LinearRegression1.ipynb

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        "import pandas as pd\n",
        "from matplotlib import pyplot as pyplot\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import numpy as np"
      ],
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        "cars = pd.read_csv('CAR DETAILS.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>2017</td>\n",
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              "      <td>2014</td>\n",
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        "cars.fuel = le.fit_transform(cars.fuel)\n",
        "cars.seller_type = le.fit_transform(cars.seller_type)\n",
        "cars.transmission = le.fit_transform(cars.transmission)\n",
        "cars.owner = le.fit_transform(cars.owner)\n",
        "cars.head()"
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              "                       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  "
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            "text/plain": [
              "      km_driven  fuel  seller_type  transmission  owner\n",
              "3858      21394     1            0             1      0\n",
              "2841       4000     1            1             1      0\n",
              "2688      80000     1            1             1      2\n",
              "4313      10000     1            1             0      0\n",
              "853       60000     4            1             0      4"
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              "      km_driven  fuel  seller_type  transmission  owner\n",
              "2814      30000     4            1             1      0\n",
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              "      selling_price\n",
              "3858         950000\n",
              "2841        1100000\n",
              "2688         385000\n",
              "4313        2800000\n",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      selling_price\n",
              "2814         229999\n",
              "874          160000\n",
              "551         2150000\n",
              "2400         285000\n",
              "1062         950000"
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2814</th>\n",
              "      <td>229999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>874</th>\n",
              "      <td>160000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>551</th>\n",
              "      <td>2150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2400</th>\n",
              "      <td>285000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1062</th>\n",
              "      <td>950000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16f3e685-28da-4d79-be2e-2c49df88c11a')\"\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",
              "  <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-16f3e685-28da-4d79-be2e-2c49df88c11a 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-16f3e685-28da-4d79-be2e-2c49df88c11a');\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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr1 = LinearRegression()"
      ],
      "metadata": {
        "id": "EeY4JWffl50D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr1.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "my27TKZuo_1Y",
        "outputId": "437e9166-0c28-480a-f7a4-72dcb874e406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=lr1.predict(x_test)"
      ],
      "metadata": {
        "id": "Hh539Y-frHW8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "8TkZ9aTFrQhh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwXoFSLrrSN2",
        "outputId": "7c0040b4-62b4-42f2-fd75-8d22128bbe46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "192988640913.67206"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    }
  ]
}