Chetan_chavan.ipynb

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      "name": "python"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WvJczU6U8DKh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_boston\n",
        "df = load_boston()"
      ],
      "metadata": {
        "id": "HI29LoJ28iHL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNFkwi5R8khh",
        "outputId": "04391f11-4733-4895-c890-eb033e92282b"
      },
      "execution_count": 4,
      "outputs": [
        {
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              "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
              "         4.9800e+00],\n",
              "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
              "         9.1400e+00],\n",
              "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
              "         4.0300e+00],\n",
              "        ...,\n",
              "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "         5.6400e+00],\n",
              "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
              "         6.4800e+00],\n",
              "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "         7.8800e+00]]),\n",
              " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
              "        18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
              "        15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
              "        13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
              "        21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
              "        35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
              "        19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
              "        20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
              "        23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
              "        33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
              "        21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
              "        20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
              "        23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
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              "        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
              "        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
              "        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
              "        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
              "        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
              "        20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
              "        26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
              "        31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
              "        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
              "        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
              "        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
              "        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
              "        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
              "        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
              "        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
              "        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
              "        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
              "        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
              "        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
              "        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
              "        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
              "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
              "        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
              "        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
              "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
              "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
              "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
              "        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
              "        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
              "        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
              "        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
              "        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\n",
              " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
              "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
              " 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
              " 'filename': 'boston_house_prices.csv',\n",
              " 'data_module': 'sklearn.datasets.data'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df)"
      ],
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        "colab": {
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        },
        "id": "RJR9lbgt9MZk",
        "outputId": "31ba3e95-bc48-4217-fd70-c825d603442a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sklearn.utils.Bunch"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.keys()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sf0h3lfI8vVQ",
        "outputId": "d5e87323-180b-4e99-a133-f2f0c16dc388"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dm00Our183Wr",
        "outputId": "1e22c6c7-bd99-4531-ea91-aa4159bf372a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.3200e-03 1.8000e+01 2.3100e+00 ... 1.5300e+01 3.9690e+02 4.9800e+00]\n",
            " [2.7310e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9690e+02 9.1400e+00]\n",
            " [2.7290e-02 0.0000e+00 7.0700e+00 ... 1.7800e+01 3.9283e+02 4.0300e+00]\n",
            " ...\n",
            " [6.0760e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 5.6400e+00]\n",
            " [1.0959e-01 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9345e+02 6.4800e+00]\n",
            " [4.7410e-02 0.0000e+00 1.1930e+01 ... 2.1000e+01 3.9690e+02 7.8800e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "boston = pd.DataFrame(df.data,columns=df.feature_names)\n",
        "boston.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "E5Xvh73Q865H",
        "outputId": "ac5f27de-9e42-463c-a1f1-dfc60e23fe90"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
              "\n",
              "   PTRATIO       B  LSTAT  \n",
              "0     15.3  396.90   4.98  \n",
              "1     17.8  396.90   9.14  \n",
              "2     17.8  392.83   4.03  \n",
              "3     18.7  394.63   2.94  \n",
              "4     18.7  396.90   5.33  "
            ],
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              "      <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",
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              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
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              "      <td>0.538</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
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              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
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        "boston.head()"
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              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
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              "      CRIM     ZN  INDUS   CHAS    NOX     RM    AGE    DIS    RAD    TAX  \\\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44c1b4a4-1c4a-4bb5-8045-a98f441177b4')\"\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-44c1b4a4-1c4a-4bb5-8045-a98f441177b4 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-44c1b4a4-1c4a-4bb5-8045-a98f441177b4');\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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "boston.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoZZMD2HGLa8",
        "outputId": "929dc445-c118-45c3-89b5-13eacd23e5a9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       0\n",
              "ZN         0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NOX        0\n",
              "RM         0\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "B          0\n",
              "LSTAT      0\n",
              "MEDV       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " from sklearn.model_selection import train_test_split\n",
        "\n",
        " X = boston.drop('MEDV' , axis=1)\n",
        " Y = boston['MEDV']\n",
        "\n",
        " X_train, X_test, Y_train, Y_test =train_test_split(X, Y, test_size = 0.15, random_state=5)\n",
        " \n",
        " print(X_train.shape)\n",
        " print(X_test.shape)\n",
        " print(Y_train.shape)\n",
        " print(Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZU_D_3VHjRk",
        "outputId": "81b7a143-adad-4f76-c987-a207d0d74fc6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(430, 13)\n",
            "(76, 13)\n",
            "(430,)\n",
            "(76,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "metadata": {
        "id": "NO0qR8UzJky7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lin_model = LinearRegression()\n",
        "\n",
        "lin_model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-p6l5EdP-RJ",
        "outputId": "abab4f10-ab6c-4361-8669-1848b3004856"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_predict = lin_model.predict(X_train)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
        "\n",
        "print(\"The model performance for training set\")\n",
        "print('RMSE is {}'.format(rmse))\n",
        "print(\"\\n\")\n",
        "\n",
        "y_test_predict = lin_model.predict(X_test)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n",
        "\n",
        "print(\"The model performance for testing set\")\n",
        "print('RMSE is {}'.format(rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKMsk9TsQqlN",
        "outputId": "20074a78-5084-4d84-9dad-2bf0707ad64d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model performance for training set\n",
            "RMSE is 4.710901797319796\n",
            "\n",
            "\n",
            "The model performance for testing set\n",
            "RMSE is 4.687543527902972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(5,5))\n",
        "plt.scatter(Y_test, y_test_predict)\n",
        "plt.plot([min(y_test_predict),max(y_test_predict)],[min(y_test_predict),max(y_test_predict)])\n",
        "plt.xlable('Actual')\n",
        "plt.ylable('Predicted')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "OKxYvTAKSvIq",
        "outputId": "b037f021-8e93-47b0-ba33-cb6f05e7165d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-7c18ac967fc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_predict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_predict\u001b[0m\u001b[0;34m)\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----> 6\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Actual'\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      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Predicted'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'xlable'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_predict = lin_model.predict(X_train)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
        "\n",
        "print(\"The model performance for training set\")\n",
        "print('RMSE is {}'.format(rmse))\n",
        "print(\"\\n\")\n",
        "\n",
        "y_test_predict = lin_model.predict(X_test)\n",
        "rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n",
        "\n",
        "print(\"The model performance for testing set\")\n",
        "print('RMSE is {}'.format(rmse))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKyT8uiUUlXD",
        "outputId": "468a168d-4099-4020-d7a0-fdb25be786f5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The model performance for training set\n",
            "RMSE is 4.710901797319796\n",
            "\n",
            "\n",
            "The model performance for testing set\n",
            "RMSE is 4.687543527902972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mL_xxINqWdfk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}