car-price-prediction.ipynb

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   },
   "source": [
    "### Reading and Understanding the Dataset"
   ]
  },
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   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_rows\", None,\"display.max_columns\", None)\n",
    "warnings.simplefilter(action='ignore')\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "execution": {
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   "outputs": [],
   "source": [
    "#load dataset\n",
    "df_main = pd.read_csv('../input/vehicle-dataset-from-cardekho/car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
     "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_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</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>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.head()"
   ]
  },
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   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    object \n",
      " 6   Seller_Type    301 non-null    object \n",
      " 7   Transmission   301 non-null    object \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_main.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2013.627907</td>\n",
       "      <td>4.661296</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>36947.205980</td>\n",
       "      <td>0.043189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.891554</td>\n",
       "      <td>5.082812</td>\n",
       "      <td>8.644115</td>\n",
       "      <td>38886.883882</td>\n",
       "      <td>0.247915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>32000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>48767.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>92.600000</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
       "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
       "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
       "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
       "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
       "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
       "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
       "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
       "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#numerical stats\n",
    "df_main.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "execution": {
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     "start_time": "2020-12-21T20:34:26.832330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing values\n",
    "df_main.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "duration": 0.033627,
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     "start_time": "2020-12-21T20:34:26.912799",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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     "start_time": "2020-12-21T20:34:26.980462",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_main['Age'] = 2020 - df_main['Year']\n",
    "df_main.drop('Year',axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:27.122978Z",
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     "exception": false,
     "start_time": "2020-12-21T20:34:27.081127",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_main.rename(columns = {'Selling_Price':'Selling_Price(lacs)','Present_Price':'Present_Price(lacs)','Owner':'Past_Owners'},inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
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     "start_time": "2020-12-21T20:34:27.159892",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "#### Univariate Analysis"
   ]
  },
  {
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   "execution_count": 10,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car_Name', 'Selling_Price(lacs)', 'Present_Price(lacs)', 'Kms_Driven',\n",
       "       'Fuel_Type', 'Seller_Type', 'Transmission', 'Past_Owners', 'Age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "end_time": "2020-12-21T20:34:27.912245",
     "exception": false,
     "start_time": "2020-12-21T20:34:27.376706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_cols = ['Fuel_Type','Seller_Type','Transmission','Past_Owners']\n",
    "i=0\n",
    "while i < 4:\n",
    "    fig = plt.figure(figsize=[10,4])\n",
    "    #ax1 = fig.add_subplot(121)\n",
    "    #ax2 = fig.add_subplot(122)\n",
    "    \n",
    "    #ax1.title.set_text(cat_cols[i])\n",
    "    plt.subplot(1,2,1)\n",
    "    sns.countplot(x=cat_cols[i], data=df_main)\n",
    "    i += 1\n",
    "    \n",
    "    #ax2.title.set_text(cat_cols[i])\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.countplot(x=cat_cols[i], data=df_main)\n",
    "    i += 1\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:28.017958Z",
     "iopub.status.busy": "2020-12-21T20:34:28.013334Z",
     "iopub.status.idle": "2020-12-21T20:34:28.441624Z",
     "shell.execute_reply": "2020-12-21T20:34:28.440937Z"
    },
    "papermill": {
     "duration": 0.491483,
     "end_time": "2020-12-21T20:34:28.441768",
     "exception": false,
     "start_time": "2020-12-21T20:34:27.950285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eXl6VD1zTpk2t/zjFW0FBQQoJCanyXHFxceUbKzw8XLm5ucrLy1OTJk0qxzRr1uyq510ulwIDA5WXl6c77rijcuyN5ridXC6X6tevL0lat26devXq5dfrlaS4uDhNmzZNycnJfr/WlJQUzZw5s/KxP6+3qKhIH3/8scaNG6f4+Hh9+OGHfr1ef1LTfsjlcllO9Y3qnyNfcuLECRljNHXqVI0cOdJqgazJE088oVOnTqlPnz5KSEjQjBkzbEeS5P2x3paa8tWvX18ul0sej0d/+tOfNHDgQEvpas539OhRHTx4UD/96U8tpfpGTflOnjyp3bt3a+zYsfrVr35l9WTmWoKcnMwYc9XjgIAAJzdxW12ZvWJt11pjTc/X9Nz15rBh69atWr9+vX7/+99XnnlXZLryv1c+/21d79q1a/XZZ59p+vTpfv29/ctf/qIHH3xQLVu2rHzOn9fbtm1bTZo0Sb1799bRo0f1i1/8QmVlZV5n/bat1x9duR/yFTV9jnxNTk6OXn/9dZ06dUo///nPtX37dp95P/71r39VVFSUVq1apYMHD+r555/Xhg0bbMeqUU37R1/j8Xj03HPPqVu3burevbvtOFXMnz9fs2fPth3jmowxat68uVatWqWlS5dq+fLlPnNyWMHRK+ORkZHKy8urfHzmzBk1a9bMyU3cVqGhoSopKZF0eacXERFR4xrDw8MVGRlZeTZ96dIlGWMUERFR5QzsWnPk5OQoPDz8Nq3qG7t27dKyZcu0cuVKNWrUyG/Xe+DAAZ0+fVqS1K5dO3k8Hr9dqyTt2LFD//jHPzRs2DCtW7dOS5cu9ev1tm7duvLHjq1atVKzZs1UWFjot+v1N9X3Q76ips/RBx98YDtWpaZNm6pTp04KCgrSXXfdpQYNGig/P992rEp79+5VdHS0pMsnzDk5OVVOkn1JTftHXzNr1izdfffdmjx5su0oVeTk5OjIkSOaNm2ahg0bpjNnzighIcF2rCqaNWumzp07S7p8K6Iv3lroaBnv0aOH/v73v0uSPv30U0VERKhhw4ZObuK2evjhhyvXs2XLFvXs2VMPPPCA9u/fr8LCQrndbu3du1edO3dWjx49tHnzZkmX70/q2rWr6tWrp3vuuUd79uypMke3bt20Y8cOlZaWKicnR2fOnNG99957W9d24cIFLVy4UMuXL1fjxo39er179uypvOKWl5enoqIiv12rJL3yyivasGGD0tLSNHToUE2cONGv17t+/XqtXr1akpSbm6uzZ89q8ODBfrtef1LTfshXXOtz5Cuio6P14Ycfqry8XPn5+SoqKvKZ+7Il6e6779a+ffskXb5NoEGDBgoKcvSH8Y6paf/oSzIyMlSvXj1NmTLFdpSrREZGauvWrUpLS1NaWpoiIiKu+i0rtvXq1avyt/n85z//UatWrSwnulqAcfhnMosXL9aePXsUEBCgOXPmqG3btk5OX2cOHDiglJQUnTx5UkFBQYqMjNTixYs1c+ZMXbx4UVFRUZo/f77q1aunzZs3a9WqVQoICFBCQoJiYmLk8Xg0e/ZsffHFFwoODtaCBQvUvHlzHT58WC+88ILKy8v1wAMPaNasWZKk1NRUZWZmKiAgQFOnTr3tP3b685//rCVLllR5Uy5YsECzZ8/2u/WWlJTo+eef1+nTp1VSUqLJkyerQ4cOmjFjht+ttbolS5aoRYsWio6O9tv1nj9/XtOmTVNRUZFKS0s1efJktWvXzm/X609q2g+lpKQoKirKYqqrVXyOBg8ebDtKFWvXrtXGjRtVXFysZ5991qf+Yprb7VZycrLOnj2rsrIyJSUl+cRn5WaO9b6S7+zZs/re975XeXGzdevWmjt3rs/kW7JkSeXJ9GOPPaZt27ZZyXatfIsXL1ZKSopyc3MVHByslJQUn7trw/EyDgAAAMA7/AucAAAAgCWUcQAAAMASyjgAAABgCWUcAAAAsIQyDgAAAFhCGQcAAN86Z86cUfv27bVixQrbUYBaoYzjlp04cUK9evWqfHzu3DnFxMTU2e8YbdOmjRISEpSYmKi4uDi98MILKigoqHFsenq61q1bVyc5AAD2vfPOO2rdurXS09NtRwFqhTIORxQXF2vChAkaM2aMHnvssTrbzh/+8AelpqbqrbfeUlRUlMaOHSuPx3PVuMGDB2vo0KF1lgMAYFd6erqSk5NVXFysTz75RJK0c+dOxcTEKDExUampqZUXjM6fP6+pU6dq1KhRGjFihDIzM21GB6rwzX+bFt8qFf+62hNPPKGnnnpKktSpUyc9++yz2rZtmy5duqQJEyYoLS1NR48e1dy5cxUdHa0//vGPysjIUGhoqEJCQrRo0SKv/zlnl8ulCRMmaMeOHXrvvfcUEhKi3/3udwoODlbfvn11+vRplZWVqbS0VI0bN9YzzzwjSVq6dKncbreSkpL0m9/8Rl9++aXKy8vVu3dvjRkzRunp6frggw9UXl6uo0ePqkWLFlqyZIkCAgLq7M8PAHBzPvroI5WVlalbt2566qmnlJ6ergcffFBz5szRsmXL1LZtW7388suV41955RX17NlTQ4YMUVFRkZ588kn16NFDTZo0sbgK4DKujKNWjDFKTk5WSUmJEhMTK58vKipShw4dtHbtWtWvX1/btm3TypUrNXHiRL399tuSpNdee03Lly/XmjVrNGrUKJ05c+amt9+pUyd9/vnnkqT9+/dr4cKFio2Nrfx6TEyMNm/eXPl406ZNevLJJ7V69WpFREQoNTVVq1ev1saNG3Xw4EFJ0ieffKKXXnpJ6enpOnjwoD777LNb+rMBANSN9evXa9CgQQoICNCQIUO0adMmnT59WsXFxWrbtq0kqW/fvpXjs7Oz9fbbbysxMVHPPPOMgoKCdOLECVvxgSq4Mo5aycvL03333adDhw4pIyNDMTExlV976KGHJEmRkZH60Y9+JEn6/ve/r8LCQklSbGysxo0bp379+ql///5q1arVTW//woULatasmSSpVatWaty4cZWvt2vXTqWlpTp+/LhKS0vlcrn0gx/8QIsWLdJXX32l3bt3S5JKS0t17NgxSdL999+vkJAQSVLz5s11/vz5m84FAKgbX3/9td599101b95c7777riTJ4/Hoo48+qjLO5XJV/n9wcLDmzJmjjh073tasgDco46iV8PBwPf300+rfv7/i4+PVunVr/fCHP5RUdUd45f9XmDVrlk6ePKmdO3dq0qRJmjFjhh555BGvt22M0b/+9S/FxMTIGKN69erVOG7AgAHavHmziouLK08WgoODNWnSJPXv37/K2PT09KuyGmO8zgQAqFuZmZnq0qVLld+ikpmZqbS0NAUGBurIkSO65557tGXLlsqvP/TQQ9q0aZM6duyokpISLViwQLNnz1ZQEDUI9nGbChzRsmVLvfjii/rlL3+p/Pz8G44/f/68lixZoubNm2vkyJGKj4/X/v37vd6eMUavv/66IiMj9eMf//i6YwcMGKDt27dr27ZtGjBggKTLO+aK21fKy8s1f/58nTt3zuvtAwDsWL9+vUaMGFHluX79+unIkSMaNWqUJk2apLFjxyo4OLiybE+ePFlffvmlRowYofj4eLVv354iDp/BOxGO6dWrl4YMGaKkpKQbjr3zzjvldrsVGxurO+64Q0FBQfrtb397w9eNHj1aknTx4kV16NBBr7766g1f07JlSwUEBKhp06aKiIiQJMXHx+vQoUMaPny4PB6PHn300atucQEA+J4NGzZc9VxwcLDef/99bd26VStWrFDLli21ZcuWytsfw8LC9MYbb9zuqIBXAgw/gwcAAH5gy5YtWrp0qRo2bCiPx6O5c+eqTZs2tmMB10UZh884fvy4kpOTa/xacnKy2rVrd5sTAQAA1C3KOAAAAGAJf4ETAAAAsIQyDgAAAFhCGQcAAAAsoYwDAAAAllDGAQAAAEv+P7V0RW0YsGSIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 936x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_cols = ['Selling_Price(lacs)','Present_Price(lacs)','Kms_Driven','Age']\n",
    "i=0\n",
    "while i < 4:\n",
    "    fig = plt.figure(figsize=[13,3])\n",
    "    #ax1 = fig.add_subplot(121)\n",
    "    #ax2 = fig.add_subplot(122)\n",
    "    \n",
    "    #ax1.title.set_text(num_cols[i])\n",
    "    plt.subplot(1,2,1)\n",
    "    sns.boxplot(x=num_cols[i], data=df_main)\n",
    "    i += 1\n",
    "    \n",
    "    #ax2.title.set_text(num_cols[i])\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.boxplot(x=num_cols[i], data=df_main)\n",
    "    i += 1\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:28.538710Z",
     "iopub.status.busy": "2020-12-21T20:34:28.537526Z",
     "iopub.status.idle": "2020-12-21T20:34:28.543231Z",
     "shell.execute_reply": "2020-12-21T20:34:28.542518Z"
    },
    "papermill": {
     "duration": 0.06315,
     "end_time": "2020-12-21T20:34:28.543351",
     "exception": false,
     "start_time": "2020-12-21T20:34:28.480201",
     "status": "completed"
    },
    "tags": []
   },
   "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_Name</th>\n",
       "      <th>Selling_Price(lacs)</th>\n",
       "      <th>Present_Price(lacs)</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Past_Owners</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>fortuner</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.23</td>\n",
       "      <td>6000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>land cruiser</td>\n",
       "      <td>35.0</td>\n",
       "      <td>92.60</td>\n",
       "      <td>78000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Car_Name  Selling_Price(lacs)  Present_Price(lacs)  Kms_Driven  \\\n",
       "64      fortuner                 33.0                36.23        6000   \n",
       "86  land cruiser                 35.0                92.60       78000   \n",
       "\n",
       "   Fuel_Type Seller_Type Transmission  Past_Owners  Age  \n",
       "64    Diesel      Dealer    Automatic            0    3  \n",
       "86    Diesel      Dealer       Manual            0   10  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main[df_main['Present_Price(lacs)'] > df_main['Present_Price(lacs)'].quantile(0.99)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:28.635865Z",
     "iopub.status.busy": "2020-12-21T20:34:28.633302Z",
     "iopub.status.idle": "2020-12-21T20:34:28.649575Z",
     "shell.execute_reply": "2020-12-21T20:34:28.648893Z"
    },
    "papermill": {
     "duration": 0.06714,
     "end_time": "2020-12-21T20:34:28.649699",
     "exception": false,
     "start_time": "2020-12-21T20:34:28.582559",
     "status": "completed"
    },
    "tags": []
   },
   "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_Name</th>\n",
       "      <th>Selling_Price(lacs)</th>\n",
       "      <th>Present_Price(lacs)</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Past_Owners</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>fortuner</td>\n",
       "      <td>23.5</td>\n",
       "      <td>35.96</td>\n",
       "      <td>47000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>fortuner</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.23</td>\n",
       "      <td>6000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>land cruiser</td>\n",
       "      <td>35.0</td>\n",
       "      <td>92.60</td>\n",
       "      <td>78000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Car_Name  Selling_Price(lacs)  Present_Price(lacs)  Kms_Driven  \\\n",
       "63      fortuner                 23.5                35.96       47000   \n",
       "64      fortuner                 33.0                36.23        6000   \n",
       "86  land cruiser                 35.0                92.60       78000   \n",
       "\n",
       "   Fuel_Type Seller_Type Transmission  Past_Owners  Age  \n",
       "63    Diesel      Dealer    Automatic            0    5  \n",
       "64    Diesel      Dealer    Automatic            0    3  \n",
       "86    Diesel      Dealer       Manual            0   10  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main[df_main['Selling_Price(lacs)'] > df_main['Selling_Price(lacs)'].quantile(0.99)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:28.745896Z",
     "iopub.status.busy": "2020-12-21T20:34:28.745178Z",
     "iopub.status.idle": "2020-12-21T20:34:28.749861Z",
     "shell.execute_reply": "2020-12-21T20:34:28.749214Z"
    },
    "papermill": {
     "duration": 0.060353,
     "end_time": "2020-12-21T20:34:28.749980",
     "exception": false,
     "start_time": "2020-12-21T20:34:28.689627",
     "status": "completed"
    },
    "tags": []
   },
   "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_Name</th>\n",
       "      <th>Selling_Price(lacs)</th>\n",
       "      <th>Present_Price(lacs)</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Past_Owners</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>innova</td>\n",
       "      <td>3.49</td>\n",
       "      <td>13.46</td>\n",
       "      <td>197176</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>Honda Karizma</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.05</td>\n",
       "      <td>213000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Activa 3g</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.52</td>\n",
       "      <td>500000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Car_Name  Selling_Price(lacs)  Present_Price(lacs)  Kms_Driven  \\\n",
       "84          innova                 3.49                13.46      197176   \n",
       "179  Honda Karizma                 0.31                 1.05      213000   \n",
       "196      Activa 3g                 0.17                 0.52      500000   \n",
       "\n",
       "    Fuel_Type Seller_Type Transmission  Past_Owners  Age  \n",
       "84     Diesel      Dealer       Manual            0   15  \n",
       "179    Petrol  Individual       Manual            0   10  \n",
       "196    Petrol  Individual    Automatic            0   12  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main[df_main['Kms_Driven'] > df_main['Kms_Driven'].quantile(0.99)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.040298,
     "end_time": "2020-12-21T20:34:28.830515",
     "exception": false,
     "start_time": "2020-12-21T20:34:28.790217",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Bivariate/Multi-Variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:28.922361Z",
     "iopub.status.busy": "2020-12-21T20:34:28.921287Z",
     "iopub.status.idle": "2020-12-21T20:34:29.291196Z",
     "shell.execute_reply": "2020-12-21T20:34:29.291674Z"
    },
    "papermill": {
     "duration": 0.420676,
     "end_time": "2020-12-21T20:34:29.291858",
     "exception": false,
     "start_time": "2020-12-21T20:34:28.871182",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_main.corr(), annot=True, cmap=\"RdBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:29.386732Z",
     "iopub.status.busy": "2020-12-21T20:34:29.385678Z",
     "iopub.status.idle": "2020-12-21T20:34:29.390392Z",
     "shell.execute_reply": "2020-12-21T20:34:29.389874Z"
    },
    "papermill": {
     "duration": 0.055784,
     "end_time": "2020-12-21T20:34:29.390508",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.334724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Selling_Price(lacs)    1.000000\n",
       "Present_Price(lacs)    0.878983\n",
       "Kms_Driven             0.029187\n",
       "Past_Owners           -0.088344\n",
       "Age                   -0.236141\n",
       "Name: Selling_Price(lacs), dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.corr()['Selling_Price(lacs)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:29.493317Z",
     "iopub.status.busy": "2020-12-21T20:34:29.489310Z",
     "iopub.status.idle": "2020-12-21T20:34:29.506446Z",
     "shell.execute_reply": "2020-12-21T20:34:29.505797Z"
    },
    "papermill": {
     "duration": 0.073727,
     "end_time": "2020-12-21T20:34:29.506562",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.432835",
     "status": "completed"
    },
    "tags": []
   },
   "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>Fuel_Type</th>\n",
       "      <th>CNG</th>\n",
       "      <th>Diesel</th>\n",
       "      <th>Petrol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dealer</th>\n",
       "      <td>3.1</td>\n",
       "      <td>10.181525</td>\n",
       "      <td>5.252388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.726857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Fuel_Type    CNG     Diesel    Petrol\n",
       "Seller_Type                          \n",
       "Dealer       3.1  10.181525  5.252388\n",
       "Individual   NaN  16.000000  0.726857"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.pivot_table(values='Selling_Price(lacs)', index = 'Seller_Type', columns= 'Fuel_Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:29.609836Z",
     "iopub.status.busy": "2020-12-21T20:34:29.604357Z",
     "iopub.status.idle": "2020-12-21T20:34:29.629846Z",
     "shell.execute_reply": "2020-12-21T20:34:29.630820Z"
    },
    "papermill": {
     "duration": 0.078626,
     "end_time": "2020-12-21T20:34:29.631053",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.552427",
     "status": "completed"
    },
    "tags": []
   },
   "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>Transmission</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Manual</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seller_Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dealer</th>\n",
       "      <td>12.246552</td>\n",
       "      <td>5.756506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual</th>\n",
       "      <td>1.968182</td>\n",
       "      <td>0.743895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Transmission  Automatic    Manual\n",
       "Seller_Type                      \n",
       "Dealer        12.246552  5.756506\n",
       "Individual     1.968182  0.743895"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.pivot_table(values='Selling_Price(lacs)', index = 'Seller_Type', columns= 'Transmission')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043203,
     "end_time": "2020-12-21T20:34:29.724490",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.681287",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.044421,
     "end_time": "2020-12-21T20:34:29.813337",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.768916",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Creating Dummies for Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:29.904605Z",
     "iopub.status.busy": "2020-12-21T20:34:29.903927Z",
     "iopub.status.idle": "2020-12-21T20:34:29.911595Z",
     "shell.execute_reply": "2020-12-21T20:34:29.911035Z"
    },
    "papermill": {
     "duration": 0.054384,
     "end_time": "2020-12-21T20:34:29.911743",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.857359",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_main.drop(labels='Car_Name',axis= 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:30.014145Z",
     "iopub.status.busy": "2020-12-21T20:34:30.013022Z",
     "iopub.status.idle": "2020-12-21T20:34:30.017768Z",
     "shell.execute_reply": "2020-12-21T20:34:30.017102Z"
    },
    "papermill": {
     "duration": 0.061072,
     "end_time": "2020-12-21T20:34:30.017883",
     "exception": false,
     "start_time": "2020-12-21T20:34:29.956811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price(lacs)</th>\n",
       "      <th>Present_Price(lacs)</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Past_Owners</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price(lacs)  Present_Price(lacs)  Kms_Driven Fuel_Type Seller_Type  \\\n",
       "0                 3.35                 5.59       27000    Petrol      Dealer   \n",
       "1                 4.75                 9.54       43000    Diesel      Dealer   \n",
       "2                 7.25                 9.85        6900    Petrol      Dealer   \n",
       "3                 2.85                 4.15        5200    Petrol      Dealer   \n",
       "4                 4.60                 6.87       42450    Diesel      Dealer   \n",
       "\n",
       "  Transmission  Past_Owners  Age  \n",
       "0       Manual            0    6  \n",
       "1       Manual            0    7  \n",
       "2       Manual            0    3  \n",
       "3       Manual            0    9  \n",
       "4       Manual            0    6  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:30.115190Z",
     "iopub.status.busy": "2020-12-21T20:34:30.113105Z",
     "iopub.status.idle": "2020-12-21T20:34:30.122575Z",
     "shell.execute_reply": "2020-12-21T20:34:30.122000Z"
    },
    "papermill": {
     "duration": 0.060741,
     "end_time": "2020-12-21T20:34:30.122703",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.061962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_main = pd.get_dummies(data = df_main,drop_first=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:30.216268Z",
     "iopub.status.busy": "2020-12-21T20:34:30.215233Z",
     "iopub.status.idle": "2020-12-21T20:34:30.228417Z",
     "shell.execute_reply": "2020-12-21T20:34:30.228951Z"
    },
    "papermill": {
     "duration": 0.061437,
     "end_time": "2020-12-21T20:34:30.229101",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.167664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selling_Price(lacs)</th>\n",
       "      <th>Present_Price(lacs)</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Past_Owners</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Seller_Type_Individual</th>\n",
       "      <th>Transmission_Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selling_Price(lacs)  Present_Price(lacs)  Kms_Driven  Past_Owners  Age  \\\n",
       "0                 3.35                 5.59       27000            0    6   \n",
       "1                 4.75                 9.54       43000            0    7   \n",
       "2                 7.25                 9.85        6900            0    3   \n",
       "3                 2.85                 4.15        5200            0    9   \n",
       "4                 4.60                 6.87       42450            0    6   \n",
       "\n",
       "   Fuel_Type_Diesel  Fuel_Type_Petrol  Seller_Type_Individual  \\\n",
       "0                 0                 1                       0   \n",
       "1                 1                 0                       0   \n",
       "2                 0                 1                       0   \n",
       "3                 0                 1                       0   \n",
       "4                 1                 0                       0   \n",
       "\n",
       "   Transmission_Manual  \n",
       "0                    1  \n",
       "1                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.04556,
     "end_time": "2020-12-21T20:34:30.319499",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.273939",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:30.416587Z",
     "iopub.status.busy": "2020-12-21T20:34:30.415568Z",
     "iopub.status.idle": "2020-12-21T20:34:30.421772Z",
     "shell.execute_reply": "2020-12-21T20:34:30.422305Z"
    },
    "papermill": {
     "duration": 0.05552,
     "end_time": "2020-12-21T20:34:30.422442",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.366922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Separating target variable and its features\n",
    "y = df_main['Selling_Price(lacs)']\n",
    "X = df_main.drop('Selling_Price(lacs)',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:30.518472Z",
     "iopub.status.busy": "2020-12-21T20:34:30.517454Z",
     "iopub.status.idle": "2020-12-21T20:34:30.658820Z",
     "shell.execute_reply": "2020-12-21T20:34:30.657002Z"
    },
    "papermill": {
     "duration": 0.189848,
     "end_time": "2020-12-21T20:34:30.658980",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.469132",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:30.773046Z",
     "iopub.status.busy": "2020-12-21T20:34:30.771967Z",
     "iopub.status.idle": "2020-12-21T20:34:30.785738Z",
     "shell.execute_reply": "2020-12-21T20:34:30.786720Z"
    },
    "papermill": {
     "duration": 0.07607,
     "end_time": "2020-12-21T20:34:30.786978",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.710908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x train:  (240, 8)\n",
      "x test:  (61, 8)\n",
      "y train:  (240,)\n",
      "y test:  (61,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "print(\"x train: \",X_train.shape)\n",
    "print(\"x test: \",X_test.shape)\n",
    "print(\"y train: \",y_train.shape)\n",
    "print(\"y test: \",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045923,
     "end_time": "2020-12-21T20:34:30.883302",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.837379",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Model Creation/Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.046023,
     "end_time": "2020-12-21T20:34:30.975436",
     "exception": false,
     "start_time": "2020-12-21T20:34:30.929413",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Applying regression models\n",
    "1. Linear Regression \n",
    "2. Ridge Regression\n",
    "3. Lasso Regression\n",
    "4. Random Forest Regression\n",
    "5. Gradient Boosting regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:31.070974Z",
     "iopub.status.busy": "2020-12-21T20:34:31.070160Z",
     "iopub.status.idle": "2020-12-21T20:34:31.073786Z",
     "shell.execute_reply": "2020-12-21T20:34:31.074396Z"
    },
    "papermill": {
     "duration": 0.053167,
     "end_time": "2020-12-21T20:34:31.074606",
     "exception": false,
     "start_time": "2020-12-21T20:34:31.021439",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:31.172080Z",
     "iopub.status.busy": "2020-12-21T20:34:31.171244Z",
     "iopub.status.idle": "2020-12-21T20:34:31.186866Z",
     "shell.execute_reply": "2020-12-21T20:34:31.187386Z"
    },
    "papermill": {
     "duration": 0.066241,
     "end_time": "2020-12-21T20:34:31.187643",
     "exception": false,
     "start_time": "2020-12-21T20:34:31.121402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "CV = []\n",
    "R2_train = []\n",
    "R2_test = []\n",
    "\n",
    "def car_pred_model(model,model_name):\n",
    "    # Training model\n",
    "    model.fit(X_train,y_train)\n",
    "            \n",
    "    # R2 score of train set\n",
    "    y_pred_train = model.predict(X_train)\n",
    "    R2_train_model = r2_score(y_train,y_pred_train)\n",
    "    R2_train.append(round(R2_train_model,2))\n",
    "    \n",
    "    # R2 score of test set\n",
    "    y_pred_test = model.predict(X_test)\n",
    "    R2_test_model = r2_score(y_test,y_pred_test)\n",
    "    R2_test.append(round(R2_test_model,2))\n",
    "    \n",
    "    # R2 mean of train set using Cross validation\n",
    "    cross_val = cross_val_score(model ,X_train ,y_train ,cv=5)\n",
    "    cv_mean = cross_val.mean()\n",
    "    CV.append(round(cv_mean,2))\n",
    "    \n",
    "    # Printing results\n",
    "    print(\"Train R2-score :\",round(R2_train_model,2))\n",
    "    print(\"Test R2-score :\",round(R2_test_model,2))\n",
    "    print(\"Train CV scores :\",cross_val)\n",
    "    print(\"Train CV mean :\",round(cv_mean,2))\n",
    "    \n",
    "    # Plotting Graphs \n",
    "    # Residual Plot of train data\n",
    "    fig, ax = plt.subplots(1,2,figsize = (10,4))\n",
    "    ax[0].set_title('Residual Plot of Train samples')\n",
    "    sns.distplot((y_train-y_pred_train),hist = False,ax = ax[0])\n",
    "    ax[0].set_xlabel('y_train - y_pred_train')\n",
    "    \n",
    "    # Y_test vs Y_train scatter plot\n",
    "    ax[1].set_title('y_test vs y_pred_test')\n",
    "    ax[1].scatter(x = y_test, y = y_pred_test)\n",
    "    ax[1].set_xlabel('y_test')\n",
    "    ax[1].set_ylabel('y_pred_test')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045748,
     "end_time": "2020-12-21T20:34:31.280392",
     "exception": false,
     "start_time": "2020-12-21T20:34:31.234644",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Standard Linear Regression or Ordinary Least Squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:31.378144Z",
     "iopub.status.busy": "2020-12-21T20:34:31.377469Z",
     "iopub.status.idle": "2020-12-21T20:34:31.862044Z",
     "shell.execute_reply": "2020-12-21T20:34:31.861308Z"
    },
    "papermill": {
     "duration": 0.535588,
     "end_time": "2020-12-21T20:34:31.862166",
     "exception": false,
     "start_time": "2020-12-21T20:34:31.326578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2-score : 0.88\n",
      "Test R2-score : 0.86\n",
      "Train CV scores : [0.89912189 0.89838688 0.82980069 0.81769076 0.76113925]\n",
      "Train CV mean : 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()\n",
    "car_pred_model(lr,\"Linear_regressor.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047284,
     "end_time": "2020-12-21T20:34:31.957285",
     "exception": false,
     "start_time": "2020-12-21T20:34:31.910001",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:32.064627Z",
     "iopub.status.busy": "2020-12-21T20:34:32.063532Z",
     "iopub.status.idle": "2020-12-21T20:34:34.193727Z",
     "shell.execute_reply": "2020-12-21T20:34:34.194382Z"
    },
    "papermill": {
     "duration": 2.189131,
     "end_time": "2020-12-21T20:34:34.194531",
     "exception": false,
     "start_time": "2020-12-21T20:34:32.005400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2-score : 0.88\n",
      "Test R2-score : 0.86\n",
      "Train CV scores : [0.89912126 0.8983878  0.82980292 0.81769155 0.73377277]\n",
      "Train CV mean : 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Creating Ridge model object\n",
    "rg = Ridge()\n",
    "# range of alpha \n",
    "alpha = np.logspace(-3,3,num=14)\n",
    "\n",
    "# Creating RandomizedSearchCV to find the best estimator of hyperparameter\n",
    "rg_rs = RandomizedSearchCV(estimator = rg, param_distributions = dict(alpha=alpha))\n",
    "\n",
    "car_pred_model(rg_rs,\"ridge.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.048923,
     "end_time": "2020-12-21T20:34:34.293641",
     "exception": false,
     "start_time": "2020-12-21T20:34:34.244718",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:34.400034Z",
     "iopub.status.busy": "2020-12-21T20:34:34.399322Z",
     "iopub.status.idle": "2020-12-21T20:34:34.402927Z",
     "shell.execute_reply": "2020-12-21T20:34:34.402239Z"
    },
    "papermill": {
     "duration": 0.059798,
     "end_time": "2020-12-21T20:34:34.403045",
     "exception": false,
     "start_time": "2020-12-21T20:34:34.343247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "ls = Lasso()\n",
    "alpha = np.logspace(-3,3,num=14) # range for alpha\n",
    "\n",
    "ls_rs = RandomizedSearchCV(estimator = ls, param_distributions = dict(alpha=alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:34.516501Z",
     "iopub.status.busy": "2020-12-21T20:34:34.515812Z",
     "iopub.status.idle": "2020-12-21T20:34:36.670672Z",
     "shell.execute_reply": "2020-12-21T20:34:36.669958Z"
    },
    "papermill": {
     "duration": 2.218354,
     "end_time": "2020-12-21T20:34:36.670926",
     "exception": false,
     "start_time": "2020-12-21T20:34:34.452572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2-score : 0.88\n",
      "Test R2-score : 0.86\n",
      "Train CV scores : [0.8990973  0.90004456 0.8301628  0.81769086 0.73120322]\n",
      "Train CV mean : 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_pred_model(ls_rs,\"lasso.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.050885,
     "end_time": "2020-12-21T20:34:36.773959",
     "exception": false,
     "start_time": "2020-12-21T20:34:36.723074",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:36.886215Z",
     "iopub.status.busy": "2020-12-21T20:34:36.885445Z",
     "iopub.status.idle": "2020-12-21T20:34:37.024691Z",
     "shell.execute_reply": "2020-12-21T20:34:37.023705Z"
    },
    "papermill": {
     "duration": 0.199623,
     "end_time": "2020-12-21T20:34:37.024843",
     "exception": false,
     "start_time": "2020-12-21T20:34:36.825220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Number of trees in Random forest\n",
    "n_estimators=list(range(500,1000,100))\n",
    "# Maximum number of levels in a tree\n",
    "max_depth=list(range(4,9,4))\n",
    "# Minimum number of samples required to split an internal node\n",
    "min_samples_split=list(range(4,9,2))\n",
    "# Minimum number of samples required to be at a leaf node.\n",
    "min_samples_leaf=[1,2,5,7]\n",
    "# Number of fearures to be considered at each split\n",
    "max_features=['auto','sqrt']\n",
    "\n",
    "# Hyperparameters dict\n",
    "param_grid = {\"n_estimators\":n_estimators,\n",
    "              \"max_depth\":max_depth,\n",
    "              \"min_samples_split\":min_samples_split,\n",
    "              \"min_samples_leaf\":min_samples_leaf,\n",
    "              \"max_features\":max_features}\n",
    "\n",
    "rf_rs = RandomizedSearchCV(estimator = rf, param_distributions = param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:34:37.167400Z",
     "iopub.status.busy": "2020-12-21T20:34:37.146397Z",
     "iopub.status.idle": "2020-12-21T20:41:00.578147Z",
     "shell.execute_reply": "2020-12-21T20:41:00.577534Z"
    },
    "papermill": {
     "duration": 383.501836,
     "end_time": "2020-12-21T20:41:00.578273",
     "exception": false,
     "start_time": "2020-12-21T20:34:37.076437",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2-score : 0.95\n",
      "Test R2-score : 0.82\n",
      "Train CV scores : [0.92642078 0.97074541 0.81416073 0.93029401 0.73000263]\n",
      "Train CV mean : 0.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_pred_model(rf_rs,'random_forest.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:41:00.688606Z",
     "iopub.status.busy": "2020-12-21T20:41:00.687571Z",
     "iopub.status.idle": "2020-12-21T20:41:00.697472Z",
     "shell.execute_reply": "2020-12-21T20:41:00.696827Z"
    },
    "papermill": {
     "duration": 0.066724,
     "end_time": "2020-12-21T20:41:00.697663",
     "exception": false,
     "start_time": "2020-12-21T20:41:00.630939",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(max_depth=8, min_samples_leaf=2, min_samples_split=8,\n",
      "                      n_estimators=900)\n"
     ]
    }
   ],
   "source": [
    "print(rf_rs.best_estimator_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.053049,
     "end_time": "2020-12-21T20:41:00.804511",
     "exception": false,
     "start_time": "2020-12-21T20:41:00.751462",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:41:00.924538Z",
     "iopub.status.busy": "2020-12-21T20:41:00.923630Z",
     "iopub.status.idle": "2020-12-21T20:41:00.926880Z",
     "shell.execute_reply": "2020-12-21T20:41:00.926321Z"
    },
    "papermill": {
     "duration": 0.067988,
     "end_time": "2020-12-21T20:41:00.926998",
     "exception": false,
     "start_time": "2020-12-21T20:41:00.859010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "gb = GradientBoostingRegressor()\n",
    "\n",
    "# Rate at which correcting is being made\n",
    "learning_rate = [0.001, 0.01, 0.1, 0.2]\n",
    "# Number of trees in Gradient boosting\n",
    "n_estimators=list(range(500,1000,100))\n",
    "# Maximum number of levels in a tree\n",
    "max_depth=list(range(4,9,4))\n",
    "# Minimum number of samples required to split an internal node\n",
    "min_samples_split=list(range(4,9,2))\n",
    "# Minimum number of samples required to be at a leaf node.\n",
    "min_samples_leaf=[1,2,5,7]\n",
    "# Number of fearures to be considered at each split\n",
    "max_features=['auto','sqrt']\n",
    "\n",
    "# Hyperparameters dict\n",
    "param_grid = {\"learning_rate\":learning_rate,\n",
    "              \"n_estimators\":n_estimators,\n",
    "              \"max_depth\":max_depth,\n",
    "              \"min_samples_split\":min_samples_split,\n",
    "              \"min_samples_leaf\":min_samples_leaf,\n",
    "              \"max_features\":max_features}\n",
    "\n",
    "gb_rs = RandomizedSearchCV(estimator = gb, param_distributions = param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-21T20:41:01.046156Z",
     "iopub.status.busy": "2020-12-21T20:41:01.040634Z",
     "iopub.status.idle": "2020-12-21T20:42:35.859300Z",
     "shell.execute_reply": "2020-12-21T20:42:35.858670Z"
    },
    "papermill": {
     "duration": 94.878382,
     "end_time": "2020-12-21T20:42:35.859423",
     "exception": false,
     "start_time": "2020-12-21T20:41:00.981041",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train R2-score : 1.0\n",
      "Test R2-score : 0.94\n",
      "Train CV scores : [0.90694506 0.93090678 0.87786231 0.94391449 0.70671455]\n",
      "Train CV mean : 0.87\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car_pred_model(gb_rs,\"gradient_boosting.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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    "execution": {
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   "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>Model</th>\n",
       "      <th>R Squared(Train)</th>\n",
       "      <th>R Squared(Test)</th>\n",
       "      <th>CV score mean(Train)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LinearRegression</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RandomForestRegressor</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GradientBoostingRegressor</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model  R Squared(Train)  R Squared(Test)  \\\n",
       "0           LinearRegression              0.88             0.86   \n",
       "1                      Ridge              0.88             0.86   \n",
       "2                      Lasso              0.88             0.86   \n",
       "3      RandomForestRegressor              0.95             0.82   \n",
       "4  GradientBoostingRegressor              1.00             0.94   \n",
       "\n",
       "   CV score mean(Train)  \n",
       "0                  0.84  \n",
       "1                  0.84  \n",
       "2                  0.84  \n",
       "3                  0.87  \n",
       "4                  0.87  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Technique = [\"LinearRegression\",\"Ridge\",\"Lasso\",\"RandomForestRegressor\",\"GradientBoostingRegressor\"]\n",
    "results=pd.DataFrame({'Model': Technique,'R Squared(Train)': R2_train,'R Squared(Test)': R2_test,'CV score mean(Train)': CV})\n",
    "display(results)"
   ]
  },
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