Code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg-sUc-Wv-i2"
      },
      "source": [
        "Mount the google drive in colab where the csv file is located"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZyZevtVmStW",
        "outputId": "ef819d59-4214-4f2c-9c46-a8192ccbcb8e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uWRxrGufwb8n"
      },
      "source": [
        "Import the required libraries for the project"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LEBIlVXKm13B"
      },
      "outputs": [],
      "source": [
        "# import the required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhIfeUnPwitH"
      },
      "source": [
        "**Data Cleaning**\n",
        "\n",
        "Here we drop the unnecessary columns which aren't required for linear regression.\n",
        "\n",
        "We are analysing only petrol and diesel cars in our project.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hQBXGLiSxpTA"
      },
      "outputs": [],
      "source": [
        "# Read the csv file into Pandas dataframe\n",
        "df = pd.read_csv('/content/drive/MyDrive/data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 417
        },
        "id": "Ja4cMX0bxxAA",
        "outputId": "2cc5e3ef-9cae-4246-af64-1b9bde43e189"
      },
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>brand</th>\n",
              "      <th>model</th>\n",
              "      <th>color</th>\n",
              "      <th>registration_date</th>\n",
              "      <th>year</th>\n",
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              "      <th>power_kw</th>\n",
              "      <th>power_ps</th>\n",
              "      <th>transmission_type</th>\n",
              "      <th>fuel_type</th>\n",
              "      <th>fuel_consumption_l_100km</th>\n",
              "      <th>fuel_consumption_g_km</th>\n",
              "      <th>mileage_in_km</th>\n",
              "      <th>offer_description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>Alfa Romeo GTV</td>\n",
              "      <td>red</td>\n",
              "      <td>10/1995</td>\n",
              "      <td>1995</td>\n",
              "      <td>1300</td>\n",
              "      <td>148</td>\n",
              "      <td>201</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>10,9 l/100 km</td>\n",
              "      <td>260 g/km</td>\n",
              "      <td>160500.0</td>\n",
              "      <td>2.0 V6 TB</td>\n",
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              "      <td>1995</td>\n",
              "      <td>24900</td>\n",
              "      <td>191</td>\n",
              "      <td>260</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>NaN</td>\n",
              "      <td>- (g/km)</td>\n",
              "      <td>190000.0</td>\n",
              "      <td>Q4 Allrad, 3.2L GTA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>Alfa Romeo Spider</td>\n",
              "      <td>black</td>\n",
              "      <td>02/1995</td>\n",
              "      <td>1995</td>\n",
              "      <td>5900</td>\n",
              "      <td>110</td>\n",
              "      <td>150</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>NaN</td>\n",
              "      <td>- (g/km)</td>\n",
              "      <td>129000.0</td>\n",
              "      <td>ALFA ROME 916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>Alfa Romeo Spider</td>\n",
              "      <td>black</td>\n",
              "      <td>07/1995</td>\n",
              "      <td>1995</td>\n",
              "      <td>4900</td>\n",
              "      <td>110</td>\n",
              "      <td>150</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>9,5 l/100 km</td>\n",
              "      <td>225 g/km</td>\n",
              "      <td>189500.0</td>\n",
              "      <td>2.0 16V Twin Spark L</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>Alfa Romeo 164</td>\n",
              "      <td>red</td>\n",
              "      <td>11/1996</td>\n",
              "      <td>1996</td>\n",
              "      <td>17950</td>\n",
              "      <td>132</td>\n",
              "      <td>179</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>7,2 l/100 km</td>\n",
              "      <td>- (g/km)</td>\n",
              "      <td>96127.0</td>\n",
              "      <td>3.0i Super V6, absoluter Topzustand !</td>\n",
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            "text/plain": [
              "   Unnamed: 0       brand              model  color registration_date  year  \\\n",
              "0           0  alfa-romeo     Alfa Romeo GTV    red           10/1995  1995   \n",
              "1           1  alfa-romeo     Alfa Romeo 164  black           02/1995  1995   \n",
              "2           2  alfa-romeo  Alfa Romeo Spider  black           02/1995  1995   \n",
              "3           3  alfa-romeo  Alfa Romeo Spider  black           07/1995  1995   \n",
              "4           4  alfa-romeo     Alfa Romeo 164    red           11/1996  1996   \n",
              "\n",
              "  price_in_euro power_kw power_ps transmission_type fuel_type  \\\n",
              "0          1300      148      201            Manual    Petrol   \n",
              "1         24900      191      260            Manual    Petrol   \n",
              "2          5900      110      150           Unknown    Petrol   \n",
              "3          4900      110      150            Manual    Petrol   \n",
              "4         17950      132      179            Manual    Petrol   \n",
              "\n",
              "  fuel_consumption_l_100km fuel_consumption_g_km  mileage_in_km  \\\n",
              "0            10,9 l/100 km              260 g/km       160500.0   \n",
              "1                      NaN              - (g/km)       190000.0   \n",
              "2                      NaN              - (g/km)       129000.0   \n",
              "3             9,5 l/100 km              225 g/km       189500.0   \n",
              "4             7,2 l/100 km              - (g/km)        96127.0   \n",
              "\n",
              "                       offer_description  \n",
              "0                              2.0 V6 TB  \n",
              "1                    Q4 Allrad, 3.2L GTA  \n",
              "2                          ALFA ROME 916  \n",
              "3                   2.0 16V Twin Spark L  \n",
              "4  3.0i Super V6, absoluter Topzustand !  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
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        }
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      "source": [
        "df.head()"
      ]
    },
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      "cell_type": "markdown",
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        "id": "PnOa8ZEFz-gD"
      },
      "source": [
        "Dropping unnecessary columns"
      ]
    },
    {
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      "execution_count": null,
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        "id": "MwN_OzYhz9CA",
        "outputId": "5e775e45-ec97-4097-97de-6829d0032919"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "brand                           0\n",
              "year                            0\n",
              "price_in_euro                   0\n",
              "power_kw                      134\n",
              "transmission_type               0\n",
              "fuel_type                       0\n",
              "fuel_consumption_l_100km    26873\n",
              "mileage_in_km                 152\n",
              "dtype: int64"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Dropping unnecessary columns\n",
        "dropped_columns = ['Unnamed: 0', 'model', 'color', 'registration_date', 'power_ps', 'fuel_consumption_g_km', 'offer_description']\n",
        "for column_name in dropped_columns:\n",
        "  df = df.drop(column_name, axis=1)\n",
        "\n",
        "df.isnull().sum()"
      ]
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>1995</td>\n",
              "      <td>1300</td>\n",
              "      <td>148</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>10,9 l/100 km</td>\n",
              "      <td>160500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>1995</td>\n",
              "      <td>24900</td>\n",
              "      <td>191</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>NaN</td>\n",
              "      <td>190000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>1995</td>\n",
              "      <td>5900</td>\n",
              "      <td>110</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>NaN</td>\n",
              "      <td>129000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>1995</td>\n",
              "      <td>4900</td>\n",
              "      <td>110</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>9,5 l/100 km</td>\n",
              "      <td>189500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alfa-romeo</td>\n",
              "      <td>1996</td>\n",
              "      <td>17950</td>\n",
              "      <td>132</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>7,2 l/100 km</td>\n",
              "      <td>96127.0</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "        brand  year price_in_euro power_kw transmission_type fuel_type  \\\n",
              "0  alfa-romeo  1995          1300      148            Manual    Petrol   \n",
              "1  alfa-romeo  1995         24900      191            Manual    Petrol   \n",
              "2  alfa-romeo  1995          5900      110           Unknown    Petrol   \n",
              "3  alfa-romeo  1995          4900      110            Manual    Petrol   \n",
              "4  alfa-romeo  1996         17950      132            Manual    Petrol   \n",
              "\n",
              "  fuel_consumption_l_100km  mileage_in_km  \n",
              "0            10,9 l/100 km       160500.0  \n",
              "1                      NaN       190000.0  \n",
              "2                      NaN       129000.0  \n",
              "3             9,5 l/100 km       189500.0  \n",
              "4             7,2 l/100 km        96127.0  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pW7cOsPpyIiD",
        "outputId": "2f51c278-51ed-4c22-e7aa-8bc334ba3077"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Manual', 'Unknown', 'Automatic', 'Semi-automatic'], dtype=object)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.transmission_type.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IPHVjkRMyM4m",
        "outputId": "959dc4ab-a4e3-45e2-d5bb-f6a48bcb96dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Petrol', 'Diesel', 'Hybrid', 'LPG', 'Other', '07/2004',\n",
              "       '74.194 km', '110.250 km', '06/2014', 'CNG', 'Diesel Hybrid',\n",
              "       '12/2016', 'Automatic', 'Electric', '12/2019', 'Unknown',\n",
              "       '06/2023', 'Ethanol', 'Manual', '300.000 km', '264.000 km',\n",
              "       'KETTE NEUE', '108.313 km', '05/2009', '180.000 km', '04/2013',\n",
              "       '03/2014', '08/2014', '01/2016', '03/2017', '04/2008', '07/2007',\n",
              "       '145.500 km', '12/2012', '25890', '10/2022', '06/2004', '09/2009',\n",
              "       '12/2014', '02/2017', '12890', '11/2018', '08/2018', '03/2019',\n",
              "       '19450', '11/2021', '20.600 km', 'Hydrogen', '07/2022', '05/2015',\n",
              "       '03/2018', '04/2022', '160.629 km', '144.919 km', '02/1996',\n",
              "       '04/2000', '200.000 km', '06/2009', '185.500 km', '13000',\n",
              "       '05/2012', '11/2014', '10/2015', '350.000 km', '49.817 km',\n",
              "       '34900', '35.487 km', '03/2021', '26890', '26990', '4.000 km',\n",
              "       '11/2005', '07/2005', '08/2011', '02/2011', '03/2011', '10/2013',\n",
              "       '09/2015', '02/2018', '02/2023', '08/2017', '99.900 km', '67780',\n",
              "       '30.450 km', '62.880 km', '116490', '119890', '29.990 km',\n",
              "       '39.990 km', '13.610 km', '139890', '179990', '9.900 km',\n",
              "       '6.900 km', '9.870 km', '25.600 km', '37.500 km', '13750',\n",
              "       '01/2021', '05/2021', '42.300 km', '08/2013', '02/2013', '04/2017',\n",
              "       '11/2017', '83.034 km', '70.300 km', '1.000 km', '01/2022', 'FSE',\n",
              "       '40919', '05/2023', '02/2009', 'DAB', '09/2019', '08/2019',\n",
              "       '12.300 km', '18790', '11/2022', '04/2023', '03/2023', '01/2023',\n",
              "       '07/1999', '02/2002', '08/2007', '05/2007', '6500', '9350',\n",
              "       'KD GEPL', '07/2015', '41600', '14900', '09/2017', '06/2017',\n",
              "       '06/2018', '06/2020'], dtype=object)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.fuel_type.unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxtESpr2yl2Q"
      },
      "source": [
        "As we can see above, the fuel type has some garbage values when we inspect the unique values in the fuel_type column.\n",
        "\n",
        "We use only Petrol and Diesel cars in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ksU2edg5zyX8"
      },
      "outputs": [],
      "source": [
        "#Data Cleaning\n",
        "# Take only petrol and diesel cars\n",
        "df['fuel_type'] = df['fuel_type'].astype(str)\n",
        "df = df[df['fuel_type'].str.contains('Petrol') | df['fuel_type'].str.contains('Diesel')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0kQfrPdYz1zZ",
        "outputId": "f4bd2031-6485-4b22-e539-16a093ee1265"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['Petrol', 'Diesel', 'Diesel Hybrid'], dtype=object)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.fuel_type.unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JaWoo7SZxLS4"
      },
      "source": [
        "Before removing null values, we see if we can do any mean or median imputation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4lyWHdSE-5y8",
        "outputId": "fc57d1d3-c438-43c5-d59f-3b2cbda46548"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "brand                           0\n",
              "year                            0\n",
              "price_in_euro                   0\n",
              "power_kw                       74\n",
              "transmission_type               0\n",
              "fuel_type                       0\n",
              "fuel_consumption_l_100km    18628\n",
              "mileage_in_km                  31\n",
              "dtype: int64"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "7rtp8yyuvoW_",
        "outputId": "6e1c3a41-8776-4252-fa2b-a5b0a13c77a2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-12-abf7239a6290>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df['power_kw'])\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='power_kw', ylabel='Density'>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(df['power_kw'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hh17FyymxaAl"
      },
      "source": [
        "For the power_kw column, we see that the distribution is skewed. Hence we perform median imputation on the power_kw column.\n",
        "\n",
        "Thus we replace missing/null values with median of power_kw."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqF9EO_xUKpp",
        "outputId": "031ef90e-a398-4893-e3ea-9fbf79b78017"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-13-a0511294fd48>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_not_null['power_kw']=df_not_null.power_kw.astype(int)\n"
          ]
        }
      ],
      "source": [
        "# Fill missing power_kw with the median value\n",
        "df_not_null = df.dropna(subset=['power_kw'])\n",
        "df_not_null['power_kw']=df_not_null.power_kw.astype(int)\n",
        "df['power_kw'] = df['power_kw'].astype(float)\n",
        "df['power_kw'].fillna(df_not_null['power_kw'].median(), inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jqx2iDXi1T00"
      },
      "source": [
        "Convert the fuel_consumption_l_100km column string values to float values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "khaLnJWfIci4"
      },
      "outputs": [],
      "source": [
        "# convert string of fuel_consumption_l_km to float values\n",
        "\n",
        "df['fuel_consumption_l_100km'] = df['fuel_consumption_l_100km'].astype(str)\n",
        "\n",
        "# Filter out entries with units other than liters per 100 kilometers\n",
        "df = df[df['fuel_consumption_l_100km'].str.contains('l/100 km')]\n",
        "\n",
        "# Extract numeric values and convert to a consistent format\n",
        "df['fuel_consumption_l_100km'] = df['fuel_consumption_l_100km'].str.extract(r'(\\d+\\.\\d+|\\d+)').astype(float)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MmqvQjb84FUQ"
      },
      "source": [
        "Check distribution plot of fuel_consumption_l_100km for mean/median imputation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "QrOsZCDM2NQx",
        "outputId": "bec3c176-dfca-4faf-e189-e04f250784fa"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-15-e7d5026cbea5>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df['fuel_consumption_l_100km'], ax=ax)\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "sns.distplot(df['fuel_consumption_l_100km'], ax=ax)\n",
        "ax.set_xlim(1,31)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIxOjTPX7BUg"
      },
      "source": [
        "Replace null values in fuel_consumption_l_100km column with the median value in that column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U9auL-xN4zEV"
      },
      "outputs": [],
      "source": [
        "df_not_null = df.dropna(subset=['fuel_consumption_l_100km'])\n",
        "df['fuel_consumption_l_100km'].fillna(df_not_null['fuel_consumption_l_100km'].median(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KHHAnCq4n0m",
        "outputId": "d8d9fc81-a641-46f7-801c-5dd0642dd328"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "brand                        0\n",
              "year                         0\n",
              "price_in_euro                0\n",
              "power_kw                     0\n",
              "transmission_type            0\n",
              "fuel_type                    0\n",
              "fuel_consumption_l_100km     0\n",
              "mileage_in_km               31\n",
              "dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxIyyndk6kMz"
      },
      "source": [
        "Follow the same process given above on the mileage_in_km column. First inspect the distribution and then decide on mean or median imputation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "fGUQMlnn4rDw",
        "outputId": "a716015f-2925-49b0-fbfc-5958ba5262ad"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-18-4f8f8fd98ad1>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df['mileage_in_km'], ax=ax)  # distplot is deprecate and replaced by histplot\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "sns.distplot(df['mileage_in_km'], ax=ax)  # distplot is deprecate and replaced by histplot\n",
        "ax.set_xlim(0.0e6,4.0e6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lyxID3oS61fK"
      },
      "source": [
        "Replace null values in mileage_in_km column with the median value in that column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtCw2MfV5-PM"
      },
      "outputs": [],
      "source": [
        "df_not_null = df.dropna(subset=['mileage_in_km'])\n",
        "df['mileage_in_km'].fillna(df_not_null['mileage_in_km'].median(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4icDewhudfP"
      },
      "outputs": [],
      "source": [
        "df['price_in_euro']=df['price_in_euro'].astype(float)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0Dxn5V24egT"
      },
      "source": [
        "Change datatype of year column from string to integer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "67qPpEzZJ-Te"
      },
      "outputs": [],
      "source": [
        "# transform year from string into integer\n",
        "df['year'] = df['year'].astype(str)\n",
        "\n",
        "# Filter out non-numeric values\n",
        "numeric_years = df[df['year'].str.isnumeric()]\n",
        "\n",
        "# Convert years to integer type\n",
        "numeric_years['year'] = numeric_years['year'].astype(int)\n",
        "\n",
        "# Filter out years outside the valid range\n",
        "valid_years = numeric_years[(numeric_years['year'] >= 1995) & (numeric_years['year'] <= 2023)]\n",
        "\n",
        "df['year'] = valid_years['year']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zB8y_7ZlCmEb"
      },
      "source": [
        "No null values now as shown below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Bg7eJbt6JMx",
        "outputId": "37b9cef4-71d2-42ea-8918-b5d47b478879"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "brand                       0\n",
              "year                        0\n",
              "price_in_euro               0\n",
              "power_kw                    0\n",
              "transmission_type           0\n",
              "fuel_type                   0\n",
              "fuel_consumption_l_100km    0\n",
              "mileage_in_km               0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGcVw1EIIbYn",
        "outputId": "1ab8c76a-81a0-44e7-8d42-1993008e1641"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 211541 entries, 0 to 251076\n",
            "Data columns (total 8 columns):\n",
            " #   Column                    Non-Null Count   Dtype  \n",
            "---  ------                    --------------   -----  \n",
            " 0   brand                     211541 non-null  object \n",
            " 1   year                      211541 non-null  int64  \n",
            " 2   price_in_euro             211541 non-null  float64\n",
            " 3   power_kw                  211541 non-null  float64\n",
            " 4   transmission_type         211541 non-null  object \n",
            " 5   fuel_type                 211541 non-null  object \n",
            " 6   fuel_consumption_l_100km  211541 non-null  float64\n",
            " 7   mileage_in_km             211541 non-null  float64\n",
            "dtypes: float64(4), int64(1), object(3)\n",
            "memory usage: 14.5+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0wvsvg7Cr04"
      },
      "source": [
        "Heatmap to show corelation between variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dgwe-GSr_Leh"
      },
      "outputs": [],
      "source": [
        "# Add new columns with imperial units\n",
        "\n",
        "df['power_hp'] = df['power_kw'] / 0.7457\n",
        "\n",
        "df['mpg'] = 235.215 / df['fuel_consumption_l_100km']\n",
        "\n",
        "df['mileage_in_miles'] = df['mileage_in_km'] / 1.609\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xixRgllJJMzT",
        "outputId": "b351d2a5-2d78-4c86-8f33-579a21713048"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DataFrame contains infinity or NaN values.\n"
          ]
        }
      ],
      "source": [
        "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "\n",
        "# Check for NaN values\n",
        "inf_check = df.isna().any(axis=None)\n",
        "\n",
        "if inf_check:\n",
        "    print(\"DataFrame contains infinity or NaN values.\")\n",
        "else:\n",
        "    print(\"DataFrame does not contain infinity or NaN values.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DD_oc4-_M0K-",
        "outputId": "261312f4-c2de-4b79-d58c-51c55548109a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "brand                        0\n",
              "year                         0\n",
              "price_in_euro                0\n",
              "power_kw                     0\n",
              "transmission_type            0\n",
              "fuel_type                    0\n",
              "fuel_consumption_l_100km     0\n",
              "mileage_in_km                0\n",
              "power_hp                     0\n",
              "mpg                         57\n",
              "mileage_in_miles             0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7bgBTqoM5GG"
      },
      "outputs": [],
      "source": [
        "df_not_null = df.dropna(subset = ['mpg'])\n",
        "df.fillna(df_not_null['mpg'].median(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "PKFJDw8sJKFZ",
        "outputId": "4997d4d7-a761-48a0-bc0a-eb56430dcc0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df1 = df.iloc[:, 1:3].copy()\n",
        "df2 = df.iloc[:,8:].copy()\n",
        "\n",
        "df_heatmap = pd.concat([df1, df2],axis = 1)\n",
        "\n",
        "sns.heatmap(df_heatmap.corr(), cmap=\"coolwarm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9dYN3NYhHlAG"
      },
      "outputs": [],
      "source": [
        "X=df[['year','power_hp', 'mpg','mileage_in_miles']]\n",
        "y=df['price_in_euro']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v5oBRdKQJn2K"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score\n",
        "import statsmodels.api as sm\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iyrHm5s0poQK"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ycIbDqddp-dz"
      },
      "source": [
        "Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "dsuaNyOSJ0pg",
        "outputId": "b3f251ec-7c06-4534-d07c-3bc7f459dd8e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = LinearRegression()\n",
        "model.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oG9TWK1nJ_GL",
        "outputId": "c3db6646-4d47-4885-dfed-24475aa9d13a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-1684580.5879554853\n"
          ]
        }
      ],
      "source": [
        "# print the intercept\n",
        "print(model.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UhIBMODTaUS",
        "outputId": "337c4f42-34e9-4cb7-ec88-2f97b96380e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:          price_in_euro   R-squared:                       0.439\n",
            "Model:                            OLS   Adj. R-squared:                  0.439\n",
            "Method:                 Least Squares   F-statistic:                 3.308e+04\n",
            "Date:                Mon, 08 Apr 2024   Prob (F-statistic):               0.00\n",
            "Time:                        16:08:53   Log-Likelihood:            -1.9615e+06\n",
            "No. Observations:              169232   AIC:                         3.923e+06\n",
            "Df Residuals:                  169227   BIC:                         3.923e+06\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "====================================================================================\n",
            "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------\n",
            "const            -1.685e+06   3.52e+04    -47.849      0.000   -1.75e+06   -1.62e+06\n",
            "year               833.9662     17.482     47.704      0.000     799.702     868.231\n",
            "power_hp           217.9314      0.793    274.949      0.000     216.378     219.485\n",
            "mpg                -31.3109      5.576     -5.616      0.000     -42.239     -20.383\n",
            "mileage_in_miles    -0.1198      0.002    -65.188      0.000      -0.123      -0.116\n",
            "==============================================================================\n",
            "Omnibus:                   610812.530   Durbin-Watson:                   1.995\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):    1417408280563.377\n",
            "Skew:                          73.784   Prob(JB):                         0.00\n",
            "Kurtosis:                   14180.130   Cond. No.                     4.15e+07\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 4.15e+07. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ],
      "source": [
        "X2 = sm.add_constant(X_train)\n",
        "est = sm.OLS(y_train, X2)\n",
        "est2 = est.fit()\n",
        "print(est2.summary())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "K9wcFwaFNt1d",
        "outputId": "3befb372-8864-49c5-bbbb-a07f984c9c32"
      },
      "outputs": [
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            "text/plain": [
              "                  Coefficient\n",
              "year               833.966220\n",
              "power_hp           217.931428\n",
              "mpg                -31.310912\n",
              "mileage_in_miles    -0.119794"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "coeff_df = pd.DataFrame(model.coef_,X.columns,columns=['Coefficient'])\n",
        "coeff_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkDwN1HRN98S",
        "outputId": "8d048444-6b2c-4bc5-c1f7-a775cdb8ea75"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 8983.742629947725\n",
            "MSE: 454409051.86292857\n",
            "RMSE: 21316.872469077836\n",
            "R squared: 0.5419645324860002\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(X_test)\n",
        "mae = metrics.mean_absolute_error(y_test, predictions)\n",
        "mse = metrics.mean_squared_error(y_test, predictions)\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, predictions))\n",
        "r_score = r2_score(y_test, predictions)\n",
        "\n",
        "print('MAE:', mae)\n",
        "print('MSE:', mse)\n",
        "print('RMSE:', rmse)\n",
        "print('R squared:',r_score )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3Umv5p0_Vlz"
      },
      "source": [
        "Decision Tree Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Om9QA_pk_VUl",
        "outputId": "779df4fc-8e8f-4a02-dcc7-70b6dd841f95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 5806.383444928376\n",
            "MSE: 346714255.3569775\n",
            "RMSE: 18620.264642506496\n"
          ]
        }
      ],
      "source": [
        "dt_model = DecisionTreeRegressor()\n",
        "\n",
        "# Train the regressor on the training data\n",
        "dt_model.fit(X_train, y_train)\n",
        "\n",
        "# Predict the housing prices for the test data\n",
        "y_pred = dt_model.predict(X_test)\n",
        "\n",
        "# Calculate the mean squared error of the regressor\n",
        "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
        "mse = metrics.mean_squared_error(y_test, y_pred)\n",
        "# print(\"Mean Squared Error:\", mse)\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "print('MAE:', mae)\n",
        "print('MSE:', mse)\n",
        "print('RMSE:', rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViKP0Fhnpzbv"
      },
      "source": [
        "Random Forest Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NVu3OjBUYIu",
        "outputId": "345d02c8-5e68-4b5f-ea63-d27f8730cc5f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 4815.949864982732\n",
            "MSE: 183692145.67021576\n",
            "RMSE: 13553.307554623549\n"
          ]
        }
      ],
      "source": [
        "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "# Evaluate model performance\n",
        "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
        "mse = metrics.mean_squared_error(y_test, y_pred)\n",
        "# print(\"Mean Squared Error:\", mse)\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "print('MAE:', mae)\n",
        "print('MSE:', mse)\n",
        "print('RMSE:', rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bq9-R-8FsE3Q"
      },
      "source": [
        "Ridge Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHtNJo0ZqSgi",
        "outputId": "a87449f5-4af1-41bd-8893-01862e620134"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 8983.742568490643\n",
            "MSE: 454409051.64318687\n",
            "RMSE: 21316.87246392366\n"
          ]
        }
      ],
      "source": [
        "ridge_model = Ridge(alpha=1.0)  # You can adjust the regularization strength (alpha) as needed\n",
        "ridge_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = ridge_model.predict(X_test)\n",
        "\n",
        "# Evaluate model performance\n",
        "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
        "mse = metrics.mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "print('MAE:', mae)\n",
        "print('MSE:', mse)\n",
        "print('RMSE:', rmse)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h09NUqMCsHUR"
      },
      "source": [
        "Gradient Boostin Machine (GBM) Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVlseiGSrKl-",
        "outputId": "21a7d422-c62a-486f-edbf-964a8a55dd73"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 5605.188144026183\n",
            "MSE: 193324475.55616716\n",
            "RMSE: 13904.117215996388\n"
          ]
        }
      ],
      "source": [
        "gbm_model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, random_state=42)  # Adjust parameters as needed\n",
        "gbm_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = gbm_model.predict(X_test)\n",
        "\n",
        "# Evaluate model performance\n",
        "mae = metrics.mean_absolute_error(y_test, y_pred)\n",
        "mse = metrics.mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "print('MAE:', mae)\n",
        "print('MSE:', mse)\n",
        "print('RMSE:', rmse)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SetmdGDvs2Om"
      },
      "source": [
        "Neural Networks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kPcaUC1s_I0"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bn0vowdAs3Pt",
        "outputId": "a0c8af0c-fcdb-464c-9403-632bce121f40"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "5289/5289 [==============================] - 11s 2ms/step - loss: 941924736.0000\n",
            "Epoch 2/50\n",
            "5289/5289 [==============================] - 12s 2ms/step - loss: 646879488.0000\n",
            "Epoch 3/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 628926528.0000\n",
            "Epoch 4/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 618400064.0000\n",
            "Epoch 5/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 606392960.0000\n",
            "Epoch 6/50\n",
            "5289/5289 [==============================] - 9s 2ms/step - loss: 596704320.0000\n",
            "Epoch 7/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 586729792.0000\n",
            "Epoch 8/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 575608320.0000\n",
            "Epoch 9/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 567590336.0000\n",
            "Epoch 10/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 558302400.0000\n",
            "Epoch 11/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 551074432.0000\n",
            "Epoch 12/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 543365824.0000\n",
            "Epoch 13/50\n",
            "5289/5289 [==============================] - 11s 2ms/step - loss: 535111872.0000\n",
            "Epoch 14/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 528939712.0000\n",
            "Epoch 15/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 522754880.0000\n",
            "Epoch 16/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 517288448.0000\n",
            "Epoch 17/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 511433856.0000\n",
            "Epoch 18/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 505921664.0000\n",
            "Epoch 19/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 502049600.0000\n",
            "Epoch 20/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 498581568.0000\n",
            "Epoch 21/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 494393024.0000\n",
            "Epoch 22/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 490601376.0000\n",
            "Epoch 23/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 487915296.0000\n",
            "Epoch 24/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 485339104.0000\n",
            "Epoch 25/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 482101216.0000\n",
            "Epoch 26/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 480244384.0000\n",
            "Epoch 27/50\n",
            "5289/5289 [==============================] - 9s 2ms/step - loss: 478913856.0000\n",
            "Epoch 28/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 477168000.0000\n",
            "Epoch 29/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 475151104.0000\n",
            "Epoch 30/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 474408256.0000\n",
            "Epoch 31/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 472887808.0000\n",
            "Epoch 32/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 471562240.0000\n",
            "Epoch 33/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 470377088.0000\n",
            "Epoch 34/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 469462592.0000\n",
            "Epoch 35/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 468378624.0000\n",
            "Epoch 36/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 467265568.0000\n",
            "Epoch 37/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 466462272.0000\n",
            "Epoch 38/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 465714528.0000\n",
            "Epoch 39/50\n",
            "5289/5289 [==============================] - 8s 2ms/step - loss: 465042720.0000\n",
            "Epoch 40/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 464792192.0000\n",
            "Epoch 41/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 463460384.0000\n",
            "Epoch 42/50\n",
            "5289/5289 [==============================] - 9s 2ms/step - loss: 463526368.0000\n",
            "Epoch 43/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 462574432.0000\n",
            "Epoch 44/50\n",
            "5289/5289 [==============================] - 10s 2ms/step - loss: 461805920.0000\n",
            "Epoch 45/50\n",
            "5289/5289 [==============================] - 9s 2ms/step - loss: 461341856.0000\n",
            "Epoch 46/50\n",
            "5289/5289 [==============================] - 9s 2ms/step - loss: 460969056.0000\n",
            "Epoch 47/50\n",
            "5289/5289 [==============================] - 13s 2ms/step - loss: 461200800.0000\n",
            "Epoch 48/50\n",
            "5289/5289 [==============================] - 11s 2ms/step - loss: 460035424.0000\n",
            "Epoch 49/50\n",
            "5289/5289 [==============================] - 8s 1ms/step - loss: 459591040.0000\n",
            "Epoch 50/50\n",
            "5289/5289 [==============================] - 9s 2ms/step - loss: 459824864.0000\n",
            "1323/1323 [==============================] - 2s 1ms/step\n",
            "MSE: 221488233.13109425\n",
            "RMSE: 14882.480745194809\n"
          ]
        }
      ],
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Create neural network model\n",
        "nn_model = Sequential()\n",
        "nn_model.add(Dense(64, input_dim=X_train_scaled.shape[1], activation='relu'))  # Input layer with 64 neurons and ReLU activation\n",
        "nn_model.add(Dense(32, activation='relu'))  # Hidden layer with 32 neurons and ReLU activation\n",
        "nn_model.add(Dense(1))  # Output layer with 1 neuron for regression\n",
        "nn_model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.001))  # Compile the model with mean squared error loss and Adam optimizer\n",
        "\n",
        "# Train the model\n",
        "nn_model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, verbose=1)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = nn_model.predict(X_test_scaled)\n",
        "\n",
        "\n",
        "# Evaluate model performance\n",
        "mse = metrics.mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
        "print('MSE:', mse)\n",
        "print('RMSE:', rmse)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "N16byg8F3Uct"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "hdq692Oe-tZN"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}