Price_predictor.ipynb

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      "source": [
        "##Price predictor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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      "execution_count": 2,
      "outputs": []
    },
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      "source": [
        "housing=pd.read_csv('/content/data.csv')"
      ],
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        "id": "M-qfF2Lp-Kh0"
      },
      "execution_count": 3,
      "outputs": []
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        "housing.head()"
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              "   CRIM        ZN    INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
              "0     0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
              "1     0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
              "2     0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
              "3     0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
              "4     0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
              "\n",
              "   PTRATIO       B  LSTAT  MEDV  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     17.8  392.83   4.03  34.7  \n",
              "3     18.7  394.63   2.94  33.4  \n",
              "4     18.7  396.90   5.33  36.2  "
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              "      <th>CRIM</th>\n",
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              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
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              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
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              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
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              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 14 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   CRIM        506 non-null    float64\n",
            " 1   ZN          506 non-null    float64\n",
            " 2   INDUS       506 non-null    float64\n",
            " 3   CHAS        506 non-null    int64  \n",
            " 4   NOX         506 non-null    float64\n",
            " 5   RM          506 non-null    float64\n",
            " 6   AGE         506 non-null    float64\n",
            " 7   DIS         506 non-null    float64\n",
            " 8   RAD         506 non-null    int64  \n",
            " 9   TAX         506 non-null    int64  \n",
            " 10  PTRATIO     506 non-null    float64\n",
            " 11  B           506 non-null    float64\n",
            " 12  LSTAT       506 non-null    float64\n",
            " 13  MEDV        506 non-null    float64\n",
            "dtypes: float64(11), int64(3)\n",
            "memory usage: 55.5 KB\n"
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      "source": [
        "housing.describe()"
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              "       CRIM              ZN         INDUS        CHAS         NOX          RM  \\\n",
              "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
              "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
              "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
              "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
              "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
              "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
              "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
              "\n",
              "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
              "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
              "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
              "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
              "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
              "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
              "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
              "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
              "\n",
              "            LSTAT        MEDV  \n",
              "count  506.000000  506.000000  \n",
              "mean    12.653063   22.532806  \n",
              "std      7.141062    9.197104  \n",
              "min      1.730000    5.000000  \n",
              "25%      6.950000   17.025000  \n",
              "50%     11.360000   21.200000  \n",
              "75%     16.955000   25.000000  \n",
              "max     37.970000   50.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677083</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-746fc1dd-cea4-442f-800a-30a621afac57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-746fc1dd-cea4-442f-800a-30a621afac57 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-746fc1dd-cea4-442f-800a-30a621afac57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "pFCMeriyBGY8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "rIza4OTjBn8Y"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing.hist(bins=50, figsize=(30,50))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "COdKHGAGBsyL",
        "outputId": "20b17f33-45c8-4bc8-d6e1-d0c69c7b5c39"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c6d43d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c6ae8d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c669ed0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c62c510>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c5e0b10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c5a5150>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c55b7d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c513d10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c513d50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c4d5490>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c442f10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c403550>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c3bab50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c37f190>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c3b4790>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd8c36ad90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x3600 with 16 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##train test splitting\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "yLhbeZ-WByUC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Test Split\n"
      ],
      "metadata": {
        "id": "CKtc36igCxQF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# def split_train_test(data,test_ratio):\n",
        "#   np.random.seed(43)\n",
        "#   shuffled=np.random.permutation(len(data))\n",
        "#   test_set_size=int(len(data)*test_ratio)\n",
        "#   test_indices = shuffled[:test_set_size]\n",
        "#   train_indices = shuffled[test_set_size:]\n",
        "#   return data.iloc[train_indices], data.iloc[test_indices]\n"
      ],
      "metadata": {
        "id": "iYaFKuPsCeuI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train_set,test_set=split_train_test(housing,0.2)"
      ],
      "metadata": {
        "id": "5Mt28Yk3D5os"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(len(test_set))"
      ],
      "metadata": {
        "id": "92ALkkOtFVDW"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_set,test_set=train_test_split(housing,test_size=0.2,random_state=43)"
      ],
      "metadata": {
        "id": "q8dGQXT-FZPW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(test_set))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T99__SZ9GW1s",
        "outputId": "b496d873-aec1-4ac1-9883-7906626b2a25"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "split=StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=43)\n",
        "for train_index, test_index in split.split(housing,housing['CHAS']):\n",
        "  strat_train_set=housing.loc[train_index]\n",
        "  strat_test_set=housing.loc[test_index]\n",
        "\n",
        "#for equal splittin of data"
      ],
      "metadata": {
        "id": "dN_oOe5OGgwH"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "strat_test_set['CHAS'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNbwFA8wHnsx",
        "outputId": "f1de8781-eb92-4637-ea77-64374ed1ccce"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    95\n",
              "1     7\n",
              "Name: CHAS, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oVyYIhGQIUzL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Looking for correlations\n"
      ],
      "metadata": {
        "id": "HZGmadpPIpSw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix=housing.corr()"
      ],
      "metadata": {
        "id": "UV4az0zUIvTY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix['MEDV'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZt43pJfI5FU",
        "outputId": "b70fa807-7b14-4511-8ad4-0de7d66499b6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MEDV          1.000000\n",
              "RM            0.695360\n",
              "ZN            0.360445\n",
              "B             0.333461\n",
              "DIS           0.249929\n",
              "CHAS          0.175260\n",
              "AGE          -0.376955\n",
              "RAD          -0.381626\n",
              "CRIM         -0.388305\n",
              "NOX          -0.427321\n",
              "TAX          -0.468536\n",
              "INDUS        -0.483725\n",
              "PTRATIO      -0.507787\n",
              "LSTAT        -0.737663\n",
              "Name: MEDV, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.plotting import scatter_matrix"
      ],
      "metadata": {
        "id": "seWhfIBMJF8l"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "attr=['MEDV','RM','B','LSTAT']"
      ],
      "metadata": {
        "id": "PPwl8wH2Jkf2"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scatter_matrix(housing[attr],figsize=(12,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "92H2OItvJx7P",
        "outputId": "ce07d792-fefd-465a-a5fd-e9075d886093"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d8651d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d7fdd90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d7c23d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d7789d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d724b10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d6f2610>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d6a8c90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d66d210>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d66d250>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d624950>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d59f410>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d555a10>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d50efd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d4d0650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d506c50>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7efd7d4ca290>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "housing.plot(kind='scatter',x='RM',y='MEDV',alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "HNxjzyIbJ5BM",
        "outputId": "e5773283-d4bb-40d1-d9a7-852a96da20aa"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efd8c7aad90>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e796729lK-Pm"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "stcS7EVpMpYw",
        "outputId": "deed332b-cde0-4da7-bc9e-fe6f1c641d79"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CRIM        ZN    INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  \\\n",
              "196     0.04011  80.0   1.52     0  0.404  7.287  34.1  7.3090    2  329   \n",
              "199     0.03150  95.0   1.47     0  0.403  6.975  15.3  7.6534    3  402   \n",
              "150     1.65660   0.0  19.58     0  0.871  6.122  97.3  1.6180    5  403   \n",
              "97      0.12083   0.0   2.89     0  0.445  8.069  76.0  3.4952    2  276   \n",
              "299     0.05561  70.0   2.24     0  0.400  7.041  10.0  7.8278    5  358   \n",
              "\n",
              "     PTRATIO       B  LSTAT  \n",
              "196     12.6  396.90   4.08  \n",
              "199     17.0  396.90   4.56  \n",
              "150     14.7  372.80  14.10  \n",
              "97      18.0  396.90   4.21  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix['MEDV'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYdQ9KhGMq8P",
        "outputId": "e74d599b-eb5e-4f84-e25a-f98efd46fa5e"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MEDV          1.000000\n",
              "RM            0.695360\n",
              "ZN            0.360445\n",
              "B             0.333461\n",
              "DIS           0.249929\n",
              "CHAS          0.175260\n",
              "AGE          -0.376955\n",
              "RAD          -0.381626\n",
              "CRIM         -0.388305\n",
              "NOX          -0.427321\n",
              "TAX          -0.468536\n",
              "INDUS        -0.483725\n",
              "PTRATIO      -0.507787\n",
              "LSTAT        -0.737663\n",
              "Name: MEDV, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# housing.plot(kind='scatter',x='TAXRM',y='MEDV',alpha=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "SAzgTjkHMvxn",
        "outputId": "3227e80f-3765-4ef4-997b-29efd0434e5e"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'TAXRM'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-128-8edaf1b7d5f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'TAXRM'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'MEDV'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataframe_kinds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"plot kind {kind} can only be used for data frames\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    286\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    289\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_make_plot\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1069\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m         scatter = ax.scatter(\n\u001b[0;32m-> 1071\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1072\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1073\u001b[0m             \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'TAXRM'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n"
      ],
      "metadata": {
        "id": "q_1bw9pANJUL"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_pipeline=Pipeline([('imputer',SimpleImputer(strategy='median')),\n",
        "                      ('std_scaler',StandardScaler()),])"
      ],
      "metadata": {
        "id": "TazjgiREPv5Y"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing_tr=train_set\n",
        "housing_num_tr=my_pipeline.fit_transform(housing_tr)"
      ],
      "metadata": {
        "id": "gIZzilF27XG6"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "housing_num_tr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Cr5ayr57u8v",
        "outputId": "9ac56ee5-3b6c-43cd-f2be-af84a671bbce"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4.52581407, -0.49169983,  1.06329337, ...,  0.43532361,\n",
              "         2.69708355, -1.94842587],\n",
              "       [-0.24210115, -0.49169983,  1.28254968, ...,  0.06741988,\n",
              "        -0.7160898 ,  0.06827633],\n",
              "       [-0.41398831,  0.04605526, -0.45224181, ...,  0.36307495,\n",
              "         0.50794478, -0.10804736],\n",
              "       ...,\n",
              "       [-0.4215159 ,  2.94993276, -1.07890005, ...,  0.41590678,\n",
              "        -0.44244745, -0.1962092 ],\n",
              "       [-0.40445681, -0.49169983, -0.52483343, ...,  0.43532361,\n",
              "        -0.74404251,  0.12337748],\n",
              "       [-0.38205612, -0.49169983, -0.52483343, ...,  0.43532361,\n",
              "        -0.90293162,  0.25562025]])"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ljaTWAsT7yHu"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecting data model\n"
      ],
      "metadata": {
        "id": "aafaUSPh8aWk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "housing = strat_train_set.drop('MEDV', axis=1)\n",
        "housing_labels= strat_train_set['MEDV'].copy()"
      ],
      "metadata": {
        "id": "ScKwdwaD8dp_"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "model= RandomForestRegressor()\n",
        "model.fit(housing_tr,housing_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlMIq51081Ho",
        "outputId": "7955ec16-1f75-40f7-f907-64bcaa59bf11"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "somedata=housing_tr.iloc[:5]\n",
        "somelabels=housing_labels.iloc[:5]"
      ],
      "metadata": {
        "id": "5k6R8x-y99yk"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prepdata=my_pipeline.transform(somedata)"
      ],
      "metadata": {
        "id": "UXqjjXMs_GQG"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(prepdata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lYKH8O3_P5R",
        "outputId": "9ea34531-182b-4262-fed9-59d6e7069377"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([35.673, 36.013, 33.448, 31.596, 32.13 ])"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(somelabels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrlE8pt5_9Hm",
        "outputId": "c1550910-da63-444a-a59d-b590c6c66ec1"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[33.3, 34.9, 21.5, 38.7, 29.0]"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "hspred=model.predict(housing_num_tr)\n",
        "linmse=mean_squared_error(housing_labels,hspred)\n",
        "lin_rmse=np.sqrt(linmse)\n",
        "print(lin_rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEYAmJpfAH53",
        "outputId": "56d02d48-9ede-4aa9-c63f-f0011b9084b4"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14.57989665796472\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "scores=cross_val_score(model,housing_num_tr,housing_labels,scoring='neg_mean_squared_error', cv=10)\n",
        "rmse_score=np.sqrt(-scores)"
      ],
      "metadata": {
        "id": "00-BSB7kA4Hc"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J1SWgYY0CP46",
        "outputId": "8d80da18-21bf-4c66-c8bd-d1bec4c30086"
      },
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 9.4616073 , 10.86223159,  9.75988074, 10.11618113, 10.19672307,\n",
              "       10.63891608, 11.27144453,  9.48505518,  9.24363896, 10.34661998])"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_scores(scores):\n",
        "  print(\"scores:\", scores)\n",
        "  print(\"Mean:\", scores.mean())\n",
        "  print(\"std deviation:\", scores.std())\n"
      ],
      "metadata": {
        "id": "LDndVGk8CSYo"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_scores(rmse_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6r-M1VWVDI_B",
        "outputId": "bd20d0ba-f110-4c2b-b582-0293caadc6df"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "scores: [ 9.4616073  10.86223159  9.75988074 10.11618113 10.19672307 10.63891608\n",
            " 11.27144453  9.48505518  9.24363896 10.34661998]\n",
            "Mean: 10.138229855813117\n",
            "std deviation: 0.6272376676150523\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump,load"
      ],
      "metadata": {
        "id": "SyslImOFDNHr"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dump(model,'price-predictor.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c4M7xMNEXP-",
        "outputId": "47e176f9-9c1c-43c3-a4a3-70ffc3719015"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['price-predictor.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test=strat_test_set\n",
        "Y_test=strat_test_set['MEDV'].copy()\n",
        "X_test_prer=my_pipeline.transform(X_test)\n",
        "finalpred=model.predict(X_test)\n",
        "finalmse=mean_squared_error(Y_test,finalpred)\n",
        "firmse=np.sqrt(finalmse)\n",
        "firmse"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_M5B2FVFFAv",
        "outputId": "56195f65-b081-418c-c6b1-688ddbe0e1ba"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.34254664362219"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(list(finalpred))\n",
        "print('test')\n",
        "print(list(Y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eguNTo99Gwjb",
        "outputId": "e38dfa4c-9464-4862-be3f-68b1d813d368"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[29.366999999999962, 22.811999999999998, 27.21199999999998, 23.939, 21.35200000000003, 21.926, 20.791999999999998, 27.32700000000003, 22.753000000000025, 20.13599999999998, 23.230999999999973, 26.720999999999993, 19.417000000000016, 23.622000000000007, 29.890999999999973, 15.472000000000005, 38.10799999999999, 14.870999999999983, 18.386000000000003, 20.189999999999973, 23.18799999999996, 15.658000000000014, 28.188999999999965, 16.715999999999998, 20.220000000000024, 17.240999999999985, 16.018999999999984, 32.81899999999997, 22.824999999999996, 21.39399999999999, 22.95500000000002, 17.35300000000001, 24.35799999999998, 24.06700000000001, 19.976000000000003, 31.250999999999966, 25.173999999999978, 22.412999999999982, 37.97500000000001, 18.74800000000002, 18.931000000000026, 20.043000000000003, 41.229, 12.165000000000003, 29.079999999999995, 21.36299999999998, 24.20299999999999, 20.767000000000003, 23.89199999999999, 23.051, 30.445, 12.948000000000006, 12.255999999999998, 31.373000000000037, 20.127000000000024, 13.375000000000018, 24.406999999999993, 15.415000000000026, 19.578999999999986, 21.52399999999997, 25.866, 19.536000000000016, 20.365000000000027, 25.154, 25.993000000000006, 42.404999999999994, 19.29200000000003, 18.954999999999995, 19.869999999999973, 24.047999999999984, 25.287000000000006, 21.402000000000015, 21.712999999999987, 15.99600000000002, 24.303999999999988, 24.519000000000005, 20.96900000000001, 19.930000000000017, 23.334, 23.987999999999996, 23.848999999999997, 21.699000000000016, 17.061000000000018, 21.632, 35.78200000000003, 25.781000000000027, 22.82299999999998, 26.366999999999994, 21.200000000000024, 38.153, 23.78799999999997, 24.400999999999975, 20.93399999999998, 24.95399999999997, 24.506000000000007, 26.78, 21.18, 21.825000000000024, 25.85600000000001, 17.383999999999983, 23.860000000000014, 20.67700000000002]\n",
            "test\n",
            "[24.2, 24.0, 10.8, 7.2, 31.1, 16.0, 23.0, 20.4, 19.5, 18.4, 14.6, 19.8, 24.1, 23.3, 23.1, 22.9, 15.3, 45.4, 19.9, 17.1, 24.6, 23.9, 13.6, 11.3, 16.1, 7.0, 7.5, 13.9, 15.6, 20.1, 28.7, 20.5, 29.8, 25.2, 37.2, 23.2, 21.2, 19.3, 33.2, 23.1, 20.1, 33.8, 29.6, 34.9, 19.4, 28.2, 23.4, 24.8, 15.2, 18.2, 26.6, 36.1, 32.0, 27.1, 12.1, 19.1, 11.7, 28.7, 37.9, 31.2, 14.9, 20.0, 13.5, 20.6, 17.2, 10.5, 17.2, 24.1, 13.1, 17.8, 8.3, 19.6, 20.8, 29.1, 17.8, 11.8, 25.0, 25.0, 13.2, 14.1, 8.8, 23.1, 31.7, 18.1, 22.0, 33.4, 13.4, 16.7, 50.0, 21.0, 13.0, 20.1, 21.2, 20.1, 50.0, 23.9, 19.4, 20.7, 23.7, 50.0, 14.6, 19.9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=load('/content/price-predictor.joblib')"
      ],
      "metadata": {
        "id": "hxKAaR14HIii"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prepdata[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m3BaNSfbH_Zs",
        "outputId": "68806c39-d99a-4b0b-e2f7-898bc9abdd61"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.52581407, -0.49169983,  1.06329337, -0.26761547,  1.24746018,\n",
              "       -1.19183179,  1.12946465, -1.13887896,  1.68801285,  1.55201972,\n",
              "        0.81464999,  0.43532361,  2.69708355, -1.94842587])"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input=np.array([[ -4.52581407, -0.49169983,  1.06329337, -0.26761547,  1.24746018,\n",
        "       -40.183179,  1.12946465, -1.13887896,  1.68801285,  1.55201972,\n",
        "        0.81464999,  8.43532361,  2.69708355, -1.94842587]])"
      ],
      "metadata": {
        "id": "4Ir3eBi4ILVf"
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tv7geiH7IaWd",
        "outputId": "0122804b-eca6-429b-ee85-c8282f96b4ce"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([36.269])"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oSLaS0K5IdO8"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}