Houseprice_Prediction_System.ipynb

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      "metadata": {
        "id": "3d8787b4-3c4d-45bd-92ab-3b55a0dfb965"
      },
      "source": [
        "## MAKING THE ML PROJECT (PREDICTION SYSTEM)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "d1fb8801-fd16-4cb0-a523-1cc269415ff0",
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        "id": "d1fb8801-fd16-4cb0-a523-1cc269415ff0"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dedd930d-98dc-4e12-b512-696ec4cb651e",
      "metadata": {
        "id": "dedd930d-98dc-4e12-b512-696ec4cb651e"
      },
      "source": [
        "## DATA COLLECTION FROM DATASET"
      ]
    },
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              "   Unnamed: 0    Price  Area  Location  No. of Bedrooms  New/Resale  \\\n",
              "0           0  4850000   720  Kharghar                1           0   \n",
              "1           1  4500000   600  Kharghar                1           0   \n",
              "2           2  6700000   650  Kharghar                1           0   \n",
              "3           3  4500000   650  Kharghar                1           0   \n",
              "4           4  5000000   665  Kharghar                1           0   \n",
              "\n",
              "   Gymnasium  Lift Available  Car Parking  Maintenance Staff  24x7 Security  \\\n",
              "0          0               1            1                  1              1   \n",
              "1          1               1            1                  1              1   \n",
              "2          1               1            1                  1              1   \n",
              "3          0               1            1                  1              1   \n",
              "4          0               1            1                  1              1   \n",
              "\n",
              "   Children's Play Area  Clubhouse  Intercom  Landscaped Gardens  \\\n",
              "0                     0          0         0                   0   \n",
              "1                     0          1         0                   0   \n",
              "2                     1          1         1                   0   \n",
              "3                     0          0         1                   1   \n",
              "4                     0          0         1                   1   \n",
              "\n",
              "   Indoor Games  Gas Connection  Jogging Track  Swimming Pool  \n",
              "0             0               0              0              0  \n",
              "1             0               0              1              1  \n",
              "2             0               0              1              1  \n",
              "3             0               0              0              0  \n",
              "4             0               0              0              0  "
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Price</th>\n",
              "      <th>Area</th>\n",
              "      <th>Location</th>\n",
              "      <th>No. of Bedrooms</th>\n",
              "      <th>New/Resale</th>\n",
              "      <th>Gymnasium</th>\n",
              "      <th>Lift Available</th>\n",
              "      <th>Car Parking</th>\n",
              "      <th>Maintenance Staff</th>\n",
              "      <th>24x7 Security</th>\n",
              "      <th>Children's Play Area</th>\n",
              "      <th>Clubhouse</th>\n",
              "      <th>Intercom</th>\n",
              "      <th>Landscaped Gardens</th>\n",
              "      <th>Indoor Games</th>\n",
              "      <th>Gas Connection</th>\n",
              "      <th>Jogging Track</th>\n",
              "      <th>Swimming Pool</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>4850000</td>\n",
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              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>6700000</td>\n",
              "      <td>650</td>\n",
              "      <td>Kharghar</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4500000</td>\n",
              "      <td>650</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>5000000</td>\n",
              "      <td>665</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "data = pd.read_csv(\"/content/Mumbai1.csv\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "992b07a7-8378-4a0a-bdf0-55cfcd7d62f1",
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        "outputId": "f5960bb9-27ef-420b-fd2c-bbce79eb3a2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6347, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "26dbfc5d-cac9-4e25-a5bd-273476e8271a",
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        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6347 entries, 0 to 6346\n",
            "Data columns (total 19 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   Unnamed: 0            6347 non-null   int64 \n",
            " 1   Price                 6347 non-null   int64 \n",
            " 2   Area                  6347 non-null   int64 \n",
            " 3   Location              6347 non-null   object\n",
            " 4   No. of Bedrooms       6347 non-null   int64 \n",
            " 5   New/Resale            6347 non-null   int64 \n",
            " 6   Gymnasium             6347 non-null   int64 \n",
            " 7   Lift Available        6347 non-null   int64 \n",
            " 8   Car Parking           6347 non-null   int64 \n",
            " 9   Maintenance Staff     6347 non-null   int64 \n",
            " 10  24x7 Security         6347 non-null   int64 \n",
            " 11  Children's Play Area  6347 non-null   int64 \n",
            " 12  Clubhouse             6347 non-null   int64 \n",
            " 13  Intercom              6347 non-null   int64 \n",
            " 14  Landscaped Gardens    6347 non-null   int64 \n",
            " 15  Indoor Games          6347 non-null   int64 \n",
            " 16  Gas Connection        6347 non-null   int64 \n",
            " 17  Jogging Track         6347 non-null   int64 \n",
            " 18  Swimming Pool         6347 non-null   int64 \n",
            "dtypes: int64(18), object(1)\n",
            "memory usage: 942.3+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for column in data.columns:\n",
        "    print(data[column].value_counts())\n",
        "    print(\"*\"*20)"
      ],
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        "colab": {
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        },
        "id": "mWyTZirvJtFS",
        "outputId": "fe5ffab0-746d-4dd5-9a6a-ec0e24519f21"
      },
      "id": "mWyTZirvJtFS",
      "execution_count": 71,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "0       1\n",
            "4239    1\n",
            "4237    1\n",
            "4236    1\n",
            "4235    1\n",
            "       ..\n",
            "2113    1\n",
            "2112    1\n",
            "2111    1\n",
            "2110    1\n",
            "6346    1\n",
            "Name: Unnamed: 0, Length: 6347, dtype: int64\n",
            "********************\n",
            "8500000     116\n",
            "9000000      93\n",
            "7500000      92\n",
            "4500000      88\n",
            "9500000      88\n",
            "           ... \n",
            "52600000      1\n",
            "5686000       1\n",
            "7881000       1\n",
            "2770000       1\n",
            "2465000       1\n",
            "Name: Price, Length: 979, dtype: int64\n",
            "********************\n",
            "650     263\n",
            "1050    146\n",
            "600     141\n",
            "1100    138\n",
            "1000     98\n",
            "       ... \n",
            "1487      1\n",
            "1558      1\n",
            "793       1\n",
            "596       1\n",
            "435       1\n",
            "Name: Area, Length: 1140, dtype: int64\n",
            "********************\n",
            "Kharghar             533\n",
            "Thane West           418\n",
            "Mira Road East       390\n",
            "Ulwe                 319\n",
            "Borivali West        176\n",
            "                    ... \n",
            "worli sea face         1\n",
            "Sea Face               1\n",
            "Shiv Sagar Estate      1\n",
            "Natakwala Lane         1\n",
            "Padle Gaon             1\n",
            "Name: Location, Length: 413, dtype: int64\n",
            "********************\n",
            "2    2601\n",
            "1    2307\n",
            "3    1201\n",
            "4     190\n",
            "5      39\n",
            "6       7\n",
            "7       2\n",
            "Name: No. of Bedrooms, dtype: int64\n",
            "********************\n",
            "0    4178\n",
            "1    2169\n",
            "Name: New/Resale, dtype: int64\n",
            "********************\n",
            "1    3690\n",
            "0    2657\n",
            "Name: Gymnasium, dtype: int64\n",
            "********************\n",
            "1    5087\n",
            "0    1260\n",
            "Name: Lift Available, dtype: int64\n",
            "********************\n",
            "1    3573\n",
            "0    2774\n",
            "Name: Car Parking, dtype: int64\n",
            "********************\n",
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            "********************\n",
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            "********************\n",
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            "Name: Children's Play Area, dtype: int64\n",
            "********************\n",
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            "********************\n",
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            "********************\n",
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            "********************\n",
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            "********************\n",
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            "Name: Gas Connection, dtype: int64\n",
            "********************\n",
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            "Name: Jogging Track, dtype: int64\n",
            "********************\n",
            "0    3438\n",
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            "Name: Swimming Pool, dtype: int64\n",
            "********************\n"
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      "id": "my2IyIVuKNq7",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              0\n",
              "Price                   0\n",
              "Area                    0\n",
              "Location                0\n",
              "No. of Bedrooms         0\n",
              "New/Resale              0\n",
              "Gymnasium               0\n",
              "Lift Available          0\n",
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              "Landscaped Gardens      0\n",
              "Indoor Games            0\n",
              "Gas Connection          0\n",
              "Jogging Track           0\n",
              "Swimming Pool           0\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 72
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              "   Unnamed: 0    Price  Area  Location  No. of Bedrooms  Gymnasium  \\\n",
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        "data.drop(columns=['Lift Available','Car Parking','Maintenance Staff','24x7 Security','Clubhouse','Intercom','Landscaped Gardens','Jogging Track','Swimming Pool','Indoor Games','New/Resale','Gas Connection'], inplace=True)\n",
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              "        Unnamed: 0         Price         Area  No. of Bedrooms    Gymnasium  \\\n",
              "count  6347.000000  6.347000e+03  6347.000000      6347.000000  6347.000000   \n",
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              "              style=\"display:none;\">\n",
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              "    </g>\n",
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              "      </button>\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-0bf0a1b7-0353-463c-901e-cdf6523db5e6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
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              "        displayQuickchartButton(document);\n",
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              "      border: none;\n",
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              "      width: 32px;\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4578c342-3348-46c1-a75b-2cfc1d6a485c 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-4578c342-3348-46c1-a75b-2cfc1d6a485c');\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": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RW4c1hW4NnLj",
        "outputId": "bf937eb7-4851-473e-d785-f7ac2e31348f"
      },
      "id": "RW4c1hW4NnLj",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6347 entries, 0 to 6346\n",
            "Data columns (total 7 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   Unnamed: 0            6347 non-null   int64 \n",
            " 1   Price                 6347 non-null   int64 \n",
            " 2   Area                  6347 non-null   int64 \n",
            " 3   Location              6347 non-null   object\n",
            " 4   No. of Bedrooms       6347 non-null   int64 \n",
            " 5   Gymnasium             6347 non-null   int64 \n",
            " 6   Children's Play Area  6347 non-null   int64 \n",
            "dtypes: int64(6), object(1)\n",
            "memory usage: 347.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "ca6c7e71-6e58-4182-9d5c-57da71c6068d",
      "metadata": {
        "id": "ca6c7e71-6e58-4182-9d5c-57da71c6068d"
      },
      "outputs": [],
      "source": [
        "data.drop(columns=data.columns[0],\n",
        "        axis=1,\n",
        "        inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "6f65d01c-e612-4862-9785-c5b19ce5cd12",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "outputId": "ea3d1633-7bc9-4afc-ae36-c607c5508738"
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          "data": {
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              "     Price  Area  Location  No. of Bedrooms  Gymnasium  Children's Play Area\n",
              "0  4850000   720  Kharghar                1          0                     0\n",
              "1  4500000   600  Kharghar                1          1                     0\n",
              "2  6700000   650  Kharghar                1          1                     1\n",
              "3  4500000   650  Kharghar                1          0                     0\n",
              "4  5000000   665  Kharghar                1          0                     0"
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f9ce3f5d-f8a7-4680-8c48-999ceef133e3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-f9ce3f5d-f8a7-4680-8c48-999ceef133e3');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
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          "metadata": {},
          "execution_count": 77
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      "execution_count": 78,
      "id": "5ef2d25f-a39c-4807-b63a-2d854b60bcdd",
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        "outputId": "666475ba-0565-465a-e834-182ea959c886"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 3, 2, 5, 6, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ],
      "source": [
        "data['No. of Bedrooms'].unique()"
      ]
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    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "f0a63e66-603c-4f66-8682-75e6169704c3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "f0a63e66-603c-4f66-8682-75e6169704c3",
        "outputId": "d36ad573-223c-4f4a-fb27-974101986640"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 720,  600,  650, ...,  250, 1162,  435])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "data['Area'].unique()"
      ]
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    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "5280a300-2fb7-492b-adb1-f39a2adbbd1e",
      "metadata": {
        "id": "5280a300-2fb7-492b-adb1-f39a2adbbd1e"
      },
      "outputs": [],
      "source": [
        "data['Price_per_sqft'] = data['Price']*100000/data['Area']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "34c2cfc9-6291-496b-b2b2-d02608c2892b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "34c2cfc9-6291-496b-b2b2-d02608c2892b",
        "outputId": "5fbb670c-33a1-446e-c8f7-6df1afa2bf72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Price  Area  Location  No. of Bedrooms  Gymnasium  Children's Play Area  \\\n",
              "0  4850000   720  Kharghar                1          0                     0   \n",
              "1  4500000   600  Kharghar                1          1                     0   \n",
              "2  6700000   650  Kharghar                1          1                     1   \n",
              "3  4500000   650  Kharghar                1          0                     0   \n",
              "4  5000000   665  Kharghar                1          0                     0   \n",
              "\n",
              "   Price_per_sqft  \n",
              "0    6.736111e+08  \n",
              "1    7.500000e+08  \n",
              "2    1.030769e+09  \n",
              "3    6.923077e+08  \n",
              "4    7.518797e+08  "
            ],
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              "\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>Location</th>\n",
              "      <th>No. of Bedrooms</th>\n",
              "      <th>Gymnasium</th>\n",
              "      <th>Children's Play Area</th>\n",
              "      <th>Price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4850000</td>\n",
              "      <td>720</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.736111e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000</td>\n",
              "      <td>600</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.500000e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6700000</td>\n",
              "      <td>650</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.030769e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4500000</td>\n",
              "      <td>650</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.923077e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5000000</td>\n",
              "      <td>665</td>\n",
              "      <td>Kharghar</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.518797e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4c08b38-deb1-4217-acbc-38f931b354ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-f833f60e-d272-4fd8-8ebc-4f042cf206da\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f833f60e-d272-4fd8-8ebc-4f042cf206da')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f833f60e-d272-4fd8-8ebc-4f042cf206da button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d4c08b38-deb1-4217-acbc-38f931b354ab 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-d4c08b38-deb1-4217-acbc-38f931b354ab');\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": 81
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4U4ALBlIPhrQ",
        "outputId": "5778cb35-037a-4cfa-c874-2b024e653860"
      },
      "id": "4U4ALBlIPhrQ",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Price         Area  No. of Bedrooms    Gymnasium  \\\n",
              "count  6.347000e+03  6347.000000      6347.000000  6347.000000   \n",
              "mean   1.515401e+07  1004.327084         1.910036     0.581377   \n",
              "std    2.015943e+07   556.375703         0.863304     0.493372   \n",
              "min    2.000000e+06   200.000000         1.000000     0.000000   \n",
              "25%    5.300000e+06   650.000000         1.000000     0.000000   \n",
              "50%    9.500000e+06   905.000000         2.000000     1.000000   \n",
              "75%    1.750000e+07  1182.000000         2.000000     1.000000   \n",
              "max    4.200000e+08  8511.000000         7.000000     1.000000   \n",
              "\n",
              "       Children's Play Area  Price_per_sqft  \n",
              "count           6347.000000    6.347000e+03  \n",
              "mean               0.559319    1.355547e+09  \n",
              "std                0.496508    9.573921e+08  \n",
              "min                0.000000    1.597444e+08  \n",
              "25%                0.000000    7.234043e+08  \n",
              "50%                1.000000    1.049383e+09  \n",
              "75%                1.000000    1.714286e+09  \n",
              "max                1.000000    1.099505e+10  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-a49ea398-9d9c-452a-8599-82494da6b8f8\">\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>No. of Bedrooms</th>\n",
              "      <th>Gymnasium</th>\n",
              "      <th>Children's Play Area</th>\n",
              "      <th>Price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.347000e+03</td>\n",
              "      <td>6347.000000</td>\n",
              "      <td>6347.000000</td>\n",
              "      <td>6347.000000</td>\n",
              "      <td>6347.000000</td>\n",
              "      <td>6.347000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.515401e+07</td>\n",
              "      <td>1004.327084</td>\n",
              "      <td>1.910036</td>\n",
              "      <td>0.581377</td>\n",
              "      <td>0.559319</td>\n",
              "      <td>1.355547e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.015943e+07</td>\n",
              "      <td>556.375703</td>\n",
              "      <td>0.863304</td>\n",
              "      <td>0.493372</td>\n",
              "      <td>0.496508</td>\n",
              "      <td>9.573921e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000e+06</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.597444e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.300000e+06</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.234043e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.500000e+06</td>\n",
              "      <td>905.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.049383e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.750000e+07</td>\n",
              "      <td>1182.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.714286e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.200000e+08</td>\n",
              "      <td>8511.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.099505e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a49ea398-9d9c-452a-8599-82494da6b8f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-59bd1de6-eb9d-4489-9d04-01bcf66676fa\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-59bd1de6-eb9d-4489-9d04-01bcf66676fa')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-59bd1de6-eb9d-4489-9d04-01bcf66676fa button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      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",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a49ea398-9d9c-452a-8599-82494da6b8f8 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-a49ea398-9d9c-452a-8599-82494da6b8f8');\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": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "dca9e258-b767-4d60-862d-145bc1c40a06",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "dd1d6462-3569-49eb-8840-eb2b6a06d4e0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Kharghar             533\n",
              "Thane West           418\n",
              "Mira Road East       390\n",
              "Ulwe                 319\n",
              "Borivali West        176\n",
              "                    ... \n",
              "worli sea face         1\n",
              "Sea Face               1\n",
              "Shiv Sagar Estate      1\n",
              "Natakwala Lane         1\n",
              "Padle Gaon             1\n",
              "Name: Location, Length: 413, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ],
      "source": [
        "data['Location'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "551f4ced-cf3c-4a51-bee1-8a5bf320ac3a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "551f4ced-cf3c-4a51-bee1-8a5bf320ac3a",
        "outputId": "5831481b-e3c8-4686-8f6b-3604296844e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kharghar             533\n",
            "Thane West           418\n",
            "Mira Road East       390\n",
            "Ulwe                 319\n",
            "Borivali West        176\n",
            "                    ... \n",
            "worli sea face         1\n",
            "Sea Face               1\n",
            "Shiv Sagar Estate      1\n",
            "Natakwala Lane         1\n",
            "Padle Gaon             1\n",
            "Name: Location, Length: 413, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "data['Location'] = data['Location'].apply(lambda x: x.strip())\n",
        "location_count = data['Location'].value_counts()\n",
        "print(location_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "23477bc2-6125-4114-8f86-31fd08b0a97e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23477bc2-6125-4114-8f86-31fd08b0a97e",
        "outputId": "82b12fcb-5928-417f-e25d-892099bcf326"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mahalaxmi            5\n",
              "Sector 5 Ulwe        5\n",
              "Central Avenue       5\n",
              "Majiwada thane       5\n",
              "Greater Khanda       5\n",
              "                    ..\n",
              "worli sea face       1\n",
              "Sea Face             1\n",
              "Shiv Sagar Estate    1\n",
              "Natakwala Lane       1\n",
              "Padle Gaon           1\n",
              "Name: Location, Length: 284, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "locations_count_less_5 = location_count[location_count<=5]\n",
        "locations_count_less_5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "7a30afb9-492a-4ab2-8bc0-98d49e5c973d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7a30afb9-492a-4ab2-8bc0-98d49e5c973d",
        "outputId": "c7b6a402-3b6b-4d4c-98e7-56274acddce2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Kharghar             533\n",
              "other                510\n",
              "Thane West           418\n",
              "Mira Road East       390\n",
              "Ulwe                 319\n",
              "                    ... \n",
              "Sector-8 Ulwe          6\n",
              "Jogeshwari East        6\n",
              "Mulund                 6\n",
              "Sector 22 Kamothe      6\n",
              "Kewale                 6\n",
              "Name: Location, Length: 130, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "data['Location'] = data['Location'].apply(lambda x:'other' if x in locations_count_less_5 else x)\n",
        "data['Location'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "e5d112a1-6107-48c0-b593-5300295be94d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e5d112a1-6107-48c0-b593-5300295be94d",
        "outputId": "72a4ac58-2eef-4c8d-fdec-596055f3be3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Price  Area  Location  bhk  GYM  bath  Price_per_sqft\n",
              "0  4850000   720  Kharghar    1    0     0    6.736111e+08\n",
              "1  4500000   600  Kharghar    1    1     0    7.500000e+08\n",
              "2  6700000   650  Kharghar    1    1     1    1.030769e+09\n",
              "3  4500000   650  Kharghar    1    0     0    6.923077e+08\n",
              "4  5000000   665  Kharghar    1    0     0    7.518797e+08"
            ],
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              "      <td>Kharghar</td>\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56ef66f2-a08a-4dbf-9590-e19835656623');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "data.rename(\n",
        "    columns=({ 'No. of Bedrooms': 'bhk','Gymnasium':'GYM',\"Children's Play Area\":'bath','24x7 Security':'Security'}),\n",
        "    inplace=True,\n",
        ")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Outlier Detection and removal"
      ],
      "metadata": {
        "id": "Yj2TRazeT40D"
      },
      "id": "Yj2TRazeT40D"
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "QVcSlZ3lUpho",
        "outputId": "223a6722-d69c-4f8e-b597-e5a8166f6bd7"
      },
      "id": "QVcSlZ3lUpho",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Price         Area          bhk          GYM         bath  \\\n",
              "count  6.347000e+03  6347.000000  6347.000000  6347.000000  6347.000000   \n",
              "mean   1.515401e+07  1004.327084     1.910036     0.581377     0.559319   \n",
              "std    2.015943e+07   556.375703     0.863304     0.493372     0.496508   \n",
              "min    2.000000e+06   200.000000     1.000000     0.000000     0.000000   \n",
              "25%    5.300000e+06   650.000000     1.000000     0.000000     0.000000   \n",
              "50%    9.500000e+06   905.000000     2.000000     1.000000     1.000000   \n",
              "75%    1.750000e+07  1182.000000     2.000000     1.000000     1.000000   \n",
              "max    4.200000e+08  8511.000000     7.000000     1.000000     1.000000   \n",
              "\n",
              "       Price_per_sqft  \n",
              "count    6.347000e+03  \n",
              "mean     1.355547e+09  \n",
              "std      9.573921e+08  \n",
              "min      1.597444e+08  \n",
              "25%      7.234043e+08  \n",
              "50%      1.049383e+09  \n",
              "75%      1.714286e+09  \n",
              "max      1.099505e+10  "
            ],
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              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Area</th>\n",
              "      <th>bhk</th>\n",
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              "      <th>count</th>\n",
              "      <td>6.347000e+03</td>\n",
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              "      <td>6347.000000</td>\n",
              "      <td>6347.000000</td>\n",
              "      <td>6347.000000</td>\n",
              "      <td>6.347000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>1004.327084</td>\n",
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              "      <td>556.375703</td>\n",
              "      <td>0.863304</td>\n",
              "      <td>0.493372</td>\n",
              "      <td>0.496508</td>\n",
              "      <td>9.573921e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000e+06</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.597444e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.300000e+06</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.234043e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.500000e+06</td>\n",
              "      <td>905.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.049383e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.750000e+07</td>\n",
              "      <td>1182.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.714286e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.200000e+08</td>\n",
              "      <td>8511.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.099505e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7540571f-c0ba-42d2-b1c3-e5525dd261b0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-193413aa-84a2-42c3-8292-848645fc01f3\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-193413aa-84a2-42c3-8292-848645fc01f3')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-193413aa-84a2-42c3-8292-848645fc01f3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7540571f-c0ba-42d2-b1c3-e5525dd261b0 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-7540571f-c0ba-42d2-b1c3-e5525dd261b0');\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": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(data['Area']/data['bhk']).describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LbQ1kYCU0yI",
        "outputId": "f767eeb0-fd72-4eb1-ba61-ee3554742c76"
      },
      "id": "_LbQ1kYCU0yI",
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6347.000000\n",
              "mean      537.443429\n",
              "std       139.233025\n",
              "min       194.333333\n",
              "25%       450.000000\n",
              "50%       533.333333\n",
              "75%       616.833333\n",
              "max      1716.666667\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=data[((data['Area']/data['bhk']>=200))]\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "vIvS3841VOjJ",
        "outputId": "64056a40-9820-4f52-aa17-b5a4c77eeea9"
      },
      "id": "vIvS3841VOjJ",
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Price         Area          bhk          GYM         bath  \\\n",
              "count  6.345000e+03  6345.000000  6345.000000  6345.000000  6345.000000   \n",
              "mean   1.515582e+07  1004.459890     1.909693     0.581245     0.559180   \n",
              "std    2.016235e+07   556.413094     0.863223     0.493394     0.496524   \n",
              "min    2.000000e+06   200.000000     1.000000     0.000000     0.000000   \n",
              "25%    5.300000e+06   650.000000     1.000000     0.000000     0.000000   \n",
              "50%    9.500000e+06   905.000000     2.000000     1.000000     1.000000   \n",
              "75%    1.750000e+07  1182.000000     2.000000     1.000000     1.000000   \n",
              "max    4.200000e+08  8511.000000     7.000000     1.000000     1.000000   \n",
              "\n",
              "       Price_per_sqft  \n",
              "count    6.345000e+03  \n",
              "mean     1.355464e+09  \n",
              "std      9.575317e+08  \n",
              "min      1.597444e+08  \n",
              "25%      7.234043e+08  \n",
              "50%      1.048387e+09  \n",
              "75%      1.714286e+09  \n",
              "max      1.099505e+10  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-35bd5bd4-faa0-472d-a5d0-5bef3efce59a\">\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>bhk</th>\n",
              "      <th>GYM</th>\n",
              "      <th>bath</th>\n",
              "      <th>Price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.345000e+03</td>\n",
              "      <td>6345.000000</td>\n",
              "      <td>6345.000000</td>\n",
              "      <td>6345.000000</td>\n",
              "      <td>6345.000000</td>\n",
              "      <td>6.345000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.515582e+07</td>\n",
              "      <td>1004.459890</td>\n",
              "      <td>1.909693</td>\n",
              "      <td>0.581245</td>\n",
              "      <td>0.559180</td>\n",
              "      <td>1.355464e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.016235e+07</td>\n",
              "      <td>556.413094</td>\n",
              "      <td>0.863223</td>\n",
              "      <td>0.493394</td>\n",
              "      <td>0.496524</td>\n",
              "      <td>9.575317e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000e+06</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.597444e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.300000e+06</td>\n",
              "      <td>650.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.234043e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.500000e+06</td>\n",
              "      <td>905.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.048387e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.750000e+07</td>\n",
              "      <td>1182.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.714286e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.200000e+08</td>\n",
              "      <td>8511.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.099505e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35bd5bd4-faa0-472d-a5d0-5bef3efce59a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-fdf21e6c-5928-43c2-9b01-0f4f25daadf1\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fdf21e6c-5928-43c2-9b01-0f4f25daadf1')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fdf21e6c-5928-43c2-9b01-0f4f25daadf1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-35bd5bd4-faa0-472d-a5d0-5bef3efce59a 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-35bd5bd4-faa0-472d-a5d0-5bef3efce59a');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "f73312d4-6520-4c87-b8b8-f4f57c0f6350",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "d9091dc4-2c3b-411d-cf58-809d99d6344d"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6345, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.Price_per_sqft.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5nLW4raUnmR",
        "outputId": "1a02da77-b372-48ec-e0b1-4f6a62e29915"
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      "id": "m5nLW4raUnmR",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6.345000e+03\n",
              "mean     1.355464e+09\n",
              "std      9.575317e+08\n",
              "min      1.597444e+08\n",
              "25%      7.234043e+08\n",
              "50%      1.048387e+09\n",
              "75%      1.714286e+09\n",
              "max      1.099505e+10\n",
              "Name: Price_per_sqft, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
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    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "871de8d7-717c-4e3a-a97f-3b22c699ce87",
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        "colab": {
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        "outputId": "3a1b83e1-7b17-4bdb-a38e-a0ee3a723c35"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Price         Area          bhk          GYM         bath  \\\n",
              "count  4.869000e+03  4869.000000  4869.000000  4869.000000  4869.000000   \n",
              "mean   1.332337e+07   992.750257     1.855206     0.582666     0.568495   \n",
              "std    1.455536e+07   516.930313     0.825950     0.493170     0.495337   \n",
              "min    2.000000e+06   200.000000     1.000000     0.000000     0.000000   \n",
              "25%    5.300000e+06   650.000000     1.000000     0.000000     0.000000   \n",
              "50%    9.000000e+06   910.000000     2.000000     1.000000     1.000000   \n",
              "75%    1.620000e+07  1160.000000     2.000000     1.000000     1.000000   \n",
              "max    2.300000e+08  7600.000000     7.000000     1.000000     1.000000   \n",
              "\n",
              "       Price_per_sqft  \n",
              "count    4.869000e+03  \n",
              "mean     1.236645e+09  \n",
              "std      7.660826e+08  \n",
              "min      2.222222e+08  \n",
              "25%      7.213115e+08  \n",
              "50%      9.740260e+08  \n",
              "75%      1.632653e+09  \n",
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              "      <td>4869.000000</td>\n",
              "      <td>4869.000000</td>\n",
              "      <td>4.869000e+03</td>\n",
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              "      <th>mean</th>\n",
              "      <td>1.332337e+07</td>\n",
              "      <td>992.750257</td>\n",
              "      <td>1.855206</td>\n",
              "      <td>0.582666</td>\n",
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              "      <td>0.825950</td>\n",
              "      <td>0.493170</td>\n",
              "      <td>0.495337</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>650.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>9.000000e+06</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>1.620000e+07</td>\n",
              "      <td>1160.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "        const buttonEl =\n",
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              "          const element = document.querySelector('#df-c04307be-7f08-41f6-bbd2-c4b15e79cf51');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "def removeOutliners(df):\n",
        "  df_Output = pd.DataFrame()\n",
        "  for key,subdf in df.groupby('Location'):\n",
        "    m = np.mean(subdf.Price_per_sqft)\n",
        "    st = np.std(subdf.Price_per_sqft)\n",
        "    gen_df = subdf[(subdf.Price_per_sqft>(m-st)) & (subdf.Price_per_sqft<=(m+st))]\n",
        "    df_Output = pd.concat([df_Output,gen_df],ignore_index=True)\n",
        "  return df_Output\n",
        "data = removeOutliners(data)\n",
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def bhk_outliners(df):\n",
        "    exclude_indices=np.array([])\n",
        "    for location, location_df in df.groupby('Location'):\n",
        "        bhk_stats={}\n",
        "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
        "            bhk_stats[bhk]={\n",
        "                'mean': np.mean(bhk_df.Price_per_sqft),\n",
        "                'std': np.std(bhk_df.Price_per_sqft),\n",
        "                'count': bhk_df.shape[0]\n",
        "            }\n",
        "        for bhk,bhk_df in location_df.groupby('bhk'):\n",
        "            stats=bhk_stats.get(bhk-1)\n",
        "            if stats and stats['count']>5:\n",
        "               exclude_indices=np.append(exclude_indices,bhk_df[bhk_df.Price_per_sqft<(stats['mean'])].index.values)\n",
        "    return df.drop(exclude_indices,axis='index')"
      ],
      "metadata": {
        "id": "LHCfw5DQzKmJ"
      },
      "id": "LHCfw5DQzKmJ",
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=bhk_outliners(data)"
      ],
      "metadata": {
        "id": "uEOvnQ240_rL"
      },
      "id": "uEOvnQ240_rL",
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aZmN3hg31nbN",
        "outputId": "07c346bd-85a2-4130-9e43-c038a28a92b8"
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      "id": "aZmN3hg31nbN",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3683, 7)"
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          "metadata": {},
          "execution_count": 96
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    {
      "cell_type": "code",
      "source": [
        "data"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4Ea8NOc91sQb",
        "outputId": "6d1b5edb-6663-4c55-ba53-df1ac78ec9a2"
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      "id": "4Ea8NOc91sQb",
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Price  Area           Location  bhk  GYM  bath  Price_per_sqft\n",
              "0      6200000   400             Airoli    1    0     0    1.550000e+09\n",
              "2     14900000  1245             Airoli    2    0     0    1.196787e+09\n",
              "3     14000000  1183             Airoli    2    1     1    1.183432e+09\n",
              "4     14800000  1245             Airoli    2    0     0    1.188755e+09\n",
              "5      6400000   495             Airoli    1    0     0    1.292929e+09\n",
              "...        ...   ...                ...  ...  ...   ...             ...\n",
              "4864   5600000  1050  taloja panchanand    2    0     0    5.333333e+08\n",
              "4865  47500000  1509    vile parle west    3    0     0    3.147780e+09\n",
              "4866  33500000  1073    vile parle west    2    0     0    3.122088e+09\n",
              "4867  58000000  1735    vile parle west    3    0     0    3.342939e+09\n",
              "4868  27500000   840    vile parle west    2    0     0    3.273810e+09\n",
              "\n",
              "[3683 rows x 7 columns]"
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              "      <th>Price</th>\n",
              "      <th>Area</th>\n",
              "      <th>Location</th>\n",
              "      <th>bhk</th>\n",
              "      <th>GYM</th>\n",
              "      <th>bath</th>\n",
              "      <th>Price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6200000</td>\n",
              "      <td>400</td>\n",
              "      <td>Airoli</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.550000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14900000</td>\n",
              "      <td>1245</td>\n",
              "      <td>Airoli</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.196787e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14000000</td>\n",
              "      <td>1183</td>\n",
              "      <td>Airoli</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.183432e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14800000</td>\n",
              "      <td>1245</td>\n",
              "      <td>Airoli</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.188755e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6400000</td>\n",
              "      <td>495</td>\n",
              "      <td>Airoli</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.292929e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4864</th>\n",
              "      <td>5600000</td>\n",
              "      <td>1050</td>\n",
              "      <td>taloja panchanand</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.333333e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4865</th>\n",
              "      <td>47500000</td>\n",
              "      <td>1509</td>\n",
              "      <td>vile parle west</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.147780e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4866</th>\n",
              "      <td>33500000</td>\n",
              "      <td>1073</td>\n",
              "      <td>vile parle west</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.122088e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4867</th>\n",
              "      <td>58000000</td>\n",
              "      <td>1735</td>\n",
              "      <td>vile parle west</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.342939e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4868</th>\n",
              "      <td>27500000</td>\n",
              "      <td>840</td>\n",
              "      <td>vile parle west</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.273810e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3683 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdf18ae9-c7aa-426e-809f-c9c9d8891cd8')\"\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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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-22429b87-8c92-45d2-a32c-403c8e26a601\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-22429b87-8c92-45d2-a32c-403c8e26a601')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-22429b87-8c92-45d2-a32c-403c8e26a601 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fdf18ae9-c7aa-426e-809f-c9c9d8891cd8 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-fdf18ae9-c7aa-426e-809f-c9c9d8891cd8');\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": [
        "data.drop(columns=['bath','Price_per_sqft'],inplace=True)"
      ],
      "metadata": {
        "id": "Ix5eGIAQ1yrV"
      },
      "id": "Ix5eGIAQ1yrV",
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AigIhWfP2FZ2",
        "outputId": "557c56de-7af1-4796-97b3-e7adc068e99c"
      },
      "id": "AigIhWfP2FZ2",
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price  Area Location  bhk  GYM\n",
              "0   6200000   400   Airoli    1    0\n",
              "2  14900000  1245   Airoli    2    0\n",
              "3  14000000  1183   Airoli    2    1\n",
              "4  14800000  1245   Airoli    2    0\n",
              "5   6400000   495   Airoli    1    0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c80b5c80-e035-46cc-8c34-d753f620cd3c\">\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c80b5c80-e035-46cc-8c34-d753f620cd3c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c80b5c80-e035-46cc-8c34-d753f620cd3c');\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": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CLEANED DATA"
      ],
      "metadata": {
        "id": "Rixq-sRq2pfe"
      },
      "id": "Rixq-sRq2pfe"
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "id": "e63e328c-ce2f-4356-a452-6b3c5b8edd85",
      "metadata": {
        "id": "e63e328c-ce2f-4356-a452-6b3c5b8edd85"
      },
      "outputs": [],
      "source": [
        "data.to_csv(\"cleaned_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "id": "e1dd3803-232d-40e4-949f-791890b4d31c",
      "metadata": {
        "id": "e1dd3803-232d-40e4-949f-791890b4d31c"
      },
      "outputs": [],
      "source": [
        "X=data.drop(columns=['Price'])\n",
        "Y=data['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "85a1c687-bce3-4de9-abac-dfcedc117eb4",
      "metadata": {
        "id": "85a1c687-bce3-4de9-abac-dfcedc117eb4"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "e0e2a1b6-d8f4-4fc3-8c67-95aaa07c7c1a",
      "metadata": {
        "id": "e0e2a1b6-d8f4-4fc3-8c67-95aaa07c7c1a"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "id": "82f7c4d0-aa3b-4405-bf83-5a527e899a9d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82f7c4d0-aa3b-4405-bf83-5a527e899a9d",
        "outputId": "d0b19352-571a-4242-8a70-297dcfe02282"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2946, 4)\n",
            "(737, 4)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Applying Linear Regression"
      ],
      "metadata": {
        "id": "MxKoYzuy4zjG"
      },
      "id": "MxKoYzuy4zjG"
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "id": "cf5d180b-a53d-4aea-aa04-bff5019165f2",
      "metadata": {
        "id": "cf5d180b-a53d-4aea-aa04-bff5019165f2"
      },
      "outputs": [],
      "source": [
        "column_trans=make_column_transformer((OneHotEncoder(sparse=False),['Location']),remainder='passthrough')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "0ccc5aa0-a457-4131-91b3-4aee79f4b716",
      "metadata": {
        "id": "0ccc5aa0-a457-4131-91b3-4aee79f4b716"
      },
      "outputs": [],
      "source": [
        "scaler=StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "id": "ae6da101-a8f4-4f50-8925-f9949568da37",
      "metadata": {
        "id": "ae6da101-a8f4-4f50-8925-f9949568da37"
      },
      "outputs": [],
      "source": [
        "lr = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "id": "3bf66179-d2bc-45a2-a5f8-b2eef9a60a16",
      "metadata": {
        "id": "3bf66179-d2bc-45a2-a5f8-b2eef9a60a16"
      },
      "outputs": [],
      "source": [
        "pipe= make_pipeline(column_trans,scaler,lr)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "3pNOkyg28mLl",
        "outputId": "19575ba9-0bcb-40ba-8785-bac4bcde669b"
      },
      "id": "3pNOkyg28mLl",
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  ['Location'])])),\n",
              "                ('standardscaler', StandardScaler()),\n",
              "                ('linearregression', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  [&#x27;Location&#x27;])])),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  [&#x27;Location&#x27;])])),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;onehotencoder&#x27;, OneHotEncoder(sparse=False),\n",
              "                                 [&#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Area&#x27;, &#x27;bhk&#x27;, &#x27;GYM&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "6fb0b4db-3f1e-4e34-bf92-824b724c8557",
      "metadata": {
        "id": "6fb0b4db-3f1e-4e34-bf92-824b724c8557"
      },
      "outputs": [],
      "source": [
        "Y_pred= pipe.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "id": "262ef1d5-e188-4906-bc59-a58ea558209c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "262ef1d5-e188-4906-bc59-a58ea558209c",
        "outputId": "16803733-c0fd-4b84-c640-41719daaac8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8966413165240867"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "r2_score(Y_test,Y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Applying Lasso"
      ],
      "metadata": {
        "id": "Mx18YDpP9Uyt"
      },
      "id": "Mx18YDpP9Uyt"
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "bb110eed-bbd2-4f5a-be73-0e42ed8471bf",
      "metadata": {
        "id": "bb110eed-bbd2-4f5a-be73-0e42ed8471bf"
      },
      "outputs": [],
      "source": [
        "lasso= Lasso()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "id": "ddd5e204-5a0b-4a88-835f-6f9ba81d6403",
      "metadata": {
        "id": "ddd5e204-5a0b-4a88-835f-6f9ba81d6403"
      },
      "outputs": [],
      "source": [
        "pipe= make_pipeline(column_trans,scaler,lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "id": "4539d5e5-5ccf-44da-ab90-56e82c381e42",
      "metadata": {
        "id": "4539d5e5-5ccf-44da-ab90-56e82c381e42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "48f57d90-7a46-4ac8-9894-63f2fad50093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  ['Location'])])),\n",
              "                ('standardscaler', StandardScaler()),\n",
              "                ('linearregression', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  [&#x27;Location&#x27;])])),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  [&#x27;Location&#x27;])])),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;onehotencoder&#x27;, OneHotEncoder(sparse=False),\n",
              "                                 [&#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Area&#x27;, &#x27;bhk&#x27;, &#x27;GYM&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ],
      "source": [
        "pipe.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "id": "fcb90572-5c18-4c70-b2ee-3f9366486ad5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcb90572-5c18-4c70-b2ee-3f9366486ad5",
        "outputId": "c6d9ebb4-aa62-4d14-8096-7359488aeef4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8966413165240867"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "Y_pred_lasso= pipe.predict(X_test)\n",
        "r2_score(Y_test,Y_pred_lasso)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Applying Ridge"
      ],
      "metadata": {
        "id": "a4AUakSq-PdB"
      },
      "id": "a4AUakSq-PdB"
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "id": "939d83ba-4b3e-4cb0-b6a3-b4616e004acd",
      "metadata": {
        "id": "939d83ba-4b3e-4cb0-b6a3-b4616e004acd"
      },
      "outputs": [],
      "source": [
        "ridge= Ridge()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "id": "68312c31-012a-428e-835e-0647c27698a9",
      "metadata": {
        "id": "68312c31-012a-428e-835e-0647c27698a9"
      },
      "outputs": [],
      "source": [
        "pipe= make_pipeline(column_trans,scaler,ridge)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "id": "f1f3a935-10d5-494e-aef5-aa3be85ab917",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "f1f3a935-10d5-494e-aef5-aa3be85ab917",
        "outputId": "e03f2219-b55c-4364-e154-834981744e67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  ['Location'])])),\n",
              "                ('standardscaler', StandardScaler()), ('ridge', Ridge())])"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  [&#x27;Location&#x27;])])),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;ridge&#x27;, Ridge())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;columntransformer&#x27;,\n",
              "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;onehotencoder&#x27;,\n",
              "                                                  OneHotEncoder(sparse=False),\n",
              "                                                  [&#x27;Location&#x27;])])),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()), (&#x27;ridge&#x27;, Ridge())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">columntransformer: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;onehotencoder&#x27;, OneHotEncoder(sparse=False),\n",
              "                                 [&#x27;Location&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">onehotencoder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Location&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(sparse=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Area&#x27;, &#x27;bhk&#x27;, &#x27;GYM&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "pipe.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "id": "a369efbf-a97a-4345-afb9-211c63207bce",
      "metadata": {
        "id": "a369efbf-a97a-4345-afb9-211c63207bce",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "608fd65e-e383-4d67-aabd-be1b9ebfa886"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8968390639344369"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "Y_pred_ridge= pipe.predict(X_test)\n",
        "r2_score(Y_test,Y_pred_ridge)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "id": "f6b47101-3d81-456c-bb7c-a812053203b6",
      "metadata": {
        "id": "f6b47101-3d81-456c-bb7c-a812053203b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05edf958-ded3-40e8-81a8-b499dfcec54d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No Regularization:  0.8966413165240867\n",
            "Lasso:  0.8966413165240867\n",
            "Ridge:  0.8968390639344369\n"
          ]
        }
      ],
      "source": [
        "print(\"No Regularization: \",r2_score(Y_test,Y_pred))\n",
        "print(\"Lasso: \", r2_score(Y_test,Y_pred_lasso))\n",
        "print(\"Ridge: \", r2_score(Y_test,Y_pred_ridge))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "7eb4e7f7-7da9-4793-822d-16c5143c972f",
      "metadata": {
        "id": "7eb4e7f7-7da9-4793-822d-16c5143c972f"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(pipe,open('RidgeModel.pkl','wb'))"
      ],
      "metadata": {
        "id": "rCW25s5rppCX"
      },
      "id": "rCW25s5rppCX",
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