FinalProjectGroup042-Fa21.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project: Predicting Used Car Prices on Craigslist During the Bubble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Video: https://drive.google.com/file/d/1oG-toF0hvNxIhka35dthbd5SRfKxXtzk/view?usp=sharing"
   ]
  },
  {
   "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": [
    "For this project, a linear regression model was created to predict the price of a used car listed on Craigslist between April - May 2021. 3 different models were created using combinations of features from the following: miles driven, title status, manufacturer of the car, condition of the car, and the number of cylinders the car has. Using the mean squared error (MSE) and root mean squared error (RMSE) to evaluate the models, it was shown that the model using the miles driven, manufacturer, and title status was the best performing model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Kenneth Nguyen\n",
    "- Mandy Lee\n",
    "- Matthew Lund\n",
    "- Yash Potdar\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using features of a car such as the manufacturer, miles driven, title status, condition, and number of cylinders from the observations in a used car Craigslist listing dataset between April to May 2021, can we accurately predict the price of a used car during the used car bubble?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "# Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The year 2021 was out of the ordinary due to the recurring waves of the pandemic followed by flashes of recovery. The economy, which suffered a downturn in 2020, recovered and exceeded the pre-pandemic level, and with this growth came inflation. According to the New York Times, the used car market experienced a significant increase in prices perpetuated by a chip shortage affecting the production and supply of new cars, along with an increased demand for cars as individuals moved out of big cities. Compared to used car prices from 2020, the prices of used cars and trucks in August 2021 were around 32 percent higher **<sup>(1)</sup>**. This used car bubble affects a wide population in the United States due to the supply shortages and high demand, which is why we decided to focus on this topic.\n",
    "\n",
    "Estimating the value of a used car is a challenge in the automotive industry. The price of a used car is not only based on the car’s intrinsic features, but also on the market trends. Based on prior experience and research on used cars, we are aware that cars can either be sold directly from a seller to a buyer, or from a seller to a buyer with a dealership in between. Moreover, since we will be working with data from Craigslist, we are aware that sellers are individuals on the Internet, and there are no constraints for the information they put on their listing. The information can be inaccurate due to entry error or lack of knowledge of a specific feature. There will undoubtedly be missing data with used car listings, as well as listings with extraordinarily high prices. This means there will be a heavily involved process for data cleaning and preprocessing, which would include removing outliers and reasonably imputing missing values.\n",
    "\n",
    "After researching, we determined that regression is best suited to our research question of predicting the used car price and determining which features are important. As noted in the Statistics by Jim tutorial, in regression, you determine relevant variables, specify a model, fit the data, and make predictions. You can then evaluate the model to determine how important features were **<sup>(2)</sup>**. In other words, regression analysis is a useful method to identify variables that can impact our topics of interest. It effectively predicts continuous dependent variables based on the independent variables. This regression model will provide a listing price for individuals who wish to sell their used car during the used car bubble. We can also train models based on a combination of features and determine which combinations of features are the best for predicting price after assessing the model.\n",
    "\n",
    "We were also able to research prior work with used car data. One of the projects we came across had the same objective of predicting used car data. This project, done by Mounir Zaitri, dealt with used car data in Canada. Although our dataset was different, their preprocessing steps look relevant to ours since they are using a mix of categorical and numerical variables. They utilized a one-hot encoding strategy to encode nominal variables such as make, model, and vehicle type. In this project, Zaitri utilized gradient boosting for regression and achieved a reasonable accuracy of around 91% with a test error of around 9% on the test data **<sup>(3)</sup>**. We would be conducting similar steps, but would use ordinal encoding for some categorical features such as the number of cylinders and title status since certain values are objectively better than others. Another project, conducted by Enes Gokce, utilized the same Craigslist car dataset, but ran hypothesis tests isolating price and another feature of interest, such as odometer or condition. During their data cleaning process, they used mean imputation of odometer depending on the condition group a specific row belonged to. They also removed outliers with extraordinarily high or low prices and odometer readings **<sup>(4)</sup>**. Although our question is different, we drew inspiration from this project with regards to the data preprocessing steps. We reasonably dropped outliers as well as used probabilistic imputation to fill missing values.\n",
    "\n",
    "\n",
    "References:\n",
    "- 1) https://urldefense.proofpoint.com/v2/url?u=https-3A__www.nytimes.com_2021_09_27_business_used-2Dcars-2Dinflation.html&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=dDqFLBXlcIvTyKKWDuoUfAUIL8FF6Kzn30b1P0AX2pc&m=lVX8gXYeD5HerG6paJwqcAIHs1cc3XKvnXabbN0tSnaAXHYgXZrOgiI-2K9oknZo&s=jnHWGsMTYDxgH1wM3mbA_7JM__uciGYYoFLWhFxjjOk&e=  \n",
    "- 2) https://urldefense.proofpoint.com/v2/url?u=https-3A__statisticsbyjim.com_regression_predictions-2Dregression_-255Cn&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=dDqFLBXlcIvTyKKWDuoUfAUIL8FF6Kzn30b1P0AX2pc&m=lVX8gXYeD5HerG6paJwqcAIHs1cc3XKvnXabbN0tSnaAXHYgXZrOgiI-2K9oknZo&s=W7dX2kaUchBWCkyxt6P5KwE4ui5q34CMllhXq7ZbyK8&e= \n",
    "- 3) https://urldefense.proofpoint.com/v2/url?u=https-3A__blog.jovian.ai_predicting-2Dused-2Dcars-2Dprices-2Dd2e840d49c2a-255Cn&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=dDqFLBXlcIvTyKKWDuoUfAUIL8FF6Kzn30b1P0AX2pc&m=lVX8gXYeD5HerG6paJwqcAIHs1cc3XKvnXabbN0tSnaAXHYgXZrOgiI-2K9oknZo&s=JHooPd4OE3oG4xN5kcXjt80NYwAgRWNgqNowEwikwfs&e= \n",
    "- 4) https://urldefense.proofpoint.com/v2/url?u=https-3A__towardsdatascience.com_predicting-2Dused-2Dcar-2Dprices-2Dwith-2Dmachine-2Dlearning-2Dtechniques-2D8a9d8313952-255Cn&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=dDqFLBXlcIvTyKKWDuoUfAUIL8FF6Kzn30b1P0AX2pc&m=lVX8gXYeD5HerG6paJwqcAIHs1cc3XKvnXabbN0tSnaAXHYgXZrOgiI-2K9oknZo&s=J1HkQNDoUVckf0Q-vGH9f1nmXPP0SdlEFXgJqppKCP4&e= "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A model using the car features of miles_driven, title_status, and manufacturer will be the best at predicting the price of a used car when compared to a model using miles_driven, num_cylinders, and title_status as features, and a model using condition, title_status, and num_cylinders. The miles_driven, title_status, and manufacturer of a car are all properties individuals prioritize when buying a used car, so these should be best at predicting the price.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Dataset name: vehicles.csv\n",
    "- Link to the data: https://urldefense.proofpoint.com/v2/url?u=https-3A__www.kaggle.com_austinreese_craigslist-2Dcarstrucks-2Ddata_version_10&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=pFSXhgpDVG1CmxiYYJ9vz2r3p3kNx5vRQ6PVwRAU0lo&m=W_qWOYHtez_783hP7CAlOBa3phldwdzWAjbBn-fKskxQkdeFMsG5N9chyKUNYNj4&s=mXbW7-F9uTWrwJlut17sR3qZNPXWLInu8OdlfiYnBnE&e= \n",
    "- Number of observations: 426,880\n",
    "\n",
    "This dataset contains web-scraped Craigslist listings posted from April to May 2021, during the used car bubble, of 426,880 used cars. Each observation in this dataset represents a used car listing on the Craislist webstie. For each listing, there is information about the odometer reading, manufacturer, title status, listing price, and more. We will be creating a prediction model that aims to estimate the listing price on Craigslist, accounting for the used cars' inflated prices during the bubble.\n",
    "\n",
    "The variables of the dataset are as follows:\n",
    "- id: the Craiglist user's unique identification number\n",
    "- url: the URL to the user's listing\n",
    "- region: the Craigslist region where the post was made\n",
    "- region_url: the URL to the Craigslist site for that region\n",
    "- price: the listing price of the car on the listing\n",
    "- year: the year when the car was made\n",
    "- manufacturer: the manufacturer of the listed car\n",
    "- model: the specific model type of the listed car\n",
    "- condition: the condition of the car listed\n",
    "- cylinders: how many cylinders the car's engine has \n",
    "- fuel: the type of fuel the car takes\n",
    "- odometer: how many miles the car has driven\n",
    "- title_status: the title status of the car\n",
    "- transmission: what type of transmission the listed car has\n",
    "- VIN: the Vehicle Identification Number that identifies the specific car\n",
    "- drive: what type of drive the car uses (all wheel drive, rear wheel, etc)\n",
    "- size: describes how large or small the car is\n",
    "- type: describes the what generic category the car falls under (sedan, truck, etc)\n",
    "- paint_color: the color of the car\n",
    "- image_url: the URL to an image of the listed car\n",
    "- description: a short description of what the listing is about or details about the car\n",
    "- county: the county where the car user is from\n",
    "- state: the state where the car uer is from\n",
    "- lat: the latitude coordinates where the car user is located\n",
    "- long: the longitude coordinate where the car user is located\n",
    "- posting_date: the date when the car listing was posted \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import seaborn as sns\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.model_selection import train_test_split\n",
    "import random\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('vehicles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'url', 'region', 'region_url', 'price', 'year', 'manufacturer',\n",
       "       'model', 'condition', 'cylinders', 'fuel', 'odometer', 'title_status',\n",
       "       'transmission', 'VIN', 'drive', 'size', 'type', 'paint_color',\n",
       "       'image_url', 'description', 'county', 'state', 'lat', 'long',\n",
       "       'posting_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We selected all the desired columns from the dataframe. These columns could be used as predictors, or features in the model. We also want to use the price to evaluate how well our model can predict the listing price.\n",
    "\n",
    "We chose to leave out the region, lat, long, and county attributes and restrict our analysis to the state since regions would be too specific and computationally expensive. We left out the paint_color attribute because customers can always repaint the car and the quality of the paint is not specified in this attribute, but would probably be more important. We left out the model attribute since this would be too computationally expensive as there are over 29,000 unique models. This would be difficult to clean as this field is user-generated, so there are many inconsistencies. We chose to use manufacturer and type instead, since those would give a general idea of what car is being sold. We left out size because cylinder information is probably more informative of a car's worth. We left out description since this was user-generated and many dealerships post listings with the exact same text, which would not be useful in predicting a car's price. We finally left out posting_date since all of these cars were posted on Craigslist around the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426880, 12)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['year', 'manufacturer', 'type', 'condition', 'cylinders', 'fuel', 'odometer', 'title_status',\n",
    "       'transmission', 'drive', 'state', 'price']]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop all the rows that have missing values more than at least 50% of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>type</th>\n",
       "      <th>condition</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>fuel</th>\n",
       "      <th>odometer</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
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       "      <td>chevrolet</td>\n",
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       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>clean</td>\n",
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       "      <td>22590</td>\n",
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       "      <th>2</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>19160.0</td>\n",
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       "      <td>39590</td>\n",
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       "      <th>3</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>30990</td>\n",
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       "      <th>4</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>truck</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>al</td>\n",
       "      <td>15000</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426115</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>nissan</td>\n",
       "      <td>sedan</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>32226.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>fwd</td>\n",
       "      <td>wy</td>\n",
       "      <td>23590</td>\n",
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       "    <tr>\n",
       "      <th>426116</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>volvo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>12029.0</td>\n",
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       "      <td>fwd</td>\n",
       "      <td>wy</td>\n",
       "      <td>30590</td>\n",
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       "    <tr>\n",
       "      <th>426117</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>diesel</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wy</td>\n",
       "      <td>34990</td>\n",
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       "    <tr>\n",
       "      <th>426118</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>lexus</td>\n",
       "      <td>sedan</td>\n",
       "      <td>good</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>30112.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>fwd</td>\n",
       "      <td>wy</td>\n",
       "      <td>28990</td>\n",
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       "      <th>426119</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>coupe</td>\n",
       "      <td>good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gas</td>\n",
       "      <td>22716.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>rwd</td>\n",
       "      <td>wy</td>\n",
       "      <td>30590</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          year manufacturer       type  condition    cylinders    fuel  \\\n",
       "0       2014.0          gmc     pickup       good  8 cylinders     gas   \n",
       "1       2010.0    chevrolet     pickup       good  8 cylinders     gas   \n",
       "2       2020.0    chevrolet     pickup       good  8 cylinders     gas   \n",
       "3       2017.0       toyota     pickup       good  8 cylinders     gas   \n",
       "4       2013.0         ford      truck  excellent  6 cylinders     gas   \n",
       "...        ...          ...        ...        ...          ...     ...   \n",
       "426115  2019.0       nissan      sedan       good  6 cylinders     gas   \n",
       "426116  2020.0        volvo      sedan       good          NaN     gas   \n",
       "426117  2020.0     cadillac  hatchback       good          NaN  diesel   \n",
       "426118  2018.0        lexus      sedan       good  6 cylinders     gas   \n",
       "426119  2019.0          bmw      coupe       good          NaN     gas   \n",
       "\n",
       "        odometer title_status transmission drive state  price  \n",
       "0        57923.0        clean        other   NaN    al  33590  \n",
       "1        71229.0        clean        other   NaN    al  22590  \n",
       "2        19160.0        clean        other   NaN    al  39590  \n",
       "3        41124.0        clean        other   NaN    al  30990  \n",
       "4       128000.0        clean    automatic   rwd    al  15000  \n",
       "...          ...          ...          ...   ...   ...    ...  \n",
       "426115   32226.0        clean        other   fwd    wy  23590  \n",
       "426116   12029.0        clean        other   fwd    wy  30590  \n",
       "426117    4174.0        clean        other   NaN    wy  34990  \n",
       "426118   30112.0        clean        other   fwd    wy  28990  \n",
       "426119   22716.0        clean        other   rwd    wy  30590  \n",
       "\n",
       "[426120 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis= 0, thresh= 6, inplace= True)\n",
    "df.reset_index(drop = True)\n",
    "df.dropna(axis= 0, thresh= 6, inplace= True)\n",
    "df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426120, 12)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use ordinal encoding for the cylinders, drive, title_status, and condition because these variables are categorical, but they have orders between the groups. For example, clean title used cars are more expensive than salvage title cars due to insurance costs. Moreover, cars with higher cylinders tend to be more expensive as they have more powerful engines. We will be using scikit-learn's OrdinalEncoder for this operation.\n",
    "\n",
    "We will also cast the year and odometer variables to integers since float values do not make sense here.\n",
    "\n",
    "We will be one-hot encoding the nominal categorical variables such as manufacturer, model, state, fuel, and transmission. We will be using scikit-learn's OneHotEncoder for this operation.\n",
    "\n",
    "Since many of the categorical columns have NaN values, we will randomly selecting a value from the probability distribution of the data for that specific column to impute reasonable values.\n",
    "\n",
    "We will be renaming columns to make their names more descriptive.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year', 'manufacturer', 'type', 'condition', 'cylinders', 'fuel',\n",
       "       'odometer', 'title_status', 'transmission', 'drive', 'state', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>car_category</th>\n",
       "      <th>condition</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>33590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>22590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>39590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>pickup</td>\n",
       "      <td>good</td>\n",
       "      <td>8 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>30990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>truck</td>\n",
       "      <td>excellent</td>\n",
       "      <td>6 cylinders</td>\n",
       "      <td>gas</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>clean</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>al</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year manufacturer car_category  condition num_cylinders fuel_type  \\\n",
       "27  2014.0          gmc       pickup       good   8 cylinders       gas   \n",
       "28  2010.0    chevrolet       pickup       good   8 cylinders       gas   \n",
       "29  2020.0    chevrolet       pickup       good   8 cylinders       gas   \n",
       "30  2017.0       toyota       pickup       good   8 cylinders       gas   \n",
       "31  2013.0         ford        truck  excellent   6 cylinders       gas   \n",
       "\n",
       "    miles_driven title_status transmission drive state  price  \n",
       "27       57923.0        clean        other   NaN    al  33590  \n",
       "28       71229.0        clean        other   NaN    al  22590  \n",
       "29       19160.0        clean        other   NaN    al  39590  \n",
       "30       41124.0        clean        other   NaN    al  30990  \n",
       "31      128000.0        clean    automatic   rwd    al  15000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.rename(columns = {'odometer':'miles_driven', 'fuel': 'fuel_type', 'cylinders': 'num_cylinders', 'type':'car_category'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_status_ordinal = {\n",
    "    'missing': 0,\n",
    "    'lien' : 1,\n",
    "    'rebuilt': 2,\n",
    "    'salvage': 3,\n",
    "    'clean': 4,\n",
    "}\n",
    "num_cyl_ordinal = {\n",
    "    'other' : 0,\n",
    "    '3 cylinders': 1,\n",
    "    '4 cylinders' : 2,\n",
    "    '5 cylinders': 3,\n",
    "    '6 cylinders': 4,\n",
    "    '8 cylinders': 5,\n",
    "    '10 cylinders': 6\n",
    "}\n",
    "condition_ordinal = {\n",
    "    'excellent': 5,\n",
    "    'new': 4, \n",
    "    'like new': 3,\n",
    "    'good': 2, \n",
    "    'fair' : 1, \n",
    "    'salvage': 0\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We ordinal encoded features such as num_cylinders, title_status, condition into ordinal integer since there exist a known relationship between the categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['num_cylinders'] = df['num_cylinders'].map(num_cyl_ordinal)\n",
    "df['title_status'] = df['title_status'].map(title_status_ordinal)\n",
    "df['condition'] = df['condition'].map(condition_ordinal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'price' and 'miles_driven' columns have obvious outliers. We will be dropping any listings with odomoter readings over 250,000 or under 500 miles, or with prices above 500,000 or under 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>424787</td>\n",
       "      <td>252648</td>\n",
       "      <td>248891</td>\n",
       "      <td>422157</td>\n",
       "      <td>418435</td>\n",
       "      <td>425922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>97882</td>\n",
       "      <td>4</td>\n",
       "      <td>75352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>210953</td>\n",
       "      <td>0</td>\n",
       "      <td>12195974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37697</td>\n",
       "      <td>4</td>\n",
       "      <td>5900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>85530</td>\n",
       "      <td>4</td>\n",
       "      <td>13980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>133533</td>\n",
       "      <td>4</td>\n",
       "      <td>26500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>10000000</td>\n",
       "      <td>4</td>\n",
       "      <td>3736928711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year condition num_cylinders miles_driven title_status       price\n",
       "count  424787    252648        248891       422157       418435      425922\n",
       "mean     2011         3             4        97882            4       75352\n",
       "std         9         1             1       210953            0    12195974\n",
       "min      1900         0             0            0            0           0\n",
       "25%      2008         2             2        37697            4        5900\n",
       "50%      2013         2             4        85530            4       13980\n",
       "75%      2017         5             5       133533            4       26500\n",
       "max      2022         5             6     10000000            4  3736928711"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().apply(lambda s: s.apply('{0:.0f}'.format))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['price'] < 500000) & (df['price'] > 100)]\n",
    "df = df[(df['miles_driven'] > 500) & (df['miles_driven'] < 250000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select desired columns\n",
    "pre_df = df[['miles_driven', 'title_status', 'num_cylinders', 'condition', 'manufacturer']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation based on mean per manufacturer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#impute manufacture\n",
    "manu_dist = pre_df['manufacturer'].value_counts(normalize = True)\n",
    "\n",
    "def impute_manu(x):\n",
    "    if pd.isna(x):\n",
    "        return random.choices(manu_dist.index, manu_dist.values)[0]\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "pre_df['manufacturer'] = pre_df['manufacturer'].apply(lambda x: impute_manu(x))\n",
    "pre_df['manufacturer'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation based on probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#impute miles_driven\n",
    "miles_per_manu = pre_df[['manufacturer', 'miles_driven']].groupby('manufacturer').mean()\n",
    "\n",
    "def impute_miles_driven(x):\n",
    "    if pd.isna(x['miles_driven']):\n",
    "        return miles_per_manu.loc[x['manufacturer']][0]\n",
    "    else:\n",
    "        return x['miles_driven']\n",
    "\n",
    "pre_df['miles_driven'] = pre_df[['manufacturer',  'miles_driven']].apply(lambda x: impute_miles_driven(x), axis = 1)\n",
    "pre_df['miles_driven'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation based on probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#impute title_status\n",
    "title_status_dist = pre_df['title_status'].value_counts(normalize = True)\n",
    "\n",
    "def impute_title_status(x):\n",
    "    if pd.isna(x):\n",
    "        return random.choices(title_status_dist.index, title_status_dist.values)[0]\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "pre_df['title_status'] = pre_df['title_status'].apply(lambda x: impute_title_status(x))\n",
    "pre_df['title_status'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation based on probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#impute num_cylinders\n",
    "num_cylinders_dist = pre_df['num_cylinders'].value_counts(normalize = True)\n",
    "\n",
    "def impute_num_cylinders(x):\n",
    "    if pd.isna(x):\n",
    "        return random.choices(num_cylinders_dist.index, num_cylinders_dist.values)[0]\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "pre_df['num_cylinders'] = pre_df['num_cylinders'].apply(lambda x: impute_num_cylinders(x))\n",
    "pre_df['num_cylinders'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imputation based on probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#impute condition\n",
    "condition_dist = pre_df['condition'].value_counts(normalize = True)\n",
    "\n",
    "def impute_condition(x):\n",
    "    if pd.isna(x):\n",
    "        return random.choices(condition_dist.index, condition_dist.values)[0]\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "pre_df['condition'] = pre_df['condition'].apply(lambda x: impute_condition(x))\n",
    "pre_df['condition'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "miles_driven     0\n",
       "title_status     0\n",
       "num_cylinders    0\n",
       "condition        0\n",
       "manufacturer     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then one hot encode features that has categorical variables. These columns includes : manufacturer, car_category, fuel_type, transmission, drive, state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_onehot = pd.get_dummies(pre_df, columns = ['manufacturer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>condition</th>\n",
       "      <th>manufacturer_acura</th>\n",
       "      <th>manufacturer_alfa-romeo</th>\n",
       "      <th>manufacturer_aston-martin</th>\n",
       "      <th>manufacturer_audi</th>\n",
       "      <th>manufacturer_bmw</th>\n",
       "      <th>manufacturer_buick</th>\n",
       "      <th>...</th>\n",
       "      <th>manufacturer_pontiac</th>\n",
       "      <th>manufacturer_porsche</th>\n",
       "      <th>manufacturer_ram</th>\n",
       "      <th>manufacturer_rover</th>\n",
       "      <th>manufacturer_saturn</th>\n",
       "      <th>manufacturer_subaru</th>\n",
       "      <th>manufacturer_tesla</th>\n",
       "      <th>manufacturer_toyota</th>\n",
       "      <th>manufacturer_volkswagen</th>\n",
       "      <th>manufacturer_volvo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>57923.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>71229.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19160.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>41124.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   miles_driven  title_status  num_cylinders  condition  manufacturer_acura  \\\n",
       "0       57923.0           4.0            5.0        2.0                   0   \n",
       "1       71229.0           4.0            5.0        2.0                   0   \n",
       "2       19160.0           4.0            5.0        2.0                   0   \n",
       "3       41124.0           4.0            5.0        2.0                   0   \n",
       "4      128000.0           4.0            4.0        5.0                   0   \n",
       "\n",
       "   manufacturer_alfa-romeo  manufacturer_aston-martin  manufacturer_audi  \\\n",
       "0                        0                          0                  0   \n",
       "1                        0                          0                  0   \n",
       "2                        0                          0                  0   \n",
       "3                        0                          0                  0   \n",
       "4                        0                          0                  0   \n",
       "\n",
       "   manufacturer_bmw  manufacturer_buick  ...  manufacturer_pontiac  \\\n",
       "0                 0                   0  ...                     0   \n",
       "1                 0                   0  ...                     0   \n",
       "2                 0                   0  ...                     0   \n",
       "3                 0                   0  ...                     0   \n",
       "4                 0                   0  ...                     0   \n",
       "\n",
       "   manufacturer_porsche  manufacturer_ram  manufacturer_rover  \\\n",
       "0                     0                 0                   0   \n",
       "1                     0                 0                   0   \n",
       "2                     0                 0                   0   \n",
       "3                     0                 0                   0   \n",
       "4                     0                 0                   0   \n",
       "\n",
       "   manufacturer_saturn  manufacturer_subaru  manufacturer_tesla  \\\n",
       "0                    0                    0                   0   \n",
       "1                    0                    0                   0   \n",
       "2                    0                    0                   0   \n",
       "3                    0                    0                   0   \n",
       "4                    0                    0                   0   \n",
       "\n",
       "   manufacturer_toyota  manufacturer_volkswagen  manufacturer_volvo  \n",
       "0                    0                        0                   0  \n",
       "1                    0                        0                   0  \n",
       "2                    0                        0                   0  \n",
       "3                    1                        0                   0  \n",
       "4                    0                        0                   0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_onehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure:\n",
    "The used car data that we used is in a tabular format of a single table (CSV file) with each row being a single observation of a used car and its information. There are 12 columns in the dataset, with the year, miles_driven, price,. and num_cylinders columns being integer types. The manufacturer, car_category, condition, fuel type, title_status, transmission, state, and drive columns being string types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>car_category</th>\n",
       "      <th>condition</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>33590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>22590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>39590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>30990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>truck</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>128000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>al</td>\n",
       "      <td>15000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year manufacturer car_category  condition  num_cylinders fuel_type  \\\n",
       "0  2014.0          gmc       pickup        2.0            5.0       gas   \n",
       "1  2010.0    chevrolet       pickup        2.0            5.0       gas   \n",
       "2  2020.0    chevrolet       pickup        2.0            5.0       gas   \n",
       "3  2017.0       toyota       pickup        2.0            5.0       gas   \n",
       "4  2013.0         ford        truck        5.0            4.0       gas   \n",
       "\n",
       "   miles_driven  title_status transmission drive state  price  \n",
       "0       57923.0           4.0        other   NaN    al  33590  \n",
       "1       71229.0           4.0        other   NaN    al  22590  \n",
       "2       19160.0           4.0        other   NaN    al  39590  \n",
       "3       41124.0           4.0        other   NaN    al  30990  \n",
       "4      128000.0           4.0    automatic   rwd    al  15000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Granularity:\n",
    "Each row in our dataset represents an observation of a used car and its information: price, condition, manufacture, latitude/longitude, and 18 other categories. All records uniformaly represents the same level of granulaity since each row represents a single observation on an individual level. The data is not summarized nor aggregated. As for summaraizing, we plan on plotting a histogram to show the distribution of different prices amoung the used cars. We also plan to evaluate the count for each title_status, and will also do a bar chart of the amount of manufactures within the dataset do determine the amount of cars under each manufacture. Some interesting aggregates includes the average price/year/miles driven for each manufacture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>car_category</th>\n",
       "      <th>condition</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>33590</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     year manufacturer car_category  condition  num_cylinders fuel_type  \\\n",
       "0  2014.0          gmc       pickup        2.0            5.0       gas   \n",
       "\n",
       "   miles_driven  title_status transmission drive state  price  \n",
       "0       57923.0           4.0        other   NaN    al  33590  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temporality:\n",
    "The dataset is comprised of used car postings on Craigslist that were posted between April to May 2021. The date allows us to generalize the findings to the present day. However, the dates at which the postings were made are not very important to our analysis. Within the dataset, we find a 'year' column which represents each vehicle's model year. These values are of particular importance given that the age of the car could affect its price.\n",
    "\n",
    "Year is stored as a float:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2014.0\n",
       "1    2010.0\n",
       "2    2020.0\n",
       "3    2017.0\n",
       "4    2013.0\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the range of years is from [1900,2022]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    372653.000000\n",
       "mean       2011.273858\n",
       "std           9.114572\n",
       "min        1900.000000\n",
       "25%        2008.000000\n",
       "50%        2013.000000\n",
       "75%        2017.000000\n",
       "max        2022.000000\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not all entries include information about the car's year.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>manufacturer</th>\n",
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       "      <th>323</th>\n",
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       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>automatic</td>\n",
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       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>22105.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>rwd</td>\n",
       "      <td>al</td>\n",
       "      <td>32995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>30934.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>fwd</td>\n",
       "      <td>al</td>\n",
       "      <td>29890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>42455.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>26495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year manufacturer car_category  condition  num_cylinders fuel_type  \\\n",
       "167   NaN          NaN          SUV        NaN            4.0       gas   \n",
       "323   NaN          NaN        sedan        NaN            2.0       gas   \n",
       "551   NaN          NaN        sedan        NaN            2.0       gas   \n",
       "651   NaN          NaN          SUV        NaN            4.0       gas   \n",
       "683   NaN          NaN        sedan        NaN            2.0       gas   \n",
       "\n",
       "     miles_driven  title_status transmission drive state  price  \n",
       "167       48784.0           4.0    automatic   fwd    al  33990  \n",
       "323       14000.0           4.0    automatic   rwd    al  35495  \n",
       "551       22105.0           4.0    automatic   rwd    al  32995  \n",
       "651       30934.0           4.0    automatic   fwd    al  29890  \n",
       "683       42455.0           4.0    automatic   NaN    al  26495  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incorrect_year = df[df['year'].isna()]\n",
    "incorrect_year.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Faithfulness:\n",
    "The faithfulness of the data is quite difficult to determine as Craigslist is based purely on individual user input that does not screen for unreasonable values. For example, some cars have a listing price of well over three billion dollars in price for a pickup truck and some cars were listed at zero dollars (which was probably done to get people to click on the post). This is also a form of falsification as the true price of the car is not being stated, which leads to more missing values and a less accurate representation of the data. We removed all of the outliers within our data cleaning section to get the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>year</th>\n",
       "      <th>condition</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>372653</td>\n",
       "      <td>230699</td>\n",
       "      <td>222326</td>\n",
       "      <td>373598</td>\n",
       "      <td>366813</td>\n",
       "      <td>373598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>91200</td>\n",
       "      <td>4</td>\n",
       "      <td>19193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>58523</td>\n",
       "      <td>0</td>\n",
       "      <td>15083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>501</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>39676</td>\n",
       "      <td>4</td>\n",
       "      <td>7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>87212</td>\n",
       "      <td>4</td>\n",
       "      <td>15900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>133685</td>\n",
       "      <td>4</td>\n",
       "      <td>27990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>249961</td>\n",
       "      <td>4</td>\n",
       "      <td>470000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         year condition num_cylinders miles_driven title_status   price\n",
       "count  372653    230699        222326       373598       366813  373598\n",
       "mean     2011         3             4        91200            4   19193\n",
       "std         9         1             1        58523            0   15083\n",
       "min      1900         0             0          501            0     101\n",
       "25%      2008         2             2        39676            4    7700\n",
       "50%      2013         2             4        87212            4   15900\n",
       "75%      2017         5             5       133685            4   27990\n",
       "max      2022         5             6       249961            4  470000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().apply(lambda s: s.apply('{0:.0f}'.format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout the whole data set we also see that many of the columns have missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                945\n",
       "manufacturer      13531\n",
       "car_category      78034\n",
       "condition        142899\n",
       "num_cylinders    151272\n",
       "fuel_type          2078\n",
       "miles_driven          0\n",
       "title_status       6785\n",
       "transmission       1390\n",
       "drive            112417\n",
       "state                 0\n",
       "price                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>car_category</th>\n",
       "      <th>condition</th>\n",
       "      <th>num_cylinders</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>miles_driven</th>\n",
       "      <th>title_status</th>\n",
       "      <th>transmission</th>\n",
       "      <th>drive</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>gmc</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>57923.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>33590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>71229.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>22590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>19160.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>39590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>pickup</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>41124.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>al</td>\n",
       "      <td>30990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1992.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>gas</td>\n",
       "      <td>192000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>automatic</td>\n",
       "      <td>4wd</td>\n",
       "      <td>al</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year manufacturer car_category  condition  num_cylinders fuel_type  \\\n",
       "0   2014.0          gmc       pickup        2.0            5.0       gas   \n",
       "1   2010.0    chevrolet       pickup        2.0            5.0       gas   \n",
       "2   2020.0    chevrolet       pickup        2.0            5.0       gas   \n",
       "3   2017.0       toyota       pickup        2.0            5.0       gas   \n",
       "10  1992.0         jeep          NaN        5.0            4.0       gas   \n",
       "\n",
       "    miles_driven  title_status transmission drive state  price  \n",
       "0        57923.0           4.0        other   NaN    al  33590  \n",
       "1        71229.0           4.0        other   NaN    al  22590  \n",
       "2        19160.0           4.0        other   NaN    al  39590  \n",
       "3        41124.0           4.0        other   NaN    al  30990  \n",
       "10      192000.0           4.0    automatic   4wd    al   4500  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isna().any(axis=1)].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scope:\n",
    "\n",
    "Shape of the dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(373598, 12)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manufacturer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='manufacturer'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {'figure.figsize':(15,8)})\n",
    "sns.countplot(y = 'manufacturer', orient = 'h', order = df['manufacturer'].value_counts().index,data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset appears to be dominated by three manufacturers in particular: Ford, Chevrolet, and Toyota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>manufacturer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acura</th>\n",
       "      <td>5543.0</td>\n",
       "      <td>21251.9</td>\n",
       "      <td>12951.4</td>\n",
       "      <td>116.0</td>\n",
       "      <td>10369.0</td>\n",
       "      <td>20289.0</td>\n",
       "      <td>31355.0</td>\n",
       "      <td>139999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alfa-romeo</th>\n",
       "      <td>828.0</td>\n",
       "      <td>29423.2</td>\n",
       "      <td>5216.7</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>27990.0</td>\n",
       "      <td>29590.0</td>\n",
       "      <td>31146.8</td>\n",
       "      <td>62950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aston-martin</th>\n",
       "      <td>21.0</td>\n",
       "      <td>61136.6</td>\n",
       "      <td>46597.1</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>37000.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>74991.0</td>\n",
       "      <td>179888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>7031.0</td>\n",
       "      <td>25149.0</td>\n",
       "      <td>15270.7</td>\n",
       "      <td>129.0</td>\n",
       "      <td>13995.0</td>\n",
       "      <td>24990.0</td>\n",
       "      <td>33990.0</td>\n",
       "      <td>160000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>13402.0</td>\n",
       "      <td>20700.8</td>\n",
       "      <td>13169.9</td>\n",
       "      <td>101.0</td>\n",
       "      <td>10988.0</td>\n",
       "      <td>17999.0</td>\n",
       "      <td>28909.0</td>\n",
       "      <td>138000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>buick</th>\n",
       "      <td>4963.0</td>\n",
       "      <td>15234.9</td>\n",
       "      <td>11414.7</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>12988.0</td>\n",
       "      <td>22590.0</td>\n",
       "      <td>123456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cadillac</th>\n",
       "      <td>6282.0</td>\n",
       "      <td>20735.0</td>\n",
       "      <td>14839.4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>8900.0</td>\n",
       "      <td>17882.5</td>\n",
       "      <td>31590.0</td>\n",
       "      <td>195000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet</th>\n",
       "      <td>47490.0</td>\n",
       "      <td>20242.4</td>\n",
       "      <td>15605.7</td>\n",
       "      <td>108.0</td>\n",
       "      <td>7995.0</td>\n",
       "      <td>16000.5</td>\n",
       "      <td>29980.0</td>\n",
       "      <td>152995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chrysler</th>\n",
       "      <td>5421.0</td>\n",
       "      <td>10952.2</td>\n",
       "      <td>9216.1</td>\n",
       "      <td>116.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>7996.0</td>\n",
       "      <td>15561.0</td>\n",
       "      <td>290000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datsun</th>\n",
       "      <td>58.0</td>\n",
       "      <td>16387.7</td>\n",
       "      <td>9045.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>8124.2</td>\n",
       "      <td>18700.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>11710.0</td>\n",
       "      <td>16243.1</td>\n",
       "      <td>14026.8</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5990.0</td>\n",
       "      <td>11995.0</td>\n",
       "      <td>23999.0</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ferrari</th>\n",
       "      <td>77.0</td>\n",
       "      <td>125314.0</td>\n",
       "      <td>71083.4</td>\n",
       "      <td>2034.0</td>\n",
       "      <td>85500.0</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>149995.0</td>\n",
       "      <td>349999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fiat</th>\n",
       "      <td>754.0</td>\n",
       "      <td>12436.3</td>\n",
       "      <td>7045.8</td>\n",
       "      <td>159.0</td>\n",
       "      <td>6496.2</td>\n",
       "      <td>10600.0</td>\n",
       "      <td>19990.0</td>\n",
       "      <td>32500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ford</th>\n",
       "      <td>61453.0</td>\n",
       "      <td>21357.2</td>\n",
       "      <td>16055.9</td>\n",
       "      <td>101.0</td>\n",
       "      <td>8800.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>30900.0</td>\n",
       "      <td>225000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gmc</th>\n",
       "      <td>14673.0</td>\n",
       "      <td>25553.6</td>\n",
       "      <td>17005.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>11600.0</td>\n",
       "      <td>24590.0</td>\n",
       "      <td>34995.0</td>\n",
       "      <td>175000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>harley-davidson</th>\n",
       "      <td>132.0</td>\n",
       "      <td>12981.6</td>\n",
       "      <td>5963.9</td>\n",
       "      <td>192.0</td>\n",
       "      <td>7995.0</td>\n",
       "      <td>11975.0</td>\n",
       "      <td>18125.0</td>\n",
       "      <td>27000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda</th>\n",
       "      <td>18959.0</td>\n",
       "      <td>11754.1</td>\n",
       "      <td>8383.4</td>\n",
       "      <td>101.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>9470.0</td>\n",
       "      <td>16222.0</td>\n",
       "      <td>123456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hyundai</th>\n",
       "      <td>9098.0</td>\n",
       "      <td>12001.4</td>\n",
       "      <td>7190.4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>6500.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>16990.0</td>\n",
       "      <td>49788.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>infiniti</th>\n",
       "      <td>4379.0</td>\n",
       "      <td>21238.8</td>\n",
       "      <td>11015.8</td>\n",
       "      <td>137.0</td>\n",
       "      <td>11995.0</td>\n",
       "      <td>21590.0</td>\n",
       "      <td>28990.0</td>\n",
       "      <td>69995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>1796.0</td>\n",
       "      <td>26408.1</td>\n",
       "      <td>14366.5</td>\n",
       "      <td>134.0</td>\n",
       "      <td>15497.0</td>\n",
       "      <td>28590.0</td>\n",
       "      <td>35590.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jeep</th>\n",
       "      <td>16618.0</td>\n",
       "      <td>20072.3</td>\n",
       "      <td>13054.5</td>\n",
       "      <td>111.0</td>\n",
       "      <td>8999.0</td>\n",
       "      <td>18793.5</td>\n",
       "      <td>28997.0</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kia</th>\n",
       "      <td>7291.0</td>\n",
       "      <td>12548.4</td>\n",
       "      <td>7488.2</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6995.0</td>\n",
       "      <td>11900.0</td>\n",
       "      <td>16990.0</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land rover</th>\n",
       "      <td>11.0</td>\n",
       "      <td>15103.0</td>\n",
       "      <td>18222.5</td>\n",
       "      <td>3199.0</td>\n",
       "      <td>5747.5</td>\n",
       "      <td>7995.0</td>\n",
       "      <td>10499.5</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lexus</th>\n",
       "      <td>7501.0</td>\n",
       "      <td>20716.3</td>\n",
       "      <td>11886.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>29798.0</td>\n",
       "      <td>114950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lincoln</th>\n",
       "      <td>3920.0</td>\n",
       "      <td>20758.5</td>\n",
       "      <td>13884.8</td>\n",
       "      <td>123.0</td>\n",
       "      <td>8900.0</td>\n",
       "      <td>18969.0</td>\n",
       "      <td>30590.0</td>\n",
       "      <td>215000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>4878.0</td>\n",
       "      <td>13517.0</td>\n",
       "      <td>9307.7</td>\n",
       "      <td>103.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>19990.8</td>\n",
       "      <td>45000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>9943.0</td>\n",
       "      <td>22171.5</td>\n",
       "      <td>17737.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>18999.0</td>\n",
       "      <td>30495.0</td>\n",
       "      <td>255000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercury</th>\n",
       "      <td>1084.0</td>\n",
       "      <td>5734.4</td>\n",
       "      <td>6074.1</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>6495.0</td>\n",
       "      <td>95000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mini</th>\n",
       "      <td>2218.0</td>\n",
       "      <td>15033.6</td>\n",
       "      <td>9232.5</td>\n",
       "      <td>109.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12997.5</td>\n",
       "      <td>21435.5</td>\n",
       "      <td>42500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>2897.0</td>\n",
       "      <td>14840.2</td>\n",
       "      <td>8671.9</td>\n",
       "      <td>128.0</td>\n",
       "      <td>6795.0</td>\n",
       "      <td>15590.0</td>\n",
       "      <td>20590.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>morgan</th>\n",
       "      <td>3.0</td>\n",
       "      <td>13100.0</td>\n",
       "      <td>20268.9</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>19150.0</td>\n",
       "      <td>36500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>16772.0</td>\n",
       "      <td>13091.6</td>\n",
       "      <td>9774.3</td>\n",
       "      <td>101.0</td>\n",
       "      <td>5999.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>17989.0</td>\n",
       "      <td>169999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pontiac</th>\n",
       "      <td>2128.0</td>\n",
       "      <td>8278.8</td>\n",
       "      <td>9155.8</td>\n",
       "      <td>109.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>98000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>1272.0</td>\n",
       "      <td>33665.4</td>\n",
       "      <td>26681.6</td>\n",
       "      <td>159.0</td>\n",
       "      <td>16375.0</td>\n",
       "      <td>29997.0</td>\n",
       "      <td>39990.0</td>\n",
       "      <td>244999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ram</th>\n",
       "      <td>15636.0</td>\n",
       "      <td>30459.4</td>\n",
       "      <td>15695.8</td>\n",
       "      <td>101.0</td>\n",
       "      <td>19900.0</td>\n",
       "      <td>30495.0</td>\n",
       "      <td>39995.0</td>\n",
       "      <td>110000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rover</th>\n",
       "      <td>1917.0</td>\n",
       "      <td>28965.2</td>\n",
       "      <td>20220.8</td>\n",
       "      <td>101.0</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>24999.0</td>\n",
       "      <td>38950.0</td>\n",
       "      <td>129888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saturn</th>\n",
       "      <td>1040.0</td>\n",
       "      <td>5229.3</td>\n",
       "      <td>4203.9</td>\n",
       "      <td>188.0</td>\n",
       "      <td>2999.8</td>\n",
       "      <td>4297.5</td>\n",
       "      <td>5495.0</td>\n",
       "      <td>22000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subaru</th>\n",
       "      <td>8666.0</td>\n",
       "      <td>13984.8</td>\n",
       "      <td>9126.7</td>\n",
       "      <td>108.0</td>\n",
       "      <td>6998.0</td>\n",
       "      <td>11980.0</td>\n",
       "      <td>19977.0</td>\n",
       "      <td>195000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tesla</th>\n",
       "      <td>836.0</td>\n",
       "      <td>39638.3</td>\n",
       "      <td>14066.6</td>\n",
       "      <td>348.0</td>\n",
       "      <td>32990.0</td>\n",
       "      <td>37990.0</td>\n",
       "      <td>40990.0</td>\n",
       "      <td>104990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota</th>\n",
       "      <td>29819.0</td>\n",
       "      <td>17499.9</td>\n",
       "      <td>12491.7</td>\n",
       "      <td>101.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>25998.0</td>\n",
       "      <td>131500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>8453.0</td>\n",
       "      <td>13614.2</td>\n",
       "      <td>8730.4</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6900.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>18900.0</td>\n",
       "      <td>149000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>3094.0</td>\n",
       "      <td>18510.9</td>\n",
       "      <td>14718.2</td>\n",
       "      <td>170.0</td>\n",
       "      <td>5950.0</td>\n",
       "      <td>13993.0</td>\n",
       "      <td>29990.0</td>\n",
       "      <td>144832.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count      mean      std     min      25%       50%  \\\n",
       "manufacturer                                                             \n",
       "acura             5543.0   21251.9  12951.4   116.0  10369.0   20289.0   \n",
       "alfa-romeo         828.0   29423.2   5216.7  1000.0  27990.0   29590.0   \n",
       "aston-martin        21.0   61136.6  46597.1  1947.0  37000.0   52000.0   \n",
       "audi              7031.0   25149.0  15270.7   129.0  13995.0   24990.0   \n",
       "bmw              13402.0   20700.8  13169.9   101.0  10988.0   17999.0   \n",
       "buick             4963.0   15234.9  11414.7   104.0   5900.0   12988.0   \n",
       "cadillac          6282.0   20735.0  14839.4   135.0   8900.0   17882.5   \n",
       "chevrolet        47490.0   20242.4  15605.7   108.0   7995.0   16000.5   \n",
       "chrysler          5421.0   10952.2   9216.1   116.0   4750.0    7996.0   \n",
       "datsun              58.0   16387.7   9045.0  2200.0   8124.2   18700.0   \n",
       "dodge            11710.0   16243.1  14026.8   103.0   5990.0   11995.0   \n",
       "ferrari             77.0  125314.0  71083.4  2034.0  85500.0  105000.0   \n",
       "fiat               754.0   12436.3   7045.8   159.0   6496.2   10600.0   \n",
       "ford             61453.0   21357.2  16055.9   101.0   8800.0   17500.0   \n",
       "gmc              14673.0   25553.6  17005.0   123.0  11600.0   24590.0   \n",
       "harley-davidson    132.0   12981.6   5963.9   192.0   7995.0   11975.0   \n",
       "honda            18959.0   11754.1   8383.4   101.0   5500.0    9470.0   \n",
       "hyundai           9098.0   12001.4   7190.4   113.0   6500.0   10800.0   \n",
       "infiniti          4379.0   21238.8  11015.8   137.0  11995.0   21590.0   \n",
       "jaguar            1796.0   26408.1  14366.5   134.0  15497.0   28590.0   \n",
       "jeep             16618.0   20072.3  13054.5   111.0   8999.0   18793.5   \n",
       "kia               7291.0   12548.4   7488.2   101.0   6995.0   11900.0   \n",
       "land rover          11.0   15103.0  18222.5  3199.0   5747.5    7995.0   \n",
       "lexus             7501.0   20716.3  11886.5   116.0  10500.0   19999.0   \n",
       "lincoln           3920.0   20758.5  13884.8   123.0   8900.0   18969.0   \n",
       "mazda             4878.0   13517.0   9307.7   103.0   5900.0    9999.0   \n",
       "mercedes-benz     9943.0   22171.5  17737.1   101.0   9999.0   18999.0   \n",
       "mercury           1084.0    5734.4   6074.1   101.0   2900.0    4500.0   \n",
       "mini              2218.0   15033.6   9232.5   109.0   7500.0   12997.5   \n",
       "mitsubishi        2897.0   14840.2   8671.9   128.0   6795.0   15590.0   \n",
       "morgan               3.0   13100.0  20268.9  1000.0   1400.0    1800.0   \n",
       "nissan           16772.0   13091.6   9774.3   101.0   5999.0   10500.0   \n",
       "pontiac           2128.0    8278.8   9155.8   109.0   3500.0    4995.0   \n",
       "porsche           1272.0   33665.4  26681.6   159.0  16375.0   29997.0   \n",
       "ram              15636.0   30459.4  15695.8   101.0  19900.0   30495.0   \n",
       "rover             1917.0   28965.2  20220.8   101.0  14900.0   24999.0   \n",
       "saturn            1040.0    5229.3   4203.9   188.0   2999.8    4297.5   \n",
       "subaru            8666.0   13984.8   9126.7   108.0   6998.0   11980.0   \n",
       "tesla              836.0   39638.3  14066.6   348.0  32990.0   37990.0   \n",
       "toyota           29819.0   17499.9  12491.7   101.0   7000.0   14500.0   \n",
       "volkswagen        8453.0   13614.2   8730.4   101.0   6900.0   12000.0   \n",
       "volvo             3094.0   18510.9  14718.2   170.0   5950.0   13993.0   \n",
       "\n",
       "                      75%       max  \n",
       "manufacturer                         \n",
       "acura             31355.0  139999.0  \n",
       "alfa-romeo        31146.8   62950.0  \n",
       "aston-martin      74991.0  179888.0  \n",
       "audi              33990.0  160000.0  \n",
       "bmw               28909.0  138000.0  \n",
       "buick             22590.0  123456.0  \n",
       "cadillac          31590.0  195000.0  \n",
       "chevrolet         29980.0  152995.0  \n",
       "chrysler          15561.0  290000.0  \n",
       "datsun            23000.0   30000.0  \n",
       "dodge             23999.0  180000.0  \n",
       "ferrari          149995.0  349999.0  \n",
       "fiat              19990.0   32500.0  \n",
       "ford              30900.0  225000.0  \n",
       "gmc               34995.0  175000.0  \n",
       "harley-davidson   18125.0   27000.0  \n",
       "honda             16222.0  123456.0  \n",
       "hyundai           16990.0   49788.0  \n",
       "infiniti          28990.0   69995.0  \n",
       "jaguar            35590.0  150000.0  \n",
       "jeep              28997.0  140000.0  \n",
       "kia               16990.0   55000.0  \n",
       "land rover        10499.5   55000.0  \n",
       "lexus             29798.0  114950.0  \n",
       "lincoln           30590.0  215000.0  \n",
       "mazda             19990.8   45000.0  \n",
       "mercedes-benz     30495.0  255000.0  \n",
       "mercury            6495.0   95000.0  \n",
       "mini              21435.5   42500.0  \n",
       "mitsubishi        20590.0   40000.0  \n",
       "morgan            19150.0   36500.0  \n",
       "nissan            17989.0  169999.0  \n",
       "pontiac            8500.0   98000.0  \n",
       "porsche           39990.0  244999.0  \n",
       "ram               39995.0  110000.0  \n",
       "rover             38950.0  129888.0  \n",
       "saturn             5495.0   22000.0  \n",
       "subaru            19977.0  195000.0  \n",
       "tesla             40990.0  104990.0  \n",
       "toyota            25998.0  131500.0  \n",
       "volkswagen        18900.0  149000.0  \n",
       "volvo             29990.0  144832.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manufacturer_price = df.groupby('manufacturer')['price']\n",
    "manufacturer_price.describe().apply(lambda s: s.apply('{0:.1f}'.format))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above shows that manufacturer is in fact important when determining used car price. This is expected given that exotic brands such as 'aston-martin' and 'ferrari' tend to sell new cars at higher prices when compared to other manufacturers. This difference can also be attributed due to the rarity of certain brands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Condition, Number of Cylinders, and Title Status:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Counts Of Cars Grouped By Title Status')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3)\n",
    "sns.countplot(data = df, x = 'condition', ax=axs[0]).set(xticklabels=[\"Salvage\", \"Fair\", \"Good\", \"Like New\", \"New\", \"Excellent\"])\n",
    "sns.countplot(data = df, x = 'num_cylinders', ax=axs[1]).set(xticklabels=[\"Other\", \"3 Cyl\", \"4 Cyl\", \"5 Cyl\", \"6 Cyl\", \"8 Cyl\", \"10 Cyl\"])\n",
    "sns.countplot(data = df, x = 'title_status', ax=axs[2]).set(xticklabels=[\"Missing\", \"Lien\", \"Rebuilt\", \"Salvage\", \"Clean\"])\n",
    "fig.tight_layout(pad=2)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "axs[0].set_xlabel(\"Condition\")\n",
    "axs[0].set_ylabel(\"Count\")\n",
    "axs[0].set_title(\"Counts Of Cars Grouped By Condition\")\n",
    "axs[1].set_xlabel(\"Number of Cylinders\")\n",
    "axs[1].set_ylabel(\"Count\")\n",
    "axs[1].set_title(\"Counts Of Cars Grouped By Number Of Cylinders\")\n",
    "axs[2].set_xlabel(\"Title Status\")\n",
    "axs[2].set_ylabel(\"Count\")\n",
    "axs[2].set_title(\"Counts Of Cars Grouped By Title Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count plots up above show that within each of our selected categorical variables, there tends to be a skew. When it comes to condition, our dataset is dominated by cars which where in good and excellent condition. Furthermore, we can see that almost all of the cars sold has a 'clean' title status. Finally, we can see that the cars within our dataset primarily have 4, 6, or 8 cylinders. The skew in our data might make our model biased, making it difficult as a metric to find the 'general' used car prices given the lack of data for certain categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Price Grouped By Title Status')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dOsRf/vKXYvP6+vqyadMmvvnmG3bv3s0XX3zBvHnzeP/990vkrIiICL766iv+9a9/kZKSQlpaGufPny9zXI6OjgQFBdG/f38efvhhOnfubM3BVZ0KC1XM+fPnSUxMxNnZ2Xrxl6ysLD788EOefvpp7rjjDjZt2sSqVauIiooCLia9ouHqcHHj49KNnqIkAheTQ+vWrXnooYfo3r07+/bts15wxjAM63yX/uH+2fovdfn5Zpe7NJYiRUn30iT566+/4uHhgWEYLFu2zJqkT506hYuLS4l13HPPPezcudM6fOmZZ57hmWeeAS4m1qKNrEtf32KxFHtNi8VSbIPu0vcjPz+/2Otd/v44OjpisVjo1asXERER1va0tDTq1atnvbDP5X3+M05OTgwcONB6Xtitt95K69at+fzzz1m3bh3x8fEllrnrrrv49ddfycrKKnbxm9TUVF577TVmz579p30veo+L5rk09tLegz9z+esVrbPoNWvVqmVtv/y9khuP8uBFyoMX2ZIHL9W4cWNiY2N55ZVXePTRR63tl8dzef8uPXXiahdDK9qZh4v9NZvNFBYW4uvraz0fF+DEiRN4enry2WeflfodAGjZsiVw8eKMt99+e7Hnvv76a+65554rdxSoV68ef/3rX9m3b1+JCy+OGTOGESNG0KZNGx5//HFWrlxJWlpasR2D0jg5OVn7WFoeL1L0fbBYLFf8zp4+fRpnZ2frZ1+3bl0SExPZu3cvO3bs4IUXXuDvf/874eHhV41Jagbl+ouU6y+61lxfXoWFhTRv3pwVK1ZY21JTU0tcH+PkyZPMmTOH1157DR8fH3x8fBg+fDjjxo0jISGhRGHhpZdeorCwkO7du/Pwww9z4sSJUrdrr/YbFBcXx48//si2bdt45513SExMtBacqjLdbrKKWbt2Le7u7nz55Zds3LiRjRs3WiuZycnJPPbYY/zrX//iwoUL1nMZO3fuzMqVK8nKygLg7bff5uWXXy6x7nPnzvHdd98xduxYAgIC+OOPP/jtt9+wWCx07tyZ7du3k5qaClDsj6ys67eVq6srbdu2JSEhAbi48TVgwABycnJo37497733njX+ooviXG7EiBGsX7+ehIQECgsLgYvV46SkJKD4hl+Rhx56iI8//thaRVy8eDH33Xcfzs7OxY6m//zzzyWOzBfF+v3333P48GHuu+8+OnfuzCeffEJaWhpwsQr/5JNPWl9r+fLlABw/fpyvv/66zO/Ppk2biiWtgQMHMm3aNO66665Sz/lr1KgRoaGhREVFWT+zrKwsXn/9ddzd3alVqxadO3fmww8/xDAM8vLyiI+P/9Orfz/00EMkJCSQm5tLbm6u9b29nKOjY4kjbr6+vmzdupXff/8duHik68SJE9x9991lfh/kxqE8qDx4ufLmwcsFBQXh5+fH+++/b22rX7++tX+pqans3LmzzPFcavXq1cDFPm3fvh1fX198fX356quvrFcQ37x5Mz179iQnJ+eq63JxcWHs2LG8/PLL1mXhYv8XLVrEmDFj/jSekSNHMnnyZI4cOQJc3HieP38+Bw8etF7Esl+/fvz3v//l+++/t440KK+HHnqIlStXYrFYOHv2rPU76erqWubv7BdffMGQIUO45557GDVqFI8++qj1M5GaT7leuf5y15rrr6S0bdP27dtz5MgRdu3aBVws6AYGBlq/F0Xq1avHtm3b+OCDD6yFgAsXLvDbb79xxx13lFj/1q1bef75560jTfbt22f9nC6d70q/QadOncLf3x93d3eGDBnCCy+8wHfffWdz368njVioYj766COeeuqpYtXBunXrMmjQIP7zn/+wbNkyYmNjefbZZ63P9+vXj9TUVB577DFMJhONGzcu9VZadevWZejQofTu3ZvatWvTqFEjOnTowJEjR/D19eXVV1/l73//O87Oztx+++3WimlZ138t3nrrLWJjY1m8eDEmk4nJkyfj4eFBXFwcEydOJDQ0lLy8PEJCQujZs2eJ5b28vFi+fDlz585l0aJFAGRnZ9O+fXvi4+Nxd3cvsUzfvn05ceIE/fr1w2Kx0KxZM+tFtEaMGEFkZCSbN2/mtttuKza0Di5e7CU+Ph6LxcLMmTOpV68enTt35tlnn+Xpp5/GZDLh6urK3LlzMZlMxMTE8Oqrr9K9e3e8vLyKXSTrcklJSezZsweTyURubi5NmzblzTfftD7fpUsX621zriQmJob58+fTv39/HB0dycvL429/+5v1NmDR0dFMmjSJ0NBQ8vPzeeihh0q9QNCl+vfvz2+//UZISAju7u7WC4Ndrlu3bkRERPD6669b21q0aEFMTAwjR46ksLCQWrVqsXDhQuvFH0UupTyoPFgRefBy0dHR7Nmzx/p40KBBjB07lsDAQJo0aUKnTp3KvK5L5ebm0rt3b/Lz84mOjrZeaGzChAm89NJLGIZhveBjWS5K2L9/f26++Waio6Ott4u89dZb+fe//11iFENpQkNDMQyDl156iYKCAnJzc2nbti3vv/++dRRGw4YNadeuHc2bN7f51nSjRo0iJiaG7t2706BBA+tt3oArfmePHj1abB1+fn5s2bKFkJAQateuTb169Zg4caJN8Uj1o1yvXF8Zub40pW2bNmjQgNmzZzNt2jRyc3MxDINp06aVuH2v2Wxm0aJFTJ8+ncWLF1O7dm1MJhO9e/e2Xqy3a9euzJgxg/z8fF588UWef/55ateujaurK/fdd5/1NLhL57vSb1CDBg0YMWIEQ4YMoVatWjg6OjJp0qRr6v/1YjI05liA33//ncTERJ577jkcHBz49NNP+de//lWsiisXtW7dmu3bt5cYKnW9/O9//yM6Opp169b96fm2IlJ2yoNlpzxYvZ06dYq+ffuyZMkSGjdubO9wRK4r5fqyU66X8tCIBQEuVj/T0tIIDQ3F0dERNzc36y15pOp45ZVX2LlzJzNnzlSCFalgyoPVg/LgtYmPj2fGjBmMGjVKRQW5ISnXVw/K9dWPRiyIiIiIiIiIiM108UYRERERERERsZkKCyIiIiIiIiJiMxUWRERERERERMRmKiyIiIiIiIiIiM1u2LtCnD6djcWi61aKSPk4OJioX//P70UvV6ccLCK2UA6uGMrBIlJef5Z/b9jCgsViKKGKiNiJcrCIiP0oB4tIRdOpECIiIiIiIiJiMxUWRERERERERMRmKiyIiNRAb7/9Nj169CA4OJj33nsPgG3bthEaGkpAQAAzZ860znvgwAHCwsIIDAxk3LhxFBQUAHD8+HHCw8MJCgpixIgRZGdnA3Du3DmGDh1K9+7dCQ8PJz09/fp3UERERESqDBUWRERqmJ07d7Jjxw7WrFnDxx9/zOLFizl48CBRUVHMnz+fpKQk9u/fz+bNmwGIiIhg/PjxbNiwAcMwiI+PByA2NpaBAweSnJxMu3btmD9/PgCzZs3Cx8eH9evX069fPyZPnmy3voqIiIiI/amwICJSw9x///188MEHmM1mTp48SWFhIefOnaNZs2Y0bdoUs9lMaGgoycnJHDt2jJycHNq3bw9AWFgYycnJ5Ofns2vXLgIDA4u1A2zatInQ0FAAQkJC2LJlC/n5+Xbpq4iIiIjYnwoLIiI1kJOTE7NnzyY4OBhfX1/S0tLw8PCwPu/p6UlqamqJdg8PD1JTUzl9+jSurq6YzeZi7UCxZcxmM66urpw6deo69k5EREREqpIb9naTIiI13ejRo3n22WcZPnw4KSkpmEwm63OGYWAymbBYLKW2F00vdfnjS5dxcChfnbphQ9dyzS8iIiIiVZcKCyIiNcwvv/xCXl4et99+OzfddBMBAQEkJyfj6OhonSc9PR1PT0+8vLyKXXwxIyMDT09PGjRoQGZmJoWFhTg6Olrnh4ujHTIyMvDy8qKgoIDs7Gzc3d3LFePJk1m6h7qIlJuDg0mFSRGRKkinQoiI1DBHjx4lOjqavLw88vLy+Pzzz+nfvz+HDx/myJEjFBYWsm7dOvz8/PD29sbFxYU9e/YAkJiYiJ+fH05OTvj4+JCUlARAQkICfn5+APj7+5OQkABAUlISPj4+ODk52aWvIiIiImJ/JsMwbshDRjpaJiK2qC5Hy+bMmcP69etxdHQkICCAUaNGsX37dt544w1yc3Px9/fn1VdfxWQycfDgQaKjo8nKyqJt27a88cYbODs7c+zYMSIjIzl58iSNGzdmxowZ1KtXjzNnzhAZGcnvv/+Om5sbcXFxNGnSpFzxKQeLiC2qSw6u6pSDRaS8/iz/qrAgIlIO2qitGMrBImIL5eCKoRwsIuX1Z/lXp0KIyFXt3bub2Nhx7N27296hiIhINaPfEKlp9J0WKZ0u3igiV7VixVIOH/6VnJwLdOjgY+9wRESkGtFviNQ0+k6LlE4jFkTkqi5cyCk2FRERKSv9hkhNo++0SOlUWBARERERERERm6mwICIiIiIiIiI2U2FBRERERERERGymwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzc2WufO7cuaxfvx4Af39/Xn75ZV599VX27NnDTTfdBMDIkSPp1q0bBw4cYNy4cWRnZ+Pj40NsbCxms5njx48TERHByZMnufXWW4mLi6NOnTqcO3eOsWPH8vvvv9OgQQNmzZqFh4dHZXZHRERERERERC5TaSMWtm3bxtatW1m9ejUJCQl8//33fPbZZ+zfv58PP/yQxMREEhMT6datGwARERGMHz+eDRs2YBgG8fHxAMTGxjJw4ECSk5Np164d8+fPB2DWrFn4+Piwfv16+vXrx+TJkyurKyIiIiIiIiJyBZVWWPDw8CAyMhJnZ2ecnJxo3rw5x48f5/jx40RFRREaGsrs2bOxWCwcO3aMnJwc2rdvD0BYWBjJycnk5+eza9cuAgMDi7UDbNq0idDQUABCQkLYsmUL+fn5ldUdERERERERESlFpZ0K0bJlS+v/U1JSWL9+PUuWLGHnzp3ExMTg5ubGsGHDWLlyJS1btix2GoOHhwepqamcPn0aV1dXzGZzsXaAtLQ06zJmsxlXV1dOnTpFo0aNKqtLIiIiIiJSjdSv54zZ2aXC1ufoaLJOPTzcKmy9AAV5uZw+m1eh6xS5Xir1GgsAP/30E8OGDePll1/mtttuY968edbnBg0aREJCAs2bN8dkMlnbDcPAZDJZp5e6/PGlyzg4lH0ARsOGruXsiciNqTJ/QEVEREQqk9nZhT3Tnqmw9eWeTrVOK3K9APe+/C6gwoJUT5VaWNizZw+jR48mKiqK4OBgDh06REpKivXUBsMwMJvNeHl5kZ6ebl0uIyMDT09PGjRoQGZmJoWFhTg6OpKeno6npycAnp6eZGRk4OXlRUFBAdnZ2bi7u5c5tpMns7BYjArtr0hNVFhoWKfp6Zl2jsb+HBxMKkyKiIiIiFyi0q6xcOLECZ5//nni4uIIDg4GLhYSpkyZwtmzZ8nPz2f58uV069YNb29vXFxc2LNnDwCJiYn4+fnh5OSEj48PSUlJACQkJODn5wdcvMtEQkICAElJSfj4+ODk5FRZ3RERERERERGRUlTaiIVFixaRm5vL1KlTrW39+/dn6NChDBgwgIKCAgICAggJCQEgLi6O6OhosrKyaNu2LYMHDwYgJiaGyMhIFixYQOPGjZkxYwYAY8aMITIykuDgYNzc3IiLi6usroiIiIiIiIjIFVRaYSE6Opro6OhSnwsPDy/R1qZNG1auXFmi3dvbm8WLF5dod3d3Z+HChdceqIiIiIiIiIjYrNJOhRARERERERGRmk+FBRERERERERGxWaXfblJE7Kde3Ztwdrm2P/OKvN1kXm4BZ89duKZ1iIjI9Ve/3k2Yncv/e1IRvyEFeQWcPqvfDhGRqkyFBZEazNnFzJRxJa9dUh6nTmZZp9e6rqjJfa9peRERsQ+zs5l98zeVe7m8/18QyDt7wablAe5+7mGblhMRketHp0KIiIiIiIiIiM1UWBARERERESkDF7NDsamIXKS/CBERERERkTIIaFGf2+rXIqBFfXuHIlKl6BoLIiIiIiIiZXC7R21u96ht7zBEqhyNWBARERERERERm6mwICIiIiIiIiI2U2FBRERERERERGymwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRZERERERERExGYqLIiIiIiIiIiIzVRYEBERERERERGbqbAgIiIiIiIiIjZTYUFEREREKoWL2bnYVEREaiYVFkRERESkUgTd8TDNPZoRdMfD9g5FREQqkdneAYiIiIhIzXRH45bc0bilvcMQEZFKphELIiIiIiIVaO7cuQQHBxMcHMy0adMA2LZtG6GhoQQEBDBz5kzrvAcOHCAsLIzAwEDGjRtHQUEBAMePHyc8PJygoCBGjBhBdnY2AOfOnWPo0KF0796d8PBw0tPTr38HRUQuo8KCiIiIiEgF2bZtG1u3bmX16tUkJCTw/fffs27dOqKiopg/fz5JSUns37+fzZs3AxAREcH48ePZsGEDhmEQHx8PQGxsLAMHDiQ5OZl27doxf/58AGbNmoWPjw/r16+nX79+TJ482W59FREposKCiEgNVNrRsldffZWAgAB69epFr169+OyzzwAdLRMRqUgeHh5ERkbi7OyMk5MTzZs3JyUlhWbNmtG0aVPMZjOhoaEkJydz7NgxcnJyaN++PQBhYWEkJyeTn5/Prl27CAwMLNYOsGnTJkJDQwEICQlhy5Yt5Ofn26WvIiJFVFgQkasyOzoVm0rVV9rRss8++4z9+/fz4YcfkpiYSGJiIt26dQN0tExEpCK1bNnSWihISUlh/fr1mEwmPDw8rPN4enqSmppKWlpasXYPDw9SU1M5ffo0rq6umM3mYu1AsWXMZjOurq6cOnXqOvVORKR0unijiFzVbU3u58iJb2jWuL29Q5EyuvRoGUDz5s05fvw4x48fJyoqitTUVLp168bIkSM5ceJEiaNls2fPpl+/fuzatYt58+ZZ25944gkiIiLYtGkTS5YsAS4eLZswYQL5+fk4Oan4JCJS5KeffmLYsGG8/PLLODo6kpKSYn3OMAxMJhMWiwWTyVSivWh6qcsfX7qMg0P5jhU2bOharvnl+vHwcLN3CCI2UWFBRK7q5vrNuLl+M3uHIeXQsuX/XYG96GjZkiVL2LlzJzExMbi5uTFs2DBWrlxJy5YtK+xoWaNGja5jL0VEqq49e/YwevRooqKiCA4OZufOncVOG0tPT8fT0xMvL69i7RkZGXh6etKgQQMyMzMpLCzE0dHROj9cHO2QkZGBl5cXBQUFZGdn4+7uXq74Tp7MwmIxKqSvVV1121FPT8+0dwgipXJwMF21KKnCgohIDXXp0bLbbrvNOvoAYNCgQSQkJNC8eXMdLRORKq867RyeOHGC559/npkzZ+Lr6wvA3XffzeHDhzly5AhNmjRh3bp19OnTB29vb1xcXNizZw/33nsviYmJ+Pn54eTkhI+PD0lJSYSGhpKQkICfnx8A/v7+JCQkMHz4cJKSkvDx8dGIMRGxOxUWRERqoMuPlh06dIiUlBTrhcAMw8BsNutomYiUib137IuO4v7ZEbOqYNGiReTm5jJ16lRrW//+/Zk6dSqjRo0iNzcXf39/goKCAIiLiyM6OpqsrCzatm3L4MGDAYiJiSEyMpIFCxbQuHFjZsyYAcCYMWOIjIwkODgYNzc34uLirn8nRUQuo8KCiEgNU9rRMsMwmDJlCp06daJ27dosX76c3r1762iZiEgFi46OJjo6utTn1qxZU6KtTZs2rFy5skS7t7c3ixcvLtHu7u7OwoULrz1QEan29u7dzdq1qwkN7U2HDj52jUWFBRGRGuZKR8uGDh3KgAEDKCgoICAggJCQEEBHy0Squ6q0YSkiItfPihVLOXz4V3JyLtg9/6uwICJSw1ztaFl4eHiJNh0tE6neqtKGpYiIlK5e3ZtwdqnY3e+8vFzrtKJPWcvLLeDsuQtlnl+FBREREZFq7MKFnGJTERGpepxdzEwZV/JAzrU4dybXOq3odUdN7luu+VVYEBEREakC6tV1xtnFpdzLOTqarFNbj1jl5eZy9lyeTcuKiIh93Nbkfo6c+IZmjdvbOxQVFkRERESqAmcXF2a8Oqzcy2WezrBObVke4KU3/gmosCAiUp3cXL8ZN9dvZu8wACjfjcdFqoi9e3cTGzuOvXt32zsUERERu2rVpAEN3G6iVZMG9g5FRERuUBqxINWSLlQlIiJyUSP3OjRyr2PvMERE5AamwoJcd3XrueDi7HxN66jIK6Dm5uVx7mzuNa1DRERERETkRqXCglx3Ls7ODHlvzDWtI+3CSev0Wtf1n6feBlRYEBERERERsYWusSDVUr27GuHSqA717mpk71BERERERERuaBqxINXSTU3cuKnJtZ0CISIiIiIiItdOIxZERERERERExGYqLIiIiIiIiIiIzVRYEBGRamnv3t3Exo5j797d9g5FRERE5IamayyIiEi1tGLFUg4f/pWcnAt06OBj73BEREREblgasSAiItXShQs5xaYiIiIiYh8qLIiIiIiIiIiIzSq1sDB37lyCg4MJDg5m2rRpAGzbto3Q0FACAgKYOXOmdd4DBw4QFhZGYGAg48aNo6CgAIDjx48THh5OUFAQI0aMIDs7G4Bz584xdOhQunfvTnh4OOnp6ZXZFREREREREREpRaUVFrZt28bWrVtZvXo1CQkJfP/996xbt46oqCjmz59PUlIS+/fvZ/PmzQBEREQwfvx4NmzYgGEYxMfHAxAbG8vAgQNJTk6mXbt2zJ8/H4BZs2bh4+PD+vXr6devH5MnT66sroiIiIiIiIjIFVRaYcHDw4PIyEicnZ1xcnKiefPmpKSk0KxZM5o2bYrZbCY0NJTk5GSOHTtGTk4O7du3ByAsLIzk5GTy8/PZtWsXgYGBxdoBNm3aRGhoKAAhISFs2bKF/Pz8yuqOiIiIiIiIiJSi0goLLVu2tBYKUlJSWL9+PSaTCQ8PD+s8np6epKamkpaWVqzdw8OD1NRUTp8+jaurK2azuVg7UGwZs9mMq6srp06dqqzuiIiIiIiIiEgpKv12kz/99BPDhg3j5ZdfxtHRkZSUFOtzhmFgMpmwWCyYTKYS7UXTS13++NJlHBzKXidp2NC1fB2RGs3Dw83eIdww9F6LiIiIiNQslVpY2LNnD6NHjyYqKorg4GB27txZ7CKL6enpeHp64uXlVaw9IyMDT09PGjRoQGZmJoWFhTg6Olrnh4ujHTIyMvDy8qKgoIDs7Gzc3d3LHNvJk1lYLEaF9VXKriruWKanZ9o7hEqh97riOTiYVJgUEREREblEpZ0KceLECZ5//nni4uIIDg4G4O677+bw4cMcOXKEwsJC1q1bh5+fH97e3ri4uLBnzx4AEhMT8fPzw8nJCR8fH5KSkgBISEjAz88PAH9/fxISEgBISkrCx8cHJyenyuqOyDXbu3c3sbHj2Lt3t71DERERERERqTCVNmJh0aJF5ObmMnXqVGtb//79mTp1KqNGjSI3Nxd/f3+CgoIAiIuLIzo6mqysLNq2bcvgwYMBiImJITIykgULFtC4cWNmzJgBwJgxY4iMjCQ4OBg3Nzfi4uIqqysiFWLFiqUcPvwrOTkX6NDBx97hiIiIiIiIVIhKKyxER0cTHR1d6nNr1qwp0damTRtWrlxZot3b25vFixeXaHd3d2fhwoXXHqjIdXLhQk6xqYiIiIiISE1QaadCiIiIiIiIiEjNV+l3hRCpCdzdnHGq5XJN63B0NFmnFXFRxfycXM5k5l3zekRERERERK6FCgsiZeBUy4WkwU9d0zrO/5FqnV7rugB6fPAeqLAgNYBb3VrUcin/xXcroliXk5tP5jmdniQiIiJyLVRYEBERu6rl4sTAl5eUe7mMjIu3Lv0jI9Om5QGWTgsnExUWRERERK6FrrEgcp04OZiKTUVERERERGoCFRZErpMH67rT1NmFB+u62zsUERERERGRCqNTIUSuk9tq3cRttW6ydxgiIiIiIiIVSiMWRERERERERMRmKiyIiIiIiIiIiM1UWBARERERERERm6mwICIiIiIiIiI2U2FBRERERERERGymwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRZERKRaMjk6FZuKiIiIiH2osCAiItWS61864OTqhetfOtg7FBEREZEbmgoLIiI10Ny5cwkODiY4OJhp06YBsG3bNkJDQwkICGDmzJnWeQ8cOEBYWBiBgYGMGzeOgoICAI4fP054eDhBQUGMGDGC7OxsAM6dO8fQoUPp3r074eHhpKenX/8OAi71mtKgdQ9c6jW1y+uLiIiIyEUqLIiI1DDbtm1j69atrF69moSEBL7//nvWrVtHVFQU8+fPJykpif3797N582YAIiIiGD9+PBs2bMAwDOLj4wGIjY1l4MCBJCcn065dO+bPnw/ArFmz8PHxYf369fTr14/Jkyfbra8iIiIiYn8qLIiI1DAeHh5ERkbi7OyMk5MTzZs3JyUlhWbNmtG0aVPMZjOhoaEkJydz7NgxcnJyaN++PQBhYWEkJyeTn5/Prl27CAwMLNYOsGnTJkJDQwEICQlhy5Yt5Ofn26WvIiIiImJ/KiyIiNQwLVu2tBYKUlJSWL9+PSaTCQ8PD+s8np6epKamkpaWVqzdw8OD1NRUTp8+jaurK2azuVg7UGwZs9mMq6srp06duk69ExEREZGqxmzvAEREpHL89NNPDBs2jJdffhlHR0dSUlKszxmGgclkwmKxYDKZSrQXTS91+eNLl3FwKF+dumFD13LNX5k8PNzsHYJIlVCV/xaqcmwiIqLCgohIjbRnzx5Gjx5NVFQUwcHB7Ny5s9hFFtPT0/H09MTLy6tYe0ZGBp6enjRo0IDMzEwKCwtxdHS0zg8XRztkZGTg5eVFQUEB2dnZuLu7lyu+kyezsFgMwP47DOnpmXZ9fZEiVflvoarE5uBgqlKFSRERuUinQoiI1DAnTpzg+eefJy4ujuDgYADuvvtuDh8+zJEjRygsLGTdunX4+fnh7e2Ni4sLe/bsASAxMRE/Pz+cnJzw8fEhKSkJgISEBPz8/ADw9/cnISEBgKSkJHx8fHBycrr+HRURERGRKkEjFkREaphFixaRm5vL1KlTrW39+/dn6tSpjBo1itzcXPz9/QkKCgIgLi6O6OhosrKyaNu2LYMHDwYgJiaGyMhIFixYQOPGjZkxYwYAY8aMITIykuDgYNzc3IiLi7v+nRQRERGRKkOFBRGRGiY6Opro6OhSn1uzZk2JtjZt2rBy5coS7d7e3ixevLhEu7u7OwsXLrz2QEVERESkRtCpECIiIiIiIiJiMxUWRERERERERMRmKiyIiIiIiFSwrKwsQkJCOHr0KACvvvoqAQEB9OrVi169evHZZ58BcODAAcLCwggMDGTcuHEUFBQAcPz4ccLDwwkKCmLEiBFkZ2cDcO7cOYYOHUr37t0JDw8vdmcfERF7UWFBRERERKQC7du3jwEDBpCSkmJt279/Px9++CGJiYkkJibSrVs3ACIiIhg/fjwbNmzAMAzi4+MBiI2NZeDAgSQnJ9OuXTvmz58PwKxZs/Dx8WH9+vX069ePyZMnX/f+iYhcToUFEREREZEKFB8fT0xMDJ6engBcuHCB48ePExUVRWhoKLNnz8ZisXDs2DFycnJo3749AGFhYSQnJ5Ofn8+uXbsIDAws1g6wadMmQkNDAQgJCWHLli3k5+df/06KiFxCd4UQEREREalAl48iyMjIoFOnTsTExODm5sawYcNYuXIlLVu2xMPDwzqfh4cHqampnD59GldXV8xmc7F2gLS0NOsyZrMZV1dXTp06RaNGja5T70RESlJhQURERESkEjVt2pR58+ZZHw8aNIiEhASaN2+OyWSythuGgclksk4vdfnjS5dxcCjfIOSGDV3LNb9cPx4ebvYOQcSqPN9HFRZERERE/sTevbtZu3Y1oaG96dDBx97hSDVz6NAhUlJSrKc2GIaB2WzGy8ur2MUXMzIy8PT0pEGDBmRmZlJYWIijoyPp6enW0yo8PT3JyMjAy8uLgoICsrOzcXd3L1c8J09mYbEYFda/qqy67ainp2faOwSpJNXtuwjFv48ODqarFiV1jQURERGRP7FixVIOHPieFSuW2jsUqYYMw2DKlCmcPXuW/Px8li9fTrdu3fD29sbFxYU9e/YAkJiYiJ+fH05OTvj4+JCUlARAQkICfn5+APj7+5OQkABAUlISPj4+ODk52aVfIiJFNGJBREREbhj16t6Es0v5N3/y8nKtU1uPOuXlFnD23AWblpXqrU2bNgwdOpQBAwZQUFBAQEAAISEhAMTFxREdHU1WVhZt27Zl8ODBAMTExBAZGcmCBQto3LgxM2bMAGDMmDFERkYSHByMm5sbcXFxduuXiEgRFRZERETkhuHsYmbKuJXlXu7cmVzr1JblAaIm97VpOam+Nm7caP1/eHg44eHhJeZp06YNK1eW/E55e3uzePHiEu3u7u4sXLiwYgMVEblGOhVCRERE5E/c1uR+3Ov+hdua3G/vUERERKocjVgQERER+RM312/GzfWb2TsMERGRKkkjFkRERERERETEZiosiIiIiIiIiIjNVFgQEREREREREZupsCAiIiIiIiIiNlNhQURqnL17dxMbO469e3fbOxQRERERkRpPd4UQkRpnxYqlHD78Kzk5F+jQwcfe4YiIiIiI1GgqLIhIlVKvrjPOLi7XtI68vFzr1MPD7drWlZvL2XN517QOEREREZGaTIUFEalSnF1cmPHqsGtaR+bpDOv0Wtf10hv/BFRYEBERERG5El1jQURqnFZNGtDA7SZaNWlg71BERERERGq8Si0sZGVlERISwtGjRwF49dVXCQgIoFevXvTq1YvPPvsMgAMHDhAWFkZgYCDjxo2joKAAgOPHjxMeHk5QUBAjRowgOzsbgHPnzjF06FC6d+9OeHg46enpldkNEalmGrnXwfd2bxq517F3KCIiIiIiNV6lFRb27dvHgAEDSElJsbbt37+fDz/8kMTERBITE+nWrRsAERERjB8/ng0bNmAYBvHx8QDExsYycOBAkpOTadeuHfPnzwdg1qxZ+Pj4sH79evr168fkyZMrqxsiIiIiIiIichWVVliIj48nJiYGT09PAC5cuMDx48eJiooiNDSU2bNnY7FYOHbsGDk5ObRv3x6AsLAwkpOTyc/PZ9euXQQGBhZrB9i0aROhoaEAhISEsGXLFvLz8yurKyIiIiIiIiJyBZV28cbLRxFkZGTQqVMnYmJicHNzY9iwYaxcuZKWLVvi4eFhnc/Dw4PU1FROnz6Nq6srZrO5WDtAWlqadRmz2YyrqyunTp2iUaNGldUdERERERERESnFdbsrRNOmTZk3b5718aBBg0hISKB58+aYTCZru2EYmEwm6/RSlz++dBkHh/INvmjY0LVc80vNdq23JLSX6hi3YhYRERERqVnKVFiIiopiypQpxdpGjx7N7Nmzy/xChw4dIiUlxXpqg2EYmM1mvLy8il18MSMjA09PTxo0aEBmZiaFhYU4OjqSnp5uPa3C09OTjIwMvLy8KCgoIDs7G3d39zLHAnDyZBYWi1GuZaRiVMWdtPT0zKs+XxVjhuoZd3WP2cHBdF0LkxWRf0VExDbKwSIiZXPVwkJMTAypqans2bOHU6dOWdsLCgr4/fffy/VChmEwZcoUOnXqRO3atVm+fDm9e/fG29sbFxcX9uzZw7333ktiYiJ+fn44OTnh4+NDUlISoaGhJCQk4OfnB4C/vz8JCQkMHz6cpKQkfHx8cHJysqH7IiJVU0XmXxERKR/lYLkR7d27m7VrVxMa2psOHXzsHY5UM1ctLPTt25effvqJQ4cOWUcaADg6OlovtlhWbdq0YejQoQwYMICCggICAgIICQkBIC4ujujoaLKysmjbti2DBw8GLib1yMhIFixYQOPGjZkxYwYAY8aMITIykuDgYNzc3IiLiytXLCIiVV1F5l8RESkf5WC5Ea1YsZTDh38lJ+eCCgtSblctLNx5553ceeedPPDAA3h5edn0Ahs3brT+Pzw8nPDw8BLztGnThpUrV5Zo9/b2ZvHixSXa3d3dWbhwoU3xiIhUBxWRf0VExDbKwXIjunAhp9hUpDzKdI2FEydOEBERwdmzZzGM/7suwdq1aystMBERUf4VEbEn5WARkbIpU2Fh/PjxhIWFcccdd1zxzgwiIlLxlH9FROxHOViqqrr1XHBxdq7QdTo6mqzTir6Ydm5eHufO5lboOqVqKVNhwWw289RTT1V2LCIichnlXxER+1EOlqrKxdmZIe+NqdB1pp5Lt04ret3/eeptQIWFmsyhLDO1bNmSQ4cOVXYsIiJyGeVfERH7UQ4WESmbMo1Y+P333+nTpw9/+ctfcHFxsbbr/DIRkcql/CsiYj/KwXIjMTk5FJuKlEeZCgsvvvhiZcchIiKlUP4VEbEf5WC5kdS7qxGZBzJwu/1me4ci1VCZCgutWrWq7DhERKQUyr8iIvajHCw3kpuauHFTk4q9aKPcOMpUWOjUqRMmkwnDMKxXxPXw8GDLli2VGpyIyI1O+VdExH6Ug0VEyqZMhYWDBw9a/5+Xl8e6des4fPhwpQUlIiIXKf+KiNiPcrCISNmU+8oczs7OhIWF8dVXX1VGPCIicgXKvyIi9qMcLCJyZWUasXDmzBnr/w3DYP/+/Zw7d66yYhIRkf/vWvJvVlYW/fv3Z+HChTRp0oRXX32VPXv2cNNNNwEwcuRIunXrxoEDBxg3bhzZ2dn4+PgQGxuL2Wzm+PHjREREcPLkSW699Vbi4uKoU6cO586dY+zYsfz+++80aNCAWbNm4eHhURndFxGxK20Di4iUTbmvsQDQsGFDxo0bV6mBiYiI7fl33759REdHk5KSYm3bv38/H374IZ6ensXmjYiIYNKkSbRv356oqCji4+MZOHAgsbGxDBw4kODgYObNm8f8+fOJiIhg1qxZ+Pj48M4775CQkMDkyZOZNWtWRXZbRKRK0DawiEjZlPsaCyIicv3Ymn/j4+OJiYnh5ZdfBuDChQscP36cqKgoUlNT6datGyNHjuTEiRPk5OTQvn17AMLCwpg9ezb9+vVj165dzJs3z9r+xBNPEBERwaZNm1iyZAkAISEhTJgwgfz8fJycnK69wyIiVYi2gUVEyqZMhQWLxcKiRYvYsmULBQUFPPjggwwfPhyzuUyLi4iIjWzNv5MnTy72OCMjg06dOhETE4ObmxvDhg1j5cqVtGzZsthpDB4eHqSmpnL69GlcXV2tr1PUDpCWlmZdxmw24+rqyqlTp2jUqFFFdl1ExO60DSwiUjZlyopvvfUWBw8e5Mknn8RisbB8+XKmTZtGVFRUZccnInJDq6j827RpU+voA4BBgwaRkJBA8+bNrbdQA6y3VLv01mpFLn986TIODuW7FnDDhq7lmr8yeXjont1y/VTl75tiK0nbwCIiZVOmwsKXX37Jxx9/bB3m+vDDD9OzZ08lVRGRSlZR+ffQoUOkpKQQGBgIXCwGmM1mvLy8SE9Pt86XkZGBp6cnDRo0IDMzk8LCQhwdHUlPT7dem8HT05OMjAy8vLwoKCggOzsbd3f3csVz8mQWFsvFc5btvTOTnp5p19eX66sqf98U25UVxebgYLquhUltA4uIlE2ZDjEZhlHs3FlnZ2edSysich1UVP41DIMpU6Zw9uxZ8vPzWb58Od26dcPb2xsXFxf27NkDQGJiIn5+fjg5OeHj40NSUhIACQkJ+Pn5AeDv709CQgIASUlJ+Pj46DdBRGokbQOLiJRNmUYstGnThilTpvDEE09gMplYvHgxrVq1quzYRERueBWVf9u0acPQoUMZMGAABQUFBAQEEBISAkBcXBzR0dFkZWXRtm1bBg8eDEBMTAyRkZEsWLCAxo0bM2PGDADGjBlDZGQkwcHBuLm5ERcXV3EdFhGpQrQNLCJSNmUqLMTExDBp0iT69++PxWLhoYce4rXXXqvs2EREbnjXmn83btxo/X94eDjh4eEl5mnTpg0rV64s0e7t7c3ixYtLtLu7u7Nw4cIyxyAiUl1pG1hEpGyueipEXl4er7zyCtu3b2fq1Kls27aNu+66C0dHR1xdq86Ft0REahrlXxER+1EOFhEpn6sWFmbPnk1WVhYdOnSwtk2cOJFz584xZ86cSg9ORORGpfwrImI/ysEiIuVz1cLCpk2beOutt2jYsKG1rVGjRkybNo3//ve/lR6ciMiNSvlXRMR+lINFRMrnqoUFJycnatWqVaLd1dUVZ2fnSgtKRORGp/xbve3du5vY2HHs3bvb3qGIiA2Ug0VEyueqhQUHBweysrJKtGdlZVFQUFBpQYmI3OiUf6u3FSuWcuDA96xYsdTeoYiIDZSDRUTK56qFhZCQEKKjozl//ry17fz580RHRxMQEFDpwYmI3KiUf6u3Cxdyik1FpHpRDhYRKZ+rFhaefPJJ3NzcePDBB3nsscfo27cvDz74IHXr1uX555+/XjGKiNxwlH9FROxHOVhEpHzMV3vSwcGBiRMnMnz4cL7//nscHBy466678PT0vF7xiYjckJR/RUTsRzlYpObYu3c3a9euJjS0Nx06+Ng7nBrrqoWFIt7e3nh7e1d2LCIichnlXxER+1EOFqn+VqxYyuHDv5KTc0GFhUp01VMhRERERERERKorXffo+lBhQURERERERERspsKCiIiIiIiIiNhMhQURERERERERsZkKCyIiIiIiIiJiszLdFUJERORGVL+eM2Znl3Iv5+hosk49PNxseu2CvFxOn82zaVkREZHqyt3NGada5f/tvZKK+E2+kvycXM5k6rcaVFgQERG5IrOzC3umPVPu5XJPp1qntiwPcO/L7wLaWBGprrKysujfvz8LFy6kSZMmbNu2jTfeeIPc3Fy6d+/Oiy++CMCBAwcYN24c2dnZ+Pj4EBsbi9ls5vjx40RERHDy5EluvfVW4uLiqFOnDufOnWPs2LH8/vvvNGjQgFmzZuHh4WHn3opUHKdaLiQNfqrC1nf+j1TrtCLXC9Djg/dAhQVAp0KIiIiIiFSoffv2MWDAAFJSUgDIyckhKiqK+fPnk5SUxP79+9m8eTMAERERjB8/ng0bNmAYBvHx8QDExsYycOBAkpOTadeuHfPnzwdg1qxZ+Pj4sH79evr168fkyZPt0kcRkUupsCAiIiIiUoHi4+OJiYnB09MTgG+//ZZmzZrRtGlTzGYzoaGhJCcnc+zYMXJycmjfvj0AYWFhJCcnk5+fz65duwgMDCzWDrBp0yZCQ0MBCAkJYcuWLeTn51//ToqIXEKnQoiIiIiIVKDLRxGkpaUVO13B09OT1NTUEu0eHh6kpqZy+vRpXF1dMZvNxdovX5fZbMbV1ZVTp07RqFGjyu6WiMgVqbAgIiIiIlKJLBYLJpPJ+tgwDEwm0xXbi6aXuvzxpcs4OJRvEHLDhq7lml+un4q+uGBVUlP7VlP7BeXrmwoLIiIiIiKVyMvLi/T0dOvj9PR0PD09S7RnZGTg6elJgwYNyMzMpLCwEEdHR+v8cHG0Q0ZGBl5eXhQUFJCdnY27u3u54jl5MguLxaiQvlV11W2nLz09s0zzVbd+gf365uRggsL/P60EN8pn5uBgumpRUtdYEBERERGpRHfffTeHDx/myJEjFBYWsm7dOvz8/PD29sbFxYU9e/YAkJiYiJ+fH05OTvj4+JCUlARAQkICfn5+APj7+5OQkABAUlISPj4+ODk52aVfItXBg3XdaerswoN13e0dSo2mEQsiIiIiIpXIxcWFqVOnMmrUKHJzc/H39ycoKAiAuLg4oqOjycrKom3btgwePBiAmJgYIiMjWbBgAY0bN2bGjBkAjBkzhsjISIKDg3FzcyMuLs5u/RKpDm6rdRO31brJ3mHUeCosiIiIiIhUgo0bN1r/7+vry5o1a0rM06ZNG1auXFmi3dvbm8WLF5dod3d3Z+HChRUbqIjINdKpECIiIhXMxexQbCoiIiJSk2mLR0REpIIFtKjPbfVrEdCivr1DEREREal0OhVCRESkgt3uUZvbPWrbOwwRERGR60IjFkRERERERETEZpVaWMjKyiIkJISjR48CsG3bNkJDQwkICGDmzJnW+Q4cOEBYWBiBgYGMGzeOgoICAI4fP054eDhBQUGMGDGC7OxsAM6dO8fQoUPp3r074eHhxe7/KyIiIiIiIiLXT6UVFvbt28eAAQNISUkBICcnh6ioKObPn09SUhL79+9n8+bNAERERDB+/Hg2bNiAYRjEx8cDEBsby8CBA0lOTqZdu3bMnz8fgFmzZuHj48P69evp168fkydPrqxuiIiIiIiIiMhVVFphIT4+npiYGDw9PQH49ttvadasGU2bNsVsNhMaGkpycjLHjh0jJyeH9u3bAxAWFkZycjL5+fns2rWLwMDAYu0AmzZtIjQ0FICQkBC2bNlCfn5+ZXVFRERERERERK6g0goLkydPxsfHx/o4LS0NDw8P62NPT09SU1NLtHt4eJCamsrp06dxdXXFbDYXa798XWazGVdXV06dOlVZXREREakx9u7dTWzsOPbu3W3vUERERKSGuG53hbBYLJhMJutjwzAwmUxXbC+aXuryx5cu4+BQvhpJw4au5ZpfajYPDzd7h2CT6hi3YhaxrxUrlnL48K/k5FygQwefP19ARERE5E9ct8KCl5dXsYsspqen4+npWaI9IyMDT09PGjRoQGZmJoWFhTg6Olrnh4ujHTIyMvDy8qKgoIDs7Gzc3d3LFc/Jk1lYLEaF9E3KpyrupKWnZ171+aoYM1TPuKt7zA4OJhUmpUqoW88FF2fnci9nNjtap7b+veXm5XHubK5Ny4qIiEjNc90KC3fffTeHDx/myJEjNGnShHXr1tGnTx+8vb1xcXFhz5493HvvvSQmJuLn54eTkxM+Pj4kJSURGhpKQkICfn5+APj7+5OQkMDw4cNJSkrCx8cHJyen69UVERERu3NxdmbIe2PKvdwF7zxcztXhtHeeTcsD/OeptwEVFkREROSi61ZYcHFxYerUqYwaNYrc3Fz8/f0JCgoCIC4ujujoaLKysmjbti2DBw8GICYmhsjISBYsWEDjxo2ZMWMGAGPGjCEyMpLg4GDc3NyIi4u7Xt0QERGp1m5q4sZNTareyCARERGpviq9sLBx40br/319fVmzZk2Jedq0acPKlStLtHt7e7N48eIS7e7u7ixcuLBiAxURERERERGRcqu0u0KIiIiIiIiISM2nwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRZERGqorKwsQkJCOHr0KADbtm0jNDSUgIAAZs6caZ3vwIEDhIWFERgYyLhx4ygoKADg+PHjhIeHExQUxIgRI8jOzgbg3LlzDB06lO7duxMeHk56evr175yIiIiIVBkqLIiI1ED79u1jwIABpKSkAJCTk0NUVBTz588nKSmJ/fv3s3nzZgAiIiIYP348GzZswDAM4uPjAYiNjWXgwIEkJyfTrl075s+fD8CsWbPw8fFh/fr19OvXj8mTJ9uljyIiIiJSNaiwICJSA8XHxxMTE4OnpycA3377Lc2aNaNp06aYzWZCQ0NJTk7m2LFj5OTk0L59ewDCwsJITk4mPz+fXbt2ERgYWKwdYNOmTYSGhgIQEhLCli1byM/Pv/6dFBEREZEqwWzvAEREpOJdPoogLS0NDw8P62NPT09SU1NLtHt4eJCamsrp06dxdXXFbDYXa798XWazGVdXV06dOkWjRo0qu1siIiIiUgWpsCAicgOwWCyYTCbrY8MwMJlMV2wvml7q8seXLuPgUL4BcA0bupZr/srk4eFm7xCuSLHVPFX5fVNsIiJiKxUWRERuAF5eXsUuspieno6np2eJ9oyMDDw9PWnQoAGZmZkUFhbi6OhonR8ujnbIyMjAy8uLgoICsrOzcXd3L1c8J09mYbEYgP13GNLTM6/4nGK7sqvFVpVV5fdNsV1ZUWwODqYqVZgUEZGLdI0FEZEbwN13383hw4c5cuQIhYWFrFu3Dj8/P7y9vXFxcWHPnj0AJCYm4ufnh5OTEz4+PiQlJQGQkJCAn58fAP7+/iQkJACQlJSEj48PTk5OdumXiIiIiNifRiyIiNwAXFxcmDp1KqNGjSI3Nxd/f3+CgoIAiIuLIzo6mqysLNq2bcvgwYMBiImJITIykgULFtC4cWNmzJgBwJgxY4iMjCQ4OBg3Nzfi4uLs1i8RERERsT8VFkREarCNGzda/+/r68uaNWtKzNOmTRtWrlxZot3b25vFixeXaHd3d2fhwoUVG6iIiIiIVFs6FUJEREREREREbKbCgoiIiIiIiIjYTIUFEREREREREbGZCgsiIiIiIiIiYjMVFkRERERERETEZiosiIiIiIiIiIjNVFgQERGRKmHv3t3Exo5j797d9g5FREREysFs7wBEREREAFasWMrhw7+Sk3OBDh187B2OiIiIlJFGLIiIiEiVcOFCTrGpiIiIVA8qLIiIiIiIiIiIzVRYEBERERERERGbqbAgIiIiIiIiIjZTYUFEREREREREbKa7QoiIiEiFcndzxqmWS7mXc3Q0WaceHm42vXZ+Ti5nMvNsWlZERERso8KCiIiIVCinWi4kDX6q3Mud/yPVOrVleYAeH7wHKiyIiIhcVzoVQkRERERERERspsKCiIiIiIiIiNhMhQURERERERERsZkKCyIiIlIlODmYik1FRESkelBhQURERKqEB+u609TZhQfruts7FBERESkH3RVCREREqoTbat3EbbVusncYIiIiUk4asSAiIiIiIiIiNlNhQURERERERERspsKCiIiIiIiIiNhMhQURERERERERsZkKCyIiIiIiUqH27t1NbOw49u7dbe9QROQ60F0hRERERESkQq1YsZTDh38lJ+cCHTr42DscEalkGrEgIiIiIiIV6sKFnGJTEanZNGJBREREROQ6GDRoEKdOncJsvrgJPmHCBLKzs3njjTfIzc2le/fuvPjiiwAcOHCAcePGkZ2djY+PD7GxsZjNZo4fP05ERAQnT57k1ltvJS4ujjp16tizWyIiGrEgIiIiIlLZDMMgJSWFxMRE67/WrVsTFRXF/PnzSUpKYv/+/WzevBmAiIgIxo8fz4YNGzAMg/j4eABiY2MZOHAgycnJtGvXjvnz59uzWyIigAoLIiIiIiKV7tdffwXg6aefpmfPnnz44Yd8++23NGvWjKZNm2I2mwkNDSU5OZljx46Rk5ND+/btAQgLCyM5OZn8/Hx27dpFYGBgsXYREXtTYUFEREREpJKdO3cOX19f5s2bx3/+8x+WLVvG8ePH8fDwsM7j6elJamoqaWlpxdo9PDxITU3l9OnTuLq6Wk+lKGoXEbE3XWNBRERERKSS3XPPPdxzzz3Wx3379mX27Nnce++91jbDMDCZTFgsFkwmU4n2oumlLn9cFg0butrQg/JxdDRZpx4ebpX+ejVFTX6vamrfamq/oHx9U2FBRERERKSS7d69m/z8fHx9fYGLxQJvb2/S09Ot86Snp+Pp6YmXl1ex9oyMDDw9PWnQoAGZmZkUFhbi6Ohonb+8Tp7MwmIxrI/d6tailovTNfTu+srJzSfzXNnuNlHddvrS0zPLNF916xfU3L7V1H5B8b45OJiuWpRUYUFEREREpJJlZmYye/Zsli1bRn5+PqtXryY2NpYXXniBI0eO0KRJE9atW0efPn3w9vbGxcWFPXv2cO+995KYmIifnx9OTk74+PiQlJREaGgoCQkJ+Pn5XXNstVycGPjykgro5f/JyLi4Q/JHRmaFr3vptHAy0W0sRaoSuxQWdKsdEREREbmRdOnShX379vHoo49isVgYOHAg99xzD1OnTmXUqFHk5ubi7+9PUFAQAHFxcURHR5OVlUXbtm0ZPHgwADExMURGRrJgwQIaN27MjBkz7NktERHADoWFolvtfPHFF9bCQk5ODkFBQSxevJjGjRszbNgwNm/ejL+/PxEREUyaNIn27dsTFRVFfHw8AwcOtN5qJzg4mHnz5jF//nwiIiKud3dERERERMrkhRde4IUXXijW5uvry5o1a0rM26ZNG1auXFmi3dvbm8WLF1dWiCIiNrnud4XQrXZEREREREREao7rXljQrXZEREREREREao7rfipEVbnVzvW4zY5UH9XxKq1QPeNWzCIiIiIiNct1LyxUlVvtXH6bHbl+quJO2p/dJqYqxgzVM+7qHvOf3WpHREREwOToVGwqIjXbdT8VIjMzk2nTppGbm0tWVharV6/mpZde4vDhwxw5coTCwkLWrVuHn59fsVvtAKXeageosFvtiIjUdIMGDSI4OJhevXrRq1cv9u3bx7Zt2wgNDSUgIICZM2da5z1w4ABhYWEEBgYybtw4CgoKADh+/Djh4eEEBQUxYsQIsrOz7dUdERGpolz/0gEnVy9c/9LB3qGIyHVw3QsLXbp0wd/fn0cffZQ+ffrQp0+fYrfa6dGjB7fddluxW+288cYbBAUFcf78+WK32omPj6dHjx7s3r27xBV27WHv3t3Exo5j797d9g5FRKSEorvyJCYmWv+1bt2aqKgo5s+fT1JSEvv372fz5s0AREREMH78eDZs2IBhGMTHxwNY78qTnJxMu3btmD9/vj27JSIiVZBLvaY0aN0Dl3pN7R2KiFwH1/1UCKi5t9pZsWIphw//Sk7OBTp08LF3OCIixVx6V54zZ87w2GOP0apVK+tdeQDrXXlatGhR4q48s2fPpl+/fuzatYt58+ZZ25944gnd7ldERETkBnbdRyzUZBcu5BSbiohUJborj4iIiIhUBruMWBARkeuvqtyVB6rWnXmq4gVDiyg22yg22yg2ERGxlQoLIiI3iKpyVx4ofmcee+8wXO1OJYrtyhSbbRSbbYpi0515RESqJp0KcYPTBSdFbhy6K4+IiIiIVAaNWPj/3OrWopbLtd1n19HRZJ1ea2U/JzefzHOVf60GXXBS5MbRpUsX9u3bx6OPPorFYmHgwIHF7sqTm5uLv79/sbvyREdHk5WVRdu2bYvdlScyMpIFCxbQuHFjZsyYYc9uiYiIiIidqbDw/9VycWLgy0uuaR0ZGReH6f2RkXnN61o6LZxMKr+woAtOitxYaupdeURERETEflRYqMbq13PG7OxyTeuoyFEWAAV5uZw+m3fN6xEREREREZHqQYWFaszs7MKeac9c0zpyT6dap9e6LoB7X34XUGFBRERERETkRqGLN97gXMwOxaYiIiIiIiIi5aG9yQpkcnQqNq0OAlrU57b6tQhoUd/eoYiIiIiIiEg1pFMhKpDrXzqQ/cd31PG6096hlNntHrW53aO2vcMQERERERGRakqFhQrkUq8pLvWa2jsMERERERERketGp0KIiIiIiIiIiM1UWBARERERERERm6mwICIiIiIiIiI2U2FBRERERERERGymwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRZERERERERExGYqLIiIiIiIiIiIzVRYEBERERERERGbqbAgIiIiIiIiIjZTYUFEREREREREbKbCgoiIiIiIiIjYTIUFEREREREREbGZCgsiIiIiIiIiYjMVFkRERERERETEZiosiIiIiIiIiIjNVFgQEREREREREZupsCAiIiIiIiIiNlNhQURERERERERspsKCiIiIiIiIiNhMhQURERERERERsZkKCyIiIiIiIiJiMxUWRERERERERMRmKiyIiIiIiIiIiM1UWBARERERERERm6mwICIiIiIiIiI2U2FBRERERERERGymwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRZERERERERExGYqLIiIiIiIiIiIzVRYEBERERERERGbqbAgIiIiIiIiIjar1oWFtWvX0qNHDwICAliyZIm9wxERuaEoB4uI2Ifyr4hUNWZ7B2Cr1NRUZs6cyapVq3B2dqZ///507NiRFi1a2Ds0EZEaTzlYRMQ+lH9FpCqqtiMWtm3bRqdOnXB3d6d27doEBgaSnJxs77BERG4IysEiIvah/CsiVVG1HbGQlpaGh4eH9bGnpyfffvttmZd3cDCVaLu5fp0Kia2ilBbj5ZzrNrwOkZRPWeK+2bXBdYik7MoS8003V8/3up577esQSdmVJea67lXrvb405rLEfyOo6Bxsz/z7Z5+pPfPsn8Vmz1z6Z7HZM2f+WWz2zIt/Fps989+fxebkVus6RVJSUWzKwdeef6F6bAf/mfJ8F6ri9vKVlKdfVW17+s+Up29Vcbv7SsrTr6q2Xf5nyrMNbDIMw6jsgCrDggULyM3N5YUXXgAgPj6e/fv3M2HCBPsGJiJyA1AOFhGxD+VfEamKqu2pEF5eXqSnp1sfp6en4+npaceIRERuHMrBIiL2ofwrIlVRtS0sPPDAA2zfvp1Tp05x4cIFPv30U/z8/OwdlojIDUE5WETEPpR/RaQqqrbXWGjUqBEvvvgigwcPJj8/n759+3LXXXfZOywRkRuCcrCIiH0o/4pIVVRtr7EgIiIiIiIiIvZXbU+FEBERERERERH7U2FBRERERERERGymwoKIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRb+v+TkZMLCwujZsyehoaG8++67V51/0KBBfP3119cpumtz9OhR2rVrR69evYr9O3HiRKnzf/TRR3z00UfXOUooKChgwYIFdO/enR49ehAYGMjChQupiBuXlPfz+vrrrxk0aFCJ9u+++45x48bZtM5LtW7dmhUrVlxTjBXh6NGjtG7dmq+++qpYe9euXTl69Oh1jaO07+iSJUsqZP1F/Vm1ahWRkZE2reOLL77gvffeq5B45Mqys7OJjY2lW7du9OzZk4EDB7J9+3YA4uPjWbduHQCRkZGsWrXqusf39ttv06NHD4KDg6/4fcjLy2PmzJmEhobSq1cvHnvsMbZt23bV9VZkf958880rfs/tFdugQYMIDg62/m3v27evysS2ceNGwsLC6N69O5MmTSp1HnvFlpiYSHBwMMHBwbz55pt2jy0rK4uQkJBivw/btm0jNDSUgIAAZs6cWepyhmHw3nvvWT//3r1788knn1z1tebMmcOcOXPKHaNUjKLtg/HjxxdrP3DgAK1bt2bVqlX06tWr3OsdN24c3333XUWFWSlK2wa8dPuvKrt8eyo0NJSuXbsye/bsqy7TtWvXcr1O0ed46Xt16W90RarJ+2ilycrKIjY2lpCQEHr16sWgQYP4/vvvr7hvUpWY7R1AVZCamsqbb77JqlWrqF+/PtnZ2QwaNIhbb72VRx55xN7hVQhPT08SExPLNO+AAQMqOZrSxcbGkpGRwfLly6lbty5ZWVk8//zzuLm5ER4ebpeYLnfnnXdy5513Vsi63nrrLTp37kzjxo0rZH22cnJy4rXXXmPNmjW4urraLY7yfEftYf/+/fYOocYzDIPhw4dz++2388knn+Ds7MwPP/zA0KFDeeutt9i7dy/333+/3eLbuXMnO3bsYM2aNRQUFNCjRw/8/f257bbbis336quv4uzszMqVK3FxceHQoUM8/fTTvP/++7Ro0aJSY9y+fTurV6/m4YcfLvV5e8RmGAYpKSl88cUXmM1X3uywR2y///47MTExrFixgoYNG/Lkk0+yefNm/P397R7bhQsXmDx5MsnJydStW5cBAwawbds2HnjgAbvEtm/fPqKjo0lJSbG25eTkEBUVxeLFi2ncuDHDhg0r9f2bOXMmP/zwAx9++CFubm788ccfPPHEE9SvX79Ef6TqcHd358svv6SwsBBHR0cAkpKSaNCgAYBNv9mTJ0+u0Bivl4rc/qtsl29PpaamEhgYSHBwMM2bN6+Q1yj6HC/dga+M3+gbYR/tUhaLhWeffZaOHTuSkJCA2Wxmx44dPPvss8TExNg7vD+lEQvA6dOnyc/PJycnB4A6deowdepUWrRowfr163nsscfo2bMnQUFB7N27t9iyI0eOZMOGDdbHYWFh/PDDD+zcuZMBAwbQu3dvHnnkEf773/8CWH9MQ0ND+cc//oGfnx9w8SjdK6+8QlhYGL169aqUit/lfvzxRwYNGkSfPn3o0qWLdZTCpUcJOnXqxDPPPEOvXr3Iz8+vtFj++OMP1qxZw9SpU6lbty4Arq6ujB8/nptvvpmMjAyGDRtGaGgovXv3ZsuWLcDFDa9//OMfhISEEBoaSkJCAnDxCE5ERATdu3fnmWee4fTp0xUSZ2nVwpMnTxISEmL9jN955x169+5Nz549mTZt2hVHXDz55JNER0eX+lxCQgK9e/emV69eREVFkZuby8SJE1m6dCkAy5cvp3v37gDk5+fj7+9v8+fj6enJAw88UOrRsNL6Mnz4cDZv3gzAjBkzeOaZZwBIS0sjJCTEphiu5Pvvv+fBBx/k1KlTnDlzhi5dunDgwAHOnDnD888/T/fu3enVq5f1iPaWLVvo27cvjz76KCNHjrzq5/7tt99a/0affvppfv/9d+BipXvatGk8/vjjdOvWjc2bN/Pzzz+zbNkyli1bxscff1yhfZT/s3PnTo4fP27dUQK44447GDFiBEOGDGHjxo3Mnj2bL7/8EoBNmzbRt29funTpwvLly4Er59JVq1YxaNAgQkNDmTFjhk3x3X///XzwwQeYzWZOnjxJYWEhtWvXLjbPkSNH+PTTT3nttddwcXEBLo5QmjFjBrVq1WLWrFnFjupGRkaSlJRkUzyXO3PmDDNnzmT48OGlPm+v2H799VcAnn76aXr27MmHH35YZWL77LPP6NGjB15eXjg5OTFz5kzuvvvuKhFbYWEhFouFCxcuUFBQQEFBgfX17RFbfHw8MTExeHp6Wtu+/fZbmjVrRtOmTTGbzYSGhpKcnFxsuezsbN5//31ee+013NzcAPDy8mLGjBl4eHiwYsUK/vGPf1jnnzNnDu+8845NMUrFqlOnDrfffju7du2ytn311VfWYlDr1q2BiwXNsLAwwsLCeOqppzh16hRZWVkMHTrU2v75558D/3c0+euvv+bpp5/mueeeIzAwkNGjR5OXlwfABx98QEBAAH369CEiIqJKjFy5dPvvyJEjPPXUU/Tu3ZsBAwbwww8/ABf/viZNmsSAAQPo2rVrldleSE9PxzAM6tSpc8Vt1NzcXMaMGUPPnj0ZOXIkZ8+eBYqPYL30Pbh8VMC2bdtK/EZXhBttH+3rr7/mxIkTjB492lqI79SpE2+88QaFhYXW+a70Hbzavl10dDSDBg2ia9euLFiwoFLiV2EBaNOmDY888gh/+9vf6Nu3L9OnT8disdC0aVOWLVvGwoULWbNmDc8880yJH7tevXpZh/OlpKSQm5vLHXfcwYcffsikSZNYvXo1kyZN4u233wYuVvi6d+/O2rVrCQoKIjU1FYAFCxbQtm1bVq1axZIlS1i4cKF1R6cipKWlFRti/u6777JixQqee+45Pv74Yz744AOmTZtWYrnTp0/z7LPPkpiYiJOTU4XFc7lvv/2W5s2bU69evWLtzZs3JzAwkIkTJ9KpUyfWrl3L7NmziYqKIiMjgzlz5lC/fn3WrVvH+++/z5w5czh48CCLFy8GYP369URHR/Pbb79VStyZmZkMHTqUkSNH8re//Y0tW7awf/9+Vq5cSUJCAqmpqaxZs6bUZZ999llOnz5d4pSIn376ifj4eJYtW0ZiYiINGzZk0aJF+Pv7s2PHDgB27NjB2bNnycjIYM+ePdxzzz3X9PlERkaydevWYqdEfPnll6X25dI4du/eza+//kphYSFffvlliaNU5XH5d7RXr16YzWYef/xxpk2bxsSJExkwYAC33347b7/9Nrfccgvr169n2rRpzJo1i1OnTvHWW2+xaNEiEhIS6Ny5M3FxcaW+Vl5eHtHR0bz11lusXr2ap556itdee836fH5+PsuXL+fVV1/l7bffpkWLFvTv35/+/fvTp08fm/soV/fdd9/Rrl07TCZTsfb77ruPm266ia5duzJ69Ggeeugh4OLnuGLFCv75z39ad56ulktTU1NZvXo1L730ks0xOjk5MXv2bIKDg/H19aVRo0bFnj9w4AB//etfSxQcOnbsSJMmTejTpw9r167FMAwuXLjAjh07Kuyoy/jx43nxxRetxdnL2Su2c+fO4evry7x58/jPf/7DsmXLSpx+Za/Yjhw5QmFhIcOHD6dXr14sXbq0xO+QvWJzdXVlzJgxdO/eHX9/f7y9venQoYPdYps8eTI+Pj7F2tLS0vDw8LA+9vT0tG7XFPn1118xm800a9asWPtdd91Fy5Yt6dGjB9u3bycrKwuAdevW2TTEXipH9+7drTtn3377La1bty6xvTF//nxef/11Vq1axQMPPMAPP/zAZ599hre3N6tWrWLy5Mns3r27xLr/97//MX78eNavX8/x48fZunUrBw8eZMmSJaxatYqlS5dy5MiR69LP8njllVeIiIhg9erVTJw4kRdffNH63B9//MHSpUtZsGBBqdvV10PR9lRQUBAdO3Zk1qxZzJ07lx9//PGK26gnT55k0KBBrFmzhqZNmzJv3rxyveYDDzxQ4je6ItwI+2iX+uGHH2jTpg0ODsV30f39/WnYsKH18ZW+g1fbtzt06BCLFi1ixYoVvPPOO5w7d67C49epEP9fbGwszz33HFu3bmXr1q089thjxMXFMW/ePDZu3Mjhw4fZuXNnqR/0hAkTyMrKYt26dfTs2ROA6dOn88UXX5CcnMy+ffvIzs4GLlZ633jjDQC6detm3QDctm0bOTk51urm+fPn+emnn2jatGmF9K+0YeZFO4P//Oc/+fHHHzl//nypy15+9KayXLozkZyczIIFC7BYLDg7O3P06FHrua9Nmzbl7rvvZt++fezYsYMpU6YA0KBBAx555BF27tzJzp07efzxxwH461//yj333FMpMcfExHDzzTcTEBAAXKzaf/vtt4SFhQEXh4n+5S9/KXVZs9nM1KlTGTx4MJ07d7a2f/311xw5coTHHnsMuLiTe8cdd/D3v/+d1157jcLCQn799Vd69OjBrl27+O6776447LmsXF1dmThxovWUiKv1ZeDAgYwYMcK6Edi6dWu+//57tmzZck3nfl3pVIjbbruNPn36UKtWLaZPnw7Arl27rEWD1q1bs3z5cr744gtOnDjB4MGDgYvDyS7fQSiSkpLC77//zogRI6xtRf0BrD+KLVu25MyZMzb3ScrHZDIVq8gXyc/PL1FsAHjkkUcwmUy0bNnSOjrlSrkULo5+uNpQ/LIaPXo0zz77LMOHDyc+Pt6aawAcHBxKHFW+VNOmTfH29mbXrl0cP34cf3//q85fVitWrKBx48b4+vpe8dx5e8V2zz33FMvBffv2ZfPmzTz44IN2j62wsJDdu3ezePFiateuzYgRI1i9erU179kztoMHD/Lxxx/zxRdf4ObmxtixY1m0aJF1lJg9YytisViK/W0ahlHib9XBwcE6Aqk0derUwd/fn88++4ymTZvStGnTEgU7sZ+uXbsya9YsLBYL69evp3v37iVGvTzyyCPWAyyPPPIIDz74ICkpKcyYMYPU1FQefvhhnn/++RLrbtmyJV5eXsDFA0lnz57lyJEjdOnSxXpqZnBwcKXsANkqOzub/fv38+qrr1rbzp8/b/0NevDBBzGZTLRq1cpu2w9F21MWi4WpU6fyyy+/8OCDDzJ9+vRSt+vuvfdebr31VmvhsFevXjZfj6oy1PR9tEv9WU6Hq38HIyMjr7hv17FjR5ydnWnYsCHu7u5kZmZe8UCErVRY4OJw2vPnz9OjRw/69OlDnz59iI+PZ8mSJcyYMYOePXty33330bp16xIXk3N2dqZLly5s3LiR5ORk/vnPfwIwcOBAOnbsSMeOHfH19WXs2LEAODo6ljo03mKxMH36dNq2bQtARkbGFXeKKsoLL7xA3bp16dKlCz169Lji0J5atWpVahwA7dq145dffiErKwtXV1eCgoIICgri6NGjDB48uMR7ZhgGhYWFV2w3mUzFnquInYnSPPvss2zevJmPPvqI8PBwCgsLefLJJ3nqqaeAi0fqis5LLE2rVq1KnBJRWFhI9+7drW3Z2dkUFhbi4uLC7bffztq1a7ntttvo2LEj27dvZ8+ePcU2NG3VuXPnYqdEXKkvderUwWKx8Omnn9KhQwduvvlmduzYwffff18pBZzMzEyys7PJzs7mzJkzNGjQALPZXGzj9ZdffqGwsJAOHTqwcOFC4OKwvqIfi8tZLBaaNGliLWQUFhaSkZFhfb4oqZe2MyuV5+6772bx4sXk5+cXOyL2zTff0K5duxLzF/1tXfo5XSmXrl279ppz2S+//EJeXh633347N910EwEBARw6dKjYPEW5LCcnp9jr/ec//8HDw4Pg4GD69OnDunXrOH78OKNGjbqmmIokJSWRnp5Or169OHv2LOfPn2fKlClERUXZPbbdu3eTn5+Pr68vcDFPX56T7RXbzTffjK+vr/Wc8b/97W/FNrztGdvWrVvx9fW1HqUKCwtj6dKlxfK9vWIr4uXlRXp6uvVxenp6sVMl4OIOY05ODsePHy9WaP/kk0/IyMjgySefpE+fPixYsIAmTZoUe+/F/urUqUObNm3Ys2cPO3bs4B//+EeJwsKQIUPo0qULX3zxhXXndcSIEaxfv54vv/ySL774gn//+98llrt0B6pou83BwQGLxXJd+maLogNelx4I+eOPP3B3dweq1vaDg4MDL7/8Mo8++iiLFi264nbd6dOni+Xky3N00fZ0QUHB9e0AN94+Wrt27Vi6dGmJIu2MGTOspyBd7Ts4evToK+7blfb3VtF0KgQXd5zfeust6zlEhmFw4MABnJ2dMZlMDB8+nI4dO/LZZ5+VejStV69evPfee7i7u+Pt7c2ZM2dISUlhzJgx+Pn58fnnn1uX8/X1Ze3atQBs3rzZWoXt1KmT9TyYtLQ0evbsecW7NlSUr776itGjR1uH8AOl9u96+Mtf/kLPnj155ZVXrO9JQUEBmzZtwsHBgU6dOrFy5Urg4sW29u7dS/v27Yu1nzp1is8//5z777/f+j5bLBaOHTtW4ryrinL77bcTExPD3LlzSU1NpVOnTiQmJpKdnU1BQQHPP/98sfO7SlN0SsT//vc/AOt37eTJkxiGweuvv877778PXKy+zps3j/vvv5/777+fzz//nNq1a1s3iq9V0SkRaWlpV+2Ln58fCxYs4P7776dTp04sXryYu++++6pFFFvFxsbyxBNPMHDgQGJjYwHw8fGxDm/75ZdfePbZZ7nrrrv45ptvOHz4MHBxaOaVhiHedtttnD171jo08+OPP7b+sFyJo6OjXX5UbyQ+Pj60aNGCKVOmWK8Zsn//fhYsWMBzzz2Ho6Pjn+aoysylR48eJTo6mry8PPLy8vj888+59957i83zl7/8hYcffpiJEyeSm5sLXBza+O6779KyZUsAgoKC2L59OxkZGRU2Iuy9995j3bp1JCYmMnr0aLp27VqsqGDP2DIzM5k2bRq5ublkZWWxevVqunXrViVi69KlC1u3buXcuXPWUXxFG4/2jq1NmzZs27aN8+fPYxgGGzduLHHxOHvFVuTuu+/m8OHD1lNK1q1bZz0vuUitWrUIDw/n9ddft44MO3r0KDNmzLBeSM7Hx4c//viDr7/+mr/97W8VGqNcu+7du/PWW2/Rrl27Ug/U9OvXj+zsbIYMGcKQIUOsF+qcM2cO3bt3JyYmxnrdhT/j6+vL5s2bycrKIi8vj08//bRK7KQXcXNz469//at1p+6rr76qMhcYL43ZbObll19m/vz53HHHHVfcrvvll1+s5+l//PHH1p3Y+vXr8/PPPwNYr5NxJWX5jS6vG20fzcfHh4YNGzJ37lxrXF9++SWrVq3i1KlTwNW/g/bet9OIBS5+YUaOHMnw4cOtG7MPPfQQ8+bNIzIyku7du2MymejcuTN79uwpsfy9995LZmam9W4K7u7u9O3bl+DgYMxmM506dSInJ4fz588zbtw4XnnlFeLj42nTpo11CMrIkSN5/fXXCQkJobCwkIiICG655ZZK7feoUaMYOHAgLi4utGnTBm9v7+t6i8HLvf7667z33nsMHjyYwsJCsrOz6dixI//617+oXbs248ePtw7xnTRpEp6enjz//PO8/vrrhIaGWs+Tbdu2La1ateKnn36ie/fueHt706pVq3LHs3v37mJH4ENDQwkODi4x31//+lfCw8OZMGEC8+bN4+DBgzz22GMUFhby0EMP0bt376u+TtEpEUVHadq0acPIkSN58sknsVgs3H777QwdOhSAhx9+mNdff53777+fevXq0bBhw2s+DeJSRadE/P3vf6dLly5kZmaW2peHH36Y9957j3vvvZfatWuTn59Ply5drum1i84JvFR+fj61atVixowZGIZBnz59SEpKYvTo0URHR9OzZ0/MZjPTpk3D09OTKVOm8MILL2CxWGjUqJH11InLOTs78/bbbzN58mRyc3NxdXW94q3citx333288sor3HzzzVX+dj/V2dy5c5k5cyYhISE4OjpSr149pk+fTseOHcnIyGDGjBnWC8CV5kq5tLTze8vL39+fb7/9lkcffRRHR0cCAgJKzQlTpkwhLi6OXr164ezszE033cT06dOteahWrVq0b9/eprx0rewRW5cuXdi3bx+PPvooFouFgQMHljq6yR6x3X333TzzzDMMHDiQ/Px8HnzwwVKvo2KP2Dp37swPP/xAWFgYTk5O3HnnndbfAnvHVsTFxYWpU6cyatQocnNz8ff3JygoqMR8L774IvPmzeOxxx7DbDbj6OjIP/7xj2KnAXbr1o0zZ85c9bQJsY8uXbowbtw4xowZU+rzL730EpGRkZjNZmrXrs2kSZO4+eabeemllwgNDcXR0ZGIiIgyDbtu1aoVgwcP5vHHH6d27drUr1+/Qk/fKavLtwEbNWpkvZ7I9OnTef3113n33XetF32tSsWPy/n5+XHPPfewe/duAgICSmzXHTt2jFtuuYV58+bx22+/0apVK+s5+6NHj2bixInMnTu32N9raR544AHrb3RpecAWN9o+mslkYv78+bzxxhuEhIRgNpupX78+77zzDpmZmdb5rvQdtPe+ncmojHEQckUffPABDzzwAC1atOD777/ntddes8u92EVE5PozDIPs7Gwef/xx63D1qkKx2UaxXRvDMMjPz+epp54iKiqqxIgRubEcPnyYzZs3M2TIEABGjBhBv3796Nq1q30DkxpP+2jXTiMWrrNmzZrx0ksvWS/OMXHiRHuHJCIi18l3333HM888w/PPP1/ldvIUm20U27VJT08nODiYfv36qaggeHt789133xESEmI9En2tIyJFykL7aNdOIxZERERERERExGa6eKOIiIiIiIiI2EyFBRERERERERGxmQoLIiIiIiIiImIzFRakWiosLOS9994jLCyMXr160aNHD6ZPn05eXl6FrH/RokVERkYCMG7cOLZt2wZAdHQ0+/fvL9EuInK9HT16lNatW7NixYpi7Zfmr4rQtWtXvvvuuwpb39VkZWXRv39/goOD+fTTT0s8/8svvzBq1ChCQ0Pp2bMnTzzxxJ/eSvTo0aPW28Z99NFHvPPOO+WKKTk5WbeYFZHrbtKkSfTq1YtevXrRrl07AgMDrY+XLl1qzWUrVqxgyZIlAMyZM4cJEyaU63Xy8/OZNm2aNa+GhoaycOFCii7Dt2nTJt5+++0/XU9Z55OaS3eFkGrp9ddf5+zZs7z//vu4ublx/vx5xo4dy7hx45g+fXqFvtbkyZOt/9+2bRuPP/54iXYREXtwcHDgzTff5N577+W2226zdzjX7MCBA5w8eZLPPvusxHO//vorTz75JG+88QYPPfQQANu3b2f48OF89NFHtGzZ8k/XX3QvcxGRqi46Otr6/65duxIXF8edd95ZYr49e/aUKf9dyfvvv8/Ro0dZvXo1ZrOZzMxMnnzySerXr8/jjz/Od999x9mzZ/90PWWdT2ouFRak2jl69Chr165l69atuLq6AlC7dm1iY2PZu3cvmZmZxMbGcvDgQUwmEw899BAvvfQSZrOZO++8k6FDh/LVV1+RlpbGM888w8CBA8nPz2fSpEls27aNhg0b0rBhQ9zc3AAYNGgQ4eHhHDhwgLS0NMaOHcu0adOIi4sjPDycoKAg/vvf/zJ37lwsFgt16tTh1Vdf5a677mLOnDkcO3aM9PR0jh07RqNGjZg+fTqenp72fAtFpIaoVasWTz31FGPHjmXZsmU4OzsXez4yMpKWLVvy97//vcTjrl27EhISwo4dOzh79izPPPMMe/fu5fvvv8dsNrNgwQIaNWoEwNKlSzl48CB5eXk89dRT9O3bF4CNGzeyYMEC8vPzqVWrFq+88gr33HMPc+bM4ZtvviEtLY3WrVsTFxdXLK7ScqarqytRUVGkpqbSq1cvli9fTq1atazL/Otf/6JPnz7WogKAr68vb731FrVq1WLBggX8/PPPvPXWWwDs3r2bSZMmMXfuXOv8c+bM4fTp04wfP56uXbvSu3dvtm/fzokTJ+jVqxcvvPACAG+//TZr167F3d2dZs2aWZfPy8sjLi6OXbt2UVhYyB133EF0dDSurq507dqVu+66i0OHDvHSSy+Rnp7OsmXLcHJywsXFhQkTJtCiRYtr/chFRKy5zNfXl40bN/LVV18Vy5cAqampTJgwgRMnTpCfn09wcDDDhw8vsa709HTy8/PJy8vDbDbj5ubGtGnTsFgs7Nu3j2XLllFYWIibmxvDhg3j9ddf58iRI5w5c4Y6deoQFxdHZmZmsfmaNWvGhg0b+Oc//wnAqlWrrI93797N1KlTsVgsAAwbNozAwMDKf9Ok0ulUCKl2vv/+e1q0aGEtKhTx8PAgMDCQSZMm4e7uztq1a/n44485dOgQ//73v4GLG4X169dn2bJlzJ49mzfeeIPc3FyWLl1KSkoKn3zyCf/+9785ceJEidd98cUX8fT0JC4ujrvvvtva/ssvvxATE8OcOXNYs2YNo0eP5rnnniMrKwu4uHH79ttvk5yczE033cSyZcsq8d0RkRvNiBEjqF27NjNnziz3srm5ucTHxzNmzBjGjx/Pk08+yZo1a2jcuDGrV6+2zufi4sLq1av597//zYwZM/jpp59ISUlh5syZvPPOOyQkJDBx4kRGjRrF+fPnATh27BirV68uUVS4Us709PRk0qRJ3HLLLSQmJpbYSN6/fz8dOnQo0Qd/f3+aNm3KY489xqZNmzhz5gwA8fHx9O/f/6r9P3/+PEuXLmXZsmX8+9//5vfff+e///0vn376KQkJCSxbtsyaywHeeecdHB0dWbVqFWvWrLH+JhRp2bIl69evp2vXrkyZMoV3332Xjz/+mMcee4w9e/aU7UMRESmjbt260bVrV4YMGUJ4eHix5yIiIujTpw+rVq1i5cqVbNu2jaSkpBLreOqpp0hNTaVTp04MGjSImTNnkpeXR6tWrbj77rvp378/PXr04MUXX2TLli3UrVuX5cuXs2HDBtq1a8eSJUtKzHc1c+bM4amnnmLVqlVMmTKFHTt2VOh7IvajEQtS7Tg4OFirnKXZsmULH330ESaTCWdnZ/r378/777/P0KFDAXjkkUcAaNu2LXl5eZw/f57t27cTEhKCs7Mzzs7OhIaGcujQoTLFs2PHDjp16kTTpk2Bi0fQGjRoYL0Ww/33328tgtxxxx0aJiYiFcrBwYHp06fz6KOP0rlz53ItGxAQAEDTpk25+eabadOmDQC33HJLsVxVtIPeqFEjHnzwQbZv346joyNpaWkMGTLEOp/JZOK3334DoH379pjNJTczrpYzTSbTFWM1mUxXzf0NGzbk4YcfJjExkUcffZStW7cSExPD6dOnr7hM0e9Bo0aNaNiwIWfPnmX79u1069bNmrf79OnD4sWLgYvnEGdmZlqvr5Ofn0/Dhg2t6/Px8QHA0dGRoKAg+vfvz8MPP0znzp3x9/e/YhwiIhXp/Pnz7Nq1i7Nnz1qve3D+/HkOHjxIjx49is3r5eXFqlWr+Pnnn/n666/5+uuvefzxx4mMjCxRrAgKCqJp06YsXryYI0eOsHPnTus1bMqqe/fuTJgwgY0bN/LAAw/w0ksvXVtnpcpQYUGqnbvuuotff/2VrKysYqMWUlNTee2117BYLMU2Ti0WCwUFBdbHLi4uANZ5ii5OcylHR8cyx3P56xWts+g1Lz3qZjKZSn09EZFr0bhxY2JjY3nllVd49NFHre2X55z8/Pxiy1166oSTk9MV1+/g8H8DHC0WC2azmcLCQnx9fZk1a5b1uRMnTuDp6clnn31G7dq1S13X1XLm1WJo374933zzDV26dCnWPnfuXG655RZ69uxJeHg4r7/+OmazmYCAAOrUqXPVwkLR7wEUf68ufc8u/T2wWCxERUVZiwTZ2dnk5uZan7+0z3Fxcfz4449s27aNd955h8TERF3YTESuC4vFgmEYLFu2jJtuugmAU6dOFct5RaZNm0a/fv1o0aIFLVq0IDw8nMTERP71r3+VKCwsXbqU+Ph4wsPDCQ0Nxd3dnaNHj5ZY59V+e/r370+XLl346quv+PLLL5k7dy7JycmlxibVi06FkGqnUaNGhIaGEhUVZR2impWVxeuvv467uzudO3fmww8/xDAM8vLyiI+P54EHHrjqOh966CESEhLIzc0lNze31KFicHED89IiBVw82rZ161Z+//13AOv5upeeLiEiUtmCgoLw8/Pj/ffft7bVr1/fOnoqNTWVnTt32rTuotMijh8/zvbt2/H19cXX15evvvqKX375BYDNmzfTs2dPcnJyrrouW3Pm3//+d1asWMHWrVutbVu2bGHx4sXWkRYdOnTAwcGBRYsW/elpEFfi5+dHcnIy586dw2KxkJiYaH2uc+fOLFmyhLy8PCwWC6+99hozZswosY5Tp07h7++Pu7s7Q4YM4YUXXrhud9YQkRtLadumrq6utG/fnvfeew+Ac+fOMWDAAD7//PMSy586dYq3336bCxcuABcLqz/99BN33HFHifVv3bqV3r17069fP2699VY2btxIYWFhifkaNGjATz/9RG5uLvn5+WzYsMH6ev379+fAgQOEhYUxceJEzp07R3p6egW/K2IPGrEg1VJMTAzz58+nf//+ODo6kpeXx9/+9jdGjRpFdnY2kyZNIjQ0lPz8fB566KFSL1Zzqf79+/Pbb78REhJS4mJdl+rWrRsRERG8/vrr1rYWLVoQExPDyJEjKSwspFatWixcuNB68UcRkeslOjq62Ln8gwYNYuzYsQQGBtKkSRM6depk03pzc3Pp3bs3+fn5REdHc+uttwIwYcIEXnrpJQzDsF7wsU6dOlddl605s1mzZixcuJBZs2bx5ptvYrFYaNCgAQsWLKBVq1bW+cLCwkhKSrIWG8rL39+fQ4cO0adPH+rWrUubNm2sox6ee+453nzzTXr37k1hYSG33357qbf2bNCgASNGjGDIkCHUqlULR0dHJk2aZFM8IiJX4+fnx9SpU0u0x8XFMXHiREJDQ8nLyyMkJISePXuWmC8mJoaZM2fSs2dPnJ2dKSgooFOnTowfPx6ATp06MXbsWCZOnMjTTz/N+PHjWblyJXBxJNmPP/5YYr5XX32V++67j+7du+Ph4UHHjh2tpxiPHTuWKVOmMGvWLEwmEyNHjqRJkyaV9fbIdWQyNC5bREREaoCCggJGjhxJz549S5xHLCIiIpVHp0KIiIhItffzzz/j6+tL/fr1CQoKsnc4IiIiNxSNWBARERERERERm2nEgoiIiIiIiIjYTIUFEREREREREbGZCgsiIvL/2rFjAQAAAIBB/tZz2F0YAQDAJhYAAACATSwAAAAAm1gAAAAAtgDmc+pmoRUKFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=3)\n",
    "sns.barplot(data = df_vis, x = 'condition', y = 'price', ax=axs[0]).set(xticklabels=[\"Salvage\", \"Fair\", \"Good\", \"Like New\", \"New\", \"Excellent\"])\n",
    "sns.barplot(data = df_vis, x = 'num_cylinders', y = 'price', ax=axs[1]).set(xticklabels=[\"Other\", \"3 Cyl\", \"4 Cyl\", \"5 Cyl\", \"6 Cyl\", \"8 Cyl\", \"10 Cyl\"])\n",
    "sns.barplot(data = df_vis, x = 'title_status', y = 'price', ax=axs[2]).set(xticklabels=[\"Missing\", \"Lien\", \"Rebuilt\", \"Salvage\", \"Clean\"])\n",
    "fig.tight_layout(pad=2)\n",
    "fig.set_figheight(5)\n",
    "fig.set_figwidth(15)\n",
    "axs[0].set_xlabel(\"Condition\")\n",
    "axs[0].set_ylabel(\"Count\")\n",
    "axs[0].set_title(\"Average Price Grouped By Condition\")\n",
    "axs[1].set_xlabel(\"Number of Cylinders\")\n",
    "axs[1].set_ylabel(\"Count\")\n",
    "axs[1].set_title(\"Average Price Grouped By Number Of Cylinders\")\n",
    "axs[2].set_xlabel(\"Title Status\")\n",
    "axs[2].set_ylabel(\"Count\")\n",
    "axs[2].set_title(\"Average Price Grouped By Title Status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the bar plots above, we can see that price does change when looking at our selected categorical variables indendently. Surprisingly, cars with a 'good' condition tend, on average, to have higher sell prices than those in 'excellent' condition. This is perhaps reflective of the fact that these categorical variables are slightly ambiguous and hard to determine from sale to sale. In general, the barplot shows that the cars with more cylinders do not necessarily have higher sell prices. This is evident by the fact that cars with 5 cylinders sold for less on average than those with either 3 or 4 cylinders. Finally, we can see that title status does impact price, potentially making title status a good predictor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### States:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='state'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data = df, y = 'state', order = df['state'].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the cars within our dataset came from California. This could perhaps affect our model given that price could vary between different regions, which our model could not accurately account for given the lack of data for certain states compared to others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantitative Variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    373598.00000\n",
       "mean      91199.51474\n",
       "std       58522.95875\n",
       "min         501.00000\n",
       "25%       39676.25000\n",
       "50%       87211.50000\n",
       "75%      133685.00000\n",
       "max      249961.00000\n",
       "Name: miles_driven, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['miles_driven'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='miles_driven', ylabel='miles_driven'>,\n",
       "        <AxesSubplot:xlabel='price', ylabel='miles_driven'>],\n",
       "       [<AxesSubplot:xlabel='miles_driven', ylabel='price'>,\n",
       "        <AxesSubplot:xlabel='price', ylabel='price'>]], dtype=object)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_scatter = df[['miles_driven', 'price']]\n",
    "pd.plotting.scatter_matrix(df_scatter, alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the scatter matrix above, we see that our quantatative variable do influence the sell price of the car. The 'miles_driven' and 'price' graph shows that price decreases as the number of miles a car has driven increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Creation & Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract price from our data since it is the varible that we will be predicting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_onehot\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>num_cylinders</th>\n",
       "      <th>condition</th>\n",
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       "      <th>manufacturer_alfa-romeo</th>\n",
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      "text/plain": [
       "   miles_driven  title_status  num_cylinders  condition  manufacturer_acura  \\\n",
       "0       57923.0           4.0            5.0        2.0                   0   \n",
       "1       71229.0           4.0            5.0        2.0                   0   \n",
       "2       19160.0           4.0            5.0        2.0                   0   \n",
       "3       41124.0           4.0            5.0        2.0                   0   \n",
       "4      128000.0           4.0            4.0        5.0                   0   \n",
       "\n",
       "   manufacturer_alfa-romeo  manufacturer_aston-martin  manufacturer_audi  \\\n",
       "0                        0                          0                  0   \n",
       "1                        0                          0                  0   \n",
       "2                        0                          0                  0   \n",
       "3                        0                          0                  0   \n",
       "4                        0                          0                  0   \n",
       "\n",
       "   manufacturer_bmw  manufacturer_buick  ...  manufacturer_pontiac  \\\n",
       "0                 0                   0  ...                     0   \n",
       "1                 0                   0  ...                     0   \n",
       "2                 0                   0  ...                     0   \n",
       "3                 0                   0  ...                     0   \n",
       "4                 0                   0  ...                     0   \n",
       "\n",
       "   manufacturer_porsche  manufacturer_ram  manufacturer_rover  \\\n",
       "0                     0                 0                   0   \n",
       "1                     0                 0                   0   \n",
       "2                     0                 0                   0   \n",
       "3                     0                 0                   0   \n",
       "4                     0                 0                   0   \n",
       "\n",
       "   manufacturer_saturn  manufacturer_subaru  manufacturer_tesla  \\\n",
       "0                    0                    0                   0   \n",
       "1                    0                    0                   0   \n",
       "2                    0                    0                   0   \n",
       "3                    0                    0                   0   \n",
       "4                    0                    0                   0   \n",
       "\n",
       "   manufacturer_toyota  manufacturer_volkswagen  manufacturer_volvo  \n",
       "0                    0                        0                   0  \n",
       "1                    0                        0                   0  \n",
       "2                    0                        0                   0  \n",
       "3                    1                        0                   0  \n",
       "4                    0                        0                   0  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions that we will use to determine accuracy of our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mean Square Error\n",
    "def MSE(pred, y_test):\n",
    "    return (((pred - y_test)**2).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Root Mean Square Error\n",
    "def RMSE(pred, y_test):\n",
    "    return np.sqrt(MSE(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Used sklean's test_train_split to split our data into training and testing set with a train size of 0.7 and test size of 0.3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decided to use Ridge regression for our regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Model 1: miles_driven, title_status, manufacturer<b/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:\t 137962234.27966952\n",
      "RMSE:\t 11745.732598678956\n"
     ]
    }
   ],
   "source": [
    "X_1 = df_onehot.drop(['num_cylinders', 'condition'], axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_1, y, test_size=0.3)\n",
    "reg = Ridge(alpha=1)\n",
    "reg.fit(X_train, y_train)\n",
    "y_pred = reg.predict(X_test)\n",
    "print('MSE:\\t', MSE(y_pred, y_test))\n",
    "print('RMSE:\\t', RMSE(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Model 2: miles_driven, title_status, num_cylinders<b/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:\t 154553029.10266316\n",
      "RMSE:\t 12431.935854993106\n"
     ]
    }
   ],
   "source": [
    "X_2 = df_onehot[['miles_driven', 'title_status', 'num_cylinders']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_2, y, test_size=0.3, random_state=42)\n",
    "reg = Ridge(alpha=1.0)\n",
    "reg.fit(X_train, y_train)\n",
    "y_pred = reg.predict(X_test)\n",
    "print('MSE:\\t', MSE(y_pred, y_test))\n",
    "print('RMSE:\\t', RMSE(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Model 3: condition, title_status, num_cylinders<b/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:\t 217253580.04708567\n",
      "RMSE:\t 14739.524417262779\n"
     ]
    }
   ],
   "source": [
    "X_3 = df_onehot[['condition', 'title_status', 'num_cylinders']]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_3, y, test_size=0.3, random_state=42)\n",
    "reg = Ridge(alpha=1.0)\n",
    "reg.fit(X_train, y_train)\n",
    "y_pred = reg.predict(X_test)\n",
    "print('MSE:\\t', MSE(y_pred, y_test))\n",
    "print('RMSE:\\t', RMSE(y_pred, y_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 1 with RMSE as 11745.73 yields the best result out of all three models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The license plate information, Craigslist user upload URLs, and VIN numbers are all privacy concerns because these data can be used to track back to a specific user. Since that is personally identifiable information, these columns will be dropped during the data cleaning process in order to protect the privacy of the users.\n",
    "\n",
    "In regards to the privacy of the data, because all of the personal identifiable information for each user is dropped, there is no way to connect a user to their car anymore, which maintains the user’s privacy. However, because the state where the car was listed is still being used for analysis, there is still some private information being used. Despite this, it would be very difficult to track down the exact user from simply knowing their state, which means that this should not be that big of a concern. Additionally, when the URLs to the posting were clicked on, they were no longer found (even though this variable was removed from the data) which is another way the user’s data was kept private. The data was also taken under the public domain usage, meaning that it was publicly available for use. \n",
    "\n",
    "In short, most of the personal identification information from the dataset has been removed and the only possible identification variable that could be used is not specific enough to trace back the user. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training and testing each of the three linear regression models, the results show that the model that used miles_driven, title_status, manufacturer as features was the most successful at predicting the price of a used car in the dataset as it had the lowest RMSE and MSE out of all the models, which supports our hypothesis. This conclusion does make sense as these features are the most important factors in determining the car as they have a large impact on how the car would run, and thus how it would be priced. \n",
    "\n",
    "The target audience of individuals who are either seeking or looking to list used cars on Craigslist would be the target audience of this model. Despite this model being the \"best\" model, the limitations of it should be considered. The dataset was heavily skewed towards cars that were made by Ford, Chevrolet, and Toyota, had a clean title status, and came from California. Taking this into consideration, it may be the case that the model does not work as well at predicting the price of a used car that does not fit these descriptions as the model would not have had as much data to train on. Additionally, because the data was collected between April - May 2021, it would not be beneficial to use this model outside the scope of these months as the bubble would have much more inflated prices than a normal year. This model would be a tool for buyers or sellers who want a price for a used car during the pandemic's bubble due to the chip shortage and increased demand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a team we all helped out each other with each of the part, but the majority of each part was done by the following:\n",
    "\n",
    "- Yash Potdar: Background, Model Creation, Data Cleaning, Overview\n",
    "- Mandy Lee: Data Cleaning, Model Creation\n",
    "- Kenneth Nguyen: EDA, Hypothesis, Conclusion, Ethics and Privacy\n",
    "- Matthew Lund: EDA, Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final Video: https://drive.google.com/file/d/1oG-toF0hvNxIhka35dthbd5SRfKxXtzk/view?usp=sharing"
   ]
  }
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