KNN_ON_HOUSING_MS.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/Housing.csv\")"
      ],
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              "        price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0    13300000  7420         4          2        3      yes        no       no   \n",
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              "543   1750000  2910         3          1        1       no        no       no   \n",
              "544   1750000  3850         3          1        2      yes        no       no   \n",
              "\n",
              "    hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0                no             yes        2      yes        furnished  \n",
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              "2                no              no        2      yes   semi-furnished  \n",
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              "4                no             yes        2       no        furnished  \n",
              "..              ...             ...      ...      ...              ...  \n",
              "540              no              no        2       no      unfurnished  \n",
              "541              no              no        0       no   semi-furnished  \n",
              "542              no              no        0       no      unfurnished  \n",
              "543              no              no        0       no        furnished  \n",
              "544              no              no        0       no      unfurnished  \n",
              "\n",
              "[545 rows x 13 columns]"
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1870439,\n        \"min\": 1750000,\n        \"max\": 13300000,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          3773000,\n          5285000,\n          1820000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"furnished\",\n          \"semi-furnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSlm5l8-Syg-",
        "outputId": "65f8a4df-545a-4f72-d4ed-0839eec7c9c2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 545 entries, 0 to 544\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   price             545 non-null    int64 \n",
            " 1   area              545 non-null    int64 \n",
            " 2   bedrooms          545 non-null    int64 \n",
            " 3   bathrooms         545 non-null    int64 \n",
            " 4   stories           545 non-null    int64 \n",
            " 5   mainroad          545 non-null    object\n",
            " 6   guestroom         545 non-null    object\n",
            " 7   basement          545 non-null    object\n",
            " 8   hotwaterheating   545 non-null    object\n",
            " 9   airconditioning   545 non-null    object\n",
            " 10  parking           545 non-null    int64 \n",
            " 11  prefarea          545 non-null    object\n",
            " 12  furnishingstatus  545 non-null    object\n",
            "dtypes: int64(6), object(7)\n",
            "memory usage: 55.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3bGHAHTS1Ij",
        "outputId": "285eb0da-01e4-405c-f3e8-2303f5b878c5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price                int64\n",
              "area                 int64\n",
              "bedrooms             int64\n",
              "bathrooms            int64\n",
              "stories              int64\n",
              "mainroad            object\n",
              "guestroom           object\n",
              "basement            object\n",
              "hotwaterheating     object\n",
              "airconditioning     object\n",
              "parking              int64\n",
              "prefarea            object\n",
              "furnishingstatus    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWEfczE0S8QK",
        "outputId": "9a75aaaf-97f5-49d4-e576-9eaa17707560"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price               0\n",
              "area                0\n",
              "bedrooms            0\n",
              "bathrooms           0\n",
              "stories             0\n",
              "mainroad            0\n",
              "guestroom           0\n",
              "basement            0\n",
              "hotwaterheating     0\n",
              "airconditioning     0\n",
              "parking             0\n",
              "prefarea            0\n",
              "furnishingstatus    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "CfSUHJTqTBis",
        "outputId": "b6158273-3b20-4faf-b672-78f4c919dace"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  13300000  7420         4          2        3      yes        no       no   \n",
              "1  12250000  8960         4          4        4      yes        no       no   \n",
              "2  12250000  9960         3          2        2      yes        no      yes   \n",
              "3  12215000  7500         4          2        2      yes        no      yes   \n",
              "4  11410000  7420         4          1        2      yes       yes      yes   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no             yes        2      yes        furnished  \n",
              "1              no             yes        3       no        furnished  \n",
              "2              no              no        2      yes   semi-furnished  \n",
              "3              no             yes        3      yes        furnished  \n",
              "4              no             yes        2       no        furnished  "
            ],
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 545,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1870439,\n        \"min\": 1750000,\n        \"max\": 13300000,\n        \"num_unique_values\": 219,\n        \"samples\": [\n          3773000,\n          5285000,\n          1820000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170,\n        \"min\": 1650,\n        \"max\": 16200,\n        \"num_unique_values\": 284,\n        \"samples\": [\n          6000,\n          2684,\n          5360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"furnished\",\n          \"semi-furnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "AlvUP1SCTZdu",
        "outputId": "5080a6d8-6b39-4a95-bfdb-5ba72683a491"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price      area  bedrooms  bathrooms   stories   parking\n",
              "price      1.000000  0.535997  0.366494   0.517545  0.420712  0.384394\n",
              "area       0.535997  1.000000  0.151858   0.193820  0.083996  0.352980\n",
              "bedrooms   0.366494  0.151858  1.000000   0.373930  0.408564  0.139270\n",
              "bathrooms  0.517545  0.193820  0.373930   1.000000  0.326165  0.177496\n",
              "stories    0.420712  0.083996  0.408564   0.326165  1.000000  0.045547\n",
              "parking    0.384394  0.352980  0.139270   0.177496  0.045547  1.000000"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23690953653765645,\n        \"min\": 0.36649402577386964,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          0.5359973457780797,\n          0.38439364863572645\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3416273662772731,\n        \"min\": 0.08399605092891993,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.5359973457780797,\n          1.0,\n          0.35298048121168235\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31342042038297463,\n        \"min\": 0.139269896865613,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.36649402577386964,\n          0.1518584855745371,\n          0.139269896865613\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3052919849047227,\n        \"min\": 0.17749582102283437,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.517545339455012,\n          0.1938195310520531,\n          0.17749582102283437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34329130373160394,\n        \"min\": 0.045547091916846645,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.42071236618861724,\n          0.08399605092891993,\n          0.045547091916846645\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34367084001182635,\n        \"min\": 0.045547091916846645,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.38439364863572645,\n          0.35298048121168235,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "FH2uVOXVTbqg",
        "outputId": "2bfeffee-1dd9-4a08-c9a6-37acdde8e472"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = np.percentile(df['price'],25,interpolation='midpoint')\n",
        "Q2 = np.percentile(df['price'],50,interpolation='midpoint')\n",
        "Q3 = np.percentile(df['price'],75,interpolation='midpoint')\n",
        "\n",
        "print(Q1)\n",
        "print(Q2)\n",
        "print(Q3)\n",
        "\n",
        "IQR = Q3-Q1\n",
        "IQR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1R7YD04JTi0c",
        "outputId": "3645cf1b-9b0a-40ec-a194-3c5ea3ddea37"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3430000.0\n",
            "4340000.0\n",
            "5740000.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2310000.0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upper_limit = Q3+1.5*IQR\n",
        "lower_limit = Q1-1.5*IQR"
      ],
      "metadata": {
        "id": "Bm0Tic6nU_SQ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "OUTLIER=[]\n",
        "for x in df['price']:\n",
        "  if((x>upper_limit) or (x<lower_limit)):\n",
        "    OUTLIER.append(x)\n",
        "OUTLIER"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QAv0gsvVTm_",
        "outputId": "3de0b1fa-fd90-47f8-ba0b-fb478fea87d4"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[13300000,\n",
              " 12250000,\n",
              " 12250000,\n",
              " 12215000,\n",
              " 11410000,\n",
              " 10850000,\n",
              " 10150000,\n",
              " 10150000,\n",
              " 9870000,\n",
              " 9800000,\n",
              " 9800000,\n",
              " 9681000,\n",
              " 9310000,\n",
              " 9240000,\n",
              " 9240000]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df['price']>upper_limit"
      ],
      "metadata": {
        "id": "RpERg51SVbpr"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "JXil5K6lVrX5",
        "outputId": "d5a4cbc1-f70b-464a-d2bf-ef6b8f8ea886"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       price   area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0   13300000   7420         4          2        3      yes        no       no   \n",
              "1   12250000   8960         4          4        4      yes        no       no   \n",
              "2   12250000   9960         3          2        2      yes        no      yes   \n",
              "3   12215000   7500         4          2        2      yes        no      yes   \n",
              "4   11410000   7420         4          1        2      yes       yes      yes   \n",
              "5   10850000   7500         3          3        1      yes        no      yes   \n",
              "6   10150000   8580         4          3        4      yes        no       no   \n",
              "7   10150000  16200         5          3        2      yes        no       no   \n",
              "8    9870000   8100         4          1        2      yes       yes      yes   \n",
              "9    9800000   5750         3          2        4      yes       yes       no   \n",
              "10   9800000  13200         3          1        2      yes        no      yes   \n",
              "11   9681000   6000         4          3        2      yes       yes      yes   \n",
              "12   9310000   6550         4          2        2      yes        no       no   \n",
              "13   9240000   3500         4          2        2      yes        no       no   \n",
              "14   9240000   7800         3          2        2      yes        no       no   \n",
              "\n",
              "   hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0               no             yes        2      yes        furnished  \n",
              "1               no             yes        3       no        furnished  \n",
              "2               no              no        2      yes   semi-furnished  \n",
              "3               no             yes        3      yes        furnished  \n",
              "4               no             yes        2       no        furnished  \n",
              "5               no             yes        2      yes   semi-furnished  \n",
              "6               no             yes        2      yes   semi-furnished  \n",
              "7               no              no        0       no      unfurnished  \n",
              "8               no             yes        2      yes        furnished  \n",
              "9               no             yes        1      yes      unfurnished  \n",
              "10              no             yes        2      yes        furnished  \n",
              "11             yes              no        2       no   semi-furnished  \n",
              "12              no             yes        1      yes   semi-furnished  \n",
              "13             yes              no        2       no        furnished  \n",
              "14              no              no        0      yes   semi-furnished  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4fd84dd7-b0c6-461d-a767-f88a21d4eb39\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>stories</th>\n",
              "      <th>mainroad</th>\n",
              "      <th>guestroom</th>\n",
              "      <th>basement</th>\n",
              "      <th>hotwaterheating</th>\n",
              "      <th>airconditioning</th>\n",
              "      <th>parking</th>\n",
              "      <th>prefarea</th>\n",
              "      <th>furnishingstatus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>13300000</td>\n",
              "      <td>7420</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12250000</td>\n",
              "      <td>8960</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12250000</td>\n",
              "      <td>9960</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12215000</td>\n",
              "      <td>7500</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>3</td>\n",
              "      <td>yes</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11410000</td>\n",
              "      <td>7420</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10850000</td>\n",
              "      <td>7500</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10150000</td>\n",
              "      <td>8580</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>10150000</td>\n",
              "      <td>16200</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>unfurnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9870000</td>\n",
              "      <td>8100</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9800000</td>\n",
              "      <td>5750</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "      <td>unfurnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9800000</td>\n",
              "      <td>13200</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>9681000</td>\n",
              "      <td>6000</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>9310000</td>\n",
              "      <td>6550</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>1</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>9240000</td>\n",
              "      <td>3500</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>9240000</td>\n",
              "      <td>7800</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      display:flex;\n",
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              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1322061,\n        \"min\": 9240000,\n        \"max\": 13300000,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          10150000,\n          13300000,\n          9310000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3055,\n        \"min\": 3500,\n        \"max\": 16200,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          3500,\n          6000,\n          7420\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"furnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df1].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mT6WCZcLVx8o",
        "outputId": "5f6dcde2-4db8-43fd-cc5b-f59f9e7c1d06"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],inplace=True)"
      ],
      "metadata": {
        "id": "I_GRHgUrV-i_"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "XH7rTtrWWHGu",
        "outputId": "881b31b5-3ea2-4c95-f877-45191e94e6d7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "15  9100000  6000         4          1        2      yes        no      yes   \n",
              "16  9100000  6600         4          2        2      yes       yes      yes   \n",
              "17  8960000  8500         3          2        4      yes        no       no   \n",
              "18  8890000  4600         3          2        2      yes       yes       no   \n",
              "19  8855000  6420         3          2        2      yes        no       no   \n",
              "\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"semi-furnished\",\n          \"unfurnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "dZ8SU5WxWJwY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "wwMQOFzdWnL-",
        "outputId": "4eae35c8-ecbb-49a5-8a06-026418266c41"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index    price  area  bedrooms  bathrooms  stories mainroad guestroom  \\\n",
              "0     15  9100000  6000         4          1        2      yes        no   \n",
              "1     16  9100000  6600         4          2        2      yes       yes   \n",
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              "3     18  8890000  4600         3          2        2      yes       yes   \n",
              "4     19  8855000  6420         3          2        2      yes        no   \n",
              "\n",
              "  basement hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0      yes              no              no        2       no   semi-furnished  \n",
              "1      yes              no             yes        1      yes      unfurnished  \n",
              "2       no              no             yes        2       no        furnished  \n",
              "3       no              no             yes        2       no        furnished  \n",
              "4       no              no             yes        1      yes   semi-furnished  "
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17</td>\n",
              "      <td>8960000</td>\n",
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              "    <tr>\n",
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              "      <td>8855000</td>\n",
              "      <td>6420</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 153,\n        \"min\": 15,\n        \"max\": 544,\n        \"num_unique_values\": 530,\n        \"samples\": [\n          155,\n          413,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"semi-furnished\",\n          \"unfurnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('index',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "-7tdBfOeWM98"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "efbyP4GVWhUg"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "k0ImTaYvWsXv",
        "outputId": "db4e40c5-fbc3-429b-c0c4-8eedc889f658"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  9100000  6000         4          1        2      yes        no      yes   \n",
              "1  9100000  6600         4          2        2      yes       yes      yes   \n",
              "2  8960000  8500         3          2        4      yes        no       no   \n",
              "3  8890000  4600         3          2        2      yes       yes       no   \n",
              "4  8855000  6420         3          2        2      yes        no       no   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no              no        2       no   semi-furnished  \n",
              "1              no             yes        1      yes      unfurnished  \n",
              "2              no             yes        2       no        furnished  \n",
              "3              no             yes        2       no        furnished  \n",
              "4              no             yes        1      yes   semi-furnished  "
            ],
            "text/html": [
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              "        document.querySelector('#df-99d7e9bc-e51b-4b63-a236-10b5466233c0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-99d7e9bc-e51b-4b63-a236-10b5466233c0');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"semi-furnished\",\n          \"unfurnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# encoding\n"
      ],
      "metadata": {
        "id": "X4V-tBYCWvXw"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()"
      ],
      "metadata": {
        "id": "Ed5AEE8VgVs3"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "KeW2m7cpgkQB",
        "outputId": "102cd70a-641a-4b1b-8f4f-3b3daa569c4a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
              "0  9100000  6000         4          1        2      yes        no      yes   \n",
              "1  9100000  6600         4          2        2      yes       yes      yes   \n",
              "2  8960000  8500         3          2        4      yes        no       no   \n",
              "3  8890000  4600         3          2        2      yes       yes       no   \n",
              "4  8855000  6420         3          2        2      yes        no       no   \n",
              "\n",
              "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
              "0              no              no        2       no   semi-furnished  \n",
              "1              no             yes        1      yes      unfurnished  \n",
              "2              no             yes        2       no        furnished  \n",
              "3              no             yes        2       no        furnished  \n",
              "4              no             yes        1      yes   semi-furnished  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      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",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dbf914ef-2c0d-4129-b61e-64102b8a1f1a 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-dbf914ef-2c0d-4129-b61e-64102b8a1f1a');\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",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-91469ea7-d229-430f-b475-961f201a1eff button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"semi-furnished\",\n          \"unfurnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['mainroad','guestroom','basement','hotwaterheating','airconditioning','prefarea','furnishingstatus']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "xLFkr8rKgvCP",
        "outputId": "6aee6160-4111-4d20-b11a-619604c66cca"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    mainroad guestroom basement hotwaterheating airconditioning prefarea  \\\n",
              "0        yes        no      yes              no              no       no   \n",
              "1        yes       yes      yes              no             yes      yes   \n",
              "2        yes        no       no              no             yes       no   \n",
              "3        yes       yes       no              no             yes       no   \n",
              "4        yes        no       no              no             yes      yes   \n",
              "..       ...       ...      ...             ...             ...      ...   \n",
              "525      yes        no      yes              no              no       no   \n",
              "526       no        no       no              no              no       no   \n",
              "527      yes        no       no              no              no       no   \n",
              "528       no        no       no              no              no       no   \n",
              "529      yes        no       no              no              no       no   \n",
              "\n",
              "    furnishingstatus  \n",
              "0     semi-furnished  \n",
              "1        unfurnished  \n",
              "2          furnished  \n",
              "3          furnished  \n",
              "4     semi-furnished  \n",
              "..               ...  \n",
              "525      unfurnished  \n",
              "526   semi-furnished  \n",
              "527      unfurnished  \n",
              "528        furnished  \n",
              "529      unfurnished  \n",
              "\n",
              "[530 rows x 7 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mainroad</th>\n",
              "      <th>guestroom</th>\n",
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              "      <th>airconditioning</th>\n",
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              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
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              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unfurnished</td>\n",
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              "      <th>2</th>\n",
              "      <td>yes</td>\n",
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              "      <td>yes</td>\n",
              "      <td>no</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
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              "    </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>525</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unfurnished</td>\n",
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              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>no</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>semi-furnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unfurnished</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>yes</td>\n",
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              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unfurnished</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "    [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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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d0352ab5-430c-42e7-883e-c6962bfd035b');\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",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b4c54540-fe52-482f-8d74-40aae4b61dec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['mainroad','guestroom','basement','hotwaterheating','airconditioning','prefarea','furnishingstatus']]\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"semi-furnished\",\n          \"unfurnished\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['mainroad','guestroom','basement','hotwaterheating','airconditioning','prefarea','furnishingstatus']:\n",
        "  df[i]=le.fit_transform(df[i])\n",
        "df[i]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOem_RU4ho8W",
        "outputId": "b69cf6b6-952d-4659-a655-4801ebed44c3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      2\n",
              "2      0\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "525    2\n",
              "526    1\n",
              "527    2\n",
              "528    0\n",
              "529    2\n",
              "Name: furnishingstatus, Length: 530, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "J7jgeA5kh1M3",
        "outputId": "744fd2f6-b373-42a1-f59b-d67ac9c67e90"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price  area  bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
              "0  9100000  6000         4          1        2         1          0         1   \n",
              "1  9100000  6600         4          2        2         1          1         1   \n",
              "2  8960000  8500         3          2        4         1          0         0   \n",
              "3  8890000  4600         3          2        2         1          1         0   \n",
              "4  8855000  6420         3          2        2         1          0         0   \n",
              "\n",
              "   hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
              "0                0                0        2         0                 1  \n",
              "1                0                1        1         1                 2  \n",
              "2                0                1        2         0                 0  \n",
              "3                0                1        2         0                 0  \n",
              "4                0                1        1         1                 1  "
            ],
            "text/html": [
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              "  <div id=\"df-a636429f-654e-4efb-9e4d-8254448227fc\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "    </tr>\n",
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              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a636429f-654e-4efb-9e4d-8254448227fc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a636429f-654e-4efb-9e4d-8254448227fc 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-a636429f-654e-4efb-9e4d-8254448227fc');\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",
              "\n",
              "\n",
              "<div id=\"df-58f4a96f-6722-4d8a-8c23-37afd29da893\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58f4a96f-6722-4d8a-8c23-37afd29da893')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-58f4a96f-6722-4d8a-8c23-37afd29da893 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
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          "height": 476
        },
        "id": "TwKXbVphh3ht",
        "outputId": "6923a498-64cf-4b39-a26d-9fbb31b59e23"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     price      area  bedrooms  bathrooms   stories  mainroad  \\\n",
              "price             1.000000  0.509856  0.332293   0.457962  0.432528  0.310712   \n",
              "area              0.509856  1.000000  0.112056   0.133472  0.063939  0.289321   \n",
              "bedrooms          0.332293  0.112056  1.000000   0.350025  0.404628 -0.024639   \n",
              "bathrooms         0.457962  0.133472  0.350025   1.000000  0.308026  0.023649   \n",
              "stories           0.432528  0.063939  0.404628   0.308026  1.000000  0.116551   \n",
              "mainroad          0.310712  0.289321 -0.024639   0.023649  0.116551  1.000000   \n",
              "guestroom         0.290195  0.155552  0.076559   0.147393  0.038356  0.091658   \n",
              "basement          0.195705  0.037246  0.100002   0.120375 -0.166370  0.041974   \n",
              "hotwaterheating   0.082829  0.002533  0.029208   0.040149  0.020024 -0.017305   \n",
              "airconditioning   0.462342  0.211505  0.146859   0.174966  0.281968  0.099208   \n",
              "parking           0.328308  0.344797  0.110234   0.127184  0.019222  0.197896   \n",
              "prefarea          0.292536  0.212723  0.066364   0.032021  0.026019  0.194901   \n",
              "furnishingstatus -0.303320 -0.170043 -0.110891  -0.145684 -0.100844 -0.153407   \n",
              "\n",
              "                  guestroom  basement  hotwaterheating  airconditioning  \\\n",
              "price              0.290195  0.195705         0.082829         0.462342   \n",
              "area               0.155552  0.037246         0.002533         0.211505   \n",
              "bedrooms           0.076559  0.100002         0.029208         0.146859   \n",
              "bathrooms          0.147393  0.120375         0.040149         0.174966   \n",
              "stories            0.038356 -0.166370         0.020024         0.281968   \n",
              "mainroad           0.091658  0.041974        -0.017305         0.099208   \n",
              "guestroom          1.000000  0.372114        -0.025219         0.135385   \n",
              "basement           0.372114  1.000000         0.000294         0.040937   \n",
              "hotwaterheating   -0.025219  0.000294         1.000000        -0.121215   \n",
              "airconditioning    0.135385  0.040937        -0.121215         1.000000   \n",
              "parking            0.030815  0.031970         0.051929         0.128370   \n",
              "prefarea           0.170439  0.228963        -0.047207         0.087934   \n",
              "furnishingstatus  -0.121165 -0.104027        -0.022760        -0.135633   \n",
              "\n",
              "                   parking  prefarea  furnishingstatus  \n",
              "price             0.328308  0.292536         -0.303320  \n",
              "area              0.344797  0.212723         -0.170043  \n",
              "bedrooms          0.110234  0.066364         -0.110891  \n",
              "bathrooms         0.127184  0.032021         -0.145684  \n",
              "stories           0.019222  0.026019         -0.100844  \n",
              "mainroad          0.197896  0.194901         -0.153407  \n",
              "guestroom         0.030815  0.170439         -0.121165  \n",
              "basement          0.031970  0.228963         -0.104027  \n",
              "hotwaterheating   0.051929 -0.047207         -0.022760  \n",
              "airconditioning   0.128370  0.087934         -0.135633  \n",
              "parking           1.000000  0.062606         -0.149128  \n",
              "prefarea          0.062606  1.000000         -0.099004  \n",
              "furnishingstatus -0.149128 -0.099004          1.000000  "
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' 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",
              "\n",
              "\n",
              "<div id=\"df-00a52d7a-6ce3-4e09-8458-731783f8a85c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-00a52d7a-6ce3-4e09-8458-731783f8a85c')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-00a52d7a-6ce3-4e09-8458-731783f8a85c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2898908396988379,\n        \"min\": -0.3033197094603121,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.2925357192169958,\n          0.46234232521108504,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2876994295381944,\n        \"min\": -0.17004320952565877,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.2127231632533319,\n          0.2115049778277179,\n          0.5098559538900821\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2828243412623176,\n        \"min\": -0.1108905091487767,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.06636391437572699,\n          0.14685888252502521,\n          0.3322928162614711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28318718427926287,\n        \"min\": -0.14568445863684482,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.0320214065534512,\n          0.1749659712321833,\n          0.4579622349481894\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3060691975372741,\n        \"min\": -0.1663701883673144,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.026018561707569398,\n          0.28196837058521235,\n          0.4325276756260766\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28189692665116367,\n        \"min\": -0.1534071447925264,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.1949014544894636,\n          0.09920800492554427,\n          0.310712163707974\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2767059387809884,\n        \"min\": -0.12116454440784567,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.1704386798082887,\n          0.1353852586570828,\n          0.29019498953291384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29163019231756,\n        \"min\": -0.1663701883673144,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.22896266530781617,\n          0.04093706520829472,\n          0.1957047851457928\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2820927020273789,\n        \"min\": -0.1212153043777877,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -0.0472072608707509,\n          -0.1212153043777877,\n          0.08282855980417422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2874734625449832,\n        \"min\": -0.13563344843029354,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.0879340802275282,\n          1.0,\n          0.46234232521108504\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27995300991335026,\n        \"min\": -0.14912833173613316,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.06260583286048804,\n          0.1283702231853239,\n          0.3283082357430689\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2735767491267784,\n        \"min\": -0.0990036319027562,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          1.0,\n          0.0879340802275282,\n          0.2925357192169958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32086073771763374,\n        \"min\": -0.3033197094603121,\n        \"max\": 1.0,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          -0.0990036319027562,\n          -0.13563344843029354,\n          -0.3033197094603121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(),cmap='coolwarm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "dkXhR8Lzh51-",
        "outputId": "f6a4579f-15ac-4147-8229-3f3aba67535d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# minmax"
      ],
      "metadata": {
        "id": "7A8QHJ_0iBix"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "xyBeR8FCimZ5",
        "outputId": "fa485744-bfb4-40f1-eae7-e988ddd64e8e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price  area  bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
              "0  9100000  6000         4          1        2         1          0         1   \n",
              "1  9100000  6600         4          2        2         1          1         1   \n",
              "2  8960000  8500         3          2        4         1          0         0   \n",
              "3  8890000  4600         3          2        2         1          1         0   \n",
              "4  8855000  6420         3          2        2         1          0         0   \n",
              "\n",
              "   hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
              "0                0                0        2         0                 1  \n",
              "1                0                1        1         1                 2  \n",
              "2                0                1        2         0                 0  \n",
              "3                0                1        2         0                 0  \n",
              "4                0                1        1         1                 1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-783b6ad4-8069-4760-8781-eec11dbeed29\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>area</th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>stories</th>\n",
              "      <th>mainroad</th>\n",
              "      <th>guestroom</th>\n",
              "      <th>basement</th>\n",
              "      <th>hotwaterheating</th>\n",
              "      <th>airconditioning</th>\n",
              "      <th>parking</th>\n",
              "      <th>prefarea</th>\n",
              "      <th>furnishingstatus</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9100000</td>\n",
              "      <td>6000</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9100000</td>\n",
              "      <td>6600</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8960000</td>\n",
              "      <td>8500</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8890000</td>\n",
              "      <td>4600</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>8855000</td>\n",
              "      <td>6420</td>\n",
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              "      <td>2</td>\n",
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              "        document.querySelector('#df-783b6ad4-8069-4760-8781-eec11dbeed29 button.colab-df-convert');\n",
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              "        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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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "        document.querySelector('#df-aa828efa-f5ad-49e8-84b8-4254a63d0d9d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df[['price','area']]\n",
        "y = df.drop(['price','area'],axis=1)"
      ],
      "metadata": {
        "id": "xsaXlEzwi6pG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "90V-cwxejmFN",
        "outputId": "03483e37-ccd2-416f-de41-87205e51d8d4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     price  area\n",
              "0  9100000  6000\n",
              "1  9100000  6600\n",
              "2  8960000  8500\n",
              "3  8890000  4600\n",
              "4  8855000  6420"
            ],
            "text/html": [
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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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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d0c6b3d3-60bb-4468-9498-715d760f58ed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1596119,\n        \"min\": 1750000,\n        \"max\": 9100000,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          3332000,\n          8043000,\n          5565000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2075,\n        \"min\": 1650,\n        \"max\": 15600,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          8880,\n          4995,\n          3290\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "minmax = MinMaxScaler()"
      ],
      "metadata": {
        "id": "80sPQjdAjouD"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newx = minmax.fit_transform(x)"
      ],
      "metadata": {
        "id": "tfVVzKZnJaPX"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = pd.DataFrame(newx,columns=['price','area'])"
      ],
      "metadata": {
        "id": "iXoHCzcbJjbq"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "r7UNuVGhJtmA",
        "outputId": "301173bd-5cbf-4639-943b-460feb31117b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      price      area\n",
              "0  1.000000  0.311828\n",
              "1  1.000000  0.354839\n",
              "2  0.980952  0.491039\n",
              "3  0.971429  0.211470\n",
              "4  0.966667  0.341935"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-157bf726-62d4-4f1f-8823-0143d351733b\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "      <th>area</th>\n",
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              "    <tr>\n",
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              "      <th>1</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.354839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.980952</td>\n",
              "      <td>0.491039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.971429</td>\n",
              "      <td>0.211470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "    </tr>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-157bf726-62d4-4f1f-8823-0143d351733b')\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\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",
              "      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-157bf726-62d4-4f1f-8823-0143d351733b 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-157bf726-62d4-4f1f-8823-0143d351733b');\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",
              "\n",
              "\n",
              "<div id=\"df-7522ca99-9a5d-4b37-8c2b-8d6b23b4021c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7522ca99-9a5d-4b37-8c2b-8d6b23b4021c')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7522ca99-9a5d-4b37-8c2b-8d6b23b4021c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21715907657170813,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          0.21523809523809526,\n          0.8561904761904762,\n          0.5190476190476191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14877774045341913,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          0.5182795698924731,\n          0.23978494623655916,\n          0.1175627240143369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([y,x],axis=1)"
      ],
      "metadata": {
        "id": "Jx_4B69bJwN9"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "dvvVDPIFJ3po",
        "outputId": "ad97eb7a-4928-4df1-bd5a-8913ec50bd18"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
              "0           4          1        2         1          0         1   \n",
              "1           4          2        2         1          1         1   \n",
              "2           3          2        4         1          0         0   \n",
              "3           3          2        2         1          1         0   \n",
              "4           3          2        2         1          0         0   \n",
              "..        ...        ...      ...       ...        ...       ...   \n",
              "525         2          1        1         1          0         1   \n",
              "526         3          1        1         0          0         0   \n",
              "527         2          1        1         1          0         0   \n",
              "528         3          1        1         0          0         0   \n",
              "529         3          1        2         1          0         0   \n",
              "\n",
              "     hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \\\n",
              "0                  0                0        2         0                 1   \n",
              "1                  0                1        1         1                 2   \n",
              "2                  0                1        2         0                 0   \n",
              "3                  0                1        2         0                 0   \n",
              "4                  0                1        1         1                 1   \n",
              "..               ...              ...      ...       ...               ...   \n",
              "525                0                0        2         0                 2   \n",
              "526                0                0        0         0                 1   \n",
              "527                0                0        0         0                 2   \n",
              "528                0                0        0         0                 0   \n",
              "529                0                0        0         0                 2   \n",
              "\n",
              "        price      area  \n",
              "0    1.000000  0.311828  \n",
              "1    1.000000  0.354839  \n",
              "2    0.980952  0.491039  \n",
              "3    0.971429  0.211470  \n",
              "4    0.966667  0.341935  \n",
              "..        ...       ...  \n",
              "525  0.009524  0.096774  \n",
              "526  0.002333  0.053763  \n",
              "527  0.000000  0.141219  \n",
              "528  0.000000  0.090323  \n",
              "529  0.000000  0.157706  \n",
              "\n",
              "[530 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dd76d9e-eaff-44d0-9334-52782d95dcef\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bedrooms</th>\n",
              "      <th>bathrooms</th>\n",
              "      <th>stories</th>\n",
              "      <th>mainroad</th>\n",
              "      <th>guestroom</th>\n",
              "      <th>basement</th>\n",
              "      <th>hotwaterheating</th>\n",
              "      <th>airconditioning</th>\n",
              "      <th>parking</th>\n",
              "      <th>prefarea</th>\n",
              "      <th>furnishingstatus</th>\n",
              "      <th>price</th>\n",
              "      <th>area</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.311828</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.354839</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.980952</td>\n",
              "      <td>0.491039</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.971429</td>\n",
              "      <td>0.211470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</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",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.341935</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.009524</td>\n",
              "      <td>0.096774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>526</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.002333</td>\n",
              "      <td>0.053763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>527</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.141219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>528</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.090323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>529</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.157706</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        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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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"furnishingstatus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21715907657170813,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          0.21523809523809526,\n          0.8561904761904762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14877774045341913,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          0.5182795698924731,\n          0.23978494623655916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  KNN ALGORITHM"
      ],
      "metadata": {
        "id": "V0kGgqg9KAxz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN - CLASSIFICATION"
      ],
      "metadata": {
        "id": "G1He31AdKJY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('furnishingstatus',axis=1)\n",
        "x1 = pd.DataFrame(x)\n",
        "y = df['furnishingstatus']\n",
        "y1 = pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "ysUnRb9MKEHL"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "pnDNv5zcMFDg",
        "outputId": "73f01529-8106-4fd1-efc0-0501b68aff56"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
              "0         4          1        2         1          0         1   \n",
              "1         4          2        2         1          1         1   \n",
              "2         3          2        4         1          0         0   \n",
              "3         3          2        2         1          1         0   \n",
              "4         3          2        2         1          0         0   \n",
              "\n",
              "   hotwaterheating  airconditioning  parking  prefarea     price      area  \n",
              "0                0                0        2         0  1.000000  0.311828  \n",
              "1                0                1        1         1  1.000000  0.354839  \n",
              "2                0                1        2         0  0.980952  0.491039  \n",
              "3                0                1        2         0  0.971429  0.211470  \n",
              "4                0                1        1         1  0.966667  0.341935  "
            ],
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              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 530,\n  \"fields\": [\n    {\n      \"column\": \"bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bathrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stories\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mainroad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"guestroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"basement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hotwaterheating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"airconditioning\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parking\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prefarea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21715907657170813,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 208,\n        \"samples\": [\n          0.21523809523809526,\n          0.8561904761904762\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14877774045341913,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 278,\n        \"samples\": [\n          0.5182795698924731,\n          0.23978494623655916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test=train_test_split(x1,y1,test_size=0.25,random_state=42)"
      ],
      "metadata": {
        "id": "DX-mja3ULmvs"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "metric_k = []\n",
        "\n",
        "neighbors = np.arange(3,15)\n",
        "\n",
        "for k in neighbors:\n",
        "  knn = KNeighborsClassifier(n_neighbors = k)\n",
        "  model = knn.fit(x1_train,y1_train)\n",
        "  y1_pred = model.predict(x1_test)\n",
        "  acc = accuracy_score(y1_test,y1_pred)\n",
        "  metric_k.append(acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTSxDbSYKntQ",
        "outputId": "79405dec-b971-41cb-851f-3ea3c0389ac8"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metric_k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-EfBviOMVN0",
        "outputId": "66c63825-6346-4f7d-ca1d-da2b935d4899"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3684210526315789,\n",
              " 0.3609022556390977,\n",
              " 0.3533834586466165,\n",
              " 0.37593984962406013,\n",
              " 0.3684210526315789,\n",
              " 0.3533834586466165,\n",
              " 0.39097744360902253,\n",
              " 0.41353383458646614,\n",
              " 0.45864661654135336,\n",
              " 0.44360902255639095,\n",
              " 0.47368421052631576,\n",
              " 0.48120300751879697]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(neighbors,metric_k,'o-')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ag8VvuwqMaNF",
        "outputId": "9a713bd9-993c-4d65-d502-c441f8816ec9"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ac56b59ae90>]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# its 14 have the highest accuracy"
      ],
      "metadata": {
        "id": "yLCAbiMxMpAO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=14)\n",
        "model = knn.fit(x1_train,y1_train)\n",
        "y1_pred = model.predict(x1_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVWOH6udMyNy",
        "outputId": "498248d5-8364-4e5b-8b93-f49946b99c51"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return self._fit(X, y)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"accuracy score is:\", accuracy_score(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Grjxh2KxNMLM",
        "outputId": "b09ce999-265b-46ca-c43b-48d450988043"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score is: 0.48120300751879697\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score\n",
        "print(precision_score(y1_test,y1_pred,average='macro'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgxPiQBVOoIh",
        "outputId": "c9156c7b-6ec5-4de0-cd93-3aaab4f9e374"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5011720581340834\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN REGRESSION"
      ],
      "metadata": {
        "id": "uLLEFgpoTKOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop('price',axis=1)\n",
        "x1 = pd.DataFrame(x)\n",
        "y = df['price']\n",
        "y1 = pd.DataFrame(y)"
      ],
      "metadata": {
        "id": "_RFhcLIjNitL"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x1_train,x1_test,y1_train,y1_test = train_test_split(x1,y1,test_size=0.20,random_state=42)"
      ],
      "metadata": {
        "id": "-n2_je4oOQIP"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "metric_k =[]\n",
        "\n",
        "neighbors = np.arange(3,15)\n",
        "\n",
        "for k in neighbors:\n",
        "  knn = KNeighborsRegressor(n_neighbors = k)\n",
        "  model = knn.fit(x1_train,y1_train)\n",
        "  y1_pred = model.predict(x1_test)\n",
        "  acc=mean_squared_error(y1_test,y1_pred)\n",
        "  metric_k.append(acc)\n",
        "\n"
      ],
      "metadata": {
        "id": "OzblGeOzP6iN"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "metric_k"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LaMQia4kRkaR",
        "outputId": "f01233d2-3e0c-452d-891c-35229acdd1df"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.02991181165618449,\n",
              " 0.027732937295597487,\n",
              " 0.027233604046652118,\n",
              " 0.025477698881615538,\n",
              " 0.02537008469964908,\n",
              " 0.026206430219002694,\n",
              " 0.02530840773069909,\n",
              " 0.0266012888097634,\n",
              " 0.026676405460796932,\n",
              " 0.027121039312204973,\n",
              " 0.027571060844073108,\n",
              " 0.02848176057044279]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(neighbors,metric_k,'o-')\n",
        "plt.grid()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "5J9chXwgRpJJ",
        "outputId": "ad4eef6e-8296-4d55-97fa-755ec5e362f3"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the highest relation is at 3\n",
        "\n",
        "knn = KNeighborsRegressor(n_neighbors=3)\n",
        "model = knn.fit(x1_train,y1_train)\n",
        "y1_pred = model.predict(x1_test)"
      ],
      "metadata": {
        "id": "Na9hOg9AR5vs"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"mean_sqaured_error is:\",mean_squared_error(y1_test,y1_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3Jd1brMSfKL",
        "outputId": "c8d67f1a-19f2-4957-cab8-1b0eb8a5bce3"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean_sqaured_error is: 0.02991181165618449\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "skFns4bbTQIV",
        "outputId": "ae729e5e-5685-4ec3-89c4-8ab103d331ea"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
              "0         4          1        2         1          0         1   \n",
              "1         4          2        2         1          1         1   \n",
              "2         3          2        4         1          0         0   \n",
              "3         3          2        2         1          1         0   \n",
              "4         3          2        2         1          0         0   \n",
              "\n",
              "   hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \\\n",
              "0                0                0        2         0                 1   \n",
              "1                0                1        1         1                 2   \n",
              "2                0                1        2         0                 0   \n",
              "3                0                1        2         0                 0   \n",
              "4                0                1        1         1                 1   \n",
              "\n",
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            }
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict([[4\t,1\t,2\t,1\t,0\t,1\t,0\t,0\t,2\t,0\t,1\t,0.311828]])"
      ],
      "metadata": {
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        },
        "id": "qM9D61PxTUIG",
        "outputId": "2d9bcbfa-e594-4543-d35e-0a2fcb977b42"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.38730159]])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    }
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}