Linear_Regression.ipynb

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    "colab": {
      "name": "Linear Regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the libraries"
      ],
      "metadata": {
        "id": "F4TJK7IMZyUS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "eRRjAGXAgB07"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the DatFrame"
      ],
      "metadata": {
        "id": "o3bT4yR1aBkg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Housing.csv')"
      ],
      "metadata": {
        "id": "vDVC9gwggTFE"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA - Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "gKltcMm6aGnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        },
        "id": "nPq-b83lgjaP",
        "outputId": "ea7ea90c-cc3e-4783-9677-79e98febd388"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes        no       no   \n",
              "1  12250000  8960         4          4        4      yes        no       no   \n",
              "2  12250000  9960         3          2        2      yes        no      yes   \n",
              "3  12215000  7500         4          2        2      yes        no      yes   \n",
              "4  11410000  7420         4          1        2      yes       yes      yes   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no             yes        2      yes        furnished  \n",
              "1              no             yes        3       no        furnished  \n",
              "2              no              no        2      yes   semi-furnished  \n",
              "3              no             yes        3      yes        furnished  \n",
              "4              no             yes        2       no        furnished  "
            ],
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              "      <th>guestroom</th>\n",
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              "      <th>0</th>\n",
              "      <td>13300000</td>\n",
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              "      <td>12250000</td>\n",
              "      <td>9960</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12215000</td>\n",
              "      <td>7500</td>\n",
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              "      <td>7420</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.info()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qEOpa6nogkg6",
        "outputId": "85677609-f78b-47ec-fcf3-0606f6ef568a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 545 entries, 0 to 544\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   price             545 non-null    int64 \n",
            " 1   area              545 non-null    int64 \n",
            " 2   bedrooms          545 non-null    int64 \n",
            " 3   bathrooms         545 non-null    int64 \n",
            " 4   stories           545 non-null    int64 \n",
            " 5   mainroad          545 non-null    object\n",
            " 6   guestroom         545 non-null    object\n",
            " 7   basement          545 non-null    object\n",
            " 8   hotwaterheating   545 non-null    object\n",
            " 9   airconditioning   545 non-null    object\n",
            " 10  parking           545 non-null    int64 \n",
            " 11  prefarea          545 non-null    object\n",
            " 12  furnishingstatus  545 non-null    object\n",
            "dtypes: int64(6), object(7)\n",
            "memory usage: 55.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking the outliers"
      ],
      "metadata": {
        "id": "6-qHhZBzaNcI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "roy_hceUgpF6",
        "outputId": "06240dcb-987a-4976-80e8-45a17eb636c7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc54c473d0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking the null values from dataset"
      ],
      "metadata": {
        "id": "xj9o0FznaWog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCqYvldPg0kj",
        "outputId": "7b773c0f-635f-407e-aa2d-95b57a9d5f98"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               0\n",
              "area                0\n",
              "bedrooms            0\n",
              "bathrooms           0\n",
              "stories             0\n",
              "mainroad            0\n",
              "guestroom           0\n",
              "basement            0\n",
              "hotwaterheating     0\n",
              "airconditioning     0\n",
              "parking             0\n",
              "prefarea            0\n",
              "furnishingstatus    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "teuHQ4Xoih2R",
        "outputId": "cfcba746-677b-4fd0-eca3-458fd20d5402"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes        no       no   \n",
              "1  12250000  8960         4          4        4      yes        no       no   \n",
              "2  12250000  9960         3          2        2      yes        no      yes   \n",
              "3  12215000  7500         4          2        2      yes        no      yes   \n",
              "4  11410000  7420         4          1        2      yes       yes      yes   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no             yes        2      yes        furnished  \n",
              "1              no             yes        3       no        furnished  \n",
              "2              no              no        2      yes   semi-furnished  \n",
              "3              no             yes        3      yes        furnished  \n",
              "4              no             yes        2       no        furnished  "
            ],
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              "\n",
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              "      <td>2</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12215000</td>\n",
              "      <td>7500</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "    }\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-85f4a6e1-125a-4277-8653-dc66f6191bd3 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-85f4a6e1-125a-4277-8653-dc66f6191bd3');\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": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Changing the categorical data into Numerical through Label Encoding"
      ],
      "metadata": {
        "id": "Bjgqc8y8adDW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "label = LabelEncoder()\n",
        "df['mainroad'] = label.fit_transform(df['mainroad'])\n",
        "df['guestroom'] = label.fit_transform(df['guestroom'])\n",
        "df['basement'] = label.fit_transform(df['basement'])\n",
        "df['hotwaterheating'] = label.fit_transform(df['hotwaterheating'])\n",
        "df['airconditioning'] = label.fit_transform(df['airconditioning'])\n",
        "df['prefarea'] = label.fit_transform(df['prefarea'])\n",
        "df['furnishingstatus'] = label.fit_transform(df['furnishingstatus'])\n"
      ],
      "metadata": {
        "id": "OdNMalyShQR6"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Label Encoding Successfully"
      ],
      "metadata": {
        "id": "BO48fOPgau0v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_t8OW4mljEmD",
        "outputId": "b3618c9f-4dac-432f-9739-4c6060a189fc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
              "0  13300000  7420         4          2        3         1          0   \n",
              "1  12250000  8960         4          4        4         1          0   \n",
              "2  12250000  9960         3          2        2         1          0   \n",
              "3  12215000  7500         4          2        2         1          0   \n",
              "4  11410000  7420         4          1        2         1          1   \n",
              "\n",
              "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
              "0         0                0                1        2         1   \n",
              "1         0                0                1        3         0   \n",
              "2         1                0                0        2         1   \n",
              "3         1                0                1        3         1   \n",
              "4         1                0                1        2         0   \n",
              "\n",
              "   furnishingstatus  \n",
              "0                 0  \n",
              "1                 0  \n",
              "2                 1  \n",
              "3                 0  \n",
              "4                 0  "
            ],
            "text/html": [
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4320a349-2c6f-426f-a14f-7375016799b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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-4320a349-2c6f-426f-a14f-7375016799b9 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-4320a349-2c6f-426f-a14f-7375016799b9');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "oaYTXURLjIvq",
        "outputId": "0e53aaee-0259-48bc-ed54-09424c3373c3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc54678950>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "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": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "FxQn9oEojRgi",
        "outputId": "71386fee-9b0f-4056-882e-b6289c270dbf"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     price      area  bedrooms  bathrooms   stories  mainroad  \\\n",
              "price             1.000000  0.535997  0.366494   0.517545  0.420712  0.296898   \n",
              "area              0.535997  1.000000  0.151858   0.193820  0.083996  0.288874   \n",
              "bedrooms          0.366494  0.151858  1.000000   0.373930  0.408564 -0.012033   \n",
              "bathrooms         0.517545  0.193820  0.373930   1.000000  0.326165  0.042398   \n",
              "stories           0.420712  0.083996  0.408564   0.326165  1.000000  0.121706   \n",
              "mainroad          0.296898  0.288874 -0.012033   0.042398  0.121706  1.000000   \n",
              "guestroom         0.255517  0.140297  0.080549   0.126469  0.043538  0.092337   \n",
              "basement          0.187057  0.047417  0.097312   0.102106 -0.172394  0.044002   \n",
              "hotwaterheating   0.093073 -0.009229  0.046049   0.067159  0.018847 -0.011781   \n",
              "airconditioning   0.452954  0.222393  0.160603   0.186915  0.293602  0.105423   \n",
              "parking           0.384394  0.352980  0.139270   0.177496  0.045547  0.204433   \n",
              "prefarea          0.329777  0.234779  0.079023   0.063472  0.044425  0.199876   \n",
              "furnishingstatus -0.304721 -0.171445 -0.123244  -0.143559 -0.104672 -0.156726   \n",
              "\n",
              "                  guestroom  basement  hotwaterheating  airconditioning  \\\n",
              "price              0.255517  0.187057         0.093073         0.452954   \n",
              "area               0.140297  0.047417        -0.009229         0.222393   \n",
              "bedrooms           0.080549  0.097312         0.046049         0.160603   \n",
              "bathrooms          0.126469  0.102106         0.067159         0.186915   \n",
              "stories            0.043538 -0.172394         0.018847         0.293602   \n",
              "mainroad           0.092337  0.044002        -0.011781         0.105423   \n",
              "guestroom          1.000000  0.372066        -0.010308         0.138179   \n",
              "basement           0.372066  1.000000         0.004385         0.047341   \n",
              "hotwaterheating   -0.010308  0.004385         1.000000        -0.130023   \n",
              "airconditioning    0.138179  0.047341        -0.130023         1.000000   \n",
              "parking            0.037466  0.051497         0.067864         0.159173   \n",
              "prefarea           0.160897  0.228083        -0.059411         0.117382   \n",
              "furnishingstatus  -0.118328 -0.112831        -0.031628        -0.150477   \n",
              "\n",
              "                   parking  prefarea  furnishingstatus  \n",
              "price             0.384394  0.329777         -0.304721  \n",
              "area              0.352980  0.234779         -0.171445  \n",
              "bedrooms          0.139270  0.079023         -0.123244  \n",
              "bathrooms         0.177496  0.063472         -0.143559  \n",
              "stories           0.045547  0.044425         -0.104672  \n",
              "mainroad          0.204433  0.199876         -0.156726  \n",
              "guestroom         0.037466  0.160897         -0.118328  \n",
              "basement          0.051497  0.228083         -0.112831  \n",
              "hotwaterheating   0.067864 -0.059411         -0.031628  \n",
              "airconditioning   0.159173  0.117382         -0.150477  \n",
              "parking           1.000000  0.091627         -0.177539  \n",
              "prefarea          0.091627  1.000000         -0.107686  \n",
              "furnishingstatus -0.177539 -0.107686          1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f9e3465-e339-49b1-8662-f197dd9a4a04\">\n",
              "    <div class=\"colab-df-container\">\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>price</th>\n",
              "      <th>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>stories</th>\n",
              "      <th>mainroad</th>\n",
              "      <th>guestroom</th>\n",
              "      <th>basement</th>\n",
              "      <th>hotwaterheating</th>\n",
              "      <th>airconditioning</th>\n",
              "      <th>parking</th>\n",
              "      <th>prefarea</th>\n",
              "      <th>furnishingstatus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.535997</td>\n",
              "      <td>0.366494</td>\n",
              "      <td>0.517545</td>\n",
              "      <td>0.420712</td>\n",
              "      <td>0.296898</td>\n",
              "      <td>0.255517</td>\n",
              "      <td>0.187057</td>\n",
              "      <td>0.093073</td>\n",
              "      <td>0.452954</td>\n",
              "      <td>0.384394</td>\n",
              "      <td>0.329777</td>\n",
              "      <td>-0.304721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area</th>\n",
              "      <td>0.535997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.151858</td>\n",
              "      <td>0.193820</td>\n",
              "      <td>0.083996</td>\n",
              "      <td>0.288874</td>\n",
              "      <td>0.140297</td>\n",
              "      <td>0.047417</td>\n",
              "      <td>-0.009229</td>\n",
              "      <td>0.222393</td>\n",
              "      <td>0.352980</td>\n",
              "      <td>0.234779</td>\n",
              "      <td>-0.171445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <td>0.366494</td>\n",
              "      <td>0.151858</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.373930</td>\n",
              "      <td>0.408564</td>\n",
              "      <td>-0.012033</td>\n",
              "      <td>0.080549</td>\n",
              "      <td>0.097312</td>\n",
              "      <td>0.046049</td>\n",
              "      <td>0.160603</td>\n",
              "      <td>0.139270</td>\n",
              "      <td>0.079023</td>\n",
              "      <td>-0.123244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bathrooms</th>\n",
              "      <td>0.517545</td>\n",
              "      <td>0.193820</td>\n",
              "      <td>0.373930</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.326165</td>\n",
              "      <td>0.042398</td>\n",
              "      <td>0.126469</td>\n",
              "      <td>0.102106</td>\n",
              "      <td>0.067159</td>\n",
              "      <td>0.186915</td>\n",
              "      <td>0.177496</td>\n",
              "      <td>0.063472</td>\n",
              "      <td>-0.143559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stories</th>\n",
              "      <td>0.420712</td>\n",
              "      <td>0.083996</td>\n",
              "      <td>0.408564</td>\n",
              "      <td>0.326165</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.121706</td>\n",
              "      <td>0.043538</td>\n",
              "      <td>-0.172394</td>\n",
              "      <td>0.018847</td>\n",
              "      <td>0.293602</td>\n",
              "      <td>0.045547</td>\n",
              "      <td>0.044425</td>\n",
              "      <td>-0.104672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mainroad</th>\n",
              "      <td>0.296898</td>\n",
              "      <td>0.288874</td>\n",
              "      <td>-0.012033</td>\n",
              "      <td>0.042398</td>\n",
              "      <td>0.121706</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.092337</td>\n",
              "      <td>0.044002</td>\n",
              "      <td>-0.011781</td>\n",
              "      <td>0.105423</td>\n",
              "      <td>0.204433</td>\n",
              "      <td>0.199876</td>\n",
              "      <td>-0.156726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>guestroom</th>\n",
              "      <td>0.255517</td>\n",
              "      <td>0.140297</td>\n",
              "      <td>0.080549</td>\n",
              "      <td>0.126469</td>\n",
              "      <td>0.043538</td>\n",
              "      <td>0.092337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.372066</td>\n",
              "      <td>-0.010308</td>\n",
              "      <td>0.138179</td>\n",
              "      <td>0.037466</td>\n",
              "      <td>0.160897</td>\n",
              "      <td>-0.118328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basement</th>\n",
              "      <td>0.187057</td>\n",
              "      <td>0.047417</td>\n",
              "      <td>0.097312</td>\n",
              "      <td>0.102106</td>\n",
              "      <td>-0.172394</td>\n",
              "      <td>0.044002</td>\n",
              "      <td>0.372066</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.004385</td>\n",
              "      <td>0.047341</td>\n",
              "      <td>0.051497</td>\n",
              "      <td>0.228083</td>\n",
              "      <td>-0.112831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hotwaterheating</th>\n",
              "      <td>0.093073</td>\n",
              "      <td>-0.009229</td>\n",
              "      <td>0.046049</td>\n",
              "      <td>0.067159</td>\n",
              "      <td>0.018847</td>\n",
              "      <td>-0.011781</td>\n",
              "      <td>-0.010308</td>\n",
              "      <td>0.004385</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.130023</td>\n",
              "      <td>0.067864</td>\n",
              "      <td>-0.059411</td>\n",
              "      <td>-0.031628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>airconditioning</th>\n",
              "      <td>0.452954</td>\n",
              "      <td>0.222393</td>\n",
              "      <td>0.160603</td>\n",
              "      <td>0.186915</td>\n",
              "      <td>0.293602</td>\n",
              "      <td>0.105423</td>\n",
              "      <td>0.138179</td>\n",
              "      <td>0.047341</td>\n",
              "      <td>-0.130023</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.159173</td>\n",
              "      <td>0.117382</td>\n",
              "      <td>-0.150477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parking</th>\n",
              "      <td>0.384394</td>\n",
              "      <td>0.352980</td>\n",
              "      <td>0.139270</td>\n",
              "      <td>0.177496</td>\n",
              "      <td>0.045547</td>\n",
              "      <td>0.204433</td>\n",
              "      <td>0.037466</td>\n",
              "      <td>0.051497</td>\n",
              "      <td>0.067864</td>\n",
              "      <td>0.159173</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.091627</td>\n",
              "      <td>-0.177539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prefarea</th>\n",
              "      <td>0.329777</td>\n",
              "      <td>0.234779</td>\n",
              "      <td>0.079023</td>\n",
              "      <td>0.063472</td>\n",
              "      <td>0.044425</td>\n",
              "      <td>0.199876</td>\n",
              "      <td>0.160897</td>\n",
              "      <td>0.228083</td>\n",
              "      <td>-0.059411</td>\n",
              "      <td>0.117382</td>\n",
              "      <td>0.091627</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.107686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>furnishingstatus</th>\n",
              "      <td>-0.304721</td>\n",
              "      <td>-0.171445</td>\n",
              "      <td>-0.123244</td>\n",
              "      <td>-0.143559</td>\n",
              "      <td>-0.104672</td>\n",
              "      <td>-0.156726</td>\n",
              "      <td>-0.118328</td>\n",
              "      <td>-0.112831</td>\n",
              "      <td>-0.031628</td>\n",
              "      <td>-0.150477</td>\n",
              "      <td>-0.177539</td>\n",
              "      <td>-0.107686</td>\n",
              "      <td>1.000000</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Removing of outlier from all dataset using a Function"
      ],
      "metadata": {
        "id": "fHorI3tpa-hs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def mod_outlier(df):\n",
        "    col_vals = df.columns\n",
        "    df1 = df.copy()\n",
        "    df = df._get_numeric_data()\n",
        "\n",
        "    q1 = df.quantile(0.25)\n",
        "    q3 = df.quantile(0.75)\n",
        "\n",
        "    iqr = q3 - q1\n",
        "\n",
        "    lower_bound = q1 - (1.5 * iqr)\n",
        "    upper_bound = q3 + (1.5 * iqr)\n",
        "    for col in col_vals:\n",
        "        for i in range(0, len(df[col])):\n",
        "            if df[col][i] < lower_bound[col]:\n",
        "                df[col][i] = lower_bound[col]\n",
        "\n",
        "            if df[col][i] > upper_bound[col]:\n",
        "                df[col][i] = upper_bound[col]\n",
        "\n",
        "    for col in col_vals:\n",
        "        df1[col] = df[col]\n",
        "\n",
        "        return(df1)"
      ],
      "metadata": {
        "id": "i52gvsGojftb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mod_outlier(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "F-k8GGP2lXwP",
        "outputId": "6bfda4de-5c31-420c-96cc-da4dd6626777"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
              "0    9205000  7420         4          2        3         1          0   \n",
              "1    9205000  8960         4          4        4         1          0   \n",
              "2    9205000  9960         3          2        2         1          0   \n",
              "3    9205000  7500         4          2        2         1          0   \n",
              "4    9205000  7420         4          1        2         1          1   \n",
              "..       ...   ...       ...        ...      ...       ...        ...   \n",
              "540  1820000  3000         2          1        1         1          0   \n",
              "541  1767150  2400         3          1        1         0          0   \n",
              "542  1750000  3620         2          1        1         1          0   \n",
              "543  1750000  2910         3          1        1         0          0   \n",
              "544  1750000  3850         3          1        2         1          0   \n",
              "\n",
              "     basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
              "0           0                0                1        2         1   \n",
              "1           0                0                1        3         0   \n",
              "2           1                0                0        2         1   \n",
              "3           1                0                1        3         1   \n",
              "4           1                0                1        2         0   \n",
              "..        ...              ...              ...      ...       ...   \n",
              "540         1                0                0        2         0   \n",
              "541         0                0                0        0         0   \n",
              "542         0                0                0        0         0   \n",
              "543         0                0                0        0         0   \n",
              "544         0                0                0        0         0   \n",
              "\n",
              "     furnishingstatus  \n",
              "0                   0  \n",
              "1                   0  \n",
              "2                   1  \n",
              "3                   0  \n",
              "4                   0  \n",
              "..                ...  \n",
              "540                 2  \n",
              "541                 1  \n",
              "542                 2  \n",
              "543                 0  \n",
              "544                 2  \n",
              "\n",
              "[545 rows x 13 columns]"
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>540</th>\n",
              "      <td>1820000</td>\n",
              "      <td>3000</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>1767150</td>\n",
              "      <td>2400</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542</th>\n",
              "      <td>1750000</td>\n",
              "      <td>3620</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>543</th>\n",
              "      <td>1750000</td>\n",
              "      <td>2910</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>544</th>\n",
              "      <td>1750000</td>\n",
              "      <td>3850</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>545 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-025a0675-63fd-4afb-877f-547a87a021ce')\"\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-025a0675-63fd-4afb-877f-547a87a021ce 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-025a0675-63fd-4afb-877f-547a87a021ce');\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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Outliers Removed Successfully"
      ],
      "metadata": {
        "id": "cIeRJkL5bIES"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "GiWmpKq9luaq",
        "outputId": "d360252c-bf6e-4055-d786-3590a304c324"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc5558c2d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": [
        ""
      ],
      "metadata": {
        "id": "4jKCsdh7nuAT"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['bedrooms','bathrooms']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "e1OYrH2hl0Gx",
        "outputId": "66db47f1-be57-400a-dcd6-0d2af92f515f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    price  area  stories  mainroad  guestroom  basement  \\\n",
              "bedrooms bathrooms                                                        \n",
              "1        1              2     2        2         2          2         2   \n",
              "2        1            128   128      128       128        128       128   \n",
              "         2              8     8        8         8          8         8   \n",
              "3        1            224   224      224       224        224       224   \n",
              "         2             72    72       72        72         72        72   \n",
              "         3              4     4        4         4          4         4   \n",
              "4        1             47    47       47        47         47        47   \n",
              "         2             53    53       53        53         53        53   \n",
              "         3              7     7        7         7          7         7   \n",
              "\n",
              "                    hotwaterheating  airconditioning  parking  prefarea  \\\n",
              "bedrooms bathrooms                                                        \n",
              "1        1                        2                2        2         2   \n",
              "2        1                      128              128      128       128   \n",
              "         2                        8                8        8         8   \n",
              "3        1                      224              224      224       224   \n",
              "         2                       72               72       72        72   \n",
              "         3                        4                4        4         4   \n",
              "4        1                       47               47       47        47   \n",
              "         2                       53               53       53        53   \n",
              "         3                        7                7        7         7   \n",
              "\n",
              "                    furnishingstatus  \n",
              "bedrooms bathrooms                    \n",
              "1        1                         2  \n",
              "2        1                       128  \n",
              "         2                         8  \n",
              "3        1                       224  \n",
              "         2                        72  \n",
              "         3                         4  \n",
              "4        1                        47  \n",
              "         2                        53  \n",
              "         3                         7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a258cbc3-eb17-4a51-b341-7d994bb4cbbe\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>area</th>\n",
              "      <th>stories</th>\n",
              "      <th>mainroad</th>\n",
              "      <th>guestroom</th>\n",
              "      <th>basement</th>\n",
              "      <th>hotwaterheating</th>\n",
              "      <th>airconditioning</th>\n",
              "      <th>parking</th>\n",
              "      <th>prefarea</th>\n",
              "      <th>furnishingstatus</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>1</th>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
              "      <th>1</th>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "      <td>224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a258cbc3-eb17-4a51-b341-7d994bb4cbbe')\"\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",
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              "  </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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    }\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-a258cbc3-eb17-4a51-b341-7d994bb4cbbe 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-a258cbc3-eb17-4a51-b341-7d994bb4cbbe');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splliting of data into X and y"
      ],
      "metadata": {
        "id": "QPAkk205bPZi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['price']"
      ],
      "metadata": {
        "id": "CTxE4SjVngiy"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, 1:]"
      ],
      "metadata": {
        "id": "l4mLSdH9t6mx"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IU-ZSO7byX8M",
        "outputId": "34202202-cdb9-4db5-93ee-ec2e88c9cea9"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      9205000\n",
              "1      9205000\n",
              "2      9205000\n",
              "3      9205000\n",
              "4      9205000\n",
              "        ...   \n",
              "540    1820000\n",
              "541    1767150\n",
              "542    1750000\n",
              "543    1750000\n",
              "544    1750000\n",
              "Name: price, Length: 545, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Pf-GOI9-bh2K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using train_test_split"
      ],
      "metadata": {
        "id": "EM7fgozKbkcl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " from sklearn.model_selection import train_test_split\n",
        " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "S5HYWt4JymfR"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using LinearRegression model"
      ],
      "metadata": {
        "id": "jghdi1grbsf4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "DDpHbZQXz_iv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G105K9vkcBBj",
        "outputId": "a57766e9-f9a4-4eea-d6eb-1f3dcbc6c4fe"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fitting/ Training the model"
      ],
      "metadata": {
        "id": "f-uKQqN9c4LD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7nvhMNpc6FZ",
        "outputId": "fff49f08-df02-4cd1-bd13-c3c49d4eb8ad"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# intercept value (cost/lost function)\n",
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9NcgahvVdDgi",
        "outputId": "6a2ec112-8ce5-4277-d735-235daeae66a1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "481479.34129941836"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# coefficient Value\n",
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0qztk-adIDn",
        "outputId": "8c658f0b-b13a-4f0d-832d-2f264ea33b40"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.08193949e+02,  4.37511029e+04,  1.02257793e+06,  5.11584303e+05,\n",
              "        8.14907253e-10,  1.62981451e-09,  5.39361554e+05,  0.00000000e+00,\n",
              "        7.41251849e+05,  2.46038182e+05,  0.00000000e+00, -2.39569003e+05])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ],
      "metadata": {
        "id": "lEnRGh5edMHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gQvg71d4dPs_",
        "outputId": "3736ab34-6794-4f49-ed34-a823798b79e1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "316    4060000\n",
              "77     6650000\n",
              "360    3710000\n",
              "90     6440000\n",
              "493    2800000\n",
              "        ...   \n",
              "15     9100000\n",
              "357    3773000\n",
              "39     7910000\n",
              "54     7350000\n",
              "155    5530000\n",
              "Name: price, Length: 109, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_y = model.predict(X_test)"
      ],
      "metadata": {
        "id": "4XUyKJiZdQXK"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdf2 = pd.DataFrame({'Actual Value':y_test, 'predicted Value':pred_y})"
      ],
      "metadata": {
        "id": "CxG8Iq94dQgh"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdf2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "gDMBTMWhdccq",
        "outputId": "e9abda47-43f4-46b3-99bb-faa6160011b2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Actual Value  predicted Value\n",
              "316       4060000     5.849414e+06\n",
              "77        6650000     6.937154e+06\n",
              "360       3710000     3.108678e+06\n",
              "90        6440000     4.701132e+06\n",
              "493       2800000     3.367343e+06\n",
              "..            ...              ...\n",
              "15        9100000     5.343263e+06\n",
              "357       3773000     5.084053e+06\n",
              "39        7910000     6.833277e+06\n",
              "54        7350000     6.277942e+06\n",
              "155       5530000     6.080894e+06\n",
              "\n",
              "[109 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-67d6981c-044e-45e3-b56d-1df428e3585d\">\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",
              "    .dataframe thead th {\n",
              "        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>Actual Value</th>\n",
              "      <th>predicted Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>4060000</td>\n",
              "      <td>5.849414e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>6650000</td>\n",
              "      <td>6.937154e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>3710000</td>\n",
              "      <td>3.108678e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>6440000</td>\n",
              "      <td>4.701132e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>2800000</td>\n",
              "      <td>3.367343e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>9100000</td>\n",
              "      <td>5.343263e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>357</th>\n",
              "      <td>3773000</td>\n",
              "      <td>5.084053e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>7910000</td>\n",
              "      <td>6.833277e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>7350000</td>\n",
              "      <td>6.277942e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>5530000</td>\n",
              "      <td>6.080894e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>109 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67d6981c-044e-45e3-b56d-1df428e3585d')\"\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-67d6981c-044e-45e3-b56d-1df428e3585d 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-67d6981c-044e-45e3-b56d-1df428e3585d');\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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(newdf2['Actual Value'],newdf2['predicted Value'])\n",
        "plt.xlabel('Actual Value')\n",
        "plt.ylabel('predicted value')\n",
        "plt.title('Actual v/s Predicted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "uAmdwudWdj3F",
        "outputId": "211503d1-af22-4475-e32c-47444a600368"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Actual v/s Predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "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": [
        "# Strength\n",
        "newdf2.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Qaol7E6PdnN-",
        "outputId": "6a32bd03-4035-4ecc-cd72-f6e54d68c412"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Actual Value  predicted Value\n",
              "Actual Value         1.000000         0.806066\n",
              "predicted Value      0.806066         1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e99761ac-b8ff-49dd-8851-415bb56f2db8\">\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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual Value</th>\n",
              "      <th>predicted Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Actual Value</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.806066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>predicted Value</th>\n",
              "      <td>0.806066</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e99761ac-b8ff-49dd-8851-415bb56f2db8')\"\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-e99761ac-b8ff-49dd-8851-415bb56f2db8 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-e99761ac-b8ff-49dd-8851-415bb56f2db8');\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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(newdf2.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "O-byOj-odqn9",
        "outputId": "12f5f0bd-06b4-4cb1-cca2-aab8e01bf18c"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc54276050>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing the model performance\n"
      ],
      "metadata": {
        "id": "Y121_cfydz-p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Eavl9Cu1XZZ",
        "outputId": "a93a0c95-f5e2-4769-e2ba-71b8b82b3cb2"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6373879419505348"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}