House_Price_Prediction.ipynb

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      "source": [
        "# **Importing The Libraries**"
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
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      "source": [
        "dataset = pd.read_csv('Bengaluru_House_Data.csv')"
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              "              area_type   availability                  location       size  \\\n",
              "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
              "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
              "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
              "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
              "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
              "\n",
              "   society total_sqft  bath  balcony   price  \n",
              "0  Coomee        1056   2.0      1.0   39.07  \n",
              "1  Theanmp       2600   5.0      3.0  120.00  \n",
              "2      NaN       1440   2.0      3.0   62.00  \n",
              "3  Soiewre       1521   3.0      1.0   95.00  \n",
              "4      NaN       1200   2.0      1.0   51.00  "
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              "      <td>Ready To Move</td>\n",
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              "      <td>3 BHK</td>\n",
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              "(13320, 9)"
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          "data": {
            "text/plain": [
              "location      0\n",
              "size          0\n",
              "total_sqft    0\n",
              "bath          0\n",
              "price         0\n",
              "dtype: int64"
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              "                       location       size total_sqft  bath   price\n",
              "0      Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
              "1              Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
              "2                   Uttarahalli      3 BHK       1440   2.0   62.00\n",
              "3            Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
              "4                      Kothanur      2 BHK       1200   2.0   51.00\n",
              "...                         ...        ...        ...   ...     ...\n",
              "13315                Whitefield  5 Bedroom       3453   4.0  231.00\n",
              "13316             Richards Town      4 BHK       3600   5.0  400.00\n",
              "13317     Raja Rajeshwari Nagar      2 BHK       1141   2.0   60.00\n",
              "13318           Padmanabhanagar      4 BHK       4689   4.0  488.00\n",
              "13319              Doddathoguru      1 BHK        550   1.0   17.00\n",
              "\n",
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              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
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              "      <td>Uttarahalli</td>\n",
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              "      <td>Lingadheeranahalli</td>\n",
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              "      <td>Kothanur</td>\n",
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              "      <th>13315</th>\n",
              "      <td>Whitefield</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>231.00</td>\n",
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              "      <th>13316</th>\n",
              "      <td>Richards Town</td>\n",
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              "      <td>400.00</td>\n",
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              "      <th>13317</th>\n",
              "      <td>Raja Rajeshwari Nagar</td>\n",
              "      <td>2 BHK</td>\n",
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              "      <td>60.00</td>\n",
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              "    <tr>\n",
              "      <th>13318</th>\n",
              "      <td>Padmanabhanagar</td>\n",
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              "      <td>4689</td>\n",
              "      <td>4.0</td>\n",
              "      <td>488.00</td>\n",
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              "    <tr>\n",
              "      <th>13319</th>\n",
              "      <td>Doddathoguru</td>\n",
              "      <td>1 BHK</td>\n",
              "      <td>550</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.00</td>\n",
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              "<p>13246 rows × 5 columns</p>\n",
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              "            '<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": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['size']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4zh6FzVLPCX",
        "outputId": "8ae0461c-3c0f-48e6-d4e2-acfca0273671"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            2 BHK\n",
              "1        4 Bedroom\n",
              "2            3 BHK\n",
              "3            3 BHK\n",
              "4            2 BHK\n",
              "           ...    \n",
              "13315    5 Bedroom\n",
              "13316        4 BHK\n",
              "13317        2 BHK\n",
              "13318        4 BHK\n",
              "13319        1 BHK\n",
              "Name: size, Length: 13246, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['BHK'] = dataset['size'].apply(lambda x: int(x.split(' ')[0]))\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bOnORZbvK6d6",
        "outputId": "ec351a33-b431-4852-f237-62e90b0f58c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   location       size total_sqft  bath   price  BHK\n",
              "0  Electronic City Phase II      2 BHK       1056   2.0   39.07    2\n",
              "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
              "2               Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
              "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
              "4                  Kothanur      2 BHK       1200   2.0   51.00    2"
            ],
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              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['BHK'].unique()\n",
        "dataset[dataset.BHK > 20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0uIHNjIQL__0",
        "outputId": "6773cc5e-1e39-4b86-dd50-0e50a7554eb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       location        size total_sqft  bath  price  BHK\n",
              "1718  2Electronic City Phase II      27 BHK       8000  27.0  230.0   27\n",
              "4684                Munnekollal  43 Bedroom       2400  40.0  660.0   43"
            ],
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              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1718</th>\n",
              "      <td>2Electronic City Phase II</td>\n",
              "      <td>27 BHK</td>\n",
              "      <td>8000</td>\n",
              "      <td>27.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4684</th>\n",
              "      <td>Munnekollal</td>\n",
              "      <td>43 Bedroom</td>\n",
              "      <td>2400</td>\n",
              "      <td>40.0</td>\n",
              "      <td>660.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\n",
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              "    }\n",
              "\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",
              "      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-f3270c4b-d0bc-4218-8ca5-09a18406605d 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-f3270c4b-d0bc-4218-8ca5-09a18406605d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.total_sqft.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUt3G2Y4M5tl",
        "outputId": "d0eea2a9-fa15-4461-caf3-26aba0e87a84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def isFloat(x) :\n",
        "    try:\n",
        "        float(x)\n",
        "    except:\n",
        "        return False\n",
        "    return True"
      ],
      "metadata": {
        "id": "e6u3orcRVOjf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[~dataset['total_sqft'].apply(isFloat)].head(10)"
      ],
      "metadata": {
        "id": "6x4QKXugVSJT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "6629ef3f-de29-46f2-9c61-2c313df03246"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               location       size      total_sqft  bath    price  BHK\n",
              "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
              "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
              "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
              "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
              "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
              "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
              "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
              "648             Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
              "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
              "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Yelahanka</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>2100 - 2850</td>\n",
              "      <td>4.0</td>\n",
              "      <td>186.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Hebbal</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>3067 - 8156</td>\n",
              "      <td>4.0</td>\n",
              "      <td>477.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>8th Phase JP Nagar</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1042 - 1105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.005</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Sarjapur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1145 - 1340</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.490</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>KR Puram</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1015 - 1540</td>\n",
              "      <td>2.0</td>\n",
              "      <td>56.800</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Kengeri</td>\n",
              "      <td>1 BHK</td>\n",
              "      <td>34.46Sq. Meter</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>Hennur Road</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1195 - 1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>63.770</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>648</th>\n",
              "      <td>Arekere</td>\n",
              "      <td>9 Bedroom</td>\n",
              "      <td>4125Perch</td>\n",
              "      <td>9.0</td>\n",
              "      <td>265.000</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>661</th>\n",
              "      <td>Yelahanka</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1120 - 1145</td>\n",
              "      <td>2.0</td>\n",
              "      <td>48.130</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Bettahalsoor</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>3090 - 5002</td>\n",
              "      <td>4.0</td>\n",
              "      <td>445.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_sqft_to_num(x):\n",
        "        tokens = x.split('-')\n",
        "        if len(tokens) == 2:\n",
        "            return ((float(tokens[0])+float(tokens[1]))/2)\n",
        "        try:\n",
        "            return float(x)\n",
        "        except:\n",
        "            return None"
      ],
      "metadata": {
        "id": "hbWhNSYJVUrt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['total_sqft'] = dataset.total_sqft.apply(convert_sqft_to_num)\n",
        "dataset.head()"
      ],
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        "id": "KGBHRydNVVaY",
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          "height": 206
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            "text/plain": [
              "                   location       size  total_sqft  bath   price  BHK\n",
              "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
              "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
              "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
              "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
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              "      <td>2.0</td>\n",
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              "                                                     [key], {});\n",
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    {
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      "source": [
        "dataset['price_per_sqft'] = dataset.price*100000/dataset.total_sqft\n",
        "dataset.head()"
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            "text/plain": [
              "                   location       size  total_sqft  bath   price  BHK  \\\n",
              "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
              "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
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              "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
              "\n",
              "   price_per_sqft  \n",
              "0     3699.810606  \n",
              "1     4615.384615  \n",
              "2     4305.555556  \n",
              "3     6245.890861  \n",
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              "\n",
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              "      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",
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              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-3c649552-df0c-4fcd-bfc5-4bb55e2a630b');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 312
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(dataset.location.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Sx7XT_VNHsO",
        "outputId": "ddf246fd-4009-45a8-f10f-1a7b7d1e5161"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1304"
            ]
          },
          "metadata": {},
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.location = dataset.location.apply(lambda x: x.strip()) # removes starting and ending spaces\n",
        "location_stats = dataset.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
        "location_stats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiIR15jFNJ6z",
        "outputId": "fa7cda96-91a3-45a8-be6d-1ca8e75beef3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location\n",
              "Whitefield               535\n",
              "Sarjapur  Road           392\n",
              "Electronic City          304\n",
              "Kanakpura Road           266\n",
              "Thanisandra              236\n",
              "                        ... \n",
              "1 Giri Nagar               1\n",
              "Kanakapura Road,           1\n",
              "Kanakapura main  Road      1\n",
              "Karnataka Shabarimala      1\n",
              "whitefiled                 1\n",
              "Name: location, Length: 1293, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(location_stats[location_stats<=10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TyMJjtbNVRY",
        "outputId": "72f789b4-54e5-40c4-e6c5-807e8b7e82eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1052"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "location_stats_less_than_10 = location_stats[location_stats<=10]\n",
        "location_stats_less_than_10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "whQy766ENZTi",
        "outputId": "b48dedd1-21c9-43d5-d26b-854f86905d4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location\n",
              "Basapura                 10\n",
              "1st Block Koramangala    10\n",
              "Gunjur Palya             10\n",
              "Kalkere                  10\n",
              "Sector 1 HSR Layout      10\n",
              "                         ..\n",
              "1 Giri Nagar              1\n",
              "Kanakapura Road,          1\n",
              "Kanakapura main  Road     1\n",
              "Karnataka Shabarimala     1\n",
              "whitefiled                1\n",
              "Name: location, Length: 1052, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(dataset.location.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cIgDyrYmNb9h",
        "outputId": "6fec1795-c223-4cef-b344-e4fc48bdf706"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1293"
            ]
          },
          "metadata": {},
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.location = dataset.location.apply(lambda x: 'other' if x in location_stats_less_than_10 else x)\n",
        "len(dataset.location.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tU-16NNtNd3Z",
        "outputId": "679451b7-d9b9-411f-d658-d4916dd6e69d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "242"
            ]
          },
          "metadata": {},
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "BEsbGGYvNmaq",
        "outputId": "086e2e1e-b6f4-4f06-9e90-ff21def437b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   location       size  total_sqft  bath   price  BHK  \\\n",
              "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
              "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
              "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
              "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
              "5                Whitefield      2 BHK      1170.0   2.0   38.00    2   \n",
              "6          Old Airport Road      4 BHK      2732.0   4.0  204.00    4   \n",
              "7              Rajaji Nagar      4 BHK      3300.0   4.0  600.00    4   \n",
              "8              Marathahalli      3 BHK      1310.0   3.0   63.25    3   \n",
              "9                     other  6 Bedroom      1020.0   6.0  370.00    6   \n",
              "\n",
              "   price_per_sqft  \n",
              "0     3699.810606  \n",
              "1     4615.384615  \n",
              "2     4305.555556  \n",
              "3     6245.890861  \n",
              "4     4250.000000  \n",
              "5     3247.863248  \n",
              "6     7467.057101  \n",
              "7    18181.818182  \n",
              "8     4828.244275  \n",
              "9    36274.509804  "
            ],
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              "      <td>Chikka Tirupathi</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>Uttarahalli</td>\n",
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              "      <td>Kothanur</td>\n",
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              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
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              "      <td>4250.000000</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Whitefield</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>2</td>\n",
              "      <td>3247.863248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Old Airport Road</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>2732.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>204.00</td>\n",
              "      <td>4</td>\n",
              "      <td>7467.057101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Rajaji Nagar</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>3300.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>4</td>\n",
              "      <td>18181.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Marathahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1310.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>63.25</td>\n",
              "      <td>3</td>\n",
              "      <td>4828.244275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>other</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>370.00</td>\n",
              "      <td>6</td>\n",
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    {
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        "dataset[dataset['total_sqft']/dataset['BHK']<300]"
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          "data": {
            "text/plain": [
              "                  location       size  total_sqft  bath  price  BHK  \\\n",
              "9                    other  6 Bedroom      1020.0   6.0  370.0    6   \n",
              "45              HSR Layout  8 Bedroom       600.0   9.0  200.0    8   \n",
              "58           Murugeshpalya  6 Bedroom      1407.0   4.0  150.0    6   \n",
              "68     Devarachikkanahalli  8 Bedroom      1350.0   7.0   85.0    8   \n",
              "70                   other  3 Bedroom       500.0   3.0  100.0    3   \n",
              "...                    ...        ...         ...   ...    ...  ...   \n",
              "13277                other  7 Bedroom      1400.0   7.0  218.0    7   \n",
              "13279                other  6 Bedroom      1200.0   5.0  130.0    6   \n",
              "13281      Margondanahalli  5 Bedroom      1375.0   5.0  125.0    5   \n",
              "13303       Vidyaranyapura  5 Bedroom       774.0   5.0   70.0    5   \n",
              "13311     Ramamurthy Nagar  7 Bedroom      1500.0   9.0  250.0    7   \n",
              "\n",
              "       price_per_sqft  \n",
              "9        36274.509804  \n",
              "45       33333.333333  \n",
              "58       10660.980810  \n",
              "68        6296.296296  \n",
              "70       20000.000000  \n",
              "...               ...  \n",
              "13277    15571.428571  \n",
              "13279    10833.333333  \n",
              "13281     9090.909091  \n",
              "13303     9043.927649  \n",
              "13311    16666.666667  \n",
              "\n",
              "[744 rows x 7 columns]"
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              "      <td>370.0</td>\n",
              "      <td>6</td>\n",
              "      <td>36274.509804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>HSR Layout</td>\n",
              "      <td>8 Bedroom</td>\n",
              "      <td>600.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>8</td>\n",
              "      <td>33333.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Murugeshpalya</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1407.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>6</td>\n",
              "      <td>10660.980810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Devarachikkanahalli</td>\n",
              "      <td>8 Bedroom</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>8</td>\n",
              "      <td>6296.296296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>other</td>\n",
              "      <td>3 Bedroom</td>\n",
              "      <td>500.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>3</td>\n",
              "      <td>20000.000000</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>13277</th>\n",
              "      <td>other</td>\n",
              "      <td>7 Bedroom</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>7</td>\n",
              "      <td>15571.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13279</th>\n",
              "      <td>other</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>6</td>\n",
              "      <td>10833.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13281</th>\n",
              "      <td>Margondanahalli</td>\n",
              "      <td>5 Bedroom</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9090.909091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13303</th>\n",
              "      <td>Vidyaranyapura</td>\n",
              "      <td>5 Bedroom</td>\n",
              "      <td>774.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9043.927649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13311</th>\n",
              "      <td>Ramamurthy Nagar</td>\n",
              "      <td>7 Bedroom</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>7</td>\n",
              "      <td>16666.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>744 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e690ac8-f96e-43ee-b4e1-5d0c479c7aaf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-4e690ac8-f96e-43ee-b4e1-5d0c479c7aaf 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-4e690ac8-f96e-43ee-b4e1-5d0c479c7aaf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[~(dataset['total_sqft']/dataset['BHK']<300)]\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3tEy-Y38ONTM",
        "outputId": "2add21b0-342b-475e-ba25-1edaeeb4d74b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       location       size  total_sqft  bath   price  BHK  \\\n",
              "0      Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
              "1              Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
              "2                   Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
              "3            Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
              "4                      Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
              "...                         ...        ...         ...   ...     ...  ...   \n",
              "13315                Whitefield  5 Bedroom      3453.0   4.0  231.00    5   \n",
              "13316                     other      4 BHK      3600.0   5.0  400.00    4   \n",
              "13317     Raja Rajeshwari Nagar      2 BHK      1141.0   2.0   60.00    2   \n",
              "13318           Padmanabhanagar      4 BHK      4689.0   4.0  488.00    4   \n",
              "13319              Doddathoguru      1 BHK       550.0   1.0   17.00    1   \n",
              "\n",
              "       price_per_sqft  \n",
              "0         3699.810606  \n",
              "1         4615.384615  \n",
              "2         4305.555556  \n",
              "3         6245.890861  \n",
              "4         4250.000000  \n",
              "...               ...  \n",
              "13315     6689.834926  \n",
              "13316    11111.111111  \n",
              "13317     5258.545136  \n",
              "13318    10407.336319  \n",
              "13319     3090.909091  \n",
              "\n",
              "[12502 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a3655317-63a2-4c7e-8ed3-303eb45ecfb0\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "      <td>3699.810606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "      <td>4615.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "      <td>4305.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "      <td>6245.890861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "      <td>4250.000000</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",
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              "    <tr>\n",
              "      <th>13315</th>\n",
              "      <td>Whitefield</td>\n",
              "      <td>5 Bedroom</td>\n",
              "      <td>3453.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>231.00</td>\n",
              "      <td>5</td>\n",
              "      <td>6689.834926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13316</th>\n",
              "      <td>other</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>3600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>400.00</td>\n",
              "      <td>4</td>\n",
              "      <td>11111.111111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13317</th>\n",
              "      <td>Raja Rajeshwari Nagar</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1141.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>60.00</td>\n",
              "      <td>2</td>\n",
              "      <td>5258.545136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13318</th>\n",
              "      <td>Padmanabhanagar</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>4689.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>488.00</td>\n",
              "      <td>4</td>\n",
              "      <td>10407.336319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13319</th>\n",
              "      <td>Doddathoguru</td>\n",
              "      <td>1 BHK</td>\n",
              "      <td>550.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.00</td>\n",
              "      <td>1</td>\n",
              "      <td>3090.909091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12502 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a3655317-63a2-4c7e-8ed3-303eb45ecfb0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean = dataset.price_per_sqft.mean()\n",
        "std = dataset.price_per_sqft.std()\n",
        "mean,std"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cbywSnzOwwb",
        "outputId": "253af75e-7b4b-4cb9-8f9a-7b16a71c4909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6308.502826047989, 4168.127338507347)"
            ]
          },
          "metadata": {},
          "execution_count": 322
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_pps_outliers(df):\n",
        "    df_out = 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",
        "        reduced_df = subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
        "        df_out = pd.concat([df_out,reduced_df],ignore_index=True)\n",
        "    return df_out\n",
        "dataset = remove_pps_outliers(dataset)\n",
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1NGHMV52PJ0A",
        "outputId": "40638fe4-a5c2-4615-cf85-952180305fd2"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
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              "(10241, 7)"
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          },
          "metadata": {},
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_bhk_outliers(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')\n",
        "dataset = remove_bhk_outliers(dataset)\n",
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLyVlxTiPTgc",
        "outputId": "e5ac8953-0d97-479f-d22c-9aa914e19065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7329, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.bath.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-VKUGc3PPbDI",
        "outputId": "568e70ff-ed97-48ea-a377-4fbf3be8cdf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
            ]
          },
          "metadata": {},
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[dataset.bath>10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BUHodPBVPbhD",
        "outputId": "9108a6b6-a785-4bc1-c08e-21dba0d7a2c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            location    size  total_sqft  bath  price  BHK  price_per_sqft\n",
              "5277  Neeladri Nagar  10 BHK      4000.0  12.0  160.0   10     4000.000000\n",
              "8486           other  10 BHK     12000.0  12.0  525.0   10     4375.000000\n",
              "8575           other  16 BHK     10000.0  16.0  550.0   16     5500.000000\n",
              "9308           other  11 BHK      6000.0  12.0  150.0   11     2500.000000\n",
              "9639           other  13 BHK      5425.0  13.0  275.0   13     5069.124424"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
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              "      <th>5277</th>\n",
              "      <td>Neeladri Nagar</td>\n",
              "      <td>10 BHK</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>10</td>\n",
              "      <td>4000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8486</th>\n",
              "      <td>other</td>\n",
              "      <td>10 BHK</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>525.0</td>\n",
              "      <td>10</td>\n",
              "      <td>4375.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8575</th>\n",
              "      <td>other</td>\n",
              "      <td>16 BHK</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>16</td>\n",
              "      <td>5500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9308</th>\n",
              "      <td>other</td>\n",
              "      <td>11 BHK</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>11</td>\n",
              "      <td>2500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9639</th>\n",
              "      <td>other</td>\n",
              "      <td>13 BHK</td>\n",
              "      <td>5425.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>275.0</td>\n",
              "      <td>13</td>\n",
              "      <td>5069.124424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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    {
      "cell_type": "code",
      "source": [
        "dataset[dataset.bath>dataset.BHK+2]"
      ],
      "metadata": {
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          "height": 175
        },
        "id": "qpClU8q0PhgN",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location       size  total_sqft  bath   price  BHK  price_per_sqft\n",
              "1626  Chikkabanavar  4 Bedroom      2460.0   7.0    80.0    4     3252.032520\n",
              "5238     Nagasandra  4 Bedroom      7000.0   8.0   450.0    4     6428.571429\n",
              "6711    Thanisandra      3 BHK      1806.0   6.0   116.0    3     6423.034330\n",
              "8411          other      6 BHK     11338.0   9.0  1000.0    6     8819.897689"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ae5c55d-27c9-4426-836d-8e823ceeb507\">\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1626</th>\n",
              "      <td>Chikkabanavar</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2460.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3252.032520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5238</th>\n",
              "      <td>Nagasandra</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6428.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6711</th>\n",
              "      <td>Thanisandra</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1806.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6423.034330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8411</th>\n",
              "      <td>other</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>11338.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>6</td>\n",
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              "</table>\n",
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              "          const element = document.querySelector('#df-3ae5c55d-27c9-4426-836d-8e823ceeb507');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset[dataset.bath<dataset.BHK+2]\n",
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pBslwvSAPjh9",
        "outputId": "9b50e1f8-1f66-46da-8f13-85a85d86bf58"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7251, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.drop(['size','price_per_sqft'],axis='columns')\n",
        "dataset.head()"
      ],
      "metadata": {
        "id": "V3cLTj1EJR8j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "1ac859c0-2589-4812-faf2-b910d18d66ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              location  total_sqft  bath  price  BHK\n",
              "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
              "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
              "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
              "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
              "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1d60b724-98bc-4cad-b24c-9e43c9eb453b\">\n",
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              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>total_sqft</th>\n",
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              "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
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        "dataset = pd.concat([dataset.drop('location',axis='columns'),dummies.drop(['other'],axis='columns')],axis='columns')\n",
        "dataset.head()"
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              "   total_sqft  bath  price  BHK  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.drop('price',axis='columns')\n",
        "X"
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      "metadata": {
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      "execution_count": null,
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10233</th>\n",
              "      <td>1800.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10236</th>\n",
              "      <td>1353.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10237</th>\n",
              "      <td>812.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10240</th>\n",
              "      <td>3600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7251 rows × 244 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f3a99f7-da68-439f-b75c-9a9fe978610b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "          document.querySelector('#df-6f3a99f7-da68-439f-b75c-9a9fe978610b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f3a99f7-da68-439f-b75c-9a9fe978610b');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = dataset['price']\n",
        "y"
      ],
      "metadata": {
        "id": "2lz-1AhwJ25C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "371cfa09-a451-4ce9-814f-062396d4f160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        428.0\n",
              "1        194.0\n",
              "2        235.0\n",
              "3        130.0\n",
              "4        148.0\n",
              "         ...  \n",
              "10232     70.0\n",
              "10233    200.0\n",
              "10236    110.0\n",
              "10237     26.0\n",
              "10240    400.0\n",
              "Name: price, Length: 7251, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 333
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "metadata": {
        "id": "sLf7XefYTYlx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the Multiple Linear Regression model on the Training set"
      ],
      "metadata": {
        "id": "oBpcI6vyTa7G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, y_train)\n",
        "regressor.score(X_test, y_test)"
      ],
      "metadata": {
        "id": "gZ3Z6iUXTddh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48937183-5517-451f-a110-7fb26036d386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8243018623639004"
            ]
          },
          "metadata": {},
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.model_selection import cross_val_score\n",
        "cv = ShuffleSplit(n_splits=5, test_size=0.2,random_state=0)\n",
        "cross_val_score(LinearRegression(),X,y,cv=cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZpO4qq6P4s4",
        "outputId": "4654cb9b-835a-4af1-da34-4735f80001fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.82, 0.77, 0.85, 0.81, 0.84])"
            ]
          },
          "metadata": {},
          "execution_count": 336
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_price(location,sqft,bath,bhk):\n",
        "    loc_index = np.where(X.columns==location)[0][0]\n",
        "    \n",
        "    x=np.zeros(len(X.columns))\n",
        "    x[0] = sqft\n",
        "    x[1] = bath\n",
        "    x[2] = bhk\n",
        "    if loc_index >= 0:\n",
        "        x[loc_index] = 1\n",
        "    \n",
        "    return regressor.predict([x])[0]"
      ],
      "metadata": {
        "id": "prNCw0DpUNwU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_price('1st Phase JP Nagar',1000,2,2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejmbAuAtURlS",
        "outputId": "1e151416-2a86-4c54-8701-a5d506332bad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "89.83010079821935"
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "pFsXg9oaTiu2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r = y_pred.reshape(len(y_pred),1)\n",
        "r"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ScQQNMFTOT3",
        "outputId": "1723816d-e737-4294-a0d0-2b7869ccc469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[54.35],\n",
              "       [52.91],\n",
              "       [84.71],\n",
              "       ...,\n",
              "       [44.82],\n",
              "       [76.46],\n",
              "       [68.15]])"
            ]
          },
          "metadata": {},
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rt = y_test.values.reshape(len(y_test),1)\n",
        "rt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "58-XPd7HRL-E",
        "outputId": "ed7cebfe-890f-4ac2-8bbd-949801753eb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[49.65],\n",
              "       [56.81],\n",
              "       [58.  ],\n",
              "       ...,\n",
              "       [41.  ],\n",
              "       [82.  ],\n",
              "       [54.  ]])"
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ]
    }
  ]
}