Multi Linear Regression.ipynb

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      "name": "Real Estate Price Prediction.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the libraries"
      ],
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        "id": "PXEImJDpfSVC"
      }
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HtCNn9_9erha"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problem Statement\n",
        "\n",
        "* we have been given a dataset that describes the functionality of house. Now, based on the given features, we have to predict the house price."
      ],
      "metadata": {
        "id": "BJ0vVZFOhP37"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Defining the DatFrame"
      ],
      "metadata": {
        "id": "zSS43fU8l-JE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Housing.csv')"
      ],
      "metadata": {
        "id": "90cCtEzCfFP1"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df"
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          "height": 488
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        "id": "PICmchVfmp8F",
        "outputId": "550544be-0bf0-4053-9157-d2e1ede2f0a9"
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      "outputs": [
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              "        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",
              "540   1820000  3000         2          1        1      yes        no      yes   \n",
              "541   1767150  2400         3          1        1       no        no       no   \n",
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              "543   1750000  2910         3          1        1       no        no       no   \n",
              "544   1750000  3850         3          1        2      yes        no       no   \n",
              "\n",
              "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0                no             yes        2      yes        furnished  \n",
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              "2                no              no        2      yes   semi-furnished  \n",
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              "543              no              no        0       no        furnished  \n",
              "544              no              no        0       no      unfurnished  \n",
              "\n",
              "[545 rows x 13 columns]"
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      "source": [
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      "source": [
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        "id": "E2lKrjCBohy7",
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      "outputs": [
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          "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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        "df.tail()"
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      "execution_count": null,
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              "       price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "540  1820000  3000         2          1        1      yes        no      yes   \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    },
    {
      "cell_type": "code",
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        "df.shape[0] # for rows"
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        "id": "85c8E1N1DGgE",
        "outputId": "be743ae2-74d7-4ba0-e6fe-9fac944e70dd"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "545"
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          },
          "metadata": {},
          "execution_count": 9
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        "df.shape[1] # for colums"
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        "outputId": "b43a100c-14ef-4aee-a790-479d31f23b01"
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      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "13"
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          "metadata": {},
          "execution_count": 10
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        "outputId": "82e73aac-91fd-48b4-e773-e24b8b4646c7"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'mainroad',\n",
              "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
              "       'parking', 'prefarea', 'furnishingstatus'],\n",
              "      dtype='object')"
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              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes        no       no   \n",
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              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ac76205-a97b-42ed-8625-74172f564dac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7ac76205-a97b-42ed-8625-74172f564dac');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPja7VXxEHMM",
        "outputId": "adf80841-1628-4d9d-9a66-f55b9c07e935"
      },
      "execution_count": null,
      "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": "code",
      "source": [
        "# checking for unique values\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IAL-k80bFI7w",
        "outputId": "89917a44-4927-4e3e-b961-6ccdcfbf3c55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               219\n",
              "area                284\n",
              "bedrooms              6\n",
              "bathrooms             4\n",
              "stories               4\n",
              "mainroad              2\n",
              "guestroom             2\n",
              "basement              2\n",
              "hotwaterheating       2\n",
              "airconditioning       2\n",
              "parking               4\n",
              "prefarea              2\n",
              "furnishingstatus      3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of null values in each columns\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHUD9rI8FlNJ",
        "outputId": "8d46b7ab-98b9-4176-bc6c-14188138cedb"
      },
      "execution_count": null,
      "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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# no. of null values in each dataset\n",
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6iCbQ34KF9TI",
        "outputId": "41938d60-1e1c-4c34-aa1c-ab09abebc07d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "4B33xwvRGNPy",
        "outputId": "791bc100-67f8-4ce6-c0ba-91db82bb6981"
      },
      "execution_count": null,
      "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  "
            ],
            "text/html": [
              "\n",
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              "  </tbody>\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f0a2d97f-9060-483d-9768-886eb178a9fe button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f0a2d97f-9060-483d-9768-886eb178a9fe');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## price       stregth\n",
        "## 10             2\n",
        "## 100            3\n",
        "## 1000           4\n",
        "## 10000          5 -->\n",
        "\n"
      ],
      "metadata": {
        "id": "-fBMeBvkGQ0F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# unique values in mainroad\n",
        "df.mainroad.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x-7RZpUHpOM",
        "outputId": "e52fd66c-7d8f-4534-e7c1-dd78aa34fb3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['yes', 'no'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.mainroad.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKdDvNqjH7el",
        "outputId": "b1fcb350-ccd1-4ec4-d794-1134a2ddc91d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "yes    468\n",
              "no      77\n",
              "Name: mainroad, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# yes--->1\n",
        "# No---->0\n",
        "newdf = df.copy()"
      ],
      "metadata": {
        "id": "SQyiEGDFIE-8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "8KL3kIRwMAtt",
        "outputId": "d69f94d2-6849-4da4-d09b-d7da731e5253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories mainroad  guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes          0       no   \n",
              "1  12250000  8960         4          4        4      yes          0       no   \n",
              "2  12250000  9960         3          2        2      yes          0      yes   \n",
              "3  12215000  7500         4          2        2      yes          0      yes   \n",
              "4  11410000  7420         4          1        2      yes          1      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",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mainroad\n",
        "df.mainroad.replace(['yes','no'],[1,0], inplace=True)"
      ],
      "metadata": {
        "id": "sZX65qglI5Lj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "4_pfxkdWJgjw",
        "outputId": "1c0ea0c6-5a5c-46de-99c6-96d67f4ef9e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom basement  \\\n",
              "0  13300000  7420         4          2        3         1          0       no   \n",
              "1  12250000  8960         4          4        4         1          0       no   \n",
              "2  12250000  9960         3          2        2         1          0      yes   \n",
              "3  12215000  7500         4          2        2         1          0      yes   \n",
              "4  11410000  7420         4          1        2         1          1      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",
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            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.mainroad.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MoBlOjWqJ80y",
        "outputId": "d00bad45-37b9-40b1-c762-e4b1e2b2e99a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.guestroom.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DEWCJZF0KXSR",
        "outputId": "cccb6240-68c0-4281-b60c-3f81626bd000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.guestroom.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaCiYHJ3Kkt2",
        "outputId": "00115cd2-7206-46f0-caaa-7273a2924846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    448\n",
              "1     97\n",
              "Name: guestroom, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# guestroom\n",
        "df.guestroom.replace(['no','yes'],[0,1], inplace=True)"
      ],
      "metadata": {
        "id": "k2pckg46KCVx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.guestroom.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_neIYmuoKuiD",
        "outputId": "b0ba9b88-0099-413d-c9cc-dbcdaa6a5022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    448\n",
              "1     97\n",
              "Name: guestroom, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.guestroom.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "3550f8c4-80d6-4c94-d008-831307925753"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.guestroom.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "skEo7DpGLRF5",
        "outputId": "525d9f24-e4ff-4eb7-f98a-b9fca4f46ffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    448\n",
              "1     97\n",
              "Name: guestroom, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.basement.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYeu2wqkMfRO",
        "outputId": "6f105417-734a-4fb8-82fe-5577ff67eb48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['no', 'yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.basement.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gj93sHYFMe45",
        "outputId": "8d8ba9d8-4ad2-41b1-c3bd-b488731918f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     354\n",
              "yes    191\n",
              "Name: basement, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Basement\n",
        "df.basement.replace(['no','yes'],[0,1], inplace=True)"
      ],
      "metadata": {
        "id": "ZmCRgQxcLRf3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.basement.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMTcawDqMrj-",
        "outputId": "7596a3f1-e88d-450e-f4ed-2dd695d33e1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    354\n",
              "1    191\n",
              "Name: basement, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hotwaterheating\n",
        "df.hotwaterheating.replace(['no','yes'],[0,1], inplace=True)"
      ],
      "metadata": {
        "id": "mbQic03cMvEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# airconditioning\n",
        "df.airconditioning.replace(['no','yes'],[0,1], inplace=True)"
      ],
      "metadata": {
        "id": "ScUhq6_PNZqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# prefarea\n",
        "df.prefarea.replace(['no','yes'],[0,1], inplace=True)"
      ],
      "metadata": {
        "id": "w6JJq9vHNZo9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Uz6r5e4CNZmO",
        "outputId": "804f0827-7cb5-4ded-8bda-e4610d6e60ce"
      },
      "execution_count": null,
      "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",
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              "4         1                0                1        2         0   \n",
              "\n",
              "  furnishingstatus  \n",
              "0        furnished  \n",
              "1        furnished  \n",
              "2   semi-furnished  \n",
              "3        furnished  \n",
              "4        furnished  "
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b53f0f7-c890-4799-92db-b1aa17a37354')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1b53f0f7-c890-4799-92db-b1aa17a37354 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-1b53f0f7-c890-4799-92db-b1aa17a37354');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.furnishingstatus.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-y34gcvNZjs",
        "outputId": "b6a46d95-a5a9-443f-b9a4-163eda1ba09d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['furnished', 'semi-furnished', 'unfurnished'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.furnishingstatus.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P1PREEPXNZf9",
        "outputId": "504d72c2-60a3-4fa8-c9ec-a51b2a47a85b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "semi-furnished    227\n",
              "unfurnished       178\n",
              "furnished         140\n",
              "Name: furnishingstatus, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# furnishingstatus\n",
        "df.furnishingstatus.replace(['unfurnished','furnished','semi-furnished'],[0,1,2], inplace=True)"
      ],
      "metadata": {
        "id": "9kwGMkJLNZd5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "kd7EmJZONZcF",
        "outputId": "d9d1063a-c1aa-4363-cce6-da7e1faa5b29"
      },
      "execution_count": null,
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        {
          "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",
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              "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                 1  \n",
              "1                 1  \n",
              "2                 2  \n",
              "3                 1  \n",
              "4                 1  "
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              "      <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>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91897687-8a90-4854-ab86-247544569626')\"\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-91897687-8a90-4854-ab86-247544569626 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-91897687-8a90-4854-ab86-247544569626');\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": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Observation\n",
        "  1. Data Strength - 545\n",
        "  2. number of random variable - 13\n",
        "  3. None of the column contain vull value\n",
        "  4. columns - price\tarea\tbedrooms\tstories,parking\t  has numerical values\n",
        "\n",
        "  5. columns - mainroad\tguestroom\tbasement\thotwaterheating\tairconditioning\t\tprefarea\tfurnishingstatus has object values"
      ],
      "metadata": {
        "id": "kuDo6uuGPIr0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Manipulation\n",
        "  1. Changed the Categorical values for the columns(mainroad\tguestroom\tbasement\thotwaterheating\tairconditioning\tprefarea\tfurnishingstatus)"
      ],
      "metadata": {
        "id": "eyIIYm_iRmZs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZX5g9lkNZU3",
        "outputId": "b933bfca-854f-49f1-a4a5-113eed12daf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               int64\n",
              "area                int64\n",
              "bedrooms            int64\n",
              "bathrooms           int64\n",
              "stories             int64\n",
              "mainroad            int64\n",
              "guestroom           int64\n",
              "basement            int64\n",
              "hotwaterheating     int64\n",
              "airconditioning     int64\n",
              "parking             int64\n",
              "prefarea            int64\n",
              "furnishingstatus    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# statistics\n",
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "HrqAXdInNZD5",
        "outputId": "b59ccace-c282-4497-e372-5e2fb9e130b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price          area    bedrooms   bathrooms     stories  \\\n",
              "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
              "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
              "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
              "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
              "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
              "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
              "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
              "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
              "\n",
              "         mainroad   guestroom    basement  hotwaterheating  airconditioning  \\\n",
              "count  545.000000  545.000000  545.000000       545.000000       545.000000   \n",
              "mean     0.858716    0.177982    0.350459         0.045872         0.315596   \n",
              "std      0.348635    0.382849    0.477552         0.209399         0.465180   \n",
              "min      0.000000    0.000000    0.000000         0.000000         0.000000   \n",
              "25%      1.000000    0.000000    0.000000         0.000000         0.000000   \n",
              "50%      1.000000    0.000000    0.000000         0.000000         0.000000   \n",
              "75%      1.000000    0.000000    1.000000         0.000000         1.000000   \n",
              "max      1.000000    1.000000    1.000000         1.000000         1.000000   \n",
              "\n",
              "          parking    prefarea  furnishingstatus  \n",
              "count  545.000000  545.000000        545.000000  \n",
              "mean     0.693578    0.234862          1.089908  \n",
              "std      0.861586    0.424302          0.858130  \n",
              "min      0.000000    0.000000          0.000000  \n",
              "25%      0.000000    0.000000          0.000000  \n",
              "50%      0.000000    0.000000          1.000000  \n",
              "75%      1.000000    0.000000          2.000000  \n",
              "max      3.000000    1.000000          2.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ff9dfdf-0b6d-4681-8331-ed2118140441\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\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>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>count</th>\n",
              "      <td>5.450000e+02</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "      <td>545.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.766729e+06</td>\n",
              "      <td>5150.541284</td>\n",
              "      <td>2.965138</td>\n",
              "      <td>1.286239</td>\n",
              "      <td>1.805505</td>\n",
              "      <td>0.858716</td>\n",
              "      <td>0.177982</td>\n",
              "      <td>0.350459</td>\n",
              "      <td>0.045872</td>\n",
              "      <td>0.315596</td>\n",
              "      <td>0.693578</td>\n",
              "      <td>0.234862</td>\n",
              "      <td>1.089908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.870440e+06</td>\n",
              "      <td>2170.141023</td>\n",
              "      <td>0.738064</td>\n",
              "      <td>0.502470</td>\n",
              "      <td>0.867492</td>\n",
              "      <td>0.348635</td>\n",
              "      <td>0.382849</td>\n",
              "      <td>0.477552</td>\n",
              "      <td>0.209399</td>\n",
              "      <td>0.465180</td>\n",
              "      <td>0.861586</td>\n",
              "      <td>0.424302</td>\n",
              "      <td>0.858130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.750000e+06</td>\n",
              "      <td>1650.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.430000e+06</td>\n",
              "      <td>3600.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.340000e+06</td>\n",
              "      <td>4600.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.740000e+06</td>\n",
              "      <td>6360.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.330000e+07</td>\n",
              "      <td>16200.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ff9dfdf-0b6d-4681-8331-ed2118140441')\"\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",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "    }\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-9ff9dfdf-0b6d-4681-8331-ed2118140441 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-9ff9dfdf-0b6d-4681-8331-ed2118140441');\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": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "QeNSosWTSYtm",
        "outputId": "fa3640f0-746d-41c5-d3eb-182d1997d802"
      },
      "execution_count": null,
      "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",
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              "\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",
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              "\n",
              "                   parking  prefarea  furnishingstatus  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(df.corr(), annot=True , cmap='Greens')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "l41_n3LgSuLx",
        "outputId": "44cf084d-2d61-4187-d214-acd069d9bc0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabae9be890>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df.area, df.price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "IkosEzutTCfH",
        "outputId": "7643cf72-4f3d-4f1f-c340-0d40516c68b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fabae5f3350>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "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": [
        "# lets say \n",
        "# 16 ft --> Rs.100\n",
        "#  16.5 ---> Rs.10000\n",
        "## exactly no , so these are outliers at area 16000"
      ],
      "metadata": {
        "id": "KWeRJuY5YOCg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "mEkPmPOwUclQ",
        "outputId": "2985ca06-af2f-4df0-ce35-ce95430232c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fabaeacb350>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2340x2340 with 182 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(50,50))\n",
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "_92RIQplVOKR",
        "outputId": "7ed96684-550c-4e80-f672-e85f2a19d67e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabaa4eb0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x3600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['area']].boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "44MilIvbYwB1",
        "outputId": "6b5b919e-9e4b-449c-daa5-3d8cf2031bf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabaa0cfa10>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "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": [
        "  #  if you remove the outliers then we use z test\n",
        "  # iteration of i means all data points\n",
        "#   z = (data(i) - mean(area))/standard deviation(area)"
      ],
      "metadata": {
        "id": "KBoOIDwZZOir"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['bedrooms']].boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "hOASVf31duMW",
        "outputId": "7d9c752b-97e0-4448-e7ea-058ace2f8b10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fabaa0d6090>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "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": [
        "# how many values are there where bedrooms is more than 4\n",
        "len(df.bedrooms.loc[df.bedrooms >4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBqfMHciduKY",
        "outputId": "7f6cba9f-f494-4112-cf60-3c1ae4283fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['mainroad']].boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "73WlZt9eduID",
        "outputId": "b98ea24b-d8e1-424b-a656-649eafe306cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faba9f6fc50>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "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": [
        "len(df.mainroad.loc[df.mainroad == 0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3MpylIKduFu",
        "outputId": "87c275b4-4984-4cb3-d70b-b7f2089e9eb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "77"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df.price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "hOa6FrD7duDa",
        "outputId": "04e04aeb-ad89-4bfe-d777-038a0b657edd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faba9ef3cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "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": [
        "# show the strght of 2 variables\n",
        "df.basement.value_counts().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "aXQOPMSEdt-6",
        "outputId": "df0c6dc9-844b-4872-e495-35792f9c84ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faba9fc7050>"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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kdej/ASYjd6uUTrWAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show all the prices of a houses where bedrooms are 4 and bathroom are 2\n",
        "\n",
        "df.loc[df.bedrooms == 4, ['price', 'bedrooms', 'bathrooms']][df.bathrooms==2]\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aSMmMsmjdt4w",
        "outputId": "58635b95-a254-4750-a52a-64b972dea56b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        price  bedrooms  bathrooms\n",
              "0    13300000         4          2\n",
              "3    12215000         4          2\n",
              "12    9310000         4          2\n",
              "13    9240000         4          2\n",
              "16    9100000         4          2\n",
              "25    8540000         4          2\n",
              "29    8400000         4          2\n",
              "32    8295000         4          2\n",
              "37    7980000         4          2\n",
              "39    7910000         4          2\n",
              "43    7700000         4          2\n",
              "44    7560000         4          2\n",
              "52    7350000         4          2\n",
              "57    7245000         4          2\n",
              "58    7210000         4          2\n",
              "62    7070000         4          2\n",
              "63    7035000         4          2\n",
              "69    6790000         4          2\n",
              "71    6755000         4          2\n",
              "75    6650000         4          2\n",
              "94    6300000         4          2\n",
              "106   6160000         4          2\n",
              "109   6090000         4          2\n",
              "122   5950000         4          2\n",
              "123   5950000         4          2\n",
              "136   5740000         4          2\n",
              "141   5652500         4          2\n",
              "142   5600000         4          2\n",
              "149   5600000         4          2\n",
              "157   5495000         4          2\n",
              "162   5460000         4          2\n",
              "169   5250000         4          2\n",
              "173   5250000         4          2\n",
              "175   5250000         4          2\n",
              "180   5215000         4          2\n",
              "212   4893000         4          2\n",
              "213   4893000         4          2\n",
              "221   4767000         4          2\n",
              "229   4690000         4          2\n",
              "238   4613000         4          2\n",
              "249   4543000         4          2\n",
              "292   4200000         4          2\n",
              "294   4200000         4          2\n",
              "316   4060000         4          2\n",
              "337   3920000         4          2\n",
              "339   3885000         4          2\n",
              "383   3570000         4          2\n",
              "523   2380000         4          2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56f1f136-d815-43ab-be08-50827fb4a2d6\">\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>price</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13300000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12215000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>9310000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>9240000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>9100000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>8540000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>8400000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8295000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>7980000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>7910000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>7700000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>7560000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>7350000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>7245000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>7210000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>7070000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>7035000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>6790000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>6755000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>6650000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>6300000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>6160000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>6090000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>5950000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>5950000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>5740000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>5652500</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>5600000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5600000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>5495000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>5460000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>5250000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>5250000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>5250000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180</th>\n",
              "      <td>5215000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>4893000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>4893000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>4767000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>4690000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>4613000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>4543000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>4200000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>4200000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>316</th>\n",
              "      <td>4060000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>3920000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>3885000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>3570000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>523</th>\n",
              "      <td>2380000</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56f1f136-d815-43ab-be08-50827fb4a2d6')\"\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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              "      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-56f1f136-d815-43ab-be08-50827fb4a2d6 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-56f1f136-d815-43ab-be08-50827fb4a2d6');\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": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Spliting the dataset"
      ],
      "metadata": {
        "id": "VKn_dm5zoMRE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "JCd6ePKorbVI",
        "outputId": "2e02f23d-aa0a-4600-c37b-43726c4ec78f"
      },
      "execution_count": null,
      "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",
              "\n",
              "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
              "0         0                0                1        2         1   \n",
              "1         0                0                1        3         0   \n",
              "\n",
              "   furnishingstatus  \n",
              "0                 1  \n",
              "1                 1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a88eb03f-de6f-4179-a6e6-7e024f094f95\">\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>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>0</th>\n",
              "      <td>13300000</td>\n",
              "      <td>7420</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12250000</td>\n",
              "      <td>8960</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a88eb03f-de6f-4179-a6e6-7e024f094f95')\"\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-a88eb03f-de6f-4179-a6e6-7e024f094f95 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-a88eb03f-de6f-4179-a6e6-7e024f094f95');\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": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,1:]\n",
        "y = df.price"
      ],
      "metadata": {
        "id": "Xb7HtVN9riXQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.price.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofixdinLtT51",
        "outputId": "d7f50674-ebb7-4113-9e01-d3e258f23a29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:,1:].ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "caGUoXgatXrp",
        "outputId": "15fb809e-59d3-4480-ed3f-188c0c9d19ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test , y_train , y_test =train_test_split(X,y,test_size=0.2 , random_state=42)"
      ],
      "metadata": {
        "id": "7HQ2uXfBdtqt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NcagEBzFskmy",
        "outputId": "4206cf3d-4177-4ee2-a1e6-6a9b94d030ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "436"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nz47LELZwE9g",
        "outputId": "6ed09a26-4a61-4cfc-bc87-d85fe076185f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBd_FPbnwE5g",
        "outputId": "058f0ca3-1690-4f00-96d6-41106758bb70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "436"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQ7eHv8pwE3h",
        "outputId": "5c094ab1-0b1e-418e-b35c-bbeb4fcd2240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "109"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Choosing the model"
      ],
      "metadata": {
        "id": "8uxZvpGKyWtC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "G0u3C0qgwE1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fitting/ Training the model"
      ],
      "metadata": {
        "id": "gU6Gw5j-yf16"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "832P1F_FwEz0",
        "outputId": "d1ac8797-5c34-448d-a064-d3cabe28ebbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# intercept value (cost/lost function)\n",
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tLWGNljwEyA",
        "outputId": "8d6619d1-abad-4908-ef06-5807cf77c670"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-153020.39767693914"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# coefficient Value\n",
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3JPL4FvwEv9",
        "outputId": "092067a7-74dc-4916-a609-0dcf4f9606a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.37359519e+02, 7.42382082e+04, 1.09878563e+06, 4.22818367e+05,\n",
              "       3.99602240e+05, 2.36368961e+05, 4.13764911e+05, 6.96228583e+05,\n",
              "       8.14587673e+05, 2.37846833e+05, 6.34405791e+05, 1.26914419e+05])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y = c + m1x1 + m2x2+ ......+mnxn # for multvalues"
      ],
      "metadata": {
        "id": "U9FfeouwzLz3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prediction"
      ],
      "metadata": {
        "id": "WxI_jBc3z_rR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lGUCDWCzlZp",
        "outputId": "8f89e6c5-4dc6-4074-97c9-678a4398460e"
      },
      "execution_count": null,
      "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": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_y = model.predict(X_test)"
      ],
      "metadata": {
        "id": "3lfuoz0RzlVz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred_y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQck-IBZzlT1",
        "outputId": "cd9a2ffb-a6a5-4f25-a74c-a1d156c3f440"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5239173.32762718, 7054067.57583382, 3129423.54574214,\n",
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              "       5963835.34944124, 2782787.13656036, 4838071.49471231,\n",
              "       5330250.73959906, 7654298.4678318 , 3300237.03688317,\n",
              "       5001631.29984533, 8012230.93813926, 3178473.5945452 ,\n",
              "       6578529.7401813 , 3392127.35949438, 6537350.98622346,\n",
              "       4237680.8829526 , 3621348.73870716, 5910621.58319737,\n",
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              "       4375178.05890928, 4614551.92546614, 3592864.9782568 ,\n",
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              "       4365957.52699338, 6850714.81147478, 4080980.8739274 ,\n",
              "       3827406.38024767, 3518199.38571399, 7422684.93118873,\n",
              "       2866587.64174594, 4125706.96659304, 4564415.84372406,\n",
              "       3765710.59536327, 2408928.83898152, 7646167.67893304,\n",
              "       2983381.67262102, 4272754.77045075, 2818628.42394886,\n",
              "       4913538.56198921, 3619664.78598349, 5227214.56351311,\n",
              "       4322318.04956622, 4796065.30613815, 4731941.70491163,\n",
              "       7025560.92519855, 3539562.29496617, 6040954.22944843,\n",
              "       6045585.13802059, 4837547.54372261, 5234378.3635673 ,\n",
              "       4275243.4636444 , 7809480.66172141, 3606169.70626103,\n",
              "       5547951.63205956, 4004856.38455291, 4762370.63632124,\n",
              "       4594406.49844883, 4298297.13435303, 7570769.11751097,\n",
              "       4111668.16816102, 6282311.18726455, 5467895.88816271,\n",
              "       2817230.00923063, 6913933.51659649, 2655507.11297418,\n",
              "       3724284.83800929, 7822370.20778785, 8185570.35869071,\n",
              "       3282356.63533296, 5731127.38417955, 3337186.04911307,\n",
              "       3891929.05700563, 7398919.25320925, 4544058.30745272,\n",
              "       5207122.84479776, 6549414.07044666, 5014267.17301434,\n",
              "       6032496.6582131 , 3912286.17209255, 6257922.95834494,\n",
              "       3795323.80302542, 6062170.23916657, 5055401.56391108,\n",
              "       4098017.51443397, 7162799.85272961, 6479293.87062649,\n",
              "       6188357.93809371])"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "newdf2 = pd.DataFrame({'Actual Value':y_test, 'predicted Value':pred_y})"
      ],
      "metadata": {
        "id": "E8vW6pPizlRz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdf2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "mewL8ugEzlPb",
        "outputId": "40097792-c3b7-4d80-db3c-421aaba3c96a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Actual Value  predicted Value\n",
              "316       4060000     5.239173e+06\n",
              "77        6650000     7.054068e+06\n",
              "360       3710000     3.129424e+06\n",
              "90        6440000     4.668933e+06\n",
              "493       2800000     3.057759e+06\n",
              "..            ...              ...\n",
              "15        9100000     5.055402e+06\n",
              "357       3773000     4.098018e+06\n",
              "39        7910000     7.162800e+06\n",
              "54        7350000     6.479294e+06\n",
              "155       5530000     6.188358e+06\n",
              "\n",
              "[109 rows x 2 columns]"
            ],
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              "\n",
              "  <div id=\"df-f6e7968d-e31e-4837-b58e-e56084f71f58\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <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",
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              "      <th>316</th>\n",
              "      <td>4060000</td>\n",
              "      <td>5.239173e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>6650000</td>\n",
              "      <td>7.054068e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>3710000</td>\n",
              "      <td>3.129424e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>6440000</td>\n",
              "      <td>4.668933e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>2800000</td>\n",
              "      <td>3.057759e+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>5.055402e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>357</th>\n",
              "      <td>3773000</td>\n",
              "      <td>4.098018e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>7910000</td>\n",
              "      <td>7.162800e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>7350000</td>\n",
              "      <td>6.479294e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>5530000</td>\n",
              "      <td>6.188358e+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-f6e7968d-e31e-4837-b58e-e56084f71f58')\"\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",
              "      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",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6e7968d-e31e-4837-b58e-e56084f71f58');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "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": "vg6PRB2QzlNk",
        "outputId": "4524f400-d10b-4171-ce8b-076399d83d67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Actual v/s Predicted')"
            ]
          },
          "metadata": {},
          "execution_count": 122
        },
        {
          "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": "rbCVI1FfzlLD",
        "outputId": "4de4b5c9-c470-4fff-f741-fdc1ade6eafe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Actual Value  predicted Value\n",
              "Actual Value         1.000000         0.822513\n",
              "predicted Value      0.822513         1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-04f27351-626d-4d21-9df0-f5b887f34901\">\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>0.822513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>predicted Value</th>\n",
              "      <td>0.822513</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04f27351-626d-4d21-9df0-f5b887f34901')\"\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-04f27351-626d-4d21-9df0-f5b887f34901 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-04f27351-626d-4d21-9df0-f5b887f34901');\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": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(newdf2.corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "9d9luoPRzlIx",
        "outputId": "6a120826-fbb3-4c14-91d1-308f8d53cee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faba76e4ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 124
        },
        {
          "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": "code",
      "source": [
        "# Try to do that in simple linear regression\n",
        "plt.scatter(X_test.area , y_test)\n",
        "plt.plot(X_test.area , pred_y, color='red')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "mhDeVZk-2Vos",
        "outputId": "492eadbd-854f-499b-8eb1-f2d13bf644d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7faba750e0d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "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": [
        "# model.predict([[__, __, __, ___]])\n",
        "# you can predict values by putting these values of features \n",
        "# and you will find the price of that house"
      ],
      "metadata": {
        "id": "A7j470ck2yFu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing the model performance\n"
      ],
      "metadata": {
        "id": "ad1eivgP5Qft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r2_score(y_test, pred_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VA2h8Pp3qKk",
        "outputId": "c1ee47d4-7c08-4c4d-b2ae-3791c4fa2274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6542221839581839"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    }
  ]
}