linar_regression_ass1.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
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    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/CAR DETAILS FROM CAR DEKHO.csv')"
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              "                       name  year  selling_price  km_driven    fuel  \\\n",
              "0             Maruti 800 AC  2007          60000      70000  Petrol   \n",
              "1  Maruti Wagon R LXI Minor  2007         135000      50000  Petrol   \n",
              "2      Hyundai Verna 1.6 SX  2012         600000     100000  Diesel   \n",
              "3    Datsun RediGO T Option  2017         250000      46000  Petrol   \n",
              "4     Honda Amaze VX i-DTEC  2014         450000     141000  Diesel   \n",
              "\n",
              "  seller_type transmission         owner  \n",
              "0  Individual       Manual   First Owner  \n",
              "1  Individual       Manual   First Owner  \n",
              "2  Individual       Manual   First Owner  \n",
              "3  Individual       Manual   First Owner  \n",
              "4  Individual       Manual  Second Owner  "
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              "      <td>Maruti 800 AC</td>\n",
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              "      <th>1</th>\n",
              "      <td>Maruti Wagon R LXI Minor</td>\n",
              "      <td>2007</td>\n",
              "      <td>135000</td>\n",
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              "      <td>2017</td>\n",
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              "      <td>Honda Amaze VX i-DTEC</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>141000</td>\n",
              "      <td>Diesel</td>\n",
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              "              year  selling_price      km_driven\n",
              "count  4340.000000   4.340000e+03    4340.000000\n",
              "mean   2013.090783   5.041273e+05   66215.777419\n",
              "std       4.215344   5.785487e+05   46644.102194\n",
              "min    1992.000000   2.000000e+04       1.000000\n",
              "25%    2011.000000   2.087498e+05   35000.000000\n",
              "50%    2014.000000   3.500000e+05   60000.000000\n",
              "75%    2016.000000   6.000000e+05   90000.000000\n",
              "max    2020.000000   8.900000e+06  806599.000000"
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              "      <th>mean</th>\n",
              "      <td>2013.090783</td>\n",
              "      <td>5.041273e+05</td>\n",
              "      <td>66215.777419</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>4.215344</td>\n",
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              "      <th>min</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
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              "      <td>2020.000000</td>\n",
              "      <td>8.900000e+06</td>\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4340 entries, 0 to 4339\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   name           4340 non-null   object\n",
            " 1   year           4340 non-null   int64 \n",
            " 2   selling_price  4340 non-null   int64 \n",
            " 3   km_driven      4340 non-null   int64 \n",
            " 4   fuel           4340 non-null   object\n",
            " 5   seller_type    4340 non-null   object\n",
            " 6   transmission   4340 non-null   object\n",
            " 7   owner          4340 non-null   object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 271.4+ KB\n"
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        "data['owner'].unique()"
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            "text/plain": [
              "array(['First Owner', 'Second Owner', 'Fourth & Above Owner',\n",
              "       'Third Owner', 'Test Drive Car'], dtype=object)"
            ]
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          "execution_count": 6
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            "text/plain": [
              "array(['Manual', 'Automatic'], dtype=object)"
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          "data": {
            "text/plain": [
              "array(['Individual', 'Dealer', 'Trustmark Dealer'], dtype=object)"
            ]
          },
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4dayUi8kCvn",
        "outputId": "3c7987b1-84b0-4ec6-8632-7713540dd6fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Petrol', 'Diesel', 'CNG', 'LPG', 'Electric'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='km_driven',y='selling_price',data=data,hue='owner')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "p3vXsZiXkNGh",
        "outputId": "dca63704-ba48-49ae-9043-df23f27a3a73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a2c4c9cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x= 'year',y='selling_price', data=data, hue='owner')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "aevAOFaulhqt",
        "outputId": "bf6f3007-124e-499a-eba9-a54826df4ddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a2c42b3a0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDTF8pi6mFWL",
        "outputId": "1de3c482-a2c7-43f6-c39f-5072fb223840"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4340 entries, 0 to 4339\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   name           4340 non-null   object\n",
            " 1   year           4340 non-null   int64 \n",
            " 2   selling_price  4340 non-null   int64 \n",
            " 3   km_driven      4340 non-null   int64 \n",
            " 4   fuel           4340 non-null   object\n",
            " 5   seller_type    4340 non-null   object\n",
            " 6   transmission   4340 non-null   object\n",
            " 7   owner          4340 non-null   object\n",
            "dtypes: int64(3), object(5)\n",
            "memory usage: 271.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "4k6u63bmHhwg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "le=LabelEncoder()"
      ],
      "metadata": {
        "id": "qq_7hHuaH_fU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.fuel=le.fit_transform(data.fuel)\n",
        "data.seller_type=le.fit_transform(data.seller_type)"
      ],
      "metadata": {
        "id": "GqzQf7ssIEXI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data.transmission=le.fit_transform(data.transmission)\n",
        "data.owner=le.fit_transform(data.owner)"
      ],
      "metadata": {
        "id": "NppCaaOQISiM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZlOj7Ee7I20U",
        "outputId": "4643a7b9-6150-4ca4-e31c-6f0a0f4c9595"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       name  year  selling_price  km_driven  fuel  \\\n",
              "0             Maruti 800 AC  2007          60000      70000     4   \n",
              "1  Maruti Wagon R LXI Minor  2007         135000      50000     4   \n",
              "2      Hyundai Verna 1.6 SX  2012         600000     100000     1   \n",
              "3    Datsun RediGO T Option  2017         250000      46000     4   \n",
              "4     Honda Amaze VX i-DTEC  2014         450000     141000     1   \n",
              "\n",
              "   seller_type  transmission  owner  \n",
              "0            1             1      0  \n",
              "1            1             1      0  \n",
              "2            1             1      0  \n",
              "3            1             1      0  \n",
              "4            1             1      2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8e8bb508-deca-4f7f-9ea2-a93b82c12a7c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>year</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>km_driven</th>\n",
              "      <th>fuel</th>\n",
              "      <th>seller_type</th>\n",
              "      <th>transmission</th>\n",
              "      <th>owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti 800 AC</td>\n",
              "      <td>2007</td>\n",
              "      <td>60000</td>\n",
              "      <td>70000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Maruti Wagon R LXI Minor</td>\n",
              "      <td>2007</td>\n",
              "      <td>135000</td>\n",
              "      <td>50000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Verna 1.6 SX</td>\n",
              "      <td>2012</td>\n",
              "      <td>600000</td>\n",
              "      <td>100000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Datsun RediGO T Option</td>\n",
              "      <td>2017</td>\n",
              "      <td>250000</td>\n",
              "      <td>46000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Honda Amaze VX i-DTEC</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>141000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e8bb508-deca-4f7f-9ea2-a93b82c12a7c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-8e8bb508-deca-4f7f-9ea2-a93b82c12a7c button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e8bb508-deca-4f7f-9ea2-a93b82c12a7c');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1st model including year"
      ],
      "metadata": {
        "id": "xghXvrLIKUhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data[['year','km_driven','fuel','seller_type','transmission','owner']]\n",
        "y=data[['selling_price']]"
      ],
      "metadata": {
        "id": "YNEXoLrEKJzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "qV7EXkZqJH2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(x, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "Qc--PQoRLDHy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VQyiyHMBLpIe",
        "outputId": "1a829e13-6211-499a-d944-9a950bf04a9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      year  km_driven  fuel  seller_type  transmission  owner\n",
              "2195  2014      76000     4            1             1      2\n",
              "116   2016      60000     1            1             1      0\n",
              "2682  2017      55000     4            1             1      0\n",
              "3581  2017      70000     1            1             1      0\n",
              "3173  2016      35000     4            1             1      0"
            ],
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              "          const element = document.querySelector('#df-2a23e7a8-331c-4b71-aae4-c2d72957813b');\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",
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            ]
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    {
      "cell_type": "code",
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        "y_train.head()"
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        "colab": {
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          "height": 206
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      selling_price\n",
              "2195         180000\n",
              "116          780000\n",
              "2682         350000\n",
              "3581         720000\n",
              "3173         300000"
            ],
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              "      <td>300000</td>\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
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              "          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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      year  km_driven  fuel  seller_type  transmission  owner\n",
              "4073  2012      30000     4            1             1      4\n",
              "4213  2015     135000     1            1             1      2\n",
              "2058  2008      90000     4            1             1      2\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2058</th>\n",
              "      <td>2008</td>\n",
              "      <td>90000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3179</th>\n",
              "      <td>2010</td>\n",
              "      <td>102000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3360</th>\n",
              "      <td>2018</td>\n",
              "      <td>8000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "            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",
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            ]
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.head()"
      ],
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        "colab": {
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          "height": 206
        },
        "id": "QdpOg3NBL8RY",
        "outputId": "6a59cf10-585f-44bf-d1bd-3b2ecb872f6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      selling_price\n",
              "4073         130000\n",
              "4213         550000\n",
              "2058         120000\n",
              "3179         160000\n",
              "3360         890000"
            ],
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-105bdaba-3a67-4e92-bb33-aabcf2d28b39 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-105bdaba-3a67-4e92-bb33-aabcf2d28b39');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr=LinearRegression()\n",
        "lr.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CcTdtSf6MAd6",
        "outputId": "8411af68-2f6b-4a6f-886f-07151a5d763b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=lr.predict(x_test)\n",
        "y_predict[ : 5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_km0lEKMMki",
        "outputId": "87b6d116-cbfe-4913-ce4c-51f7a0aecce6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[216840.97421317],\n",
              "       [534166.86998664],\n",
              "       [ 38816.68301293],\n",
              "       [368884.50823568],\n",
              "       [550711.1328952 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "xupoIolZMdiY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AJyE7hdEMj_K",
        "outputId": "9ea06eb8-b205-4af3-f84e-be51acd64e8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "211132156089.44467"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0zEnpzpMypi",
        "outputId": "d93c5344-e1bd-406d-b715-8750acdc3cf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-76471835.83711801])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyNe9D-YM2ig",
        "outputId": "071ceb59-4558-4f6e-af75-79a90d289e28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3.87779456e+04, -8.66889894e-01, -8.76382042e+04,\n",
              "        -2.39291371e+04, -8.73859411e+05, -1.45504425e+04]])"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2nd model without using year\n"
      ],
      "metadata": {
        "id": "z27ZX3p0M9gH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x2=data[['km_driven','fuel','seller_type','transmission','owner']]\n",
        "y2=data[['selling_price']]"
      ],
      "metadata": {
        "id": "7T-gehLKM6hf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x2_train,x2_test,y2_train,y2_test=train_test_split(x,y,test_size=0.3,random_state=2)"
      ],
      "metadata": {
        "id": "tMvaaAw1M83Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x2_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IgAehJglOCPq",
        "outputId": "a322068a-9f60-4d46-f63e-818994fc187e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      year  km_driven  fuel  seller_type  transmission  owner\n",
              "1452  2014      90000     1            1             1      2\n",
              "1457  2008      40000     4            1             1      0\n",
              "1556  2012      10000     4            1             1      0\n",
              "2204  2012      80000     1            1             1      2\n",
              "3858  2018      21394     1            0             1      0"
            ],
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              "            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",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x2_test.head()"
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      "metadata": {
        "colab": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      year  km_driven  fuel  seller_type  transmission  owner\n",
              "1149  2016      30000     4            0             1      0\n",
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              "4261  2006     100000     1            1             1      2\n",
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              "\n",
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              "          const element = document.querySelector('#df-75716640-4f60-4e52-a18d-9249771d0ed7');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(x2_train,y2_train)"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "atjdQXeFOTro",
        "outputId": "255b2ba4-8ed9-491b-b69c-f4e0c4720930"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y2_predict=lr.predict(x2_test)"
      ],
      "metadata": {
        "id": "Hwn0FJXdP4zH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y2_test,y2_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_gOXgn1QGFf",
        "outputId": "34bd1a6e-6c92-4086-c4c7-1e0e7e0d1c6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "144907275556.1272"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZOcMAuUpQOmo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}