notebooks/model_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "757d9a7f",
   "metadata": {},
   "source": [
    "## Importing Dependencies and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "4304a88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RepeatedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692bc6dc",
   "metadata": {},
   "source": [
    "## Features\n",
    "If you ever need to take a look at the features of this dataset, refer to the following cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0b4d85",
   "metadata": {},
   "source": [
    "- Name: Name of the Phone\n",
    "- Brand: Brand Name\n",
    "- Model: Model of the Phone\n",
    "- Battery capacity (mAh): Battery capacity in mAh\n",
    "- Screen size (inches): Screen Size in Inches across opposite corners\n",
    "- Touchscreen: Whether the phone is touchscreen supported or not\n",
    "- Resolution x: The resolution of the phone along the width of the screen\n",
    "- Resolution y: The resolution of the phone along the height of the screen\n",
    "- Processor: No. of processor cores\n",
    "- RAM (MB): RAM available in phone in MB\n",
    "- Internal storage: Internal Storage of phone in GB\n",
    "- Rear camera: Resolution of rear camera in MP (0 if unavailable)\n",
    "- Front camera: Resolution of front camera in MP (0 if unavailable)\n",
    "- Operating system: OS used in phone\n",
    "- Wi-Fi: Whether phone has WiFi functionality\n",
    "- Bluetooth: Whether phone has Bluetooth functionality\n",
    "- GPS: Whether phone has GPS functionality\n",
    "- Number of SIMs: Number of SIM card slots in phone\n",
    "- 3G: Whether phone has 3G network functionality\n",
    "- 4G/LTE: Whether phone has 4G/LTE network functionality\n",
    "- Price: Price of the phone in INR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56e52ca3",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "Here we can explore and analyze the data, provided by the *Mobile Phone Prices and Specifications* dataset, as aforementioned.\n",
    "\n",
    "Even though this data was web scraped, the original author already provided some imputation of missing values mechanism (such as assigning 0 if unavailable to certain features, like Rear Camera and Front Camera).\n",
    "\n",
    "Hence, more focused is place in the actual exploratory data analysis process than data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "a9dcf810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                             name    brand                   model  \\\n0  OnePlus 7T Pro McLaren Edition  OnePlus  7T Pro McLaren Edition   \n1                   Realme X2 Pro   Realme                  X2 Pro   \n2               iPhone 11 Pro Max    Apple       iPhone 11 Pro Max   \n3                       iPhone 11    Apple               iPhone 11   \n4                    LG G8X ThinQ       LG               G8X ThinQ   \n\n   battery_capacity  screen_size touch_screen  resolution_x  resolution_y  \\\n0              4085         6.67          Yes          1440          3120   \n1              4000         6.50          Yes          1080          2400   \n2              3969         6.50          Yes          1242          2688   \n3              3110         6.10          Yes           828          1792   \n4              4000         6.40          Yes          1080          2340   \n\n   processor    ram  ...  rear_camera  front_camera  operating_system wi_fi  \\\n0          8  12000  ...         48.0          16.0           Android   Yes   \n1          8   6000  ...         64.0          16.0           Android   Yes   \n2          6   4000  ...         12.0          12.0               iOS   Yes   \n3          6   4000  ...         12.0          12.0               iOS   Yes   \n4          8   6000  ...         12.0          32.0           Android   Yes   \n\n  bluetooth  gps number_of_sims   3g 4g_lte   price  \n0       Yes  Yes              2  Yes    Yes   58998  \n1       Yes  Yes              2  Yes    Yes   27999  \n2       Yes  Yes              2  Yes    Yes  106900  \n3       Yes  Yes              2  Yes    Yes   62900  \n4       Yes  Yes              1   No     No   49990  \n\n[5 rows x 21 columns]",
      "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>name</th>\n      <th>brand</th>\n      <th>model</th>\n      <th>battery_capacity</th>\n      <th>screen_size</th>\n      <th>touch_screen</th>\n      <th>resolution_x</th>\n      <th>resolution_y</th>\n      <th>processor</th>\n      <th>ram</th>\n      <th>...</th>\n      <th>rear_camera</th>\n      <th>front_camera</th>\n      <th>operating_system</th>\n      <th>wi_fi</th>\n      <th>bluetooth</th>\n      <th>gps</th>\n      <th>number_of_sims</th>\n      <th>3g</th>\n      <th>4g_lte</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>OnePlus 7T Pro McLaren Edition</td>\n      <td>OnePlus</td>\n      <td>7T Pro McLaren Edition</td>\n      <td>4085</td>\n      <td>6.67</td>\n      <td>Yes</td>\n      <td>1440</td>\n      <td>3120</td>\n      <td>8</td>\n      <td>12000</td>\n      <td>...</td>\n      <td>48.0</td>\n      <td>16.0</td>\n      <td>Android</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>58998</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Realme X2 Pro</td>\n      <td>Realme</td>\n      <td>X2 Pro</td>\n      <td>4000</td>\n      <td>6.50</td>\n      <td>Yes</td>\n      <td>1080</td>\n      <td>2400</td>\n      <td>8</td>\n      <td>6000</td>\n      <td>...</td>\n      <td>64.0</td>\n      <td>16.0</td>\n      <td>Android</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>27999</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>iPhone 11 Pro Max</td>\n      <td>Apple</td>\n      <td>iPhone 11 Pro Max</td>\n      <td>3969</td>\n      <td>6.50</td>\n      <td>Yes</td>\n      <td>1242</td>\n      <td>2688</td>\n      <td>6</td>\n      <td>4000</td>\n      <td>...</td>\n      <td>12.0</td>\n      <td>12.0</td>\n      <td>iOS</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>106900</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>iPhone 11</td>\n      <td>Apple</td>\n      <td>iPhone 11</td>\n      <td>3110</td>\n      <td>6.10</td>\n      <td>Yes</td>\n      <td>828</td>\n      <td>1792</td>\n      <td>6</td>\n      <td>4000</td>\n      <td>...</td>\n      <td>12.0</td>\n      <td>12.0</td>\n      <td>iOS</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>2</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>62900</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>LG G8X ThinQ</td>\n      <td>LG</td>\n      <td>G8X ThinQ</td>\n      <td>4000</td>\n      <td>6.40</td>\n      <td>Yes</td>\n      <td>1080</td>\n      <td>2340</td>\n      <td>8</td>\n      <td>6000</td>\n      <td>...</td>\n      <td>12.0</td>\n      <td>32.0</td>\n      <td>Android</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>Yes</td>\n      <td>1</td>\n      <td>No</td>\n      <td>No</td>\n      <td>49990</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_PATH = \"../data/phone_prices.csv\"\n",
    "df = pd.read_csv(DATA_PATH)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3002ee3",
   "metadata": {},
   "source": [
    "## Checking the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "38011190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "name                0\nbrand               0\nmodel               0\nbattery_capacity    0\nscreen_size         0\ntouch_screen        0\nresolution_x        0\nresolution_y        0\nprocessor           0\nram                 0\ninternal_storage    0\nrear_camera         0\nfront_camera        0\noperating_system    0\nwi_fi               0\nbluetooth           0\ngps                 0\nnumber_of_sims      0\n3g                  0\n4g_lte              0\nprice               0\ndtype: int64"
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking NA values\n",
    "df.isnull().sum() # No NAs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9832aa0b",
   "metadata": {},
   "source": [
    "## Changing the Currency of the Feature Variable\n",
    "The phone price is in Indian Rupees, hence in order to transform this, we can multiply by 0.012, the actual conversion rate as of this date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "ee755e42",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.price = df.price * 0.012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "9cdd1997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "       battery_capacity  screen_size  resolution_x  resolution_y    processor  \\\ncount       1359.000000  1359.000000   1359.000000   1359.000000  1359.000000   \nmean        2938.489330     5.291310    811.543046   1490.777778     5.551141   \nstd          873.514133     0.671357    270.707271    557.780120     2.196562   \nmin         1010.000000     2.400000    240.000000    320.000000     1.000000   \n25%         2300.000000     5.000000    720.000000   1280.000000     4.000000   \n50%         3000.000000     5.200000    720.000000   1280.000000     4.000000   \n75%         3500.000000     5.700000   1080.000000   1920.000000     8.000000   \nmax         6000.000000     7.300000   2160.000000   3840.000000    10.000000   \n\n                ram  internal_storage  rear_camera  front_camera  \\\ncount   1359.000000       1359.000000  1359.000000   1359.000000   \nmean    2488.777778         30.654864    12.070199      7.037969   \nstd     1664.440386         36.950241     8.948337      6.295448   \nmin       64.000000          0.064000     0.000000      0.000000   \n25%     1000.000000          8.000000     8.000000      2.000000   \n50%     2000.000000         16.000000    12.200000      5.000000   \n75%     3000.000000         32.000000    13.000000      8.000000   \nmax    12000.000000        512.000000   108.000000     48.000000   \n\n       number_of_sims        price  \ncount     1359.000000  1359.000000  \nmean         1.833701   137.589907  \nstd          0.374457   166.289965  \nmin          1.000000     5.928000  \n25%          2.000000    57.162000  \n50%          2.000000    83.988000  \n75%          2.000000   143.988000  \nmax          3.000000  2099.880000  ",
      "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>battery_capacity</th>\n      <th>screen_size</th>\n      <th>resolution_x</th>\n      <th>resolution_y</th>\n      <th>processor</th>\n      <th>ram</th>\n      <th>internal_storage</th>\n      <th>rear_camera</th>\n      <th>front_camera</th>\n      <th>number_of_sims</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n      <td>1359.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2938.489330</td>\n      <td>5.291310</td>\n      <td>811.543046</td>\n      <td>1490.777778</td>\n      <td>5.551141</td>\n      <td>2488.777778</td>\n      <td>30.654864</td>\n      <td>12.070199</td>\n      <td>7.037969</td>\n      <td>1.833701</td>\n      <td>137.589907</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>873.514133</td>\n      <td>0.671357</td>\n      <td>270.707271</td>\n      <td>557.780120</td>\n      <td>2.196562</td>\n      <td>1664.440386</td>\n      <td>36.950241</td>\n      <td>8.948337</td>\n      <td>6.295448</td>\n      <td>0.374457</td>\n      <td>166.289965</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1010.000000</td>\n      <td>2.400000</td>\n      <td>240.000000</td>\n      <td>320.000000</td>\n      <td>1.000000</td>\n      <td>64.000000</td>\n      <td>0.064000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>5.928000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2300.000000</td>\n      <td>5.000000</td>\n      <td>720.000000</td>\n      <td>1280.000000</td>\n      <td>4.000000</td>\n      <td>1000.000000</td>\n      <td>8.000000</td>\n      <td>8.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>57.162000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3000.000000</td>\n      <td>5.200000</td>\n      <td>720.000000</td>\n      <td>1280.000000</td>\n      <td>4.000000</td>\n      <td>2000.000000</td>\n      <td>16.000000</td>\n      <td>12.200000</td>\n      <td>5.000000</td>\n      <td>2.000000</td>\n      <td>83.988000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3500.000000</td>\n      <td>5.700000</td>\n      <td>1080.000000</td>\n      <td>1920.000000</td>\n      <td>8.000000</td>\n      <td>3000.000000</td>\n      <td>32.000000</td>\n      <td>13.000000</td>\n      <td>8.000000</td>\n      <td>2.000000</td>\n      <td>143.988000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>6000.000000</td>\n      <td>7.300000</td>\n      <td>2160.000000</td>\n      <td>3840.000000</td>\n      <td>10.000000</td>\n      <td>12000.000000</td>\n      <td>512.000000</td>\n      <td>108.000000</td>\n      <td>48.000000</td>\n      <td>3.000000</td>\n      <td>2099.880000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the most relevant summary statistics for each column\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82b46301",
   "metadata": {},
   "source": [
    "# Highest-Priced Phones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "744b13f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "       brand              model     price\n617  Samsung      Galaxy Z Flip  2099.880\n651  Samsung        Galaxy Fold  1979.988\n2      Apple  iPhone 11 Pro Max  1282.800\n630    Apple      iPhone 11 Pro  1162.800\n614  Samsung   Galaxy S20 Ultra  1115.988",
      "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>brand</th>\n      <th>model</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>617</th>\n      <td>Samsung</td>\n      <td>Galaxy Z Flip</td>\n      <td>2099.880</td>\n    </tr>\n    <tr>\n      <th>651</th>\n      <td>Samsung</td>\n      <td>Galaxy Fold</td>\n      <td>1979.988</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Apple</td>\n      <td>iPhone 11 Pro Max</td>\n      <td>1282.800</td>\n    </tr>\n    <tr>\n      <th>630</th>\n      <td>Apple</td>\n      <td>iPhone 11 Pro</td>\n      <td>1162.800</td>\n    </tr>\n    <tr>\n      <th>614</th>\n      <td>Samsung</td>\n      <td>Galaxy S20 Ultra</td>\n      <td>1115.988</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As expected, the phones with higher prices are either foldable phones, or made by Apple \n",
    "# (with the exception of the Samsung Galaxy S20)).\n",
    "df.sort_values(by='price', ascending=False)[['brand', 'model', 'price']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664f95fc",
   "metadata": {},
   "source": [
    "# Phones by Operating System\n",
    "As known, Android is the most used mobile operating system, with almost 70% of the distribution, followed by Apple's iOS, with an approximate 28%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "d708aaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['operating_system'].value_counts().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ec04849",
   "metadata": {},
   "source": [
    "# Phones by Brand\n",
    "As evidenced by the plots, most phones in this dataset are manufactured by Intex, Samsung, Micromax, Lava and Panasonic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "8151d5d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x400 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "\n",
    "category_order = df.brand.value_counts().index\n",
    "\n",
    "sns.countplot(df, x='brand', order=category_order)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad963bc0",
   "metadata": {},
   "source": [
    "# Price Distribution\n",
    "As the target variable, it is extremely important to understand its distribution. In order to do this, we plot a Histogram with a Kernel Density Curve.\n",
    "\n",
    "As evidenced below, the distribution is skewed to the right. There are some expensive phones that range from 800 - 2500`$`, but most are actually concentrated around the 0-500`$` mark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c2cc1d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.price, stat='density', kde=True, linewidth=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ede118",
   "metadata": {},
   "source": [
    "## Log Transformation\n",
    "In order to mitigate the skewness, we can try to log-transform the data so that the target variable approaches a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "53221ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(np.log1p(df.price), stat='density', kde=True, linewidth=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2adede64",
   "metadata": {},
   "source": [
    "## Feature Selection and Train-Test Split\n",
    "Separating the features from the target variable and doing the train-test-validation split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "3d9463f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the `names` and `model` features, which are redundant and unnecesary, only useful to query the data\n",
    "features = ['brand', 'battery_capacity', 'screen_size',\n",
    "       'touch_screen', 'resolution_x', 'resolution_y', 'processor', 'ram',\n",
    "       'internal_storage', 'rear_camera', 'front_camera', 'operating_system',\n",
    "       'wi_fi', 'bluetooth', 'gps', 'number_of_sims', '3g', '4g_lte']\n",
    "target = ['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "dc2bafcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting a seed (random state)\n",
    "custom_seed = 287\n",
    "\n",
    "df_full_train, df_test = train_test_split(df[features + target], test_size=0.2, shuffle=True, random_state=custom_seed)\n",
    "df_train, df_validation = train_test_split(df_full_train, test_size=0.25, shuffle=True, random_state=custom_seed)\n",
    "\n",
    "df_full_train = df_full_train.reset_index(drop=True)\n",
    "df_train = df_train.reset_index(drop=True)\n",
    "df_validation = df_validation.reset_index(drop=True)\n",
    "df_test = df_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "f8ca4e54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full: 1359\n",
      "Full Train Set : 1087\n",
      "Training Set : 815\n",
      "Validation Set: 272\n",
      "Test Set : 272\n"
     ]
    }
   ],
   "source": [
    "# Printing the number of records in each dataset\n",
    "print(f\"Full: {len(df)}\")\n",
    "print(f\"Full Train Set : {len(df_full_train)}\")\n",
    "print(f\"Training Set : {len(df_train)}\")\n",
    "print(f\"Validation Set: {len(df_validation)}\")\n",
    "print(f\"Test Set : {len(df_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "04d90ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying a Log Transformation to the Target Variable\n",
    "y_train = np.log1p(df_train[target].values)\n",
    "y_validation = np.log1p(df_validation[target].values)\n",
    "y_test = np.log1p(df_test[target].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "85f2398d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the target variable\n",
    "del df_train[target[0]], df_validation[target[0]], df_test[target[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e81a2aa8",
   "metadata": {},
   "source": [
    "## Baseline Model\n",
    "A baseline model is essentially a simple model that acts as a reference in a machine learning project. Its main function is to contextualize the results of trained models. Baseline models usually lack complexity and may have little predictive power.\n",
    "\n",
    "In this case, we use a Linear Regression Model with default parameters as a Baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "04e82e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(train_data, validation_data, model, selected_features):\n",
    "    dict_vectorizer = DictVectorizer(sparse=False)\n",
    "    dtrain = train_data[selected_features].to_dict(orient='records')\n",
    "    x_train = dict_vectorizer.fit_transform(dtrain)\n",
    "    model.fit(x_train, y_train)\n",
    "\n",
    "    dval = validation_data[selected_features].to_dict(orient='records')\n",
    "    x_val = dict_vectorizer.transform(dval)\n",
    "    y_pred = model.predict(x_val)\n",
    "    return np.sqrt(mean_squared_error(y_validation, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "75a867b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7756.21541\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the baseline model\n",
    "baseline_model = LinearRegression()\n",
    "baseline_model.rmse = evaluate_model(df_train, df_validation, baseline_model, features)\n",
    "print(round(baseline_model.rmse, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "7c3155bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "[('Baseline Linear Regression', 7756.215405299715)]"
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['model', 'rmse']\n",
    "models = [(\"Baseline Linear Regression\", baseline_model.rmse)]\n",
    "models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c4ff8b",
   "metadata": {},
   "source": [
    "# Decision Trees\n",
    "\n",
    "Now that we have a quantifiable Baseline Model, with its RMSE, we can try to tune the parameters so as to minimize the RMSE, as well as compare it with other models."
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We proceed to train a Decision Stump, a Decision Tree with max depth of 1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.62038\n"
     ]
    }
   ],
   "source": [
    "decision_stump = DecisionTreeRegressor(max_depth=1, random_state=custom_seed)\n",
    "decision_stump.rmse = evaluate_model(df_train, df_validation, decision_stump, features)\n",
    "print(round(decision_stump.rmse, 5))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "outputs": [
    {
     "data": {
      "text/plain": "[('Baseline Linear Regression', 7756.215405299715),\n ('Decision Stump', 0.6203796205666162)]"
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.append((\"Decision Stump\", decision_stump.rmse))\n",
    "models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, we fit a Decision Tree Regressor with default `max_depth` and `min_samples_leaf` parameters."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.51709\n"
     ]
    }
   ],
   "source": [
    "decision_tree_model = DecisionTreeRegressor(random_state=custom_seed)\n",
    "decision_tree_model.rmse = evaluate_model(df_train, df_validation, decision_tree_model, features)\n",
    "print(round(decision_tree_model.rmse, 5))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "outputs": [
    {
     "data": {
      "text/plain": "[('Baseline Linear Regression', 7756.215405299715),\n ('Decision Stump', 0.6203796205666162),\n ('Decision Tree Regressor', 0.5170882625542527)]"
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.append((\"Decision Tree Regressor\", decision_tree_model.rmse))\n",
    "models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "outputs": [],
   "source": [
    "dict_vectorizer = DictVectorizer(sparse=False)\n",
    "dtrain = df_train[features].to_dict(orient='records')\n",
    "x_train = dict_vectorizer.fit_transform(dtrain)\n",
    "baseline_model.fit(x_train, y_train)\n",
    "\n",
    "dval = df_validation[features].to_dict(orient='records')\n",
    "x_val = dict_vectorizer.transform(dval)\n",
    "y_pred = baseline_model.predict(x_val)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Tuning the model"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "outputs": [],
   "source": [
    "tree_model = DecisionTreeRegressor()\n",
    "\n",
    "df_train_copy = df_train.copy()\n",
    "df_val_copy = df_validation.copy()\n",
    "dtrain_copy = df_train_copy.to_dict(orient='records')\n",
    "\n",
    "dict_vectorizer = DictVectorizer(sparse=False)\n",
    "x_train_copy = dict_vectorizer.fit_transform(dtrain_copy)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "outputs": [],
   "source": [
    "# Setting up the Grid Search\n",
    "\n",
    "max_depths = [1, 2, 3, 4, 5, 6, 10, 15, 20, None]\n",
    "min_samples_leafs = [1, 2, 5, 10, 15, 20, 100, 200, 500]\n",
    "max_features = [1.0, \"sqrt\", \"log2\", None]\n",
    "criteria = [\"squared_error\", \"friedman_mse\", \"absolute_error\", \"poisson\"]\n",
    "\n",
    "grid = dict(max_depth=max_depths, min_samples_leaf=min_samples_leafs, max_features=max_features, criterion=criteria)\n",
    "cv = RepeatedKFold(n_repeats=3, random_state=5)\n",
    "grid_search = GridSearchCV(tree_model, param_grid=grid, cv=cv, scoring='neg_root_mean_squared_error', n_jobs=-1)\n",
    "gridr = grid_search.fit(x_train_copy, y_train)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "outputs": [],
   "source": [
    "\n",
    "mean_scores = gridr.cv_results_['mean_test_score']\n",
    "std_scores = gridr.cv_results_['std_test_score']\n",
    "params = gridr.cv_results_['params']\n",
    "\n",
    "columns_grid = [\"mean score\", \"std score\", \"params\"]\n",
    "grid_results = []\n",
    "\n",
    "for mean, std, param in zip(mean_scores, std_scores, params):\n",
    "    grid_results.append((mean, std, param))\n",
    "grid_results_df = pd.DataFrame(grid_results, columns=columns_grid)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "outputs": [
    {
     "data": {
      "text/plain": "      mean score  std score                                             params\n1292   -0.482495   0.040008  {'criterion': 'poisson', 'max_depth': 6, 'max_...\n1265   -0.482495   0.040008  {'criterion': 'poisson', 'max_depth': 6, 'max_...\n1364   -0.483546   0.040612  {'criterion': 'poisson', 'max_depth': 15, 'max...\n1400   -0.483546   0.040612  {'criterion': 'poisson', 'max_depth': 20, 'max...\n1373   -0.483546   0.040612  {'criterion': 'poisson', 'max_depth': 20, 'max...\n...          ...        ...                                                ...\n791    -0.760339   0.054219  {'criterion': 'absolute_error', 'max_depth': 2...\n800    -0.760339   0.054219  {'criterion': 'absolute_error', 'max_depth': 3...\n980    -0.760339   0.054219  {'criterion': 'absolute_error', 'max_depth': 1...\n809    -0.760339   0.054219  {'criterion': 'absolute_error', 'max_depth': 3...\n1034   -0.760339   0.054219  {'criterion': 'absolute_error', 'max_depth': 2...\n\n[1440 rows x 3 columns]",
      "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>mean score</th>\n      <th>std score</th>\n      <th>params</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1292</th>\n      <td>-0.482495</td>\n      <td>0.040008</td>\n      <td>{'criterion': 'poisson', 'max_depth': 6, 'max_...</td>\n    </tr>\n    <tr>\n      <th>1265</th>\n      <td>-0.482495</td>\n      <td>0.040008</td>\n      <td>{'criterion': 'poisson', 'max_depth': 6, 'max_...</td>\n    </tr>\n    <tr>\n      <th>1364</th>\n      <td>-0.483546</td>\n      <td>0.040612</td>\n      <td>{'criterion': 'poisson', 'max_depth': 15, 'max...</td>\n    </tr>\n    <tr>\n      <th>1400</th>\n      <td>-0.483546</td>\n      <td>0.040612</td>\n      <td>{'criterion': 'poisson', 'max_depth': 20, 'max...</td>\n    </tr>\n    <tr>\n      <th>1373</th>\n      <td>-0.483546</td>\n      <td>0.040612</td>\n      <td>{'criterion': 'poisson', 'max_depth': 20, 'max...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>791</th>\n      <td>-0.760339</td>\n      <td>0.054219</td>\n      <td>{'criterion': 'absolute_error', 'max_depth': 2...</td>\n    </tr>\n    <tr>\n      <th>800</th>\n      <td>-0.760339</td>\n      <td>0.054219</td>\n      <td>{'criterion': 'absolute_error', 'max_depth': 3...</td>\n    </tr>\n    <tr>\n      <th>980</th>\n      <td>-0.760339</td>\n      <td>0.054219</td>\n      <td>{'criterion': 'absolute_error', 'max_depth': 1...</td>\n    </tr>\n    <tr>\n      <th>809</th>\n      <td>-0.760339</td>\n      <td>0.054219</td>\n      <td>{'criterion': 'absolute_error', 'max_depth': 3...</td>\n    </tr>\n    <tr>\n      <th>1034</th>\n      <td>-0.760339</td>\n      <td>0.054219</td>\n      <td>{'criterion': 'absolute_error', 'max_depth': 2...</td>\n    </tr>\n  </tbody>\n</table>\n<p>1440 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_results_df.sort_values(by='mean score', ascending=False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "outputs": [
    {
     "data": {
      "text/plain": "{'criterion': 'poisson',\n 'max_depth': 6,\n 'max_features': 1.0,\n 'min_samples_leaf': 20}"
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_results_df.iloc[1265]['params']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.505389148710412\n"
     ]
    }
   ],
   "source": [
    "tuned_decision_tree = DecisionTreeRegressor(criterion='poisson', max_depth=6, max_features=1.0, min_samples_leaf=20)\n",
    "tuned_decision_tree.rmse = evaluate_model(df_train_copy, df_val_copy, tuned_decision_tree, features)\n",
    "print(tuned_decision_tree.rmse)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "outputs": [
    {
     "data": {
      "text/plain": "[('Baseline Linear Regression', 7756.215405299715),\n ('Decision Stump', 0.6203796205666162),\n ('Decision Tree Regressor', 0.5170882625542527),\n ('Tuned Decision Tree Regressor', 0.505389148710412)]"
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.append((\"Tuned Decision Tree Regressor\", tuned_decision_tree.rmse))\n",
    "models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# XGBoost"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\JonAreas\\.virtualenvs\\Phone-Price-Prediction-E2pVZFWk\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "dv = DictVectorizer(sparse=False)\n",
    "\n",
    "dict_train = df_train.to_dict(orient='records')\n",
    "x_train = dv.fit_transform(dict_train)\n",
    "features = dv.get_feature_names()\n",
    "dtrain = xgb.DMatrix(x_train, label=y_train)\n",
    "\n",
    "dict_val = df_validation.to_dict(orient='records')\n",
    "x_val = dv.transform(dict_val)\n",
    "dval = xgb.DMatrix(x_val, label=y_validation)\n",
    "\n",
    "xgboost_seed = 20\n",
    "xgb_params = {'seed': xgboost_seed, 'eval_metric': 'rmse', 'n_jobs': -1}\n",
    "xgboost_model = xgb.train(xgb_params, dtrain)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "outputs": [
    {
     "data": {
      "text/plain": "0.47006492825230467"
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = xgboost_model.predict(dval)\n",
    "xgboost_model.rmse = np.sqrt(mean_squared_error(y_validation, y_pred))\n",
    "xgboost_model.rmse"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "outputs": [
    {
     "data": {
      "text/plain": "[('Baseline Linear Regression', 7756.215405299715),\n ('Decision Stump', 0.6203796205666162),\n ('Decision Tree Regressor', 0.5170882625542527),\n ('Tuned Decision Tree Regressor', 0.505389148710412),\n ('XGBoost', 0.47006492825230467)]"
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models.append((\"XGBoost\", xgboost_model.rmse))\n",
    "models"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Comparing the Models\n",
    "Here, we compare all of the models fitted, which include XGBoost, a Decision Tree Regressor with Hyperparameter Tuning, a Default Decision Tree Regressor, a Decision Stump and a Default Linear Regression Model."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "outputs": [
    {
     "data": {
      "text/plain": "                           Model         RMSE\n4                        XGBoost     0.470065\n3  Tuned Decision Tree Regressor     0.505389\n2        Decision Tree Regressor     0.517088\n1                 Decision Stump     0.620380\n0     Baseline Linear Regression  7756.215405",
      "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>Model</th>\n      <th>RMSE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>XGBoost</td>\n      <td>0.470065</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Tuned Decision Tree Regressor</td>\n      <td>0.505389</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Decision Tree Regressor</td>\n      <td>0.517088</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Decision Stump</td>\n      <td>0.620380</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Baseline Linear Regression</td>\n      <td>7756.215405</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models_df = pd.DataFrame(models, columns=['Model', 'RMSE'])\n",
    "models_df.sort_values(by='RMSE', ascending=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# BentoML\n",
    "Finally, we save our model using BentoML"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "outputs": [
    {
     "data": {
      "text/plain": "Model(tag=\"xgboost_phone_predictor:t5ymq4dbcw73dhfw\", path=\"C:\\Users\\JonAreas\\bentoml\\models\\xgboost_phone_predictor\\t5ymq4dbcw73dhfw\\\")"
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BentoML\n",
    "import bentoml\n",
    "bentoml.xgboost.save_model(\"xgboost_phone_predictor\", xgboost_model,\n",
    "                           custom_objects={\n",
    "                               \"dictVectorizer\":dv\n",
    "                           },\n",
    "                           signatures={\n",
    "                               \"predict\": {\n",
    "                                   \"batchable\":True,\n",
    "                                   \"batch_dim\":0,\n",
    "                               }\n",
    "                           })"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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