FinalProjectGroup033-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project (Video Games and GPU Sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [X] YES - make available\n",
    "* [  ] NO - keep private"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GPUs are best known for its use in modern videos and games and gained popularity through video games.[3][4] \"GPUs were originally designed to accelerate the rendering of 3D graphics. Over time, they became more flexible ... This allowed graphics programmers to create more interesting visual effects and realistic scenes with advanced lighting and shadowing techniques.\"[5] \n",
    "A market for GPUs was needed in the form of video games since GPUs provided acceleration of rendering 3D graphics which boosted the popularity of GPUs in the earlier years. Video games still have a major impact on the GPU market[6] but, is the demand for accelerated 3D rendering still as prevalent or do video games impact the GPU market because video games are simply popular?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Cameron VanderTuig\n",
    "- Erdogan Ergit\n",
    "- Henry Chan\n",
    "- Tony Diep\n",
    "- Wilson Tan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"+1\"> How does the release of popular and graphically intensive games correlate with GPU sales?</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphics processing unit (GPU) and the central processing unit (CPU) are perhaps the two of the most important parts of PC gaming. \"A GPU is a single-chip processor that’s used chiefly to manage and enhance video and graphics performance.\"[1] A key difference between the two is the GPU's ability to parallel process which gives accelerated 3D rendering times.[3] Therefore, powerful games usually require a powerful GPU. Video games have also been getting more advanced in terms of their video quality and AI.[2] They're gradually becoming more computationally intensive as graphics continue to improve.[5] As video game graphics and GPU performance grows, it's unclear how the relation between video game graphics and the GPU market has grown. We know that video games still have a massive impact on the GPU market today since \"The gaming GPU market is expected to grow at 14.1% CAGR during the forecast period of 2021-2026.\"[6] However, there has been a lack of research and understanding, beyond the traditional perspective, about how the new releases drive hardware purchases. So, we aim to determine if the new game releases affect computer hardware sales. \n",
    "\n",
    "References (include links):\n",
    "- 1)  https://www.hp.com/us-en/shop/tech-takes/gpu-vs-cpu-for-pc-gaming\n",
    "- 2)  https://www.thelogocreative.co.uk/the-evolution-of-video-game-graphics/ \n",
    "- 3)  https://medium.com/analytics-vidhya/gpu-for-deep-learning-7f4ef099b702\n",
    "- 4)  https://www.investopedia.com/terms/g/graphics-processing-unit-gpu.asp\n",
    "- 5)  https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html\n",
    "- 6)  https://www.mordorintelligence.com/industry-reports/gaming-gpu-market\n",
    "- 7)  https://www.statista.com/statistics/552623/number-games-released-steam/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that the release of popular, graphically intensive video games is positively correlated with computer hardware sales. Traditionally, computer hardware sales would increase with the arrival of new popular games. Most of the time, new games would come with better graphics, which would require better hardware. So, in this case, one might predict that computer hardware sales might increase with the arrival of new popular games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset Name: Steam Hardware & Software Survey\n",
    "- Link to the dataset: https://web.archive.org/web/yyyymmddhhmmss/http://store.steampowered.com/hwsurvey/videocard\n",
    "- yyyymmddhhmmss: date of the range in 2017 to 2020\n",
    "- Number of observations: 2162\n",
    "- This dataset is the survey that steam users can opt into to measure which hardware they have.\n",
    "---\n",
    "- Dataset Name: Benchmarking Data\n",
    "- Link to the dataset: https://benchmarks.ul.com/hardware/gpu/\n",
    "- Number of observations: 110\n",
    "- Contains the relative performance statistics for many graphics hardware systems. It gives us a way to equate a GPU to the performance it is capable of.\n",
    "---\n",
    "- Dataset Name: Game_Main\n",
    "- Link to the dataset: https://www.kaggle.com/deepann/80000-steam-games-dataset/version/1\n",
    "- Number of observations: 81048\n",
    "- This dataset is a compiled set of scrapable steam game data. This particular set contains review statistics, game names, release dates, and urls to the steam page.\n",
    "---\n",
    "Ultimately, we want to know how releases of videogames affect sales of computer hardware, so we need an avenue to get from video games and their popularity to computer hardware sales.  \n",
    "The way we anticipate doing that now is like so:  \n",
    "Video game popularity and release date (Game_Main.csv) -->   \n",
    "Video game's hardware requirements (Game_Requirements.csv) -->   \n",
    "Hardware in those requirements (Game_Requirements.csv) -->   \n",
    "Level of graphical intensity (Benchmarking_Data.csv) -->   \n",
    "Hardware that can handle that intensity (Benchmarking_Data.csv) -->   \n",
    "Sales and release date of hardware (Steam Hardware & Software_Survey.csv)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run only if you have not install waybackpy\n",
    "#pip install waybackpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run only if you have not install aiohttp\n",
    "#pip install aiohttp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import aiohttp\n",
    "import asyncio\n",
    "import requests\n",
    "import patsy\n",
    "import os\n",
    "import re\n",
    "# import time\n",
    "# import timeit\n",
    "import waybackpy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from bs4 import BeautifulSoup\n",
    "from dateutil.parser import parse\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to get webpages content\n",
    "async def fetch(session, url):\n",
    "    async with session.get(url) as response:\n",
    "        if response.status != 200:\n",
    "            response.raise_for_status()\n",
    "        return await response.text()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "async def fetch_all(session, urls):\n",
    "    tasks = []\n",
    "    for url in urls:\n",
    "        task = asyncio.create_task(fetch(session, url))\n",
    "        tasks.append(task)\n",
    "    results = await asyncio.gather(*tasks)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_year = '2017'\n",
    "end_year = '2020'\n",
    "dates = pd.date_range(start=start_year, end=end_year, freq='M', closed=None) + pd.DateOffset(days=15)\n",
    "\n",
    "dates_times = dates.strftime('%Y%m%d%H%M%S')\n",
    "dates_months = dates.strftime('%h_%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://store.steampowered.com/hwsurvey/videocard'\n",
    "wayback_url = 'https://web.archive.org/web/'\n",
    "wayback = waybackpy.Url(url)\n",
    "\n",
    "steam_url = [wayback_url + date + '/' + url for date in dates_times]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<aiohttp.connector._DeprecationWaiter at 0x1a1015a9250>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read wayback machine webpage\n",
    "connector = aiohttp.TCPConnector(limit=10)\n",
    "async with aiohttp.ClientSession(connector=connector) as session:\n",
    "    pages = await fetch_all(session, steam_url)\n",
    "connector.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Web Scraping: GPUs\n",
    "\n",
    "Using the code below, we scraped for GPU data per month in a span of 2017-2019 from Steam and generated CSV files per month within the time span. This time span was chosen because months in 2016 didn't contain DirectX 12 GPUs and there existed a chip shortage for years after 2019 (2020, 2021).  \n",
    "NOTE: The code below will take a bit of time (5-10 minutes).\n",
    "\n",
    "The data we get from this is the name and change in percentage of usage per month. The name will be used later to determine if a game was graphically demanding for the time it was released in. Since we were unable to find hard sales number of GPUs, we will use the change of usage of GPUs as an estimation to sales number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the output directory\n",
    "outdir = './GPU_Data'\n",
    "if not os.path.exists(outdir):\n",
    "    os.mkdir(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Content as .csv file\n",
    "for i in range(len(dates)):\n",
    "    \n",
    "    soup = BeautifulSoup(pages[i], 'html.parser')\n",
    "    \n",
    "    # Get names and increase/decrease percentages\n",
    "    name = soup.find_all('div', {'class', 'substats_col_left'})\n",
    "    stat = soup.find_all('span', {'class', 'stat_increase', 'stat_decrease', 'stat_unchanged'})\n",
    "\n",
    "    names = []\n",
    "    stats = []\n",
    "    performances = []\n",
    "    directx_version = []\n",
    "\n",
    "    for tag in stat:\n",
    "        stats.append(tag.text.strip())\n",
    "\n",
    "    for _ in range(1, 7):\n",
    "        stats.pop(0)\n",
    "\n",
    "    temp = ''\n",
    "    unwanted = ['overall distribution of cards', 'directx 11 gpus', 'directx 10 gpus', 'directx 9 shader model 2b and 3.0 gpus',\n",
    "                'directx 9 shader model 2.0 gpus', 'directx 8 gpus and below', 'all video cards']\n",
    "\n",
    "    # Directx 12 gpus\n",
    "    directx = 0\n",
    "\n",
    "    # Append only the wanted names\n",
    "    for tag in name:\n",
    "        temp = tag.text.strip()\n",
    "        if (temp.lower() in unwanted):\n",
    "            directx = 0\n",
    "            continue\n",
    "\n",
    "        # Limit to 3 most recent DirectX versions \n",
    "        if (temp.lower() == 'directx 12 gpus'):\n",
    "            directx = 12\n",
    "            continue\n",
    "\n",
    "        directx_version.append(directx)\n",
    "        names.append(temp)\n",
    "\n",
    "    df = pd.DataFrame({'GPU Name': names, 'Change (%)': stats, 'DirectX Version': directx_version})\n",
    "    df = df[df['DirectX Version'] != 0]\n",
    "    df.set_index(np.array(range(0, df.shape[0])), inplace=True)\n",
    "    df.to_csv('./GPU_Data/' + dates_months[i] + '_GPUs.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the code above, we get multiple CSV files. These files are contained in the list \"files\". Since every month will have duplicate GPU names, we can create a set, \"GPUs\", to get unique GPUs only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMD Radeon HD 7480D',\n",
       " 'AMD Radeon HD 7540D',\n",
       " 'AMD Radeon HD 7700 Series',\n",
       " 'AMD Radeon HD 7800 Series',\n",
       " 'AMD Radeon HD 7900 Series',\n",
       " 'AMD Radeon HD 8370D',\n",
       " 'AMD Radeon HD 8470D',\n",
       " 'AMD Radeon HD 8500 Series',\n",
       " 'AMD Radeon HD 8600 Series',\n",
       " 'AMD Radeon HD 8800 Series',\n",
       " 'AMD Radeon R4 Graphics',\n",
       " 'AMD Radeon R5 Graphics',\n",
       " 'AMD Radeon R5 M330',\n",
       " 'AMD Radeon R7 200 Series',\n",
       " 'AMD Radeon R7 300 Series',\n",
       " 'AMD Radeon R7 Graphics',\n",
       " 'AMD Radeon R9 200 Series',\n",
       " 'AMD Radeon R9 380 Series',\n",
       " 'AMD Radeon R9 390 Series',\n",
       " 'AMD Radeon RX 460',\n",
       " 'AMD Radeon RX 470',\n",
       " 'AMD Radeon RX 480',\n",
       " 'AMD Radeon RX 550',\n",
       " 'AMD Radeon RX 560',\n",
       " 'AMD Radeon RX 570',\n",
       " 'AMD Radeon RX 580',\n",
       " 'AMD Radeon RX Vega',\n",
       " 'AMD Radeon Vega 8 Graphics',\n",
       " 'Intel HD Graphics 4400',\n",
       " 'Intel HD Graphics 4600',\n",
       " 'Intel HD Graphics 5000',\n",
       " 'Intel HD Graphics 520',\n",
       " 'Intel HD Graphics 530',\n",
       " 'Intel HD Graphics 5500',\n",
       " 'Intel HD Graphics 6000',\n",
       " 'Intel HD Graphics 620',\n",
       " 'Intel HD Graphics 630',\n",
       " 'Intel Iris Graphics 5100',\n",
       " 'Intel Iris Graphics 6100',\n",
       " 'Intel Iris Plus Graphics 640',\n",
       " 'Intel Iris Pro Graphics 5200',\n",
       " 'Intel UHD Graphics 620',\n",
       " 'Intel UHD Graphics 630',\n",
       " 'NVIDIA GeForce 610M',\n",
       " 'NVIDIA GeForce 840M',\n",
       " 'NVIDIA GeForce 920M',\n",
       " 'NVIDIA GeForce 920MX',\n",
       " 'NVIDIA GeForce 940M',\n",
       " 'NVIDIA GeForce 940MX',\n",
       " 'NVIDIA GeForce GT 1030',\n",
       " 'NVIDIA GeForce GT 430',\n",
       " 'NVIDIA GeForce GT 440',\n",
       " 'NVIDIA GeForce GT 540M',\n",
       " 'NVIDIA GeForce GT 610',\n",
       " 'NVIDIA GeForce GT 630',\n",
       " 'NVIDIA GeForce GT 630M',\n",
       " 'NVIDIA GeForce GT 640',\n",
       " 'NVIDIA GeForce GT 650M',\n",
       " 'NVIDIA GeForce GT 710',\n",
       " 'NVIDIA GeForce GT 720M',\n",
       " 'NVIDIA GeForce GT 730',\n",
       " 'NVIDIA GeForce GT 740',\n",
       " 'NVIDIA GeForce GT 740M',\n",
       " 'NVIDIA GeForce GT 750M',\n",
       " 'NVIDIA GeForce GTS 450',\n",
       " 'NVIDIA GeForce GTX 1050',\n",
       " 'NVIDIA GeForce GTX 1050 Ti',\n",
       " 'NVIDIA GeForce GTX 1060',\n",
       " 'NVIDIA GeForce GTX 1070',\n",
       " 'NVIDIA GeForce GTX 1070 Ti',\n",
       " 'NVIDIA GeForce GTX 1080',\n",
       " 'NVIDIA GeForce GTX 1080 Ti',\n",
       " 'NVIDIA GeForce GTX 1650',\n",
       " 'NVIDIA GeForce GTX 1660',\n",
       " 'NVIDIA GeForce GTX 1660 Ti',\n",
       " 'NVIDIA GeForce GTX 550 Ti',\n",
       " 'NVIDIA GeForce GTX 560',\n",
       " 'NVIDIA GeForce GTX 560 Ti',\n",
       " 'NVIDIA GeForce GTX 650',\n",
       " 'NVIDIA GeForce GTX 650 Ti',\n",
       " 'NVIDIA GeForce GTX 660',\n",
       " 'NVIDIA GeForce GTX 660 Ti',\n",
       " 'NVIDIA GeForce GTX 670',\n",
       " 'NVIDIA GeForce GTX 680',\n",
       " 'NVIDIA GeForce GTX 745',\n",
       " 'NVIDIA GeForce GTX 750',\n",
       " 'NVIDIA GeForce GTX 750 Ti',\n",
       " 'NVIDIA GeForce GTX 760',\n",
       " 'NVIDIA GeForce GTX 770',\n",
       " 'NVIDIA GeForce GTX 780',\n",
       " 'NVIDIA GeForce GTX 850M',\n",
       " 'NVIDIA GeForce GTX 860M',\n",
       " 'NVIDIA GeForce GTX 950',\n",
       " 'NVIDIA GeForce GTX 950M',\n",
       " 'NVIDIA GeForce GTX 960',\n",
       " 'NVIDIA GeForce GTX 960M',\n",
       " 'NVIDIA GeForce GTX 965M',\n",
       " 'NVIDIA GeForce GTX 970',\n",
       " 'NVIDIA GeForce GTX 970M',\n",
       " 'NVIDIA GeForce GTX 980',\n",
       " 'NVIDIA GeForce GTX 980 Ti',\n",
       " 'NVIDIA GeForce GTX 980M',\n",
       " 'NVIDIA GeForce MX150',\n",
       " 'NVIDIA GeForce RTX 2060',\n",
       " 'NVIDIA GeForce RTX 2060 SUPER',\n",
       " 'NVIDIA GeForce RTX 2070',\n",
       " 'NVIDIA GeForce RTX 2070 SUPER',\n",
       " 'NVIDIA GeForce RTX 2080',\n",
       " 'NVIDIA GeForce RTX 2080 SUPER',\n",
       " 'NVIDIA GeForce RTX 2080 Ti'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GPUs = set([])\n",
    "for date in dates_months:\n",
    "    df = pd.read_csv('./GPU_Data/' + date + '_GPUs.csv')\n",
    "    df = df[df['GPU Name'] != 'Other']\n",
    "    df['GPU Name'].apply(lambda x: GPUs.add(x))\n",
    "    \n",
    "GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "GPUs = list(GPUs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Web Scraping: Performance\n",
    "\n",
    "Data from Steam only provided us with the name and change in usage percentage. However, in order to determine the performance of a GPU, we would need to scrape a different site, \"benchmarks.ul.com\".\n",
    "\n",
    "Performance will tell us whether a game is demanding for when it was released, and how new/old a GPU is. Newer GPUs have higher performances, so when given a list of GPUs and their performances, we're able to determine which GPU are newest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<aiohttp.connector._DeprecationWaiter at 0x1a1029da9a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connector = aiohttp.TCPConnector()\n",
    "ul_url = ('https://benchmarks.ul.com/hardware/gpu/' + gpu + ' review' for gpu in GPUs)\n",
    "async with aiohttp.ClientSession(connector=connector) as session:\n",
    "    ul_ratings = await fetch_all(session, ul_url)\n",
    "connector.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "performances = []\n",
    "for rating in ul_ratings:\n",
    "    soup = BeautifulSoup(rating, 'html.parser')\n",
    "\n",
    "    try:\n",
    "        performance = soup.find_all('span', {'class', 'result-pimp-badge-score-item'})[0].text.strip()\n",
    "        performances.append(performance)\n",
    "    except:\n",
    "        performances.append(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the list of GPUs and performances, we can create a dictionary to easily grab the performance of the GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AMD Radeon R7 300 Series': 0,\n",
       " 'NVIDIA GeForce RTX 2060': '7592',\n",
       " 'AMD Radeon HD 7900 Series': 0,\n",
       " 'AMD Radeon RX 550': '1193',\n",
       " 'AMD Radeon RX Vega': 0,\n",
       " 'NVIDIA GeForce 940M': '514',\n",
       " 'Intel UHD Graphics 630': '449',\n",
       " 'AMD Radeon R7 Graphics': 0,\n",
       " 'AMD Radeon RX 470': '3643',\n",
       " 'NVIDIA GeForce GTX 560': 0,\n",
       " 'NVIDIA GeForce GTX 980 Ti': '5802',\n",
       " 'NVIDIA GeForce GTX 750 Ti': '1285',\n",
       " 'AMD Radeon HD 7700 Series': 0,\n",
       " 'AMD Radeon HD 8370D': 0,\n",
       " 'AMD Radeon HD 7480D': 0,\n",
       " 'NVIDIA GeForce 610M': 0,\n",
       " 'NVIDIA GeForce RTX 2070': '9125',\n",
       " 'NVIDIA GeForce RTX 2070 SUPER': '10171',\n",
       " 'AMD Radeon R9 200 Series': 0,\n",
       " 'NVIDIA GeForce RTX 2080 Ti': '14761',\n",
       " 'Intel HD Graphics 630': '408',\n",
       " 'NVIDIA GeForce GTX 1070': '6083',\n",
       " 'NVIDIA GeForce GTX 1070 Ti': '6825',\n",
       " 'Intel HD Graphics 530': '381',\n",
       " 'NVIDIA GeForce GTX 660': '1323',\n",
       " 'NVIDIA GeForce GTX 560 Ti': '389',\n",
       " 'NVIDIA GeForce RTX 2080 SUPER': '11670',\n",
       " 'NVIDIA GeForce 940MX': '599',\n",
       " 'NVIDIA GeForce GTX 1660': '5469',\n",
       " 'NVIDIA GeForce GTX 965M': '1809',\n",
       " 'AMD Radeon Vega 8 Graphics': 0,\n",
       " 'AMD Radeon R5 Graphics': 0,\n",
       " 'NVIDIA GeForce GT 430': 0,\n",
       " 'NVIDIA GeForce GTX 860M': '1156',\n",
       " 'AMD Radeon R5 M330': 0,\n",
       " 'Intel Iris Plus Graphics 640': 0,\n",
       " 'AMD Radeon HD 8600 Series': 0,\n",
       " 'NVIDIA GeForce GTX 1660 Ti': '6379',\n",
       " 'NVIDIA GeForce GTX 950M': 0,\n",
       " 'NVIDIA GeForce RTX 2080': '11091',\n",
       " 'NVIDIA GeForce GTX 680': '2019',\n",
       " 'NVIDIA GeForce GTX 1080': '7584',\n",
       " 'AMD Radeon R4 Graphics': 0,\n",
       " 'AMD Radeon RX 580': '4294',\n",
       " 'NVIDIA GeForce GT 640': 0,\n",
       " 'NVIDIA GeForce GT 650M': '414',\n",
       " 'NVIDIA GeForce GTX 850M': '983',\n",
       " 'Intel HD Graphics 5500': '234',\n",
       " 'AMD Radeon HD 8470D': 0,\n",
       " 'AMD Radeon HD 7800 Series': 0,\n",
       " 'NVIDIA GeForce GTX 745': '656',\n",
       " 'AMD Radeon HD 8500 Series': 0,\n",
       " 'NVIDIA GeForce GTX 780': '2844',\n",
       " 'Intel HD Graphics 5000': 0,\n",
       " 'Intel Iris Graphics 5100': 0,\n",
       " 'NVIDIA GeForce 840M': '500',\n",
       " 'NVIDIA GeForce GTX 1650': '3649',\n",
       " 'Intel HD Graphics 520': 0,\n",
       " 'NVIDIA GeForce GTX 970M': '2282',\n",
       " 'Intel UHD Graphics 620': '355',\n",
       " 'NVIDIA GeForce GT 740': '513',\n",
       " 'AMD Radeon RX 480': '4087',\n",
       " 'NVIDIA GeForce GT 630': '166',\n",
       " 'Intel Iris Graphics 6100': 0,\n",
       " 'NVIDIA GeForce RTX 2060 SUPER': '8813',\n",
       " 'NVIDIA GeForce GTX 670': '1856',\n",
       " 'NVIDIA GeForce GTX 970': '3659',\n",
       " 'AMD Radeon R9 380 Series': 0,\n",
       " 'NVIDIA GeForce GTX 760': '1690',\n",
       " 'Intel HD Graphics 6000': 0,\n",
       " 'NVIDIA GeForce GT 610': '88',\n",
       " 'NVIDIA GeForce GTS 450': 0,\n",
       " 'NVIDIA GeForce GT 540M': 0,\n",
       " 'NVIDIA GeForce GTX 660 Ti': '1639',\n",
       " 'AMD Radeon RX 570': '3812',\n",
       " 'NVIDIA GeForce GTX 1080 Ti': '9926',\n",
       " 'Intel HD Graphics 4600': '194',\n",
       " 'NVIDIA GeForce GTX 650': '545',\n",
       " 'NVIDIA GeForce GTX 750': '1058',\n",
       " 'NVIDIA GeForce GT 440': 0,\n",
       " 'NVIDIA GeForce GTX 1050': '1738',\n",
       " 'AMD Radeon R9 390 Series': 0,\n",
       " 'NVIDIA GeForce GT 630M': 0,\n",
       " 'NVIDIA GeForce GTX 980M': '2932',\n",
       " 'NVIDIA GeForce GT 750M': '461',\n",
       " 'NVIDIA GeForce GTX 550 Ti': '322',\n",
       " 'NVIDIA GeForce GTX 960M': '1240',\n",
       " 'NVIDIA GeForce MX150': '996',\n",
       " 'NVIDIA GeForce GTX 770': '2158',\n",
       " 'NVIDIA GeForce 920M': '326',\n",
       " 'NVIDIA GeForce 920MX': '384',\n",
       " 'Intel HD Graphics 4400': 0,\n",
       " 'NVIDIA GeForce GTX 650 Ti': '906',\n",
       " 'AMD Radeon HD 7540D': 0,\n",
       " 'NVIDIA GeForce GT 730': '299',\n",
       " 'AMD Radeon RX 460': '1739',\n",
       " 'NVIDIA GeForce GT 710': '200',\n",
       " 'NVIDIA GeForce GT 720M': 0,\n",
       " 'NVIDIA GeForce GTX 950': '1922',\n",
       " 'AMD Radeon RX 560': '1840',\n",
       " 'NVIDIA GeForce GTX 1050 Ti': '2356',\n",
       " 'Intel Iris Pro Graphics 5200': 0,\n",
       " 'NVIDIA GeForce GTX 960': '2308',\n",
       " 'NVIDIA GeForce GTX 980': '4383',\n",
       " 'Intel HD Graphics 620': '335',\n",
       " 'NVIDIA GeForce GT 1030': '1091',\n",
       " 'AMD Radeon R7 200 Series': 0,\n",
       " 'NVIDIA GeForce GT 740M': '345',\n",
       " 'NVIDIA GeForce GTX 1060': '3740',\n",
       " 'AMD Radeon HD 8800 Series': 0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GPU_dict = dict(zip(GPUs, performances))\n",
    "GPU_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have GPU names, percentage change per month, and performances, we have complete web scraping for GPUs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Games\n",
    "\n",
    "With GPU names and their performances, we're able to determine whether the GPU is new/old and whether a game is demanding or not. However, we would also need a list of games within our time frame to measure whether release of popular, demanding games affect the sales number of GPUs.\n",
    "\n",
    "Because we're using Steam data, we will only be taking a look at games on Steam.\n",
    "\n",
    "By using the Steam reviews of the games, we can determine the popularity of the game. URLs and names will be used to simply indicate what game we're looking at and date will be their release date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>all_reviews</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>Overwhelmingly Positive(224,878)- 95% of the 2...</td>\n",
       "      <td>16-Nov-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://store.steampowered.com/app/730/Counter...</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>Very Positive(4,843,904)- 87% of the 4,843,904...</td>\n",
       "      <td>21-Aug-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/1097150/Fal...</td>\n",
       "      <td>Fall Guys: Ultimate Knockout</td>\n",
       "      <td>Very Positive(223,706)- 80% of the 223,706 use...</td>\n",
       "      <td>3-Aug-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/1158310/Cru...</td>\n",
       "      <td>Crusader Kings III</td>\n",
       "      <td>Very Positive(18,951)- 92% of the 18,951 user ...</td>\n",
       "      <td>1-Sep-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>Very Positive(284,689)- 86% of the 284,689 use...</td>\n",
       "      <td>1-Oct-19</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>https://store.steampowered.com/bundle/3237/Sho...</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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      "text/plain": [
       "                                                     url  \\\n",
       "0      https://store.steampowered.com/app/945360/Amon...   \n",
       "1      https://store.steampowered.com/app/730/Counter...   \n",
       "2      https://store.steampowered.com/app/1097150/Fal...   \n",
       "3      https://store.steampowered.com/app/1158310/Cru...   \n",
       "4      https://store.steampowered.com/app/1085660/Des...   \n",
       "...                                                  ...   \n",
       "81043  https://store.steampowered.com/bundle/2961/Ste...   \n",
       "81044  https://store.steampowered.com/bundle/3123/Det...   \n",
       "81045  https://store.steampowered.com/bundle/3175/Fea...   \n",
       "81046  https://store.steampowered.com/bundle/3176/Fea...   \n",
       "81047  https://store.steampowered.com/bundle/3237/Sho...   \n",
       "\n",
       "                                   name  \\\n",
       "0                              Among Us   \n",
       "1      Counter-Strike: Global Offensive   \n",
       "2          Fall Guys: Ultimate Knockout   \n",
       "3                    Crusader Kings III   \n",
       "4                             Destiny 2   \n",
       "...                                 ...   \n",
       "81043                                 -   \n",
       "81044                                 -   \n",
       "81045                                 -   \n",
       "81046                                 -   \n",
       "81047                                 -   \n",
       "\n",
       "                                             all_reviews       date  \n",
       "0      Overwhelmingly Positive(224,878)- 95% of the 2...  16-Nov-18  \n",
       "1      Very Positive(4,843,904)- 87% of the 4,843,904...  21-Aug-12  \n",
       "2      Very Positive(223,706)- 80% of the 223,706 use...   3-Aug-20  \n",
       "3      Very Positive(18,951)- 92% of the 18,951 user ...   1-Sep-20  \n",
       "4      Very Positive(284,689)- 86% of the 284,689 use...   1-Oct-19  \n",
       "...                                                  ...        ...  \n",
       "81043                                                  -          -  \n",
       "81044                                                  -          -  \n",
       "81045                                                  -          -  \n",
       "81046                                                  -          -  \n",
       "81047                                                  -          -  \n",
       "\n",
       "[81048 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe (Game name)\n",
    "df_game_main = pd.read_csv('./Data/Game_Main.csv')\n",
    "df_game_main"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Requirements of a game will be needed to determine how demanding a game is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th>url</th>\n",
       "      <th>requirements</th>\n",
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       "      <th>0</th>\n",
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       "      <td>https://store.steampowered.com/app/730/Counter...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/1097150/Fal...</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
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       "    <tr>\n",
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       "      <td>https://store.steampowered.com/app/1158310/Cru...</td>\n",
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       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>https://store.steampowered.com/bundle/3237/Sho...</td>\n",
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      "text/plain": [
       "                                                     url  \\\n",
       "0      https://store.steampowered.com/app/945360/Amon...   \n",
       "1      https://store.steampowered.com/app/730/Counter...   \n",
       "2      https://store.steampowered.com/app/1097150/Fal...   \n",
       "3      https://store.steampowered.com/app/1158310/Cru...   \n",
       "4      https://store.steampowered.com/app/1085660/Des...   \n",
       "...                                                  ...   \n",
       "81043  https://store.steampowered.com/bundle/2961/Ste...   \n",
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       "81045  https://store.steampowered.com/bundle/3175/Fea...   \n",
       "81046  https://store.steampowered.com/bundle/3176/Fea...   \n",
       "81047  https://store.steampowered.com/bundle/3237/Sho...   \n",
       "\n",
       "                                            requirements  \n",
       "0      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "1      System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "2      System RequirementsMinimum:Requires a 64-bit p...  \n",
       "3      System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "4      System RequirementsMinimum:Requires a 64-bit p...  \n",
       "...                                                  ...  \n",
       "81043                                                  -  \n",
       "81044                                                  -  \n",
       "81045                                                  -  \n",
       "81046                                                  -  \n",
       "81047                                                  -  \n",
       "\n",
       "[81048 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dataframe (Game name)\n",
    "df_game_requirements = pd.read_csv('./Data/Game_Requirements.csv')\n",
    "df_game_requirements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning: GPUs\n",
    "\n",
    "Although we scraped for the GPU data, we still have a few issues to clean up.\n",
    "\n",
    "Remove Other and DirectX Version:\n",
    "Data from Steam included an \"Other\" GPU label. This is unusable and needs to be dropped. During setup, we used the DirectX Version to pick out only DirectX Version 12 GPUs, but is no longer needed.\n",
    "\n",
    "Get Performance list:\n",
    "Earlier in our setup, we created a dictionary which used the GPU names as keys and performance as values. We use that here to grab the performance values for the GPUs in a month.\n",
    "\n",
    "Replace 0 values:\n",
    "When scraping for performances, some values came back as 0 which indicated that it didn't have a performance score. We can fill in these 0 values by using the mean performance of the GPUs for that month.\n",
    "NOTE: The mean performance score was intentionally left as an integer. Performance data scraped are either originally integers, or were converted to integers. Also, the performance score will be used to determine the higher performing GPUs that month and keeping it as an integer will have no effect.\n",
    "\n",
    "Get GPUs above 75th percentile:\n",
    "To determine which GPUs are high performing, we are taking everything above the 75th percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in dates_months:\n",
    "    \n",
    "    # Remove Other and DirectX Version\n",
    "    performances = []\n",
    "    df = pd.read_csv('./GPU_Data/' + x + '_GPUs.csv')\n",
    "    df = df[df['GPU Name'] != 'Other']\n",
    "    df.drop(labels='DirectX Version', axis=1, inplace=True)\n",
    "    \n",
    "    # Get performance list\n",
    "    performances = list(df['GPU Name'].apply(lambda x: GPU_dict.get(x)))\n",
    "    \n",
    "    df['Performance (3DMark)'] = performances\n",
    "    df['Performance (3DMark)'] = df['Performance (3DMark)'].apply(lambda x: int(x))\n",
    "    \n",
    "    # Replace 0 values. Took mean as an integer because Performance scores were also converted to ints\n",
    "    mean = int(df['Performance (3DMark)'].mean())\n",
    "    df['Performance (3DMark)'] = df['Performance (3DMark)'].replace(0, mean)\n",
    "    \n",
    "    # Get GPUs above 75th percentile\n",
    "    percentile = df['Performance (3DMark)'].quantile([0.75]).loc[0.75]\n",
    "    df = df.loc[df['Performance (3DMark)'] > percentile]\n",
    "    df = df.set_index(np.array(range(0, df.shape[0])))\n",
    "    df.to_csv('./GPU_Data/' + x + '_75_ptile.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Cleaning: Games\n",
    "\n",
    "We can merge df_game_main and df_game_requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game = pd.merge(df_game_main, df_game_requirements)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then drop any empty row (when it equal to '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Overwhelmingly Positive(224,878)- 95% of the 2...</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>Overwhelmingly Positive(224,878)- 95% of the 2...</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>Overwhelmingly Positive(224,878)- 95% of the 2...</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>Overwhelmingly Positive(224,878)- 95% of the 2...</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81379</th>\n",
       "      <td>https://store.steampowered.com/app/1346541/STA...</td>\n",
       "      <td>STAR WARS™: The Old Republic™ - Sith Bundles</td>\n",
       "      <td>21 Jul, 2020</td>\n",
       "      <td>21 Jul, 2020</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 or la...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81380</th>\n",
       "      <td>https://store.steampowered.com/app/1347970/Pat...</td>\n",
       "      <td>Patch Quest</td>\n",
       "      <td>Late 2020</td>\n",
       "      <td>Late 2020</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 10Proce...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81381</th>\n",
       "      <td>https://store.steampowered.com/app/1349120/_/?...</td>\n",
       "      <td>球球少女</td>\n",
       "      <td>6 Nov, 2020</td>\n",
       "      <td>6 Nov, 2020</td>\n",
       "      <td>System RequirementsMinimum:OS: WIN7 SP1/WIN8/W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81382</th>\n",
       "      <td>https://store.steampowered.com/app/1349170/Fur...</td>\n",
       "      <td>Furries &amp; Scalies &amp; Bears OH MY! 2: Return to ...</td>\n",
       "      <td>20 Apr, 2021</td>\n",
       "      <td>20 Apr, 2021</td>\n",
       "      <td>System RequirementsWindowsSteamOS + LinuxMinim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81402</th>\n",
       "      <td>https://store.steampowered.com/app/597700/OVIV...</td>\n",
       "      <td>OVIVO</td>\n",
       "      <td>12 May, 2017</td>\n",
       "      <td>12 May, 2017</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74725 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url  \\\n",
       "0      https://store.steampowered.com/app/945360/Amon...   \n",
       "1      https://store.steampowered.com/app/945360/Amon...   \n",
       "2      https://store.steampowered.com/app/945360/Amon...   \n",
       "3      https://store.steampowered.com/app/945360/Amon...   \n",
       "4      https://store.steampowered.com/app/945360/Amon...   \n",
       "...                                                  ...   \n",
       "81379  https://store.steampowered.com/app/1346541/STA...   \n",
       "81380  https://store.steampowered.com/app/1347970/Pat...   \n",
       "81381  https://store.steampowered.com/app/1349120/_/?...   \n",
       "81382  https://store.steampowered.com/app/1349170/Fur...   \n",
       "81402  https://store.steampowered.com/app/597700/OVIV...   \n",
       "\n",
       "                                                    name  \\\n",
       "0                                               Among Us   \n",
       "1                                               Among Us   \n",
       "2                                               Among Us   \n",
       "3                                               Among Us   \n",
       "4                                               Among Us   \n",
       "...                                                  ...   \n",
       "81379       STAR WARS™: The Old Republic™ - Sith Bundles   \n",
       "81380                                        Patch Quest   \n",
       "81381                                               球球少女   \n",
       "81382  Furries & Scalies & Bears OH MY! 2: Return to ...   \n",
       "81402                                              OVIVO   \n",
       "\n",
       "                                             all_reviews          date  \\\n",
       "0      Overwhelmingly Positive(224,878)- 95% of the 2...     16-Nov-18   \n",
       "1      Overwhelmingly Positive(224,878)- 95% of the 2...     16-Nov-18   \n",
       "2      Overwhelmingly Positive(224,878)- 95% of the 2...     16-Nov-18   \n",
       "3      Overwhelmingly Positive(224,878)- 95% of the 2...     16-Nov-18   \n",
       "4      Overwhelmingly Positive(224,878)- 95% of the 2...     16-Nov-18   \n",
       "...                                                  ...           ...   \n",
       "81379                                       21 Jul, 2020  21 Jul, 2020   \n",
       "81380                                          Late 2020     Late 2020   \n",
       "81381                                        6 Nov, 2020   6 Nov, 2020   \n",
       "81382                                       20 Apr, 2021  20 Apr, 2021   \n",
       "81402                                       12 May, 2017  12 May, 2017   \n",
       "\n",
       "                                            requirements  \n",
       "0      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "1      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "2      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "3      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "4      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "...                                                  ...  \n",
       "81379  System RequirementsMinimum:OS: Windows 7 or la...  \n",
       "81380  System RequirementsMinimum:OS: Windows 10Proce...  \n",
       "81381  System RequirementsMinimum:OS: WIN7 SP1/WIN8/W...  \n",
       "81382  System RequirementsWindowsSteamOS + LinuxMinim...  \n",
       "81402  System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "\n",
       "[74725 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game.replace('-', np.nan, inplace=True)\n",
    "df_game.dropna(inplace=True)\n",
    "\n",
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>all_reviews</th>\n",
       "      <th>date</th>\n",
       "      <th>requirements</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>Overwhelmingly Positive(224,878)- 95% of the 2...</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://store.steampowered.com/app/730/Counter...</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>Very Positive(4,843,904)- 87% of the 4,843,904...</td>\n",
       "      <td>21-Aug-12</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/1097150/Fal...</td>\n",
       "      <td>Fall Guys: Ultimate Knockout</td>\n",
       "      <td>Very Positive(223,706)- 80% of the 223,706 use...</td>\n",
       "      <td>3-Aug-20</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/1158310/Cru...</td>\n",
       "      <td>Crusader Kings III</td>\n",
       "      <td>Very Positive(18,951)- 92% of the 18,951 user ...</td>\n",
       "      <td>1-Sep-20</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>Very Positive(284,689)- 86% of the 284,689 use...</td>\n",
       "      <td>1-Oct-19</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63460</th>\n",
       "      <td>https://store.steampowered.com/app/1340160/RIO...</td>\n",
       "      <td>RIO - Raised In Oblivion</td>\n",
       "      <td>30 Oct, 2020</td>\n",
       "      <td>30 Oct, 2020</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63461</th>\n",
       "      <td>https://store.steampowered.com/app/1340360/Pol...</td>\n",
       "      <td>Polycalypse: Last bit of Hope</td>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 or la...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63462</th>\n",
       "      <td>https://store.steampowered.com/app/1341230/Sup...</td>\n",
       "      <td>Super Buckyball Tournament Preseason</td>\n",
       "      <td>Coming Soon</td>\n",
       "      <td>Coming Soon</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7+ / 8....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63463</th>\n",
       "      <td>https://store.steampowered.com/app/1342050/Lis...</td>\n",
       "      <td>Lisa and the Grimoire</td>\n",
       "      <td>TBA</td>\n",
       "      <td>TBA</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows® 7/8/8....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63464</th>\n",
       "      <td>https://store.steampowered.com/app/1343340/HOR...</td>\n",
       "      <td>HORROR TALES: The Beggar</td>\n",
       "      <td>1 Dec, 2020</td>\n",
       "      <td>1 Dec, 2020</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1 6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63465 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     url  \\\n",
       "0      https://store.steampowered.com/app/945360/Amon...   \n",
       "1      https://store.steampowered.com/app/730/Counter...   \n",
       "2      https://store.steampowered.com/app/1097150/Fal...   \n",
       "3      https://store.steampowered.com/app/1158310/Cru...   \n",
       "4      https://store.steampowered.com/app/1085660/Des...   \n",
       "...                                                  ...   \n",
       "63460  https://store.steampowered.com/app/1340160/RIO...   \n",
       "63461  https://store.steampowered.com/app/1340360/Pol...   \n",
       "63462  https://store.steampowered.com/app/1341230/Sup...   \n",
       "63463  https://store.steampowered.com/app/1342050/Lis...   \n",
       "63464  https://store.steampowered.com/app/1343340/HOR...   \n",
       "\n",
       "                                       name  \\\n",
       "0                                  Among Us   \n",
       "1          Counter-Strike: Global Offensive   \n",
       "2              Fall Guys: Ultimate Knockout   \n",
       "3                        Crusader Kings III   \n",
       "4                                 Destiny 2   \n",
       "...                                     ...   \n",
       "63460              RIO - Raised In Oblivion   \n",
       "63461         Polycalypse: Last bit of Hope   \n",
       "63462  Super Buckyball Tournament Preseason   \n",
       "63463                 Lisa and the Grimoire   \n",
       "63464              HORROR TALES: The Beggar   \n",
       "\n",
       "                                             all_reviews          date  \\\n",
       "0      Overwhelmingly Positive(224,878)- 95% of the 2...     16-Nov-18   \n",
       "1      Very Positive(4,843,904)- 87% of the 4,843,904...     21-Aug-12   \n",
       "2      Very Positive(223,706)- 80% of the 223,706 use...      3-Aug-20   \n",
       "3      Very Positive(18,951)- 92% of the 18,951 user ...      1-Sep-20   \n",
       "4      Very Positive(284,689)- 86% of the 284,689 use...      1-Oct-19   \n",
       "...                                                  ...           ...   \n",
       "63460                                       30 Oct, 2020  30 Oct, 2020   \n",
       "63461                                               2020          2020   \n",
       "63462                                        Coming Soon   Coming Soon   \n",
       "63463                                                TBA           TBA   \n",
       "63464                                        1 Dec, 2020   1 Dec, 2020   \n",
       "\n",
       "                                            requirements  \n",
       "0      System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "1      System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "2      System RequirementsMinimum:Requires a 64-bit p...  \n",
       "3      System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "4      System RequirementsMinimum:Requires a 64-bit p...  \n",
       "...                                                  ...  \n",
       "63460  System RequirementsMinimum:Requires a 64-bit p...  \n",
       "63461  System RequirementsMinimum:OS: Windows 7 or la...  \n",
       "63462  System RequirementsMinimum:OS: Windows 7+ / 8....  \n",
       "63463  System RequirementsMinimum:OS: Windows® 7/8/8....  \n",
       "63464  System RequirementsMinimum:OS: Windows 7 SP1 6...  \n",
       "\n",
       "[63465 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game.drop_duplicates(subset='name', keep='first', inplace=True)\n",
    "df_game.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're only interested in number of reviews and percentage of likes. We can use regular expressions and the apply function to get rid of useless information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           224878 95   224878       \n",
       "1         4843904 87   4843904       \n",
       "2           223706 80   223706       \n",
       "3             18951 92   18951       \n",
       "4           284689 86   284689       \n",
       "                     ...             \n",
       "63460                        30  2020\n",
       "63461                            2020\n",
       "63462                                \n",
       "63463                                \n",
       "63464                         1  2020\n",
       "Name: all_reviews, Length: 63465, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game['all_reviews'] = df_game['all_reviews'].apply(lambda x: re.sub('[^0-9\\s]', '', x))\n",
    "\n",
    "df_game['all_reviews']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the bottom 5 reviews are different from the top 5. Some reviews are unusable to us, so we can get rid of those rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>all_reviews</th>\n",
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       "      <th>requirements</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>224878 95   224878</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://store.steampowered.com/app/730/Counter...</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>4843904 87   4843904</td>\n",
       "      <td>21-Aug-12</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/1097150/Fal...</td>\n",
       "      <td>Fall Guys: Ultimate Knockout</td>\n",
       "      <td>223706 80   223706</td>\n",
       "      <td>3-Aug-20</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/1158310/Cru...</td>\n",
       "      <td>Crusader Kings III</td>\n",
       "      <td>18951 92   18951</td>\n",
       "      <td>1-Sep-20</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>284689 86   284689</td>\n",
       "      <td>1-Oct-19</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4227</th>\n",
       "      <td>https://store.steampowered.com/app/1236180/The...</td>\n",
       "      <td>The Whipper</td>\n",
       "      <td>4 20211 2022</td>\n",
       "      <td>Q4 2021/Q1 2022</td>\n",
       "      <td>System RequirementsWindowsSteamOS + LinuxMinim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4228</th>\n",
       "      <td>https://store.steampowered.com/app/1361980/Wel...</td>\n",
       "      <td>Welcome To... Chichester 2 - Part III : NightFall</td>\n",
       "      <td>3  4 2021</td>\n",
       "      <td>Q3 - Q4 2021</td>\n",
       "      <td>System RequirementsWindowsSteamOS + LinuxMinim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4229</th>\n",
       "      <td>https://store.steampowered.com/app/1333910/Siz...</td>\n",
       "      <td>Sizeable</td>\n",
       "      <td>3  4 2020</td>\n",
       "      <td>Coming Q3 - Q4 2020</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 or ne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4230</th>\n",
       "      <td>https://store.steampowered.com/app/346290/Penu...</td>\n",
       "      <td>Penumbra: Necrologue</td>\n",
       "      <td>508 88   508</td>\n",
       "      <td>6 Feb, 2015</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4231</th>\n",
       "      <td>https://store.steampowered.com/app/1082450/Gol...</td>\n",
       "      <td>Gold Hunter</td>\n",
       "      <td>4 2020   2022</td>\n",
       "      <td>public demo Q4 2020 [Release Steam 2022]</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4232 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "0     https://store.steampowered.com/app/945360/Amon...   \n",
       "1     https://store.steampowered.com/app/730/Counter...   \n",
       "2     https://store.steampowered.com/app/1097150/Fal...   \n",
       "3     https://store.steampowered.com/app/1158310/Cru...   \n",
       "4     https://store.steampowered.com/app/1085660/Des...   \n",
       "...                                                 ...   \n",
       "4227  https://store.steampowered.com/app/1236180/The...   \n",
       "4228  https://store.steampowered.com/app/1361980/Wel...   \n",
       "4229  https://store.steampowered.com/app/1333910/Siz...   \n",
       "4230  https://store.steampowered.com/app/346290/Penu...   \n",
       "4231  https://store.steampowered.com/app/1082450/Gol...   \n",
       "\n",
       "                                                   name  \\\n",
       "0                                              Among Us   \n",
       "1                      Counter-Strike: Global Offensive   \n",
       "2                          Fall Guys: Ultimate Knockout   \n",
       "3                                    Crusader Kings III   \n",
       "4                                             Destiny 2   \n",
       "...                                                 ...   \n",
       "4227                                        The Whipper   \n",
       "4228  Welcome To... Chichester 2 - Part III : NightFall   \n",
       "4229                                           Sizeable   \n",
       "4230                               Penumbra: Necrologue   \n",
       "4231                                        Gold Hunter   \n",
       "\n",
       "                       all_reviews                                      date  \\\n",
       "0        224878 95   224878                                        16-Nov-18   \n",
       "1      4843904 87   4843904                                        21-Aug-12   \n",
       "2        223706 80   223706                                         3-Aug-20   \n",
       "3          18951 92   18951                                         1-Sep-20   \n",
       "4        284689 86   284689                                         1-Oct-19   \n",
       "...                            ...                                       ...   \n",
       "4227                  4 20211 2022                           Q4 2021/Q1 2022   \n",
       "4228                     3  4 2021                              Q3 - Q4 2021   \n",
       "4229                     3  4 2020                       Coming Q3 - Q4 2020   \n",
       "4230           508 88   508                                      6 Feb, 2015   \n",
       "4231                 4 2020   2022  public demo Q4 2020 [Release Steam 2022]   \n",
       "\n",
       "                                           requirements  \n",
       "0     System RequirementsMinimum:OS: Windows 7 SP1+P...  \n",
       "1     System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "2     System RequirementsMinimum:Requires a 64-bit p...  \n",
       "3     System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "4     System RequirementsMinimum:Requires a 64-bit p...  \n",
       "...                                                 ...  \n",
       "4227  System RequirementsWindowsSteamOS + LinuxMinim...  \n",
       "4228  System RequirementsWindowsSteamOS + LinuxMinim...  \n",
       "4229  System RequirementsMinimum:OS: Windows 7 or ne...  \n",
       "4230  System RequirementsWindowsMac OS XSteamOS + Li...  \n",
       "4231  System RequirementsMinimum:Requires a 64-bit p...  \n",
       "\n",
       "[4232 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game = df_game[df_game['all_reviews'].str.split().apply(len) == 3]\n",
    "df_game.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the useless information gone, we can get the number of reviews and percetange of likes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4232 4232\n"
     ]
    }
   ],
   "source": [
    "num_reviews = []\n",
    "pct_like = []\n",
    "\n",
    "for x in range(0, df_game.shape[0]):\n",
    "    review = df_game.loc[x, 'all_reviews'].split()\n",
    "    \n",
    "    num_reviews.append(int(review[0]))\n",
    "    pct_like.append(int(review[1]))\n",
    "    \n",
    "print(len(num_reviews), len(pct_like))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding number of reviews, percentage of likes, and number of positive review columns. Also getting rid of the \"all_reviews\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      <td>https://store.steampowered.com/app/945360/Amon...</td>\n",
       "      <td>Among Us</td>\n",
       "      <td>16-Nov-18</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 SP1+P...</td>\n",
       "      <td>224878</td>\n",
       "      <td>95</td>\n",
       "      <td>213634</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://store.steampowered.com/app/730/Counter...</td>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>21-Aug-12</td>\n",
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       "      <td>4843904</td>\n",
       "      <td>87</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/1097150/Fal...</td>\n",
       "      <td>Fall Guys: Ultimate Knockout</td>\n",
       "      <td>3-Aug-20</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "      <td>223706</td>\n",
       "      <td>80</td>\n",
       "      <td>178964</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/1158310/Cru...</td>\n",
       "      <td>Crusader Kings III</td>\n",
       "      <td>1-Sep-20</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "      <td>18951</td>\n",
       "      <td>92</td>\n",
       "      <td>17434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>1-Oct-19</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "      <td>284689</td>\n",
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       "      <td>System RequirementsWindowsSteamOS + LinuxMinim...</td>\n",
       "      <td>4</td>\n",
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       "      <td>808</td>\n",
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       "    <tr>\n",
       "      <th>4228</th>\n",
       "      <td>https://store.steampowered.com/app/1361980/Wel...</td>\n",
       "      <td>Welcome To... Chichester 2 - Part III : NightFall</td>\n",
       "      <td>Q3 - Q4 2021</td>\n",
       "      <td>System RequirementsWindowsSteamOS + LinuxMinim...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <th>4229</th>\n",
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       "      <td>Sizeable</td>\n",
       "      <td>Coming Q3 - Q4 2020</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 or ne...</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4230</th>\n",
       "      <td>https://store.steampowered.com/app/346290/Penu...</td>\n",
       "      <td>Penumbra: Necrologue</td>\n",
       "      <td>6 Feb, 2015</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "      <td>508</td>\n",
       "      <td>88</td>\n",
       "      <td>447</td>\n",
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       "    <tr>\n",
       "      <th>4231</th>\n",
       "      <td>https://store.steampowered.com/app/1082450/Gol...</td>\n",
       "      <td>Gold Hunter</td>\n",
       "      <td>public demo Q4 2020 [Release Steam 2022]</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "      <td>4</td>\n",
       "      <td>2020</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4232 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "0     https://store.steampowered.com/app/945360/Amon...   \n",
       "1     https://store.steampowered.com/app/730/Counter...   \n",
       "2     https://store.steampowered.com/app/1097150/Fal...   \n",
       "3     https://store.steampowered.com/app/1158310/Cru...   \n",
       "4     https://store.steampowered.com/app/1085660/Des...   \n",
       "...                                                 ...   \n",
       "4227  https://store.steampowered.com/app/1236180/The...   \n",
       "4228  https://store.steampowered.com/app/1361980/Wel...   \n",
       "4229  https://store.steampowered.com/app/1333910/Siz...   \n",
       "4230  https://store.steampowered.com/app/346290/Penu...   \n",
       "4231  https://store.steampowered.com/app/1082450/Gol...   \n",
       "\n",
       "                                                   name  \\\n",
       "0                                              Among Us   \n",
       "1                      Counter-Strike: Global Offensive   \n",
       "2                          Fall Guys: Ultimate Knockout   \n",
       "3                                    Crusader Kings III   \n",
       "4                                             Destiny 2   \n",
       "...                                                 ...   \n",
       "4227                                        The Whipper   \n",
       "4228  Welcome To... Chichester 2 - Part III : NightFall   \n",
       "4229                                           Sizeable   \n",
       "4230                               Penumbra: Necrologue   \n",
       "4231                                        Gold Hunter   \n",
       "\n",
       "                                          date  \\\n",
       "0                                    16-Nov-18   \n",
       "1                                    21-Aug-12   \n",
       "2                                     3-Aug-20   \n",
       "3                                     1-Sep-20   \n",
       "4                                     1-Oct-19   \n",
       "...                                        ...   \n",
       "4227                           Q4 2021/Q1 2022   \n",
       "4228                              Q3 - Q4 2021   \n",
       "4229                       Coming Q3 - Q4 2020   \n",
       "4230                               6 Feb, 2015   \n",
       "4231  public demo Q4 2020 [Release Steam 2022]   \n",
       "\n",
       "                                           requirements  num_reviews  \\\n",
       "0     System RequirementsMinimum:OS: Windows 7 SP1+P...       224878   \n",
       "1     System RequirementsWindowsMac OS XSteamOS + Li...      4843904   \n",
       "2     System RequirementsMinimum:Requires a 64-bit p...       223706   \n",
       "3     System RequirementsWindowsMac OS XSteamOS + Li...        18951   \n",
       "4     System RequirementsMinimum:Requires a 64-bit p...       284689   \n",
       "...                                                 ...          ...   \n",
       "4227  System RequirementsWindowsSteamOS + LinuxMinim...            4   \n",
       "4228  System RequirementsWindowsSteamOS + LinuxMinim...            3   \n",
       "4229  System RequirementsMinimum:OS: Windows 7 or ne...            3   \n",
       "4230  System RequirementsWindowsMac OS XSteamOS + Li...          508   \n",
       "4231  System RequirementsMinimum:Requires a 64-bit p...            4   \n",
       "\n",
       "      pct_like  num_positive_reviews  \n",
       "0           95                213634  \n",
       "1           87               4214196  \n",
       "2           80                178964  \n",
       "3           92                 17434  \n",
       "4           86                244832  \n",
       "...        ...                   ...  \n",
       "4227     20211                   808  \n",
       "4228         4                     0  \n",
       "4229         4                     0  \n",
       "4230        88                   447  \n",
       "4231      2020                    80  \n",
       "\n",
       "[4232 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pos_reviews = []\n",
    "df_game = df_game.assign(num_reviews=num_reviews, pct_like=pct_like)\n",
    "\n",
    "df_game['num_reviews'].apply(lambda x: int(x))\n",
    "df_game['pct_like'].apply(lambda x: int(x))\n",
    "\n",
    "for x in range(0, len(num_reviews)):\n",
    "    pos_reviews = (float(num_reviews[x]) * (float(pct_like[x])/100))\n",
    "    num_pos_reviews.append(int(pos_reviews))\n",
    "\n",
    "df_game = df_game.assign(num_positive_reviews=num_pos_reviews)\n",
    "df_game.drop(columns='all_reviews', inplace=True)\n",
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reformatting the date\n",
    "\n",
    "Since the dates have different formats, we need a way to standardize them. In this case, we can write our own function to do this. Months will be rewritten as two digits numbers, years will be rewritten as four.\n",
    "\n",
    "Our GPU data goes by months, so we can simply get rid of the days.\n",
    "\n",
    "Because our time span of GPUs ranges from 2017-2019, we will only consider games within this time span."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardize_date(date):\n",
    "    try:\n",
    "        return parse(date).strftime('%Y/%m')\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://store.steampowered.com/app/578080/PLAY...</td>\n",
       "      <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n",
       "      <td>2017/12</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "      <td>1316559</td>\n",
       "      <td>52</td>\n",
       "      <td>684610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://store.steampowered.com/app/304930/Untu...</td>\n",
       "      <td>Unturned</td>\n",
       "      <td>2017/07</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "      <td>402298</td>\n",
       "      <td>91</td>\n",
       "      <td>366091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/252490/Rust...</td>\n",
       "      <td>Rust</td>\n",
       "      <td>2018/02</td>\n",
       "      <td>System RequirementsWindowsMac OS XMinimum:Requ...</td>\n",
       "      <td>375523</td>\n",
       "      <td>84</td>\n",
       "      <td>315439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/346110/ARK_...</td>\n",
       "      <td>ARK: Survival Evolved</td>\n",
       "      <td>2017/08</td>\n",
       "      <td>System RequirementsWindowsMac OS XSteamOS + Li...</td>\n",
       "      <td>289957</td>\n",
       "      <td>78</td>\n",
       "      <td>226166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>2019/10</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "      <td>284689</td>\n",
       "      <td>86</td>\n",
       "      <td>244832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>https://store.steampowered.com/app/1172520/Col...</td>\n",
       "      <td>Colorgrid</td>\n",
       "      <td>2019/11</td>\n",
       "      <td>System RequirementsWindowsMac OS XMinimum:OS: ...</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>https://store.steampowered.com/app/1018130/Cas...</td>\n",
       "      <td>Castle Break</td>\n",
       "      <td>2019/02</td>\n",
       "      <td>System RequirementsMinimum:OS: Windows 7 or ne...</td>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>https://store.steampowered.com/app/763710/Rive...</td>\n",
       "      <td>River City Melee Mach!!</td>\n",
       "      <td>2019/10</td>\n",
       "      <td>System RequirementsMinimum:Requires a 64-bit p...</td>\n",
       "      <td>41</td>\n",
       "      <td>85</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>https://store.steampowered.com/app/705210/Cube...</td>\n",
       "      <td>Cube Racer</td>\n",
       "      <td>2017/10</td>\n",
       "      <td>System RequirementsWindowsSteamOS + LinuxMinim...</td>\n",
       "      <td>37</td>\n",
       "      <td>89</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>https://store.steampowered.com/app/523230/Supe...</td>\n",
       "      <td>Super Spring Ninja</td>\n",
       "      <td>2017/03</td>\n",
       "      <td>System RequirementsMinimum:OS: 8Processor: Dua...</td>\n",
       "      <td>16</td>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1754 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "0     https://store.steampowered.com/app/578080/PLAY...   \n",
       "1     https://store.steampowered.com/app/304930/Untu...   \n",
       "2     https://store.steampowered.com/app/252490/Rust...   \n",
       "3     https://store.steampowered.com/app/346110/ARK_...   \n",
       "4     https://store.steampowered.com/app/1085660/Des...   \n",
       "...                                                 ...   \n",
       "1749  https://store.steampowered.com/app/1172520/Col...   \n",
       "1750  https://store.steampowered.com/app/1018130/Cas...   \n",
       "1751  https://store.steampowered.com/app/763710/Rive...   \n",
       "1752  https://store.steampowered.com/app/705210/Cube...   \n",
       "1753  https://store.steampowered.com/app/523230/Supe...   \n",
       "\n",
       "                               name     date  \\\n",
       "0     PLAYERUNKNOWN'S BATTLEGROUNDS  2017/12   \n",
       "1                          Unturned  2017/07   \n",
       "2                              Rust  2018/02   \n",
       "3             ARK: Survival Evolved  2017/08   \n",
       "4                         Destiny 2  2019/10   \n",
       "...                             ...      ...   \n",
       "1749                      Colorgrid  2019/11   \n",
       "1750                   Castle Break  2019/02   \n",
       "1751        River City Melee Mach!!  2019/10   \n",
       "1752                     Cube Racer  2017/10   \n",
       "1753             Super Spring Ninja  2017/03   \n",
       "\n",
       "                                           requirements  num_reviews  \\\n",
       "0     System RequirementsMinimum:Requires a 64-bit p...      1316559   \n",
       "1     System RequirementsWindowsMac OS XSteamOS + Li...       402298   \n",
       "2     System RequirementsWindowsMac OS XMinimum:Requ...       375523   \n",
       "3     System RequirementsWindowsMac OS XSteamOS + Li...       289957   \n",
       "4     System RequirementsMinimum:Requires a 64-bit p...       284689   \n",
       "...                                                 ...          ...   \n",
       "1749  System RequirementsWindowsMac OS XMinimum:OS: ...           50   \n",
       "1750  System RequirementsMinimum:OS: Windows 7 or ne...           45   \n",
       "1751  System RequirementsMinimum:Requires a 64-bit p...           41   \n",
       "1752  System RequirementsWindowsSteamOS + LinuxMinim...           37   \n",
       "1753  System RequirementsMinimum:OS: 8Processor: Dua...           16   \n",
       "\n",
       "      pct_like  num_positive_reviews  \n",
       "0           52                684610  \n",
       "1           91                366091  \n",
       "2           84                315439  \n",
       "3           78                226166  \n",
       "4           86                244832  \n",
       "...        ...                   ...  \n",
       "1749       100                    50  \n",
       "1750        95                    42  \n",
       "1751        85                    34  \n",
       "1752        89                    32  \n",
       "1753        87                    13  \n",
       "\n",
       "[1754 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game['date'] = df_game['date'].apply(standardize_date)\n",
    "df_game.dropna(inplace=True)\n",
    "df_game = df_game[(df_game['date'] >= '2017-01') & (df_game['date'] <= '2020-01')]\n",
    "df_game = df_game.sort_values(by='num_reviews', ascending=False)\n",
    "df_game.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Web scraping for requirements\n",
    "\n",
    "Some of the requirements are missing. However, since we have the urls for the games, we can easily web scrape for them instead.  \n",
    "NOTE: This processes takes a while (2 - 3 minutes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<aiohttp.connector._DeprecationWaiter at 0x1a106c35550>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connector = aiohttp.TCPConnector(limit=10)\n",
    "cookies = {'birthtime': '283993201', 'mature_content': '1'}\n",
    "async with aiohttp.ClientSession(connector=connector, cookies=cookies) as session:\n",
    "    page = await fetch_all(session, df_game['url'])\n",
    "connector.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1754 1754\n"
     ]
    }
   ],
   "source": [
    "min_cards = []\n",
    "rec_cards = []\n",
    "minimum = \"\"\n",
    "recommended = \"\"\n",
    "contains_graphics_section = False\n",
    "\n",
    "for index in range(0, df_game.shape[0]):\n",
    "    try:\n",
    "        soup = BeautifulSoup(page[index], 'html.parser')\n",
    "\n",
    "        card = soup.find('div', {'class': 'game_area_sys_req sysreq_content active'})\n",
    "        card = card.find_all('ul', {'class':'bb_ul'})\n",
    "\n",
    "        for x in range(0, len(card)):\n",
    "            contains_graphics_section = False\n",
    "            rec = card[x].find_all('li')\n",
    "            for tag in rec:\n",
    "                if ('Graphics' in tag.text.strip()):\n",
    "                    contains_graphics_section = True\n",
    "                    if (x == 0):\n",
    "                        minimum = tag.text.strip()\n",
    "                        recommended = tag.text.strip()\n",
    "                    elif (x == 1):\n",
    "                        recommended = tag.text.strip()\n",
    "\n",
    "        if (not contains_graphics_section):\n",
    "            min_cards.append(np.nan)\n",
    "            rec_cards.append(np.nan)\n",
    "        else:\n",
    "            min_cards.append(minimum)\n",
    "            rec_cards.append(recommended)\n",
    "\n",
    "    except:\n",
    "        min_cards.append(np.nan)\n",
    "        rec_cards.append(np.nan)\n",
    "print(len(min_cards), len(rec_cards))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can add columns for minimum and recommended hardware.\n",
    "\n",
    "Some of these games may not be on Steam anymore and the GPU requirements can no longer be scraped. In this case, we can just remove them from our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>pct_like</th>\n",
       "      <th>num_positive_reviews</th>\n",
       "      <th>minimum</th>\n",
       "      <th>recommended</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://store.steampowered.com/app/578080/PLAY...</td>\n",
       "      <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n",
       "      <td>2017/12</td>\n",
       "      <td>1316559</td>\n",
       "      <td>52</td>\n",
       "      <td>684610</td>\n",
       "      <td>Graphics: NVIDIA GeForce GTX 960 2GB / AMD Rad...</td>\n",
       "      <td>Graphics: NVIDIA GeForce GTX 1060 3GB / AMD Ra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/252490/Rust...</td>\n",
       "      <td>Rust</td>\n",
       "      <td>2018/02</td>\n",
       "      <td>375523</td>\n",
       "      <td>84</td>\n",
       "      <td>315439</td>\n",
       "      <td>Graphics: GTX 670 2GB / AMD R9 280 better</td>\n",
       "      <td>Graphics: GTX 980 / AMD R9 Fury</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/346110/ARK_...</td>\n",
       "      <td>ARK: Survival Evolved</td>\n",
       "      <td>2017/08</td>\n",
       "      <td>289957</td>\n",
       "      <td>78</td>\n",
       "      <td>226166</td>\n",
       "      <td>Graphics: NVIDIA GTX 670 2GB/AMD Radeon HD 787...</td>\n",
       "      <td>Graphics: NVIDIA GTX 670 2GB/AMD Radeon HD 787...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>2019/10</td>\n",
       "      <td>284689</td>\n",
       "      <td>86</td>\n",
       "      <td>244832</td>\n",
       "      <td>Graphics: NVIDIA® GeForce® GTX 660 2GB or GTX ...</td>\n",
       "      <td>Graphics: NVIDIA® GeForce® GTX 970 4GB or GTX ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>https://store.steampowered.com/app/444090/Pala...</td>\n",
       "      <td>Paladins®</td>\n",
       "      <td>2018/05</td>\n",
       "      <td>280677</td>\n",
       "      <td>85</td>\n",
       "      <td>238575</td>\n",
       "      <td>Graphics: Nvidia GeForce 8800 GT</td>\n",
       "      <td>Graphics: Nvidia GeForce GTX 660 or ATI Radeon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1741</th>\n",
       "      <td>https://store.steampowered.com/app/930310/Puzz...</td>\n",
       "      <td>Puzzle Plunder</td>\n",
       "      <td>2018/10</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>Graphics: Videocard with at least 512MB</td>\n",
       "      <td>Graphics: Videocard with at least 512MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1742</th>\n",
       "      <td>https://store.steampowered.com/app/926520/Love...</td>\n",
       "      <td>Love Letter</td>\n",
       "      <td>2018/10</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>83</td>\n",
       "      <td>Graphics: Nvidia 450 GTS / Radeon HD 5750 or b...</td>\n",
       "      <td>Graphics: Nvidia 650 GTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1749</th>\n",
       "      <td>https://store.steampowered.com/app/1172520/Col...</td>\n",
       "      <td>Colorgrid</td>\n",
       "      <td>2019/11</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>Graphics: Graphics card supporting DirectX 9.0c</td>\n",
       "      <td>Graphics: Graphics card supporting DirectX 9.0c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750</th>\n",
       "      <td>https://store.steampowered.com/app/1018130/Cas...</td>\n",
       "      <td>Castle Break</td>\n",
       "      <td>2019/02</td>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>42</td>\n",
       "      <td>Graphics: existing</td>\n",
       "      <td>Graphics: working with 1920x1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752</th>\n",
       "      <td>https://store.steampowered.com/app/705210/Cube...</td>\n",
       "      <td>Cube Racer</td>\n",
       "      <td>2017/10</td>\n",
       "      <td>37</td>\n",
       "      <td>89</td>\n",
       "      <td>32</td>\n",
       "      <td>Graphics: 512 MB</td>\n",
       "      <td>Graphics: 1 GB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1493 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "0     https://store.steampowered.com/app/578080/PLAY...   \n",
       "2     https://store.steampowered.com/app/252490/Rust...   \n",
       "3     https://store.steampowered.com/app/346110/ARK_...   \n",
       "4     https://store.steampowered.com/app/1085660/Des...   \n",
       "5     https://store.steampowered.com/app/444090/Pala...   \n",
       "...                                                 ...   \n",
       "1741  https://store.steampowered.com/app/930310/Puzz...   \n",
       "1742  https://store.steampowered.com/app/926520/Love...   \n",
       "1749  https://store.steampowered.com/app/1172520/Col...   \n",
       "1750  https://store.steampowered.com/app/1018130/Cas...   \n",
       "1752  https://store.steampowered.com/app/705210/Cube...   \n",
       "\n",
       "                               name     date  num_reviews  pct_like  \\\n",
       "0     PLAYERUNKNOWN'S BATTLEGROUNDS  2017/12      1316559        52   \n",
       "2                              Rust  2018/02       375523        84   \n",
       "3             ARK: Survival Evolved  2017/08       289957        78   \n",
       "4                         Destiny 2  2019/10       284689        86   \n",
       "5                         Paladins®  2018/05       280677        85   \n",
       "...                             ...      ...          ...       ...   \n",
       "1741                 Puzzle Plunder  2018/10           94        93   \n",
       "1742                    Love Letter  2018/10           92        91   \n",
       "1749                      Colorgrid  2019/11           50       100   \n",
       "1750                   Castle Break  2019/02           45        95   \n",
       "1752                     Cube Racer  2017/10           37        89   \n",
       "\n",
       "      num_positive_reviews                                            minimum  \\\n",
       "0                   684610  Graphics: NVIDIA GeForce GTX 960 2GB / AMD Rad...   \n",
       "2                   315439          Graphics: GTX 670 2GB / AMD R9 280 better   \n",
       "3                   226166  Graphics: NVIDIA GTX 670 2GB/AMD Radeon HD 787...   \n",
       "4                   244832  Graphics: NVIDIA® GeForce® GTX 660 2GB or GTX ...   \n",
       "5                   238575                   Graphics: Nvidia GeForce 8800 GT   \n",
       "...                    ...                                                ...   \n",
       "1741                    87            Graphics: Videocard with at least 512MB   \n",
       "1742                    83  Graphics: Nvidia 450 GTS / Radeon HD 5750 or b...   \n",
       "1749                    50    Graphics: Graphics card supporting DirectX 9.0c   \n",
       "1750                    42                                 Graphics: existing   \n",
       "1752                    32                                   Graphics: 512 MB   \n",
       "\n",
       "                                            recommended  \n",
       "0     Graphics: NVIDIA GeForce GTX 1060 3GB / AMD Ra...  \n",
       "2                       Graphics: GTX 980 / AMD R9 Fury  \n",
       "3     Graphics: NVIDIA GTX 670 2GB/AMD Radeon HD 787...  \n",
       "4     Graphics: NVIDIA® GeForce® GTX 970 4GB or GTX ...  \n",
       "5     Graphics: Nvidia GeForce GTX 660 or ATI Radeon...  \n",
       "...                                                 ...  \n",
       "1741            Graphics: Videocard with at least 512MB  \n",
       "1742                           Graphics: Nvidia 650 GTS  \n",
       "1749    Graphics: Graphics card supporting DirectX 9.0c  \n",
       "1750                   Graphics: working with 1920x1080  \n",
       "1752                                     Graphics: 1 GB  \n",
       "\n",
       "[1493 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game = df_game.assign(minimum=min_cards, recommended=rec_cards)\n",
    "df_game = df_game.drop(labels='requirements', axis=1)\n",
    "df_game.dropna(inplace=True)\n",
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove \"Graphics:\" from the added columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game['minimum'] = df_game['minimum'].apply(lambda x: (x.replace('Graphics:', '')).strip())\n",
    "df_game['recommended'] = df_game['recommended'].apply(lambda x: (x.replace('Graphics:', '')).strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                     False\n",
       "name                    False\n",
       "date                    False\n",
       "num_reviews             False\n",
       "pct_like                False\n",
       "num_positive_reviews    False\n",
       "minimum                 False\n",
       "recommended             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game.reset_index(drop=True, inplace=True)\n",
    "df_game.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Popularity\n",
    "\n",
    "For our measurement of popularity, we orignially had the number of reviews and the percentage of positive reviews. From these two values, we got the number of positive reviews. \n",
    "\n",
    "In our analysis, we can measure popularity in three ways. Number of reviews, percentage of positive reviews, and number of postive reviews.\n",
    "\n",
    "Similar to how we determined whether a GPU is high performing, we can take the 75th percentile for each of these categories to determine if a game is considered popular in each of the number of reviews categories.\n",
    "\n",
    "Taking the percentile of the percentage of likes for a game isn't an effective measurement since many games with a small number of reviews is volatile and can result in high numbers for percentage of likes. Instead, we will go by Steam's cutoff of 70%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "percentile_revs = df_game['num_reviews'].quantile([0.75]).loc[0.75]\n",
    "percentile_pos_revs = df_game['num_positive_reviews'].quantile([0.75]).loc[0.75]\n",
    "percentile_pct = 70\n",
    "\n",
    "popular_revs = [(x > percentile_revs) for x in df_game['num_reviews']]\n",
    "popular_pct = [(x > percentile_pct) for x in df_game['pct_like']]\n",
    "popular_pos_revs = [(x > percentile_pos_revs) for x in df_game['num_positive_reviews']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game = df_game.assign(popular_revs=popular_revs, popular_pos_revs=popular_pos_revs, popular_pct_likes=popular_pct)\n",
    "df_game.replace([True, False], [1, 0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>pct_like</th>\n",
       "      <th>num_positive_reviews</th>\n",
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       "      <th>recommended</th>\n",
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       "      <th>popular_pos_revs</th>\n",
       "      <th>popular_pct_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://store.steampowered.com/app/578080/PLAY...</td>\n",
       "      <td>PLAYERUNKNOWN'S BATTLEGROUNDS</td>\n",
       "      <td>2017/12</td>\n",
       "      <td>1316559</td>\n",
       "      <td>52</td>\n",
       "      <td>684610</td>\n",
       "      <td>NVIDIA GeForce GTX 960 2GB / AMD Radeon R7 370...</td>\n",
       "      <td>NVIDIA GeForce GTX 1060 3GB / AMD Radeon RX 58...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://store.steampowered.com/app/252490/Rust...</td>\n",
       "      <td>Rust</td>\n",
       "      <td>2018/02</td>\n",
       "      <td>375523</td>\n",
       "      <td>84</td>\n",
       "      <td>315439</td>\n",
       "      <td>GTX 670 2GB / AMD R9 280 better</td>\n",
       "      <td>GTX 980 / AMD R9 Fury</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://store.steampowered.com/app/346110/ARK_...</td>\n",
       "      <td>ARK: Survival Evolved</td>\n",
       "      <td>2017/08</td>\n",
       "      <td>289957</td>\n",
       "      <td>78</td>\n",
       "      <td>226166</td>\n",
       "      <td>NVIDIA GTX 670 2GB/AMD Radeon HD 7870 2GB or b...</td>\n",
       "      <td>NVIDIA GTX 670 2GB/AMD Radeon HD 7870 2GB or b...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://store.steampowered.com/app/1085660/Des...</td>\n",
       "      <td>Destiny 2</td>\n",
       "      <td>2019/10</td>\n",
       "      <td>284689</td>\n",
       "      <td>86</td>\n",
       "      <td>244832</td>\n",
       "      <td>NVIDIA® GeForce® GTX 660 2GB or GTX 1050 2GB /...</td>\n",
       "      <td>NVIDIA® GeForce® GTX 970 4GB or GTX 1060 6GB /...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://store.steampowered.com/app/444090/Pala...</td>\n",
       "      <td>Paladins®</td>\n",
       "      <td>2018/05</td>\n",
       "      <td>280677</td>\n",
       "      <td>85</td>\n",
       "      <td>238575</td>\n",
       "      <td>Nvidia GeForce 8800 GT</td>\n",
       "      <td>Nvidia GeForce GTX 660 or ATI Radeon HD 7950</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>https://store.steampowered.com/app/930310/Puzz...</td>\n",
       "      <td>Puzzle Plunder</td>\n",
       "      <td>2018/10</td>\n",
       "      <td>94</td>\n",
       "      <td>93</td>\n",
       "      <td>87</td>\n",
       "      <td>Videocard with at least 512MB</td>\n",
       "      <td>Videocard with at least 512MB</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>https://store.steampowered.com/app/926520/Love...</td>\n",
       "      <td>Love Letter</td>\n",
       "      <td>2018/10</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>83</td>\n",
       "      <td>Nvidia 450 GTS / Radeon HD 5750 or better</td>\n",
       "      <td>Nvidia 650 GTS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>https://store.steampowered.com/app/1172520/Col...</td>\n",
       "      <td>Colorgrid</td>\n",
       "      <td>2019/11</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>50</td>\n",
       "      <td>Graphics card supporting DirectX 9.0c</td>\n",
       "      <td>Graphics card supporting DirectX 9.0c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>https://store.steampowered.com/app/1018130/Cas...</td>\n",
       "      <td>Castle Break</td>\n",
       "      <td>2019/02</td>\n",
       "      <td>45</td>\n",
       "      <td>95</td>\n",
       "      <td>42</td>\n",
       "      <td>existing</td>\n",
       "      <td>working with 1920x1080</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>https://store.steampowered.com/app/705210/Cube...</td>\n",
       "      <td>Cube Racer</td>\n",
       "      <td>2017/10</td>\n",
       "      <td>37</td>\n",
       "      <td>89</td>\n",
       "      <td>32</td>\n",
       "      <td>512 MB</td>\n",
       "      <td>1 GB</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1493 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "0     https://store.steampowered.com/app/578080/PLAY...   \n",
       "1     https://store.steampowered.com/app/252490/Rust...   \n",
       "2     https://store.steampowered.com/app/346110/ARK_...   \n",
       "3     https://store.steampowered.com/app/1085660/Des...   \n",
       "4     https://store.steampowered.com/app/444090/Pala...   \n",
       "...                                                 ...   \n",
       "1488  https://store.steampowered.com/app/930310/Puzz...   \n",
       "1489  https://store.steampowered.com/app/926520/Love...   \n",
       "1490  https://store.steampowered.com/app/1172520/Col...   \n",
       "1491  https://store.steampowered.com/app/1018130/Cas...   \n",
       "1492  https://store.steampowered.com/app/705210/Cube...   \n",
       "\n",
       "                               name     date  num_reviews  pct_like  \\\n",
       "0     PLAYERUNKNOWN'S BATTLEGROUNDS  2017/12      1316559        52   \n",
       "1                              Rust  2018/02       375523        84   \n",
       "2             ARK: Survival Evolved  2017/08       289957        78   \n",
       "3                         Destiny 2  2019/10       284689        86   \n",
       "4                         Paladins®  2018/05       280677        85   \n",
       "...                             ...      ...          ...       ...   \n",
       "1488                 Puzzle Plunder  2018/10           94        93   \n",
       "1489                    Love Letter  2018/10           92        91   \n",
       "1490                      Colorgrid  2019/11           50       100   \n",
       "1491                   Castle Break  2019/02           45        95   \n",
       "1492                     Cube Racer  2017/10           37        89   \n",
       "\n",
       "      num_positive_reviews                                            minimum  \\\n",
       "0                   684610  NVIDIA GeForce GTX 960 2GB / AMD Radeon R7 370...   \n",
       "1                   315439                    GTX 670 2GB / AMD R9 280 better   \n",
       "2                   226166  NVIDIA GTX 670 2GB/AMD Radeon HD 7870 2GB or b...   \n",
       "3                   244832  NVIDIA® GeForce® GTX 660 2GB or GTX 1050 2GB /...   \n",
       "4                   238575                             Nvidia GeForce 8800 GT   \n",
       "...                    ...                                                ...   \n",
       "1488                    87                      Videocard with at least 512MB   \n",
       "1489                    83          Nvidia 450 GTS / Radeon HD 5750 or better   \n",
       "1490                    50              Graphics card supporting DirectX 9.0c   \n",
       "1491                    42                                           existing   \n",
       "1492                    32                                             512 MB   \n",
       "\n",
       "                                            recommended  popular_revs  \\\n",
       "0     NVIDIA GeForce GTX 1060 3GB / AMD Radeon RX 58...             1   \n",
       "1                                 GTX 980 / AMD R9 Fury             1   \n",
       "2     NVIDIA GTX 670 2GB/AMD Radeon HD 7870 2GB or b...             1   \n",
       "3     NVIDIA® GeForce® GTX 970 4GB or GTX 1060 6GB /...             1   \n",
       "4          Nvidia GeForce GTX 660 or ATI Radeon HD 7950             1   \n",
       "...                                                 ...           ...   \n",
       "1488                      Videocard with at least 512MB             0   \n",
       "1489                                     Nvidia 650 GTS             0   \n",
       "1490              Graphics card supporting DirectX 9.0c             0   \n",
       "1491                             working with 1920x1080             0   \n",
       "1492                                               1 GB             0   \n",
       "\n",
       "      popular_pos_revs  popular_pct_likes  \n",
       "0                    1                  0  \n",
       "1                    1                  1  \n",
       "2                    1                  1  \n",
       "3                    1                  1  \n",
       "4                    1                  1  \n",
       "...                ...                ...  \n",
       "1488                 0                  1  \n",
       "1489                 0                  1  \n",
       "1490                 0                  1  \n",
       "1491                 0                  1  \n",
       "1492                 0                  1  \n",
       "\n",
       "[1493 rows x 11 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graphically Demanding\n",
    "\n",
    "Using the minimum and recommended requirements variables can help us determine whether a game is graphically demanding. However, the reason why we had to scrape GPU data month by month is because games have different release dates. We can't use GPU data from 2019 to evaluate a game from 2017 because GPUs from 2019 are higher performing than GPUs from 2017.\n",
    "\n",
    "Replace company name from names:\n",
    "Steam would sometimes include multiple GPUs in their minimum/recommended hardware section. When two or more GPUs from the same company are listed, Steam would only include the full title of one GPU and the partial title for the other GPU. For example, assume two Nvidia cards, Nvidia Geforce GTX 1070 and Nvidia Geforce GTX 1080 are listed. Steam would list this as Nvidia GeForce GTX 1070 / GTX 1080. So removing \"Nvidia GeForce\" would allow us to search for the GTX 1080 part as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = './GPU_Data/' + dates_months + '_75_ptile.csv'\n",
    "\n",
    "def replace_company_name(str_in):\n",
    "    str_in = str_in.replace('NVIDIA GeForce', '').strip()\n",
    "    str_in = str_in.replace('AMD Radeon', '').strip()\n",
    "    str_in = str_in.replace('Intel', '').strip()\n",
    "    return str_in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>num_reviews</th>\n",
       "      <th>pct_like</th>\n",
       "      <th>num_positive_reviews</th>\n",
       "      <th>minimum</th>\n",
       "      <th>recommended</th>\n",
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       "      <th>popular_pos_revs</th>\n",
       "      <th>popular_pct_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>https://store.steampowered.com/app/418370/Resi...</td>\n",
       "      <td>Resident Evil 7 Biohazard</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>20879</td>\n",
       "      <td>93</td>\n",
       "      <td>19417</td>\n",
       "      <td>NVIDIA® GeForce® GTX 760 or AMD Radeon™ R7 260...</td>\n",
       "      <td>NVIDIA® GeForce® GTX 1060 with 3GB VRAM</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1435</th>\n",
       "      <td>https://store.steampowered.com/app/556180/Myst...</td>\n",
       "      <td>Mysterium: A Psychic Clue Game</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>230</td>\n",
       "      <td>78</td>\n",
       "      <td>179</td>\n",
       "      <td>Intel HD Graphics 3000</td>\n",
       "      <td>Intel HD Graphics 4000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>955</th>\n",
       "      <td>https://store.steampowered.com/app/571880/Ange...</td>\n",
       "      <td>Angels with Scaly Wings</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>1039</td>\n",
       "      <td>98</td>\n",
       "      <td>1018</td>\n",
       "      <td>DirectX Compatible Card</td>\n",
       "      <td>DirectX Compatible Card</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>https://store.steampowered.com/app/559610/Love...</td>\n",
       "      <td>Love Ribbon</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>1396</td>\n",
       "      <td>93</td>\n",
       "      <td>1298</td>\n",
       "      <td>DirectX or OpenGL compatible card</td>\n",
       "      <td>DirectX or OpenGL compatible card</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>https://store.steampowered.com/app/568320/Pict...</td>\n",
       "      <td>Pictopix</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>499</td>\n",
       "      <td>96</td>\n",
       "      <td>479</td>\n",
       "      <td>Shader Model 2.0</td>\n",
       "      <td>Shader Model 2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>https://store.steampowered.com/app/1201400/Not...</td>\n",
       "      <td>Not For Broadcast: Prologue</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>510</td>\n",
       "      <td>92</td>\n",
       "      <td>469</td>\n",
       "      <td>Dedicated video card is required</td>\n",
       "      <td>Dedicated video card is required</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>https://store.steampowered.com/app/393420/Hurt...</td>\n",
       "      <td>Hurtworld</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>21386</td>\n",
       "      <td>75</td>\n",
       "      <td>16039</td>\n",
       "      <td>GeForce 460/Radeon HD 5850/Intel HD 4600 with ...</td>\n",
       "      <td>GeForce 660/Radeon HD 6970</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>https://store.steampowered.com/app/601220/Zup_...</td>\n",
       "      <td>Zup! F</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>1071</td>\n",
       "      <td>97</td>\n",
       "      <td>1038</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>https://store.steampowered.com/app/1066780/Tra...</td>\n",
       "      <td>Transport Fever 2</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>7742</td>\n",
       "      <td>84</td>\n",
       "      <td>6503</td>\n",
       "      <td>NVIDIA GeForce GTX 560 or AMD Radeon HD 7850, ...</td>\n",
       "      <td>NVIDIA GeForce GTX 1060 or AMD Radeon RX 580, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>https://store.steampowered.com/app/351290/SURV...</td>\n",
       "      <td>SURVIVAL: Postapocalypse Now</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>7614</td>\n",
       "      <td>54</td>\n",
       "      <td>4111</td>\n",
       "      <td>NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD s...</td>\n",
       "      <td>NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD s...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1493 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "98    https://store.steampowered.com/app/418370/Resi...   \n",
       "1435  https://store.steampowered.com/app/556180/Myst...   \n",
       "955   https://store.steampowered.com/app/571880/Ange...   \n",
       "807   https://store.steampowered.com/app/559610/Love...   \n",
       "1257  https://store.steampowered.com/app/568320/Pict...   \n",
       "...                                                 ...   \n",
       "1253  https://store.steampowered.com/app/1201400/Not...   \n",
       "96    https://store.steampowered.com/app/393420/Hurt...   \n",
       "936   https://store.steampowered.com/app/601220/Zup_...   \n",
       "236   https://store.steampowered.com/app/1066780/Tra...   \n",
       "242   https://store.steampowered.com/app/351290/SURV...   \n",
       "\n",
       "                                name     date  num_reviews  pct_like  \\\n",
       "98         Resident Evil 7 Biohazard  2017/01        20879        93   \n",
       "1435  Mysterium: A Psychic Clue Game  2017/01          230        78   \n",
       "955          Angels with Scaly Wings  2017/01         1039        98   \n",
       "807                      Love Ribbon  2017/01         1396        93   \n",
       "1257                        Pictopix  2017/01          499        96   \n",
       "...                              ...      ...          ...       ...   \n",
       "1253     Not For Broadcast: Prologue  2019/12          510        92   \n",
       "96                         Hurtworld  2019/12        21386        75   \n",
       "936                           Zup! F  2019/12         1071        97   \n",
       "236                Transport Fever 2  2019/12         7742        84   \n",
       "242     SURVIVAL: Postapocalypse Now  2019/12         7614        54   \n",
       "\n",
       "      num_positive_reviews                                            minimum  \\\n",
       "98                   19417  NVIDIA® GeForce® GTX 760 or AMD Radeon™ R7 260...   \n",
       "1435                   179                             Intel HD Graphics 3000   \n",
       "955                   1018                            DirectX Compatible Card   \n",
       "807                   1298                  DirectX or OpenGL compatible card   \n",
       "1257                   479                                   Shader Model 2.0   \n",
       "...                    ...                                                ...   \n",
       "1253                   469                   Dedicated video card is required   \n",
       "96                   16039  GeForce 460/Radeon HD 5850/Intel HD 4600 with ...   \n",
       "936                   1038                                  Intel HD Graphics   \n",
       "236                   6503  NVIDIA GeForce GTX 560 or AMD Radeon HD 7850, ...   \n",
       "242                   4111  NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD s...   \n",
       "\n",
       "                                            recommended  popular_revs  \\\n",
       "98              NVIDIA® GeForce® GTX 1060 with 3GB VRAM             1   \n",
       "1435                             Intel HD Graphics 4000             0   \n",
       "955                             DirectX Compatible Card             0   \n",
       "807                   DirectX or OpenGL compatible card             0   \n",
       "1257                                   Shader Model 2.0             0   \n",
       "...                                                 ...           ...   \n",
       "1253                   Dedicated video card is required             0   \n",
       "96                           GeForce 660/Radeon HD 6970             1   \n",
       "936                                   Intel HD Graphics             0   \n",
       "236   NVIDIA GeForce GTX 1060 or AMD Radeon RX 580, ...             1   \n",
       "242   NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD s...             1   \n",
       "\n",
       "      popular_pos_revs  popular_pct_likes  \n",
       "98                   1                  1  \n",
       "1435                 0                  1  \n",
       "955                  0                  1  \n",
       "807                  0                  1  \n",
       "1257                 0                  1  \n",
       "...                ...                ...  \n",
       "1253                 0                  1  \n",
       "96                   1                  1  \n",
       "936                  0                  1  \n",
       "236                  1                  1  \n",
       "242                  1                  0  \n",
       "\n",
       "[1493 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game = df_game.sort_values(by='date')\n",
    "df_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_game.set_index(np.array(range(0, df_game.shape[0])), inplace=True)\n",
    "df_GPU = pd.read_csv(files[0])\n",
    "df_GPU['GPU Name'] = df_GPU['GPU Name'].apply(lambda x: replace_company_name(x))\n",
    "df_GPU['Change (%)'] = df_GPU['Change (%)'].apply(lambda x: float(x.replace('%', '')))\n",
    "change_pct = df_GPU['Change (%)'].sum()\n",
    "previous_date = df_game.loc[0, 'date']\n",
    "\n",
    "min_demanding = np.zeros(df_game.shape[0], dtype=int)\n",
    "rec_demanding = np.zeros(df_game.shape[0], dtype=int)\n",
    "gpu_change_pct = []\n",
    "\n",
    "count = 0\n",
    "\n",
    "for x in range(0, df_game.shape[0]):\n",
    "    if (df_game.loc[x, 'date'] != previous_date):\n",
    "        df_GPU = pd.read_csv(files[count+1])\n",
    "        df_GPU['GPU Name'] = df_GPU['GPU Name'].apply(lambda x: replace_company_name(x))\n",
    "        df_GPU['Change (%)'] = df_GPU['Change (%)'].apply(lambda x: float(x.replace('%', '')))\n",
    "        count += 1\n",
    "        previous_date = df_game.loc[x, 'date']\n",
    "        change_pct = df_GPU['Change (%)'].sum()\n",
    "        \n",
    "    gpu_change_pct.append(change_pct)\n",
    "    for name in df_GPU['GPU Name']:\n",
    "        if name in df_game.loc[x, 'minimum']:\n",
    "            min_demanding[x] = 1\n",
    "            rec_demanding[x] = 1\n",
    "            break\n",
    "        elif name in df_game.loc[x, 'recommended']:\n",
    "            rec_demanding[x] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've determined which games are and aren't demanding, we can create columns for these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>popular_revs</th>\n",
       "      <th>popular_pos_revs</th>\n",
       "      <th>popular_pct_likes</th>\n",
       "      <th>min_demanding</th>\n",
       "      <th>rec_demanding</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resident Evil 7 Biohazard</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mysterium: A Psychic Clue Game</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angels with Scaly Wings</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Love Ribbon</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pictopix</td>\n",
       "      <td>2017/01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1488</th>\n",
       "      <td>Not For Broadcast: Prologue</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>Hurtworld</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>Zup! F</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1491</th>\n",
       "      <td>Transport Fever 2</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1492</th>\n",
       "      <td>SURVIVAL: Postapocalypse Now</td>\n",
       "      <td>2019/12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1493 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name     date  popular_revs  popular_pos_revs  \\\n",
       "0          Resident Evil 7 Biohazard  2017/01             1                 1   \n",
       "1     Mysterium: A Psychic Clue Game  2017/01             0                 0   \n",
       "2            Angels with Scaly Wings  2017/01             0                 0   \n",
       "3                        Love Ribbon  2017/01             0                 0   \n",
       "4                           Pictopix  2017/01             0                 0   \n",
       "...                              ...      ...           ...               ...   \n",
       "1488     Not For Broadcast: Prologue  2019/12             0                 0   \n",
       "1489                       Hurtworld  2019/12             1                 1   \n",
       "1490                          Zup! F  2019/12             0                 0   \n",
       "1491               Transport Fever 2  2019/12             1                 1   \n",
       "1492    SURVIVAL: Postapocalypse Now  2019/12             1                 1   \n",
       "\n",
       "      popular_pct_likes  min_demanding  rec_demanding  change_pct  \n",
       "0                     1              1              1        0.58  \n",
       "1                     1              0              0        0.58  \n",
       "2                     1              0              0        0.58  \n",
       "3                     1              0              0        0.58  \n",
       "4                     1              0              0        0.58  \n",
       "...                 ...            ...            ...         ...  \n",
       "1488                  1              0              0        1.22  \n",
       "1489                  1              0              0        1.22  \n",
       "1490                  1              0              0        1.22  \n",
       "1491                  1              0              0        1.22  \n",
       "1492                  0              0              0        1.22  \n",
       "\n",
       "[1493 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game = df_game.assign(min_demanding=min_demanding, rec_demanding=rec_demanding, change_pct=gpu_change_pct)\n",
    "df_game.drop(columns=['url', 'num_reviews', 'pct_like', 'num_positive_reviews', 'minimum', 'recommended'], inplace=True)\n",
    "df_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                 False\n",
       "date                 False\n",
       "popular_revs         False\n",
       "popular_pos_revs     False\n",
       "popular_pct_likes    False\n",
       "min_demanding        False\n",
       "rec_demanding        False\n",
       "change_pct           False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_game.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>popular_revs</th>\n",
       "      <th>popular_pos_revs</th>\n",
       "      <th>popular_pct_likes</th>\n",
       "      <th>min_demanding</th>\n",
       "      <th>rec_demanding</th>\n",
       "      <th>change_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.800000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/02</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.680000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/03</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.250000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/04</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.140000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/05</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-2.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017/06</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.260000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017/07</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-3.469447e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017/08</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.370000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017/09</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.380000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017/10</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.360000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017/11</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.920000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017/12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.160000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018/01</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.665335e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018/02</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-1.580000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018/03</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.300000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018/04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-6.150000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018/05</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.300000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018/06</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.100000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018/07</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-2.300000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018/08</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.960000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018/09</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018/10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.600000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018/11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-3.600000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018/12</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.900000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019/01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-2.800000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019/02</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.320000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019/03</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.700000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019/04</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.800000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019/05</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.800000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019/06</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.900000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2019/07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-1.340000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2019/08</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.300000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2019/09</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.520000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2019/10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.900000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2019/11</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.610000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2019/12</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.220000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  popular_revs  popular_pos_revs  popular_pct_likes  min_demanding  \\\n",
       "0   2017/01           5.0               5.0               13.0            1.0   \n",
       "1   2017/02          12.0              12.0               20.0            1.0   \n",
       "2   2017/03          11.0              11.0               35.0            2.0   \n",
       "3   2017/04          11.0              12.0               29.0            2.0   \n",
       "4   2017/05          12.0              13.0               37.0            3.0   \n",
       "5   2017/06           7.0               9.0               25.0            1.0   \n",
       "6   2017/07          10.0              10.0               23.0            3.0   \n",
       "7   2017/08          14.0              14.0               40.0            5.0   \n",
       "8   2017/09          12.0              11.0               41.0            1.0   \n",
       "9   2017/10          12.0              14.0               27.0            2.0   \n",
       "10  2017/11           8.0               9.0               27.0            3.0   \n",
       "11  2017/12           6.0               7.0               26.0            5.0   \n",
       "12  2018/01           7.0               6.0               24.0            2.0   \n",
       "13  2018/02          10.0               9.0               30.0            1.0   \n",
       "14  2018/03          12.0              12.0               43.0            1.0   \n",
       "15  2018/04          10.0               9.0               22.0            4.0   \n",
       "16  2018/05          13.0              13.0               37.0            2.0   \n",
       "17  2018/06          12.0              11.0               22.0            2.0   \n",
       "18  2018/07           4.0               4.0               24.0            2.0   \n",
       "19  2018/08          14.0              15.0               44.0            2.0   \n",
       "20  2018/09          15.0              16.0               50.0            4.0   \n",
       "21  2018/10           8.0               6.0               34.0            1.0   \n",
       "22  2018/11          11.0              12.0               48.0            3.0   \n",
       "23  2018/12          15.0              14.0               32.0            6.0   \n",
       "24  2019/01          12.0              13.0               35.0            3.0   \n",
       "25  2019/02          11.0              11.0               32.0            4.0   \n",
       "26  2019/03          11.0              10.0               36.0            3.0   \n",
       "27  2019/04          13.0              14.0               42.0            1.0   \n",
       "28  2019/05          10.0              11.0               42.0            3.0   \n",
       "29  2019/06          13.0              12.0               47.0            4.0   \n",
       "30  2019/07           5.0               5.0               35.0            3.0   \n",
       "31  2019/08           3.0               5.0               46.0            2.0   \n",
       "32  2019/09          14.0              14.0               41.0            1.0   \n",
       "33  2019/10          10.0               9.0               47.0            0.0   \n",
       "34  2019/11           9.0               6.0               54.0            0.0   \n",
       "35  2019/12          11.0               9.0               43.0            0.0   \n",
       "\n",
       "    rec_demanding    change_pct  \n",
       "0             1.0  5.800000e-01  \n",
       "1             6.0  1.680000e+00  \n",
       "2            11.0  2.250000e+00  \n",
       "3             7.0  1.140000e+00  \n",
       "4            11.0 -2.000000e-02  \n",
       "5            10.0  3.260000e+00  \n",
       "6             8.0 -3.469447e-18  \n",
       "7            13.0  5.370000e+00  \n",
       "8            13.0  2.380000e+00  \n",
       "9             8.0  8.360000e+00  \n",
       "10            7.0  2.920000e+00  \n",
       "11           12.0  2.160000e+00  \n",
       "12            4.0  1.665335e-16  \n",
       "13           13.0 -1.580000e+00  \n",
       "14           13.0  8.300000e-01  \n",
       "15           10.0 -6.150000e+00  \n",
       "16           10.0  2.300000e-01  \n",
       "17           10.0  2.100000e-01  \n",
       "18            7.0 -2.300000e-01  \n",
       "19           15.0  1.960000e+00  \n",
       "20           20.0  5.000000e-01  \n",
       "21           14.0  5.600000e-01  \n",
       "22           16.0 -3.600000e-01  \n",
       "23           21.0  3.900000e-01  \n",
       "24           15.0 -2.800000e-01  \n",
       "25           17.0  2.320000e+00  \n",
       "26           15.0  1.700000e-01  \n",
       "27           14.0  7.800000e-01  \n",
       "28           24.0  5.800000e-01  \n",
       "29           15.0  6.900000e-01  \n",
       "30           17.0 -1.340000e+00  \n",
       "31           14.0  6.300000e-01  \n",
       "32           15.0  3.520000e+00  \n",
       "33           11.0  5.900000e-01  \n",
       "34           12.0  1.610000e+00  \n",
       "35            3.0  1.220000e+00  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final = pd.DataFrame({'date':[],'popular_revs':[],'popular_pos_revs':[],'popular_pct_likes':[],'min_demanding':[],'rec_demanding':[],'change_pct':[]})\n",
    "dates = df_game['date'].unique()\n",
    "\n",
    "temp = df_game[df_game['date'] == '2017/01']\n",
    "for x in dates:\n",
    "    temp = df_game[df_game['date'] == x]\n",
    "    try:\n",
    "        pop_revs = (temp['popular_revs'].value_counts()[1])\n",
    "    except:\n",
    "        pop_revs = 0\n",
    "    \n",
    "    try:\n",
    "        pop_pos_revs = (temp['popular_pos_revs'].value_counts()[1])\n",
    "    except:\n",
    "        pop_pos_revs = 0\n",
    "        \n",
    "    try:\n",
    "        pop_pct_likes = (temp['popular_pct_likes'].value_counts()[1])\n",
    "    except:\n",
    "        pop_pct_likes = 0\n",
    "        \n",
    "    try:\n",
    "        min_demand = (temp['min_demanding'].value_counts()[1])\n",
    "    except:\n",
    "        min_demand = 0\n",
    "\n",
    "    try:\n",
    "        rec_demand = (temp['rec_demanding'].value_counts()[1])\n",
    "    except:\n",
    "        rec_demand = 0\n",
    "        \n",
    "    change_pct = (temp['change_pct'].unique()[0])\n",
    "    \n",
    "    df2 = pd.DataFrame({'date': x,\n",
    "    'popular_revs':[pop_revs],\n",
    "    'popular_pos_revs':[pop_pos_revs],\n",
    "    'popular_pct_likes':[pop_pct_likes],\n",
    "    'min_demanding':[min_demand],\n",
    "    'rec_demanding':[rec_demand],\n",
    "    'change_pct':[change_pct]}, index=[0])\n",
    "    \n",
    "    df_final = df_final.append(df2, ignore_index=True)\n",
    "\n",
    "df_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have our final dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary:\n",
    "\n",
    "The variables we will use for data analysis.\n",
    "\n",
    "### Independent Variables\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- popular_pct_likes\n",
    "- min_demanding\n",
    "- rec_demanding\n",
    "\n",
    "### Dependent Variable\n",
    "- change_pct\n",
    "\n",
    "\n",
    "The following information gives the definition for all of the variables contained in our dataframe.\n",
    "\n",
    "#### Identification\n",
    "date:\n",
    "- Time frame we're looking at\n",
    "\n",
    "#### Popularity \n",
    "popular_revs: \n",
    "- Number of games that are popular depending on the number of reviews\n",
    "\n",
    "popular_pos_revs: \n",
    "- Number of games that are popular depending on the number of positive reviews\n",
    "\n",
    "popular_pct_likes: \n",
    "- Number of games that are popular depending on the percetange of likes\n",
    "\n",
    "#### Graphically Intensive\n",
    "min_demanding: \n",
    "- Number of games that have demanding minimum requirements\n",
    "\n",
    "rec_demanding: \n",
    "- Number of games that have demanding recommended requirements\n",
    "\n",
    "#### Usage/sales\n",
    "change_pct:\n",
    "- The change in usage percentage for higher performing/latest GPUs for the month when the game was released."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to determine if there's a correlation between release of popular, graphically demanding games and GPU sales. We were unable to attain sales numbers of specific GPUs so as an alternative, we will be using the change in usage percentage. We know as sales for newer GPUs increase, it's likely that usage rate of newer GPUs also increase. There are some problems with this that we will address later in the issues section.\n",
    "\n",
    "First, lets look at the distribution of usage percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Change in Usage Percentage')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.histplot(x=df_final['change_pct'], kde=True, stat=\"density\", linewidth=0).set(xlabel='Change in Usage Percentage')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the distribution for change in usage percentage of higher performing/newer GPUs, we can see that it is normal with a mean greater than 0 as well as some larger values in both the negative and positive directions. We can determine when these occur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.lineplot(x='date', y='change_pct', data=df_final)\n",
    "plt.xlabel('Date', fontsize = 13)\n",
    "plt.ylabel('Change in usage percentage', fontsize = 13)\n",
    "plt.title('Change in usage percentage plotted over time')\n",
    "plt.xticks(fontsize=12, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were able to identify where the large values from the distribution plot occurs, but more importantly, we can see that the change in usage percentage has periods of sudden increase and some periods show greater increase than others. Now that we know there exists periods where usage rate of higher performing/newer GPUs, we can begin to analyze if there's any correlation to our popular/demanding variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking for Patterns (Eyeball Test)\n",
    "\n",
    "Lets see if any of the popularity variables show any similar patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.lineplot(x='date', y='popular_revs', data=df_final)\n",
    "\n",
    "plt.xticks(fontsize=12, rotation=90)\n",
    "plt.title('Popular games based on number of reviews', fontsize = 13)\n",
    "plt.ylabel('Popular games', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.lineplot(x='date', y='popular_pos_revs', data=df_final)\n",
    "plt.ylabel('Popular games', fontsize = 13)\n",
    "plt.title('Popular games based on number of positive reviews')\n",
    "plt.xticks(fontsize=12, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.lineplot(x='date', y='popular_pct_likes', data=df_final)\n",
    "plt.title('Popular games based on percentage of likes')\n",
    "plt.ylabel('Popular games', fontsize = 13)\n",
    "plt.xticks(fontsize=12, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What do we find about the popularity graphs?\n",
    "\n",
    "None of them show any significant patterns to the change in usage percentage graph.\n",
    "\n",
    "There's a few small differences between the number of games with a popular number of reviews and the number of games with a popular number of positive reviews, but in general, they share a similar pattern. Their slope throughout the years looks to be horizontal, but it isn't entirely clear by simply looking at the graphs.\n",
    "\n",
    "The graph for number of games with a popular percentage of likes has an upward sloping pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.lineplot(x='date', y='min_demanding', data=df_final)\n",
    "plt.title('Number of games that have demanding minimum requirements over time')\n",
    "plt.ylabel('Number of games', fontsize = 13)\n",
    "\n",
    "plt.xticks(fontsize=12, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.lineplot(x='date', y='rec_demanding', data=df_final)\n",
    "plt.title('Number of games that have demanding recommended requirements over time')\n",
    "plt.ylabel('Number of games', fontsize = 13)\n",
    "\n",
    "plt.xticks(fontsize=12, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What do we find about the demanding graphs?\n",
    "\n",
    "There doesn't appear to be any significant patterns relating to change in usage percentage.\n",
    "\n",
    "The graph for the number of games with a demanding minimum requirement looks to have a horizontal slope.\n",
    "\n",
    "The slope of the graph for the number of games with a demanding recommended requirement looks to be positive, but it's not entirely clear since it begins to drop significantly in the later months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression\n",
    "\n",
    "Instead of simply looking for patterns, linear regression models can be used to look for correlations. We can start by plotting the graph to get a visual, then take a look at the Ordinary Least Squares (OLS) regression results for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Change in usage percentage')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('Change in usage percentage plotted over popular reviews')\n",
    "\n",
    "sns.regplot(x='popular_revs', y='change_pct', data=df_final)\n",
    "plt.xlabel('Popular reviews', fontsize = 13)\n",
    "plt.ylabel('Change in usage percentage', fontsize = 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.256</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:18</td>     <th>  Log-Likelihood:    </th> <td> -78.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   161.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    34</td>      <th>  BIC:               </th> <td>   164.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>    <td>   -0.4226</td> <td>    1.305</td> <td>   -0.324</td> <td> 0.748</td> <td>   -3.075</td> <td>    2.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th> <td>    0.1398</td> <td>    0.121</td> <td>    1.156</td> <td> 0.256</td> <td>   -0.106</td> <td>    0.385</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.732</td> <th>  Durbin-Watson:     </th> <td>   1.523</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  27.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.180</td> <th>  Prob(JB):          </th> <td>9.00e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.293</td> <th>  Cond. No.          </th> <td>    38.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.038\n",
       "Model:                            OLS   Adj. R-squared:                  0.010\n",
       "Method:                 Least Squares   F-statistic:                     1.337\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.256\n",
       "Time:                        23:55:18   Log-Likelihood:                -78.499\n",
       "No. Observations:                  36   AIC:                             161.0\n",
       "Df Residuals:                      34   BIC:                             164.2\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "================================================================================\n",
       "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "Intercept       -0.4226      1.305     -0.324      0.748      -3.075       2.230\n",
       "popular_revs     0.1398      0.121      1.156      0.256      -0.106       0.385\n",
       "==============================================================================\n",
       "Omnibus:                       10.732   Durbin-Watson:                   1.523\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               27.842\n",
       "Skew:                           0.180   Prob(JB):                     9.00e-07\n",
       "Kurtosis:                       7.293   Cond. No.                         38.7\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for popular_revs is 0.256 meaning that there is a %25.6 chance that popular_revs has no affect on change_pct (dependent variable). The relationship is not statistically significant (p > 0.05). R-squared is 0.038, which means that %3.8 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Change in usage percentage')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.title('Change in usage percentage plotted over popular positive reviews')\n",
    "\n",
    "sns.regplot(x='popular_pos_revs', y='change_pct', data=df_final)\n",
    "plt.xlabel('Popular positive reviews', fontsize = 13)\n",
    "plt.ylabel('Change in usage percentage', fontsize = 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3.406</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td>0.0737</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:19</td>     <th>  Log-Likelihood:    </th> <td> -77.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   158.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    34</td>      <th>  BIC:               </th> <td>   162.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -1.1260</td> <td>    1.219</td> <td>   -0.923</td> <td> 0.362</td> <td>   -3.604</td> <td>    1.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th> <td>    0.2077</td> <td>    0.113</td> <td>    1.846</td> <td> 0.074</td> <td>   -0.021</td> <td>    0.436</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.041</td> <th>  Durbin-Watson:     </th> <td>   1.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.011</td> <th>  Jarque-Bera (JB):  </th> <td>  19.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.018</td> <th>  Prob(JB):          </th> <td>5.68e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.610</td> <th>  Cond. No.          </th> <td>    37.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.091\n",
       "Model:                            OLS   Adj. R-squared:                  0.064\n",
       "Method:                 Least Squares   F-statistic:                     3.406\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):             0.0737\n",
       "Time:                        23:55:19   Log-Likelihood:                -77.474\n",
       "No. Observations:                  36   AIC:                             158.9\n",
       "Df Residuals:                      34   BIC:                             162.1\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -1.1260      1.219     -0.923      0.362      -3.604       1.352\n",
       "popular_pos_revs     0.2077      0.113      1.846      0.074      -0.021       0.436\n",
       "==============================================================================\n",
       "Omnibus:                        9.041   Durbin-Watson:                   1.474\n",
       "Prob(Omnibus):                  0.011   Jarque-Bera (JB):               19.551\n",
       "Skew:                           0.018   Prob(JB):                     5.68e-05\n",
       "Kurtosis:                       6.610   Cond. No.                         37.3\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pos_revs', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for popular_pos_revs is 0.074 meaning that there is a %7.4 chance that popular_pos_revs has no affect on change_pct (dependent variable). So, the relationship is not statistically significant (p > 0.05). R-squared is 0.091, which means that %9.1 of the variance in change_pct can be explained by our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Change in usage percentage plotted over percentage of likes')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.regplot(x='popular_pct_likes', y='change_pct', data=df_final)\n",
    "plt.xlabel('Percentage of likes', fontsize = 13)\n",
    "plt.ylabel('Change in usage percentage', fontsize = 13)\n",
    "plt.title('Change in usage percentage plotted over percentage of likes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.2195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.642</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:19</td>     <th>  Log-Likelihood:    </th> <td> -79.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   162.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    34</td>      <th>  BIC:               </th> <td>   165.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    0.3993</td> <td>    1.388</td> <td>    0.288</td> <td> 0.775</td> <td>   -2.422</td> <td>    3.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0180</td> <td>    0.038</td> <td>    0.469</td> <td> 0.642</td> <td>   -0.060</td> <td>    0.096</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.082</td> <th>  Durbin-Watson:     </th> <td>   1.583</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  29.875</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.437</td> <th>  Prob(JB):          </th> <td>3.26e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.376</td> <th>  Cond. No.          </th> <td>    135.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.006\n",
       "Model:                            OLS   Adj. R-squared:                 -0.023\n",
       "Method:                 Least Squares   F-statistic:                    0.2195\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.642\n",
       "Time:                        23:55:19   Log-Likelihood:                -79.077\n",
       "No. Observations:                  36   AIC:                             162.2\n",
       "Df Residuals:                      34   BIC:                             165.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             0.3993      1.388      0.288      0.775      -2.422       3.221\n",
       "popular_pct_likes     0.0180      0.038      0.469      0.642      -0.060       0.096\n",
       "==============================================================================\n",
       "Omnibus:                       12.082   Durbin-Watson:                   1.583\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               29.875\n",
       "Skew:                           0.437   Prob(JB):                     3.26e-07\n",
       "Kurtosis:                       7.376   Cond. No.                         135.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for popular_pct_likes is 0.642 meaning that there is a %64.2 chance that popular_pct_likes has no affect on change_pct (dependent variable). Therefore, the relationship is not statistically significant (p > 0.05). R-squared is 0.006, which means that %0.6 of the variance in change_pct can be explained by our model. As we can see, effects of popular_pct_likes on our dependent variable is minimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Change in usage percentage plotted over # games that have demanding min requirements')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HUxxruzci1lEcjzVbUz7GiPjhKKaCWErxY9V+iv/LTtr36UyOL7d3ZxSDVPzRUW6v0/anfI4y82sU7yvvAD6dmXeWq7p+3yhfWx+jeH9dHhEPojgOrOVI3zemMxefA63/3ccy+XPgSMz02uwn0/3//ifFZ8tvR8RQFK3rj6jc9hvAOeV72DKKVu0jMZv31um2NSefGeX+Py8iTszMUYr/kZ5Pq6HBY9DTopKZf0nx5fLVFB/U2ylCySe6uO23KI6p+U+KN+2HUBxH1K3zKI4Zu53iwPePUByzRBlinkHxa3errt9jfv5HX1Ru+3aKASKqv0aeC3wlIvZStE69NDNvoDhe8FMUg2fcRPFls9o15yJgB8XB/Z+j+BLSemwNii8zP1quv43iS101TLT7EMUXzjsovpyfV25rD8UB88+maK26lYkD7w9Tue/7URwPsYMiSJCZHy9ve1kUXcr+B3hKp+10cCWwBbg1Im4rl/0GcFFE7KH4QWH819nM3EJxTMxlFMFzD8UxVwfLq1xM8Xx/prz9f1G0bkzlVoog/n2Krm4vzsytHR7/vwOvoWgluYUi1D+7cpXXAu+NoivSL5Tb+DCwrVx2xnS1lY/rNyn21y1lTdPNw/cSil/otwFfKm/3rkq9XynXn0Hxemstv4bidfu28j6+Q3HMy5FqTafwEOCbbd3FDtPFfns5RaDfQ/Fl9iMdNtOVGR7jUooBj26j2PerKN4rOnkn8KBy/32ii7t+K8WgFbdR7Nt/a1t/MUWL2+6I+KsuH05VN8/RhymOPxwP97N43/gtim5yt1Ict/XuyraO6H1jOnPwOfDLFP8/d1C8x73vSGso65jptdk3pnttZ+Yh4P+Wl3dTvD9/rHLb/6X4fPkcxQieRzoX3ZG+t073OOb6M+O5wI3ltl5MMeCZNKdihs85SfMkiqkLtmZm+y/oC15E/DrFYAw/MYvbvodiQJBXz3lhfSKK7qh3UnRnu+EIb7uRYvCGOW3t1cyOZr9J82mQXpuL4TNAOlZs0ZOOkbKr0A9FMa/Pkyla8D7R47LmREScHhGPKR/bD1Mc9/PxmW63mETEz5bdylZQDIX+TYqR7dTH3G/qV742Jc3EoCcdO6dRDJe9F/gr4NfL41MGwRKK4cr3UHRR+Sfgb3paUf95BkW3se8D96do8bRLRf9zv6lf+dqUNC27bkqSJEnSgLFFT5IkSZIGjEFPkiRJkgZMp0mEF4R73eteefbZZ/e6DEmSJEnqiWuvvfa2zDy107oFG/TOPvtsrrnmml6XIUmSJEk9ERE3TbXOrpuSJEmSNGAMepIkSZI0YAx6kiRJkjRgDHqSJEmSNGAMepIkSZI0YAx6kiRJkjRgDHqSJEmSNGAMepIkSZI0YAx6kiRJkjRgDHqSJEmSNGAMepIkSZI0YAx6kiRJkjRgDHqSJEmSNGAMepIkSZI0YAx6kiRJkjRghnpdgCQtVJu27uSSzdvYvnsfa1Yu54INa9m4blWvy5IkSbJFT5JmY9PWnVx4xRZ27jnASSPD7NxzgAuv2MKmrTt7XZokSZJBT5Jm45LN2xiuB8uXDBFR/B2uB5ds3tbr0iRJkgx6kjQb23fvY2S4PmnZyHCdHbv39agiSZKkCQY9SZqFNSuXs3+0MWnZ/tEGq1cu71FFkiRJEwx6kjQLF2xYy2gj2XdojMzi72gjuWDD2l6XJkmSZNCTpNnYuG4VFz39HFYdv4y79o+y6vhlXPT0cxx1U5Ik9QWnV5CkWdq4bpXBTpIk9SVb9CRJkiRpwPRN0IuI/xcRWyLifyLiwxGxrNc1SZIkSdJC1BdBLyLuA/w2sD4zHwzUgWf3tipJkiRJWpj6IuiVhoCRiBgClgPf73E9kiRJkrQg9UXQy8ybgTcD3wNuAe7KzM+0Xy8izo+IayLiml27dh3rMiVJkiRpQeiLoBcRK4FnAPcFzgBWRMRz2q+XmZdm5vrMXH/qqace6zIlSZIkaUHoi6AHPBG4ITN3ZeYo8DHg0T2uSZIkSZIWpH4Jet8DHhURyyMigCcA1/W4JkmSJElakPoi6GXmV4CPAl8FvklR16U9LUqSJEmSFqihXhfQkpl/BPxRr+uQJEmSpIWuL1r0JEmSJElzx6AnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA6Zvgl5EnBQRH42IrRFxXUT8eK9rkiRJkqSFaKjXBVRcDPxbZj4rIpYAy3tdkCRJkiQtRH0R9CLiBGAD8HyAzDwEHOplTZIkSZK0UPVL1821wC7g3RHxtYh4R0Ss6HVRkiRJkrQQ9UvQGwIeBvxtZv4YcA/w++1XiojzI+KaiLhm165dx7pGSZIkSVoQ+iXo7QB2ZOZXyssfpQh+k2TmpZm5PjPXn3rqqce0QEmSJElaKPoi6GXmrcD2iPjhctETgG/1sCRJkiRJWrD6YjCW0kuAD5Yjbm4DXtDjeiRJkiRpQeqboJeZXwfW97oOSZIkSVro+qLrpiRJkiRp7hj0JEmSJGnAGPQkSZIkacAY9CRJkiRpwBj0JEmSJGnAGPQkSZIkacAY9CRJkiRpwBj0JEmSJGnAGPQkSZIkacAM9boASZIkqZ9t2rqTSzZvY/vufaxZuZwLNqxl47pVvS5LmpYtepIkSdIUNm3dyYVXbGHnngOcNDLMzj0HuPCKLWzaurPXpUnTMuhJkiRJU7hk8zaG68HyJUNEFH+H68Elm7f1ujRpWgY9SZIkaQrbd+9jZLg+adnIcJ0du/f1qCKpOwY9SZIkaQprVi5n/2hj0rL9ow1Wr1zeo4qk7hj0JEmSpClcsGEto41k36ExMou/o43kgg1re12aNC2DniRJkjSFjetWcdHTz2HV8cu4a/8oq45fxkVPP8dRN9X3nF5BkiRJmsbGdasMdlpwbNGTJEmSpAFj0JMkSZKkAWPQkyRJkqQBY9CTJEmSpAFj0JMkSZKkAWPQkyRJkqQBY9CTJEmSpAFj0JMkSZKkAWPQkyRJkqQBY9CTJEmSpAFj0JMkSZKkAWPQkyRJkqQBY9CTJEmSpAFj0JMkSZKkAWPQkyRJkqQBY9CTJEmSpAFj0JMkSZKkAWPQkyRJkqQBY9CTJEmSpAFzREEvIs6IiEfNVzGSJEmSpKPXVdCLiFUR8TlgB/C5ctkvRsTfzGdxkiRJkqQj122L3l8BNwCnAqPlsiuBn5qPoiRJkiRJszfU5fV+EjgrMw9ERAJk5q6IOHX+SpMkSZIkzUa3LXoHaQuFEXEycMecVyRJkiRJOirdBr3PAH8REcOVZa8F/mXOK5IkSZIkHZVuu26+AvgEsBtYFhF3Al8HnjkfRUmSJEmSZq+roJeZdwAbImI9cDZwE3BNZuY81iZJkiRJmoVuW/QAyMxrgGvmqRZJkiRJ0hzoKuhFxOeBTq13Byla9z6UmZvnsjBJkiRJ0ux0OxjL14CHU0yY/h/A9vLyd4ATgM9ExPOOtpiIqEfE1yLik0e7LUmSJElarLrtunk/4P9k5pWtBRGxEXhZZj49Ip4BvAF471HW81LgOorwKEmSJEmahW5b9DYCm9qWbS6XA3wSOPNoComI1cDPAO84mu1IkiRJ0mLXbdDbDjyrbdn/pejKCXA8xfF6R+OtFNM4NKe6QkScHxHXRMQ1u3btOsq7kyRJkqTB1G3QewXwvojYFBHvjYhNwPvL5QCPBd4z2yIi4mnAzsy8drrrZealmbk+M9efeuqps707SZIkSRpoXQW9zPwX4Bzgs8A95d9zMvOT5fpPZubvHUUdjwGeHhE3ApcBj4+IDxzF9iRJkiRp0ep6Hr3M/C7FgCtzLjP/APgDGB/k5eWZ+Zz5uC9JkiRJGnRdB72IWEcx+MqpQLSWZ+ZFc1+WJEmSJGm2up0w/ZcojsH7b+BHyr8PpRh5c05l5iYOH+FTkiRJktSlbgdj+UPguZl5LrCv/Pti4KvzVpkkSZIkaVa6DXpnAv/Qtux9wHPnthxJkiRJ0tHqNujdCZxYnv9BRDwQOBlYMR9FSZIkSZJmr9ug9zng/5TnLy8vXwX823wUJUmSJEmava4GY8nMF1Yu/hGwFTiBo5gkXZIkSZI0P7oddfO5mfl+gMxM4EPl8vOAD85feVL/27R1J5ds3sb23ftYs3I5F2xYy8Z1q3pdliRJkhaxbrtuvn2K5X89V4VIC9GmrTu58Iot7NxzgJNGhtm55wAXXrGFTVt39ro0SZIkLWLdBr04bEHE2cDYnFYjLTCXbN7GcD1YvmSIiOLvcD24ZPO2XpcmSZKkRWzarpsRMQokUI+IQ22r68DfzFdh0kKwffc+ThoZnrRsZLjOjt37elSRJEmSNPMxek+kaM37V+ApleVN4NbMvH6+CpMWgjUrl7NzzwGWL5n4V9o/2mD1yuU9rEqSJEmL3bRBLzO/ABARP5SZtxybkqSF44INa7nwii3sOzTGyHCd/aMNRhvJBRvW9ro0SZIkLWLdTq9wS0SsAX4UOL5t3YfmoS5pQdi4bhUXURyrt2P3PlY76qYkSZL6QLfTK5wPvA24E7insmp8qgVpscteFyBJkiSVuh118zXAL2bmqsy8b+Vk/zQtak6vIEmSpH7UbdA7LjM/Pq+VSAuQ0ytIkiSpH3Ub9P4hIn5mXiuRFqDtu/cxMlyftMzpFSRJktRrXR2jBywDLo+IK4FJo29m5vlzXpW0QDi9giRJkvpRty16DeBy4DZguO0kLVoXbFjLaCPZd2iMzOKv0ytIkiSp17qdXuEF812ItBA5vYIkSZL6UbddN4mIE4GfAVZn5p9FxGlALTO/P2/VSQvAxnWrDHaSJEnqK1113YyIhwHfAX6fYqoFgB8B/nqe6pIkSZIkzVK3LXoXA6/IzHdHxO5y2ZeBd89PWZIk9adNW3dyyeZtbN+9jzV215Yk9aluB2M5B3hPeT4BMnMvsGIeapIkqS9t2rqTC6/Yws49BzhpZJidew5w4RVb2LR1Z69LkyRpkm6D3i7gzOqCiLgfcPOcVyRJUp+6ZPM2huvB8iVDRBR/h+vBJZu39bo0SZIm6TbovRe4LCIeC0REPBx4B/D381aZJEl9ZvvufYwM1yctGxmus2P3vh5VJElSZ90GvTcBnwf+FTixPP8l4K/mqS5JkvrOmpXL2T/amLRs/2iD1SuX96giSZI66yroZWYjM1+VmScAqzLzhMx8dWY257k+SZL6xgUb1jLaSPYdGiOz+DvaSC7YsLbXpUmSNEm30ys8OiLWAmTmbeWytRHx6PksTpKkfrJx3Souevo5rDp+GXftH2XV8cu46OnnOOqmJKnvdDu9wiXAM9uWRbn8IXNZkCRJ/WzjulUGO0lS3+v2GL2zMvO71QXl5bPmviRJkiRJ0tHoenqFiGifXuEs4I65L0mSJEmSdDS6DXofB94fEesioh4R64B3Ax+bv9IkSZIkSbPRbdD7I+BW4FvAIWALxSTqr5mnuhas2/ce5K79o+w/1GC04aCkkiRJko69GQdjiYg6cA7wXOC3gLOBGzNz1/yWtjDdtX900uWIYKgWLBmqMVyvMVQPltRrLKnXqNWiR1VKkiRJi1dm0kxoZtLMJMfPF3+zObEuIjh5xZJel3zEZgx6mdmIiM8Dx5XhzoB3BDKT0UZ2bN0bqtUYHgqG60UIXFKvsWSoRt0AKEmSJE1yJOGsmdNfPzO7vt96bUCDXulbFCNs3jh/pSw+Y80mY4dgP41Jy+tlC2Ar+LXORxgAJUmStHD0Kpyp+6D3fuATEfHnwE3AePNUZn55PgpbzBrNZP+hxqQAGBEM14sAuLReL/4O2f2zH2zaupNLNm9j++59rFm5nAs2rHWOLUmStOA1m0XYalQCV6M5OZA1mmk461PdBr23ln/f37Y8gfqcVaMpZSaHxpJDY032Mja+fLhs9Vs6VGOoXmOoFtRrxXGBtgDOv01bd3LhFVsYrgcnjQyzc88BLrxiCxeBYU+SBow/7KmftULZeEtZQtK55SwpzpMUYYyJUGZIGxxdBb3M7HZ0Th1jo40mo40m9xw8fF19PPQVx/21uoIO14Ohurt0LlyyeRvD9WD5kuJfafmSIfYdGuOSzdv88JekAeIPezpWGs1Wq1nxt5FJs1k9P9GylpVWNKldty16AETRRHRaZt4yT/VoDrXeKA5x+EAw9VplEJjyGMBWS6DdQbu3ffc+ThoZnrRsZLjOjt37elSRjiV/3ZcWD3/Y02xkTg5oY80mzSY0si3MVc5Lc6WroBcRxwEXA+cBDWBFRDwTeGhm/vH8laf5UrypNDgw2jhsXS3KlsB60Ro4VAuGy5ZAB4WZbM3K5ezcc2D8gx9g/2iD1SuX97AqHQv+ui8tLv6wp2YZ2KqhbLx1rdXq1uo6WQlvUq9026L3F8C9gccAnyuXXQ38CWDQGzDNTJqNpMiAhwfB4fEuoJWuoLXFOS3EBRvWcuEVW9h3aIyR4Tr7RxuMNpILNqztdWmaZ/66Ly0u/rA3ONqPZauGtsZ4l8hKqCuXe7yaFppug97TgAdl5l0RkQCZeXNEnDF/palftY4LbNeaHL7aElivB8O1wT0ucOO6VVxE8aV/x+59rLb73qLhr/vS4uIPe3NvppEaO/6lHGSkNcAIMw8YUr3+bFvYrtp2B5ddvZ1b7t7P6SeM8Oxz1/CItSfPalvSsdJt0Atg/6QFRXfOvXNekRasicnhoVNL4PgUEfVihNDh+sRxggu5NXDjulUGu0XIX/elxcUf9oqWsNZojVPNh1Yd5fGw61QGD+kmoPWLq7bdwcVXXs9QLThh2RC333OQi6+8npdyf8Oe+lq3Qe8/gD9gcjfNlwCfn/OKNLCqU0S0q0V5HGA5SMzQgIRADS5/3ZcWn4X2w143E1V3G8wW87Fml129naFaMDJczCjWes+/7OrtBj31tW6D3suAKyPiOcBxEfFNYBh4wlwUERFrgPcBp1FMxn5pZl48F9vWwtDM5OBogw6zRBAR1KPoBlovB4qpV+YLHC5bBx0kRseSv+5L6pWxRpOxZhan1vlGjo/oWJ0TTUfvlrv3c8KyyV+Zlw3XuPXu/VPcQuoP3c6jtz0iHgz8LHA2cBPwycycq1f4GPC7mfnViDgeuDYiPpuZ35qj7WsBy0zGMunQEDhJqwWwdTxga8qI1kmaawvt131J/aXRnAhnY81mZZj9zq1xmQ4K0gunnzDC7fccHG/RAzgw2uS0E0Z6WJU0s67n0cvMg8BH56OIcl6+W8rzeyLiOuA+gEFPXZtqkJiWei2oRTFYTD2K+QLH/46fZ7zV0BZCzcR59CR1MtZojo/YONYsht0fa07Ml9ZabmBbGJ597houvvJ69o82WDZc48Bo0Yr67HPX9Lo0aVrdzqNXB14OvABYDewA3gP8eWYePurGUYiIs4EfA74yl9uVGs2kQWuwmJm1WgKH67VyNNGJqSTsKirn0ZMG20zHtzUaE3OnjTWd8HqQPWLtybyU+3PZ1du59e79nOaom1ogum3RexNFt80/A24E7gv8LnBq+XdOlCN5/iPwO5l5d4f15wPnA5x55plzdbdSR61fXTsNHgOUk8oXA8gMld1EDYKLh/PoSf2n2mLWKANas3q5Nbl1OcgIFMezQTkEf7kdW9rU7hFrTzbYacHpNug9B/jxzLyhtSAirgT+kzkKehExTBHyPpiZH+t0ncy8FLgUYP369b4Lq6eKLxOdB5CBYiTR6qAxra6ihw0qU3Yf1cLiPHrS/Og0mXUzGZ/Eunq+kcUgJLakSdLhug16TeDmtmXfL5cftSiaPt4JXJeZfzkX25R6rZlJs9FdV9HWZPPjYbAMh/V6OQl9OQG9gbB/OI+eFpJsDebROj++/PAJqFvBirb5ztpvU1xobfPwlrHWfbY6N1R7OVTfyZqVlrbFPIS/JM21boPeW4G/iIiXZ+bBiFgGvBGYq1D2GOC5wDcj4uvlsldl5r/O0falvtaabL7DPPOTtFoJJ881aHfRXnAePc2lblqxYHKXwmokynKUxobzn0mSSt0GvRcBZwG/FhE7gVVAHbgxIl7UulJmPmA2RWTml5j8A5+kDiZaCQ9vTG+1Cg7Vi9FFI4pgWCvnIYxa63Jlea24bEA8cs6jp6pms2jPag3I0RoGvzUpdfV4sWZrXSXceUyYJGmudRv0Xj+vVUg6aq1WwW5HFa1qBb9ajfHjBiPapp0op6eYFBYXeVdS59E7tiYFo0p3w+rk0Alkc2L9RNfDsmthl3mq2k2xuFycGb+f1rbLv5Ik9ZtuJ0x/73wXIi1UV227g8uu3s4td+/n9AU65PJ4965ZHHXbCn8RlKci/LUiYAREeWn8WJ3WjdvW1aK4VGtrgQyC9kbH6nfrrHx7j7bOAdXbVe8jwtbMY6UV0BqVYDTdpNDjXRjtfihJ0qx1PWG6pMNdte0OLr7yeoZqwQnLhrj9noNcfOX1vJT7L7iwN1uD8CW8Ffam6z43HmaZHGqrjZrThcyJ6xy+IJgInePBtsbkQFq532pYbbVitUISbeenq6c1aEbr+pNaqjo9T9XbMrmVbFIrG9gtUZKkHjPozZFNW3dyyeZtbLtt74Jt1dGRu+zq7QzVgpHhOsD4oByXXb3d/b+AdBNEmuOTbBlaJElS/6v1uoBBsGnrTi68Ygs79xyY1Kpz1bY7el2a5tktd+9n2fDkf6NlwzVuvXt/jyqSJEmSDHpz4pLN2xiuB8uXDBEUrTtDteCyq7f3ujTNs9NPGOHA6OQD2w6MNjnthJEeVSRJkiQdQdCLiLUR8aqIeHt5+Ycj4pz5K23h2L5733jXvRZbdRaHZ5+7hrFmsn+0QVL8HWsmzz53Ta9LkyRJ0iLWVdCLiCcB3wAeRTGxOcC9gDfPU10LypqVy9nfNqa9rTqLwyPWnsxLH39/TlmxlD0HxjhlxVJe+vjFMxCLJEmS+lO3g7G8Efj5zPy3iNhdLvsq8LD5KWthuWDDWi68Ygv7Do1RiyLk2aqzeDxi7ckGO0mSJPWVbrtu/lBm/lt5vhh3LnM/MDwvVS0wG9et4qKnn8Oq45fZqiNJkiSp57pt0dseEQ/OzP9pLYiIhwI3zktVC9DGdavYuG4V23bt7XUpkiRJkha5blv0/gr4WEQ8B6hHxM8BHwDeMm+VSZIkSZJmpasWvcz8+4gI4JVAHfhj4K2Z+f75LG4huWvfKG/69FYOHGowXA+Gh2oM14vTknqMnx+u1xgeCpaMX47yOhPrJm5XrK/XguLplyRJkqSZddt1k8y8FLh0HmtZ0D695RY+9JXvzcu2A8rgWA2IxeWhGYNkW5gcmnzdSbcdagudrXVDE6Gzdd16zeApSZIk9auugl5EnDnFqoOZ+YM5rGdB2rR1J2/99+tZsbQOCY1m0kw4/cRlLB2uMdpIRhtNDo01GW00xy+PNpo0c+btJ3BorMmhMbiHxozXPxZqQRkcO7dMDo1frgbJ9utNXndYYK0HS9q2P+m2QxOXh+pBzVZPSZIkCei+Re9GytE220XEQeAy4Hcy8+45qmtBuWTzNkaG65yyYikHy/n09o82WLl8CX/5iw+d9raNZnKo0WS0EgIPNZrjQXB0LBltVs6Xyw9VwuKhTreddN2JcDnWdttO1+tGM+HAWJMDY82jfv7mylAtJkLhUIeWyQ7nlwzVDlvXHiQP737b+XbtraZDdrkdeFdtu4PLrt7OLXfv5/QTRnj2uWscbVeSJPWFboPer1FMlP464CbgLOAPgcspQuDrKCZPP3/uS+x/23fv46SRyTNNLBuucevd+2e8bb0WjNTqjAzX56u8I5KZjDVzPAROCp2tlskymI6Nh9RKcOwYQqe+XL3+WNv2x8NpN82ewFgzGWs22D86z0/SETisu+2kYzAPb8Hs3I22rbvtUIeWz2mO8ax2v7XL7dy5atsdXHzl9QzVghOWDXH7PQe5+MrreSlOrSJJknqv26D3CuCxmXlbefm7EfE/wObMXBcR1wOb56XCBWDNyuXs3HOA5Usmns4Do01OO2Gkh1XNTkSMhwOW9LqaQjOTsUmtlR1aPid1j+0QJMcmX/dQ9XZjnUPqaKfzY0UQ7TJ7lrdrQB91uT2sBbNj99gj63I7VOvU/Xa67U+0mi7ULreXXb2doVqM/0gzMlxn/2iDy67ebtCTJEk9123QOw1ob57aVy4nM7dFxIlzWdhCcsGGtVx4xRb2HRqjFkXIG2smzz53Ta9LGwi1CJYMFd0l+0WjmYcHx7ZjMDt3o50cQie3mrZ1o52mpXR0LBlrVrrljjU7961u00w4ONbkYB91ua3XouNAQxPHYE537ObMXW6HOhxD2n6MZ3Wb3Xa5veXu/ZywbPJbaLct+ZIkSfOt26D3ReA9EfFyYDtwJvDGcjkR8RDg1nmpcAHYuG4VF1Ecq3fDbXs5zWN1Bl69FtRrdZb1UZfbInx2OMazGjondY+dLki2B9bJ6zq1fM62y22jWdR+YLR/wueUXW5rE+f3HWxw1/7RIhgSRBSPZWRJnUs3b+uqy203U6wsGbLLrSRJOnJHcozeh4AbmBiUZRPwy+X5AF40p5UtMBvXrWLjulVs27W316VoEYoIhurBUB1G6I/wOVOX27H2ls9J3WPbWj4b2XHU2kNttzusNbUHXW7vOVR035xLM3W5HarN8tjNjiPedjjvFCuSJC043U6Y/gPgCRGxGjgDuDkzb66s/+95qk/SArWQu9yONdq6205xjOf22/ex9dY93HNojGXDdVavXM7xy4Yqgw91PuZzELrc1gKWTBrdtnOX26H20HlYWO0wt+cU07Z06nJb3aaj3EqSNKHrCdMBMnMHsGOeapGkebXQu9weNtDQWIeWz47HhrZ3v51q+8X5bjSzOB6537rcto6zXDIpEE5u1TxsmpUOQbLj8aFDbdedpsvtcD2oO8WKJKmHup0wfQR4NfAE4FSKrpoAZOba+SlNkgZbP3a5zczDgl9799v2kHj4/JwdRrcdD6dTjIzbtv1qQG102ed2osttfwgYH7V2Sb1Wjk47uXVz1l1up5yOxS63kqRCty16bwEeC/wt8CbglcBvAR+cp7qkBcNJszVIog+73DYzu5hWpT0kTjetyuRWzcO66nax/W6yZwKHxpocGoN7+miKlSX12mHdYzuPXHvkXW6Hau0j4069/dZlWz0laX5E5syfVhFxM/C4chqFOzPzpIh4EPDXmfmEea+yg/Xr1+c111zTi7ueloOxLC5XbbuDN316K/ccGqPZTGq1YMWSIV750+sMe9IAazQPP8Zz6u63s+9yO2UI7dA1d6EaqlVGoB2a3DI5VIbIyeF0dl1uh2qHb39SCB2a6Ppr+JRUVa8FZ52yotdldBQR12bm+k7rum3ROy4zt5XnD0XEksz8VkScOzclSgvTpZu/y937R6lFUIsgm3D3/lEu3fxdg540wOq1YKRWZ6SPjvcca04enfawLrfNTt1jZ9vldu6mWBlrJmPNBvtH5/lJ6lIAQ1NNsTLjsZttx28OdWj5bN9mrX37h3e/tcutpNnoNujdEBEPzMzrgK3ACyPiTuCueatMWgC237mfWkCt/BCOAJrJ9judNFvSsRMR4+GgX7RPsdLeBbZ1eSKgdjh2c8rut5PXzXWX26OZYmU+zDTFSucg2T5o0HTTqhxZl9uhevHjpqT+1m3Q+1OKSdKvA14HfBxYCvz6PNUlSZIWsH6dYqXV8jhWOd/edXaqbrPf3bmXr33vTu4+MMqKpUPcf9VxnHLc0sndaMc6BNaj7HLbj1OsTOpy26Eb7fRBskMInbL7rV1updnqdh69j1TOfzYiVgJLMvOeeatMWgDWrFzOTbffA5lEQGbxgXzWyct7XZqOAQfikRaWo+lye9W2O/jotTsYqgdnnLSMA6NNtt12Dz/7I2fM+v++1eV2PAQ2Dw+c7V1iOw481N5Nt9PxoWNTtHwOSJdb4LABhCZ1wW2bYqVT19wlHQNpETyHOt3WLrfqc91Or3AicCgz90dEDTgPGI2ID2U3o7ksApu27uSSzdvYdttev/AtIuc/bm0xGMvBMRqNpF4LThgZ5vzHOevIoLtq2x1cfOX1DNWCE5YNcfs9B7n4yut5Kff3f18aQJddvZ2hWoyHxJHhOvtHG1x29fZZ/89P6nK7ZC6rnb32LrejY01Gx8Po5IA46y63zcrtppi2ZSKcdtflFhZOl9uJ1tAjnGJlii63M3fpnWgZtcvt4tJt181/AV4GXAW8Fvg1YAw4B3jVvFS2gGzaupMLr9jCcN0vfIvNI9aezMPPPIkrv72LZkI2k4efeZL7fRGYjy99WhhsyV2cbrl7Pycsm/y1adlwjVvvHqxjsvu1y+1hwfGwYzwPPz7zUFsIHR2bYrChct1Ys9PIuId3tx0da9JN9uzHLrf1WhzeotnqOltr70Z7BFOstIXQyQMaHT6tSnWbdrmdP90GvQcC15bnzwOeBNwN/AcGPS7ZvI3herB8yRAHRxt+4VtE3v/lG7ny27uoBdRrRdfNK7+9izUrb+S5jz671+VpHi2WL32azJbcxev0E0a4/Z6Dk7p9HhhtctoJIz2sanGo14J6rc6yPhrltgif002r0t49drpjN6fvcjta7d7bqeXzCI73bDSL2g+M9k/4POw4z3ptVl1uW91r21s9W8F0SacQ2qH7bX2AjvfsNujVM7MREWdRHJu3BSAiTpq3yhaQ7bv3cdLI8KRlfuFbHC6/dkcZ8spfPgNoNrn82h0GvQHnl77FyZbcxevZ567hTZ/eyg/uPkCjWXTVX7F0iN/ceL9el6ZjLCIYqgdDdRihf8Jne/A7bIqVKbvHdpjbs8OotYfabjf1tC3F5UaXfW77rcttwPiotdVguWJpnX/6zccysqQ/9nk3ug1634yIV1OMvPkZgIg4HXB2cIoBOXbuOcDyJRNPp1/4Fof9ow3qbb1bIorlGmzPPncNF195PftHGywbrnFgtOhC9Oxz1/S6NM0jW3JFlFPpDMYP/hoQ0YddbpuZHD4/5/RToEx/jGd7q2ZbK+kcdblN4NBYk0NjcE9b+FxoA+x0G/ReArwdOAQ8v1z2JMrQt9hdsGEtF16xhX2HxqgFfuHrMxEx/nkcAUFQbZFvnW8107dft1Z+oLfOR5TLgBVLil/zq9vLLJafsmLpUdWd5dtRa7ijpPjFrnW+ta51PSZdr1he/C1u1+2wSbVa8fhqUTxPrcngW499/Pli8nPWbS+HnFxuuay41MzifDOLD4hmJs1mcb79cUxsZ/rHNjEa6tyOG/WItSfzUu7PZVdv59a793Oax2otCrbkLl6XXb2d45YOcepxE+/ttuZKU6tFsHS4ztLhma97LHTqcjvWZZAcaybHLx1iuD6AQS8zvw48pm3Z+4D3zUNNC87Gdau4iOJYvRtu2+sXvjlWrxVBo14LhmpBrRbUI6jXJwJIK5S0h7H57mP9oset5eIrv0Mjk1qUQYXgRY9by4nL++SdTePGGk0aZXhsZNJoZPG3mRPBOCeCZbOZ46G5UxeUR6w92f/zRcaW3MXL1lxpYZttl9urtt3BR67Zzq69B/n0lh9wwYa1bFy3ah4rnTvdTq/w6KnWZeaX566chWvjulVsXLeKbbvszQoTrUARE0GtVoN6q3WoFuVyxof6nQhrxy6oHa3ffuIDAHjHl27gnkMNViyp82uPve/4cvWXoXqt624MnbR+DWy1OLZCYSOTLMNj0QpZXKcxfj7HWyu1sNmSu3jZmistPq0BuIbrwUkjw+zcc4ALr9jCRbAgwl5088UjIjoNzZMAmdmTIxLXr1+f11xzTS/ueloLNehVg9m0XfcIokbHlrRWcOv3cCb1SisoNqohsVm0MI41J7c2NpvFRMqGQ6k/VEdcrbbmvvTxjrgqDaqXfeQbxQ88S+osHSoiz75DY6w6fhkfPv9RPa6uEBHXZub6Tuu67bo56cjOiDgDeD3wyaMvT0cqoq0lrFYcg1Y9jqpWCWidgln7cVeS5t94t5EjuE0r8DWaxRxPjcrl1qkVGCXNH1tzpcWnU5ftkeE6O3bv61FFR2ZWvZgy8/sR8VLgq8DH5rakwVdtPYsOwaxeOQatXunyOEjzekiDYNPWnVyyeRvbd+9jzcrl89Jvv1YLloyP8jV9B4pq8GtmEQjHGslYeSD5WKMIi5Jmx+NypcVlvMt2ZUqF/aMNVq9c3sOqunc0h6ssBfq/c+oxduLIcNGFsTWAiEFNGkibtu7k5R/9BnsPjtFoJrftPcjLP/oN3vysh/as3369PPZ1Oq25lhrNYqSxsbaRxSRJUqE1ANeB0QZL6jX2jzYYbSQXbFjb69K60u1gLK9qW7QCeAbw2TmvaIE75bijG1Jf0sLwxk9dx537RosfcSLIJty5b5Q3fuq6vj5AuzXXEhw+6linCXfHmsVw07YETrhq2x1cdvV2brl7P6fbfU+SBlary/ZHrtnObXsPsnqeeu/Ml25b9J7Udnkv8A/AW+a2HElaGG64fV/R5bpsQYuAbCY33L4w+u13Mt2Eu9UQONZIRpvNtuMHF8fAMdUBOU5YNsTt9xzk4iuv56U4IIckDaJHrD2ZH7/fKZx1yopel3LEuh2M5SfnuxBJUv+aLgS2VINfY3xE0RwfRbRRGWW0GHl04QXDy67ezlAtxofYHxmuO2m2JKkvHc0xepK0aK291wqu37mXyCxa8xKaCfc/deH94jdXJg8cM7PWIDGtYNgaLKYxPnBM/4VBJ82WJC0UfRP0IuLJwMUUw8q9IzPf2OOSJGlKr3zyOl76ka9x9/4xEgjghJEhXvnkdb0ubcEYqtcYmmEm1lYYHG0UAbB6DGEvppQ4/YQRbr7zHvYebDDaaDJcr3Hc0jr3OWnxBnxJUn/qi6AXEXXg7RTHAu4Aro6IKzLzW72tTJKmNlyvsXS4RqOZ1GvBcH3qbo2anVYYXDZ8eCJslAHwUKPJ6NjE6KHz2RL4Y2tO5Bs77qS19bFmkwOjTZ72kBPn5f4kSZqtvgh6wCOA72TmNoCIuIxiVE+DnqS+dMnmbZw4MszpJ46ML9t3aIxLNm9bMKNxLXTFdBL1jiFwogVwYvCYVpfQo5lG4gvX3zbl8uc++uxZb1eSpLnWL0HvPsD2yuUdwCPbrxQR5wPnA5x55pnHpjJJ6mD77n2cNDI8adnIcJ0duxfuqJuDZLheY3iKlsDMYg7B0XIy+db50bEmzRlaArfv3sdQDWq1idbbZrPJdve7JKnPdDuP3r2Bi4D1wPHVdZn5gDmoo9PR+4d92mbmpcClAOvXr++vI/QlLSprVi5n554DLF8y8Ta6f7TB6pXLe1iVuhERLB2qs7TDJ2CzmTSzNVIo4+ezHCk06PDhJElSH+q2Re+9wHHAO4F75qGOHcCayuXVwPfn4X4kaU5csGEtF16xhX2HxsaH2B9tJBdsWNvr0nQUarWgRkz54bj2Xiv4zq57yObk0VbPPnmE4XrtqLqFSpI0l7oNej8O3Ccz985THVcD94+I+wI3A88Gfnme7kuSjtrGdau4iOJYvR2797F65XIu2LDW4/MG3O8/5YG8/KPfYO/BsWIQnnpwwtJhXvO0c1hz8nKazaJb6MGxJofGJgaKmalLqCRJc63boLcDGJ7xWrOUmWMR8VvApymmV3hXZm6Zr/uTpLnkV/jFY+O6Vbz5WQ+dMuDXasGyDgPEtELfobEmB8caHBrrzfQQkqTFo9ug96fAeyPitcCt1RWZOSddLDPzX4F/nYttSdJ827R1JxdesYXhenDSyDA79xzgwiu2cBHYqrdIHElMWzJUY8lQDZZOLGsNBHNwdCIE2vVTkjRXug167yv/Po2Jz7bWMekzTHcrSYPnks3bGK7H+GAsy5cMOb3CIjCXAb+YI7DG8iUTy1pdP1tdPlsB0NY/SdKR6jbo3Xdeq5CkBcbpFRanSzZvY7TR4Pa9YxxqNFlSr3HCyNCcBfypun42mslYszU3YE66PFZe9jhASVJVV0EvM2+a70IkaSFxeoXF6fqde7hr3yi1WlCvBWPN5LY9hxht7JnX+21NDj+dzGSsWYS+RrM1RcTEsrFmMW+grYOStDhMGfQi4uWZ+eby/Kumul5m/sl8FCZJ/czpFRanQ2NNCKhFMf1rBDQji+U9FhEM14MOc8RPUg2Eo2XwO9RoMtYoWgfHmr1/LJKkozddi97jgTeX5580xXUSMOhJWnScXmFxGq4H+0eLY+la8+gBLKlHbws7AtVA2N5FFIrHNtpsMtooWgBHy/BXhMAk7SIqSQvClEEvM59aOf+Tx6YcSVo4Nq5bZbBbZB5w7xO44ba97DkwcYze8cuGue+9jut1aXOmVguW1uosneIbwlijyVjZGjjWKEKhrYGS1H+6HYxFkqRFr9Vl97QThxZtl91itNDOrYGZOd4C2GoRHPP4QEnqCYOeJEldssvu9CKCJUPBEmod17eCYKPsHtooWwRbo4faNVSS5o5BT5KkI2CX3dlrBUGAkSmm4W1vBRxrTnQLHWs4jYQkdcugJ0mS+kara+hUWoPFtOYUbHUJHW0mDY8TlKRxRxT0IiKA0zLzlnmqR5IkaUqtwWKm0t49dKxxeMugJC0GXQW9iDgOuBg4D2gAKyLimcBDM/OP5688SZKk7s3UPdTjBCUtFt226P0FcG/gMcDnymVXU8yhZ9CTJEkLQjfHCVYnkx8/PtDRQyUtMN0GvacBD8rMuyIiATLz5og4Y/5KkyRJOvbqtaA+Q/fQ9kFiiiDoxPKS+ke3QS+A/ZMWFN059855RZIkSX0sIhiuB8VUgkfeKjg61nT0UEnzrtug9x/AHzC5m+ZLgM/PeUWSJEkL3Eytgq0gONooWgFHG01GDYGS5lC3Qe9lwJUR8RzguIj4JjAMPGHeKpMkSRpQrSC4bPjwMDjWaDLaSA6VQfDQWHO8dVCSutVV0MvM7RHxYOBngbOBm4BPZub+aW8oSZKkI9KaS7B9sJhWK+ChRpPRsdZfp4yQ1FnX8+hl5kHgo/NYiyRJkqYwVStgsznR+jfayPEWwNGGAVBazLqdR+9dU6w6SNG699HM/M6cVSVJkqSu1GrBsg4BMLMVAItj/0YbTQ6ONR0VVFokum3RGwZ+AfgKRbA7E3gU8HHgocAfR8QzM/NT81KlJEmSjkhEsHSoztIhYOnE8tak8a1Wv0PlcYBjjXQgGGmAdBv0EnheZl7WWhARvwD8TGY+OiLOB14PGPQkSZL6WGvS+CVDtcPWHTYaaGtuwIbHAkoLTbdB7+nA89uWfRS4tDz/HuDP56YkSZIk9cJ0o4E2msXxf4fGmhxsNMpjAe0GKvWrboPebcDjgC9Ulj0OuKM8Pwz4M48kSdKAqteCkSV1RpbUKb76TRwH2Ap9rekgPA5Q6r1ug96fAp+KiMspjtE7C3gW8NJy/U9jt01JkqRFZeI4wMMHgmk/DnC0kYw5H6B0zHQ7j947I+J/gV8BzgVuBp6SmV8s138M+Ni8VSlJkqQF40iPAzQESnPvSObR+yLwxXmsRZIkSQNupuMAJ4XARpPRZjE9hCOCSkem66AXEWuAHwWOry7PzA/NcU2SJElahLoNga0WwEMNp4WQptLthOnnA28D7gTuqaxKwKAnSVo0Nm3dySWbt7F99z7WrFzOBRvWsnHdql6XJQ286ULgWKMYAKYV/AyBUvcteq8BfjEzPz6fxUiS1M82bd3JhVdsYbgenDQyzM49B7jwii1cBIY9qYeG6jWG6nQVAqutgo4MqkHWbdA7zpAnSVrsLtm8jeF6sHxJ8fG5fMkQ+w6NccnmbQY9qU/NFAJHWwPClNNCOD2EBkW3Qe8fIuJnMvNf5rUaSZL62Pbd+zhpZHjSspHhOjt27+tRRZKORisEjnB4CJw0KmhlnkBDoBaKboPeMuDyiLgSuKW6IjPPn/OqJEnqQ2tWLmfnngPjLXoA+0cbrF65vIdVSZoPw/UawzOEwKI7qCFQ/anboNcALi/PD093RUmSBtUFG9Zy4RVb2HdojJHhOvtHG4w2kgs2rO11aZKOoZlCYOsYwENjzfHLzhGoY63bCdNfMN+FSJLU7zauW8VFFMfq7di9j9WOuimpTRECD58ovtXyd6jRHA+Ah5wfUPOo63n0ACJiBLgXEK1lmfm9uS5KWkgcal1aXDauW+X/uKQjNtXxgK2pIA6NNSsh0C6gOnrdzqO3FvgA8MgOqw9vs5YWCYdalyRJR6MIgDWWL5m8vBX8RicFwGZvitSC1G2L3tuA7cD5wJeAxwAXAf88T3VJC4JDrUuSpPmwZKjGkqEaLJ1YlpkcrHT7LIJgMtY0AOpw3Qa9RwJnZ+aeiCAzt0TEBcAXgPfMW3VSn3OodUmSdKxEBMuG64fNCdhoFqN+Hhzz+D9N6DboNYH95fm9EXEScAdw5nwUJS0UDrUuSZJ6rV4L6rXDA6DH/y1u3Qa9LRTdNb8AfAV4C3APcMM81SUtCA61LkmS+tV0x/+NVo77OzjmHICDqNug99tAa8//HvB3wAnABfNRlLRQONS6JElaaFrH/61oO/5vtDIJ/KFyUnjnAFy4up1H778r57cBPzVvFUkLjEOtS5KkhS4iWDIUxQAwbZrNZLRZdPtszQc4WgZBB4LpX13PoxcRy4D7A8dXl2fml+e6KEmSJEn9oVYLltbqLO2QHDJzUuvfaBn+HA2097qdR+/pwHuBE9tWJc6jJ0mSJC1KEcHSoalDYLX1r2gVnAiFml/dtuj9BfDHwKWZOafjxkfEnwM/CxwCvgu8IDPvnMv7kCRJknRsTdcdtBUCW61/o82JAOjAMHOj26B378x86zzV8FngDzJzLCLeBPwB8Mp5ui9JkiRJPTYeAqlB26igmclYs9IVtHpcoCGwa90Gvc9ExKMy87/muoDM/Ezl4n8Bz5rr+5AkSZK0MEQEw/VguH54SyAw3v2zNUKoIbCzKYNeRLyqcvFG4IqI+AhwS/V6mfknc1jPC4GPzOH2JEmSJA2Q4XqN4TqMdBgqZDz4NZuMlvMDtloGF1sInK5F70ltl7cADy5PLQnMGPQi4nPAaR1W/WFm/lN5nT8ExoAPTrOd84HzAc4888yZ7laSJEnSIlJMEj91CBxrTowSWp0vsDmAITD6IdlGxPOAFwNP6Hawl/Xr1+c111wzv4VJkiRJGniN8Za/ylQRzSIMApx1yooeV9hZRFybmes7rZv2GL2IuDfwE5l5eYd1Pw98ITN3HmVxT6YYfOUn5npET0mSJEmaSb0W1Gt1lg0f3hLYbPa+YWw2Oh/hOOGVFJOkd/JDzM3omG+jmIT9sxHx9Yj4uznYpiRJkiQdtVotel3CrMw06uZTgZ+YYt27gC8Cv3s0BWTm/Y7m9pIkSZKkyWZq0TstM3/QaUXZZbPTACuSJEmSpB6aKegdiojTO60ol4/OfUmSJEmSpKMxU9D7D+AlU6z7TYqum5IkSZKkPjLTMXpvAL4YEacCHwZuBu4D/BJwHvDY+S1PkiRJknSkpg16mXlNRDwdeDvwqxQTpAfwHeDpmfnV+S9RkiRJknQkZmrRIzM/CzwgIu4PnArsyszr570ySZIkSdKszBj0WspwZ8CTJEmSpD4302AskiRJkqQFxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA8agJ0mSJEkDxqAnSZIkSQPGoCdJkiRJA6Zvgl5EvDwiMiLu1etaJEmSJGkh64ugFxFrgCcB3+t1LZIkSZK00PVF0APeArwCyF4XIkmSJEkLXc+DXkQ8Hbg5M7/RxXXPj4hrIuKaXbt2HYPqJEmSJGnhGToWdxIRnwNO67DqD4FXAT/VzXYy81LgUoD169fb+idJkiRJHRyToJeZT+y0PCIeAtwX+EZEAKwGvhoRj8jMW49FbZIkSZI0aI5J0JtKZn4TWNW6HBE3Ausz87aeFSVJkiRJC1zPj9GTJEmSJM2tnrbotcvMs3tdgyRJkiQtdLboSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgOmLoBcRL4mIb0fEloj4s17XI0mSJEkL2VCvC4iInwSeAfxIZh6MiFW9rkmSJEmSFrJ+aNH7deCNmXkQIDN39rgeSZIkSVrQ+iHoPQB4XER8JSK+EBHn9rogSZIkSVrIjknXzYj4HHBah1V/WNawEngUcC5weUSszczssJ3zgfMBzjzzzPkrWJIkSZIWsGMS9DLziVOti4hfBz5WBrurIqIJ3AvY1WE7lwKXAqxfv/6wIChJkjRfNm3dySWbt7F99z7WrFzOBRvWsnGdQwtI6k/90HXzE8DjASLiAcAS4LZeFiRJklS1aetOLrxiCzv3HOCkkWF27jnAhVdsYdNWhxaQ1J/6Iei9C1gbEf8DXAY8r1O3TUmSpF65ZPM2huvB8iVDRBR/h+vBJZu39bo0Seqo59MrZOYh4Dm9rkOSJGkq23fv46SR4UnLRobr7Ni9r0cVSdL0+qFFT5Ikqa+tWbmc/aONScv2jzZYvXJ5jyqSpOkZ9CRJkmZwwYa1jDaSfYfGyCz+jjaSCzas7XVpktSRQU+SJGkGG9et4qKnn8Oq45dx1/5RVh2/jIuefo6jbkrqWz0/Rk+SJGkh2LhulcFO0oJhi54kSZIkDRiDniRJkiQNGIOeJEmSJA0Yg54kSZIkDRiDniRJkiQNGIOeJEmSJA0Yg54kSZIkDRiDniRJkiQNGIOeJEmSJA0Yg54kSZIkDRiDniRJkiQNGIOeJEmSJA0Yg54kSZIkDRiDniRJkiQNGIOeJEmSJA2YyMxe1zArEbELuKnXdXRwL+C2XhehnnDfL17u+8XJ/b54ue8XL/f94tWv+/6szDy104oFG/T6VURck5nre12Hjj33/eLlvl+c3O+Ll/t+8XLfL14Lcd/bdVOSJEmSBoxBT5IkSZIGjEFv7l3a6wLUM+77xct9vzi53xcv9/3i5b5fvBbcvvcYPUmSJEkaMLboSZIkSdKAMejNkYh4ckR8OyK+ExG/3+t6dOxExLsiYmdE/E+va9GxExFrIuLzEXFdRGyJiJf2uiYdGxGxLCKuiohvlPv+j3tdk46tiKhHxNci4pO9rkXHTkTcGBHfjIivR8Q1va5Hx0ZEnBQRH42IreVn/o/3uqZu2XVzDkREHfhf4EnADuBq4Jcy81s9LUzHRERsAPYC78vMB/e6Hh0bEXE6cHpmfjUijgeuBZ7p//3gi4gAVmTm3ogYBr4EvDQz/6vHpekYiYiXAeuBEzLzab2uR8dGRNwIrM/MfpxLTfMkIt4LfDEz3xERS4DlmXlnj8vqii16c+MRwHcyc1tmHgIuA57R45p0jGTmZuCOXtehYyszb8nMr5bn9wDXAffpbVU6FrKwt7w4XJ781XSRiIjVwM8A7+h1LZLmV0ScAGwA3gmQmYcWSsgDg95cuQ+wvXJ5B37hkxaNiDgb+DHgKz0uRcdI2XXv68BO4LOZ6b5fPN4KvAJo9rgOHXsJfCYiro2I83tdjI6JtcAu4N1ld+13RMSKXhfVLYPe3IgOy/x1V1oEIuI44B+B38nMu3tdj46NzGxk5o8Cq4FHRITdtheBiHgasDMzr+11LeqJx2Tmw4CnAL9ZHrqhwTYEPAz428z8MeAeYMGMxWHQmxs7gDWVy6uB7/eoFknHSHl81j8CH8zMj/W6Hh17ZReeTcCTe1uJjpHHAE8vj9W6DHh8RHygtyXpWMnM75d/dwIfpzh0R4NtB7Cj0mvjoxTBb0Ew6M2Nq4H7R8R9y4M0nw1c0eOaJM2jckCOdwLXZeZf9roeHTsRcWpEnFSeHwGeCGztaVE6JjLzDzJzdWaeTfFZf2VmPqfHZekYiIgV5cBblF33fgpwtO0Bl5m3Atsj4ofLRU8AFsyga0O9LmAQZOZYRPwW8GmgDrwrM7f0uCwdIxHxYWAjcK+I2AH8UWa+s7dV6Rh4DPBc4JvlsVoAr8rMf+1dSTpGTgfeW464XAMuz0yH2ZcG272Bjxe/8TEEfCgz/623JekYeQnwwbIxZxvwgh7X0zWnV5AkSZKkAWPXTUmSJEkaMAY9SZIkSRowBj1JkiRJGjAGPUmSJEkaMAY9SZIkSRowBj1Ji05EnBwRn46IuyLi2l7Xc6xEREbEY+f5Pl4bEZ+bz/s4WhHxjoh4T6/rOBIR8Zxyku7W5U9FxCt6WNJhIuLvIuJtva7jSPXjcylJc8F59CT1vYj4HeCHMvMlEfEXwM7MfNNRbPLFwHHAKZk5Nhc19pOIOBu4AViTmTvmcLubgM9l5uvnapuancx8Sq9raJeZL+51DbPRj89lN8ofK8Yy89d6XYuk/mSLnqSF4JHAV8rzj6qcn621wHWDGPKkQRERw31QQz0i/K4kaUHyzUvSQvAo4CsRsQR4KHDNdFeOiFMi4n0RcUtE3BoR742Ik8t1/ww8D3heROyNiD+eYhuPjIhrI2JPRHwpIi5s6zr30ojYWq7/XkT8aUTUK+szIn4rIq6JiHsi4ssRsToi/l9EbI+I2yPiDW33+eCyS+ltlW0Ol+uWRMSlEbEzIu6OiP+NiGdN8RR8o/z77fIxvqay7kci4uqy7v+KiHWV+392RHyj3P4tEXFJRKwo170NeBzwmnKb355+F8SflLXurD7HEbE8Ij5W7pe7I+KrEfGkct1Qeb/PaNvYeyPiXZXLL4qI/ym73n4tIn5qmlqIiBdGxHfL+3s/sKxt/ZkR8dHyvm8pn+fjK+uPaF9O9xjL9c+PiO9ExG9HxI6I2F0+19XXzyPK+9sbEV+i+HGiWvOmiHh1ef7sssbnRsS3yn37mYg4vXL90yLin8vn7H8j4lfL25w9xXP22oj494h4U0TsKh/jyyLirIi4sryPayPigZXbvCci3tH2vP3GVK+3Dvf5noj4YES8OyLuAP6qXP7M8r7ujIjrIuK86fZvRHwgyq65ledmdfvzP8Nz+asR8S1gH7AqiveUd5b7e1dEXB4R965s48aIeHVEfL7cZ9+MiB+JiF8q9/VdUXQZHqrcppvXXcfnL4qupucx8T62N4pQ+mNRvF/dFRF3lK/VlVM955IGXGZ68uTJU9+dgJ8A7gTuAprl+T3AWHl+yzS3/Tfgn4GV5elfgH+prH8P8I5pbn8icDvwe8Aw8GPAzcCNlev8HHBfIMr1PwAuqKxP4L+A1cBy4Ergf4GLgFZgPQg8urz+qvI+LyjX34ci0F5Yrj8f+BpFd1OANcCDpqj/7PL+V7ctT+Aq4ExgKfAPwGcr658CnEPxI+D9gG8Bf1pZvwl49Qz77bXAKEX32CGK1thR4DHl+uOA5wDHl8/t7wF3A6eW6/8M+ERle8cBe4HHVZ6H75TPXw14arn+flPU8zhgP/Cksp5fKet5T7l+Wbm9i4CR8vXyr8C7jmJfzvQYn1/W8IZyP9wPuAM4r+319/vl9s8FbmXy6298X1T29yeBewEnAP8B/H3l+v8O/GO5blV5+wTOnmE//hpQL18bDeBzwAPLx/UB4DNT/V8xw+utw32+BzgE/GJ5n8vL/XZ7uR9rwCOA3cCGLvdv67lZXbmf5wPfmeG5/HfgtPL5rwNfBN5R7pvlwDuBf69s40bg+rbn5rvApcCK8jnYCfzyEb7upvt/nfR8l8u+DFxY1jxM8SPZil6/n3vy5Kk3p54X4MmTJ0/TncovZZeV5/8IeNMM1z+j/IJ0/8qyHy6XnV5ePuwLUts2ngPcBERl2euofNHucJs3A5dXLifw85XLv0HxZb9WWXYV8NLy/MuBK9u2+XOtL6Tl83B9+cV2aIbn4LAvt1PU9DPA7mm281vAVZXL41+Ip7nNa2kL4cDVrcc5xW1uA55ann8gxZf9VeXlFwL/W7nu/wC/0nb7f56qLuDvgfe3LfsPJoLAs4Dvtq1/OEVwq89mX3bxGJ9f3r5eWf8PwFvK8+cB32t7/b2BmYPeuZX1vwl8rTy/uly/trL+Ccwc9Nr3407g9yqXn1p9/dA56B3J6+09HP4/8EnKHzsqy/66dT9d7N/Wc3OkQW9DZf16ipa9pZVlp1S3SxH02p+bpAz35bLLK/t4Nq+7Sc9f+/NdeSzvmGq/evLkaXGd7Lopqd/9JEULCsBGii8y01lT/r2hsuy7betmch/ge5mZlWU3Va9Qdsm6uuzSdhfFF+tT27ZzS+X8PopBZJpty1pdte4LPKbsnnZnRNwJvIuiVQGKFoJ3AG8Bbi+7Bt6vy8czVU33VO6fiHhSRHyx7Jp2N/CmDo/pSO9j0v1ExEhE/HVEbCu72t1J0ZpxKkBmXgd8lSJsA7wAeHdlW/cF3t72PP0kxT7rZDXFl/Cq6mvjvsCZbdv7d4ov2adVrtf1vpzpMZZ2Zmajcrm6L1YDN7W9/qo1T2Wqfdt6br5XWT/p9dzF9qB4jO3Pw/FMb8rX2xRubLt8X+CVbfvn+RQ/6MDM+3e2qtu8L0WL2g8qNXwXOEDR2tbS/tw0MnNX27Lq//uRvu66ef5eQNHy+aWIuCEiXlftLippcTHoSepLlS8/zwXeXIapnwAuK9c9cIqbbi//nl1ZtrZt3UxupvgSFpVl41/oImINRfB6PUUr4YnA2ym6cc7WTRQjWp5UOZ2YmccBZOZYZr4pM9cDZ1F8aXzXFNtqTrF8SlEc//gJ4DLgzMw8AXglkx/TEW+3g5dR7McnACdm5kkUXfGq9/Nu4PllkH0U8L7KupuAF7Y9T8dl5q9PcX83M/m1AMWX7Or2/rdteydl5rLMvHkeH+N0bgbOanv93XeqK3e5PZgcSs7sdMU+0P4auwl4bdu+OT4zn1qun2n/7i3/rqgsO4OZVeu4iSJkndxWx0hmfrmLbXUyF6+7w/4fM/OGzHxhZq4Gnk7R9fZXZlmjpAXOoCepL5Vfjh8LfLsMHedRBKETyy9E101xu+8DnwH+IiJOKgci+AvgU5nZ3kIxlU9S/HL+sogYjoiHUvxS3nIcxfvnLmA0Ih5FEUiPxvuA9eXAEssiohYRayPiyQAR8fiIeHgUg7Psp/jiOdWoobsovgTe/wjufwnFcUO7M3N/RDyIoutm1a0Ux5MdjRMouqfdDiyJiAuBk9quc1l5P39FcUxS9YvvW4DXRsSPRmEkIh4bUw/y8T7gWRHxhCgGe3kOxXFeLZ8EhiPiVRFxfLnN+0TE/5nnxzidT1K8xn6vfP09jKIL66xkMcXGJuCN5WNcBbx6tts7xt4K/E5EPK4cbGRJ+X+wvlw/7f7NzNsofxwob/8Q4EVHWMM1wNeBiyPiFICIODUinn0Uj2suXne3AmujMipoRDwvIlpB9k6K9whHF5YWKYOepH72c8DHKuc/2uXtnkMxcMvW8nQnR/CrdmbeSXE8zHkULTFvpzge5mC5/jqK4wX/qdz27wMf7nb7U9znrRRdEJ9J0W1sN/BxJloj7w28v1x+C0Wr3gVTbGs/8Brgw2Xr5x92cf97gV8H/iwi9lI85g+1Xe0tFGH0zojYciSPr+IvKZ6z71N0f9tHW9e7zLyL4rE/hbZWy8z8e4oBW95N8Vx8j+KxdhyKPzM3Ay+h6PZ6B/Bk4COV9fsoWt4eRPFauYuiC92PzvLxQRePcTqV198vUjzGvwL+9ijqAfhlikFEdgBfojgmEMrXdL/KzM9QDMDz5xTHOd5C8TpstXRPu39LzwOeRrFv/5JiIJUjqaFJ8X9ZA66NiD0UU7xsnMVDam1zLl5376Boqby9/J+sA48va9wL/CfF//AHZ1unpIUtJh8CIEnqJCL+FHh4Zk47lL+0EETET1P8UDGSA/ZFIIopHoYy8/m9rkWSeskWPUnqoByY5PSyC+XjKFoVjqrVTuqViHhoFPO6RUSspTi+9CODFvIkSRMciUmSOnsIRVfJEyi64P058N6eViTN3skUUxGcTtFN8FPA7/a0IknSvLLrpiRJkiQNGLtuSpIkSdKAMehJkiRJ0oAx6EmSJEnSgDHoSZIkSdKAMehJkiRJ0oAx6EmSJEnSgPn/bk/NuKVWemcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.regplot(x='min_demanding', y='change_pct', data=df_final)\n",
    "plt.xlabel('# of games that have demanding min requirements', fontsize = 13)\n",
    "plt.ylabel('Change in usage percentage', fontsize = 13)\n",
    "plt.title('Change in usage percentage plotted over # games that have demanding min requirements')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.2428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.625</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:19</td>     <th>  Log-Likelihood:    </th> <td> -79.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   162.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    34</td>      <th>  BIC:               </th> <td>   165.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    1.3183</td> <td>    0.701</td> <td>    1.881</td> <td> 0.069</td> <td>   -0.106</td> <td>    2.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th> <td>   -0.1268</td> <td>    0.257</td> <td>   -0.493</td> <td> 0.625</td> <td>   -0.650</td> <td>    0.396</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.593</td> <th>  Durbin-Watson:     </th> <td>   1.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>  27.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.402</td> <th>  Prob(JB):          </th> <td>8.60e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.240</td> <th>  Cond. No.          </th> <td>    5.63</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.007\n",
       "Model:                            OLS   Adj. R-squared:                 -0.022\n",
       "Method:                 Least Squares   F-statistic:                    0.2428\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.625\n",
       "Time:                        23:55:19   Log-Likelihood:                -79.065\n",
       "No. Observations:                  36   AIC:                             162.1\n",
       "Df Residuals:                      34   BIC:                             165.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         1.3183      0.701      1.881      0.069      -0.106       2.743\n",
       "min_demanding    -0.1268      0.257     -0.493      0.625      -0.650       0.396\n",
       "==============================================================================\n",
       "Omnibus:                       11.593   Durbin-Watson:                   1.623\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               27.933\n",
       "Skew:                           0.402   Prob(JB):                     8.60e-07\n",
       "Kurtosis:                       7.240   Cond. No.                         5.63\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ min_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for min_demanding is 0.625 meaning that there is a %62.5 chance that min_demanding has no affect on change_pct (dependent variable). The relationship is not statistically significant (p > 0.05). R-squared is 0.007, which means that %0.7 of the variance in change_pct can be explained by our model. Effect of min_demanding on our dependent variable is minimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Change in usage percentage plotted over # games that have demanding recommended requirements')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.regplot(x='rec_demanding', y='change_pct', data=df_final)\n",
    "plt.xlabel('# of games that have demanding recommended requirements', fontsize = 13)\n",
    "plt.ylabel('Change in usage percentage', fontsize = 13)\n",
    "plt.title('Change in usage percentage plotted over # games that have demanding recommended requirements')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.2256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.638</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:20</td>     <th>  Log-Likelihood:    </th> <td> -79.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   162.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    34</td>      <th>  BIC:               </th> <td>   165.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    1.4652</td> <td>    0.997</td> <td>    1.469</td> <td> 0.151</td> <td>   -0.562</td> <td>    3.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th> <td>   -0.0366</td> <td>    0.077</td> <td>   -0.475</td> <td> 0.638</td> <td>   -0.193</td> <td>    0.120</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.014</td> <th>  Durbin-Watson:     </th> <td>   1.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  29.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.205</td> <th>  Prob(JB):          </th> <td>4.45e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.397</td> <th>  Cond. No.          </th> <td>    34.8</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.007\n",
       "Model:                            OLS   Adj. R-squared:                 -0.023\n",
       "Method:                 Least Squares   F-statistic:                    0.2256\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.638\n",
       "Time:                        23:55:20   Log-Likelihood:                -79.074\n",
       "No. Observations:                  36   AIC:                             162.1\n",
       "Df Residuals:                      34   BIC:                             165.3\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         1.4652      0.997      1.469      0.151      -0.562       3.492\n",
       "rec_demanding    -0.0366      0.077     -0.475      0.638      -0.193       0.120\n",
       "==============================================================================\n",
       "Omnibus:                       11.014   Durbin-Watson:                   1.669\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               29.253\n",
       "Skew:                           0.205   Prob(JB):                     4.45e-07\n",
       "Kurtosis:                       7.397   Cond. No.                         34.8\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ rec_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for rec_demanding is 0.638 meaning that there is a %63.8 chance that rec_demanding has no affect on change_pct (dependent variable). The relationship is not statistically significant (p > 0.05). R-squared is 0.007, which means that %0.7 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary from Simple Linear Regression\n",
    "\n",
    "Individually, none of them show correlation to change in usage percentage. The 95% confidence interval for all of our popularity variables and demanding variables contained 0. This can be seen in the summaries and in the shaded blue regions of the graphs.\n",
    "\n",
    "One thing of note is the R-squared value for all models is relatively small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Linear Regression\n",
    "\n",
    "Although none of our dependent variables show correlation to the change in number of usage percentage individually, We can try to show correlation through the combination of multiple independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Popular Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independent variables\n",
    "- popular_revs\n",
    "- popular_pct_likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.6515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.528</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:20</td>     <th>  Log-Likelihood:    </th> <td> -78.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   163.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   167.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.4899</td> <td>    1.629</td> <td>   -0.301</td> <td> 0.765</td> <td>   -3.804</td> <td>    2.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>      <td>    0.1365</td> <td>    0.131</td> <td>    1.041</td> <td> 0.306</td> <td>   -0.130</td> <td>    0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0029</td> <td>    0.041</td> <td>    0.071</td> <td> 0.944</td> <td>   -0.081</td> <td>    0.086</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.796</td> <th>  Durbin-Watson:     </th> <td>   1.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  27.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.209</td> <th>  Prob(JB):          </th> <td>9.37e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.282</td> <th>  Cond. No.          </th> <td>    164.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.038\n",
       "Model:                            OLS   Adj. R-squared:                 -0.020\n",
       "Method:                 Least Squares   F-statistic:                    0.6515\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.528\n",
       "Time:                        23:55:20   Log-Likelihood:                -78.496\n",
       "No. Observations:                  36   AIC:                             163.0\n",
       "Df Residuals:                      33   BIC:                             167.7\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.4899      1.629     -0.301      0.765      -3.804       2.824\n",
       "popular_revs          0.1365      0.131      1.041      0.306      -0.130       0.403\n",
       "popular_pct_likes     0.0029      0.041      0.071      0.944      -0.081       0.086\n",
       "==============================================================================\n",
       "Omnibus:                       10.796   Durbin-Watson:                   1.519\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               27.762\n",
       "Skew:                           0.209   Prob(JB):                     9.37e-07\n",
       "Kurtosis:                       7.282   Cond. No.                         164.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of both of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.038, which means that %3.8 of the variance in change_pct can be explained by our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independent variables\n",
    "- popular_pct_likes\n",
    "- popular_pos_revs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.656</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.206</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:20</td>     <th>  Log-Likelihood:    </th> <td> -77.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   160.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   165.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -1.0580</td> <td>    1.583</td> <td>   -0.668</td> <td> 0.509</td> <td>   -4.279</td> <td>    2.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th>  <td>    0.2102</td> <td>    0.120</td> <td>    1.754</td> <td> 0.089</td> <td>   -0.034</td> <td>    0.454</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>   -0.0027</td> <td>    0.039</td> <td>   -0.069</td> <td> 0.946</td> <td>   -0.082</td> <td>    0.077</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.063</td> <th>  Durbin-Watson:     </th> <td>   1.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.011</td> <th>  Jarque-Bera (JB):  </th> <td>  19.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.007</td> <th>  Prob(JB):          </th> <td>5.35e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.621</td> <th>  Cond. No.          </th> <td>    164.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.091\n",
       "Model:                            OLS   Adj. R-squared:                  0.036\n",
       "Method:                 Least Squares   F-statistic:                     1.656\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.206\n",
       "Time:                        23:55:20   Log-Likelihood:                -77.472\n",
       "No. Observations:                  36   AIC:                             160.9\n",
       "Df Residuals:                      33   BIC:                             165.7\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -1.0580      1.583     -0.668      0.509      -4.279       2.163\n",
       "popular_pos_revs      0.2102      0.120      1.754      0.089      -0.034       0.454\n",
       "popular_pct_likes    -0.0027      0.039     -0.069      0.946      -0.082       0.077\n",
       "==============================================================================\n",
       "Omnibus:                        9.063   Durbin-Watson:                   1.477\n",
       "Prob(Omnibus):                  0.011   Jarque-Bera (JB):               19.670\n",
       "Skew:                          -0.007   Prob(JB):                     5.35e-05\n",
       "Kurtosis:                       6.621   Cond. No.                         164.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pos_revs + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of both of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.091, which means that %9.1 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independent variables\n",
    "- popular_revs\n",
    "- popular_pos_revs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.866</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td>0.0712</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:20</td>     <th>  Log-Likelihood:    </th> <td> -76.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   158.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   163.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -0.5991</td> <td>    1.250</td> <td>   -0.479</td> <td> 0.635</td> <td>   -3.142</td> <td>    1.943</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>     <td>   -0.4724</td> <td>    0.318</td> <td>   -1.485</td> <td> 0.147</td> <td>   -1.119</td> <td>    0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th> <td>    0.6292</td> <td>    0.305</td> <td>    2.066</td> <td> 0.047</td> <td>    0.009</td> <td>    1.249</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.712</td> <th>  Durbin-Watson:     </th> <td>   1.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.035</td> <th>  Jarque-Bera (JB):  </th> <td>  10.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.087</td> <th>  Prob(JB):          </th> <td> 0.00650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.585</td> <th>  Cond. No.          </th> <td>    54.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.148\n",
       "Model:                            OLS   Adj. R-squared:                  0.096\n",
       "Method:                 Least Squares   F-statistic:                     2.866\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):             0.0712\n",
       "Time:                        23:55:20   Log-Likelihood:                -76.310\n",
       "No. Observations:                  36   AIC:                             158.6\n",
       "Df Residuals:                      33   BIC:                             163.4\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -0.5991      1.250     -0.479      0.635      -3.142       1.943\n",
       "popular_revs        -0.4724      0.318     -1.485      0.147      -1.119       0.175\n",
       "popular_pos_revs     0.6292      0.305      2.066      0.047       0.009       1.249\n",
       "==============================================================================\n",
       "Omnibus:                        6.712   Durbin-Watson:                   1.494\n",
       "Prob(Omnibus):                  0.035   Jarque-Bera (JB):               10.072\n",
       "Skew:                          -0.087   Prob(JB):                      0.00650\n",
       "Kurtosis:                       5.585   Cond. No.                         54.7\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + popular_pos_revs', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that the p-value of popular_pos_revs is slightly lower than 0.05, which was not the case when we used a single variable regression. This might indicate that the affect of popular_pos_revs might be significant when used together with popular_revs. However, we still need further analysis to confirm that"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independent variables\n",
    "- All popular variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.154</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:20</td>     <th>  Log-Likelihood:    </th> <td> -76.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   160.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   166.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.8203</td> <td>    1.563</td> <td>   -0.525</td> <td> 0.603</td> <td>   -4.005</td> <td>    2.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>      <td>   -0.4891</td> <td>    0.330</td> <td>   -1.482</td> <td> 0.148</td> <td>   -1.161</td> <td>    0.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0095</td> <td>    0.039</td> <td>    0.242</td> <td> 0.810</td> <td>   -0.071</td> <td>    0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th>  <td>    0.6353</td> <td>    0.310</td> <td>    2.049</td> <td> 0.049</td> <td>    0.004</td> <td>    1.267</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.397</td> <th>  Durbin-Watson:     </th> <td>   1.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.041</td> <th>  Jarque-Bera (JB):  </th> <td>   9.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.030</td> <th>  Prob(JB):          </th> <td> 0.00991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.480</td> <th>  Cond. No.          </th> <td>    171.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.150\n",
       "Model:                            OLS   Adj. R-squared:                  0.070\n",
       "Method:                 Least Squares   F-statistic:                     1.876\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.154\n",
       "Time:                        23:55:20   Log-Likelihood:                -76.277\n",
       "No. Observations:                  36   AIC:                             160.6\n",
       "Df Residuals:                      32   BIC:                             166.9\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.8203      1.563     -0.525      0.603      -4.005       2.364\n",
       "popular_revs         -0.4891      0.330     -1.482      0.148      -1.161       0.183\n",
       "popular_pct_likes     0.0095      0.039      0.242      0.810      -0.071       0.090\n",
       "popular_pos_revs      0.6353      0.310      2.049      0.049       0.004       1.267\n",
       "==============================================================================\n",
       "Omnibus:                        6.397   Durbin-Watson:                   1.494\n",
       "Prob(Omnibus):                  0.041   Jarque-Bera (JB):                9.229\n",
       "Skew:                          -0.030   Prob(JB):                      0.00991\n",
       "Kurtosis:                       5.480   Cond. No.                         171.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + popular_pct_likes + popular_pos_revs', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05 (with one being ~ 0.05). So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.150, which means that %1.5 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demanding Variables\n",
    "\n",
    "We find little point in using min_demanding and rec_demanding together as independent variables because if a game has a demanding minimum requirement, then it also has a demanding recommended requirement. However, it is only one linear regression model and including it will not take long.\n",
    "\n",
    "NOTE: In the multivariate regressions with popular and demanding variables combined, we will separate min_demanding and rec_demanding. In other words, none of the models will contain both min_demanding and rec_demanding.\n",
    "\n",
    "#### Independent Variables\n",
    "- min_demanding\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.1576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.855</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:20</td>     <th>  Log-Likelihood:    </th> <td> -79.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   164.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   168.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    1.5282</td> <td>    1.031</td> <td>    1.482</td> <td> 0.148</td> <td>   -0.570</td> <td>    3.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th> <td>   -0.0902</td> <td>    0.292</td> <td>   -0.309</td> <td> 0.759</td> <td>   -0.684</td> <td>    0.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th> <td>   -0.0245</td> <td>    0.087</td> <td>   -0.281</td> <td> 0.781</td> <td>   -0.202</td> <td>    0.153</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.170</td> <th>  Durbin-Watson:     </th> <td>   1.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>  27.781</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.315</td> <th>  Prob(JB):          </th> <td>9.28e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.257</td> <th>  Cond. No.          </th> <td>    36.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.009\n",
       "Model:                            OLS   Adj. R-squared:                 -0.051\n",
       "Method:                 Least Squares   F-statistic:                    0.1576\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.855\n",
       "Time:                        23:55:20   Log-Likelihood:                -79.022\n",
       "No. Observations:                  36   AIC:                             164.0\n",
       "Df Residuals:                      33   BIC:                             168.8\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         1.5282      1.031      1.482      0.148      -0.570       3.626\n",
       "min_demanding    -0.0902      0.292     -0.309      0.759      -0.684       0.503\n",
       "rec_demanding    -0.0245      0.087     -0.281      0.781      -0.202       0.153\n",
       "==============================================================================\n",
       "Omnibus:                       11.170   Durbin-Watson:                   1.653\n",
       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               27.781\n",
       "Skew:                           0.315   Prob(JB):                     9.28e-07\n",
       "Kurtosis:                       7.257   Cond. No.                         36.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ min_demanding + rec_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are significantly greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary of Multivariate Regression while Popular and Demanding variables are separated\n",
    "\n",
    "No correlation could be found while keeping popular and demanding variables separate.\n",
    "\n",
    "It doesn't seem that popularity of a video game nor demanding games correlates to change in usage percentage individually. However, popular and demanding video games may show a correlation to change in usage percentage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### min_demanding and Popular variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_revs\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.9242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.407</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -78.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   162.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   167.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>   -0.1636</td> <td>    1.362</td> <td>   -0.120</td> <td> 0.905</td> <td>   -2.934</td> <td>    2.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>  <td>    0.1569</td> <td>    0.124</td> <td>    1.265</td> <td> 0.215</td> <td>   -0.095</td> <td>    0.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th> <td>   -0.1891</td> <td>    0.260</td> <td>   -0.728</td> <td> 0.472</td> <td>   -0.718</td> <td>    0.340</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.750</td> <th>  Durbin-Watson:     </th> <td>   1.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  24.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.358</td> <th>  Prob(JB):          </th> <td>5.57e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.952</td> <th>  Cond. No.          </th> <td>    41.0</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.053\n",
       "Model:                            OLS   Adj. R-squared:                 -0.004\n",
       "Method:                 Least Squares   F-statistic:                    0.9242\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.407\n",
       "Time:                        23:55:21   Log-Likelihood:                -78.212\n",
       "No. Observations:                  36   AIC:                             162.4\n",
       "Df Residuals:                      33   BIC:                             167.2\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept        -0.1636      1.362     -0.120      0.905      -2.934       2.607\n",
       "popular_revs      0.1569      0.124      1.265      0.215      -0.095       0.409\n",
       "min_demanding    -0.1891      0.260     -0.728      0.472      -0.718       0.340\n",
       "==============================================================================\n",
       "Omnibus:                       10.750   Durbin-Watson:                   1.508\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               24.195\n",
       "Skew:                           0.358   Prob(JB):                     5.57e-06\n",
       "Kurtosis:                       6.952   Cond. No.                         41.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + min_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.053, which means that %5.3 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_pct_likes\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.2058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.815</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -78.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   163.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   168.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    0.7256</td> <td>    1.585</td> <td>    0.458</td> <td> 0.650</td> <td>   -2.500</td> <td>    3.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0163</td> <td>    0.039</td> <td>    0.418</td> <td> 0.679</td> <td>   -0.063</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th>     <td>   -0.1163</td> <td>    0.262</td> <td>   -0.444</td> <td> 0.660</td> <td>   -0.649</td> <td>    0.416</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>12.378</td> <th>  Durbin-Watson:     </th> <td>   1.585</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.002</td> <th>  Jarque-Bera (JB):  </th> <td>  28.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.530</td> <th>  Prob(JB):          </th> <td>7.08e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.214</td> <th>  Cond. No.          </th> <td>    153.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.012\n",
       "Model:                            OLS   Adj. R-squared:                 -0.048\n",
       "Method:                 Least Squares   F-statistic:                    0.2058\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.815\n",
       "Time:                        23:55:21   Log-Likelihood:                -78.970\n",
       "No. Observations:                  36   AIC:                             163.9\n",
       "Df Residuals:                      33   BIC:                             168.7\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             0.7256      1.585      0.458      0.650      -2.500       3.951\n",
       "popular_pct_likes     0.0163      0.039      0.418      0.679      -0.063       0.096\n",
       "min_demanding        -0.1163      0.262     -0.444      0.660      -0.649       0.416\n",
       "==============================================================================\n",
       "Omnibus:                       12.378   Durbin-Watson:                   1.585\n",
       "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               28.321\n",
       "Skew:                           0.530   Prob(JB):                     7.08e-07\n",
       "Kurtosis:                       7.214   Cond. No.                         153.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pct_likes + min_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.012, which means that %1.2 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_pos_revs\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.121</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -76.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   159.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   164.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -0.8470</td> <td>    1.247</td> <td>   -0.679</td> <td> 0.502</td> <td>   -3.383</td> <td>    1.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th> <td>    0.2401</td> <td>    0.117</td> <td>    2.059</td> <td> 0.047</td> <td>    0.003</td> <td>    0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th>    <td>   -0.2666</td> <td>    0.255</td> <td>   -1.045</td> <td> 0.304</td> <td>   -0.786</td> <td>    0.253</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.334</td> <th>  Durbin-Watson:     </th> <td>   1.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.016</td> <th>  Jarque-Bera (JB):  </th> <td>  14.541</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.258</td> <th>  Prob(JB):          </th> <td>0.000696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.070</td> <th>  Cond. No.          </th> <td>    39.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.120\n",
       "Model:                            OLS   Adj. R-squared:                  0.067\n",
       "Method:                 Least Squares   F-statistic:                     2.254\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.121\n",
       "Time:                        23:55:21   Log-Likelihood:                -76.889\n",
       "No. Observations:                  36   AIC:                             159.8\n",
       "Df Residuals:                      33   BIC:                             164.5\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -0.8470      1.247     -0.679      0.502      -3.383       1.689\n",
       "popular_pos_revs     0.2401      0.117      2.059      0.047       0.003       0.477\n",
       "min_demanding       -0.2666      0.255     -1.045      0.304      -0.786       0.253\n",
       "==============================================================================\n",
       "Omnibus:                        8.334   Durbin-Watson:                   1.466\n",
       "Prob(Omnibus):                  0.016   Jarque-Bera (JB):               14.541\n",
       "Skew:                           0.258   Prob(JB):                     0.000696\n",
       "Kurtosis:                       6.070   Cond. No.                         39.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pos_revs + min_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-value for popular_pos_revs is barely lower than 0.05. So, we might say that there is probably no significant relationship between our variables. R-squared is 0.120, which means that %12 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_revs\n",
    "- popular_pct_likes\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.5986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.621</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -78.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   164.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   170.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.1037</td> <td>    1.727</td> <td>   -0.060</td> <td> 0.952</td> <td>   -3.622</td> <td>    3.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>      <td>    0.1599</td> <td>    0.136</td> <td>    1.175</td> <td> 0.249</td> <td>   -0.117</td> <td>    0.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th>     <td>   -0.1919</td> <td>    0.268</td> <td>   -0.716</td> <td> 0.479</td> <td>   -0.738</td> <td>    0.354</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>   -0.0024</td> <td>    0.042</td> <td>   -0.058</td> <td> 0.954</td> <td>   -0.088</td> <td>    0.083</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.646</td> <th>  Durbin-Watson:     </th> <td>   1.512</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.005</td> <th>  Jarque-Bera (JB):  </th> <td>  24.128</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.337</td> <th>  Prob(JB):          </th> <td>5.76e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.954</td> <th>  Cond. No.          </th> <td>    173.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.053\n",
       "Model:                            OLS   Adj. R-squared:                 -0.036\n",
       "Method:                 Least Squares   F-statistic:                    0.5986\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.621\n",
       "Time:                        23:55:21   Log-Likelihood:                -78.210\n",
       "No. Observations:                  36   AIC:                             164.4\n",
       "Df Residuals:                      32   BIC:                             170.8\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.1037      1.727     -0.060      0.952      -3.622       3.415\n",
       "popular_revs          0.1599      0.136      1.175      0.249      -0.117       0.437\n",
       "min_demanding        -0.1919      0.268     -0.716      0.479      -0.738       0.354\n",
       "popular_pct_likes    -0.0024      0.042     -0.058      0.954      -0.088       0.083\n",
       "==============================================================================\n",
       "Omnibus:                       10.646   Durbin-Watson:                   1.512\n",
       "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               24.128\n",
       "Skew:                           0.337   Prob(JB):                     5.76e-06\n",
       "Kurtosis:                       6.954   Cond. No.                         173.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + min_demanding + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.053, which means that %5.3 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_pos_revs\n",
    "- popular_pct_likes\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.237</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -76.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   161.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   168.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.5596</td> <td>    1.648</td> <td>   -0.340</td> <td> 0.736</td> <td>   -3.917</td> <td>    2.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th>  <td>    0.2517</td> <td>    0.126</td> <td>    2.001</td> <td> 0.054</td> <td>   -0.004</td> <td>    0.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th>     <td>   -0.2804</td> <td>    0.264</td> <td>   -1.063</td> <td> 0.296</td> <td>   -0.818</td> <td>    0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>   -0.0108</td> <td>    0.040</td> <td>   -0.272</td> <td> 0.787</td> <td>   -0.092</td> <td>    0.070</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.054</td> <th>  Durbin-Watson:     </th> <td>   1.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.018</td> <th>  Jarque-Bera (JB):  </th> <td>  14.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.171</td> <th>  Prob(JB):          </th> <td>0.000767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.074</td> <th>  Cond. No.          </th> <td>    172.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.122\n",
       "Model:                            OLS   Adj. R-squared:                  0.040\n",
       "Method:                 Least Squares   F-statistic:                     1.485\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.237\n",
       "Time:                        23:55:21   Log-Likelihood:                -76.847\n",
       "No. Observations:                  36   AIC:                             161.7\n",
       "Df Residuals:                      32   BIC:                             168.0\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.5596      1.648     -0.340      0.736      -3.917       2.798\n",
       "popular_pos_revs      0.2517      0.126      2.001      0.054      -0.004       0.508\n",
       "min_demanding        -0.2804      0.264     -1.063      0.296      -0.818       0.257\n",
       "popular_pct_likes    -0.0108      0.040     -0.272      0.787      -0.092       0.070\n",
       "==============================================================================\n",
       "Omnibus:                        8.054   Durbin-Watson:                   1.480\n",
       "Prob(Omnibus):                  0.018   Jarque-Bera (JB):               14.346\n",
       "Skew:                           0.171   Prob(JB):                     0.000767\n",
       "Kurtosis:                       6.074   Cond. No.                         172.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pos_revs + min_demanding + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.122, which means that %12.2 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td>0.0723</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -75.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   158.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   165.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -0.1654</td> <td>    1.276</td> <td>   -0.130</td> <td> 0.898</td> <td>   -2.765</td> <td>    2.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>     <td>   -0.5439</td> <td>    0.319</td> <td>   -1.707</td> <td> 0.098</td> <td>   -1.193</td> <td>    0.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th>    <td>   -0.3382</td> <td>    0.252</td> <td>   -1.344</td> <td> 0.188</td> <td>   -0.851</td> <td>    0.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th> <td>    0.7341</td> <td>    0.311</td> <td>    2.361</td> <td> 0.024</td> <td>    0.101</td> <td>    1.367</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.270</td> <th>  Durbin-Watson:     </th> <td>   1.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.072</td> <th>  Jarque-Bera (JB):  </th> <td>   5.837</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.184</td> <th>  Prob(JB):          </th> <td>  0.0540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.938</td> <th>  Cond. No.          </th> <td>    57.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.194\n",
       "Model:                            OLS   Adj. R-squared:                  0.118\n",
       "Method:                 Least Squares   F-statistic:                     2.560\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):             0.0723\n",
       "Time:                        23:55:21   Log-Likelihood:                -75.321\n",
       "No. Observations:                  36   AIC:                             158.6\n",
       "Df Residuals:                      32   BIC:                             165.0\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -0.1654      1.276     -0.130      0.898      -2.765       2.434\n",
       "popular_revs        -0.5439      0.319     -1.707      0.098      -1.193       0.105\n",
       "min_demanding       -0.3382      0.252     -1.344      0.188      -0.851       0.174\n",
       "popular_pos_revs     0.7341      0.311      2.361      0.024       0.101       1.367\n",
       "==============================================================================\n",
       "Omnibus:                        5.270   Durbin-Watson:                   1.543\n",
       "Prob(Omnibus):                  0.072   Jarque-Bera (JB):                5.837\n",
       "Skew:                           0.184   Prob(JB):                       0.0540\n",
       "Kurtosis:                       4.938   Cond. No.                         57.3\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + min_demanding + popular_pos_revs', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for popular_pos_revs is below 0.05, which was not the case when we used single variable linear regression. This could mean that popular_pos_revs might have a significant affect on our dependent variable in this given context."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- All popular variables\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.142</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:21</td>     <th>  Log-Likelihood:    </th> <td> -75.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   160.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    31</td>      <th>  BIC:               </th> <td>   168.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.1937</td> <td>    1.620</td> <td>   -0.120</td> <td> 0.906</td> <td>   -3.498</td> <td>    3.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>      <td>   -0.5456</td> <td>    0.329</td> <td>   -1.657</td> <td> 0.108</td> <td>   -1.217</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>min_demanding</th>     <td>   -0.3370</td> <td>    0.259</td> <td>   -1.301</td> <td> 0.203</td> <td>   -0.865</td> <td>    0.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th>  <td>    0.7345</td> <td>    0.316</td> <td>    2.324</td> <td> 0.027</td> <td>    0.090</td> <td>    1.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0011</td> <td>    0.039</td> <td>    0.029</td> <td> 0.977</td> <td>   -0.079</td> <td>    0.082</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.264</td> <th>  Durbin-Watson:     </th> <td>   1.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.072</td> <th>  Jarque-Bera (JB):  </th> <td>   5.803</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.189</td> <th>  Prob(JB):          </th> <td>  0.0549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.930</td> <th>  Cond. No.          </th> <td>    180.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.194\n",
       "Model:                            OLS   Adj. R-squared:                  0.090\n",
       "Method:                 Least Squares   F-statistic:                     1.860\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.142\n",
       "Time:                        23:55:21   Log-Likelihood:                -75.320\n",
       "No. Observations:                  36   AIC:                             160.6\n",
       "Df Residuals:                      31   BIC:                             168.6\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.1937      1.620     -0.120      0.906      -3.498       3.111\n",
       "popular_revs         -0.5456      0.329     -1.657      0.108      -1.217       0.126\n",
       "min_demanding        -0.3370      0.259     -1.301      0.203      -0.865       0.191\n",
       "popular_pos_revs      0.7345      0.316      2.324      0.027       0.090       1.379\n",
       "popular_pct_likes     0.0011      0.039      0.029      0.977      -0.079       0.082\n",
       "==============================================================================\n",
       "Omnibus:                        5.264   Durbin-Watson:                   1.543\n",
       "Prob(Omnibus):                  0.072   Jarque-Bera (JB):                5.803\n",
       "Skew:                           0.189   Prob(JB):                       0.0549\n",
       "Kurtosis:                       4.930   Cond. No.                         180.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + min_demanding + popular_pos_revs + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the p-value of popular_pos_revs is below 0.05. We might infer that popular_pos_revs have a significant impact when used in this given context. Also, the value R-squared went up compared to other results, which can mean that we can explain more of the variance by this set of independent variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### rec_demanding and Popular variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_revs\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.341</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:22</td>     <th>  Log-Likelihood:    </th> <td> -78.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   162.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   166.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>     <td>    0.0548</td> <td>    1.402</td> <td>    0.039</td> <td> 0.969</td> <td>   -2.797</td> <td>    2.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>  <td>    0.1824</td> <td>    0.129</td> <td>    1.412</td> <td> 0.167</td> <td>   -0.080</td> <td>    0.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th> <td>   -0.0766</td> <td>    0.081</td> <td>   -0.945</td> <td> 0.352</td> <td>   -0.242</td> <td>    0.088</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.382</td> <th>  Durbin-Watson:     </th> <td>   1.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>  27.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.023</td> <th>  Prob(JB):          </th> <td>1.36e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.244</td> <th>  Cond. No.          </th> <td>    63.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.063\n",
       "Model:                            OLS   Adj. R-squared:                  0.006\n",
       "Method:                 Least Squares   F-statistic:                     1.113\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.341\n",
       "Time:                        23:55:22   Log-Likelihood:                -78.018\n",
       "No. Observations:                  36   AIC:                             162.0\n",
       "Df Residuals:                      33   BIC:                             166.8\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept         0.0548      1.402      0.039      0.969      -2.797       2.906\n",
       "popular_revs      0.1824      0.129      1.412      0.167      -0.080       0.445\n",
       "rec_demanding    -0.0766      0.081     -0.945      0.352      -0.242       0.088\n",
       "==============================================================================\n",
       "Omnibus:                       10.382   Durbin-Watson:                   1.601\n",
       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               27.021\n",
       "Skew:                          -0.023   Prob(JB):                     1.36e-06\n",
       "Kurtosis:                       7.244   Cond. No.                         63.3\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + rec_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.063, which means that %6.3 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_pct_likes\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.5059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.608</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:22</td>     <th>  Log-Likelihood:    </th> <td> -78.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   163.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   168.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>    0.5857</td> <td>    1.408</td> <td>    0.416</td> <td> 0.680</td> <td>   -2.279</td> <td>    3.451</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0414</td> <td>    0.047</td> <td>    0.887</td> <td> 0.381</td> <td>   -0.053</td> <td>    0.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th>     <td>   -0.0833</td> <td>    0.094</td> <td>   -0.891</td> <td> 0.379</td> <td>   -0.274</td> <td>    0.107</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.804</td> <th>  Durbin-Watson:     </th> <td>   1.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>  27.263</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.462</td> <th>  Prob(JB):          </th> <td>1.20e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.162</td> <th>  Cond. No.          </th> <td>    144.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.030\n",
       "Model:                            OLS   Adj. R-squared:                 -0.029\n",
       "Method:                 Least Squares   F-statistic:                    0.5059\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.608\n",
       "Time:                        23:55:22   Log-Likelihood:                -78.649\n",
       "No. Observations:                  36   AIC:                             163.3\n",
       "Df Residuals:                      33   BIC:                             168.0\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept             0.5857      1.408      0.416      0.680      -2.279       3.451\n",
       "popular_pct_likes     0.0414      0.047      0.887      0.381      -0.053       0.136\n",
       "rec_demanding        -0.0833      0.094     -0.891      0.379      -0.274       0.107\n",
       "==============================================================================\n",
       "Omnibus:                       11.804   Durbin-Watson:                   1.650\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               27.263\n",
       "Skew:                           0.462   Prob(JB):                     1.20e-06\n",
       "Kurtosis:                       7.162   Cond. No.                         144.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pct_likes + rec_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.030, which means that %3.0 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_pos_revs\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td>0.0910</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:22</td>     <th>  Log-Likelihood:    </th> <td> -76.578</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   159.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   163.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>   -0.5071</td> <td>    1.298</td> <td>   -0.391</td> <td> 0.699</td> <td>   -3.148</td> <td>    2.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th> <td>    0.2664</td> <td>    0.120</td> <td>    2.215</td> <td> 0.034</td> <td>    0.022</td> <td>    0.511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th>    <td>   -0.1023</td> <td>    0.079</td> <td>   -1.298</td> <td> 0.203</td> <td>   -0.263</td> <td>    0.058</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.387</td> <th>  Durbin-Watson:     </th> <td>   1.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>  18.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.268</td> <th>  Prob(JB):          </th> <td>7.60e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.516</td> <th>  Cond. No.          </th> <td>    61.1</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.135\n",
       "Model:                            OLS   Adj. R-squared:                  0.083\n",
       "Method:                 Least Squares   F-statistic:                     2.580\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):             0.0910\n",
       "Time:                        23:55:22   Log-Likelihood:                -76.578\n",
       "No. Observations:                  36   AIC:                             159.2\n",
       "Df Residuals:                      33   BIC:                             163.9\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept           -0.5071      1.298     -0.391      0.699      -3.148       2.134\n",
       "popular_pos_revs     0.2664      0.120      2.215      0.034       0.022       0.511\n",
       "rec_demanding       -0.1023      0.079     -1.298      0.203      -0.263       0.058\n",
       "==============================================================================\n",
       "Omnibus:                        9.387   Durbin-Watson:                   1.599\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):               18.970\n",
       "Skew:                          -0.268   Prob(JB):                     7.60e-05\n",
       "Kurtosis:                       6.516   Cond. No.                         61.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pos_revs + rec_demanding', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value of popular_pos_revs is below 0.05. So, the effect of this independent variable could be significant in this given context. We can also see that the value of R-squared went up compared to other results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_revs\n",
    "- popular_pct_likes\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.8581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.473</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:22</td>     <th>  Log-Likelihood:    </th> <td> -77.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   163.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   169.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.4418</td> <td>    1.623</td> <td>   -0.272</td> <td> 0.787</td> <td>   -3.748</td> <td>    2.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>      <td>    0.1656</td> <td>    0.133</td> <td>    1.243</td> <td> 0.223</td> <td>   -0.106</td> <td>    0.437</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th>     <td>   -0.1062</td> <td>    0.095</td> <td>   -1.123</td> <td> 0.270</td> <td>   -0.299</td> <td>    0.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0295</td> <td>    0.047</td> <td>    0.625</td> <td> 0.537</td> <td>   -0.067</td> <td>    0.126</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>10.242</td> <th>  Durbin-Watson:     </th> <td>   1.600</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.006</td> <th>  Jarque-Bera (JB):  </th> <td>  24.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.193</td> <th>  Prob(JB):          </th> <td>4.41e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.037</td> <th>  Cond. No.          </th> <td>    173.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.074\n",
       "Model:                            OLS   Adj. R-squared:                 -0.012\n",
       "Method:                 Least Squares   F-statistic:                    0.8581\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.473\n",
       "Time:                        23:55:22   Log-Likelihood:                -77.800\n",
       "No. Observations:                  36   AIC:                             163.6\n",
       "Df Residuals:                      32   BIC:                             169.9\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.4418      1.623     -0.272      0.787      -3.748       2.864\n",
       "popular_revs          0.1656      0.133      1.243      0.223      -0.106       0.437\n",
       "rec_demanding        -0.1062      0.095     -1.123      0.270      -0.299       0.086\n",
       "popular_pct_likes     0.0295      0.047      0.625      0.537      -0.067       0.126\n",
       "==============================================================================\n",
       "Omnibus:                       10.242   Durbin-Watson:                   1.600\n",
       "Prob(Omnibus):                  0.006   Jarque-Bera (JB):               24.665\n",
       "Skew:                           0.193   Prob(JB):                     4.41e-06\n",
       "Kurtosis:                       7.037   Cond. No.                         173.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + rec_demanding + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-values of our independent variables are greater than 0.05. So, there is no statistically significant relationship between our independent variables and dependent variable. R-squared is 0.074, which means that %7.4 of the variance in change_pct can be explained by our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables:\n",
    "- popular_pos_revs\n",
    "- popular_pct_likes\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1.846</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td> 0.159</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:22</td>     <th>  Log-Likelihood:    </th> <td> -76.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   160.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   167.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -1.0803</td> <td>    1.557</td> <td>   -0.694</td> <td> 0.493</td> <td>   -4.252</td> <td>    2.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th>  <td>    0.2567</td> <td>    0.122</td> <td>    2.102</td> <td> 0.043</td> <td>    0.008</td> <td>    0.505</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th>     <td>   -0.1341</td> <td>    0.092</td> <td>   -1.454</td> <td> 0.156</td> <td>   -0.322</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0304</td> <td>    0.045</td> <td>    0.679</td> <td> 0.502</td> <td>   -0.061</td> <td>    0.121</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.261</td> <th>  Durbin-Watson:     </th> <td>   1.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.016</td> <th>  Jarque-Bera (JB):  </th> <td>  15.840</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.069</td> <th>  Prob(JB):          </th> <td>0.000363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.247</td> <th>  Cond. No.          </th> <td>    173.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.148\n",
       "Model:                            OLS   Adj. R-squared:                  0.068\n",
       "Method:                 Least Squares   F-statistic:                     1.846\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):              0.159\n",
       "Time:                        23:55:22   Log-Likelihood:                -76.320\n",
       "No. Observations:                  36   AIC:                             160.6\n",
       "Df Residuals:                      32   BIC:                             167.0\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -1.0803      1.557     -0.694      0.493      -4.252       2.092\n",
       "popular_pos_revs      0.2567      0.122      2.102      0.043       0.008       0.505\n",
       "rec_demanding        -0.1341      0.092     -1.454      0.156      -0.322       0.054\n",
       "popular_pct_likes     0.0304      0.045      0.679      0.502      -0.061       0.121\n",
       "==============================================================================\n",
       "Omnibus:                        8.261   Durbin-Watson:                   1.625\n",
       "Prob(Omnibus):                  0.016   Jarque-Bera (JB):               15.840\n",
       "Skew:                          -0.069   Prob(JB):                     0.000363\n",
       "Kurtosis:                       6.247   Cond. No.                         173.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_pos_revs + rec_demanding + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value of popular_pos_revs is slighyly under 0.05, which is a recurring pattern so far in our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td>0.0736</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:23</td>     <th>  Log-Likelihood:    </th> <td> -75.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   158.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    32</td>      <th>  BIC:               </th> <td>   165.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>    0.0237</td> <td>    1.322</td> <td>    0.018</td> <td> 0.986</td> <td>   -2.668</td> <td>    2.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>     <td>   -0.4738</td> <td>    0.314</td> <td>   -1.507</td> <td> 0.142</td> <td>   -1.114</td> <td>    0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th>    <td>   -0.1027</td> <td>    0.077</td> <td>   -1.328</td> <td> 0.194</td> <td>   -0.260</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th> <td>    0.6894</td> <td>    0.305</td> <td>    2.264</td> <td> 0.030</td> <td>    0.069</td> <td>    1.310</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.670</td> <th>  Durbin-Watson:     </th> <td>   1.634</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.022</td> <th>  Jarque-Bera (JB):  </th> <td>  11.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.323</td> <th>  Prob(JB):          </th> <td> 0.00344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.673</td> <th>  Cond. No.          </th> <td>    75.2</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.193\n",
       "Model:                            OLS   Adj. R-squared:                  0.117\n",
       "Method:                 Least Squares   F-statistic:                     2.543\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):             0.0736\n",
       "Time:                        23:55:23   Log-Likelihood:                -75.344\n",
       "No. Observations:                  36   AIC:                             158.7\n",
       "Df Residuals:                      32   BIC:                             165.0\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "Intercept            0.0237      1.322      0.018      0.986      -2.668       2.716\n",
       "popular_revs        -0.4738      0.314     -1.507      0.142      -1.114       0.167\n",
       "rec_demanding       -0.1027      0.077     -1.328      0.194      -0.260       0.055\n",
       "popular_pos_revs     0.6894      0.305      2.264      0.030       0.069       1.310\n",
       "==============================================================================\n",
       "Omnibus:                        7.670   Durbin-Watson:                   1.634\n",
       "Prob(Omnibus):                  0.022   Jarque-Bera (JB):               11.342\n",
       "Skew:                          -0.323   Prob(JB):                      0.00344\n",
       "Kurtosis:                       5.673   Cond. No.                         75.2\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + rec_demanding + popular_pos_revs', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value of popular_pos_revs is lower than 0.05, which can mean that it's effects might be significant. We need further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables\n",
    "- All popular variables\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>change_pct</td>    <th>  R-squared:         </th> <td>   0.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   2.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 10 Dec 2021</td> <th>  Prob (F-statistic):</th>  <td>0.0891</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:55:23</td>     <th>  Log-Likelihood:    </th> <td> -74.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   159.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    31</td>      <th>  BIC:               </th> <td>   167.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   -0.8109</td> <td>    1.518</td> <td>   -0.534</td> <td> 0.597</td> <td>   -3.907</td> <td>    2.286</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_revs</th>      <td>   -0.5614</td> <td>    0.323</td> <td>   -1.737</td> <td> 0.092</td> <td>   -1.221</td> <td>    0.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rec_demanding</th>     <td>   -0.1546</td> <td>    0.090</td> <td>   -1.713</td> <td> 0.097</td> <td>   -0.339</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pos_revs</th>  <td>    0.7518</td> <td>    0.309</td> <td>    2.435</td> <td> 0.021</td> <td>    0.122</td> <td>    1.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>popular_pct_likes</th> <td>    0.0494</td> <td>    0.045</td> <td>    1.105</td> <td> 0.278</td> <td>   -0.042</td> <td>    0.141</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 5.217</td> <th>  Durbin-Watson:     </th> <td>   1.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.074</td> <th>  Jarque-Bera (JB):  </th> <td>   5.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.123</td> <th>  Prob(JB):          </th> <td>  0.0514</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.974</td> <th>  Cond. No.          </th> <td>    180.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             change_pct   R-squared:                       0.223\n",
       "Model:                            OLS   Adj. R-squared:                  0.123\n",
       "Method:                 Least Squares   F-statistic:                     2.226\n",
       "Date:                Fri, 10 Dec 2021   Prob (F-statistic):             0.0891\n",
       "Time:                        23:55:23   Log-Likelihood:                -74.649\n",
       "No. Observations:                  36   AIC:                             159.3\n",
       "Df Residuals:                      31   BIC:                             167.2\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            -0.8109      1.518     -0.534      0.597      -3.907       2.286\n",
       "popular_revs         -0.5614      0.323     -1.737      0.092      -1.221       0.098\n",
       "rec_demanding        -0.1546      0.090     -1.713      0.097      -0.339       0.029\n",
       "popular_pos_revs      0.7518      0.309      2.435      0.021       0.122       1.381\n",
       "popular_pct_likes     0.0494      0.045      1.105      0.278      -0.042       0.141\n",
       "==============================================================================\n",
       "Omnibus:                        5.217   Durbin-Watson:                   1.773\n",
       "Prob(Omnibus):                  0.074   Jarque-Bera (JB):                5.936\n",
       "Skew:                          -0.123   Prob(JB):                       0.0514\n",
       "Kurtosis:                       4.974   Cond. No.                         180.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('change_pct ~ popular_revs + rec_demanding + popular_pos_revs + popular_pct_likes', df_final)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value of popular_pos_revs is lower than 0.05, and our R-squared is 0.223, which might mean that we are onto something. We are going to further analyze these variables using variance inflation factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary of Multivariate Regressions while Popular and Demanding variables were combined\n",
    "\n",
    "Above we have 14 different combinations of popular and demanding variables. These are the correlations we were able to find:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### min_demanding and Popular variables Summary\n",
    "\n",
    "##### Independent variables\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- min_demanding\n",
    "\n",
    "When using popular_revs and popular_pos_revs only, we get a correlation for popular_pos_revs with a slope of 0.7341 and an R-square value of 0.194.\n",
    "\n",
    "##### Independent variables\n",
    "- All popular variables\n",
    "- min_demanding\n",
    "\n",
    "When using all popular variables, we get a correlation for popular_pos_revs with a slope of 0.7345 and an R-square value of 0.194.\n",
    "\n",
    "While using these two combinations of variables, we were able to find a correlation between change in usage percentage and number of games with popular number of positive reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### rec_demanding and Popular variables Summary\n",
    "\n",
    "##### Independent variables\n",
    "- popular_pos_revs\n",
    "- rec_demanding\n",
    "\n",
    "When using popular_pos_revs only, we get a correlation for popular_pos_revs with a slope of 0.2664, R-squared value of 0.135\n",
    "\n",
    "##### Independent variables\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- rec_demanding\n",
    "\n",
    "When using popular_revs and popular_pos_revs only, we get a correlation for popular_pos_revs with a slope of 0.6894, R-squared value of 0.193\n",
    "\n",
    "##### Independent variables\n",
    "- All popular variables\n",
    "- rec_demanding\n",
    "\n",
    "When using all popular variables, we get a correlation for popular pos_revs with a slope of 0.7518, R-squared value of 0.223\n",
    "\n",
    "While using these three combinations of variables, we were able to find a correlation between change in usage percentage and number of games with popular number of positive reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary of Multivariate Regression\n",
    "\n",
    "We weren't able to find any correlations when keeping popular and demanding variables separate, however, when combining these two, we were able to find a correlation. Specifically with the number of games with popular number of positive reviews. \n",
    "\n",
    "In multiple regression models with combined popular and demanding variables, we were able to find a slight positive correlation between change in usage percentage and number of games with popular number of positive reviews. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is the correlation significant?\n",
    "\n",
    "In the multivariate regression, we found correlation for four models. One concern is that our independent variables are collinear and to check for collinearity, we looked at variance inflation factor (VIF). The higher the VIF of an independent variable, the more correlated it is with the other independent variables. It's common that a VIF > 10 is an indicator of multicollinearity and so we will use that as our threshold.\n",
    "\n",
    "We can check the VIF of the four models that gave us a correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independent variables:\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>popular_revs</td>\n",
       "      <td>88.909308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>popular_pos_revs</td>\n",
       "      <td>94.463866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>min_demanding</td>\n",
       "      <td>3.657769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variables        VIF\n",
       "0      popular_revs  88.909308\n",
       "1  popular_pos_revs  94.463866\n",
       "2     min_demanding   3.657769"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "var = df_final[['popular_revs', 'popular_pos_revs', 'min_demanding']]\n",
    "vif['variables'] = var.columns\n",
    "vif['VIF'] = [variance_inflation_factor(var.values, i) for i in range(len(var.columns))]\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "graph = sns.barplot(x='VIF', y='variables', data=vif, palette= ['blue', 'orange', 'green'])\n",
    "graph.axvline(10, c = 'k', linestyle = '--')\n",
    "\n",
    "plt.xlabel('Variance Inflation Factor (VIF)', fontsize = 13)\n",
    "plt.ylabel('Variables', fontsize = 13)\n",
    "plt.title('First Set of Independent Variables Against VIF', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VIF for popular variables are too high! These values are way beyond the acceptable VIF values. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independent variables:\n",
    "- All popular variables\n",
    "- min_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>popular_revs</td>\n",
       "      <td>100.091775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>popular_pos_revs</td>\n",
       "      <td>94.554075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>min_demanding</td>\n",
       "      <td>3.659232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>popular_pct_likes</td>\n",
       "      <td>10.472384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           variables         VIF\n",
       "0       popular_revs  100.091775\n",
       "1   popular_pos_revs   94.554075\n",
       "2      min_demanding    3.659232\n",
       "3  popular_pct_likes   10.472384"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "var = df_final[['popular_revs', 'popular_pos_revs', 'min_demanding', 'popular_pct_likes']]\n",
    "vif['variables'] = var.columns\n",
    "vif['VIF'] = [variance_inflation_factor(var.values, i) for i in range(len(var.columns))]\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CWOpYZ52nDx1BkiRJUs8cGi113HXXn7jrrj8NHUOSJElSj+wRljquvPK1gM8RliRJkmYze4QlSZIkSSPFQliSJEmSNFIshJcjybwkFw6dQ5IkSZK0YlgIr2BJ5kxhmSTx2EuSJEnSAFb5Yqztsf1lkuOS/CzJqUnmJtk9yU+SXJDkk0nWbJdflOR9Sc5pXw9up89Pslen3Zsm2dZ3kvy4fe3aTt8tyVlJPgtcsIycv0hyBPBjYMsk/5bkR23ud7TLvS/JQZ313p7kdUk2T/LtJOcnuTDJ36/Aw6jWBhu8hA02eMnQMSRJkiT1aJUvhFvbAEdV1SOAG4DXAvOBvatqO5q7Y7+is/wNVbUz8DHg8Gls5wrgyVX1KGBv4KOdeTsDh1TVtsvJ+emqemT7/iHtejsAOyZ5PHBi2/aY5wKnAM8DTq+qHYDtgfPHN57kwCTnJjl3yZKrp7FbGjN37h7MnbvH0DEkSZIk9Wi2FMKXVtX32vcnALsDC6vq4nbaccDjO8t/rvNzl2lsZ3Xg6CQX0BSn3aL3nKpauJz1f1dVZ7fv/0/7+glND/HfAA+pqp8AmyXZIsn2wLVV9XvgR8ALk7wd2K6qbhzfeFUdVVU7VdVOc+ZsPI3d0pg777yEO++8ZOgYkiRJkno0W54jXPdg+bH3d9H+YSBJgDUmWO81wOU0PbL3Am7rzLt5CtvtLhPg0Ko6coLlTgX2Au5L00NMVX277TF+GnB8kg9U1aensE1Nw1VXHQL4HGFJkiRpNpstPcIPSDLWs7svcAYwb+z6X2B/4Fud5ffu/PxB+34RsGP7/lk0vb/jbQAsrqqlbZvLvTHWMpwOvCjJugBJ7pdks3beicA+NMXwqe38BwJXVNXRwLHAo+7BtiVJkiRpZM2WHuFfAC9IciTwa+DVwNnAKUlWoxlW/InO8msm+SHNHwL2bacdDXwpyTnAmUzcw3sE8PkkzwHOmmSZKamq/03yt8APmg5obgL2oyl2f55kPeCPVbW4XWU34N+S3Nku+/y7u21JkiRJGmWpmu6o4pVLknnAV6vq4VNcfhGwU1Vd1Weuoa255iNqiy2+PHSMVc7ixfsADo2WJEmSlmfhwnlDR1imJOdV1U4TzZstQ6MlSZIkSZqSVX5odFUtAqbUG9wuP6+3MECSjWmGVo+3e1X5TKOV3IYbvnLoCJIkSZJ6tsoXwiubttjdYegcunvWXvtxQ0eQJEmS1DOHRksdt99+EbffftHQMSRJkiT1yB5hqeOaa94JeLMsSZIkaTazR1iSJEmSNFIshCVJkiRJI8VCWJIkSZI0UiyEJUmSJEkjxZtlSR0bbfRvQ0eQJEmS1DMLYaljrbV2HDqCJEmSpJ45NFrquO2287jttvOGjiFJkiSpR/YISx3XXvsBwOcIS5IkSbOZPcKSJEmSpJFiISxJkiRJGikWwpIkSZKkkWIhLEmSJEkaKd4sS+q4973fNnQESZIkST2zEJY61lxz26EjSJIkSeqZQ6Oljltv/S633vrdoWNIkiRJ6pE9wlLHddd9DIC1137cwEkkSZIk9cUeYUmSJEnSSLEQliRJkiSNFAthSZIkSdJIsRCWJEmSJI0Ub5YldWyyyXuGjiBJkiSpZxbCUsfqqz9o6AiSJEmSeubQaKnjllvO4JZbzhg6hiRJkqQe2SMsdVx//TEAzJ27x8BJJEmSJPXFHmFJkiRJ0kixEJYkSZIkjRQLYUmSJEnSSLEQliRJkiSNFG+WJXVsuumHho4gSZIkqWcWwlLHaqttMXQESZIkST1zaLTUcfPNX+Xmm786dAxJkiRJPbJHWOq44YYTAFhnnacPnESSJElSX+wRliRJkiSNFHuEZ6nttluDc8+dN3SMVc5uu60FwIIF84YNIkmSJKk39ghLkiRJkkaKhbAkSZIkaaQ4NFrqOPXUU4eOIEmSJKlnFsJSxyabbDJ0BEmSJEk9c2i01DF//nzmz58/dAxJkiRJPbIQljoshCVJkqTZz0JYkiRJkjRSLIQlSZIkSSPFQliSJEmSNFIshCVJkiRJI8XHJ0kdp5122tARJEmSJPXMQljqmDt37tARJEmSJPXModFSxxFHHMERRxwxdAxJkiRJPbIQljpOPvlkTj755KFjSJIkSeqRhbAkSZIkaaRYCEuSJEmSRoqFsCRJkiRppFgIS5IkSZJGio9PkjoWLFgwdARJkiRJPbNHWJIkSZI0UiyEpY7DDjuMww47bOgYkiRJknrk0OhZ6o6rLmDRsVsNHWOVc+rRiwHYa6P/GjiJJEmSRs28Fy8cOsLIsEdYkiRJkjRSLIQlSZIkSSPFQliSJEmSNFK8RljqWGsN/zYkSZIkzXYWwlLH/IPvM3QESZIkST2z+0uSJEmSNFIshKWOj37lOj76leuGjiFJkiSpRxbCUsf3f3Er3//FrUPHkCRJktQjC2FJkiRJ0kixEJYkSZIkjRQLYUmSJEnSSPHxSVLHRuvOGTqCJEmSpJ5ZCEsdHz9os6EjSJIkSeqZQ6MlSZIkSSPFQljqeP/nr+X9n7926BiSJEmSeuTQaKnjx5fcNnQESZIkST2zR1iSJEmSNFIshCVJkiRJI8VCWJIkSZI0UrxGWOq470b+k5AkSZJmO3/rlzoOf+mmQ0eQJEmS1DOHRkuSJEmSRoqFsNTxzhOv5p0nXj10DEmSJEk9cmi01HHR7+8YOoIkSZKkntkjLEmSJEkaKRbCkiRJkqSRYiEsSZIkSRopXiMsdWx139WHjiBJkiSpZxbCUsehz99k6AiSJEmSeubQaEmSJEnSSLEQljre9OmreNOnrxo6hiRJkqQeOTRa6lh42Z1DR5AkSZLUM3uEJUmSJEkjxUJYkiRJkjRSLIQlSZIkSSPFa4Sljm0fsMbQESRJkiT1zEJY6njbPhsPHUGSJElSzxwaLUmSJEkaKRbCUsfBR1/JwUdfOXQMSZIkST2aFYVwknlJLhw6h1Z9l117F5dde9fQMSRJkiT1aFYUwtOVZM7A2/fabEmSJEkayIwUwm2P7S+THJfkZ0lOTTI3ye5JfpLkgiSfTLJmu/yiJO9Lck77enA7fX6SvTrt3jTJtr6T5Mfta9d2+m5JzkryWeCC6eRs502W9T+TXNQuf9gyjsH8JB9KchbwviQPSvL1JOe1ef8myQbtvt+rXWdukkuTrJ7kVZ3tnHh3vwtJkiRJGnUz2SO8DXBUVT0CuAF4LTAf2LuqtqO5g/UrOsvfUFU7Ax8DDp/Gdq4AnlxVjwL2Bj7ambczcEhVbTuNnAclWWuirEnuDTwbeFi7/LuXk+2hwB5V9TrgKOBfq2pH4PXAEVV1PfBT4Ant8s8ATq+qO4E3Ao9st/PyiRpPcmCSc5Oce/WNS5YTRZIkSZJG00wWwpdW1ffa9ycAuwMLq+ridtpxwOM7y3+u83OXaWxndeDoJBcApwDdovecqlo4zZyPoymOJ8p6A3AbcEyS/wvcspy2T6mqJUnWBXYFTklyPnAksHm7zEk0BTzAPu1ngJ8Bn0myHzDhRaxVdVRV7VRVO2283qCjv1dZj3rQWjzqQWsNHUOSJElSj2byWtW6B8uPvb+LtnhPEmCNCdZ7DXA5sH277G2deTffjZwFZMIFq+5KsjNNUb8P8ErgSctoe2z79wKuq6odJljmy8ChbW/zjsA32+lPoym+nwm8NcnDqsq7Oq1gb9hzo6EjSJIkSerZTPYIPyDJWM/uvsAZwLyx63+B/YFvdZbfu/PzB+37RTTFIcCzaHp/x9sAWFxVS9s2p9s1Oj7nd4FfTpS17dndoKpOAw4GdpjKBqrqBmBhkudAU9Qn2b6ddxNwDvAR4KttD/K9gC2r6izgDcCGwLrT3C9JkiRJEjPbI/wL4AVJjgR+DbwaOJtmePBqwI+AT3SWXzPJD2mK9X3baUcDX0pyDnAmE/fwHgF8vi0yz5pkmenk/HhV3ZbkhRNkvXebZy2aXuPXTGM7/wx8PMlbaAr6E2muD4ZmOPQpwG7t5znACUk2aLfz4aq6bpr7pSl4xRFXAPDxgzYbOIkkSZKkvqRquiOW78ZGknk0vZsPn+Lyi4CdquqqPnNNsN15TCPnyuwR89asL791i6FjrHL2ef9iAE58w+bLWVKSJElasea9eHm3M9J0JDmvqnaaaN5IPkdYkiRJkjS6ZmRodFUtAqbcy1pV83oLAyTZmGZo9Xi739Pe4CSHAM8ZN/mUqnrPPWlXkiRJkrRizOQ1wiuNqrqaKd7Y6m60/R7AoleSJEmSVlIjWQhLk9n1b9ceOoIkSZKknlkISx2vesaGQ0eQJEmS1DNvliVJkiRJGikWwlLHAYdfzgGHXz50DEmSJEk9cmi01HHbHUuHjiBJkiSpZ/YIS5IkSZJGioWwJEmSJGmkWAhLkiRJkkaK1whLHU/afu7QESRJkiT1zEJY6jjwHzYYOoIkSZKknjk0WpIkSZI0UiyEpY593r+Yfd6/eOgYkiRJknpkISxJkiRJGikWwpIkSZKkkWIhLEmSJEkaKRbCkiRJkqSR4uOTpI6nPXqdoSNIkiRJ6pmFsNSx/xPXHzqCJEmSpJ5NaWh0kn9JskP7fsckv0vymyQ79ZpOmmG33r6UW29fOnQMSZIkST2a6jXCrwP+2L5/N3AiMB/4YA+ZpMG88COX88KPXD50DEmSJEk9murQ6I2r6sokawK7As8G7gRe21sySZIkSZJ6MNVC+KYkWwDbAT+rqtuSrAHM6S+aJEmSJEkr3lQL4fnAD4E1gTe30x4N/KaHTJIkSZIk9WZKhXBVHZJkAXBHVX2rnXw78Pq+gkmSJEmS1IcpPz6pqr6RxuZVtbiqzu0zmDSEvR677tARJEmSJPVsSoVwknWBw4H9gCXAOkn+Cdi+qt7RWzpphu312PWGjiBJkiSpZ1N9fNIHgfsCjwXuaKf9CNi7j1DSUK65cQnX3Lhk6BiSJEmSejTVodFPB7atquuTFEBV/bG9k7Q0axz08SsAOPENmw+cRJIkSVJfptojHODWv5rQDJe+aYUnkiRJkiSpR1MthL8HvGnctH8FzlqxcSRJkiRJ6tdUh0a/Fvhmkv2AdZNcAKwO7N5bMkmSJEmSejDV5whfmuThwDOAecDvgK9W1a3LXFGSJEmSpJXMdJ4jfDtwao9ZpMHtt9v6Q0eQJEmS1LNJC+Ekb55KA1X13hUXRxrW03deZ+gIkiRJknq2rB7hJ09h/QIshFdCa2yyHfNefO7QMVY5l156KQBbbrnlwEkkSZIk9WXSQriqnjiTQaSVwf777w/AggULhg0iSZIkqTdTfXwSAEnum2SnJPftK5AkSZIkSX2aUiGcZNMkpwN/As4B/pjk9CSb9ZpOkiRJkqQVbKo9wkcBNwMPoXl+8DbAje10SZIkSZJWGVN9fNITgAdU1U3t598keRHN84QlSZIkSVplTLUQvhJYG7ipM20t4IoVnkga0Ote97qhI0iSJEnq2bKeI7xF5+P7gVOSvJ2mF3ge8FbgfX2Gk2baM57xjKEjSJIkSerZsnqE/0DznGCAtD+/2U4b+/wE4JP9RJNm3q9+9SsAttlmm4GTSJIkSerLsgrhrWYshbSSeNnLXgb4HGFJkiRpNpu0EK4qb4QlSZIkSZp1pnqzLJI8Gdgd2JS/DI2mql7UQy5JkiRJknoxpecIJ3k18CXgQcDzgPWA5zKNQlqSJEmSpJXBlAph4JXAU6vqOcBt7c+9gTt7SyZJkiRJUg+m2qN736pa0L4fu5P0acBxwItXdChpKG95y1uGjiBJkiSpZ1MthK9Icp+quhz4Q5LHAFcx9R5laZWwxx57DB1BkiRJUs+mWsieSHOjLIBjgLOAnwCf6yOUNJTzzz+f888/f+gYkiRJkno0pR7hqjqk8/6jSc6juWHW6X0Fk4Zw8MEHAz5HWJIkSZrN7tZdn6vqeys6iCRJkiRJM2HSQjjJR6vqVe37oyZbrqoO7COYJEmSJEl9WFaP8OqTvJckSZIkaZU1aSFcVa8ASBLg34GrqmrpTAWTJEmSJKkPqaplL5DcC7gZWK+q7pqRVLrHVrvParXe89ab0W1e++FrZ3R7ffj+978PwK677jpwEkmSJEn3RJLzqmqnieYt92ZZVbU0yW+BjYArV3Q4aWViASxJkiTNflN9jvAHgc8meWyS+yfZYuzVZzhppn3/+9//c6+wJEmSpNlpqo9POqb9uTswNpY67fs5KzqUNJQ3v/nNgM8RliRJkmazqRbCW/WaQpIkSZKkGTKlQriqftd3EEmSJEmSZsJUe4RJ8mSaodGb0gyLBqCqXtRDLkmSJEmSejGlm2UleTXwJeBBwPOA9YDnMo1CWpIkSZKklcFUC9lXAk+tqgVJrq2q5yR5GvB/e8wmzbjDDz986AiSJEmSejbVQvi+VbWgfT921+jTgOOAF6/oUNJQdthhh6EjSJIkSerZVJ8jfEWS+7Tv/5DkMcDW01hfWiWcccYZnHHGGUPHkCRJktSjZfYIJ/kocBRwIs2Nsj5L80zhs4C7gOP7DijNpHe/+90A7LHHHgMnkSRJktSX5Q2N3g44Hzgb+FWStarqo0nOBdYHTu85nyRJkiRJK9QyhzZX1ROBvwW+D3wA+GOSDwPXVtXXq6qWtb4kSZIkSSub5V7jW1W/rqo3APcHDqLpJb4wyXeS7Nd3QEmSJEmSVqQp3+yqqu6sqpOqag9gR2ALmrtGS5IkSZK0ypjq45MASPJo4EBgb+Bq4K19hJKGcuSRRw4dQZIkSVLPllsIJ1kf2B94KbAt8D/Ac4HTvUZYs80222wzdARJkiRJPVve45OOA/YErqJ5bNKxVbV4JoJJQ/jKV74CwDOe8YyBk0iSJEnqy/J6hDcAngN4h2iNhA9+8IOAhbAkSZI0my2zEK6qf5qhHJIkSZIkzYgp3zVakiRJkqTZwEJYkiRJkjRSLIQlSZIkSSNlWs8Rlma7448/fugIkiRJknpmISx1bLnllkNHkCRJktQzh0ZLHSeddBInnXTS0DEkSZIk9cgeYanj4x//OAB77733wEkkSZIk9cUeYUmSJEnSSLEQliRJkiSNFAthSZIkSdJIsRCWJEmSJI0Ub5YldZx66qlDR5AkSZLUMwthqWOTTTYZOoIkSZKknjk0WuqYP38+8+fPHzqGJEmSpB5ZCEsdFsKSJEnS7GchLEmSJEkaKRbCkiRJkqSRYiEsSZIkSRopFsKSJEmSpJHi45OkjtNOO23oCJIkSZJ6ttL3CCd5ZpI3roB25ifZa0VkuqeS3NT+3CLJqUPn0V/MnTuXuXPnDh1DkiRJUo9W+h7hqvoy8OWhc/Shqv4ErBTFuRpHHHEEAAcddNDASSRJkiT1ZdAe4STzkvwyyTFJLkzymSR7JPlekl8n2TnJAUk+1i4/P8lHk3w/yW+X1cObxseSXJTkf4DNOvN2TPKtJOclOT3J5u30BUk+nOTbSX6R5NFJ/rvN8u7O+l9s1/15kgM7029K8p4kP01ydpL7tNO3SvKDJD9K8q5x+39h+/6Adltfb7f3/s5yL05ycZvv6LHjoRXv5JNP5uSTTx46hiRJkqQerQxDox8MfAR4BPA3wPOAxwGvB948wfKbt/OfDvznMtp9NrANsB3wUmBXgCSrA/8P2KuqdgQ+Cbyns94dVfV44BPAl4B/AR4OHJBk43aZF7Xr7gS8qjN9HeDsqtoe+Ha7Xdr9+3hVPRq4bBmZdwD2bjPvnWTLJFsAbwX+Dnhye4wmlOTAJOcmOXfprUuXsRlJkiRJGl0rw9DohVV1AUCSnwNnVlUluQCYN8HyX6yqpcBFYz2uk3g88LmqWgL8Kck32+nb0BS230gCMAdY3FlvbBj2BcDPq2pxm+23wJbA1TTF77Pb5bYEHtJOvwP4ajv9PJrCFeCxwJ7t++OB902S+cyqur7d3kXAA4FNgG9V1TXt9FOAh060clUdBRwFsNp9VqtJtiFJkiRJI21lKIRv77xf2vm8lInzdZfPctqeqBgMTYG7y3LydLP8OU+S3YA9gF2q6pYkC4C12mXurKqxbS4Zl38qhWl3e2PrL28fJUmSJEnTsDIMje7Lt4F9ksxprwF+Yjv9V8CmSXaBZqh0kodNo90NgGvbIvhvaIYsL8/3gH3a9/88jW0BnAM8IclGSVbjLz3LkiRJkqS7YWXoEe7LF4An0Qxxvhj4FkBV3dHeZOujSTagOQaHAz+fYrtfB16e5Gc0RfXZU1jn1cBnk7wa+Px0dqKq/pjkvcAPgT8BFwHXT6cNTd2CBQuGjiBJkiSpZ/nLSF6trJKsW1U3tT3CXwA+WVVfWNY6q91ntVrveevNTMDWtR++dka3J0mSJEmTSXJeVe000bzZPDR6Nnl7kvOBC4GFwBcHTTOLHXbYYRx22GFDx5AkSZLUo1V+aHSS7WjuxNx1e1U9Zog8faiq1w+dYVR89avNTb9f/3oPuSRJkjRbrfKFcPvopR2GziFJkiRJWjU4NFqSJEmSNFIshCVJkiRJI2WVHxotrUhrr7320BEkSZIk9cxCWOr42te+NnQESZIkST1zaLQkSZIkaaRYCEsd73rXu3jXu941dAxJkiRJPbIQljrOPPNMzjzzzKFjSJIkSeqRhbAkSZIkaaRYCEuSJEmSRoqFsCRJkiRppPj4JKlj4403HjqCJEmSpJ5ZCEsdn//854eOIEmSJKlnDo2WJEmSJI0UC2Gp401vehNvetObho4hSZIkqUcOjZY6fvCDHwwdQZIkSVLP7BGWJEmSJI0UC2FJkiRJ0kixEJYkSZIkjRSvEZY67n//+w8dQZIkSVLPLISljhNOOGHoCJIkSZJ65tBoSZIkSdJIsRCWOg4++GAOPvjgoWNIkiRJ6pFDo6WO888/f+gIkiRJknpmj7AkSZIkaaRYCEuSJEmSRoqFsCRJkiRppHiNsNTx0Ic+dOgIkiRJknpmISx1HHXUUUNHkCRJktQzh0ZLkiRJkkaKhbDUceCBB3LggQcOHUOSJElSjxwaLXVcfPHFQ0eQJEmS1DN7hCVJkiRJI8VCWJIkSZI0UiyEJUmSJEkjxWuEZ6kdttyBcz987tAxVjk77LDD0BEkSZIk9cxCWOo4/PDDh44gSZIkqWcOjZYkSZIkjRQLYaljv/32Y7/99hs6hiRJkqQeOTRa6vjDH/4wdARJkiRJPbNHWJIkSZI0UiyEJUmSJEkjxUJYkiRJkjRSvEZY6thll12GjiBJkiSpZxbCUsehhx46dARJkiRJPXNotCRJkiRppFgISx177rkne+6559AxJEmSJPXIodFSx9VXXz10BEmSJEk9s0dYkiRJkjRSLIQlSZIkSSPFQliSJEmSNFK8Rljq2H333YeOIEmSJKlnFsJSx1vf+tahI0iSJEnqmUOjJUmSJEkjxUJY6njKU57CU57ylKFjSJIkSeqRQ6OljltvvXXoCJIkSZJ6Zo+wJEmSJGmk2CM8S91xwQUs2mqroWOscm5bvBjgr47dvIULh4ojSZIkqQf2CEuSJEmSRoo9wlLHk+bOHTqCJEmSpJ5ZCEsdB26wwdARJEmSJPXModGSJEmSpJFiISx17LN4Mfu0N8ySJEmSNDtZCEuSJEmSRoqFsCRJkiRppFgIS5IkSZJGioWwJEmSJGmk+PgkqeNp66wzdARJkiRJPbMQljr2X3/9oSNIkiRJ6plDo6WOW5cu5dalS4eOIUmSJKlH9ghLHS+8/HIATtx884GTSJIkSeqLPcKSJEmSpJFiISxJkiRJGikWwpIkSZKkkWIhLEmSJEkaKd4sS+rYa911h44gSZIkqWcWwlLHXuutN3QESZIkST1zaLTUcc2SJVyzZMnQMSRJkiT1yB5hqeOgK64AfI6wJEmSNJvZIyxJkiRJGikWwpIkSZKkkWIhLEmSJEkaKRbCkiRJkqSR4s2ypI791l9/6AiSJEmSemYhLHU8fZ11ho4gSZIkqWcOjZY6/nTXXfzprruGjiFJkiSpR/YISx2vvfJKwOcIS5IkSbOZPcKSJEmSpJFiISxJkiRJGikWwpIkSZKkkWIhLEmSJEkaKd4sS+p4yQYbDB1BkiRJUs8shKWOPebOHTqCJEmSpJ45NFrquOTOO7nkzjuHjiFJkiSpR/YISx2HXHUV4HOEJUmSpNnMHmFJkiRJ0kixEJYkSZIkjRQLYUmSJEnSSJk1hXCSeUkuHGC7OyR56nKWOSDJx9r3L0/y/Pb9giQ7zUROSZIkSVJjZG+WlWROVS1ZAU3tAOwEnDaVhavqEytgm+rJKzfccOgIkiRJkno2Yz3CbY/tL5Mcl+RnSU5NMjfJ7kl+kuSCJJ9Msma7/KIk70tyTvt6cDt9fpK9Ou3eNMm2vpPkx+1r13b6bknOSvJZ4ILp5GznPTrJ95P8tM20AfBOYO8k5yfZewrH4e1JXj9u2r3a7b07yZwkH0jyo3b7L2uX2TzJt9vtXJjk7ydo+8Ak5yY59+olK6LGHz2PW3ttHrf22kPHkCRJktSjmR4avQ1wVFU9ArgBeC0wH9i7qraj6aF+RWf5G6pqZ+BjwOHT2M4VwJOr6lHA3sBHO/N2Bg6pqm2nkfOgJGsAJwGvrqrtgT2Am4G3ASdV1Q5VddI0Mo5ZDfgMcHFVvQV4MXB9VT0aeDTw0iRbAc8DTq+qHYDtgfPHN1RVR1XVTlW108Zz5tyNKLro9tu56Pbbh44hSZIkqUczXQhfWlXfa9+fAOwOLKyqi9tpxwGP7yz/uc7PXaaxndWBo5NcAJwCdIvec6pq4TRzPo6mOF5cVT8CqKobququaWSazJHAhVX1nvbz/wGen+R84IfAxsBDgB8BL0zydmC7qrpxBWxb47zzmmt45zXXDB1DkiRJUo9muhCue7D82Pu7aHMnCbDGBOu9Bricpud0p3HL3Hw3chaQCaavCN8HnphkrfZzgH9te5h3qKqtqup/q+rbNH8k+CNw/NgNtyRJkiRJ0zPThfADkoz17O4LnAHMG7v+F9gf+FZn+b07P3/Qvl8E7Ni+fxZN7+94G9D03i5t25zuOOHxOb8L/BLYIsmjAZKsl2Q14EZgvWm233UszY22TmnbOx14RZLV2+08NMk6SR4IXFFVR7frPOoebFOSJEmSRtZMF8K/AF6Q5GfAvYEPAy+kKQIvAJYC3bsqr5nkh8CraXp5AY4GnpDkHOAxTNzDe0S7nbOBh06yzHRyfryq7qApyP9fkp8C3wDWAs4Ctp3qzbImUlUfAn4MHA8cA1wE/Lh9HNSRNNcR7wacn+QnwJ7AR+7OtiRJkiRp1KWqj9G+E2womQd8taoePsXlFwE7VdVVfeaaYLvzmEbOldUj1lyzvrzFFkPHWOXss3gxACduvvmfp81buLxLyiVJkiStbJKcV1U7TTRvZJ8jLE3k3zbaaOgIkiRJkno2Y4VwVS0CptzLWlXzegsDJNkYOHOCWbvf3d7gJC+kGcbd9b2q+pe7055m3o5rrbX8hSRJkiSt0ka2R7iqrgZ2WMFtfgr41IpsUzPrvNtuAyyIJUmSpNlsZAthaSIfuPZa4K+vEZYkSZI0u8z0XaMlSZIkSRqUhbAkSZIkaaRYCEuSJEmSRoqFsCRJkiRppHizLKnjbfe+99ARJEmSJPXMQljq2HbNNYeOIEmSJKlnDo2WOr57661899Zbh44hSZIkqUf2CEsdH7vuOgAet/bawwaRJEmS1Bt7hCVJkiRJI8VCWJIkSZI0UiyEJUmSJEkjxUJYkiRJkjRSvFmW1PGeTTYZOoIkSZKknlkISx0PWn31oSNIkiRJ6plDo6WOM265hTNuuWXoGJIkSZJ6ZI+w1HHM9dcDsMfcuQMnkSRJktQXe4QlSZIkSSPFQliSJEmSNFIshCVJkiRJI8VCWJIkSZI0UrxZltTxoU03HTqCJEmSpJ5ZCEsdW6zmPwlJkiRptnNotNTx1Ztv5qs33zx0DEmSJEk9svtL6jjhhhsAePo66wycRJIkSVJf7BGWJEmSJI0UC2FJkiRJ0kixEJYkSZIkjRQLYUmSJEnSSPFmWVLHEZttNnQESZIkST2zEJY67j1nztARJEmSJPXModFSx6k33sipN944dAxJkiRJPbIQljpOvekmTr3ppqFjSJIkSeqRhbAkSZIkaaR4jfAstcZ22zHv3HOHjrHKWWu33QCYt2DBoDkkSZIk9cceYUmSJEnSSLEQliRJkiSNFIdGSx2nnXba0BEkSZIk9cxCWOqYO3fu0BEkSZIk9cyh0VLHEUccwRFHHDF0DEmSJEk9shCWOk4++WROPvnkoWNIkiRJ6pGFsCRJkiRppFgIS5IkSZJGioWwJEmSJGmkWAhLkiRJkkZKqmroDOpBkhuBXw2dQyNpE+CqoUNoJHnuaUiefxqK556Gsiqcew+sqk0nmuFzhGevX1XVTkOH0OhJcq7nnobguachef5pKJ57Gsqqfu45NFqSJEmSNFIshCVJkiRJI8VCePY6augAGlmeexqK556G5PmnoXjuaSir9LnnzbIkSZIkSSPFHmFJkiRJ0kixEJ5lkvxjkl8l+U2SNw6dR7NXki2TnJXkF0l+nuTV7fR7J/lGkl+3PzcaOqtmpyRzkvwkyVfbz557mhFJNkxyapJftv8N3MXzTzMhyWva/+demORzSdby3FNfknwyyRVJLuxMm/R8S/Kmtgb5VZJ/GCb11FkIzyJJ5gD/BTwF2BbYN8m2w6bSLHYX8Lqq+lvg74B/ac+3NwJnVtVDgDPbz1IfXg38ovPZc08z5SPA16vqb4Dtac5Dzz/1Ksn9gFcBO1XVw4E5wD547qk/84F/HDdtwvOt/R1wH+Bh7TpHtLXJSstCeHbZGfhNVf22qu4ATgSeNXAmzVJVtbiqfty+v5HmF8H70Zxzx7WLHQf80yABNasluT/wNOCYzmTPPfUuyfrA44FjAarqjqq6Ds8/zYzVgLWTrAbMBf6E5556UlXfBq4ZN3my8+1ZwIlVdXtVLQR+Q1ObrLQshGeX+wGXdj7/oZ0m9SrJPOCRwA+B+1TVYmiKZWCzAaNp9joceAOwtDPNc08zYWvgSuBT7dD8Y5Ksg+efelZVfwQOA34PLAaur6r/xXNPM2uy822Vq0MshGeXTDDN24KrV0nWBT4PHFxVNwydR7NfkqcDV1TVeUNn0UhaDXgU8PGqeiRwMw5F1Qxor8V8FrAVsAWwTpL9hk0l/dkqV4dYCM8ufwC27Hy+P82QGakXSVanKYI/U1X/3U6+PMnm7fzNgSuGyqdZ67HAM5MsorkE5ElJTsBzTzPjD8AfquqH7edTaQpjzz/1bQ9gYVVdWVV3Av8N7IrnnmbWZOfbKleHWAjPLj8CHpJkqyRr0Fyw/uWBM2mWShKaa+R+UVUf6sz6MvCC9v0LgC/NdDbNblX1pqq6f1XNo/nv3Deraj889zQDquoy4NIk27STdgcuwvNP/fs98HdJ5rb/D96d5v4cnnuaSZOdb18G9kmyZpKtgIcA5wyQb8pStVL3WGuakjyV5tq5OcAnq+o9wybSbJXkccB3gAv4y3Wab6a5Tvhk4AE0/9N+TlWNv9GCtEIk2Q14fVU9PcnGeO5pBiTZgeZGbWsAvwVeSNO54PmnXiV5B7A3zZMbfgK8BFgXzz31IMnngN2ATYDLgf8Avsgk51uSQ4AX0ZyfB1fV12Y+9dRZCEuSJEmSRopDoyVJkiRJI8VCWJIkSZI0UiyEJUmSJEkjxUJYkiRJkjRSLIQlSZIkSSPFQliSpGlI8okkHxs6x4qS5I1JLk9yU5JHJ1mQ5C33sM2fJ9l7RWWcDZK8PMnxPbZ/UpIX99W+JM02FsKSpFkpyZeSfHqSeWfd3WK2ql5eVa+8Z+n6keSAJL+ZxvL3B94LPLGq1q2qH01ze/OSVNvOn1XVw6rqpOm0Nc3tLkhye1u8j72OuYdtzr+nbSyj7XWAdwJvbz9P6dxMsijJfu37sWN987j93q1d9T+A9yZZu499kKTZxkJYkjRbHQnslWTD7sQkDwGeABw1ncaSzEky2/6/OQ9YWlUXDR3kbnhXW7yPvV4ydKAkq08yaz/ggqq6pP18T87Nbcbt9wKAqvol8Btg33uwC5I0Mmbb/9AlSRrzdeBKYP9x0w8Ezq6qnyV5b5Lftj1rlyQ5eGyhTg/ci5NcBNwCbDa+53CKbeyf5KIkNyb53ySbd5ZZN8lhbRs3tsOKH9fOWy3Jm5NcnOS6JN9LsuNUD0Dbc/rBJJ9v274kybPaeXsD3wDmjGWfpI1PJbm0Xf+iJM/rzP5p+/NXbRtvbdf5c09m+/kJSX6Y5Pokv0zyss683ZLclWTvNt/1SU5Ost5U97PT1j5JfprkhiSLkxzZ9saOzZ/wWCd5A/DPwAs6Pa1z2nVekeRXba6zk/x9p723J/lm2+blwJcnifZPNMd6zHLPzenue+sb7bYkScthISxJmpWqailwDPDSsWlJ1gBewF963C4CHges1y53aJJ/GNfU84AntctcOcGmptLG3sDjgfsBY8NkxxwLPAbYHVifppC5rJ33TuBZwD8CGwOfBE5PstHy9r/jBcCHgA2AjwHHJZnbDl1+CrCk7Vl80CTrfxfYAdiwzTM/ybbtvO3bn2O9lO8av3KSrWgKv0+0+3AAzTF6TmexOcD/adt7KPBI4FXT2Mcx19N8XxsCf9++utc7T3isq+r9wGeA4zo9rUuS7Au8C3h+m/1o4OtJHthp8/HAYmBLYM9Jcj2K5jwBpnxu3h0XtNuSJC2HhbAkaTY7FvjbJI9pPz8bWB04GaCqTqiqP1Xjm8D/0BRJXe+oqsuq6o6qWjJ+A9No46qqugH4LLATQJLNgOcCL6+qhW0bv66q3yQJ8K/Av1XVb6tqSVUdS1N0PW0ax+CkqvpeW3wdRVMQP2SqK1fVsVV1dbv9E4GfAbtNY/v7Aj+uqk9V1V1VdTbN0ODxQ5nfWFU3VdXlwBdpj9EyHNL2ko+9/q6qvlZVP6+qpVX1G+AI2u9iWcd6Gdt4IXBkVf2wzX5su//dXvHfV9UH2/Pjlkna2Qi4Ydy0ZZ6by/Dzzj7/eNy8G4B7L2d9SRIWwpKkWayq/kRTmB7YTjoQOH6sYEnyqiQXJLk2yXXAM4BNxzWzaFnbmGIbizvvb6bpPYbmGl2AiydoehNgXeAr3YIP2Bq4/wTLT+bP266qm9u3Uxp2nOReSd7ZGRp8HU2v7fj9W5Ytgd+Om3ZJO33Mkqrq9rZ3j9Fk3lNVG3ZeZyd5cpLvJLkyyQ3A+zpZ57U/JzrW9yT7oim0cy1ND/SfLe/cXIaHdfZ5fO/v+sA1U8gjSSPPQliSNNsdCeyd5JHAE2mHniZ5LE2h9DJgk6raEPgKkHHrL52s4Wm0MZlF7c+JemivoikI9xhX8K1TVf85xfbvqX1pem73BDZq9++n/GX/Jj02HZcCW42btnU7fYVphxZ/ETgReEBVrQ/8O3/Juqj9OVlv+ET7MpXsUzkGPwG2nWD6hOfmPfDwdluSpOWwEJYkzXan0xSVnwd+UFUXttPXB5bQXPdbSZ5Gc83sdNyjNqrqCuBU4Ig0N9ZKkgcneXBVFfAR4LA0dxMeu9nTPyTZYpo57671gbto9u9eSV7EX64Lpp2+lGUPtf4csGOS56e5+dfONH84OHYFZ10DWAu4tqpuba9j/vNjrpZ1rNtFLgO2zl/fGXw+8LIkO7fZD6C5Xvpz08z2RWCPCaZPdm7eXU9utyVJWg4LYUnSrNZeG3s0Tc9et8ftdOB44ByaYmQv4AvTbH5FtPEi4HzgW8CNwJeA+7bz/qP9/KV2qO+vgZczc///Pg74Ic1jef5I06v5nbGZVXUr8Fbgc+3Q7UPGN1BVC4Gn0hSlV9Mcr7dV1fKuhZ2WqroJeAXw/iQ3Af9Fcz1217KO9TE0NzK7ut2XOVX1WeAdwAlt9oOAp1bVomnGOx7YPsnW4zJPdm5OW5JtaP4gMX6fJUkTSPMHZ0mSJPUlycuBx1bV+Ecmraj2PwecWVXHLHdhSZKFsCRJkiRptDg0WpIkSZI0UiyEJUmSJEkjxUJYkiRJkjRSLIQlSZIkSSPFQliSJEmSNFIshCVJkiRJI8VCWJIkSZI0UiyEJUmSJEkj5f8DG7xB0d3AOO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "graph = sns.barplot(x='VIF', y='variables', data=vif, palette = ['blue', 'orange', 'green', 'red'])\n",
    "graph.axvline(10, c = 'k', linestyle = '--')\n",
    "\n",
    "plt.xlabel('Variance Inflation Factor (VIF)', fontsize = 13)\n",
    "plt.ylabel('Variables', fontsize = 13)\n",
    "plt.title('Second Set of Independent Variables Against VIF', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Popular variables too high!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Independent variables:\n",
    "- popular_revs\n",
    "- popular_pos_revs\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>popular_revs</td>\n",
       "      <td>89.009600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>popular_pos_revs</td>\n",
       "      <td>90.509127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rec_demanding</td>\n",
       "      <td>7.271300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variables        VIF\n",
       "0      popular_revs  89.009600\n",
       "1  popular_pos_revs  90.509127\n",
       "2     rec_demanding   7.271300"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "var = df_final[['popular_revs', 'popular_pos_revs', 'rec_demanding']]\n",
    "vif['variables'] = var.columns\n",
    "vif['VIF'] = [variance_inflation_factor(var.values, i) for i in range(len(var.columns))]\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "graph = sns.barplot(x='VIF', y='variables', data=vif, palette = ['blue', 'orange', 'pink'])\n",
    "graph.axvline(10, c = 'k', linestyle = '--')\n",
    "\n",
    "plt.xlabel('Variance Inflation Factor (VIF)', fontsize = 13)\n",
    "plt.ylabel('Variables', fontsize = 13)\n",
    "plt.title('Third Set of Independent Variables Against VIF', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Popular variables too high!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independent variables:\n",
    "- popular_pos_revs\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>popular_pos_revs</td>\n",
       "      <td>7.196824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rec_demanding</td>\n",
       "      <td>7.196824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          variables       VIF\n",
       "0  popular_pos_revs  7.196824\n",
       "1     rec_demanding  7.196824"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "var = df_final[['popular_pos_revs', 'rec_demanding']]\n",
    "vif['variables'] = var.columns\n",
    "vif['VIF'] = [variance_inflation_factor(var.values, i) for i in range(len(var.columns))]\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "graph = sns.barplot(x='VIF', y='variables', data=vif, palette = ['orange', 'pink'])\n",
    "graph.axvline(10, c= 'k', linestyle = '--')\n",
    "\n",
    "plt.xlabel('Variance Inflation Factor (VIF)', fontsize = 13)\n",
    "plt.ylabel('Variables', fontsize = 13)\n",
    "plt.title('Fourth Set of Independent Variables Against VIF', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passed the VIF test! VIF values are still relatively high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Independent variables:\n",
    "- All popular variables\n",
    "- rec_demanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>popular_revs</td>\n",
       "      <td>100.883778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>popular_pos_revs</td>\n",
       "      <td>92.716103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>popular_pct_likes</td>\n",
       "      <td>16.437015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rec_demanding</td>\n",
       "      <td>11.417294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           variables         VIF\n",
       "0       popular_revs  100.883778\n",
       "1   popular_pos_revs   92.716103\n",
       "2  popular_pct_likes   16.437015\n",
       "3      rec_demanding   11.417294"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "var = df_final[['popular_revs', 'popular_pos_revs', 'popular_pct_likes', 'rec_demanding']]\n",
    "vif['variables'] = var.columns\n",
    "vif['VIF'] = [variance_inflation_factor(var.values, i) for i in range(len(var.columns))]\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "\n",
    "graph = sns.barplot(x='VIF', y='variables', data=vif, palette = ['blue', 'orange', 'red', 'pink'])\n",
    "graph.axvline(10, c= 'k', linestyle = '--')\n",
    "\n",
    "plt.xlabel('Variance Inflation Factor (VIF)', fontsize = 13)\n",
    "plt.ylabel('Variables', fontsize = 13)\n",
    "plt.title('Fifth Set of Independent Variables Against VIF', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All variables too high!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Variance Inflation Factor Results\n",
    "\n",
    "Checking VIF values for the four models that showed correlation indicates collinearity issue for all four multivariate regression models. \n",
    "\n",
    "##### Popular Variables\n",
    "\n",
    "The VIF for the popular variables in all four models are too high! \n",
    "\n",
    "##### Demanding Variables\n",
    "\n",
    "The VIF for min_demanding is within acceptable range for both models.\n",
    "The VIF for rec_demanding is relatively high in both models. It exceeds the threshold in the model with all popular variables.\n",
    "\n",
    "#### Variance Inflation Factor Summary\n",
    "\n",
    "From these results, it's highly probable that the correlations seen in the multivarite regression models are a result of collinearity and are not significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data sets we used are publicly available through the steam hardware and software website. Althought steam only give us the latest data, we were able to capture previous survey data by accessing wayback machine. Wayback machine is an initiative of the Internet Archive under 501(c)(3). Wayback machine and the no person can alter it once it have been captured. Steam is the largest digital distribution platfrom for PC gaming. Steam collect its user hardware and calculate the change compare to previous month.  \n",
    "\n",
    "Regarding privacy, the datasets we used do not contain sensitive personal information and therefore do not infringe on any person’s privacy. User are chosen randomly and require the user consent before practicipe. All of the data are keep anonymous and will not be associate to the user steam account.  \n",
    "\n",
    "Some biases that could be present in our analysis would come from the usere who are practiciping. Since most user who are on steam are aroung 20's, they are more likely to buy a new hardware once a while.\n",
    "\n",
    "Another bias is using the popular variables we decided to go with. Popular has many definitions such as anticipation/pre-order buys, largest player base, and more. We beleived anticipation/pre-order could have been a better measurement of popularity in this case. However, when deciding for popularity variable measures, we chose variables that are easier to attain. We decided to do this because of two reasons. Firstly, we believe our variables can still give us a good measurement on GPU sales, just not as good as anticipation/pre-order buys and secondly, we are unsure if enough information on anticipation/pre-order buys was attainable since games on \"Steam\" don't have pre-order. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "GPUs can be used for many reasons, such as machine learning or video editing to name a couple, but GPUs are also utilized to play video games. Many video games contain graphics that require the use of GPUs to render. Since GPUs and video games have this relation to them, we hypothesized that the release of a popular, demanding video game is positively correlated to an increase in GPU sales. \n",
    "\n",
    "In an attempt to find the effect of popular and demanding games on GPU sales, we took a look at \"Steam\", the largest online market for PC video games. Web scraping \"Steam\" yielded several measures on popularity of a game and how demanding a game is, as well as the change in usage percentages of GPUs per month, which became our measure for GPU sales.\n",
    "\n",
    "We began by performing an eyeball test to look for any noticeable patterns between our popular or demanding measures with the change in usage percentages. Each variable was plotted on separate line graphs to show patterns over time. Results showed that two of the popular variables, games with a popular number of reviews and games with a popular number of positive reviews, had almost identical patterns, but no other noticeable patterns.\n",
    "\n",
    "Unable to find anything relating our popular and demanding variables to the variable of interest from the eyeball test, we turned to simple linear regression. Results from all simple linear models contained 0 in their 95% confidence interval which means no correlation could be found.\n",
    "\n",
    "Still with no correlation found, we tried using multivariate regression. Our multivariate regression models found correlation for five models. To test whether the correlations were actually significant, VIF tests were performed on each model and four models that showed correlation were determined to be insignificant. The VIF tests for each of these four OLS regression models showed severe collinearity in popular variables for all four models. In addition to this, the VIF values for games with demanding recommended requirements were high in both models it was tested in, which could indicate that there's correlation between games with demanding recommended requirements and popularity. The one model that didn't fail the VIF test still had high VIF values.\n",
    "\n",
    "From the result of the analysis, one correlation could be found between popular and demanding video games on GPU sales. The release of a video game that achieves a popular positive review correlates with GPU sales.\n",
    "\n",
    "#### Limitations:\n",
    "We were able to find a correlation in our analysis, but this may have been due to some data limitations. One such limitation comes from our games dataset. Originally, the games dataset had over 80000 entries, but only 1493 of them went into the constructing the final dataset. In comparison, 24132 games were released on \"Steam\" over the 3 year interval this analysis was restricted to[7]. Since the values for the independent variables are counts of video games, the amount of games that go into the analysis can have substantial impact on the independent variables. In addition to this, the games dataset only considers \"Steam\" games which means console video games are not included. Consoles also make up a considerable portion of video games and the exclusion of console video games also directly impact the independent variables. \n",
    "\n",
    "Another limitation comes from our measure of variables. We were unable to attain GPU sales numbers and as a substitute, used the change in usage percentage of GPUs. A problem with this is we're only able to make correlation claims about popular, demanding games and change in usage percentage and from this, and therefore, the results of this analysis is only one possible indication to GPU sales. For popularity, a measurement that we considered, but also could not find the data for is the anticipation of a video game.\n",
    "\n",
    "#### Discussion:\n",
    "Our analysis found one correlation between popular and demanding games with GPU sales which could indicate that the demand for accelerated 3D rendering has not diminished over time. However, from the VIF tests, there is potentially a correlation between popularity and the demand of a video game. If a correlation can be found with GPU sales and popularity, then it's possible that popularity of video games could be a mediator variable between the graphical demand of video games and GPU sales. More analysis is needed in order to say there's a correlation for certain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each team member was assigned a specific portion of the report to write:\n",
    "\n",
    "\n",
    "- Cameron VanderTuig: Background & Prior Work, Hypothesis, Dataset(s), Setup, Double Checking, Video\n",
    "- Erdogan Ergit: Background & Prior Work, Documentation, Data Visualization\n",
    "- Henry Chan: Background & Prior Work, Dataset(s), Setup, Ethics & Privacy, Code Optimization, Double Checking\n",
    "- Tony Diep: N/A\n",
    "- Wilson Tan: Overview, Background & Prior Work, Dataset(s), Setup, Data Cleaning, Data Analysis & Results, Conclusion & Discussion, Double Checking, Documentation"
   ]
  }
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