Car_Selling_Price_Project(Scaling & Building Models).ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-Owned Car Selling Price Prediction ML Project\n",
    "\n",
    "In this notebook, I had performed scaling and model building on a processed dataset, saved from a previous notebook. I had applied nine different regression models to predict used car prices, and detailed instructions for each step are provided throughout the notebook. Finally, you can find the highest-scoring model saved using the pickle library for deployment on the web with the Streamlit library.\n",
    "\n",
    "The columns in the given Processed dataset are as follows:\n",
    "\n",
    "- Car_Brand\n",
    "- Car_Name\n",
    "- Year\n",
    "- Selling_Price\n",
    "- Km_Driven\n",
    "- Fuel\n",
    "- Seller_Type\n",
    "- Transmission\n",
    "- Owner\n",
    "\n",
    "\n",
    "### Context\n",
    "\n",
    "- Importing Libraries\n",
    "- Uploading Dataset\n",
    "- Applying Scaling\n",
    "    1) [Label Encoding](#label)\n",
    "    2) [Standardization](#scale)\n",
    "- [Checking Correlation](#corr)\n",
    "- [Modal Building](#mb)\n",
    "- [Checking Important Feature](#feature)\n",
    "- Modal\n",
    "    1) Basic Regression Algorithms\n",
    "        * [Linear Regression](#lr)\n",
    "        * [Ridge Regression](#ri)\n",
    "        * [Lasso Regression](#la)\n",
    "        * [KNeighbour Regression](#knr)\n",
    "    2) Ensemble Techniques \n",
    "        \n",
    "        <i>Bagging Algorithms</i>\n",
    "        * [Decision Tree Regression](#dt)\n",
    "        * [Random Forest Regression](#rf)\n",
    "\n",
    "        <i>Bagging Algorithms</i>\n",
    "        * [AdaBoost Regression](#ab)\n",
    "        * [Gradient Boosting Regression](#gb)\n",
    "        * [XGBoost Regression](xg#)\n",
    "- [Saving and Loading the modal](#save)\n",
    "- [Obtaining sample dataset](#sam)\n",
    "- [Producing prediction through saved modal](#pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Modules and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor,GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "import pickle as pkl\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uploading the preprocessed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Car_Details_Cleaned_Dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of dataset\n",
    "data = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3250, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking shape of dataset\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seprating the categorical and numerical columns\n",
    "categorical_col = ['Car_Brand', 'Car_Name', 'Fuel', 'Seller_Type', 'Transmission', 'Owner']\n",
    "numerical_col = ['Year', 'Selling_Price', 'Km_Driven']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying Label Encoding\n",
    "<a id='label'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LabelEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.LabelEncoder.html\">?<span>Documentation for LabelEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LabelEncoder()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding categorical data\n",
    "label_encoder = {} \n",
    "for i in categorical_col:\n",
    "    le = LabelEncoder()\n",
    "    data[i] = le.fit_transform(data[i])\n",
    "    label_encoder[i] = le\n",
    "le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Car_Brand</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Km_Driven</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>60000</td>\n",
       "      <td>70000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>142</td>\n",
       "      <td>2007</td>\n",
       "      <td>135000</td>\n",
       "      <td>50000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>139</td>\n",
       "      <td>2012</td>\n",
       "      <td>600000</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>108</td>\n",
       "      <td>2017</td>\n",
       "      <td>250000</td>\n",
       "      <td>46000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>2014</td>\n",
       "      <td>450000</td>\n",
       "      <td>141000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand  Car_Name  Year  Selling_Price  Km_Driven  Fuel  Seller_Type  \\\n",
       "0         12         5  2007          60000      70000     4            1   \n",
       "1         12       142  2007         135000      50000     4            1   \n",
       "2         10       139  2012         600000     100000     1            1   \n",
       "3          5       108  2017         250000      46000     4            1   \n",
       "4          9        13  2014         450000     141000     1            1   \n",
       "\n",
       "   Transmission  Owner  \n",
       "0             1      0  \n",
       "1             1      0  \n",
       "2             1      0  \n",
       "3             1      0  \n",
       "4             1      2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the dataset after applying label encoding\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Correlation between features\n",
    "<a id='corr'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "corr = corr[corr>=0.7]\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr, annot=True, fmt='.2f', cmap='coolwarm')\n",
    "plt.title(\"Correlation Heatmap Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>There is no correlation between features with each other.So, there is no need to further dive in the dataset. Now, lets proceed with the ML model building."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modal Building\n",
    "<a id='mb'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating Dependent and Independent Variables\n",
    "x = data.drop([\"Selling_Price\"], axis=1)\n",
    "y = data[\"Selling_Price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Importance in Dataset\n",
    "<a id='feature'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D+qmurs7EiRMXuHps0003rVAqWLgZM2bUeW6bNm0WYRL46p5++un0798/bdu2zeTJk/PCCy+kZ8+e+dWvfpUpU6bkj3/8Y6UjQqHVV1892223Xc4444y0aNGi0nHgS9lggw3y61//OltuuWWlo/A5SnQaxHXXXZeLLrool19+edZYY40kyQsvvJD9998/BxxwQPbYY48KJ4SFGzRoUFq3bp3LL788K6ywQp566qn07Nkz48aNy/7775+XXnqp0hFhoaqq5r/NSalUSrlcTqlUyrx58yqQCurn4Ycfzu67755XX301//1XVNcxi7Oqqqpaqx0XxH+PWVL0798/6623Xs4666y0bt265u/FDz74YHbfffdMnjy50hGhUMuWLfPMM8+kZ8+elY4CX9ro0aNz3HHH5dRTT13gAilvyFeGEp0Gscoqq+SWW25J3759a40//vjj+dGPfpRJkyZVKBnUzQorrJAHH3wwa6yxRq1/LEyePDm9evXK7NmzKx0RFurVV19d6HHbvLAk6NOnT1ZfffUMHz483/jGN+YrJdu2bVuhZLBw9957b53n9uvXbxEmga+ubdu2GT9+fFZZZZVafy9+9dVXs8Yaa+Sjjz6qdEQotNNOO2XXXXfN4MGDKx0FvrTPL5D6/N+HvSFfWfZEp0H8+9//zieffDLf+Lx58/Lmm29WIBHUT3V19QL/R/Svf/0rrVu3rkAiqB8lOUuDl156KbfccktWXXXVSkeBelGMszRp2rTpArcoevHFF7PiiitWIBHU3fbbb59jjjkmEyZMyNprr53llluu1vGBAwdWKBnU3dixYysdgQWwEp0GscMOO+T111/PZZddlvXWWy/Jp6vQf/rTn6ZLly7585//XOGEsHC77LJL2rZtm0suuSStW7fO008/nRVXXDE//OEPs/LKK+fKK6+sdESokwkTJmTKlCmZO3durXH/YGBJsMUWW+TnP/95ttlmm0pHga/k73//e37/+9/nlVdeyc0335wuXbrk6quvTo8ePfL973+/0vFgoX7yk5/k3XffzU033ZTll18+Tz/9dBo1apRBgwZl0003zXnnnVfpiFBoQVscfsYKXuCrUKLTIN5+++3svffeGT16dM07vZ988kkGDBiQq666Kh07dqxwQli4f/3rXxkwYEDK5XJeeumlbLDBBnnppZfSoUOH3Hfffa5hFnuvvPJKdtxxxzzzzDM1e6En//n1P/9gYEnwP//zP/nVr36VY445ZoGrx9wglyXBrbfemr322it77LFHrr766kyYMCE9e/bMBRdckL/+9a/561//WumIsFDTp0/Pj370ozz22GP54IMP0rlz50ydOjUbb7xx/vrXv863Ny8ADc8b8osfJToN6sUXX8zzzz+fJFlzzTWz+uqrVzgR1N0nn3ySG264IU8//XRmzpyZ9dZbL3vssUeaN29e6WjwhXbYYYc0atQol112WXr06JFHH3007777bo466qicffbZ2WSTTSodEb6QG+SyNOjbt2+OPPLIDBkypNZ+0k888US23XbbTJ06tdIRoU7uv//+Wn8v7t+/f6UjASwTvCG/eFKiA8BSoEOHDrnnnnuyzjrrpG3btnn00Uezxhpr5J577slRRx2VJ554otIR4Qu5QS5LgxYtWmTChAnp3r17rRL9lVdeSa9evdyUEWARu/fee3P22WfnueeeS5L06tUrxxxzjEUlLDG8Ib94cmNRGsS8efNy1VVXZcyYMXnrrbdSXV1d6/g999xToWRQd2+88Ubuv//+BV7Dhx12WIVSQd3Mmzev5ia4HTp0yBtvvJE11lgj3bp1ywsvvFDhdFA3SnKWBp06dcrEiRPTvXv3WuP3339/evbsWZlQUE9jxozJyJEja0rItdZaK0cccYTV6Cz2rrnmmgwdOjQ77bRTzb/hHnjggWy55Za56qqrsvvuu1c4IXyxF154IZtuuul8423bts20adO+/kAkUaLTQA4//PBcddVV2X777dO7d++aPXhhSXHVVVflgAMOSJMmTbLCCivUuoZLpZISncVe796989RTT6VHjx7ZaKONctZZZ6VJkya55JJLlDYsUa6++uqMGjUqkyZNykMPPZRu3brlvPPOS48ePfLDH/6w0vHgC+2///45/PDDc8UVV6RUKuWNN97IQw89lKOPPjonnHBCpePBF7roooty+OGH50c/+lEOP/zwJMnDDz+c7bbbLiNHjszBBx9c4YRQ7PTTT89ZZ52VI488smbssMMOy7nnnptTTz1Vic4SwRvyiyfbudAgOnTokD/+8Y/ZbrvtKh0FvpSuXbvmZz/7WY477riF3tEdFld33XVXZs2alZ122ikTJ07MD37wg7z44otZYYUVcuONN2aLLbaodET4QhdffHFOPPHEHHHEETn99NPzz3/+Mz179sxVV12VP/zhDxk7dmylI8IXKpfLOeOMMzJixIjMnj07SdK0adMcffTROfXUUyucDr7YN7/5zRx77LE55JBDao1feOGFOeOMM/L6669XKBl8saZNm+bZZ5/NqquuWmt84sSJ6d27ty21WCKMGDEi11xzTa644opstdVW+etf/5pXX301Rx55ZE444YQceuihlY64TFKi0yA6d+6ccePGuZEoS6wVVlghjz76aFZZZZVKR4EG895776V9+/Z+O4glRq9evXLGGWdk0KBBtfZ//Oc//5nNNtss77zzTqUjQp3NnTs3EydOzMyZM9OrV6+0atWq0pGgTlq1apUnn3xyvhLypZdeSt++fTNz5swKJYMvtuqqq+aYY47JAQccUGt81KhROeecc/LSSy9VKBnUnTfkF09KdBrEOeeck1deeSUXXHCBsoYl0s9//vMsv/zyOfbYYysdBb6SiRMn5uWXX86mm26a5s2bp1wu++8yS4zmzZvn+eefT7du3WqV6C+99FLWWWedfPjhh5WOCLDU23333dO3b98cc8wxtcbPPvvsPPbYY7nhhhsqlAy+2MUXX5wjjjgi++67b7773e8m+XRP9Kuuuiq//e1v5yvXYXHmDfnFiz3RaRD3339/xo4dmzvvvDPf+ta3stxyy9U6ftttt1UoGdTNiBEj8oMf/CCjR4/O2muvPd81fO6551YoGdTNu+++m8GDB2fs2LEplUp56aWX0rNnz+y3335p3759zjnnnEpHhC/Uo0ePPPnkk/PdYHT06NFZa621KpQK6mfzzTdf6JuX99xzz9eYBuqvV69eOf300zNu3LhsvPHGST7dE/2BBx7IUUcdlfPPP79mrvsGsbg58MAD06lTp5xzzjm56aabknx6Y9wbb7zRvVVY4jRp0iS9evWqdAz+PyU6DaJdu3bZcccdKx0DvrQRI0bkrrvuyhprrJEk891YFBZ3Rx55ZJZbbrlMmTKlVtm4yy67ZNiwYUp0lgjDhg3LwQcfnI8++ijlcjmPPvporr/++owYMSKXXXZZpeNBnfTp06fW448//jhPPvlk/vnPf2bvvfeuTCioh8svvzzt27fPhAkTMmHChJrxdu3a5fLLL695XCqVlOgslnbccUf9BEu0WbNm5cwzz8yYMWPy1ltvpbq6utbxV155pULJlm22cwFI0r59+4wcOTL77LNPpaPAl9KpU6fcddddWXfddWttg/HKK69knXXWsX8pS4xrr702J598cl5++eUkn953Zfjw4dlvv/0qnAy+mpNPPjkzZ87M2WefXekoAMBibLfddsu9996bvfbaK9/4xjfmW9h3+OGHVyjZsk2JDpBPC8i///3vWW211SodBb6U1q1bZ/z48VlttdVqleiPPfZYBgwYkHfffbfSEaFeZs+enZkzZ6Zjx46VjgINYuLEidlwww3z3nvvVToKLNTYsWOz+eabVzoG1Nnyyy+fF198MR06dEj79u0X+pvE/hvMkqBdu3a544478r3vfa/SUfgc27nQYG655ZbcdNNNmTJlSubOnVvr2Pjx4yuUCurm8MMPz+9+97taezzCkuCNN95I586ds8kmm+SPf/xjzd3aS6VSqqurc9ZZZ/mHMEuM0047LXvssUd69OiRFi1apEWLFpWOBA3moYceSrNmzSodA77QNttsk29+85sZOnRo9t5773Tt2rXSkWChRo4cmdatW9d8bjtOlnTt27fP8ssvX+kY/Bcr0WkQ559/fo4//vjss88+ueSSSzJ06NC8/PLL+cc//pGDDz44p59+eqUjwkLtuOOOueeee7LCCiu4OS5LlPbt2+fCCy/Muuuumy222CLrrbde7rnnngwcODDPPvts3nvvvTzwwANZZZVVKh0VvtC6666bf/7zn9loo42y5557ZvDgwenQoUOlY0G97LTTTrUel8vl/Pvf/85jjz2WE044ISeddFKFkkHdvPPOO7n66qvzhz/8Ic8++2y22GKL7Lfffhk0aFCaNGlS6XgAS71rrrkmf/rTn/KHP/zBopLFiBKdBrHmmmvmpJNOym677VZrG4ETTzwx7733Xi644IJKR4SFGjp06EKPX3nllV9TEqifiy66KL/4xS+yzTbbZNSoURk1alSeeuqpzJw5M+utt14OPvjgfOMb36h0TKizZ599Ntdee21uuOGG/Otf/8pWW22VPfbYI4MGDfKPCBZrr7zySrp37z7f/v1VVVVZccUVs8UWW2TrrbeuUDr4csaPH58rr7wy119/fZJk9913z3777Zd11123wslgwcaPH5/lllsua6+9dpLkT3/6U6688sr06tUrJ598sjeCWCL07ds3L7/8csrlcrp37z7fIj+7PVSGEp0G0aJFizz33HPp1q1bOnbsmLvvvjvrrrtuXnrppXznO9+xFy/AIjRp0qTst99+mTBhQi655JIMHDiw0pGgQTzwwAO57rrrcvPNN+ejjz7KjBkzKh0JCjVq1Cj//ve/a/bx32WXXXL++ednpZVWqnAy+GreeOONXHLJJTnzzDPTuHHjfPTRR9l4440zatSofOtb36p0PKjl29/+do499tjsvPPOeeWVV9KrV6/stNNO+cc//pHtt98+5513XqUjwhcaPnz4Qo/7rbbKsCc6DaJTp05577330q1bt6y88sp5+OGHs+6662bSpEnxPg3AotWjR4/cc889ueCCC7LzzjtnrbXWSuPGtf8Xb7UCS6KWLVumefPmadKkST744INKx4GF+u+/8955552ZNWtWhdLAV/Pxxx/nT3/6U6644orcfffd2WCDDXLBBRdkt912y9tvv51f/epX+fGPf5wJEyZUOirU8uKLL6ZPnz5Jkptvvjn9+vXLddddlwceeCC77rqrEp0lgpJ88aREp0FsscUW+fOf/5y+fftm6NChOfLII3PLLbfksccem29fSFhcuTkuS7JXX301t912W9q3b58f/vCH85XosKSYNGlSrrvuulx33XV54YUX0q9fvwwfPjw/+tGPKh0N6sVCEpY0W2yxRW677baccMIJuf7661Mul7PXXnvlrLPOSu/evWvmtWzZMmeffXY6d+5cwbSwYOVyOdXV1UmSv/3tb/nBD36QJOnatWveeeedSkaDOjvxxBOz+eabZ+ONN3ZT8sWIf2HTIC655JKa/1EdfPDBWWGFFfLggw9m4MCBOeCAAyqcDr7Y52+O+6c//Wm+m+PC4uzSSy/NUUcdlf79++fZZ5/NiiuuWOlI8KV85zvfyT/+8Y+ss846GTp0aHbbbbd06dKl0rGgTkqlUkql0nxjsKS49957M3fu3EyYMCG/+93vstNOO6Vp06YLnNuhQ4eMHTv2a04IX2yDDTbIaaedlv79++fee+/NxRdfnOTTN+ltr8WS4qGHHsq5556bTz75JN/+9rfTr1+/bLbZZvne976X5s2bVzreMsue6Hxln3zySc4444zsu++++eY3v1npOPCluDkuS6ptttkmjz76aM4777wMGTKk0nHgKzn++OOzxx57pFevXpWOAvVWVVWVbbfdtqZ0/N///d9sscUWadmyZa15t912WyXiwReqqqrK1KlTa/b1hyXR008/nT322CNTpkzJsGHDarbFOPTQQ/Puu+/muuuuq3BCqJtPPvkkjzzySO67777ce++9efDBBzNnzpx8+9vfzv3331/peMskJToNolWrVvnnP/+Z7t27VzoKfClujsuSaquttsqVV17pTUyAChs6dGid5l155ZWLOAl8OVVVVbnnnnuy/PLLL3TeOuus8zUlgobz0UcfpVGjRlluueUqHQXq5cUXX8zYsWPzt7/9Lbfffnvatm1ra6IKsZ0LDWLLLbfMvffeq0RnieXmuCyp7r777kpHgK9k2LBhOfXUU9OyZcsMGzZsoXPPPffcrykV1J9ynKXBlltuucC/+5ZKpZTL5ZRKpcybN68CyaBuXnvttZRKpZoFJo8++miuu+669OrVKz/96U8rnA7q5pJLLsm4ceNy7733Zs6cOdlkk02y2Wab5Ve/+pU3MitIiU6D2HbbbXPsscfmmWeeyfrrrz/fr60OHDiwQsmgbtwcF6AynnjiiXz88cdJPr2Jc9Ee0vaWBlj0HnnkEfdWYYm2++6756c//Wn22muvTJ06NVtttVW+9a1v5dprr83UqVNz4oknVjoifKGf/exnWXHFFXPUUUfloIMOSqtWrSodidjOhQZSVVVVeMxqBZYE1dXVqa6uTuPGn763eMMNN+TBBx/MaqutlgMOOCBNmjSpcEIAAFh07InO0qB9+/Z5+OGHs8Yaa+T888/PjTfemAceeCD/93//l5/97Gd55ZVXKh0RvtDtt9+e++67L+PGjctzzz2Xvn37ZrPNNstmm22W73//+2nRokWlIy6TlOgAAFTcxx9/nObNm+fJJ59M7969Kx0HYJmjRGdp8Pn7tQ0cODDf+9738otf/CJTpkzJGmuskQ8//LDSEaFepk+fnr///e+5+eabc/3116eqqiofffRRpWMtk2znAizTpkyZUqd5K6+88iJOArBsW2655bLyyiv77TWACunXr1+9fvvy+uuvz8CBA+fbyhMq6Vvf+lZGjRqV7bffPnfffXdOPfXUJMkbb7yRFVZYocLpoO7efffd3HvvvRk3blzGjRuXZ599Nu3bt88mm2xS6WjLLCvR+co++OCDvPjii1ljjTXSqlWrjB8/Puedd14+/PDDDBo0KHvssUelI0KhqqqqBe6z+9mNk5JPtyT65JNPvu5oAMucyy+/PLfddluuvvrqLL/88pWOA8BCtGnTJk8++WR69uxZ6ShQY9y4cdlxxx0zY8aM7L333rniiiuSJL/85S/z/PPP57bbbqtwQvhia6+9diZMmJDll18+m266aTbbbLP069fPTUUrTInOV3LfffflBz/4QWbOnJn27dvn+uuvz49+9KN06dIljRo1ynPPPZdRo0Zl//33r3RUWKCnnnpqgePlcjk33HBDzj///LRq1SpvvfXW15wMYNnTt2/fTJw4MR9//HG6des23+rG8ePHVygZAP+tdevWeeqpp5ToLHbmzZuXGTNmpH379jVjkydPTosWLWxXxBLhwgsvzGabbZaOHTumVCqlQ4cOlY5EbOfCV/SrX/0qP/7xj3PKKafkiiuuyC677JJDDjkkZ5xxRpLktNNOy4UXXqhEZ7G17rrrzjf2t7/9Lccee2xefPHF/PznP89RRx1VgWQAy55BgwZVOgIAsIRr1KhRrQI9Sbp3716ZMFBP06ZNy4QJE3LSSSfl/fffT/LpDXN33XXXnHbaaWnXrl1lAy7DrETnK2nXrl0efvjhrLnmmpk7d26aN2+e8ePH1xSTEydOTN++ffPBBx9UOCl8sfHjx+cXv/hF/v73v+cnP/lJTjzxRCsVAABgAaxEZ3Gx3nrrZcyYMWnfvn369u27wO06P+O32licvffee9l4443z+uuvZ4899shaa62VJJkwYUKuu+66dO3aNQ8++OB8bxLx9bASna9kxowZNXuWNmnSJC1atEjr1q1rjrdu3TqzZ8+uVDyok5dffjm//OUvc+utt2bw4MGZMGGCfwwAVEC5XM7jjz+eyZMnp1QqpUePHl/4j2EAYNn2wx/+ME2bNk3it9pYsp1yyilp0qRJXn755ay00krzHdt6661zyimnZOTIkRVKuGyzEp2vpFGjRpk6dWpWXHHFJJ/eXOapp55Kjx49kiRvvvlmOnfunHnz5lUyJhQ66KCDcvnll2fzzTfPmWeemT59+lQ6EsAyaezYsdlvv/3y6quv5rO/nn5WpF9xxRXZdNNNK5wQgM+zEh2gYXXv3j2///3vM2DAgAUeHz16dH72s59l8uTJX28wkijR+YqqqqrSu3fvNG786S81PP3001lzzTXTpEmTJMknn3ySZ599VonOYquqqirNmjX7f+3de0yW9f/H8dcN4ZGDGImmqCDkCXGeXVq6KBWWeIqsnGkup5jJTE3JqXkWNbHUyrScmnMeV7o85oHEWUtR0NQUxGN5AlRQkcL794f7su4fIiBwf7jh+djc5Ppcfzz/+EzhzXV/LjVp0uSJ9/GxPwAoPUlJSWrZsqU6dOigyMhINWnSRFarVSdPntQXX3yhw4cPKzExkUENAJSynJwcHTx4UEFBQQWeuxsYGKjt27fLx8fHPnFAEWVmZurhw4c219zd3Q3VAAWrXLmykpOTVa9evceuX758Wf7+/srKyrJzGSSOc0ExTZkyxebrXr165bmnX79+9soBiuz/72EAgP0tXLhQHTt21J49e2yuN2nSRH369NGrr76qmJgYLVq0yFAhAFQMzs7O6tatm06dOlXgEP3EiRP2iQKKICUlRSNHjtT+/fttBo1Wq1UWi4UH/FCmeXl56fz58/kO0VNSUnKPVIb98SQ67OrgwYNq27Zt7nllgKNhDwNAyQsMDNTs2bPVs2fPx65v3bpVUVFRDGwAwA7atm2r6OhoBQcHm04BiqxTp06yWq2KjIyUt7d3nveqdOnSxVAZULAhQ4YoOTlZu3fvzj3h4X8ePHig7t27y8/PT999952hwoqNITrsyt3dXceOHePj2HBY7GEAKHnu7u5KTExUw4YNH7uekpKioKAgZWRk2DcMACqgHTt2KCoqStOnT1ebNm1UvXp1m3WOw0BZ5urqqiNHjqhx48amU4Aiu3z5cu5Dex988EHuEYenTp3Sl19+qQcPHujw4cMco2UIx7nArvidDRwdexgASl5mZqaqVauW73q1atV07949OxYBQMUVGhoqSQoLC7N5ipfjMOAI2rVrp0uXLjFEh0OqV6+eDh06pBEjRigqKip3/mCxWPTaa69p8eLFDNANYogOAAAA406ePKmrV68+du3mzZt2rgGAimvfvn2mE4Cntnz5cg0fPlxXrlxRYGCgXFxcbNaDgoIMlQGF4+vrq+3btys9PV1nz56VJPn7+3MWehnAEB0AAADGBQcHP/bTPhaLJffpRwBA6ePMaDiyGzduKDk5We+9917utf9+L8EnKeAoPD091b59e9MZ+A+G6AAAADAqJSXFdAIA4D8OHDigpUuX6ty5c9qwYYPq1q2r1atXy9fXV507dzadB+RryJAhatWqldauXfvYF4sCwNNiiA674j8wODr2MACUvAYNGhTp/hEjRmjatGny8vIqpSIAqLg2bdqkgQMHasCAAYqPj9eDBw8kSbdv39asWbO0bds2w4VA/i5cuKAtW7bI39/fdAqAcsbJdAAcn9Vq1cWLF5WVlVWoe4Gyhj0MAI7l+++/1507d0xnAEC5NGPGDH399ddatmyZzXnSnTp1Unx8vMEyoGCvvPKKEhISTGcAKId4Eh3FZrVa5e/vrz/++EMBAQFPvDcjI8NOVUDhsYcBwLHwC00AKD1//vmnXn755TzXPTw8dOvWLfsHAUXQs2dPjR49WsePH1eLFi3yvFg0LCzMUBkAR8cQHcXm5OSkgIAApaamFjiABMoi9jAAAADwSO3atZWUlKSGDRvaXI+Li5Ofn5+ZKKCQhg8fLkmaNm1anjVeLAqgODjOBSVizpw5GjdunE6cOGE6BXgq7GEAAABAGjp0qCIjI/Xbb7/JYrHor7/+0po1azR27FhFRESYzgOe6OHDh/n+YYAOoDgsVj4PixLg6empe/fu6d9//1WlSpVUtWpVm/W0tDRDZUDhsIcBwHG4ubkpISGBJyIBoBRYrVbNmjVLs2fP1r179yRJlStX1tixYzV9+nTDdUDR3bp1SzVq1DCdAcDBcZwLSsTChQtNJwDFwh4GAAAAHh15MXHiRI0bN05JSUnKzMxUs2bN5OrqajoNKFB0dLQaNmyo/v37S5LCw8O1adMm1alTR9u2bVPLli0NFwJwVDyJDgAAAIcSERGh6dOny8vLy3QKAAAoQ3x9fbVmzRq9+OKL2r17t958802tW7dO69ev18WLF7Vr1y7TiQAcFEN0lLisrCxlZ2fbXHN3dzdUAxQdexgAzMnKylJiYqKuX7+uhw8f2qyFhYUZqgKA8q1v376Fvnfz5s2lWAIUT9WqVXXmzBn5+PgoMjJSWVlZWrp0qc6cOaMOHTooPT3ddCIAB8VxLigRd+/e1fjx47V+/XqlpqbmWecFHijr2MMAYN6OHTv07rvv6ubNm3nWLBYL/xYDQCnx8PAwnQCUCE9PT126dEk+Pj7asWOHZsyYIenRWf98HwGgOBiio0R8/PHH2rdvn7766isNHDhQS5Ys0ZUrV7R06VLNmTPHdB5QIPYwAJj34YcfKjw8XJMnT5a3t7fpHACoMFasWGE6ASgRffv21TvvvKOAgAClpqYqJCREknT06FH5+/sbrgPgyDjOBSWifv36WrVqlbp27Sp3d3fFx8fL399fq1ev1tq1a7Vt2zbTicATsYcBwDx3d3cdPXpUjRo1Mp0CAAAc0D///KPPP/9cly5d0uDBg9WqVStJUkxMjNzc3PT+++8bLgTgqHgSHSUiLS1Nfn5+kh79AJyWliZJ6ty5syIiIkymAYXCHgYA89544w3t37+fIToA2FmrVq1ksVgKdW98fHwp1wBPz8XFRWPHjs1zffTo0QZqAJQnDNFRIvz8/JSSkqL69eurSZMmWr9+vdq3b6+tW7eqRo0apvOAArGHAcC8xYsXKzw8XAcOHFCLFi3k4uJisz5q1ChDZQBQvvXu3dt0AlBizp49q3379j32JeWTJ082VAXA0XGcC0pETEyMnJ2dNWrUKP3888/q2bOnrFarsrOzFRMTo8jISNOJwBOxhwHAvG+//VbDhw9XlSpV9Oyzz9o8FWmxWHTu3DmDdQAAoKxbtmyZIiIi5OXlpdq1a+f5XoJPUgB4WgzRUSouXLigI0eOKCAgQC1atDCdAxQZexgA7K927doaNWqUJkyYICcnJ9M5AFBh3bp1Sxs3blRycrLGjRunmjVrKj4+Xt7e3qpbt67pPCBfDRo00IgRIzR+/HjTKQDKGX46QbHs3btXzZo10507d2yuN2jQQMHBwXrrrbd04MABQ3VAwdjDAFB2ZGdnq3///gzQAcCgxMREvfDCC4qOjtb8+fN169YtSdLmzZsVFRVlNg4oQHp6usLDw01nACiH+AkFxbJw4UINHTpU7u7uedY8PDw0bNgwLViwwEAZUDjsYQAoOwYNGqR169aZzgCACu2jjz7S4MGDdfbsWVWpUiX3emhoqH755ReDZUDBwsPDtWvXLtMZAMohXiyKYklISFB0dHS+6926ddP8+fPtWAQUDXsYAMqOnJwczZ07Vzt37lRQUFCeF4vyS00AKH2///67li5dmud63bp1dfXqVQNFQOH5+/tr0qRJ+vXXX3lJOYASxRAdxXLt2rU8/yn91zPPPKMbN27YsQgoGvYwAJQdx48fV6tWrSRJJ06csFnLyMgwkQQAFU7lypXzHHUoSWfOnNFzzz1noAgovG+++Uaurq6KjY1VbGyszZrFYmGIDuCpMURHsdStW1cnTpyQv7//Y9cTExNVp04dO1cBhcceBgDzYmJiNHr0aO3bt++x6xkZGerRo4edqwCgYgoLC9O0adO0fv16SY8GjxcvXtT48ePVr18/w3XAk6WkpJhOAFBOcSY6iiU0NFSTJk1SVlZWnrX79+9rypQpev311w2UAYXDHgYA8z755BOtWrXqsWt3795VSEiIUlNT7VwFABXTZ599pszMTNWqVUv3799Xly5d1KhRI7m6umrmzJmm8wAAMMJitVqtpiPguK5du6bWrVvL2dlZI0eOVOPGjSVJp0+f1pIlS5STk6P4+Hh5e3sbLgUejz0MAOZt3LhRAwcO1Lp16xQWFpZ7PTMzUz169ND169e1f/9+Pf/88wYrAaBiiYuLU2JiojIzM9WmTRsFBwebTgIK5fLly9qyZYsuXryo7OxsmzXerwLgaTFER7FduHBBERER2rlzp/63nSwWi7p3764lS5bI19fXcCHwZOxhADBv+fLlioyM1E8//aSuXbvq7t276tGjh65evarY2FgG6ABQyg4dOqTU1FSbT2GuXLlSU6ZM0b1799S7d28tWrRIlStXNlgJPNmePXsUFhYmPz8/nT59WoGBgTp//rysVqtat26tvXv3mk4E4KAYoqPEpKenKykpSVarVQEBAfL09DSdBBQJexgAzJo7d65mzpypH3/8UZMnT9aVK1cUGxurevXqmU4DgHIvJCREXbt21fjx4yU9etlzmzZtNGjQIDVt2lTz5s3TsGHD9Omnn5oNBZ6gffv2CgkJ0dSpU+Xm5qaEhATVqlVLAwYMUI8ePRQREWE6EYCDYogOAACAMmPChAmaN2+eGjZsqP3798vHx8d0EgBUCHXq1NHWrVvVtm1bSdLEiRMVGxuruLg4SdKGDRs0ZcoUnTx50mQm8ERubm46duyYGjVqJE9PT8XFxal58+ZKSEhQr169dP78edOJABzUM6YDAAAAULH17dvX5msXFxd5eXkpMjLS5vrmzZvtmQUAFUp6errNe4BiY2MVEhKS+3W7du106dIlE2lAoVWvXj33HPQ6deooOTlZzZs3lyTdvHnTZBoAB8cQHQAAAEZ5eHjYfP32228bKgGAisvb21spKSny8fFRdna24uPjNXXq1Nz1jIwMubi4GCwECtaxY0fFxcWpadOmCg0N1ZgxY3T8+HFt3rxZHTt2NJ0HwIExRAcAAIBRK1asMJ0AABVeaGioJkyYoOjoaP3www+qVq2aXnrppdz1xMRENWrUyGAhULAFCxYoMzNTkjR16lRlZmZq3bp1CggI0IIFCwzXAXBknIkOAAAAAEAFd/PmTfXt21dxcXFydXXVypUr1adPn9z14OBgdezYUTNnzjRYCeQvJydHBw8eVFBQkGrUqGE6B0A5wxAdAAAAAABIkm7fvi1XV1c5OzvbXE9LS5Orq6sqVapkqAwoWJUqVXTq1Cn5+vqaTgFQzjiZDgAAAAAAAGWDh4dHngG6JNWsWZMBOsq8wMBAnTt3znQGgHKIJ9EBAAAAAADg8Hbs2KGoqChNnz5dbdq0UfXq1W3W3d3dDZUBcHQM0QEAAAAAAOCwpk2bpjFjxsjNzS33msViyf271WqVxWJRTk6OiTwA5QBDdAAAAAAAADgsZ2dn/f333zp16tQT7+vSpYudigCUNwzRAQAAAAAA4LCcnJx09epV1apVy3QKgHKKF4sCAAAAAADAof33+BYAKGk8iQ4AAAAAAACH5eTkJA8PjwIH6WlpaXYqAlDePGM6AAAAAAAAACiOqVOnysPDw3QGgHKKJ9EBAAAAAADgsDgTHUBp40x0AAAAAAAAOCzOQwdQ2hiiAwAAAAAAwGFxyAKA0sZxLgAAAAAAAAAA5IMn0QEAAAAAAAAAyAdDdAAAAAAAAAAA8sEQHQAAAAAAAACAfDBEBwAAAAAAAAAgHwzRAQAAAAAAAADIB0N0AAAAAAAAAADywRAdAAAAAAAAAIB8MEQHAAAAAAAAACAf/wfw1WY3CilHaAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forest = RandomForestRegressor()\n",
    "forest.fit(x, y)\n",
    "importances = forest.feature_importances_\n",
    "std = np.std([tree.feature_importances_ for tree in forest.estimators_], axis=0)\n",
    "forest_importances = pd.Series(importances, index=x.columns)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "forest_importances.plot.bar(yerr=std, ax=ax)\n",
    "ax.set_title(\"Feature importances using MDI\")\n",
    "ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<i>This plot demonstrates the importance of different features in predicting the target variable using a Random Forest regression model through MDI(mean decrease in impurity), helping in identify the Year features contribute most to the model's predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into Training and Testing Dataset\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implementing Standardization\n",
    "<a id='scale'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.39427591, -1.65638215, -1.7656602 , ...,  0.4464156 ,\n",
       "        -3.87722771,  0.87237108],\n",
       "       [-0.94964843, -0.7956773 ,  1.74201389, ...,  0.4464156 ,\n",
       "         0.25791624, -0.71358067],\n",
       "       [-0.46810594,  1.15115511, -0.26237131, ...,  0.4464156 ,\n",
       "         0.25791624, -0.71358067],\n",
       "       ...,\n",
       "       [-0.94964843, -0.44729676, -2.01620835, ...,  0.4464156 ,\n",
       "         0.25791624,  2.45832282],\n",
       "       [-0.46810594, -0.93912811,  0.73982129, ...,  0.4464156 ,\n",
       "         0.25791624,  0.87237108],\n",
       "       [-0.46810594, -0.73419838,  1.49146574, ...,  0.4464156 ,\n",
       "         0.25791624, -0.71358067]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardize numerical column\n",
    "sc = StandardScaler()\n",
    "sc.fit_transform(x_train)\n",
    "sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating variable to store model evaluation data\n",
    "model_scores = {}\n",
    "\n",
    "# Creating  Model Evaluation Function \n",
    "def evaluate(model, model_name, x_train, y_train, x_test, y_test):\n",
    "    y_pred = model.predict(x_test)\n",
    "\n",
    "    # Calculating Model Evaluation\n",
    "    training_score = model.score(x_train,y_train)\n",
    "    testing_score = model.score(x_test,y_test)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    print(\"Model\",model_name)\n",
    "    print(\"Training Score\",training_score)\n",
    "    print(\"Testing Score\",testing_score)\n",
    "    print(\"Mean Squared Error\",mse)\n",
    "    print(\"Mean Absolute Error\",mae)\n",
    "    print(\"R2 Score\",r2)\n",
    "    \n",
    "    # Storing score of each model in model_scores variable    \n",
    "    model_scores[model_name] = {'Training Score': training_score,'Testing Score': testing_score,'R² Score': r2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Regression Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.Linear Regression \n",
    "<a id='lr'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Linear Regression\n",
      "Training Score 0.5124452737735408\n",
      "Testing Score 0.5382629439096264\n",
      "Mean Squared Error 29071426521.83147\n",
      "Mean Absolute Error 131145.68102530134\n",
      "R2 Score 0.5382629439096264\n"
     ]
    }
   ],
   "source": [
    "linear = LinearRegression()\n",
    "linear.fit(x_train,y_train)\n",
    "evaluate(linear,\"Linear Regression\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.Ridge Regression\n",
    "<a id='ri'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Ridge Regression\n",
      "Training Score 0.5124438663512985\n",
      "Testing Score 0.5381318412255883\n",
      "Mean Squared Error 29079680877.84547\n",
      "Mean Absolute Error 131164.27825863552\n",
      "R2 Score 0.5381318412255883\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge()\n",
    "ridge.fit(x_train,y_train)\n",
    "evaluate(ridge,\"Ridge Regression\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.Lasso Regression\n",
    "<a id='la'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Lasso Regression\n",
      "Training Score 0.5124452734171158\n",
      "Testing Score 0.5382609004817511\n",
      "Mean Squared Error 29071555178.09274\n",
      "Mean Absolute Error 131145.9063757513\n",
      "R2 Score 0.5382609004817511\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "lasso.fit(x_train,y_train)\n",
    "evaluate(lasso,\"Lasso Regression\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.KNeighbors Regression\n",
    "<a id='knr'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model KNeighbors Regressor\n",
      "Training Score 0.47405133886586115\n",
      "Testing Score 0.2213330357067549\n",
      "Mean Squared Error 49025650289.15076\n",
      "Mean Absolute Error 166937.86888068877\n",
      "R2 Score 0.2213330357067549\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsRegressor()\n",
    "knn.fit(x_train,y_train)\n",
    "evaluate(knn,\"KNeighbors Regressor\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ensemble Techniques\n",
    "a) Bagging Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Decision Tree Regression\n",
    "<a id='dt'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Decision Tree\n",
      "Training Score 0.9964248452716197\n",
      "Testing Score 0.5590784843741485\n",
      "Mean Squared Error 27760859290.666634\n",
      "Mean Absolute Error 111574.86326363265\n",
      "R2 Score 0.5590784843741485\n"
     ]
    }
   ],
   "source": [
    "decision = DecisionTreeRegressor()\n",
    "decision.fit(x_train,y_train)\n",
    "evaluate(decision,\"Decision Tree\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Random Forest Regression\n",
    "<a id='rf'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Random Forest\n",
      "Training Score 0.9648231940210549\n",
      "Testing Score 0.7729353574818341\n",
      "Mean Squared Error 14296216826.445896\n",
      "Mean Absolute Error 83556.70206747984\n",
      "R2 Score 0.7729353574818341\n"
     ]
    }
   ],
   "source": [
    "RF = RandomForestRegressor()\n",
    "RF.fit(x_train,y_train)\n",
    "evaluate(RF,\"Random Forest\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Boosting Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. AdaBoost Regression\n",
    "<a id='ab'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model AdaBoost\n",
      "Training Score 0.4360221382408246\n",
      "Testing Score 0.38467705113054407\n",
      "Mean Squared Error 38741347828.392296\n",
      "Mean Absolute Error 167348.96983417572\n",
      "R2 Score 0.38467705113054407\n"
     ]
    }
   ],
   "source": [
    "ada = AdaBoostRegressor()\n",
    "ada.fit(x_train,y_train)\n",
    "evaluate(ada,\"AdaBoost\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Gradient Boosting Regression\n",
    "<a id='gb'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Gradient Boosting\n",
      "Training Score 0.7912981311474394\n",
      "Testing Score 0.763553045658626\n",
      "Mean Squared Error 14886936555.728172\n",
      "Mean Absolute Error 89256.52604356421\n",
      "R2 Score 0.763553045658626\n"
     ]
    }
   ],
   "source": [
    "gradient = GradientBoostingRegressor()\n",
    "gradient.fit(x_train,y_train)\n",
    "evaluate(gradient,\"Gradient Boosting\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### 9. XGBoost Regression\n",
    " <a id='xg'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model XGBoost\n",
      "Training Score 0.9671026468276978\n",
      "Testing Score 0.8080800771713257\n",
      "Mean Squared Error 12083469805.83906\n",
      "Mean Absolute Error 75008.2543614987\n",
      "R2 Score 0.8080800771713257\n"
     ]
    }
   ],
   "source": [
    "xgb =XGBRegressor()\n",
    "xgb.fit(x_train,y_train)\n",
    "evaluate(xgb,\"XGBoost\",x_train,y_train,x_test,y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieving models with their scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Training Score</th>\n",
       "      <th>Testing Score</th>\n",
       "      <th>R² Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.512445</td>\n",
       "      <td>0.538263</td>\n",
       "      <td>0.538263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ridge Regression</th>\n",
       "      <td>0.512444</td>\n",
       "      <td>0.538132</td>\n",
       "      <td>0.538132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso Regression</th>\n",
       "      <td>0.512445</td>\n",
       "      <td>0.538261</td>\n",
       "      <td>0.538261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighbors Regressor</th>\n",
       "      <td>0.474051</td>\n",
       "      <td>0.221333</td>\n",
       "      <td>0.221333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.996425</td>\n",
       "      <td>0.559078</td>\n",
       "      <td>0.559078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.964823</td>\n",
       "      <td>0.772935</td>\n",
       "      <td>0.772935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>0.436022</td>\n",
       "      <td>0.384677</td>\n",
       "      <td>0.384677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosting</th>\n",
       "      <td>0.791298</td>\n",
       "      <td>0.763553</td>\n",
       "      <td>0.763553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.967103</td>\n",
       "      <td>0.808080</td>\n",
       "      <td>0.808080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Training Score  Testing Score  R² Score\n",
       "Linear Regression           0.512445       0.538263  0.538263\n",
       "Ridge Regression            0.512444       0.538132  0.538132\n",
       "Lasso Regression            0.512445       0.538261  0.538261\n",
       "KNeighbors Regressor        0.474051       0.221333  0.221333\n",
       "Decision Tree               0.996425       0.559078  0.559078\n",
       "Random Forest               0.964823       0.772935  0.772935\n",
       "AdaBoost                    0.436022       0.384677  0.384677\n",
       "Gradient Boosting           0.791298       0.763553  0.763553\n",
       "XGBoost                     0.967103       0.808080  0.808080"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(model_scores).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The XGBoost model achieved the best accuracy at 80%, outperforming Gradient Boosting at 76% and other models, despite showing signs of overfitting. This overfitting is likely due to the imbalanced nature of our dataset. From various plots in my notebook, CapStone_Project (Preprocessing and Graphical Analysis), you can observe that our dataset is imbalanced: approximately 40% of car brands have five or fewer data points, making it difficult for the model to accurately predict selling prices for those brands.\n",
    "- Despite Gradient Boosting having similar training and testing scores, and fitting the mean model perfectly, XGBoost is preferable due to its superior testing accuracy and robust handling of complex data structures, making it the most reliable choice for this dataset.\n",
    "\n",
    "In the future, acquiring an additional dataset with the same features could help my machine learning model overcome overfitting, as more data can allow the model to learn broader patterns rather than memorizing the training data. Currently, the dataset is too small, leading to overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the Model \n",
    "<a id='save'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl.dump(xgb,open('app.pkl','wb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uploading the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pkl.load(open('app.pkl','rb'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Sample Dataset\n",
    "<a id='sam'></a>\n",
    "Take the original data set and make another dataset by randomly picking 20 data points from the PROCESSED CAR DETAILS dataset and apply the saved model on the same Dataset and test the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Car_Brand</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Km_Driven</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>i20</td>\n",
       "      <td>2015</td>\n",
       "      <td>525000</td>\n",
       "      <td>70378</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hyundai</td>\n",
       "      <td>Santro</td>\n",
       "      <td>2012</td>\n",
       "      <td>300000</td>\n",
       "      <td>50000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Honda</td>\n",
       "      <td>City</td>\n",
       "      <td>2011</td>\n",
       "      <td>310000</td>\n",
       "      <td>110000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Spark</td>\n",
       "      <td>2009</td>\n",
       "      <td>70000</td>\n",
       "      <td>60000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Third Owner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maruti</td>\n",
       "      <td>Swift</td>\n",
       "      <td>2015</td>\n",
       "      <td>415000</td>\n",
       "      <td>28217</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Trustmark Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First Owner</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand Car_Name  Year  Selling_Price  Km_Driven    Fuel  \\\n",
       "0    Hyundai      i20  2015         525000      70378  Petrol   \n",
       "1    Hyundai   Santro  2012         300000      50000  Petrol   \n",
       "2      Honda     City  2011         310000     110000  Petrol   \n",
       "3  Chevrolet    Spark  2009          70000      60000  Petrol   \n",
       "4     Maruti    Swift  2015         415000      28217  Petrol   \n",
       "\n",
       "        Seller_Type Transmission        Owner  \n",
       "0            Dealer       Manual  First Owner  \n",
       "1        Individual       Manual  First Owner  \n",
       "2        Individual       Manual  First Owner  \n",
       "3        Individual       Manual  Third Owner  \n",
       "4  Trustmark Dealer       Manual  First Owner  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data = df.sample(n = 20, random_state = 40).reset_index(drop=True)  # Reordering index - just for better visualization.\n",
    "sample_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the Sample Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data.to_csv(\"Sample_Dataset.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Car_Brand</th>\n",
       "      <th>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Km_Driven</th>\n",
       "      <th>Fuel</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>155</td>\n",
       "      <td>2015</td>\n",
       "      <td>525000</td>\n",
       "      <td>70378</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>117</td>\n",
       "      <td>2012</td>\n",
       "      <td>300000</td>\n",
       "      <td>50000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>2011</td>\n",
       "      <td>310000</td>\n",
       "      <td>110000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>122</td>\n",
       "      <td>2009</td>\n",
       "      <td>70000</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>127</td>\n",
       "      <td>2015</td>\n",
       "      <td>415000</td>\n",
       "      <td>28217</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Car_Brand  Car_Name  Year  Selling_Price  Km_Driven  Fuel  Seller_Type  \\\n",
       "0         10       155  2015         525000      70378     4            0   \n",
       "1         10       117  2012         300000      50000     4            1   \n",
       "2          9        33  2011         310000     110000     4            1   \n",
       "3          3       122  2009          70000      60000     4            1   \n",
       "4         12       127  2015         415000      28217     4            2   \n",
       "\n",
       "   Transmission  Owner  \n",
       "0             1      0  \n",
       "1             1      0  \n",
       "2             1      0  \n",
       "3             1      4  \n",
       "4             1      0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying Scaling\n",
    "for i in categorical_col:\n",
    "    sample_data[i] = label_encoder[i].transform(sample_data[i])\n",
    "sample_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating prediction through Model on Sample Dataset\n",
    "<a id='pred'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Predictions on 20 Data Points:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>Difference in %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>525000</td>\n",
       "      <td>545565</td>\n",
       "      <td>-3.917143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300000</td>\n",
       "      <td>257234</td>\n",
       "      <td>14.255333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>310000</td>\n",
       "      <td>312039</td>\n",
       "      <td>-0.657742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70000</td>\n",
       "      <td>92662</td>\n",
       "      <td>-32.374286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>415000</td>\n",
       "      <td>447666</td>\n",
       "      <td>-7.871325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>750000</td>\n",
       "      <td>745760</td>\n",
       "      <td>0.565333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>200000</td>\n",
       "      <td>199942</td>\n",
       "      <td>0.029000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>600000</td>\n",
       "      <td>463004</td>\n",
       "      <td>22.832667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1025000</td>\n",
       "      <td>832185</td>\n",
       "      <td>18.811220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>145000</td>\n",
       "      <td>150952</td>\n",
       "      <td>-4.104828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>80000</td>\n",
       "      <td>48584</td>\n",
       "      <td>39.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>85000</td>\n",
       "      <td>65744</td>\n",
       "      <td>22.654118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1200000</td>\n",
       "      <td>1136763</td>\n",
       "      <td>5.269750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>350000</td>\n",
       "      <td>471848</td>\n",
       "      <td>-34.813714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>320000</td>\n",
       "      <td>294580</td>\n",
       "      <td>7.943750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>210000</td>\n",
       "      <td>258955</td>\n",
       "      <td>-23.311905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>470000</td>\n",
       "      <td>466797</td>\n",
       "      <td>0.681489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1085000</td>\n",
       "      <td>1064846</td>\n",
       "      <td>1.857512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>500000</td>\n",
       "      <td>550823</td>\n",
       "      <td>-10.164600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>650000</td>\n",
       "      <td>682095</td>\n",
       "      <td>-4.937692</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted  Difference in %\n",
       "0    525000     545565        -3.917143\n",
       "1    300000     257234        14.255333\n",
       "2    310000     312039        -0.657742\n",
       "3     70000      92662       -32.374286\n",
       "4    415000     447666        -7.871325\n",
       "5    750000     745760         0.565333\n",
       "6    200000     199942         0.029000\n",
       "7    600000     463004        22.832667\n",
       "8   1025000     832185        18.811220\n",
       "9    145000     150952        -4.104828\n",
       "10    80000      48584        39.270000\n",
       "11    85000      65744        22.654118\n",
       "12  1200000    1136763         5.269750\n",
       "13   350000     471848       -34.813714\n",
       "14   320000     294580         7.943750\n",
       "15   210000     258955       -23.311905\n",
       "16   470000     466797         0.681489\n",
       "17  1085000    1064846         1.857512\n",
       "18   500000     550823       -10.164600\n",
       "19   650000     682095        -4.937692"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting Input and Target variables\n",
    "sample_x = sample_data.drop([\"Selling_Price\"], axis=1)\n",
    "sample_y = sample_data[\"Selling_Price\"]\n",
    "\n",
    "# Generating predicion using the loaded model\n",
    "sample_pred = model.predict(sample_x).astype(int)\n",
    "\n",
    "# Comparing the actual and predicted data \n",
    "results = pd.DataFrame({'Actual': sample_y, 'Predicted': sample_pred, 'Difference in %': ((sample_y - sample_pred)/sample_y)*100})\n",
    "print(\"\\nRandom Predictions on 20 Data Points:\")\n",
    "results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MSE : 4355573319.0\n",
      "MAE : 43858.3\n",
      "R2 Score : 0.9595027224535052\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the score on model\n",
    "print('\\nMSE :',mean_squared_error(sample_y,sample_pred))\n",
    "print('MAE :',mean_absolute_error(sample_y,sample_pred))\n",
    "print('R2 Score :',r2_score(sample_y,sample_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the model gives an R² score of approximately 0.96 on unseen data. Although my XGBoost model was initially overfitting, it is still performing well on unseen data or may be due to the learning specific patterns.However, by obtaining more data, the model will have the opportunity to learn more generalized patterns, which could reduce the risk of overfitting and further improve its performance on new, unseen data, providing predictions that are closer to the actual values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "* Ensuring the dataset is free from missing and duplicate values is crucial. Missing values can be handled either by dropping or imputing them, depending on the dataset's structure. Addressing this issue is important to avoid errors during model training.\n",
    "* Remove the outliers from the data as it would completely change some of the important predictions and lead to an increase in the error respectively.\n",
    "* Before applying data to machine learning models for prediction, it is essential to convert all categorical features into numerical values. Machine learning algorithms typically only work with numerical data, so if categorical features are passed directly without being converted, the models will not function correctly and may result in errors, impacting the overall prediction process.\n",
    "* It is always a good practice to shuffle the data before splitting it into training and testing sets. Shuffling introduces more randomness, ensuring that the training and testing sets are diverse and representative of the overall dataset. This helps the machine learning models learn better patterns and generalize more effectively, improving their performance on new, unseen data.\n",
    "* Using different machine learning models would lead to different values of mean absolute error, mean squared error and r2 score respectively.\n",
    "* While making predictions with a saved model, it's important to apply the same preprocessing steps that were applied before separating the dataset into dependent and independent features. If you don't, you might end up getting an error message while performing on the model."
   ]
  }
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