usedcar_price_pred.ipynb

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      "source": [
        "# **Forecasting Used Car Prices Using Regression**"
      ],
      "metadata": {
        "id": "7MjWEK3xNMH0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This project aims to develop a regression model that accurately predicts the prices of used cars based on key features, enabling buyers and sellers to make informed decisions and optimize value in the pre-owned car market."
      ],
      "metadata": {
        "id": "JdyI0VvPPa5T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.Importing Libraries, Reading and Understanding the data**"
      ],
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      "source": [
        "# Importing the libraries\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "cp63-XbVswoc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Reading the dataset\n",
        "df=pd.read_csv('/content/drive/MyDrive/datasets/train-data.csv')"
      ],
      "metadata": {
        "id": "4pArQmjBioJA"
      },
      "execution_count": 3,
      "outputs": []
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      "source": [
        "#Understand the data\n",
        "df.head()"
      ],
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        "outputId": "b9c5aa43-0ca4-4c64-9241-72851e249c23"
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      "execution_count": 4,
      "outputs": [
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              "   Unnamed: 0                              Name    Location  Year  \\\n",
              "0           0            Maruti Wagon R LXI CNG      Mumbai  2010   \n",
              "1           1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
              "2           2                      Honda Jazz V     Chennai  2011   \n",
              "3           3                 Maruti Ertiga VDI     Chennai  2012   \n",
              "4           4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
              "\n",
              "   Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage   Engine  \\\n",
              "0              72000       CNG       Manual      First  26.6 km/kg   998 CC   \n",
              "1              41000    Diesel       Manual      First  19.67 kmpl  1582 CC   \n",
              "2              46000    Petrol       Manual      First   18.2 kmpl  1199 CC   \n",
              "3              87000    Diesel       Manual      First  20.77 kmpl  1248 CC   \n",
              "4              40670    Diesel    Automatic     Second   15.2 kmpl  1968 CC   \n",
              "\n",
              "       Power  Seats  New_Price  Price  \n",
              "0  58.16 bhp    5.0        NaN   1.75  \n",
              "1  126.2 bhp    5.0        NaN  12.50  \n",
              "2   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
              "3  88.76 bhp    7.0        NaN   6.00  \n",
              "4  140.8 bhp    5.0        NaN  17.74  "
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              "      <td>Maruti Wagon R LXI CNG</td>\n",
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              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
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      "cell_type": "code",
      "source": [
        "#Checking for shape of the data\n",
        "df.shape"
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        "id": "z-_Km5t_QEt4",
        "outputId": "35c290d5-b0be-4b97-97cb-48cc12e1ec74"
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            "text/plain": [
              "(6019, 14)"
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          "metadata": {},
          "execution_count": 5
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      "source": [
        "**2.Data Cleaning**"
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      "metadata": {
        "id": "SkI5flTpKcm4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#changing column 'Name' to 'Brand' to list only the brand name\n",
        "df['Brand']=df['Name'].apply(lambda x:x.split()[0])"
      ],
      "metadata": {
        "id": "1fuUahtVi5Is"
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      "execution_count": 6,
      "outputs": []
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        "df"
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        "outputId": "875f5d24-a926-43de-98d8-f4ae1c0d4863"
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      "execution_count": 7,
      "outputs": [
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            "text/plain": [
              "      Unnamed: 0                              Name    Location  Year  \\\n",
              "0              0            Maruti Wagon R LXI CNG      Mumbai  2010   \n",
              "1              1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
              "2              2                      Honda Jazz V     Chennai  2011   \n",
              "3              3                 Maruti Ertiga VDI     Chennai  2012   \n",
              "4              4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
              "...          ...                               ...         ...   ...   \n",
              "6014        6014                  Maruti Swift VDI       Delhi  2014   \n",
              "6015        6015          Hyundai Xcent 1.1 CRDi S      Jaipur  2015   \n",
              "6016        6016             Mahindra Xylo D4 BSIV      Jaipur  2012   \n",
              "6017        6017                Maruti Wagon R VXI     Kolkata  2013   \n",
              "6018        6018             Chevrolet Beat Diesel   Hyderabad  2011   \n",
              "\n",
              "      Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage  \\\n",
              "0                 72000       CNG       Manual      First  26.6 km/kg   \n",
              "1                 41000    Diesel       Manual      First  19.67 kmpl   \n",
              "2                 46000    Petrol       Manual      First   18.2 kmpl   \n",
              "3                 87000    Diesel       Manual      First  20.77 kmpl   \n",
              "4                 40670    Diesel    Automatic     Second   15.2 kmpl   \n",
              "...                 ...       ...          ...        ...         ...   \n",
              "6014              27365    Diesel       Manual      First   28.4 kmpl   \n",
              "6015             100000    Diesel       Manual      First   24.4 kmpl   \n",
              "6016              55000    Diesel       Manual     Second   14.0 kmpl   \n",
              "6017              46000    Petrol       Manual      First   18.9 kmpl   \n",
              "6018              47000    Diesel       Manual      First  25.44 kmpl   \n",
              "\n",
              "       Engine      Power  Seats  New_Price  Price      Brand  \n",
              "0      998 CC  58.16 bhp    5.0        NaN   1.75     Maruti  \n",
              "1     1582 CC  126.2 bhp    5.0        NaN  12.50    Hyundai  \n",
              "2     1199 CC   88.7 bhp    5.0  8.61 Lakh   4.50      Honda  \n",
              "3     1248 CC  88.76 bhp    7.0        NaN   6.00     Maruti  \n",
              "4     1968 CC  140.8 bhp    5.0        NaN  17.74       Audi  \n",
              "...       ...        ...    ...        ...    ...        ...  \n",
              "6014  1248 CC     74 bhp    5.0  7.88 Lakh   4.75     Maruti  \n",
              "6015  1120 CC     71 bhp    5.0        NaN   4.00    Hyundai  \n",
              "6016  2498 CC    112 bhp    8.0        NaN   2.90   Mahindra  \n",
              "6017   998 CC   67.1 bhp    5.0        NaN   2.65     Maruti  \n",
              "6018   936 CC   57.6 bhp    5.0        NaN   2.50  Chevrolet  \n",
              "\n",
              "[6019 rows x 15 columns]"
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              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>New_Price</th>\n",
              "      <th>Price</th>\n",
              "      <th>Brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Maruti Wagon R LXI CNG</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>26.6 km/kg</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>58.16 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.75</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67 kmpl</td>\n",
              "      <td>1582 CC</td>\n",
              "      <td>126.2 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.50</td>\n",
              "      <td>Hyundai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "      <td>Honda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.74</td>\n",
              "      <td>Audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6014</th>\n",
              "      <td>6014</td>\n",
              "      <td>Maruti Swift VDI</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>28.4 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.88 Lakh</td>\n",
              "      <td>4.75</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</th>\n",
              "      <td>6015</td>\n",
              "      <td>Hyundai Xcent 1.1 CRDi S</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.4 kmpl</td>\n",
              "      <td>1120 CC</td>\n",
              "      <td>71 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Hyundai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</th>\n",
              "      <td>6016</td>\n",
              "      <td>Mahindra Xylo D4 BSIV</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2012</td>\n",
              "      <td>55000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.0 kmpl</td>\n",
              "      <td>2498 CC</td>\n",
              "      <td>112 bhp</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.90</td>\n",
              "      <td>Mahindra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</th>\n",
              "      <td>6017</td>\n",
              "      <td>Maruti Wagon R VXI</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.9 kmpl</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>67.1 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.65</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6018</th>\n",
              "      <td>6018</td>\n",
              "      <td>Chevrolet Beat Diesel</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2011</td>\n",
              "      <td>47000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>25.44 kmpl</td>\n",
              "      <td>936 CC</td>\n",
              "      <td>57.6 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.50</td>\n",
              "      <td>Chevrolet</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6019 rows × 15 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0941638b-9764-4f58-a00b-13628ca10c55');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for missing values\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkgqcsibRcQv",
        "outputId": "7b248dd1-1402-466e-f764-96a433a4af69"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              0\n",
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 2\n",
              "Engine                 36\n",
              "Power                  36\n",
              "Seats                  42\n",
              "New_Price            5195\n",
              "Price                   0\n",
              "Brand                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping unwanted columns\n",
        "df.drop(['Unnamed: 0','New_Price','Name'],axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "FNn8dDbpkwD4",
        "outputId": "8764cacb-4c01-4b47-f09c-cc32c9fd27b6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Location  Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  \\\n",
              "0         Mumbai  2010              72000       CNG       Manual      First   \n",
              "1           Pune  2015              41000    Diesel       Manual      First   \n",
              "2        Chennai  2011              46000    Petrol       Manual      First   \n",
              "3        Chennai  2012              87000    Diesel       Manual      First   \n",
              "4     Coimbatore  2013              40670    Diesel    Automatic     Second   \n",
              "...          ...   ...                ...       ...          ...        ...   \n",
              "6014       Delhi  2014              27365    Diesel       Manual      First   \n",
              "6015      Jaipur  2015             100000    Diesel       Manual      First   \n",
              "6016      Jaipur  2012              55000    Diesel       Manual     Second   \n",
              "6017     Kolkata  2013              46000    Petrol       Manual      First   \n",
              "6018   Hyderabad  2011              47000    Diesel       Manual      First   \n",
              "\n",
              "         Mileage   Engine      Power  Seats  Price      Brand  \n",
              "0     26.6 km/kg   998 CC  58.16 bhp    5.0   1.75     Maruti  \n",
              "1     19.67 kmpl  1582 CC  126.2 bhp    5.0  12.50    Hyundai  \n",
              "2      18.2 kmpl  1199 CC   88.7 bhp    5.0   4.50      Honda  \n",
              "3     20.77 kmpl  1248 CC  88.76 bhp    7.0   6.00     Maruti  \n",
              "4      15.2 kmpl  1968 CC  140.8 bhp    5.0  17.74       Audi  \n",
              "...          ...      ...        ...    ...    ...        ...  \n",
              "6014   28.4 kmpl  1248 CC     74 bhp    5.0   4.75     Maruti  \n",
              "6015   24.4 kmpl  1120 CC     71 bhp    5.0   4.00    Hyundai  \n",
              "6016   14.0 kmpl  2498 CC    112 bhp    8.0   2.90   Mahindra  \n",
              "6017   18.9 kmpl   998 CC   67.1 bhp    5.0   2.65     Maruti  \n",
              "6018  25.44 kmpl   936 CC   57.6 bhp    5.0   2.50  Chevrolet  \n",
              "\n",
              "[6019 rows x 12 columns]"
            ],
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              "      <th>0</th>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2010</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.50</td>\n",
              "      <td>Honda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.00</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>17.74</td>\n",
              "      <td>Audi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6014</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>28.4 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.75</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</th>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.4 kmpl</td>\n",
              "      <td>1120 CC</td>\n",
              "      <td>71 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Hyundai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</th>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2012</td>\n",
              "      <td>55000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.0 kmpl</td>\n",
              "      <td>2498 CC</td>\n",
              "      <td>112 bhp</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.90</td>\n",
              "      <td>Mahindra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</th>\n",
              "      <td>Kolkata</td>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.9 kmpl</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>67.1 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.65</td>\n",
              "      <td>Maruti</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6018</th>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2011</td>\n",
              "      <td>47000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>25.44 kmpl</td>\n",
              "      <td>936 CC</td>\n",
              "      <td>57.6 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.50</td>\n",
              "      <td>Chevrolet</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6019 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6db4206a-cbda-493a-ba53-b16e24b8b7c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-f7948e3e-6a7a-403c-804f-e39921d70211\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f7948e3e-6a7a-403c-804f-e39921d70211')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f7948e3e-6a7a-403c-804f-e39921d70211 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6db4206a-cbda-493a-ba53-b16e24b8b7c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6db4206a-cbda-493a-ba53-b16e24b8b7c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhVkN7dJllHl",
        "outputId": "cdef5122-1508-441b-c694-b7e71822161c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location              0\n",
              "Year                  0\n",
              "Kilometers_Driven     0\n",
              "Fuel_Type             0\n",
              "Transmission          0\n",
              "Owner_Type            0\n",
              "Mileage               2\n",
              "Engine               36\n",
              "Power                36\n",
              "Seats                42\n",
              "Price                 0\n",
              "Brand                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.Exploratory Data Analysis(EDA)**"
      ],
      "metadata": {
        "id": "L-b9qnsyK1H8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Count Plot"
      ],
      "metadata": {
        "id": "1cln8UTCMS-J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data = df, x = df['Fuel_Type'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "m8dlDFtGLBKV",
        "outputId": "8be6fbb2-897e-4bb5-8e35-c699bec8a7f9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Fuel_Type', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot against km driven and prices of the car\n",
        "plt.plot(df['Kilometers_Driven'],df['Price'])\n",
        "plt.xlabel('KM DRIVEN')\n",
        "plt.ylabel('PRICE')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "tzLq9ouBMm7w",
        "outputId": "0ef00ea7-1743-4096-a1c4-5482b10e73f5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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cEAQsXLgQAQEBcHV1RWxsLE6dOmXRpri4GFOmTIGnpye8vLwwY8YMlJWVifgumoazsYiIiMQladgpLy9HZGQkVq1aVe/jy5Ytw8qVK7FmzRocOHAAbm5uiIuLQ1VVlbnNlClTcPz4cezYsQNbt25FcnIyZs2aJdZbaDJmHSIiInEppHzxMWPGYMyYMfU+JggCVqxYgVdeeQV33303AOCTTz6BRqPB5s2bMXnyZKSnp2Pbtm04dOgQoqKiAABvv/02xo4di+XLlyMwMLDec+v1euj1evN9nU7XzO+slqmeZFPfMSIiIrIdux2zk5WVhby8PMTGxpqPqdVqREdHIyUlBQCQkpICLy8vc9ABgNjYWMjlchw4cKDBcycmJkKtVptvQUFBNnkPyacK6xzj3lhERETistuwk5eXBwDQaDQWxzUajfmxvLw8+Pn5WTyuUCjg7e1tblOfhIQEaLVa8y03N7eZq69VWW2sc4wDlImIiMQl6WUsqahUKqhUKpu/jrNT3SzJy1hERETistueHX9/fwBAfn6+xfH8/HzzY/7+/igoKLB4vKamBsXFxeY2UlIq6n68nI1FREQkLrsNOyEhIfD398fOnTvNx3Q6HQ4cOICYmBgAQExMDEpKSpCammpus2vXLphMJkRHR4te8/Xq7dlh1iEiIhKVpJexysrKcPr0afP9rKwspKWlwdvbG8HBwXj22WfxxhtvoGvXrggJCcGCBQsQGBiIiRMnAgAiIiIwevRozJw5E2vWrIHBYMCcOXMwefLkBmdiiam+nh2O2SEiIhKXpGHn8OHDGDlypPl+fHw8AGDatGlYv349XnzxRZSXl2PWrFkoKSnBkCFDsG3bNri4uJif89lnn2HOnDkYNWoU5HI5Jk2ahJUrV4r+XuqjrLdnh2GHiIhITDJB4LevTqeDWq2GVquFp6dns503I68UcSuSLY7NHx2OJ0d0brbXICIiaq0a+/1tt2N2HIGzk6zOMfbsEBERiYthx4bqHbPDEcpERESiYtixofpmY3EFZSIiInEx7NiQXFbPZSz27BAREYmKYceG5HWzDtfZISIiEhnDjg3V17PDy1hERETiYtixIV7GIiIikh7Djg3J6vl0OfWciIhIXAw7NlTvZSyTBIUQERG1Ygw7NlT/AGX27BAREYmJYceG6h2zw7BDREQkKoYdG6on68DIAcpERESiYtixodziyjrHmHWIiIjExbBjQ9PXH6pzrKK6RoJKiIiIWi+GHRvKKa6ocyxPWyVBJURERK0Xw47ICkr1UpdARETUqjDsiCxfx54dIiIiMTHs2FB9s7Eqqo3iF0JERNSKNSnsLFu2DJWV12YY7du3D3r9tcsypaWleOqpp5qvuhZOUd+qgkRERCSqJoWdhIQElJaWmu+PGTMGFy5cMN+vqKjAe++913zVtXBODDtERESSa1LYEa5b/ff6+2TJqb7rWERERCQqjtmxIfbsEBERSY9hx4YUTg1/vFUGI8r0XGCQiIjI1hRNfcIHH3wAd3d3AEBNTQ3Wr18PHx8fALAYz0MN9+wIgoCIhdsgCMDJ10fDxdlJ5MqIiIhajyaFneDgYLz//vvm+/7+/vj000/rtKFaDY3ZKSqrxtXhTgU6PYLbtRGxKiIiotalSWHn3LlzNirDMeU1sIBgTnG5+ec2KvbqEBER2RLH7EjgXNG1PbPclE2+kkhERERN0KSws2vXLnTr1g06na7OY1qtFt27d0dycnKzFeeosi9f69lRKZg3iYiIbKlJ37QrVqzAzJkz4enpWecxtVqNxx9/HG+++WazFeeosv+2G7qc09OJiIhsqklh548//sDo0aMbfPzOO+9EamrqLRfl6M5drrhxIyIiImoWTQo7+fn5cHZ2bvBxhUKBwsLCWy7K0f39MhYRERHZVpPCTvv27XHs2LEGH//zzz8REBBwy0VdZTQasWDBAoSEhMDV1RWdO3fG66+/brFNhSAIWLhwIQICAuDq6orY2FicOnWq2WqwhZIKg9QlEBERtRpNCjtjx47FggULUFVVd0p1ZWUlFi1ahPHjxzdbcUuXLsXq1avxzjvvID09HUuXLsWyZcvw9ttvm9ssW7YMK1euxJo1a3DgwAG4ubkhLi6u3hqJiIio9ZEJTdjNMz8/H3379oWTkxPmzJmDsLAwAMDJkyexatUqGI1GHDlyBBqNplmKGz9+PDQaDT788EPzsUmTJsHV1RX/+9//IAgCAgMDMW/ePDz//PMAameFaTQarF+/HpMnT673vHq9Hnq93nxfp9MhKCgIWq223sHXN6vTSz/csM25JeOa7fWIiIhaE51OB7VafcPv7yb17Gg0Gvz222/o0aMHEhIScM899+Cee+7Byy+/jB49emDv3r3NFnQAYNCgQdi5cycyMzMB1A6Q3rt3L8aMGQMAyMrKQl5eHmJjY83PUavViI6ORkpKSoPnTUxMhFqtNt+CgoKarWYiIiKyL01e0a5jx4748ccfceXKFZw+fRqCIKBr165o27Ztsxf30ksvQafTITw8HE5OTjAajfj3v/+NKVOmAADy8vIAoE7A0mg05sfqk5CQgPj4ePP9qz07RERE5Hhuevnetm3bon///s1ZSx1ffvklPvvsM2zYsAHdu3dHWloann32WQQGBmLatGk3fV6VSgWVStWMlRIREZG9alLYuffeexvV7ttvv72pYq73wgsv4KWXXjKPvenZsyeys7ORmJiIadOmwd/fH0DtWKK/zwLLz89H7969m6UGIiIiatmaFHbUarWt6qhXRUUF5HLLYUVOTk4wmUwAgJCQEPj7+2Pnzp3mcKPT6XDgwAE8+eSTotZKRERE9qlJYWfdunW2qqNeEyZMwL///W8EBweje/fu+P333/Hf//4X06dPBwDIZDI8++yzeOONN9C1a1eEhIRgwYIFCAwMxMSJE0WtlYiIiOxTs2+5/fXXX+O+++5rlnO9/fbbWLBgAZ566ikUFBQgMDAQjz/+OBYuXGhu8+KLL6K8vByzZs1CSUkJhgwZgm3btsHFxaVZaiAiIqKWrUnr7ABATU0NTp48CaVSidDQUPPx7777DgsXLsTJkyct1rBpCRo7T7+puM4OERGR7dhknZ1jx46hS5cuiIyMREREBO69917k5+dj+PDhmD59OsaMGYMzZ87ccvFEREREzaVJl7Hmz5+PLl264J133sHnn3+Ozz//HOnp6ZgxYwa2bdsGV1dXW9VJREREdFOaFHYOHTqEn3/+Gb1798bQoUPx+eef4+WXX8Yjjzxiq/qIiIiIbkmTLmMVFRUhMDAQQO00dDc3NwwcONAmhRERERE1hyb17MhkMpSWlsLFxQWCIEAmk6GyshI6nc6iXXMO8nU0KoUc+hqT1GUQERG1Gk0KO4IgWMzAEgQBffr0sbgvk8lgNBqbr0IH06Spb0RERHTLmhR2du/ebas6Wg2jiXGHiIhITE0KO0OGDMHy5cvx/fffo7q6GqNGjcKiRYs4C6sJGHaIiIjE1aQByosXL8bLL78Md3d3tG/fHm+99RZmz55tq9qIiIiIblmTws4nn3yCd999F9u3b8fmzZuxZcsWfPbZZ+aNOYmIiIjsTZPCTk5ODsaOHWu+HxsbC5lMhosXLzZ7YURERETNoUlhp6amps4Gm87OzjAYDM1aFBEREVFzafLU80cffRQqlcp8rKqqCk888QTc3NzMx7799tvmq5CIiIjoFjQp7EybNq3OsYcffrjZiiEiIiJqbk0KO+vWrbNVHa1WdY0JSkWTriYSERFRE/BbVmKXy/VSl0BEROTQGHYkVlRaLXUJREREDo1hx4ac5LIbtiksqxKhEiIiotaLYceGnJ0aEXZKeRmLiIjIlhh2bMjZ6cYfb1EZL2MRERHZEsOODSkbEXbYs0NERGRbDDs21Jgp5Qw7REREtsWwY0ONuYzFsENERI7MZBIgCIKkNTRpUUFqGkUjBigXlTHsEBGRYzlbWIbFP57EL+n55mMHXh4FjaeLlWfZDsOODXHMDhERtQbVNSZ8knIOb/yQ3mAbuezGHQC2wrBjQ40Zs1Oqr0GVwQgXZycRKiIiImoexy5o8frWEziQVWy13eyRnfHE8M7wcHEWqbK6GHZsqDFjdoDa3p0g7zY2roaIiOjmVVTXYG3yWaz45ZTVdv06tsUr4yLQJ7itSJXdGMOODTVmUUEAKCxj2CEiIvuTcuYyXtt6AumXdFbbvTQmHI8O6mS3VykYdmyoKT07REREUiupqMbKnafx0b4sq+1GhPni5bERCNV4iFTZrWHYsaHGDFAGOCOLiIikIQgCdpzIx2tbT+D8lcoG27k4y7FwfHc8ENUBikZ+t9kThh0bYs8OERHZmzxtFZb/nIGvU89bbXdXZCBeiAtziGEWdh92Lly4gPnz5+Onn35CRUUFunTpgnXr1iEqKgpAbSpdtGgR3n//fZSUlGDw4MFYvXo1unbtKnHlwLbjeY1qx7BDRES2YjQJ2PT7Bby+9QS0lYYG2/l6qLBoQjeM7REAuVy6aeK2YNdh58qVKxg8eDBGjhyJn376Cb6+vjh16hTatr02wnvZsmVYuXIlPv74Y4SEhGDBggWIi4vDiRMn4OIizeJFTcXLWERE1Jyyisqx5Kd0bD+eb7XdwwOD8cyoUPh6qESqTBp2HXaWLl2KoKAgrFu3znwsJCTE/LMgCFixYgVeeeUV3H333QCATz75BBqNBps3b8bkyZNFr/lmsGeHiIhuhb7GiA0HcvDa1hOwtjNDZ183LBjfDcNDfSGTcJE/sdl12Pn+++8RFxeH+++/H0lJSWjfvj2eeuopzJw5EwCQlZWFvLw8xMbGmp+jVqsRHR2NlJSUBsOOXq+HXn8tYOh01qfU2Vohe3aIiKiJjl3Q4t8/pCPl7GWr7Z4Y3hlPjewMTwkX9ZOaXYeds2fPYvXq1YiPj8fLL7+MQ4cO4emnn4ZSqcS0adOQl1c7Jkaj0Vg8T6PRmB+rT2JiIl599VWb1t4UhaV6CILQqlI2ERE1Tbm+Bh/uzcJ/d2Rabdc7yAsLxkegb3Bbfq/8xa7DjslkQlRUFBYvXgwA6NOnD44dO4Y1a9Zg2rRpN33ehIQExMfHm+/rdDoEBQXdcr3Xk8lgtTtR7eoMbaUBVQYTyquNcFfZ9T8HERGJSBAE7D9bjH//eALHLli/AvHi6DA8NigErkr7XNRPanb97RoQEIBu3bpZHIuIiMA333wDAPD39wcA5OfnIyAgwNwmPz8fvXv3bvC8KpUKKpXtB2O5KJxQaTA2+LhXG2fUGGuDTmGpnmGHiKiVu1JejVW7T+ODvdYX9RsW6ouEMeGICPAUqbKWza6/XQcPHoyMjAyLY5mZmejYsSOA2sHK/v7+2Llzpznc6HQ6HDhwAE8++aTY5dbRRmk97KgUcvh6qFB+uQKFpXqE+LiJWB0REUnNZBKw82QBXt96AjnFFQ22UyrkWDi+Gx6ICmrUJtNkya7DznPPPYdBgwZh8eLFeOCBB3Dw4EGsXbsWa9euBQDIZDI8++yzeOONN9C1a1fz1PPAwEBMnDhR2uIBeLgocLm8usHHlQo51K7OOHe5gtPPiYhaiUvaSry5IxNfHra+qN+4XgF44c4wdOIfwrfMrsNO//79sWnTJiQkJOC1115DSEgIVqxYgSlTppjbvPjiiygvL8esWbNQUlKCIUOGYNu2bXaxxo7a1frId5XCCT7utZfTOP2ciMgxGYwmbPnjIl7fegJXKhpe1M/HXYkF47thXM+AFrklgz2z67ADAOPHj8f48eMbfFwmk+G1117Da6+9JmJVjeN5w7AjZ9ghInJAZwrLsHx7Bn46Zn0l/YcGBOOZUV3hr5b+D3RHZvdhpyW70ZoGSoUcn+7PBgCkZl8RoyQiIrKBKoMRXx7OxatbTsBoanga7m0+bvh/4yIwIswPTg62JYM9Y9ixITeV9SmAqr8NMjt0rtjW5RARUTMRBAHHLuiwdNtJ7D1dZLXt48Nvw+PDOsPbTSlSdXQ9hh0bcrvBVHKl4loYqrHylwAREUlPV2XAJ7+dw/KfrS/qF9lBjYSxEYgO8eaifnaCYceGTDcIMEoOQCMislsmk4D9WZex5KeT+PO81mrbF+LCMDWmIzxa8ZYM9oxhx4bkN7geazCaLO5zywgiImkVlurx/q9nsTb5rNV2Q7v64MW4cPRo78nf2y0Aw44NKW4QdiqqayzuaysN8GrDa7pERGIxGE3Yk1GIxT+mI6uovMF2CrkMCyd0w339OqCNkl+dLQ3/xWzISW79MlW53nJ15cJSPcMOEZGN5RZX4N09p/H5wVyr7cb29Ef8HaHo4uchUmVkKww7NnTOyl8JQG1Pzt8VlunRVcP/UxERNacqgxE/HbuEN7amW13V3ttNiVfGRWBszwC4OHNDTUfCsGND245bX0zqxCXLXWy5sCAR0a0TBAGnCsrw1i+n8MPRS1bbPhgVhDm3d0GQdxuRqiMpMOzYEYYdIqKbU1plwObfL+D1removm7yx991bNcGL4+NwO3hfnDmjNhWg2HHjhSVNdy9SkRE15hMAv68oMV/fs7Ar6esL+o3c2gIZg69DX6e3JKhtWLYsSPs2SEialhxeTU+P5iD/9ueYbVdz/ZqzB8djpjO7bglAwFg2LErhWUMO0REV9UYTTiYVYyl2zPwR26J1bbxd4Ti4YEduSUD1Ythx44UsWeHiFq5S9pKfPxbNtYknbHablDndph3Zxj6BHndcAFXIoYdO8KeHSJqbfQ1RuzJKMTSbSdxtrDh5TrkMuCVcd0wqV8HqF25JQM1DcOOHfBq44ySCgMul+lhNAm8xkxEDksQBGQVleODvVnYcCDHatvR3f0xd1QXdAvglgx0axh2bEguAxqzmXmA2hXaSgNMAnCloho+7irbF0dEJJJyfQ1+PpGHxT+etDoRw6uNM14eW7uon7uKX0/UfPhfkw01JugAgJvSCe3clCgqq0ZhqZ5hh4haNEEQcOKSDmuSzmLLHxettn0gqgNmDeuMzr5u7L0hm2HYsQMqZzl83FXmsBMRIHVFRERNU1JRjS1/XMQbP6RDX9Pwon5B3q54aXTton6uSm7JQOJg2LEDLgon+HqocDKvlGvtEFGLYDQJ+D3nCt7edRpJmYVW284YEoJpMZ0Q3I5bMpA0GHbsgMpZbp5dUMQZWURkp/J1VfjmyHks22Z9Ub/ugZ54/s4wDOrSDioFe29Iegw7NuTiLEeVoeHu3KtUCif4eNSO02HPDhHZi+oaE/afvYwVv2TiSE6J1bbPxnbFg/2DEKB2Fac4oiZg2LGhxgQdoDYU+f41KJlr7RCRlLIvl+OLQ7l4d4/1Rf0G3uaNZ0aFIqpTW26oSXaPYceG3FUKlOlrbthO9deYHYCXsYhIXBXVNUjOLMTynzNxuqDMattXxkXg7t7tzb+viFoKhh0bCvRyQWa+9V8eAKBSyM2/PHgZi4hsSRAEZOaX4dP95/C//dYX9bujmwZPjuiMyA5eXOyUWjSGHRvSeDY+7FxdW4dhh4iam7bCgF/S87Fs+0nk6xr+HePhokDCmAiM6eGPttxQkxwIw44NebVp3C8LlfO1y1hXKgwwGE28Bk5EN81oEnD0ghbr9mXhuzTri/pN6tsBjw3uhG4BntxQkxwWw44N+TZyJWSVQg4vV2co5DLUmARcLquGv9rFxtURkSMpKK3CtmN5SPzxJCoNxgbbtfdyxQtxYbg9wg+eLtxQk1oHhh0b0lYaGtVO5ewEuVyGdu5K5Ov0KCzVM+wQkVXVNSakZl/B2uQz2J1hfVG/Rwd1wj+ig9HVz51bMlCrxLBjQ95ujfurSaWovWTl66GqDTtlVQDUNqyMiFqi3OIKbPnz4g0X9Qv398CzsaEY2tUHbtxQk4hhx5YaOz3Txbl2hdGrl72KSqttVhMRtRyV1UaknC3C6j1ncOjcFatt597eBff27YAQHzeRqiNqOVpU2FmyZAkSEhLwzDPPYMWKFQCAqqoqzJs3Dxs3boRer0dcXBzeffddaDQaaYvFtRBzI1d7dny4sCBRqyYIAk4XlGHT7xduuKjfgBBvPDWiMwbe1q7Rv2uIWqsWE3YOHTqE9957D7169bI4/txzz+GHH37AV199BbVajTlz5uDee+/Fvn37JKr0msZeGf/7ZSyA08+JWhNdlQG/ZhZh5c5TyMgvtdp2/uhwjO8VgCBvbqhJ1BQtIuyUlZVhypQpeP/99/HGG2+Yj2u1Wnz44YfYsGEDbr/9dgDAunXrEBERgf3792PgwIFSlVxbXyMHKJsvY3mwZ4fI0ZlMAo5f1OGr1Fx8kpJttW1shB+mDwlBVEdvKBVcjoLoZrWIsDN79myMGzcOsbGxFmEnNTUVBoMBsbGx5mPh4eEIDg5GSkpKg2FHr9dDr78WKHQ6nU3qXv5zZqPasWeHyLEVlemx62QB3tyRiUvaqgbbuasUeHF0GOK6+0PjyRmZRM3F7sPOxo0bceTIERw6dKjOY3l5eVAqlfDy8rI4rtFokJeX1+A5ExMT8eqrrzZ3qTdNpajt2fExD1Bm2CFqyQxGE37PKcHnB3Ow6fcLVtve06c9Hh4YjMgOXlBwMVEim7DrsJObm4tnnnkGO3bsgItL8/2Vk5CQgPj4ePN9nU6HoKCgZjt/U6mcr+vZ4WUsohbnQkkldhzPw/KfM61uABygdsFzd4Ti9nA/8x84RGRbdh12UlNTUVBQgL59+5qPGY1GJCcn45133sH27dtRXV2NkpISi96d/Px8+Pv7N3helUoFlcp+fslcP2antKoGVQYjZ1gQ2bEqgxEHs4rxSco5/JJeYLXtIwM74v6oDugRqOaWDEQSsOuwM2rUKBw9etTi2GOPPYbw8HDMnz8fQUFBcHZ2xs6dOzFp0iQAQEZGBnJychATEyNFyTfl6pgdD5UCSoUc1TUmFJbqOeOCyI4IgoCzReXYdiwP/7fd+qJ+YRoPzL69C4Z39YW6DbdkIJKaXYcdDw8P9OjRw+KYm5sb2rVrZz4+Y8YMxMfHw9vbG56enpg7dy5iYmIkn4nVFFfDjkwmg6+7ChdKKlFUxrBDJLXSKgP2nb6Mj/Zm4eC5YqttnxjeGXf3DkS4vwe3ZCCyM3YddhrjzTffhFwux6RJkywWFWxJrg5QBmovZV0oqeSMLCIJmEwCTlzS4Yejl7D6Bov6RXVsi5nDbsOgzu3gwQ01iexaiws7e/bssbjv4uKCVatWYdWqVdIU1Aycna79FchVlInEdblMj19PFWFN0hmczLO+qN+8O0IxpmcAOvu6sfeGqAVpcWHHEf39lybX2iGyrRqjCWm5Jfj+j4s3XNRvZJgvpsZ0wsDb2sFVyQkDRC0Vw46duRp2itizQ9RsLmkrsSejEO/sOo0LJZUNtnN1dsK8O0NxRzcNOrbjhppEjoJhx86wZ4fo1lUZjDh87gq+PXIe395gUb+7IgPxQFQQ+oe0tRg/R0SOg2HHzvi6KwEw7BA1hSAIOHe5AjvT8/HWzlMorWp4UT+Npwpzb++KkeF+aO/lKmKVRCQVhh07YDIJ5oXGrl3GqpayJCK7V6avQcqZy/jycC52nMi32vahAcGY2DsQfTu2hTO3ZCBqdRh27MCVimq0+2sWlq977bYYhaV6CILAGR9EfxEEAemXSrEzPR//2WF9k92ufu6YNew2DA/1hR831CRq9Rh27EBhmd4cdnw8ai9jVRqMKK82wl3FfyJqva6UV2Pv6SJ8diAb+89aX9Rv5tAQjOkZgMgOXnDilgxE9Df8JrUDRaXVwF9bebVRKuCmdEJ5tRFFpXqGHWpVjCYBf5wvwfbjeXgv6azVtn2DvTBtUCcM7eoLbzelSBUSUUvEb1I7cP00c18PFcovV6CwTI9OPpz+So4tX1eFpIxCrP/tHE5c0llt+/TtXXBnd390C/DkhppE1GgMO3bgotZy3Q8fdxXOXa7gjCxySPoaI1LPXcGPxy7hf/tzrLYdHuqLyf2DMKiLD9Su3JKBiG4Ow44d+L/tGXhqRBfzfS4sSI4m+3I5kjIL8f6vZ5Fb3PCifiqFHM/EdsWocA1CNe4coE9EzYJhxw4IguV9LixILV1FdQ32n72MLX9cwqYbLOo3rmcA7unTHjGd28GNY9SIyAb4m8UO+boz7FDLIggCMvPLsDujAKv3nIG20tBgW18PFZ4Y3hkjw3wR4sMNNYnI9hh27JAPe3aoBdBWGLD3dBE2p1244aJ+D0R1wLhegYgO8YaLM7dkICJxMezYoas9OxyzQ/bEaBJw9IIWu08W4O1dp2ASGm57m68bHhscghGhvgjybiNekURE9WDYsRNGk2BeCI1jdsheFJRWITmzCN+knkfK2ctW206L6Yi47v6I6uQNpYJbMhCR/WDYsRNXKqrhY15F+dr+WNwygsRUXWPCkZwr2Jmej/d/zbLaNjLIC/8YEIRhob4IUHNDTSKyXww7NiSXwWpX/98VlemvhZ2/dj6vNpqgq6yBug3XFyHbyS2uQFJmITYeysGxC9YX9Xt8+G2IjdCgT5AXFNxQk4haCIYdG2ps0AFqL1mF/7VlhErhBLWrM7SVBhSWVTHsULOqrDZif9Zl7DiRjw0HrC/qN6SLD+7t2x5Du/qaL68SEbU0DDs21EbphIpqY6PaXj8Y2cddCW2lAQWlenTx87BFedRKCIKA0wVlSMosxKf7s5F9uaLBtkonOZ4c0Rkjw/3Qs72aG2oSkUNg2LGhoLZtkJFf2qi2RaXVFvd9PVQ4U1iOorLqBp5B1DBtpQG/nS7C9uN52Jx20WrbuO4ajO8ViCFdfNCWG2oSkQNi2LEh4/VLI1tRWGczUJfa45yRRY1gMgk4dlGLpIxCrPvtHIrLGw7JPu7K2mnhYb6I8OeGmkTk+Bh2bKhbgCdOF5Q1qm3RdaGGqyjTjRSV6fHrqUL88Gcefkm3vqjfPX3aI667PwZ1aQdPF44BI6LWhWHHhjq0bfx03Ot7dnw8ai8nMOzQVQajCb/nlGBPRgHWJp9FjZUR8CE+bpgSHYzhob7o4scNNYmodWPYsaGrU8kbI+WM5YJtXEWZAOD8lQokZxZhyx8Xb7io30MDgnFHNz8MvK0d2ij5f20ioqv4G9GG3Juwg3ONSUBmfilCNbUzr7iKcutUZTDiQFYx9mQUYN2+c1bb9mjvifv7BWF4qC86+biJUyARUQvEsGND3xw536T2D76Xgi8ej0GoxsPcK3T95S1yLIIg4ExhOZIzC7Hp9ws4ekFrtf1jgzvh9nA/9O/EDTWJiBqLYceG/jxv/YvrelcqDHho7X58Pmsg/P7q2Skur7bYN4tavtIqA347cxm70gvwxeFcq20H3uaNuyLbY1ioDzq05YaaREQ3g2HHhioNjVtQ8O8ul1fjobX78emMaMhktRuE/n3fLGp5TCYBJy7pkJRZiK8O5+KclUX9nOQyzBx6G0aE+aJvcFtuqElE1AwYduxMj/aeOHZBh0c+PICry/QUluoZdlqYy2V67D1dhB0n8rH1z0tW294e7ofRPfwxPNQXGk8XkSokImo9GHbszP9mROPhDw9YbMjIGVn2r8ZoQlpuCZIyC/H5wRyrK197tXHG1IEdMTzMD5Ed1NxQk4jIxuw+7CQmJuLbb7/FyZMn4erqikGDBmHp0qUICwszt6mqqsK8efOwceNG6PV6xMXF4d1334VGo5Gw8pvz2pYT+GzGQEz5cL858Ow9XYShXX0lroyud7GkEsmZhdhxIh87TxZYbTuuVwDuiNBgaFcftGMvHRGRqGSC0IQ9DSQwevRoTJ48Gf3790dNTQ1efvllHDt2DCdOnICbW+102yeffBI//PAD1q9fD7VajTlz5kAul2Pfvn2Neg2dTge1Wg2tVgtPT89mq73TSz/c1PM+mBqF/p28Efnaz+Zj258dhjB/bggqpSqDEYfPXUFSZgE+TslGdY2pwbZB3q54oF8Qhof5okegmlsyEBHZQGO/v+0+7FyvsLAQfn5+SEpKwrBhw6DVauHr64sNGzbgvvvuAwCcPHkSERERSElJwcCBA294TnsLOwCwc95wrE06a56t4+2mxOczBzLwiEgQBJy7XIGkjAL8dCwPB7KKrbaf1LcDbg/3w5AuPlC34ZYMRES21tjvb7u/jHU9rbZ2Ore3tzcAIDU1FQaDAbGxseY24eHhCA4ObjDs6PV66PXXxsHodLo6baR255vJmD2is/l+cXk1Hnp/PwOPjZXpa5By5jL2ZBTgswM5VtuG+3tgYp/2GB7qi3B/D27JQERkp1pU2DGZTHj22WcxePBg9OjRAwCQl5cHpVIJLy8vi7YajQZ5eXn1nicxMRGvvvqqrcttsjCNB2Qy4GReKYwmASt3nQZQO0NLBhmOXtDioff3Y8PMaIT7N18PVGsmCALSL5UiKbMQPx69dMNF/f4RHYwRob4Y1MWnSStkExGRdFrUb+vZs2fj2LFj2Lt37y2dJyEhAfHx8eb7Op0OQUFBt1reLcvIL8WvL47E0GW7LY5X15jw1eOD8MhHB/DneS3+8f4BBp5bcKW8Gr+eLkJSRuENV7mO6tgWY3oGYHioLzr7urH3hoioBWoxYWfOnDnYunUrkpOT0aFDB/Nxf39/VFdXo6SkxKJ3Jz8/H/7+/vWeS6VSQaWyzxkxgV6uWDShG17dcsJ8LDO/DOo2zvh0ejQDz00wmgSk5ZYgObMQ3/9xEVlF5Q22lcmAaTGdMDzMFwND2sFVyS0ZiIhaOrsPO4IgYO7cudi0aRP27NmDkJAQi8f79esHZ2dn7Ny5E5MmTQIAZGRkICcnBzExMVKUfEuKy6sxLaYTtv55CanZV8zH/8gtQWSQFz6dEY1HPrwWeD77ZzQiAhh4rpevq0JSZiF2n6wdXGzN0K4+uLObBsNCfdGxHTfUJCJyNHY/G+upp57Chg0b8N1331msraNWq+Hq6gqgdur5jz/+iPXr18PT0xNz584FAPz222+Neg17mo31wdQoxHbT4HRBGe58Mwmmv/51FHIZDrw8Cu3cVdBWGsyBx9tNycADQF9jROq5K0jKrL00ZW1RPw8XBR4aEIzhob6I6tQWKgV7b4iIWiKHmXre0BiJdevW4dFHHwVwbVHBzz//3GJRwYYuY13PnsLObb5u2DVvBABg1e7T+L/tGebHBoR447N/RsPZSW4ReNq2ccaGmQNbXeDJvlyOpMxC7EwvQFJmodW2d3TTYFS4H4aF+iLQy1WkComIyJYcJuyIwZ7CDgD8+uJIBHm3gcFoQtf/95PFY48M7IjXJ9bORNNWGjD1wwP4o5UEnorq2mnhSZmF+OJQLvRWFvULVLvgnr7tMTzUD32CveDMLRmIiByOw66z0xr4edYOnnZ2kkPjqUK+7tqaQJ/uz0a3QE88NCAYaldnfDIj2hx4/vH+focKPIIgICO/1Lwlw6FzV6y2H98rACPD/DA01Ad+HtxQk4iIajHs2KG/jyEZ1tUXX6VaTo9e+N0xdPVzR1Qn73oDz2f/HIhugS0z8GgrDNh7ughJmQX48rD1aeFd/dwxrlfttPBeHbzgxC0ZiIioHgw7ds7Xo+4UeYNRwBP/O4Lv5wxGoJfrtcDz0UH8kVuCKR+0nMBjNAk4ekGLpIxCbD+ehxOXrK9mfW/f9hgR5oehXXzQ1k0pUpVERNSSMezYuathJ1Dtgku6KlwdYVVUpsfjn6biqydi4OLsVBt4pg9oEYGnoLQKyZlFSMosxJY/Llpt26uDGnHd/TE81BfdAjy5oSYRETUZw44d0lUZ4OlSu5Gkj3tt2AnyboO4Hv5Yt+8cAEDpJMfRC1q89M2fePPB3pDJZFC7OuPTGQPwyIf2FXiqa0xIza6dFr7t2CWcu1xhtf3k/kEYEVa7JcPVz4GIiOhmMezYofSLOkTf1g7AtZ6dwjI9XogLwy/p+cgtrkTHdm1wtqgcm9MuolugJ2YNq9001NPFMvD844P92CBB4MktrsCezEIkZRTil/R8q20HhHgjNsIPI8L80NXPnVsyEBFRs2LYsUMPrt2PT6YPwLBQ32thp1SPNkoFltzbC1M+OIBTBWUY1zMAPxy9hCU/nUSYvyeGh/oCqD/wfPV4DLpqbLdbemW1EfvP1k4L3/rnRauL+rkpnXBv3w4YHuqLmM7t4MYNNYmIyIb4LWOnpn50ENMHh2BcrwAAQGlVDaoMRgzu4oPJ/YOw8VAuTlzS4e7egfgu7SLmbjiC7+YMQYhP7XYHni7OeGxQJzz7RRpKKgz4OvU8EsZGNFt9giDgdEFZ7ZYMGQXYd/qy1fbDQn0xMswXw0N9EeLDDTWJiEg8DDt27KN9WfhoX5b5/vkrleji546Xx0Vgd0YBsorKMSLMF32DvXAkpwQzPzmMTU8NgrOTHIk/puPjlGwAQESAJ6YO6nTL9WgrDfjtdO3A4s1pF1BlaHhRPz8PFcb3CsTwMF9Eh3jDxZlbMhARkTS4gjLsbwXlq9oonaCvMcFouvZP9OigTvhHdDByLlfgn58chlwGrH0kCv9v81Hk6/QI8naFi8IJpwrKAAD/HBKCF0aH3dT+TyaTgGMXa6eF784owJGcEqvtYyM0GPFX702Qd5smvx4REVFTcAVlB7D/5VE4W1iOiav2mY+t/+0c1v92Dv06tgUAmARg+c8ZeOcffXH/mhTkFlcCqJ3F9Z8HIs3jeBqrsFSPX08VIjmzEJvTrE8L79SudobY8FBfRHX0hlLBLRmIiMj+MOzYsUNZxZjx8WHz/bjuGgDAL+kFSM2+tnXCybxS3L8mxeK5T4/q0qigYzCa8HtOCZIyC7AzvQAn80qtth/Xs3bF4mGhvvBXc0sGIiKyfww7duzvQQcAugWo8UxsVxToqvBV6nl8fjAH569U1vvcxB9PIqqjd71Tzs9fqfhrUb8CbD9ufVp4RIAnYiP8MDzUF72DvKDghppERNTCMOy0IIVlVQAAP08XzBx6G66UV+ODvVn1tq00GDF25a84suAOtFE64UBWMZIyCrEjPc98qashE3vXDiwe2tXXvKghERFRS8Ww04L8b38OfN1d4O2uxLq9WThbVA4AuKObBjtO1N9D0/f1HTc8b+8gL9weXtt706O9mhtqEhGRQ2HYaWHe/CXT4n6g2gUTIgPR0btNg70811Mp5Lgrsrb3ZkgXH3i14YaaRETkuBh2WriL2io8/fnvTXpOuL8Hojq1xcgwP65eTEREDo/fdK2Ih4sCVQYj/jivxR/nj+L1rem4u3cgHhoQjB7t1VKXR0REZBMMOw7ESS6zWIDweqVVNRb3y/Q1+OxADj47kINeHdR4aEAwJkQGwv263p6LJZX4/GAOPv7tHHR/naNvsBe+eDwGzpydRUREdo5hpwVr7+Vq3i08+jZvtFEqYDIJuP+9FKRmX0GA2gVPjeyC5MzCBgcwX/XneS3+PH8UCd8ebdRrH8kpgZz7WxERUQvAsNPCeLgo8PydYRge6otOf236+XdyuQxLJ/XC2JW/4pK2Cm5KJ7w/NQo5lysw7P92m9tFdlDjj/PaJr++r4cKjw7qhIejO3LWFhERtQgMOy3Mskm9MKZngNU2Xfzc8cyorvi/7Rl4besJDO1au9rxvDtC8Z8dtbO5mhp0Hh3UCS+PjeCWEERE1OIw7LQwhWX6G7YxmgSMCPPF/23PQEmFAf3//Uujzj0q3A8xndtBpZDj99wSpGZfQfblCgC1e3Ld06c9IoO8bqV8IiIi0THstBCPDOyIT/dno7DUMuwIgoBzlyuw73QR/rc/+4Z7W13v+zmD0auDV93Xi6n934LSKhzJvoJqo4BeHThji4iIWh6GnRbi0/3ZAIC03BJs/fMivk49jz0ZhU06x57nR6BjuzbQ15gwee1+pOWWYN6Xf2DT7MF1ZmBd5efhgtE9rF82IyIismcyQRAanqvcSuh0OqjVami1Wnh61t0482Z1eumHZjvXzYqN8ENshAbjr5tSnq+rwoS396KgVI87u2mw5uF+kHPAMRERtSCN/f7maFMH9uSIznh/ahQmDwiu03Oj8XTBe4/0g9JJjp9P5OOtnackqpKIiMi2eBnLgQzt6oMwjQc8XJzRRukEV6UTNqddgKuzAm2UTn/drv3cVeOBNyb2wIvf/Im3dp5CRIAHL1kREZHDYdhxIL+eKsKvp4pu+vnxX/6BXh28EOjl2oxVERERSYthp4XoHuiJhDERULs6o6K6BhUGIyqrjSjX16DSYERFtREV+pra/zVc+/nqY3XaGYy4frRWRbURBaV6hh0iInIoHKAM2w1Q/uDXs3jjh/RmO5+LsxyHX7mjwZlTTSEIAvQ1Josg5K5SMOgQEVGL0eoGKK9atQqdOnWCi4sLoqOjcfDgQalLgsF46znyjm4a889VBhN6LNqO1XvO3PJ5ZTIZXJyd4O2mRJB3G4RqPBh0iIjIITlE2Pniiy8QHx+PRYsW4ciRI4iMjERcXBwKCgokrevwueJben57L1csndQLZxaPxe3hfubjS7edhLbScKvlERERtQoOEXb++9//YubMmXjsscfQrVs3rFmzBm3atMFHH30kaV3P3RF6S8+fd2covN2UcJLL8NGj/fHriyPh7abEQwOCmuVSFhERUWvQ4r8xq6urkZqaioSEBPMxuVyO2NhYpKSk1PscvV4Pvf7atgs6nc4mtfVor8aDUUFIOXsZ7/yjD+56Z1+dNgFqF1zSVuG+fh1wd+9AKORybD+eh24Bnri3bweLtkHebXBkwR02qZWIiMhRtfiwU1RUBKPRCI1GY3Fco9Hg5MmT9T4nMTERr776qhjlYel9vcw/n1syrlHPienczlblEBERtToOcRmrqRISEqDVas233NxcqUsiIiIiG2nxPTs+Pj5wcnJCfn6+xfH8/Hz4+/vX+xyVSgWVSiVGeURERCSxFt+zo1Qq0a9fP+zcudN8zGQyYefOnYiJiZGwMiIiIrIHLb5nBwDi4+Mxbdo0REVFYcCAAVixYgXKy8vx2GOPSV0aERERScwhws6DDz6IwsJCLFy4EHl5eejduze2bdtWZ9AyERERtT7cLgK22y6CiIiIbKfVbRdBREREVB+GHSIiInJoDDtERETk0Bh2iIiIyKEx7BAREZFDY9ghIiIih8awQ0RERA6NYYeIiIgcmkOsoHyrrq6rqNPpJK6EiIiIGuvq9/aN1kdm2AFQWloKAAgKCpK4EiIiImqq0tJSqNXqBh/ndhGo3SX94sWL8PDwgEwma7bz6nQ6BAUFITc3l9tQ/A0/l7r4mdTFz6R+/Fzq4mdSv9bwuQiCgNLSUgQGBkIub3hkDnt2AMjlcnTo0MFm5/f09HTY/9BuBT+XuviZ1MXPpH78XOriZ1I/R/9crPXoXMUBykREROTQGHaIiIjIoTHs2JBKpcKiRYugUqmkLsWu8HOpi59JXfxM6sfPpS5+JvXj53INBygTERGRQ2PPDhERETk0hh0iIiJyaAw7RERE5NAYdoiIiMihMezY0KpVq9CpUye4uLggOjoaBw8elLokSSUnJ2PChAkIDAyETCbD5s2bpS5JcomJiejfvz88PDzg5+eHiRMnIiMjQ+qyJLV69Wr06tXLvBBaTEwMfvrpJ6nLsitLliyBTCbDs88+K3UpkvrXv/4FmUxmcQsPD5e6LMlduHABDz/8MNq1awdXV1f07NkThw8flrosSTHs2MgXX3yB+Ph4LFq0CEeOHEFkZCTi4uJQUFAgdWmSKS8vR2RkJFatWiV1KXYjKSkJs2fPxv79+7Fjxw4YDAbceeedKC8vl7o0yXTo0AFLlixBamoqDh8+jNtvvx133303jh8/LnVpduHQoUN477330KtXL6lLsQvdu3fHpUuXzLe9e/dKXZKkrly5gsGDB8PZ2Rk//fQTTpw4gf/85z9o27at1KVJSyCbGDBggDB79mzzfaPRKAQGBgqJiYkSVmU/AAibNm2Sugy7U1BQIAAQkpKSpC7FrrRt21b44IMPpC5DcqWlpULXrl2FHTt2CMOHDxeeeeYZqUuS1KJFi4TIyEipy7Ar8+fPF4YMGSJ1GXaHPTs2UF1djdTUVMTGxpqPyeVyxMbGIiUlRcLKyN5ptVoAgLe3t8SV2Aej0YiNGzeivLwcMTExUpcjudmzZ2PcuHEWv1tau1OnTiEwMBC33XYbpkyZgpycHKlLktT333+PqKgo3H///fDz80OfPn3w/vvvS12W5Bh2bKCoqAhGoxEajcbiuEajQV5enkRVkb0zmUx49tlnMXjwYPTo0UPqciR19OhRuLu7Q6VS4YknnsCmTZvQrVs3qcuS1MaNG3HkyBEkJiZKXYrdiI6Oxvr167Ft2zasXr0aWVlZGDp0KEpLS6UuTTJnz57F6tWr0bVrV2zfvh1PPvkknn76aXz88cdSlyYp7npOZCdmz56NY8eOtfoxBwAQFhaGtLQ0aLVafP3115g2bRqSkpJabeDJzc3FM888gx07dsDFxUXqcuzGmDFjzD/36tUL0dHR6NixI7788kvMmDFDwsqkYzKZEBUVhcWLFwMA+vTpg2PHjmHNmjWYNm2axNVJhz07NuDj4wMnJyfk5+dbHM/Pz4e/v79EVZE9mzNnDrZu3Yrdu3ejQ4cOUpcjOaVSiS5duqBfv35ITExEZGQk3nrrLanLkkxqaioKCgrQt29fKBQKKBQKJCUlYeXKlVAoFDAajVKXaBe8vLwQGhqK06dPS12KZAICAur8URAREdHqL+8x7NiAUqlEv379sHPnTvMxk8mEnTt3ctwBWRAEAXPmzMGmTZuwa9cuhISESF2SXTKZTNDr9VKXIZlRo0bh6NGjSEtLM9+ioqIwZcoUpKWlwcnJSeoS7UJZWRnOnDmDgIAAqUuRzODBg+ssX5GZmYmOHTtKVJF94GUsG4mPj8e0adMQFRWFAQMGYMWKFSgvL8djjz0mdWmSKSsrs/iLKysrC2lpafD29kZwcLCElUln9uzZ2LBhA7777jt4eHiYx3Sp1Wq4urpKXJ00EhISMGbMGAQHB6O0tBQbNmzAnj17sH37dqlLk4yHh0edcVxubm5o165dqx7f9fzzz2PChAno2LEjLl68iEWLFsHJyQkPPfSQ1KVJ5rnnnsOgQYOwePFiPPDAAzh48CDWrl2LtWvXSl2atKSeDubI3n77bSE4OFhQKpXCgAEDhP3790tdkqR2794tAKhzmzZtmtSlSaa+zwOAsG7dOqlLk8z06dOFjh07CkqlUvD19RVGjRol/Pzzz1KXZXc49VwQHnzwQSEgIEBQKpVC+/bthQcffFA4ffq01GVJbsuWLUKPHj0ElUolhIeHC2vXrpW6JMnJBEEQJMpZRERERDbHMTtERETk0Bh2iIiIyKEx7BAREZFDY9ghIiIih8awQ0RERA6NYYeIiIgcGsMOEREROTSGHSIiIrKJ5ORkTJgwAYGBgZDJZNi8eXOTzyEIApYvX47Q0FCoVCq0b98e//73v5t0Dm4XQURERDZRXl6OyMhITJ8+Hffee+9NneOZZ57Bzz//jOXLl6Nnz54oLi5GcXFxk87Bnh0iajaPPvooJk6caHHs66+/houLC/7zn/+Y28hkMjzxxBN1nj979mzIZDI8+uijDb7Gnj17IJPJIJPJIJfLoVar0adPH7z44ou4dOmSRdt//etf5rZOTk4ICgrCrFmz6vyi7NSpE1asWIHq6mr4+PhgyZIl9b7266+/Do1GA4PBgPXr15vP/febi4uLxechk8nqnG/z5s2QyWQNvkciRzFmzBi88cYbuOeee+p9XK/X4/nnn0f79u3h5uaG6Oho7Nmzx/x4eno6Vq9eje+++w533XUXQkJC0K9fP9xxxx1NqoNhh4hs5oMPPsCUKVOwevVqzJs3z3w8KCgIGzduRGVlpflYVVUVNmzY0OhNYTMyMnDx4kUcOnQI8+fPxy+//IIePXrg6NGjFu26d++OS5cuIScnB+vWrcO2bdvw5JNP1ntOpVKJhx9+GOvWravzmCAIWL9+PaZOnQpnZ2cAgKenJy5dumRxy87Otniei4sLli5diitXrjTqfRG1JnPmzEFKSgo2btyIP//8E/fffz9Gjx6NU6dOAQC2bNmC2267DVu3bkVISAg6deqEf/7zn+zZISL7sGzZMsydOxcbN27EY489ZvFY3759ERQUhG+//dZ87Ntvv0VwcDD69OnTqPP7+fnB398foaGhmDx5Mvbt2wdfX986QUahUMDf3x/t27dHbGws7r//fuzYsaPB886YMQOZmZnYu3evxfGkpCScPXsWM2bMMB+TyWTw9/e3uGk0GovnxcbGwt/fH4mJiY16X0StxdU/QL766isMHToUnTt3xvPPP48hQ4aY/+A4e/YssrOz8dVXX+GTTz7B+vXrkZqaivvuu69Jr8WwQ0TNbv78+Xj99dexdevWBruvp0+fbtGD8tFHH9UJRU3h6uqKJ554Avv27UNBQUG9bc6dO4ft27dDqVQ2eJ6ePXuif//++OijjyyOr1u3DoMGDUJ4eHiT6nJycsLixYvx9ttv4/z58016LpEjO3r0KIxGI0JDQ+Hu7m6+JSUl4cyZMwAAk8kEvV6PTz75BEOHDsWIESPw4YcfYvfu3cjIyGj0azHsEFGz+umnn7Bs2TJ89913GDVqVIPtHn74YezduxfZ2dnIzs7Gvn378PDDD9/Sa18NIufOnTMfO3r0KNzd3eHq6oqQkBAcP34c8+fPt3qeGTNm4KuvvkJZWRkAoLS0FF9//TWmT59u0U6r1Vr8knZ3d8eYMWPqnO+ee+5B7969sWjRolt6f0SOpKysDE5OTkhNTUVaWpr5lp6ejrfeegsAEBAQAIVCgdDQUPPzIiIiANT2DDUWZ2MRUbPq1asXioqKsGjRIgwYMADu7u71tvP19cW4ceOwfv16CIKAcePGwcfH55ZeWxAEALAY/BsWFobvv/8eVVVV+N///oe0tDTMnTvX6nkeeughPPfcc/jyyy8xffp0fPHFF5DL5XjwwQct2nl4eODIkSMWx1xdXes959KlS3H77bfj+eefv5m3RuRw+vTpA6PRiIKCAgwdOrTeNoMHD0ZNTQ3OnDmDzp07AwAyMzMBAB07dmz0a7Fnh4iaVfv27bFnzx5cuHABo0ePRmlpaYNtp0+fjvXr1+Pjjz+u02tyM9LT0wHUzq66SqlUokuXLujRoweWLFkCJycnvPrqq1bP4+npifvuu898mW3dunV44IEH6gQ3uVyOLl26WNzat29f7zmHDRuGuLg4JCQk3MI7JGpZysrKzD02AJCVlYW0tDTk5OQgNDQUU6ZMwdSpU/Htt98iKysLBw8eRGJiIn744QcAtWPe+vbti+nTp+P3339HamoqHn/8cdxxxx0WvT03wrBDRM2uY8eOSEpKQl5entXAM3r0aFRXV8NgMCAuLu6WXrOyshJr167FsGHD4Ovr22C7V155BcuXL8fFixetnm/GjBnYu3cvtm7dit9++81iYPLNWrJkCbZs2YKUlJRbPhdRS3D48GH06dPHPPEgPj4effr0wcKFCwHU/iExdepUzJs3D2FhYZg4cSIOHTpknpUpl8uxZcsW+Pj4YNiwYRg3bhwiIiKwcePGJtXBy1hEZBNBQUHYs2cPRo4cibi4OGzbtg2enp4WbZycnMy9MU5OTk06f0FBAaqqqlBaWorU1FQsW7YMRUVFFjO86hMTE4NevXph8eLFeOeddxpsN2zYMHTp0gVTp05FeHg4Bg0aVKeNIAjIy8urc9zPzw9yed2/JXv27IkpU6Zg5cqVjXiHRC3fiBEjzJeX6+Ps7IxXX33Vam9rYGAgvvnmm1uqgz07RGQzHTp0wJ49e1BUVIS4uDjodLo6bTw9PeuEoMYICwtDYGAg+vXrhyVLliA2NhbHjh1Dt27dbvjc5557Dh988AFyc3MbbCOTyTB9+nRcuXKlwUtsOp0OAQEBdW4NzQYDgNdeew0mk+nGb5CImo1MsBa5iIiIiFo49uwQERGRQ2PYISIiIofGsENEREQOjWGHiIiIHBrDDhERETk0hh0iIiJyaAw7RERE5NAYdoiIiMihMewQERGRQ2PYISIiIofGsENEREQO7f8D+dpEHPpmn6sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heat Map"
      ],
      "metadata": {
        "id": "F_G0kPatbLee"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "OWTWNnqJbK4_",
        "outputId": "77f7dc5e-4e70-459d-c9dc-3ca1fe181f22"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4.Data Preprocessing**"
      ],
      "metadata": {
        "id": "tqOaBzYZRhOK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for datatype\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PlOuaMu2ln4I",
        "outputId": "632aa857-2a23-4b56-a1f4-b62901528e40"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location              object\n",
              "Year                   int64\n",
              "Kilometers_Driven      int64\n",
              "Fuel_Type             object\n",
              "Transmission          object\n",
              "Owner_Type            object\n",
              "Mileage               object\n",
              "Engine                object\n",
              "Power                 object\n",
              "Seats                float64\n",
              "Price                float64\n",
              "Brand                 object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting categorical(non-numeric) data into numerical labels\n",
        "le=LabelEncoder()\n",
        "df['Location']=le.fit_transform(df['Location'])\n",
        "df['Fuel_Type']=le.fit_transform(df['Fuel_Type'])\n",
        "df['Transmission']=le.fit_transform(df['Transmission'])\n",
        "df['Owner_Type']=le.fit_transform(df['Owner_Type'])\n",
        "df['Brand']=le.fit_transform(df['Brand'])"
      ],
      "metadata": {
        "id": "okpaMQRvmRsg"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYWHE5Dcn2h3",
        "outputId": "75223ed7-4098-4bb9-ce8a-621b5e428329"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location               int64\n",
              "Year                   int64\n",
              "Kilometers_Driven      int64\n",
              "Fuel_Type              int64\n",
              "Transmission           int64\n",
              "Owner_Type             int64\n",
              "Mileage               object\n",
              "Engine                object\n",
              "Power                 object\n",
              "Seats                float64\n",
              "Price                float64\n",
              "Brand                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#changing the columns containing string values\n",
        "df['Mileage']=df['Mileage'].str.replace('km/kg',\"\")\n",
        "df['Mileage']=df['Mileage'].str.replace('kmpl',\"\")\n",
        "df['Engine']=df['Engine'].str.replace('CC',\"\")\n",
        "df['Power']=df['Power'].str.replace('null',\"0\")\n",
        "df['Power']=df['Power'].str.replace('bhp',\"\")\n"
      ],
      "metadata": {
        "id": "mEQHb2jzoQjQ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#changing datatype to float\n",
        "df['Mileage']=df['Mileage'].astype(float)\n",
        "df['Engine']=df['Engine'].astype(float)\n",
        "df['Power']=df['Power'].astype(float)"
      ],
      "metadata": {
        "id": "DNw2Zmq1p2pP"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#again checking if all datatypes are converted\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1reX-ah8rrLh",
        "outputId": "636e9a7d-56f3-4c06-e22b-704b41f0ef1f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location               int64\n",
              "Year                   int64\n",
              "Kilometers_Driven      int64\n",
              "Fuel_Type              int64\n",
              "Transmission           int64\n",
              "Owner_Type             int64\n",
              "Mileage              float64\n",
              "Engine               float64\n",
              "Power                float64\n",
              "Seats                float64\n",
              "Price                float64\n",
              "Brand                  int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling missing values using mean and mode\n",
        "df['Power']=df['Power'].fillna(df['Power'].mean())\n",
        "df['Mileage']=df['Mileage'].fillna(df['Mileage'].mean())\n",
        "df['Engine']=df['Engine'].fillna(df['Engine'].mode()[0])\n",
        "df['Seats']=df['Seats'].fillna(df['Seats'].mode()[0])\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24WDYKCxrZLO",
        "outputId": "d1d712bd-fd93-4a39-995e-4fc48bbf5e72"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Location             0\n",
              "Year                 0\n",
              "Kilometers_Driven    0\n",
              "Fuel_Type            0\n",
              "Transmission         0\n",
              "Owner_Type           0\n",
              "Mileage              0\n",
              "Engine               0\n",
              "Power                0\n",
              "Seats                0\n",
              "Price                0\n",
              "Brand                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting X values\n",
        "X=df.drop(['Price'],axis=1)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kL3wmk4Isp6h",
        "outputId": "9115d886-0d72-4c5e-90aa-310b2949cdfe"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Location  Year  Kilometers_Driven  Fuel_Type  Transmission  Owner_Type  \\\n",
              "0            9  2010              72000          0             1           0   \n",
              "1           10  2015              41000          1             1           0   \n",
              "2            2  2011              46000          4             1           0   \n",
              "3            2  2012              87000          1             1           0   \n",
              "4            3  2013              40670          1             0           2   \n",
              "...        ...   ...                ...        ...           ...         ...   \n",
              "6014         4  2014              27365          1             1           0   \n",
              "6015         6  2015             100000          1             1           0   \n",
              "6016         6  2012              55000          1             1           2   \n",
              "6017         8  2013              46000          4             1           0   \n",
              "6018         5  2011              47000          1             1           0   \n",
              "\n",
              "      Mileage  Engine   Power  Seats  Brand  \n",
              "0       26.60   998.0   58.16    5.0     18  \n",
              "1       19.67  1582.0  126.20    5.0     10  \n",
              "2       18.20  1199.0   88.70    5.0      9  \n",
              "3       20.77  1248.0   88.76    7.0     18  \n",
              "4       15.20  1968.0  140.80    5.0      1  \n",
              "...       ...     ...     ...    ...    ...  \n",
              "6014    28.40  1248.0   74.00    5.0     18  \n",
              "6015    24.40  1120.0   71.00    5.0     10  \n",
              "6016    14.00  2498.0  112.00    8.0     17  \n",
              "6017    18.90   998.0   67.10    5.0     18  \n",
              "6018    25.44   936.0   57.60    5.0      4  \n",
              "\n",
              "[6019 rows x 11 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
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              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
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              "      <th>Brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9</td>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.60</td>\n",
              "      <td>998.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>10</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
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              "      <td>1</td>\n",
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              "      <td>19.67</td>\n",
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              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18.20</td>\n",
              "      <td>1199.0</td>\n",
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              "      <td>5.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>20.77</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>88.76</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>15.20</td>\n",
              "      <td>1968.0</td>\n",
              "      <td>140.80</td>\n",
              "      <td>5.0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6014</th>\n",
              "      <td>4</td>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28.40</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>74.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</th>\n",
              "      <td>6</td>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>24.40</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>71.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</th>\n",
              "      <td>6</td>\n",
              "      <td>2012</td>\n",
              "      <td>55000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>14.00</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>112.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</th>\n",
              "      <td>8</td>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18.90</td>\n",
              "      <td>998.0</td>\n",
              "      <td>67.10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>18</td>\n",
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              "      <th>6018</th>\n",
              "      <td>5</td>\n",
              "      <td>2011</td>\n",
              "      <td>47000</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>25.44</td>\n",
              "      <td>936.0</td>\n",
              "      <td>57.60</td>\n",
              "      <td>5.0</td>\n",
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              "<p>6019 rows × 11 columns</p>\n",
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              "  quickchartButtonEl.style.display =\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85112102-9cb0-4376-80ba-dd31bd064d5f');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting for y values\n",
        "y=df['Price']\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAuY9sf6s33g",
        "outputId": "99edcffc-fbc7-4728-e6dc-b6d6a3594065"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1.75\n",
              "1       12.50\n",
              "2        4.50\n",
              "3        6.00\n",
              "4       17.74\n",
              "        ...  \n",
              "6014     4.75\n",
              "6015     4.00\n",
              "6016     2.90\n",
              "6017     2.65\n",
              "6018     2.50\n",
              "Name: Price, Length: 6019, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5.Feature Selection**"
      ],
      "metadata": {
        "id": "QpGMIVok2F_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "TmKXvXBekZm1",
        "outputId": "71c3b336-1062-43f9-fd01-016069874c9c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Location      Year  Kilometers_Driven  Fuel_Type  \\\n",
              "Location           1.000000 -0.020117          -0.042751   0.115048   \n",
              "Year              -0.020117  1.000000          -0.173048  -0.129526   \n",
              "Kilometers_Driven -0.042751 -0.173048           1.000000  -0.102953   \n",
              "Fuel_Type          0.115048 -0.129526          -0.102953   1.000000   \n",
              "Transmission       0.040174 -0.097059           0.025663   0.125219   \n",
              "Owner_Type        -0.033889 -0.386160           0.079924   0.043017   \n",
              "Mileage            0.018773  0.321534          -0.065253  -0.152125   \n",
              "Engine            -0.079206 -0.046288           0.090476  -0.403143   \n",
              "Power             -0.063908  0.070520           0.024197  -0.262923   \n",
              "Seats             -0.037607  0.015204           0.082782  -0.299306   \n",
              "Price             -0.116620  0.305327          -0.011493  -0.305207   \n",
              "Brand              0.026295  0.018578           0.028232  -0.068246   \n",
              "\n",
              "                   Transmission  Owner_Type   Mileage    Engine     Power  \\\n",
              "Location               0.040174   -0.033889  0.018773 -0.079206 -0.063908   \n",
              "Year                  -0.097059   -0.386160  0.321534 -0.046288  0.070520   \n",
              "Kilometers_Driven      0.025663    0.079924 -0.065253  0.090476  0.024197   \n",
              "Fuel_Type              0.125219    0.043017 -0.152125 -0.403143 -0.262923   \n",
              "Transmission           1.000000    0.003002  0.333096 -0.498935 -0.632029   \n",
              "Owner_Type             0.003002    1.000000 -0.154188  0.046247  0.005638   \n",
              "Mileage                0.333096   -0.154188  1.000000 -0.581302 -0.444878   \n",
              "Engine                -0.498935    0.046247 -0.581302  1.000000  0.839684   \n",
              "Power                 -0.632029    0.005638 -0.444878  0.839684  1.000000   \n",
              "Seats                  0.074838    0.002219 -0.299631  0.393849  0.109025   \n",
              "Price                 -0.586025   -0.094559 -0.306588  0.657196  0.757711   \n",
              "Brand                  0.168697   -0.027582 -0.013374  0.009390 -0.177018   \n",
              "\n",
              "                      Seats     Price     Brand  \n",
              "Location          -0.037607 -0.116620  0.026295  \n",
              "Year               0.015204  0.305327  0.018578  \n",
              "Kilometers_Driven  0.082782 -0.011493  0.028232  \n",
              "Fuel_Type         -0.299306 -0.305207 -0.068246  \n",
              "Transmission       0.074838 -0.586025  0.168697  \n",
              "Owner_Type         0.002219 -0.094559 -0.027582  \n",
              "Mileage           -0.299631 -0.306588 -0.013374  \n",
              "Engine             0.393849  0.657196  0.009390  \n",
              "Power              0.109025  0.757711 -0.177018  \n",
              "Seats              1.000000  0.052811  0.189674  \n",
              "Price              0.052811  1.000000 -0.132929  \n",
              "Brand              0.189674 -0.132929  1.000000  "
            ],
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              "\n",
              "\n",
              "  <div id=\"df-d0340420-69e4-4cc9-a897-a9179b3796cd\">\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Location</th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>Price</th>\n",
              "      <th>Brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Location</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.020117</td>\n",
              "      <td>-0.042751</td>\n",
              "      <td>0.115048</td>\n",
              "      <td>0.040174</td>\n",
              "      <td>-0.033889</td>\n",
              "      <td>0.018773</td>\n",
              "      <td>-0.079206</td>\n",
              "      <td>-0.063908</td>\n",
              "      <td>-0.037607</td>\n",
              "      <td>-0.116620</td>\n",
              "      <td>0.026295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>-0.020117</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.173048</td>\n",
              "      <td>-0.129526</td>\n",
              "      <td>-0.097059</td>\n",
              "      <td>-0.386160</td>\n",
              "      <td>0.321534</td>\n",
              "      <td>-0.046288</td>\n",
              "      <td>0.070520</td>\n",
              "      <td>0.015204</td>\n",
              "      <td>0.305327</td>\n",
              "      <td>0.018578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <td>-0.042751</td>\n",
              "      <td>-0.173048</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.102953</td>\n",
              "      <td>0.025663</td>\n",
              "      <td>0.079924</td>\n",
              "      <td>-0.065253</td>\n",
              "      <td>0.090476</td>\n",
              "      <td>0.024197</td>\n",
              "      <td>0.082782</td>\n",
              "      <td>-0.011493</td>\n",
              "      <td>0.028232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel_Type</th>\n",
              "      <td>0.115048</td>\n",
              "      <td>-0.129526</td>\n",
              "      <td>-0.102953</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.125219</td>\n",
              "      <td>0.043017</td>\n",
              "      <td>-0.152125</td>\n",
              "      <td>-0.403143</td>\n",
              "      <td>-0.262923</td>\n",
              "      <td>-0.299306</td>\n",
              "      <td>-0.305207</td>\n",
              "      <td>-0.068246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transmission</th>\n",
              "      <td>0.040174</td>\n",
              "      <td>-0.097059</td>\n",
              "      <td>0.025663</td>\n",
              "      <td>0.125219</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>0.333096</td>\n",
              "      <td>-0.498935</td>\n",
              "      <td>-0.632029</td>\n",
              "      <td>0.074838</td>\n",
              "      <td>-0.586025</td>\n",
              "      <td>0.168697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Owner_Type</th>\n",
              "      <td>-0.033889</td>\n",
              "      <td>-0.386160</td>\n",
              "      <td>0.079924</td>\n",
              "      <td>0.043017</td>\n",
              "      <td>0.003002</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.154188</td>\n",
              "      <td>0.046247</td>\n",
              "      <td>0.005638</td>\n",
              "      <td>0.002219</td>\n",
              "      <td>-0.094559</td>\n",
              "      <td>-0.027582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mileage</th>\n",
              "      <td>0.018773</td>\n",
              "      <td>0.321534</td>\n",
              "      <td>-0.065253</td>\n",
              "      <td>-0.152125</td>\n",
              "      <td>0.333096</td>\n",
              "      <td>-0.154188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.581302</td>\n",
              "      <td>-0.444878</td>\n",
              "      <td>-0.299631</td>\n",
              "      <td>-0.306588</td>\n",
              "      <td>-0.013374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Engine</th>\n",
              "      <td>-0.079206</td>\n",
              "      <td>-0.046288</td>\n",
              "      <td>0.090476</td>\n",
              "      <td>-0.403143</td>\n",
              "      <td>-0.498935</td>\n",
              "      <td>0.046247</td>\n",
              "      <td>-0.581302</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.839684</td>\n",
              "      <td>0.393849</td>\n",
              "      <td>0.657196</td>\n",
              "      <td>0.009390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Power</th>\n",
              "      <td>-0.063908</td>\n",
              "      <td>0.070520</td>\n",
              "      <td>0.024197</td>\n",
              "      <td>-0.262923</td>\n",
              "      <td>-0.632029</td>\n",
              "      <td>0.005638</td>\n",
              "      <td>-0.444878</td>\n",
              "      <td>0.839684</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.109025</td>\n",
              "      <td>0.757711</td>\n",
              "      <td>-0.177018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Seats</th>\n",
              "      <td>-0.037607</td>\n",
              "      <td>0.015204</td>\n",
              "      <td>0.082782</td>\n",
              "      <td>-0.299306</td>\n",
              "      <td>0.074838</td>\n",
              "      <td>0.002219</td>\n",
              "      <td>-0.299631</td>\n",
              "      <td>0.393849</td>\n",
              "      <td>0.109025</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.052811</td>\n",
              "      <td>0.189674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>-0.116620</td>\n",
              "      <td>0.305327</td>\n",
              "      <td>-0.011493</td>\n",
              "      <td>-0.305207</td>\n",
              "      <td>-0.586025</td>\n",
              "      <td>-0.094559</td>\n",
              "      <td>-0.306588</td>\n",
              "      <td>0.657196</td>\n",
              "      <td>0.757711</td>\n",
              "      <td>0.052811</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.132929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brand</th>\n",
              "      <td>0.026295</td>\n",
              "      <td>0.018578</td>\n",
              "      <td>0.028232</td>\n",
              "      <td>-0.068246</td>\n",
              "      <td>0.168697</td>\n",
              "      <td>-0.027582</td>\n",
              "      <td>-0.013374</td>\n",
              "      <td>0.009390</td>\n",
              "      <td>-0.177018</td>\n",
              "      <td>0.189674</td>\n",
              "      <td>-0.132929</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0340420-69e4-4cc9-a897-a9179b3796cd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-77bf4c36-ee17-46f4-ba35-8d8724666ad1\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-77bf4c36-ee17-46f4-ba35-8d8724666ad1')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d0340420-69e4-4cc9-a897-a9179b3796cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d0340420-69e4-4cc9-a897-a9179b3796cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PSBynsmhhsO",
        "outputId": "00515af0-f8f8-4c36-f396-0a1d79be44e8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Location', 'Year', 'Kilometers_Driven', 'Fuel_Type', 'Transmission',\n",
              "       'Owner_Type', 'Mileage', 'Engine', 'Power', 'Seats', 'Brand'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#removing columns 'Kilometers_Driven' and 'Owner_Type' as it have less correlation.\n",
        "X=df[['Year', 'Fuel_Type', 'Transmission', 'Mileage', 'Engine', 'Power', 'Brand','Location','Seats']]"
      ],
      "metadata": {
        "id": "e1Fz1jTAhpRa"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "7n17b8eAKLp5",
        "outputId": "d14b11da-69f3-481f-cf49-4f000bb2f67e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Fuel_Type  Transmission  Mileage  Engine   Power  Brand  Location  \\\n",
              "0  2010          0             1    26.60   998.0   58.16     18         9   \n",
              "1  2015          1             1    19.67  1582.0  126.20     10        10   \n",
              "2  2011          4             1    18.20  1199.0   88.70      9         2   \n",
              "\n",
              "   Seats  \n",
              "0    5.0  \n",
              "1    5.0  \n",
              "2    5.0  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d0e6b9f4-a359-484f-b440-c6430e488557\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>26.60</td>\n",
              "      <td>998.0</td>\n",
              "      <td>58.16</td>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>19.67</td>\n",
              "      <td>1582.0</td>\n",
              "      <td>126.20</td>\n",
              "      <td>10</td>\n",
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              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>18.20</td>\n",
              "      <td>1199.0</td>\n",
              "      <td>88.70</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0e6b9f4-a359-484f-b440-c6430e488557')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-6f80492e-6914-4b99-84f2-36c0c59dd528\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f80492e-6914-4b99-84f2-36c0c59dd528')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-6f80492e-6914-4b99-84f2-36c0c59dd528 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d0e6b9f4-a359-484f-b440-c6430e488557 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d0e6b9f4-a359-484f-b440-c6430e488557');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#scaling the X values\n",
        "sc=StandardScaler()\n",
        "X=sc.fit_transform(X)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6w62GqttEDu",
        "outputId": "106156bc-f901-4a86-ef66-4d9301c5327b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.02713851, -1.57030559,  0.63252909, ...,  0.33132605,\n",
              "         1.14365818, -0.34329324],\n",
              "       [ 0.50216112, -0.90577143,  0.63252909, ..., -0.66226004,\n",
              "         1.48198899, -0.34329324],\n",
              "       [-0.72127858,  1.08783104,  0.63252909, ..., -0.7864583 ,\n",
              "        -1.22465746, -0.34329324],\n",
              "       ...,\n",
              "       [-0.41541866, -0.90577143,  0.63252909, ...,  0.20712779,\n",
              "         0.12866576,  3.37750269],\n",
              "       [-0.10955873,  1.08783104,  0.63252909, ...,  0.33132605,\n",
              "         0.80532737, -0.34329324],\n",
              "       [-0.72127858, -0.90577143,  0.63252909, ..., -1.4074496 ,\n",
              "        -0.20966505, -0.34329324]])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#train-test splitting\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=13)"
      ],
      "metadata": {
        "id": "Dx8JB5HqtVeI"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyAiUScu69Nf",
        "outputId": "626a459a-7677-468b-c069-082ea4e1155c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4213, 9), (1806, 9), (4213,), (1806,))"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6.Model Building and Evaluation**"
      ],
      "metadata": {
        "id": "BjH3zhiOWrJo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#converting x values into polynomials\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly=PolynomialFeatures(degree=2)\n",
        "x_poly1=poly.fit_transform(X_train)\n"
      ],
      "metadata": {
        "id": "WXvQ2A_Am1J5"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_poly2=poly.transform(X_test)"
      ],
      "metadata": {
        "id": "4M81WBzgnY--"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multiple Linear Regression with Polynomial Features"
      ],
      "metadata": {
        "id": "zaiI4j-u2caD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mlr=LinearRegression()\n",
        "mlr.fit(x_poly1,y_train)\n",
        "y_pred=mlr.predict(x_poly2)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIXC5yBHtfqb",
        "outputId": "efa38a0a-05ac-4158-a71d-1d644990f03b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.40673828,  2.58896637, 20.34881592, ...,  5.72541428,\n",
              "       22.49697495, -6.09660721])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model evaluation\n",
        "print('r2_score:',r2_score(y_test,y_pred))\n",
        "print('mean_absolute_error:',mean_absolute_error(y_test,y_pred))\n",
        "print('mean_squared_error:',mean_squared_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6PzgFl3twnE",
        "outputId": "dcafce1f-8fb1-4f49-dc7d-680750329fb2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score: 0.8469077375695623\n",
            "mean_absolute_error: 2.390649997292959\n",
            "mean_squared_error: 19.16099989288426\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot against one of the input features and y-actual & y-predicted\n",
        "plt.scatter(X_test[:,0],y_test)\n",
        "plt.scatter(X_test[:,0],y_pred,color='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "s1cnhbQFlUoi",
        "outputId": "4e696d92-6c3c-4fe7-ad10-99e119257c4c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "KNeighbors Regressor"
      ],
      "metadata": {
        "id": "ge4tTqR93mhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn=KNeighborsRegressor()\n",
        "knn.fit(X_train,y_train)\n",
        "y_pred_knn=knn.predict(X_test)\n",
        "y_pred_knn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfTxKhFai-H-",
        "outputId": "ba6c0828-d0cc-4da3-a30a-d20daa6561a8"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.898,  4.398, 23.458, ...,  6.11 , 11.782,  3.478])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('r2_score:',r2_score(y_test,y_pred_knn))\n",
        "print('mean_absolute_error:',mean_absolute_error(y_test,y_pred_knn))\n",
        "print('mean_squared_error:',mean_squared_error(y_test,y_pred_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOH59rM-jmBp",
        "outputId": "9fcdce7a-94a3-481b-b126-a520ec5be8af"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score: 0.8453931712669912\n",
            "mean_absolute_error: 1.9688150609080843\n",
            "mean_squared_error: 19.35056273754153\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Regressor"
      ],
      "metadata": {
        "id": "d48vrvBh3x0E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sv=SVR(kernel='poly')\n",
        "sv.fit(X_train,y_train)\n",
        "y_pred_sv=sv.predict(X_test)\n",
        "y_pred_sv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjvIkrqojqaS",
        "outputId": "9b7e1fe5-a2ea-4da4-ea80-7b06f3f54774"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.18120285,  4.59993346, 20.01713473, ...,  5.49096166,\n",
              "       16.68383896,  5.19237715])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('r2_score:',r2_score(y_test,y_pred_sv))\n",
        "print('mean_absolute_error:',mean_absolute_error(y_test,y_pred_sv))\n",
        "print('mean_squared_error:',mean_squared_error(y_test,y_pred_sv))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "by0xz04rjrIf",
        "outputId": "6eb12ad5-ab22-4153-c5df-90c07bfa3039"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score: 0.8278124545907601\n",
            "mean_absolute_error: 2.3598762007157164\n",
            "mean_squared_error: 21.550962058853774\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RandomForest Regressor"
      ],
      "metadata": {
        "id": "PMJ-wrOTGKid"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor(n_estimators=155, random_state=17, max_features='sqrt')\n",
        "rf.fit(X_train,y_train)\n",
        "y_pred_rf=rf.predict(X_test)"
      ],
      "metadata": {
        "id": "tqmrTdif79sw"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('r2_score:',r2_score(y_test,y_pred_rf))\n",
        "print('mean_absolute_error:',mean_absolute_error(y_test,y_pred_rf))\n",
        "print('mean_squared_error:',mean_squared_error(y_test,y_pred_rf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6XVGA3fGUzd",
        "outputId": "38413542-1d15-44ea-b6d9-9aec4bca36ba"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score: 0.8960973293123077\n",
            "mean_absolute_error: 1.51285740870906\n",
            "mean_squared_error: 13.004439481857421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7.Hyper Parameter Tuning**"
      ],
      "metadata": {
        "id": "6KLTDHBnrxXW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Applying GridSearchCV to explore different combinations of hyperparameter values (e.g., n_neighbors, weights, etc.), evaluating performance with cross-validation.\n",
        "Select the best hyperparameters based on the search results, which optimize the KNeighborsRegressor's performance"
      ],
      "metadata": {
        "id": "uxCPbKbj4BpH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV,RandomizedSearchCV"
      ],
      "metadata": {
        "id": "ylcD-1b3r2n-"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn1=KNeighborsRegressor()\n",
        "params = {'n_neighbors': [3, 5, 7, 9],                  # Try different values for 'n_neighbors'\n",
        "              'weights': ['uniform', 'distance'],           # Try different weight options\n",
        "              'p': [1, 2],                                  # Try different distance metrics (1 for Manhattan distance, 2 for Euclidean distance)\n",
        "              'algorithm':['auto','ball_tree','kd_tree','brute']}"
      ],
      "metadata": {
        "id": "OFECaHJOsA62"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf=GridSearchCV(knn1,params,cv=5,scoring='r2')\n",
        "clf.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "cf60WaDNsUqO",
        "outputId": "1fedae82-b212-4410-e89f-b9653adbe227"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
              "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
              "                         'n_neighbors': [3, 5, 7, 9], 'p': [1, 2],\n",
              "                         'weights': ['uniform', 'distance']},\n",
              "             scoring='r2')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
              "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
              "                         &#x27;n_neighbors&#x27;: [3, 5, 7, 9], &#x27;p&#x27;: [1, 2],\n",
              "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KNeighborsRegressor(),\n",
              "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
              "                         &#x27;n_neighbors&#x27;: [3, 5, 7, 9], &#x27;p&#x27;: [1, 2],\n",
              "                         &#x27;weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n",
              "             scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWmVGsI9scYY",
        "outputId": "36851680-8e6b-4aca-c8cf-cc76415e4dd3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'ball_tree', 'n_neighbors': 7, 'p': 1, 'weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn=KNeighborsRegressor(algorithm='auto',p=1,n_neighbors=3, weights='uniform')\n",
        "knn.fit(X_train,y_train)\n",
        "y_pred_knn=knn.predict(X_test)\n",
        "y_pred_knn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJSLO2RHsh6e",
        "outputId": "6e8f9932-6db3-48ad-9e1d-51ce167c1857"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.86333333,  3.06666667, 19.83333333, ...,  5.41666667,\n",
              "       12.75      ,  2.13333333])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('r2_score:',r2_score(y_test,y_pred_knn))\n",
        "print('mean_absolute_error:',mean_absolute_error(y_test,y_pred_knn))\n",
        "print('mean_squared_error:',mean_squared_error(y_test,y_pred_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BglgRk_s-CT",
        "outputId": "0e21906e-b40a-4c0e-e7c8-a84f11521a88"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "r2_score: 0.8565208739770542\n",
            "mean_absolute_error: 1.8295884090070136\n",
            "mean_squared_error: 17.957821477790084\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After applying hyperparameter tuning, the accuracy of the KNeighborsRegressor model only showed a slight improvement, indicating that the default hyperparameters were already reasonably well-suited for the dataset, leaving limited room for further enhancement through tuning."
      ],
      "metadata": {
        "id": "CUoKs_Vx5a2D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**8.Prediction on new data**"
      ],
      "metadata": {
        "id": "FuKYcMrjJmlX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use the trained and tuned model to make predictions on new, unseen data."
      ],
      "metadata": {
        "id": "aoKipr-BJrjW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting new data\n",
        "new_pred=rf.predict(sc.transform([[2010,0,1,26.60,998.0,58.16,18,9,5.0]]))\n",
        "print('predicted price of the car with given details(in lakh): ',new_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCbYS1bDJuow",
        "outputId": "27ab8843-d662-4fe9-d2e3-28ce91a42723"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted price of the car with given details(in lakh):  [2.02133333]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In conclusion, the RandomForest Regressor achieved a promising R2 score of 0.8960, which is the highest of the used regressors demonstrating its effectiveness in predicting used car prices based on the selected features."
      ],
      "metadata": {
        "id": "pCbaRofL5eOO"
      }
    }
  ]
}