predict.ipynb

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      "source": [
        "# <center>Data Mining Project </center>\n",
        "## <center> Lung Cancer Prediction </center>\n",
        "#### <center> Srilekha Malraju(msrilekh@iu.edu), Rohith Venkata Reddy(rohi@iu.edu) </center>"
      ],
      "metadata": {
        "id": "QkcdbpTr3wAt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Importing required libraries"
      ],
      "metadata": {
        "id": "vwT3TSkCQBCO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JbXCbfdjM1SZ"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Reading data"
      ],
      "metadata": {
        "id": "KS7HdvZUQHnI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('cancer patient data sets.csv')\n",
        "df.head()"
      ],
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          "height": 348
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      "execution_count": null,
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              "   index Patient Id  Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0      0         P1   33       1              2            4             5   \n",
              "1      1        P10   17       1              3            1             5   \n",
              "2      2       P100   35       1              4            5             6   \n",
              "3      3      P1000   37       1              7            7             7   \n",
              "4      4       P101   46       1              6            8             7   \n",
              "\n",
              "   OccuPational Hazards  Genetic Risk  chronic Lung Disease  ...  Fatigue  \\\n",
              "0                     4             3                     2  ...        3   \n",
              "1                     3             4                     2  ...        1   \n",
              "2                     5             5                     4  ...        8   \n",
              "3                     7             6                     7  ...        4   \n",
              "4                     7             7                     6  ...        3   \n",
              "\n",
              "   Weight Loss  Shortness of Breath  Wheezing  Swallowing Difficulty  \\\n",
              "0            4                    2         2                      3   \n",
              "1            3                    7         8                      6   \n",
              "2            7                    9         2                      1   \n",
              "3            2                    3         1                      4   \n",
              "4            2                    4         1                      4   \n",
              "\n",
              "   Clubbing of Finger Nails  Frequent Cold  Dry Cough  Snoring   Level  \n",
              "0                         1              2          3        4     Low  \n",
              "1                         2              1          7        2  Medium  \n",
              "2                         4              6          7        2    High  \n",
              "3                         5              6          7        5    High  \n",
              "4                         2              4          2        3    High  \n",
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              "[5 rows x 26 columns]"
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              "      <td>7</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performing Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "XC-_2qQU1Oql"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Checking for unique values count of Patient ID"
      ],
      "metadata": {
        "id": "8wEBTju4p566"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Patient Id'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9F6AchI1QX6F",
        "outputId": "555345df-756f-4937-c2e9-21a6c3692302"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Checking for info of the dataset"
      ],
      "metadata": {
        "id": "L3mGcbMip-T-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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        "outputId": "daf4b2a9-7ec5-40cf-8d45-fbec7292424b"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 26 columns):\n",
            " #   Column                    Non-Null Count  Dtype \n",
            "---  ------                    --------------  ----- \n",
            " 0   index                     1000 non-null   int64 \n",
            " 1   Patient Id                1000 non-null   object\n",
            " 2   Age                       1000 non-null   int64 \n",
            " 3   Gender                    1000 non-null   int64 \n",
            " 4   Air Pollution             1000 non-null   int64 \n",
            " 5   Alcohol use               1000 non-null   int64 \n",
            " 6   Dust Allergy              1000 non-null   int64 \n",
            " 7   OccuPational Hazards      1000 non-null   int64 \n",
            " 8   Genetic Risk              1000 non-null   int64 \n",
            " 9   chronic Lung Disease      1000 non-null   int64 \n",
            " 10  Balanced Diet             1000 non-null   int64 \n",
            " 11  Obesity                   1000 non-null   int64 \n",
            " 12  Smoking                   1000 non-null   int64 \n",
            " 13  Passive Smoker            1000 non-null   int64 \n",
            " 14  Chest Pain                1000 non-null   int64 \n",
            " 15  Coughing of Blood         1000 non-null   int64 \n",
            " 16  Fatigue                   1000 non-null   int64 \n",
            " 17  Weight Loss               1000 non-null   int64 \n",
            " 18  Shortness of Breath       1000 non-null   int64 \n",
            " 19  Wheezing                  1000 non-null   int64 \n",
            " 20  Swallowing Difficulty     1000 non-null   int64 \n",
            " 21  Clubbing of Finger Nails  1000 non-null   int64 \n",
            " 22  Frequent Cold             1000 non-null   int64 \n",
            " 23  Dry Cough                 1000 non-null   int64 \n",
            " 24  Snoring                   1000 non-null   int64 \n",
            " 25  Level                     1000 non-null   object\n",
            "dtypes: int64(24), object(2)\n",
            "memory usage: 203.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Describing the dataset"
      ],
      "metadata": {
        "id": "9_ZmuwFYqCPK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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              "             index          Age       Gender  Air Pollution  Alcohol use  \\\n",
              "count  1000.000000  1000.000000  1000.000000      1000.0000  1000.000000   \n",
              "mean    499.500000    37.174000     1.402000         3.8400     4.563000   \n",
              "std     288.819436    12.005493     0.490547         2.0304     2.620477   \n",
              "min       0.000000    14.000000     1.000000         1.0000     1.000000   \n",
              "25%     249.750000    27.750000     1.000000         2.0000     2.000000   \n",
              "50%     499.500000    36.000000     1.000000         3.0000     5.000000   \n",
              "75%     749.250000    45.000000     2.000000         6.0000     7.000000   \n",
              "max     999.000000    73.000000     2.000000         8.0000     8.000000   \n",
              "\n",
              "       Dust Allergy  OccuPational Hazards  Genetic Risk  chronic Lung Disease  \\\n",
              "count   1000.000000           1000.000000   1000.000000           1000.000000   \n",
              "mean       5.165000              4.840000      4.580000              4.380000   \n",
              "std        1.980833              2.107805      2.126999              1.848518   \n",
              "min        1.000000              1.000000      1.000000              1.000000   \n",
              "25%        4.000000              3.000000      2.000000              3.000000   \n",
              "50%        6.000000              5.000000      5.000000              4.000000   \n",
              "75%        7.000000              7.000000      7.000000              6.000000   \n",
              "max        8.000000              8.000000      7.000000              7.000000   \n",
              "\n",
              "       Balanced Diet  ...  Coughing of Blood      Fatigue  Weight Loss  \\\n",
              "count    1000.000000  ...        1000.000000  1000.000000  1000.000000   \n",
              "mean        4.491000  ...           4.859000     3.856000     3.855000   \n",
              "std         2.135528  ...           2.427965     2.244616     2.206546   \n",
              "min         1.000000  ...           1.000000     1.000000     1.000000   \n",
              "25%         2.000000  ...           3.000000     2.000000     2.000000   \n",
              "50%         4.000000  ...           4.000000     3.000000     3.000000   \n",
              "75%         7.000000  ...           7.000000     5.000000     6.000000   \n",
              "max         7.000000  ...           9.000000     9.000000     8.000000   \n",
              "\n",
              "       Shortness of Breath     Wheezing  Swallowing Difficulty  \\\n",
              "count          1000.000000  1000.000000            1000.000000   \n",
              "mean              4.240000     3.777000               3.746000   \n",
              "std               2.285087     2.041921               2.270383   \n",
              "min               1.000000     1.000000               1.000000   \n",
              "25%               2.000000     2.000000               2.000000   \n",
              "50%               4.000000     4.000000               4.000000   \n",
              "75%               6.000000     5.000000               5.000000   \n",
              "max               9.000000     8.000000               8.000000   \n",
              "\n",
              "       Clubbing of Finger Nails  Frequent Cold    Dry Cough      Snoring  \n",
              "count               1000.000000    1000.000000  1000.000000  1000.000000  \n",
              "mean                   3.923000       3.536000     3.853000     2.926000  \n",
              "std                    2.388048       1.832502     2.039007     1.474686  \n",
              "min                    1.000000       1.000000     1.000000     1.000000  \n",
              "25%                    2.000000       2.000000     2.000000     2.000000  \n",
              "50%                    4.000000       3.000000     4.000000     3.000000  \n",
              "75%                    5.000000       5.000000     6.000000     4.000000  \n",
              "max                    9.000000       7.000000     7.000000     7.000000  \n",
              "\n",
              "[8 rows x 24 columns]"
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              "      <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>index</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Air Pollution</th>\n",
              "      <th>Alcohol use</th>\n",
              "      <th>Dust Allergy</th>\n",
              "      <th>OccuPational Hazards</th>\n",
              "      <th>Genetic Risk</th>\n",
              "      <th>chronic Lung Disease</th>\n",
              "      <th>Balanced Diet</th>\n",
              "      <th>...</th>\n",
              "      <th>Coughing of Blood</th>\n",
              "      <th>Fatigue</th>\n",
              "      <th>Weight Loss</th>\n",
              "      <th>Shortness of Breath</th>\n",
              "      <th>Wheezing</th>\n",
              "      <th>Swallowing Difficulty</th>\n",
              "      <th>Clubbing of Finger Nails</th>\n",
              "      <th>Frequent Cold</th>\n",
              "      <th>Dry Cough</th>\n",
              "      <th>Snoring</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.0000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>499.500000</td>\n",
              "      <td>37.174000</td>\n",
              "      <td>1.402000</td>\n",
              "      <td>3.8400</td>\n",
              "      <td>4.563000</td>\n",
              "      <td>5.165000</td>\n",
              "      <td>4.840000</td>\n",
              "      <td>4.580000</td>\n",
              "      <td>4.380000</td>\n",
              "      <td>4.491000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.859000</td>\n",
              "      <td>3.856000</td>\n",
              "      <td>3.855000</td>\n",
              "      <td>4.240000</td>\n",
              "      <td>3.777000</td>\n",
              "      <td>3.746000</td>\n",
              "      <td>3.923000</td>\n",
              "      <td>3.536000</td>\n",
              "      <td>3.853000</td>\n",
              "      <td>2.926000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>288.819436</td>\n",
              "      <td>12.005493</td>\n",
              "      <td>0.490547</td>\n",
              "      <td>2.0304</td>\n",
              "      <td>2.620477</td>\n",
              "      <td>1.980833</td>\n",
              "      <td>2.107805</td>\n",
              "      <td>2.126999</td>\n",
              "      <td>1.848518</td>\n",
              "      <td>2.135528</td>\n",
              "      <td>...</td>\n",
              "      <td>2.427965</td>\n",
              "      <td>2.244616</td>\n",
              "      <td>2.206546</td>\n",
              "      <td>2.285087</td>\n",
              "      <td>2.041921</td>\n",
              "      <td>2.270383</td>\n",
              "      <td>2.388048</td>\n",
              "      <td>1.832502</td>\n",
              "      <td>2.039007</td>\n",
              "      <td>1.474686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>249.750000</td>\n",
              "      <td>27.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>499.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>749.250000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.0000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.0000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7a0c7e5-ae76-4f22-91c2-95dacde80a14')\"\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",
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              "  </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7a0c7e5-ae76-4f22-91c2-95dacde80a14 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-e7a0c7e5-ae76-4f22-91c2-95dacde80a14');\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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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Checking for null values"
      ],
      "metadata": {
        "id": "fRPfy786qKID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SJQ4ojeQgZi",
        "outputId": "73c0b832-a771-45f7-c957-670358ff0fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index                       0\n",
              "Patient Id                  0\n",
              "Age                         0\n",
              "Gender                      0\n",
              "Air Pollution               0\n",
              "Alcohol use                 0\n",
              "Dust Allergy                0\n",
              "OccuPational Hazards        0\n",
              "Genetic Risk                0\n",
              "chronic Lung Disease        0\n",
              "Balanced Diet               0\n",
              "Obesity                     0\n",
              "Smoking                     0\n",
              "Passive Smoker              0\n",
              "Chest Pain                  0\n",
              "Coughing of Blood           0\n",
              "Fatigue                     0\n",
              "Weight Loss                 0\n",
              "Shortness of Breath         0\n",
              "Wheezing                    0\n",
              "Swallowing Difficulty       0\n",
              "Clubbing of Finger Nails    0\n",
              "Frequent Cold               0\n",
              "Dry Cough                   0\n",
              "Snoring                     0\n",
              "Level                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop(['Patient Id','index'],axis=1)"
      ],
      "metadata": {
        "id": "K7tFpc6jjfpm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cancer Level count plot\n",
        "- There are 3 levels of Cancer in the dataset : Low, Medium and High risk of cancer. Plotting the counts of it in order to check if there exists no basis to any of the classes."
      ],
      "metadata": {
        "id": "sjNTVBd7qhh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (5, 5))\n",
        "df_lung_cancer = df[df[\"Level\"]=='High']\n",
        "sns.countplot(df[\"Level\"], color ='grey')\n",
        "plt.title(\"Cancer Level\")\n",
        "plt.xticks([0, 1, 2], ('Low', 'Medium', 'High'))\n",
        "plt.ylabel('Number of Patients');\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "p1sADXP0drL4",
        "outputId": "4ab20157-e02d-475d-87fe-20fc548c970a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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": "markdown",
      "source": [
        "#### Gender Distribution count plot"
      ],
      "metadata": {
        "id": "8Kq8HNARP_96"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Plotting Gender vs Count of population in the dataset to check if the dataset is gender basis.\n",
        "- Male is 1 and Female 2"
      ],
      "metadata": {
        "id": "oXCvQj3e3X4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (5, 5))\n",
        "sns.catplot('Gender',data=df,kind='count',palette='Reds')\n",
        "plt.xticks([0, 1], ('Male', 'Female'))\n",
        "plt.xlabel('Gender');\n",
        "plt.ylabel('Number of Patients');\n",
        "plt.title('Gender distribution');\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "W_8A4J8jeG3T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "outputId": "437c6730-8f93-4484-af5b-a84c85e57b4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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AT5+xKiRpHppqLohrZrIQSZpvRnkQQ5I0BgawJHUy1X3AZ7fPH5i5ciRp/pjqj3DbJXkBcECSE1ntNrSqumislUnSHDdVAL+TwZwP2wN/tdq2Al46rqIkaT6Y6i6Ik4GTk7yjqnwCTpI2sLXOhlZVxyQ5gMGjyQDnVtWXxluWJM19a70LIsmfAUcD320fRyf503EXJklz3SjzAb8S2L2qHgRIsgy4GHj7OAuTpLlu1PuAFwwtbzXKDkkeleSCJN9KclmS97T2nZOcn2RFks8m2by1P7Ktr2jbd1qXL0SSNjajBPCfMXgz8ifb1e+FwPtG2O9e4KVV9Rxgd2C/JHsBHwCOrapdgNuAI1v/I4HbWvuxrZ8kzVlrDeCq+kdgL+DzwOeAvavqsyPsV1X147a6WfuYuH3t5Na+DDioLR/Y1mnb903iFJiS5qxRxoCpqhsYvA9unSTZhMEV8y7Ah4H/D9xeVfe3LiuBxW15MXBtO9/9Se5g8DaOW1Y75lJgKcCOO+64riVJ0qwx1rkgquqBqtqdwcMcewK7bYBjHl9VS6pqycKFC6ddoyT1MiOT8VTV7cBXgL2BBUkmrry3B65ry9cBOwC07VsBP5yJ+iSphykDOMkmSa5YnwMnWZhkQVveAngZcDmDID6kdTsCOKUtn9rWadvPqapan3NL0sZgyjHg9kLOK5PsWFU/WMdjbwcsa+PAjwBOqqovJfkucGKS9zK4n/iE1v8E4O+TrABuBQ5dx/NJ0kZllD/CbQ1cluQC4K6Jxqo6YKqdqupS4LmTtF/NYDx49fZ7gFeNUI8kzQmjBPA7xl6FJM1Do0zGc16SJwG7VtW/Jnk0sMn4S5OkuW2UyXh+g8GDER9vTYuBL46zKEmaD0a5De0o4IXAjwCq6irg8eMsSpLmg1EC+N6qum9ipd2j6+1hkjRNowTweUneDmyR5GXAPwH/PN6yJGnuGyWA3wqsAr4N/CZwGvDH4yxKkuaDUe6CeLBNQ3k+g6GHK31CTZKmb60BnOSVwMcYzGQWYOckv1lV/zLu4iRpLhvlQYy/BF5SVSsAkjwF+DJgAEvSNIwyBnznRPg2VwN3jqkeSZo31ngFnOTgtrg8yWnASQzGgF8FfHMGapOkOW2qIYhfHlq+CXhxW14FbDG2iiRpnlhjAFfV62eyEEmab0a5C2Jn4I3ATsP91zYdpSRpaqPcBfFFBpOl/zPw4HjLkaT5Y5QAvqeqjht7JZI0z4wSwB9K8i7gTODeicaqumhsVUnSPDBKAP8s8BrgpTw0BFFtXZK0nkYJ4FcBTx6eklKSNH2jPAn3HWDBuAuRpPlmlCvgBcAVSb7Jw8eAvQ1NkqZhlAB+19irkKR5aKS3Is9EIZI034zyJNydPPQOuM2BzYC7qupnxlmYJM11o1wBP3ZiOUmAA4G9xlmUJM0Ho9wF8VM18EXgF8dUjyTNG6MMQRw8tPoIYAlwz9gqkqR5YpS7IIbnBb4f+D6DYQhJ0jSMMgbsvMCSNAZTvZLonVPsV1V1zBjqkaR5Y6or4LsmadsSOBJ4HGAAS9I0TPVKor+cWE7yWOBo4PXAiQxeVS9JmoYpx4CTbAO8CTgcWAbsUVW3zURhkjTXTTUG/EHgYOB44Ger6sczVpUkzQNTPYjxZuCJwB8D1yf5Ufu4M8mPZqY8SZq7phoDXqen5CRJ68aQlaRODGBJ6sQAlqROxhbASXZI8pUk301yWZKjW/s2Sc5KclX7vHVrT5LjkqxIcmmSPcZVmyTNBuO8Ar4feHNVPYPB/MFHJXkG8Fbg7KraFTi7rQPsD+zaPpYCHx1jbZLU3dgCuKpuqKqL2vKdwOXAYgYzqS1r3ZYBB7XlA4FPtTmHvwEsSLLduOqTpN5mZAw4yU7Ac4HzgUVVdUPbdCOwqC0vBq4d2m1la1v9WEuTLE+yfNWqVWOrWZLGbewBnOQxwOeA36+qhz3AUVXFQ++bG0lVHV9VS6pqycKFCzdgpZI0s8YawEk2YxC+n66qz7fmmyaGFtrnm1v7dcAOQ7tv39okaU4a510QAU4ALq+qvxradCpwRFs+AjhlqP217W6IvYA7hoYqJGnOGeWVROvrhcBrgG8nuaS1vR14P3BSkiOBa4BXt22nAa8AVgB3M5j6UpLmrLEFcFX9O5A1bN53kv4FHDWueiRptvFJOEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE4MYEnqxACWpE7GFsBJPpHk5iTfGWrbJslZSa5qn7du7UlyXJIVSS5Nsse46pKk2WKcV8CfBPZbre2twNlVtStwdlsH2B/YtX0sBT46xrokaVYYWwBX1VeBW1drPhBY1paXAQcNtX+qBr4BLEiy3bhqk6TZYKbHgBdV1Q1t+UZgUVteDFw71G9la/tvkixNsjzJ8lWrVo2vUkkas25/hKuqAmo99ju+qpZU1ZKFCxeOoTJJmhkzHcA3TQwttM83t/brgB2G+m3f2iRpzprpAD4VOKItHwGcMtT+2nY3xF7AHUNDFZI0J206rgMn+UdgH2DbJCuBdwHvB05KciRwDfDq1v004BXACuBu4PXjqkuSZouxBXBVHbaGTftO0reAo8ZViyTNRj4JJ0mdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1IkBLEmdGMCS1MmsCuAk+yW5MsmKJG/tXY8kjdOsCeAkmwAfBvYHngEcluQZfauSpPGZNQEM7AmsqKqrq+o+4ETgwM41SdLYbNq7gCGLgWuH1lcCz1+9U5KlwNK2+uMkV85AbXPBtsAtvYuYEYcd3buC+WL+/JsCSKaz9+lVtd/qjbMpgEdSVccDx/euY2OTZHlVLeldh+YO/01N32wagrgO2GFoffvWJklz0mwK4G8CuybZOcnmwKHAqZ1rkqSxmTVDEFV1f5LfBc4ANgE+UVWXdS5rLnHYRhua/6amKVXVuwZJmpdm0xCEJM0rBrAkdWIAb8SSVJJ/GFrfNMmqJF9ay377rK2P5rYkDyS5ZOhjpzGe6/tJth3X8Tdms+aPcFovdwHPSrJFVf0EeBneuqfR/KSqdu9dxHznFfDG7zTglW35MOAfJzYk2TPJ15NcnOQ/kjxt9Z2TbJnkE0kuaP18/HueSvJzSc5LcmGSM5Js19rPTXJskuVJLk/yvCSfT3JVkvcO7f/Ftu9l7YnVyc7xP9u/tUuSfLzNATNvGcAbvxOBQ5M8Cng2cP7QtiuAX6iq5wLvBP50kv3/N3BOVe0JvAT4YJItx1yz+ttiaPjhC0k2A/4aOKSqfg74BPC+of73tafePgacAhwFPAt4XZLHtT5vaPsuAX5vqB2AJE8Hfg14Ybv6fgA4fIxf46znEMRGrqoubeN3hzG4Gh62FbAsya5AAZtNcoiXAwck+cO2/ihgR+DysRSs2eJhQxBJnsUgUM/KYM6DTYAbhvpPPBT1beCyqrqh7Xc1gydYf8ggdH+l9dsB2LW1T9gX+Dngm+0cWwA3b9gva+NiAM8NpwJ/AewDDF91HAN8pap+pYX0uZPsG+BXq8pJjea3MAjWvdew/d72+cGh5Yn1TZPsA/wPYO+qujvJuQx+mK9+jmVV9bYNVvVGziGIueETwHuq6turtW/FQ3+Ue90a9j0DeGPaJUmS546lQs12VwILk+wNkGSzJM9ch/23Am5r4bsbsNckfc4GDkny+HaObZI8abqFb8wM4DmgqlZW1XGTbPpz4M+SXMyaf9s5hsHQxKVJLmvrmmfaHNyHAB9I8i3gEuAF63CI0xlcCV8OvB/4xiTn+C7wx8CZSS4FzgK2m27tGzMfRZakTrwClqRODGBJ6sQAlqRODGBJ6sQAlqRODGDNGUkWJflMkqvbnARfH3oyazrHdfY4jYUBrDmhPUjyReCrVfXkNifBoQxe7jrTtfiEqUZiAGuueCmDCWM+NtFQVddU1V8n2STJB5N8M8mlSX4Tfnple26Sk5NckeTTQ08E7tfaLgIOnjjmmmaPS/K6JKcmOYfBE1/SWvmTWnPFM4GL1rDtSOCOqnpekkcCX0tyZtv23Lbv9cDXgBcmWQ78DYNQXwF8duhYE7PHvSHJAuCCJP/atu0BPLuqbt2QX5jmLgNYc1KSDwM/D9wHXAM8O8khbfNWDGbqug+4oKpWtn0uAXYCfgx8r6quau3/AEzMb7um2eMAzjJ8tS4MYM0VlwG/OrFSVUe11+AsB34AvLGqzhjeoc3gNTyz1wOs/f+JSWePS/J8Bm8okUbmGLDminOARyX57aG2R7fPZwC/3SYdJ8lT1zLp/BXATkme0tYPG9rm7HHaYAxgzQk1mFXqIODFSb6X5AJgGfAW4G+B7wIXJfkO8HGmuNKtqnsYDDl8uf0RbnjScGeP0wbjbGiS1IlXwJLUiQEsSZ0YwJLUiQEsSZ0YwJLUiQEsSZ0YwJLUyX8BFaWFgqB218cAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gender Wise Age Distribution histogram plot"
      ],
      "metadata": {
        "id": "J9piAk8aqvN3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Checked if the the balance between age and gender exists for each class "
      ],
      "metadata": {
        "id": "RTPOO7gi5pKH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (5, 5))\n",
        "sns.histplot(data=df,x='Age',hue=\"Gender\",kde=True,multiple='stack',color='grey')\n",
        "plt.xlabel('Age');\n",
        "plt.ylabel('Number of Patients');\n",
        "plt.title('Gender wise Age distribution');\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "nDwR4zQVL1hL",
        "outputId": "386b1266-8e06-465b-fa4e-e4c406b47ebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "source": [
        "#### Multiple box plots for all the attributes with level of the attribute values "
      ],
      "metadata": {
        "id": "Cc65bwyVrO89"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.columns = [c.replace(' ', '_') for c in df.columns]\n",
        "\n",
        "f, axes = plt.subplots(5,5,figsize=(40,30))\n",
        "\n",
        "sns.boxplot(data = df, x = 'Air_Pollution', y = 'Level' ,ax=axes[0,0])\n",
        "sns.boxplot(data = df, x=\"Alcohol_use\",y = 'Level' , ax=axes[0,1])\n",
        "sns.boxplot(data = df,  x=\"Dust_Allergy\", y = 'Level' ,ax=axes[0,2])\n",
        "sns.boxplot(data = df,  x=\"OccuPational_Hazards\",y = 'Level' , ax=axes[0,3])\n",
        "sns.boxplot(data = df,  x=\"Genetic_Risk\",y = 'Level' , ax=axes[0,4])\n",
        "sns.boxplot(data = df,  x=\"chronic_Lung_Disease\",y = 'Level' , ax=axes[1,0])\n",
        "sns.boxplot(data = df,  x=\"Balanced_Diet\",y = 'Level' , ax=axes[1,1])\n",
        "sns.boxplot(data = df,  x=\"Obesity\", y = 'Level' ,ax=axes[1,2])\n",
        "sns.boxplot(data = df,  x=\"Smoking\",y = 'Level' , ax=axes[1,3])\n",
        "sns.boxplot(data = df,  x=\"Passive_Smoker\", y = 'Level' ,ax=axes[1,4])\n",
        "sns.boxplot(data = df,  x=\"Chest_Pain\",y = 'Level' , ax=axes[2,0])\n",
        "sns.boxplot(data = df,  x=\"Coughing_of_Blood\", y = 'Level' ,ax=axes[2,1])\n",
        "sns.boxplot(data = df, x=\"Fatigue\",y = 'Level' , ax=axes[2,2])\n",
        "sns.boxplot(data = df,  x=\"Weight_Loss\", y = 'Level' ,ax=axes[2,3])\n",
        "sns.boxplot(data = df,  x=\"Shortness_of_Breath\",y = 'Level' , ax=axes[2,4])\n",
        "sns.boxplot(data = df,  x=\"Wheezing\", y = 'Level' ,ax=axes[3,0])\n",
        "sns.boxplot(data = df,  x=\"Swallowing_Difficulty\",y = 'Level' , ax=axes[3,1])\n",
        "sns.boxplot(data = df,  x=\"Clubbing_of_Finger_Nails\",y = 'Level' , ax=axes[3,2])\n",
        "sns.boxplot(data = df,  x=\"Frequent_Cold\", y = 'Level' ,ax=axes[3,3])\n",
        "sns.boxplot(data = df,  x=\"Dry_Cough\", y = 'Level' ,ax=axes[3,4])\n",
        "sns.boxplot(data = df,  x=\"Snoring\",y = 'Level' , ax=axes[4,0])\n",
        "plt.show()\n",
        "\n",
        "df.columns = [c.replace('_', ' ') for c in df.columns]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "Mq4L5TPqRo9d",
        "outputId": "0d48f0cc-adf0-45aa-99d9-3ec5185789ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2880x2160 with 25 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Multiple count plots for all the attributes with level of the attribute values "
      ],
      "metadata": {
        "id": "iuimMqgyrMSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = [c.replace(' ', '_') for c in df.columns]\n",
        "\n",
        "f, axes = plt.subplots(5,5,figsize=(30,30))\n",
        "\n",
        "sns.countplot(df.Air_Pollution, x='Air_Pollution',ax=axes[0,0])\n",
        "sns.countplot(df.Alcohol_use, x=\"Alcohol_use\", ax=axes[0,1])\n",
        "sns.countplot(df.Dust_Allergy, x=\"Dust_Allergy\", ax=axes[0,2])\n",
        "sns.countplot(df.OccuPational_Hazards, x=\"OccuPational_Hazards\", ax=axes[0,3])\n",
        "sns.countplot(df.Genetic_Risk, x=\"Genetic_Risk\", ax=axes[0,4])\n",
        "sns.countplot(df.chronic_Lung_Disease, x=\"chronic_Lung_Disease\", ax=axes[1,0])\n",
        "sns.countplot(df.Balanced_Diet, x=\"Balanced_Diet\", ax=axes[1,1])\n",
        "sns.countplot(df.Obesity, x=\"Obesity\", ax=axes[1,2])\n",
        "sns.countplot(df.Smoking, x=\"Smoking\", ax=axes[1,3])\n",
        "sns.countplot(df.Passive_Smoker, x=\"Passive_Smoker\", ax=axes[1,4])\n",
        "sns.countplot(df.Chest_Pain, x=\"Chest_Pain\", ax=axes[2,0])\n",
        "sns.countplot(df.Coughing_of_Blood, x=\"Coughing_of_Blood\", ax=axes[2,1])\n",
        "sns.countplot(df.Fatigue, x=\"Fatigue\", ax=axes[2,2])\n",
        "sns.countplot(df.Weight_Loss, x=\"Weight_Loss\", ax=axes[2,3])\n",
        "sns.countplot(df.Shortness_of_Breath, x=\"Shortness_of_Breath\", ax=axes[2,4])\n",
        "sns.countplot(df.Wheezing, x=\"Wheezing\", ax=axes[3,0])\n",
        "sns.countplot(df.Swallowing_Difficulty, x=\"Swallowing_Difficulty\", ax=axes[3,1])\n",
        "sns.countplot(df.Clubbing_of_Finger_Nails, x=\"Clubbing_of_Finger_Nails\", ax=axes[3,2])\n",
        "sns.countplot(df.Frequent_Cold, x=\"Frequent_Cold\", ax=axes[3,3])\n",
        "sns.countplot(df.Dry_Cough, x=\"Dry_Cough\", ax=axes[3,4])\n",
        "sns.countplot(df.Snoring, x=\"Snoring\", ax=axes[4,0])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "df.columns = [c.replace('_', ' ') for c in df.columns]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xXaA4jfpe6xB",
        "outputId": "ef8194fc-88ae-462b-c2cd-816c8818c5fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x2160 with 25 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Changing the level values to int to check correlation"
      ],
      "metadata": {
        "id": "QvTYft3lrK1S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chaning the values of Low, Meduim and High classes of the level of cancer to 0, 1 and 2 in order to check correlation."
      ],
      "metadata": {
        "id": "5SRMPDtV5z-f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Level'] = df['Level'].replace(['Low', 'Medium', 'High'], ['0', '1', '2'])"
      ],
      "metadata": {
        "id": "N_sXPe1EyAFH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Level']=df['Level'].astype(str).astype(int)"
      ],
      "metadata": {
        "id": "GkGkfTCPzt3q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaKJPCQBzVrB",
        "outputId": "4025e8a2-1f05-4de9-bbc4-607a91e8ff5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                         int64\n",
              "Gender                      int64\n",
              "Air Pollution               int64\n",
              "Alcohol use                 int64\n",
              "Dust Allergy                int64\n",
              "OccuPational Hazards        int64\n",
              "Genetic Risk                int64\n",
              "chronic Lung Disease        int64\n",
              "Balanced Diet               int64\n",
              "Obesity                     int64\n",
              "Smoking                     int64\n",
              "Passive Smoker              int64\n",
              "Chest Pain                  int64\n",
              "Coughing of Blood           int64\n",
              "Fatigue                     int64\n",
              "Weight Loss                 int64\n",
              "Shortness of Breath         int64\n",
              "Wheezing                    int64\n",
              "Swallowing Difficulty       int64\n",
              "Clubbing of Finger Nails    int64\n",
              "Frequent Cold               int64\n",
              "Dry Cough                   int64\n",
              "Snoring                     int64\n",
              "Level                       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "FKA7vFqFdzka",
        "outputId": "67f36c41-2991-4598-dc5c-560f324a61b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Gender  Air Pollution  Alcohol use  Dust Allergy  \\\n",
              "0   33       1              2            4             5   \n",
              "1   17       1              3            1             5   \n",
              "2   35       1              4            5             6   \n",
              "3   37       1              7            7             7   \n",
              "4   46       1              6            8             7   \n",
              "\n",
              "   OccuPational Hazards  Genetic Risk  chronic Lung Disease  Balanced Diet  \\\n",
              "0                     4             3                     2              2   \n",
              "1                     3             4                     2              2   \n",
              "2                     5             5                     4              6   \n",
              "3                     7             6                     7              7   \n",
              "4                     7             7                     6              7   \n",
              "\n",
              "   Obesity  ...  Fatigue  Weight Loss  Shortness of Breath  Wheezing  \\\n",
              "0        4  ...        3            4                    2         2   \n",
              "1        2  ...        1            3                    7         8   \n",
              "2        7  ...        8            7                    9         2   \n",
              "3        7  ...        4            2                    3         1   \n",
              "4        7  ...        3            2                    4         1   \n",
              "\n",
              "   Swallowing Difficulty  Clubbing of Finger Nails  Frequent Cold  Dry Cough  \\\n",
              "0                      3                         1              2          3   \n",
              "1                      6                         2              1          7   \n",
              "2                      1                         4              6          7   \n",
              "3                      4                         5              6          7   \n",
              "4                      4                         2              4          2   \n",
              "\n",
              "   Snoring  Level  \n",
              "0        4      0  \n",
              "1        2      1  \n",
              "2        2      2  \n",
              "3        5      2  \n",
              "4        3      2  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c351c2c-1ab3-474c-84d8-60585842ec5b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Air Pollution</th>\n",
              "      <th>Alcohol use</th>\n",
              "      <th>Dust Allergy</th>\n",
              "      <th>OccuPational Hazards</th>\n",
              "      <th>Genetic Risk</th>\n",
              "      <th>chronic Lung Disease</th>\n",
              "      <th>Balanced Diet</th>\n",
              "      <th>Obesity</th>\n",
              "      <th>...</th>\n",
              "      <th>Fatigue</th>\n",
              "      <th>Weight Loss</th>\n",
              "      <th>Shortness of Breath</th>\n",
              "      <th>Wheezing</th>\n",
              "      <th>Swallowing Difficulty</th>\n",
              "      <th>Clubbing of Finger Nails</th>\n",
              "      <th>Frequent Cold</th>\n",
              "      <th>Dry Cough</th>\n",
              "      <th>Snoring</th>\n",
              "      <th>Level</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c351c2c-1ab3-474c-84d8-60585842ec5b')\"\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",
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              "          document.querySelector('#df-8c351c2c-1ab3-474c-84d8-60585842ec5b 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-8c351c2c-1ab3-474c-84d8-60585842ec5b');\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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Finding attributes that are highly correlated with Level"
      ],
      "metadata": {
        "id": "b6swyaXOrv8U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlations = df.corr()['Level'].sort_values()\n",
        "print('Most Correlated features with Level of Lung Cancer:\\n', correlations.tail(7))\n",
        "print('\\nLeast Correlated features with Level of Lung Cancer:\\n', correlations.head(7))"
      ],
      "metadata": {
        "id": "jk_hKqyEoDqJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72c8b35a-43a1-4a85-bf62-21aa52379dc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most Correlated features with Level of Lung Cancer:\n",
            " Passive Smoker       0.703594\n",
            "Balanced Diet        0.706273\n",
            "Dust Allergy         0.713839\n",
            "Alcohol use          0.718710\n",
            "Coughing of Blood    0.782092\n",
            "Obesity              0.827435\n",
            "Level                1.000000\n",
            "Name: Level, dtype: float64\n",
            "\n",
            "Least Correlated features with Level of Lung Cancer:\n",
            " Gender                     -0.164985\n",
            "Age                         0.060048\n",
            "Wheezing                    0.242794\n",
            "Swallowing Difficulty       0.249142\n",
            "Clubbing of Finger Nails    0.280063\n",
            "Snoring                     0.289366\n",
            "Weight Loss                 0.352738\n",
            "Name: Level, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Finding each attributes correlation with Level"
      ],
      "metadata": {
        "id": "hoUa1rwwtcu2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_df = df.corr()['Level'].sort_values()\n",
        "corr_df = corr_df.reset_index().rename(columns={'index':'Attributes','Level':'Correlation'})\n",
        "corr_df"
      ],
      "metadata": {
        "id": "IvUnjzgOsvWv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "a1d4de04-506d-4c88-80d0-2db139868397"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Attributes  Correlation\n",
              "0                     Gender    -0.164985\n",
              "1                        Age     0.060048\n",
              "2                   Wheezing     0.242794\n",
              "3      Swallowing Difficulty     0.249142\n",
              "4   Clubbing of Finger Nails     0.280063\n",
              "5                    Snoring     0.289366\n",
              "6                Weight Loss     0.352738\n",
              "7                  Dry Cough     0.373968\n",
              "8              Frequent Cold     0.444017\n",
              "9        Shortness of Breath     0.497024\n",
              "10                   Smoking     0.519530\n",
              "11      chronic Lung Disease     0.609971\n",
              "12                   Fatigue     0.625114\n",
              "13             Air Pollution     0.636038\n",
              "14                Chest Pain     0.645461\n",
              "15      OccuPational Hazards     0.673255\n",
              "16              Genetic Risk     0.701303\n",
              "17            Passive Smoker     0.703594\n",
              "18             Balanced Diet     0.706273\n",
              "19              Dust Allergy     0.713839\n",
              "20               Alcohol use     0.718710\n",
              "21         Coughing of Blood     0.782092\n",
              "22                   Obesity     0.827435\n",
              "23                     Level     1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91777fa0-d999-4b2c-a385-e1032850e92c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Attributes</th>\n",
              "      <th>Correlation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Gender</td>\n",
              "      <td>-0.164985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Age</td>\n",
              "      <td>0.060048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Wheezing</td>\n",
              "      <td>0.242794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Swallowing Difficulty</td>\n",
              "      <td>0.249142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Clubbing of Finger Nails</td>\n",
              "      <td>0.280063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Snoring</td>\n",
              "      <td>0.289366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Weight Loss</td>\n",
              "      <td>0.352738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Dry Cough</td>\n",
              "      <td>0.373968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Frequent Cold</td>\n",
              "      <td>0.444017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Shortness of Breath</td>\n",
              "      <td>0.497024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Smoking</td>\n",
              "      <td>0.519530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>chronic Lung Disease</td>\n",
              "      <td>0.609971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Fatigue</td>\n",
              "      <td>0.625114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Air Pollution</td>\n",
              "      <td>0.636038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Chest Pain</td>\n",
              "      <td>0.645461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>OccuPational Hazards</td>\n",
              "      <td>0.673255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Genetic Risk</td>\n",
              "      <td>0.701303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Passive Smoker</td>\n",
              "      <td>0.703594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Balanced Diet</td>\n",
              "      <td>0.706273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Dust Allergy</td>\n",
              "      <td>0.713839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Alcohol use</td>\n",
              "      <td>0.718710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Coughing of Blood</td>\n",
              "      <td>0.782092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Obesity</td>\n",
              "      <td>0.827435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Level</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91777fa0-d999-4b2c-a385-e1032850e92c')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </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",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91777fa0-d999-4b2c-a385-e1032850e92c 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-91777fa0-d999-4b2c-a385-e1032850e92c');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Correlation heat map"
      ],
      "metadata": {
        "id": "wsGH0ZQctbzn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "sns.heatmap(df.drop('Level',axis=1).corr(),annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "X6BqPOGweE4P",
        "outputId": "6797bf7b-798d-4c84-9271-be74ca36ce49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0b77a51d00>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Applying Machine Learning models\n"
      ],
      "metadata": {
        "id": "-AFQkOIz36Ng"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First let's import important libraries, which helps me in the process of splitting the data in to train and test and evaluating the macine learning models we model across a common metrics "
      ],
      "metadata": {
        "id": "nMhaBwxbkhNJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default).\n",
        "from sklearn.model_selection import train_test_split,KFold,cross_val_score\n",
        "# Build a text report showing the main classification metrics, computes subset accuracy\n",
        "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix,mean_squared_error\n",
        "# THe below import Standardize features by removing the mean and scaling to unit variance.\n",
        "from sklearn.preprocessing import StandardScaler \n",
        "# Compute precision, recall, F-measure and support for each class.\n",
        "from sklearn.metrics import precision_recall_fscore_support as score"
      ],
      "metadata": {
        "id": "SRolZ3oR4GIK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop('Level',axis=1) # Created the Predictors i.e seperated the target paramenter from data and stored as X\n",
        "# Created the target variable from the data frame.\n",
        "Y=df['Level']\n",
        "# mseTracker is an dictionary object which would be used to store the MSE- Mean squared error for each of the model.\n",
        "mseTracker={}\n",
        "# accuracyTracker is an dictionary object which would be used to store the model accuries for each of the model.\n",
        "accuracyTracker={}\n",
        "# KFoldAccuries is a dataframe created and will be used for saving the max, min and mean accuraies obtained by K -fold cross validation technique \n",
        "dict = {'Model':[],\n",
        "        'Min Accuracy':[],\n",
        "        'Mean Accuracy':[],\n",
        "        'Max Accuracy':[],\n",
        "       }\n",
        "KFoldAccuries=pd.DataFrame(dict);\n",
        "# scoreTracker is an dictionary object which would be used to store the model Precision,Recall and F1-score for each of the model.\n",
        "scoreTracker=pd.DataFrame({\n",
        "    'Model':[],\n",
        "    'Precision':[],\n",
        "    'Recall':[],\n",
        "    'F1-score':[]\n",
        "})"
      ],
      "metadata": {
        "id": "bkCiNmn__wkJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- function getMSEForModel(params) would caluclated the values for the model Precision,Recall and F1-score based on the acutal labesl and the predicted labels and save it in scoreTracker variable using model name as the key."
      ],
      "metadata": {
        "id": "sdpaYgH6wEB2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def getMSEForModel(modelName,y_test,y_pred):\n",
        "  mse=mean_squared_error(y_test,y_pred)\n",
        "  precision,recall,fscore,support=score(y_test,y_pred,average='macro')\n",
        "  mseTracker[modelName]=mse\n",
        "  scoreTracker.loc[len(scoreTracker.index)] = [modelName,precision,recall,fscore] \n",
        "  return mse"
      ],
      "metadata": {
        "id": "fqTDeQX3v-pF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- function testKFold(params) would caluclated the values for the model min,max and mean accuraies based on K value given and save it in KFoldAccuries variable using model name as the index.\n",
        "- function getAccuracyForModel(params) would call the above method and also calculate the regularised accuracy score based on test labels and the predicted labels which will be passed as args to the function."
      ],
      "metadata": {
        "id": "qTEau_5AwiEU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def testKFold(model,K):\n",
        "  kfold_validation=KFold(K)\n",
        "  results=cross_val_score(model,X_train,Y_train,cv=kfold_validation)\n",
        "  KFoldAccuries.loc[len(KFoldAccuries.index)] = [type(model).__name__,np.min(results),np.mean(results),np.max(results)] \n",
        "  min,mean,max=np.min(results),np.mean(results),np.max(results)\n",
        "  print()\n",
        "  print('Accuracy of ',type(model).__name__,' model when K Fold= ',K,': ')\n",
        "  print('Min Accuracy',min)\n",
        "  print('Max Accuracy',max)\n",
        "  print('Mean Accuracy',mean)\n",
        "  print()\n",
        "  return np.min(results),np.mean(results),np.max(results)\n",
        "\n",
        "def getAccuracyForModel(model,y_test,y_pred):\n",
        "  K=10\n",
        "  score=accuracy_score(y_test,y_pred)\n",
        "  accuracyTracker[type(model).__name__]=score\n",
        "  testKFold(model,K);\n",
        "  return accuracyTracker[type(model).__name__]"
      ],
      "metadata": {
        "id": "JTtlzqewDZeC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Here, we would split the data to train and test data based on the ratio given (30% test data in our case). train data would be used to train(fit) the model and test data would be used to evaluate/validate the trained model"
      ],
      "metadata": {
        "id": "4C23zD-yxFZh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=44)"
      ],
      "metadata": {
        "id": "BSNnpWte3_wD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape,Y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbjNu3BP4KQI",
        "outputId": "1fd5b272-e091-4b26-8846-28b1f6c5b0e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((700, 23), (700,))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- StandardScaler removes the mean and scales each feature/variable to unit variance. This operation is performed feature-wise in an independent way. "
      ],
      "metadata": {
        "id": "81XIZCXIxbba"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "Mmm1mQ0Z8oOi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression"
      ],
      "metadata": {
        "id": "1CC8zn-w41l4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "Cvz6zJLGDRTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- In the multiclass classification case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’. which is done in the below line\n",
        "- Model is trained in the second line using .fit()"
      ],
      "metadata": {
        "id": "l5q7MnO6x4Wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logmodel = LogisticRegression(random_state=0, multi_class='ovr')\n",
        "logmodel.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sS9cBf1W5ltJ",
        "outputId": "2485774e-96ff-4987-b2f2-e8c28d515885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(multi_class='ovr', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0HpZH85v9tH",
        "outputId": "e62c953a-2c86-4b8d-cf28-1e21ed3aa7d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "357    0\n",
              "645    1\n",
              "271    0\n",
              "400    1\n",
              "342    1\n",
              "      ..\n",
              "571    1\n",
              "173    0\n",
              "753    2\n",
              "419    1\n",
              "788    2\n",
              "Name: Level, Length: 700, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmFULH5s84UF",
        "outputId": "d5bca2ea-0265-4ab0-b715-dc94b0fea8e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.17116393,  1.23944822,  0.08190761, ...,  0.20840258,\n",
              "        -0.4403713 , -1.34657148],\n",
              "       [ 1.31208526,  1.23944822, -1.41379651, ...,  0.20840258,\n",
              "        -0.92353731,  1.40554542],\n",
              "       [ 0.11327531,  1.23944822, -0.41666043, ...,  0.74870557,\n",
              "        -1.40670332, -1.34657148],\n",
              "       ...,\n",
              "       [ 0.627051  , -0.80681063,  1.07904368, ...,  0.20840258,\n",
              "         0.52596071,  0.02948697],\n",
              "       [ 0.71268028, -0.80681063,  1.07904368, ..., -0.33190041,\n",
              "         0.0427947 , -0.65854226],\n",
              "       [ 2.33963665, -0.80681063,  1.07904368, ..., -0.33190041,\n",
              "        -1.40670332,  0.71751619]])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next line, the trained model is tested on the test data to predict the outcome based on the input."
      ],
      "metadata": {
        "id": "FHu0zwjzykxe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = logmodel.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_diw38Qg7oz0",
        "outputId": "65e7aa55-92eb-4d3f-9a17-0820f0d69507"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 2, 0, 1, 0, 0, 0,\n",
              "       2, 1, 1, 1, 1, 1, 1, 0, 1, 0, 2, 2, 1, 0, 0, 0, 0, 1, 2, 2, 1, 1,\n",
              "       2, 2, 1, 2, 1, 1, 0, 2, 0, 2, 1, 2, 0, 1, 0, 1, 2, 0, 0, 2, 2, 0,\n",
              "       1, 1, 1, 2, 2, 2, 1, 0, 2, 1, 2, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, 0,\n",
              "       2, 1, 2, 1, 0, 2, 2, 1, 1, 2, 1, 2, 1, 0, 1, 0, 0, 0, 2, 0, 2, 1,\n",
              "       1, 0, 2, 2, 1, 1, 1, 0, 0, 0, 1, 2, 2, 1, 0, 0, 1, 2, 0, 1, 1, 1,\n",
              "       2, 0, 2, 0, 0, 2, 2, 1, 2, 0, 1, 0, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2,\n",
              "       0, 0, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 0, 1, 1, 2, 0, 2, 1, 0, 1, 1,\n",
              "       0, 1, 1, 0, 2, 2, 1, 2, 0, 2, 1, 0, 0, 1, 2, 2, 1, 2, 2, 0, 1, 2,\n",
              "       1, 2, 0, 1, 0, 2, 1, 2, 1, 0, 2, 0, 0, 2, 0, 1, 2, 2, 0, 0, 2, 1,\n",
              "       1, 0, 1, 0, 2, 0, 1, 1, 2, 0, 2, 0, 0, 2, 2, 1, 2, 0, 2, 0, 0, 1,\n",
              "       2, 0, 1, 1, 2, 1, 0, 0, 1, 1, 2, 2, 2, 1, 1, 1, 1, 0, 0, 2, 0, 0,\n",
              "       2, 0, 2, 0, 0, 0, 0, 2, 1, 1, 2, 0, 0, 0, 2, 1, 0, 1, 0, 2, 0, 2,\n",
              "       2, 2, 2, 2, 2, 0, 2, 1, 0, 1, 0, 0, 2, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Computes confusion matrix to evaluate the accuracy of a classification."
      ],
      "metadata": {
        "id": "ZE6MuWFzy0qJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)\n"
      ],
      "metadata": {
        "id": "nAXI4SV-8vRo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7d8e70c-cf13-467c-ed56-12fc6cd83980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 94,   1,   0],\n",
              "       [  5, 105,   0],\n",
              "       [  0,   0,  95]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a text report showing the main classification metrics such has precision,f1-score and recall values for the actual and predicted values."
      ],
      "metadata": {
        "id": "vN0Cz1iBzbUi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UP8QdW_U_EZ9",
        "outputId": "f5969d1d-f3d5-4de7-e3b5-fbf9fb8e323a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97        95\n",
            "           1       0.99      0.95      0.97       110\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           0.98       300\n",
            "   macro avg       0.98      0.98      0.98       300\n",
            "weighted avg       0.98      0.98      0.98       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "dSa2CCPSvQT9",
        "outputId": "35dc63d5-e462-4599-f67b-ead7e426cdef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(logmodel).__name__,': ',getMSEForModel(type(logmodel).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(logmodel,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(logmodel).__name__,' model: ',Accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BqHJurxND26Q",
        "outputId": "6f9941bc-71e5-45b8-a615-1d2c59833619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  LogisticRegression :  0.02\n",
            "\n",
            "Accuracy of  LogisticRegression  model when K Fold=  10 : \n",
            "Min Accuracy 0.9428571428571428\n",
            "Max Accuracy 1.0\n",
            "Mean Accuracy 0.9771428571428572\n",
            "\n",
            "Accuracy of  LogisticRegression  model:  0.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Linear Regression"
      ],
      "metadata": {
        "id": "E4OgpEQC_66E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation."
      ],
      "metadata": {
        "id": "6T6wWNpizv26"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "_azFPa3_A1Vx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape,Y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69X7mohbFt-y",
        "outputId": "dbb44029-cb75-4280-ff3c-af36441dcf60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((300, 23), (700,))"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "regModel = LinearRegression()\n",
        "regModel.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "979q3rKE_5gf",
        "outputId": "141c7e77-c33d-45e0-d60d-6506b5d6ba7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the results from test data X\n",
        "y_pred = regModel.predict(X_test)"
      ],
      "metadata": {
        "id": "7wQ-D1Y1AeeY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Rounding off the values since the required values are discrete and not Continous. \n",
        "Y_pred=np.abs(np.round(y_pred))"
      ],
      "metadata": {
        "id": "X9DYI8KmGcuF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fllkpqpxAl7Q",
        "outputId": "254192f3-fd9c-431f-d3be-6911ac206e0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 82,  13,   0],\n",
              "       [  0, 110,   0],\n",
              "       [  0,   3,  92]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a text report showing the main classification metrics such has precision,f1-score and recall values for the actual and predicted values."
      ],
      "metadata": {
        "id": "OyZ3SvXq6f7o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTV6F3bAAv69",
        "outputId": "70b8c0fb-e355-4b73-ea90-e8454247fdcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.86      0.93        95\n",
            "           1       0.87      1.00      0.93       110\n",
            "           2       1.00      0.97      0.98        95\n",
            "\n",
            "    accuracy                           0.95       300\n",
            "   macro avg       0.96      0.94      0.95       300\n",
            "weighted avg       0.95      0.95      0.95       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(regModel).__name__,': ',getMSEForModel(type(regModel).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(regModel,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(regModel).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLEqxmu5C7kQ",
        "outputId": "bc3ccbc6-05f3-42df-c651-e59677a2010b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  LinearRegression :  0.05333333333333334\n",
            "\n",
            "Accuracy of  LinearRegression  model when K Fold=  10 : \n",
            "Min Accuracy 0.881772615649318\n",
            "Max Accuracy 0.9483815073032689\n",
            "Mean Accuracy 0.9283261585490743\n",
            "\n",
            "Accuracy of  LinearRegression  model:  0.9466666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Plotting a heatmap of the confusion matrix with flags annot=True which plots with numbers of strength and fmt='g' signifies the values to be displayed as integer."
      ],
      "metadata": {
        "id": "pOcMFxP00LEs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "2s0k75R7Axl-",
        "outputId": "b8a522a5-1496-4608-9e33-762aa87bff41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Gaussian Naive Bayes"
      ],
      "metadata": {
        "id": "H5mF4cQZDMyB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Gaussian Naïve Bayes: GN Bayes’s rule provides us with the formula for the probability of Y given some feature X."
      ],
      "metadata": {
        "id": "3pYpUD050uCk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "metadata": {
        "id": "Ea6WsW-PDL16"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = GaussianNB()\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdwfGI-BB57Y",
        "outputId": "befbed79-21b5-44c1-8345-d1a416f2d974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1sC2KASB57b",
        "outputId": "b8b77fd7-8271-44a0-f135-2cf3eb1fcce4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0, 2, 0, 2, 1, 2, 0, 2, 0, 0, 2,\n",
              "       2, 1, 1, 1, 1, 1, 2, 0, 1, 0, 2, 2, 1, 0, 0, 0, 1, 1, 2, 2, 1, 1,\n",
              "       2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 2, 2, 0, 1, 0, 1, 2, 0, 0, 2, 2, 0,\n",
              "       1, 1, 1, 2, 2, 2, 1, 0, 2, 1, 2, 0, 2, 1, 0, 1, 0, 1, 0, 1, 2, 0,\n",
              "       2, 1, 2, 1, 0, 2, 2, 2, 1, 2, 1, 2, 1, 0, 1, 0, 0, 0, 2, 0, 1, 1,\n",
              "       1, 0, 2, 2, 1, 1, 1, 2, 0, 0, 1, 2, 2, 2, 0, 2, 1, 2, 0, 1, 1, 2,\n",
              "       2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 1, 2, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2,\n",
              "       0, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 2, 0, 2, 1, 0, 1, 1,\n",
              "       0, 1, 1, 0, 2, 1, 1, 2, 0, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 0, 1, 2,\n",
              "       1, 2, 0, 1, 0, 2, 1, 2, 2, 0, 2, 0, 0, 2, 1, 1, 2, 2, 2, 2, 2, 1,\n",
              "       1, 0, 1, 0, 2, 0, 1, 1, 2, 0, 2, 2, 1, 2, 2, 2, 2, 0, 2, 0, 1, 1,\n",
              "       2, 0, 2, 1, 2, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1, 1, 1, 0, 0, 2, 1, 0,\n",
              "       2, 0, 2, 2, 0, 0, 0, 2, 1, 2, 2, 0, 0, 0, 2, 1, 0, 1, 0, 2, 0, 2,\n",
              "       2, 2, 2, 2, 2, 0, 2, 1, 0, 1, 0, 0, 2, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_diCWlStB57c",
        "outputId": "34e665b6-6fdd-407d-d7ef-b3ce2a3296f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[83,  7,  5],\n",
              "       [ 0, 87, 23],\n",
              "       [ 0,  3, 92]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yks8WTJrB57d",
        "outputId": "e8dd3ec6-1edb-40f3-a93f-1216418a746e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.87      0.93        95\n",
            "           1       0.90      0.79      0.84       110\n",
            "           2       0.77      0.97      0.86        95\n",
            "\n",
            "    accuracy                           0.87       300\n",
            "   macro avg       0.89      0.88      0.88       300\n",
            "weighted avg       0.89      0.87      0.87       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(model).__name__,': ',getMSEForModel(type(model).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(model,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(model).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MLRLLm3JGyW",
        "outputId": "b388a08c-2e78-47b2-c3a4-6e0de25c5a35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  GaussianNB :  0.17666666666666667\n",
            "\n",
            "Accuracy of  GaussianNB  model when K Fold=  10 : \n",
            "Min Accuracy 0.8571428571428571\n",
            "Max Accuracy 0.9714285714285714\n",
            "Mean Accuracy 0.9\n",
            "\n",
            "Accuracy of  GaussianNB  model:  0.8733333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "0BYpCMjdB57d",
        "outputId": "cf173b08-c612-4b34-f3a1-f4a65f349f70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEGCAYAAABIGw//AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaHUlEQVR4nO3deZxU5ZX/8c+p7oYGRWRRdgUDLmiMRHCBiLiCioJLXOIvYRKVLI5bNIkTJz8nxjhRMhhHTZRhkRhBUBQUsV1wQyMIIjICGhEQ2cQFRJCtq878UbdJq9BV3V1V9+ni++Z1X1TdW3XvoV68Tp8+93meMndHRETCk4g7ABER2TklaBGRQClBi4gESglaRCRQStAiIoEqjTuAXdny6ngNL8mzA8+4Je4Qit7m5La4Q9gtfPTZO1bfc2z/eEnWOaes9QH1vl42VEGLiAQq2ApaRKSgUsm4I/gaJWgREYBkZdwRfI0StIgI4J6KO4SvUYIWEQFIKUGLiIRJFbSISKB0k1BEJFCqoEVEwuQaxSEiEijdJBQRCZRaHCIigdJNQhGRQKmCFhEJlG4SiogESjcJRUTC5K4etIhImNSDFhEJlFocIiKBUgUtIhKo5Pa4I/gaJWgREVCLQ0QkWGpxiIgEShW0iEiglKBFRMLkAd4kTMQdgIhIEDyV/ZaBmV1jZgvM7C0zG29m5WbWxcxmmdliM5tgZo0ynUcJWkQE0i2ObLcamFkH4Eqgp7sfBpQAFwK3Are7e1dgHXBJppCUoEVEIKcVNOn2cRMzKwWaAquBE4GHo+NjgcGZTqIELSICtaqgzWyomc2ptg2tOo27rwT+CCwnnZg/A14H1rt71ZqmK4AOmULSTUIREajVOGh3HwGM2NkxM2sBDAK6AOuBh4ABdQlJCVpEBKAyZwv2nwwsdfePAMzsEaAPsLeZlUZVdEdgZaYTKUHX0f1PvcojL87FDLp1bMNNlwzilvunsXDZKtyd/du24neXDqZpeeO4Qy0KB3TtzN2jhu14vl/njgz/z7sZdc/fYoyq+Lw+fzobN24ilUxRmUxySr9z4w6pcHI3k3A5cIyZNQU2AycBc4DngfOAB4EhwJRMJ1KCroMP121g3DOzePSWyylvVMYv7p5Ixay3+MX3+rNnk3IAho2vYPyzr3HJwONijrY4LFm8jNOO/y4AiUSC1xZMp2Lq9JijKk5nDxzCp5+uizuMwsvRRBV3n2VmDwNzgUrgDdLtkCeAB83s5mjfqEznUoKuo2QqxdZt2yktSbB523b2adFsR3J2d7Zuq8TMYo6yOPU5/miWL/uAlStWxx2KFJMcrsXh7jcCN35l9xLgqNqcRwm6Dtq02IshA3rT/9rbKW9UxrGHfoPeh3UF4DcjJ/Py/Hc5oP0+XHvhqTFHWpzOOuc0pkx6Mu4wipIDD00ehbszdswE7r9vYtwhFc7uNNXbzA4mfSezaijJSuAxd1+Ur2sWyoZNm3n+jbeZNuxqmjUt5xd3T2Tq399kYO9v8btLB5NMpfjD36bx1GsLGHxcj7jDLSplZaWcMqAft950R9yhFKWB/S9izeq1tG7dkocmj2HxP5bw6t/nxB1WYQS4ml1exkGb2a9IN8INeC3aDBhvZtfX8L4dYwtHTQ63vzhzwRI6tG5By732oKy0hJN6HsKbiz/YcbwkkWDA0Yfx7JyFMUZZnPqdfBxvzV/Exx99EncoRWnN6rUAfPzxp0yb+gw9jjw85ogKqLIy+61A8lVBXwIc6u5fWn3EzIYDC4A/7OxN1ccWbnl1vOcptnpr26o5899bweat2yhvVMashUvp3rk9yz/8hP3atMLdeeGNd+jSrnXcoRadQeeqvZEvTZs2wRIJNm3cRNOmTeh3Yh/+69Y/xx1W4Xh4KSdfCToFtAfe/8r+dtGxBu3wb3TklF7dufDGeykpSXDwfu04r9+RXHbrWDZu2Yq7c1Cnttww5Iy4Qy0qTZo24bh+x/Jv19wUdyhFaZ99W3Hf3+4GoLS0hEcenspz02fEHFUBBdiDNs/DTw0zGwDcBbwLVP3uvx/QFfhXd6/IdI6QK+hiceAZt8QdQtHbnNwWdwi7hY8+e6feQ6Y2P/CbrHNOk4t/V5AhWnmpoN29wswOJD2kpPpNwtnunszHNUVE6iXAm4R5G8Xh7ilgZr7OLyKSU8nwakeNgxYRgSB70ErQIiKgBC0iEqzdqQctItKQeCq8gWNK0CIioBaHiEiwNIpDRCRQqqBFRAKlBC0iEqjdaLEkEZGGRRW0iEigNMxORCRQGsUhIhImV4tDRCRQanGIiARKa3GIiARKFbSISKAqdZNQRCRManGIiARKLQ4RkTBpmJ2ISKhUQYuIBEoJWkQkUJrqLSISJn0noYhIqJSgRUQCpVEcIiKBUgUtIhIoJWgRkTB5Ui2OrO15/HVxh1D0Ns0fF3cIRe+nA+6MOwTJVoAVdCLuAEREQuApz3rLxMz2NrOHzextM1tkZseaWUsze8bM3o3+bpHpPErQIiKQrqCz3TK7A6hw94OBbwGLgOuB6e7eDZgePa+RErSICECqFlsNzKw50BcYBeDu29x9PTAIGBu9bCwwOFNIStAiIoBXprLezGyomc2ptg2tdqouwEfAGDN7w8xGmtkeQBt3Xx29Zg3QJlNMwd4kFBEpqFoM4nD3EcCIXRwuBb4NXOHus8zsDr7SznB3N7OMvRJV0CIi5PQm4QpghbvPip4/TDphf2hm7QCiv9dmOpEStIgI5KwH7e5rgA/M7KBo10nAQuAxYEi0bwgwJVNIanGIiJDz1eyuAB4ws0bAEuCHpAviiWZ2CfA+cH6mkyhBi4hArXrQmbj7PKDnTg6dVJvzKEGLiABeGXcEX6cELSICeHhLcShBi4gAOW1x5IoStIgIqqBFRIKlBC0iEihPWtwhfI0StIgIqqBFRILlKVXQIiJBUgUtIhIod1XQIiJBUgUtIhKolEZxiIiESTcJRUQCpQQtIhIoz+ly0LmxywRtZncCuwzZ3a/MS0QiIjFoaBX0nIJFISISswY1zM7dxxYyEBGROCUb4igOM9sH+BXQHSiv2u/uJ+YxLhGRggqxgs7mW70fABYBXYDfAsuA2XmMSUSk4DxlWW+Fkk2CbuXuo4Dt7v6iu/8IUPUsIkXFPfutULIZZrc9+nu1mZ0BrAJa5i8kEZHCa2ijOKrcbGbNgWuBO4G9gGvyGpWISIElU9k0FAorY4J296nRw8+AE/IbTsPU/9R+DB9+EyWJBKPHjOe2YXfHHVJRuH/KMzzyzMtgRrf9O/C7K/+FoTfezhebtwDw6frPOezAztzx68tjjrThatGuFZcOv4LmrZvjDi+Of4Znx0zj7J9fyBGn9MI9xYaPNzD6urtYv3Zd3OHmVYOaqFLFzMawkwkrUS96t5dIJPjvO37PgNMvYsWK1cx8dRqPT32aRYvejTu0Bu3DT9bxwNTnmHzXbylv3IjrbruXihmzGfufv9zxmmv+8BdOOOqIGKNs+FKVSSbcPJblC5ZSvkc5///x21g4Yz5PjpjCo8MfBODkfzmdM6/6LvffMCLmaPMr1UBHcUwFnoi26aRbHBvzGVRDclSvHrz33jKWLl3O9u3bmThxCmed2T/usIpCMpli67btVCaTbNm6jX1aNt9xbOMXm3lt/juceIwSdH189tF6li9YCsCWTVtY/d5K9m7bki0bN+94TaOmjcMsL3PM3bLeCiWbFsek6s/NbDzwct4iamDad2jLBytW7Xi+YuVqjurVI8aIikObVi0YcvapnHrp9ZQ3KuPYI7rTu8ehO44/N3MeRx9+MHs2bRJjlMWlVcd92K97Z5bMS//2d851F9H7nOP54vMvGHbRf8QbXAGE+DOoLl3xbsC+db2gmf2whmNDzWyOmc1JpTbV9RJSBDZs3MTzs+bx5IhbeHbMbWzeupWpL8zccfzJGa9xWt9eMUZYXBo3Lefyv1zH+Jvu21E9P/LH8VzX+yfMnDKDE4cMiDnC/Eu5Zb0VSsYEbWafm9mGqg14nPTMwrr67a4OuPsId+/p7j0TiT3qcYnCWbVyDZ06tt/xvGOHdqxatSbGiIrDzDcX0bFNa1o2b0ZZaSknHfNt5r39HgDrNnzOW+8uo2/Pw2OOsjiUlJZw+T3XMXPyDOY+Netrx2dOnsGRA46JIbLCSqYSWW+Fkk2Lo1ltT2pm83d1CGhT2/OFbPaceXTt2oXOnTuxcuUazj9/EN//gUYV1Ffb1i2Z/84SNm/dSnmjRsya/zaHdt0fgGdemUvfnofTuFFZzFEWhx/e+jNWL17B06Om7ti3b+e2rF2WLjR6nNKLNe+tjCu8ggmww5HVKI7p7n5Spn1f0QboD3x1XI4Bf691lAFLJpNcdfW/M+2JcZQkEtw3dgILF/4j7rAavMMPOoCTex/JBdfcTElJCYcc0Inz+h8HQMXLs/nRucX/K3chdOt5ML3PPZ4PFr3Pf0wbBsCk28Zx3AUn0faA9njK+WTlR/y1yEdwQJijOMx30Rk3s3KgKfA80I90coX0KI4Kdz94lyc1GwWMcfev3Uw0s3Hu/r1MgZU26hDiD7Sismn+uLhDKHo/HXBn3CHsFkYve7je2fWVtudlnXP6rKn/9bJRUwX9Y+BqoD3wOv9M0BuAu2o6qbtfUsOxjMlZRKTQAvxS7xrXg74DuMPMrnB3lQEiUtSc8Foc2dyOTJnZ3lVPzKyFmf0sjzGJiBRcpVvWW6Fkk6Avc/f1VU/cfR1wWf5CEhEpPMey3golm9XsSszMPLqbaGYlQKP8hiUiUlgNqgddTQUwwczujZ7/GHgyfyGJiBReQ+1B/wp4DvhJtP0voAUQRKSopGqxZcPMSszsDTObGj3vYmazzGyxmU0ws4ydiIwJ2t1TwCzS30V4FOmvu1qUZYwiIg1CEst6y9JVfDlX3grc7u5dSU/i2+Vw5Cq7TNBmdqCZ3Whmb5P+JpXlAO5+grvXOA5aRKShSVn2WyZm1hE4AxgZPTfSxe3D0UvGAoMznaemHvTbwAxgoLsvji6ir7oSkaKUqkUP2syGAkOr7Rrh7tXnw/8J+CVQtZZRK2C9u1dGz1cAHTJdp6YEfQ5wIfC8mVUAD0KAXXQRkRyozdoSUTLe6QIlZjYQWOvur5tZv/rEVNNMwsnAZDPbAxhEetr3vmb2F+BRd3+6PhcWEQlJDofZ9QHOMrPTgXLS6xfdAextZqVRFd0RyLhEYDY3CTe5+zh3PzM66RvUbz1oEZHgpMyy3mri7v/m7h3dvTPpLsRz7n4x6YXnzoteNgSYkimmWq087e7rokX1a1pqVESkwUnWYqujXwE/N7PFpHvSozK9IZuJKiIiRS+b0Rm15e4vAC9Ej5eQHqqcNSVoERFqN4qjUJSgRURooF95JSKyO8hHi6O+lKBFRGi4q9mJiBS9pCpoEZEwqYIWEQmUErSISKAK+FWDWVOCFhFBFbSISLDqMYU7b5SgRUTQOGgRkWCpxSEiEiglaBGRQGktDhGRQKkHLSISKI3ikKDsc+QP4w6h6H3y/rNxhyBZSgXY5FCCFhFBNwlFRIIVXv2sBC0iAqiCFhEJVqWFV0MrQYuIoBaHiEiw1OIQEQmUhtmJiAQqvPSsBC0iAqjFISISrGSANbQStIgIqqBFRILlqqBFRMKkClpEJFAaZiciEqjw0rMStIgIAJUBpmglaBERdJNQRCRYukkoIhIoVdAiIoFSBS0iEqikh1dBJ+IOQEQkBCk8660mZtbJzJ43s4VmtsDMror2tzSzZ8zs3ejvFpliUoIWESHdg872TwaVwLXu3h04BrjczLoD1wPT3b0bMD16XiMlaBER0j3obLeauPtqd58bPf4cWAR0AAYBY6OXjQUGZ4pJPWgREfIz1dvMOgM9gFlAG3dfHR1aA7TJ9H5V0CIi1K7FYWZDzWxOtW3oV89nZnsCk4Cr3X3Dl67l7mQxu1wVtIgItRvF4e4jgBG7Om5mZaST8wPu/ki0+0Mza+fuq82sHbA203VUQYuIkNNRHAaMAha5+/Bqhx4DhkSPhwBTMsWkClpEhJxOVOkDfB/4XzObF+37NfAHYKKZXQK8D5yf6URK0CIi5G6qt7u/DNguDp9Um3MpQYuIoAX7i1b/U/sxfPhNlCQSjB4zntuG3R13SEWnceNGVDw9gUaNG1FaUsKUyRXc8vs/xR1WUbh/4mQmPVaBu3PeWQP4/gVn88e7RvLiK7MoLSulU4d23Pzrn7NXsz3jDjWvXFO9i08ikeC/7/g9A8/8f3zzWydwwQWDOeSQbnGHVXS2bt3GwNMvps8xZ9Dn2IGcfEpfevU6Iu6wGrx3lyxj0mMVjB/5JyaN/TMv/v01lq9YxbG9evDo/ffw6F//QudOHRh5/4S4Q827JJ71VihK0PV0VK8evPfeMpYuXc727duZOHEKZ53ZP+6witKmTV8AUFZWSmlZaZAVT0OzZNkHfPPQg2hSXk5paQk9j/gmz774Cn2OPpLS0hIADj/0YD5c+3HMkeZfrkZx5JISdD2179CWD1as2vF8xcrVtG/fNsaIilcikeDlV6fy3rLZPP/cK8yZ82bcITV4XQ/Yn7lvLmD9ZxvYvGULM16dzZoPP/rSax594mm+c2yvmCIsHHfPeiuUvPWgzexg0vPPZ7n7xmr7B7h7Rb6uK8UrlUrxnWMH0rx5Mx4Yfw+HdD+QRQv/EXdYDdo3Ou/Hjy7+LkOvuYEm5eUc1O0AEol/1m33jh1PSUkJA089IcYoCyPEm4R5qaDN7ErSg7CvAN4ys0HVDt9Sw/t2TJ9MpTblI7ScW7VyDZ06tt/xvGOHdqxatSbGiIrfZ599zoyXZnLyKX3jDqUonHtmfyaOvpOxfx7GXs2a0Xm/jgBMfuIZXnrlNW698Zek514UtxyuZpcz+WpxXAYc6e6DgX7Ab6rWRGXX4wNx9xHu3tPdeyYSe+QptNyaPWceXbt2oXPnTpSVlXH++YN4fOrTcYdVdFq1bknz5s0AKC9vzAknfod331kSc1TF4ZN16wFYvWYt0198hdNP6cfLM+cwetxD3HnrjTQpL485wsJIume9FUq+WhyJqraGuy8zs37Aw2a2PzUk6IYomUxy1dX/zrQnxlGSSHDf2Aks1K/dOde27b7cM2IYJSUlJBLGo5OmUVHxXNxhFYVrfn0z6zdsoLS0lBuu/Rl7NduT3w//M9u2b+eyq28A0jcKb/zlFTFHml8htjgsHw1vM3sO+Lm7z6u2rxQYDVzs7iWZzlHaqEN4n1aRaVrWOO4Qit4n7z8bdwi7hbLWB9S78Du2wwlZ55xXVz5fkEIzXxX0D0h/q8AO7l4J/MDM7s3TNUVE6izEYZt5SdDuvqKGY6/k45oiIvURYotDU71FRMjdYkm5pAQtIgIkPYcLjuaIErSICLtRD1pEpKFRD1pEJFDqQYuIBCqlFoeISJhUQYuIBEqjOEREAqUWh4hIoNTiEBEJlCpoEZFAqYIWEQlU0pNxh/A1StAiImiqt4hIsDTVW0QkUKqgRUQCpVEcIiKB0igOEZFAaaq3iEig1IMWEQmUetAiIoFSBS0iEiiNgxYRCZQqaBGRQGkUh4hIoHSTUEQkUCG2OBJxByAiEgKvxZ9MzGyAmb1jZovN7Pq6xqQKWkSE3FXQZlYC3A2cAqwAZpvZY+6+sLbnUoIWESGnPeijgMXuvgTAzB4EBgHFk6Art620uGOoLTMb6u4j4o6jmOkzzr/d9TOuTc4xs6HA0Gq7RlT7zDoAH1Q7tgI4ui4xqQedW0Mzv0TqSZ9x/ukzzsDdR7h7z2pbXn6gKUGLiOTWSqBTtecdo321pgQtIpJbs4FuZtbFzBoBFwKP1eVEwfagG6jdrm8XA33G+afPuB7cvdLM/hV4CigBRrv7grqcy0IcnC0iImpxiIgESwlaRCRQStA5kKtpnbJrZjbazNaa2Vtxx1KszKyTmT1vZgvNbIGZXRV3TLs79aDrKZrW+Q+qTesELqrLtE7ZNTPrC2wE/uruh8UdTzEys3ZAO3efa2bNgNeBwfq/HB9V0PW3Y1qnu28DqqZ1Sg65+0vAp3HHUczcfbW7z40efw4sIj0rTmKiBF1/O5vWqf/U0qCZWWegBzAr3kh2b0rQIvIlZrYnMAm42t03xB3P7kwJuv5yNq1TJG5mVkY6OT/g7o/EHc/uTgm6/nI2rVMkTmZmwChgkbsPjzseUYKuN3evBKqmdS4CJtZ1WqfsmpmNB14FDjKzFWZ2SdwxFaE+wPeBE81sXrSdHndQuzMNsxMRCZQqaBGRQClBi4gESglaRCRQStAiIoFSghYRCZQStOSFmSWjYVpvmdlDZta0Hue6z8zOix6PNLPuNby2n5n1rsM1lplZ67rGKJIPStCSL5vd/Yho5bltwE+qHzSzOn3dmrtfmmF1tX5ArRO0SIiUoKUQZgBdo+p2hpk9Biw0sxIzG2Zms81svpn9GNIz2szsrmiN7WeBfatOZGYvmFnP6PEAM5trZm+a2fRogZ+fANdE1ftxZraPmU2KrjHbzPpE721lZk9H6x6PBKywH4lIZvrSWMmrqFI+DaiIdn0bOMzdl5rZUOAzd+9lZo2BV8zsadKrqB0EdAfaAAuB0V857z7A/wB9o3O1dPdPzeweYKO7/zF63Tjgdnd/2cz2Iz3j8xDgRuBld7/JzM4ANDNRgqMELfnSxMzmRY9nkF7joTfwmrsvjfafChxe1V8GmgPdgL7AeHdPAqvM7LmdnP8Y4KWqc7n7rtaKPhnonl5mAoC9otXa+gLnRO99wszW1fHfKZI3StCSL5vd/YjqO6Ikuan6LuAKd3/qK6/L5foPCeAYd9+yk1hEgqYetMTpKeCn0RKXmNmBZrYH8BJwQdSjbgecsJP3zgT6mlmX6L0to/2fA82qve5p4IqqJ2ZW9UPjJeB70b7TgBY5+1eJ5IgStMRpJOn+8tzoy2DvJf1b3aPAu9Gxv5Jexe5L3P0jYCjwiJm9CUyIDj0OnF11kxC4EugZ3YRcyD9Hk/yWdIJfQLrVsTxP/0aROtNqdiIigVIFLSISKCVoEZFAKUGLiARKCVpEJFBK0CIigVKCFhEJlBK0iEig/g9xJtUNSMmHYwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Bernoulli Naive Bayes"
      ],
      "metadata": {
        "id": "7Ml4Fk18B81v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- In the multivariate Bernoulli event model, features are independent Booleans (binary variables) describing inputs. Like the multinomial model, this model is popular for document classification tasks\n",
        "\n",
        "- https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html"
      ],
      "metadata": {
        "id": "VYdN6J4T6ws-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import BernoulliNB"
      ],
      "metadata": {
        "id": "3nrahvkTB81w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Naive Bayes classifier for multivariate Bernoulli models.\n",
        "- Model is trained in the second line using .fit()"
      ],
      "metadata": {
        "id": "O2s6KW667acl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = BernoulliNB()\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UP9tU3QKB81x",
        "outputId": "d905bd64-8bce-4a90-d232-e13677095873"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BernoulliNB()"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next line, the trained model is tested on the test data to predict the outcome based on the input."
      ],
      "metadata": {
        "id": "-WO5DF9x7mhg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQa2rjPfB81y",
        "outputId": "aef6a616-3e39-4fb4-a802-0e924511e24a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 2, 2, 2, 1, 0, 1, 1, 1, 0, 2, 0, 2, 1, 2, 0, 2, 0, 0, 2,\n",
              "       2, 1, 1, 1, 1, 1, 2, 0, 1, 0, 2, 2, 1, 0, 0, 0, 0, 1, 2, 2, 1, 1,\n",
              "       2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 2, 2, 0, 1, 0, 1, 2, 0, 0, 2, 2, 0,\n",
              "       1, 1, 1, 2, 2, 2, 1, 0, 2, 1, 2, 0, 2, 1, 0, 1, 0, 1, 0, 1, 2, 0,\n",
              "       2, 1, 2, 1, 0, 2, 2, 2, 1, 2, 1, 2, 0, 0, 1, 0, 0, 0, 2, 0, 2, 1,\n",
              "       1, 0, 2, 2, 1, 1, 1, 2, 0, 0, 1, 2, 2, 2, 0, 2, 1, 2, 0, 1, 1, 2,\n",
              "       2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 1, 2, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2,\n",
              "       0, 0, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 0, 1, 1, 2, 0, 2, 1, 0, 1, 1,\n",
              "       0, 1, 1, 0, 2, 2, 1, 2, 0, 2, 1, 2, 0, 2, 2, 2, 2, 2, 2, 0, 1, 2,\n",
              "       1, 2, 0, 1, 0, 2, 1, 2, 2, 0, 2, 0, 0, 2, 0, 1, 2, 2, 2, 2, 2, 1,\n",
              "       1, 0, 0, 0, 2, 0, 1, 1, 2, 0, 2, 2, 1, 2, 2, 2, 2, 0, 2, 0, 1, 1,\n",
              "       2, 0, 2, 1, 2, 1, 0, 0, 1, 1, 2, 2, 2, 2, 1, 1, 1, 0, 0, 2, 1, 0,\n",
              "       2, 0, 2, 2, 0, 0, 0, 2, 1, 2, 2, 0, 0, 0, 2, 1, 0, 1, 0, 2, 0, 2,\n",
              "       2, 2, 2, 2, 2, 0, 2, 1, 0, 1, 0, 0, 2, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Computes confusion matrix to evaluate the accuracy of a classification."
      ],
      "metadata": {
        "id": "PN4umPqK7tdN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yrBdVR9rB81z",
        "outputId": "49c52dbc-a4cc-4e8f-b013-ae5682bcd34b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[85,  5,  5],\n",
              "       [ 3, 84, 23],\n",
              "       [ 0,  0, 95]])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a text report showing the main classification metrics such has precision,f1-score and recall values for the actual and predicted values."
      ],
      "metadata": {
        "id": "XhNrn_bD7xXT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc42SSJ6B81z",
        "outputId": "9bb0270d-6528-40e7-c1e0-4e8abda79fd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.89      0.93        95\n",
            "           1       0.94      0.76      0.84       110\n",
            "           2       0.77      1.00      0.87        95\n",
            "\n",
            "    accuracy                           0.88       300\n",
            "   macro avg       0.89      0.89      0.88       300\n",
            "weighted avg       0.90      0.88      0.88       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Plotting a heatmap of the confusion matrix with flags annot=True which plots with numbers of strength and fmt='g' signifies the values to be displayed as integer."
      ],
      "metadata": {
        "id": "lN8rqhO973m5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "tHXVIUoVB81z",
        "outputId": "043bf017-231c-46eb-e1a9-1acbb59c2406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(model).__name__,': ',getMSEForModel(type(model).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(model,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(model).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWRv4wLhIdCR",
        "outputId": "3bb7caa8-0f1f-4770-b914-a05be513209a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  BernoulliNB :  0.17\n",
            "\n",
            "Accuracy of  BernoulliNB  model when K Fold=  10 : \n",
            "Min Accuracy 0.8285714285714286\n",
            "Max Accuracy 0.9428571428571428\n",
            "Mean Accuracy 0.8942857142857144\n",
            "\n",
            "Accuracy of  BernoulliNB  model:  0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Random Forest"
      ],
      "metadata": {
        "id": "5_o3ugsvCDit"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. \n",
        "- The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree."
      ],
      "metadata": {
        "id": "-EJ-OG0Z8Q-q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "metadata": {
        "id": "YCTf7VEJCDiv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html\n",
        "- Model is trained in the second line using .fit(), params being training data"
      ],
      "metadata": {
        "id": "ze4uJ2z48WG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = RandomForestRegressor()\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5NeC0vPCDix",
        "outputId": "11255ef5-a0be-4fd3-d38f-99ace1104a2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next line, the trained model is tested on the test data to predict the outcome based on the input."
      ],
      "metadata": {
        "id": "g4hHncuO8kx3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yb3UsYFLCDix",
        "outputId": "52828231-bd1f-41cf-bcb7-8e2bccecacc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.  , 1.  , 0.  , 2.  , 2.  , 2.  , 1.  , 1.  , 1.  , 1.  , 1.  ,\n",
              "       0.  , 1.  , 0.  , 1.  , 1.  , 2.  , 0.  , 1.  , 0.  , 0.  , 0.  ,\n",
              "       2.  , 1.  , 1.  , 1.  , 1.  , 1.  , 1.  , 0.  , 1.  , 0.  , 2.  ,\n",
              "       2.  , 1.  , 0.  , 0.  , 0.  , 0.  , 1.  , 2.  , 2.  , 1.  , 1.  ,\n",
              "       2.  , 2.  , 1.  , 2.  , 1.  , 1.  , 0.  , 2.  , 0.  , 2.  , 1.  ,\n",
              "       2.  , 0.  , 1.  , 0.  , 1.  , 2.  , 0.  , 0.  , 2.  , 2.  , 0.  ,\n",
              "       0.  , 1.  , 1.  , 2.  , 2.  , 2.  , 1.  , 0.  , 2.  , 1.  , 2.  ,\n",
              "       0.  , 2.  , 1.  , 0.  , 1.  , 0.  , 0.  , 0.  , 1.  , 1.  , 0.  ,\n",
              "       2.  , 1.  , 2.  , 1.  , 0.  , 2.  , 2.  , 1.  , 1.  , 2.  , 1.  ,\n",
              "       2.  , 1.  , 0.  , 1.  , 0.  , 0.  , 0.  , 2.  , 0.01, 2.  , 1.  ,\n",
              "       1.  , 0.  , 2.  , 2.  , 1.  , 1.  , 1.  , 1.  , 0.  , 0.  , 1.  ,\n",
              "       2.  , 2.  , 1.  , 0.  , 1.  , 1.  , 2.  , 0.  , 1.  , 1.  , 1.  ,\n",
              "       2.  , 0.  , 2.  , 0.  , 0.  , 2.  , 2.  , 1.  , 2.  , 0.  , 1.  ,\n",
              "       0.  , 2.  , 1.  , 1.  , 1.  , 2.  , 2.  , 2.  , 0.  , 2.  , 2.  ,\n",
              "       0.  , 0.01, 1.  , 1.  , 1.  , 2.  , 1.  , 1.  , 1.  , 1.  , 1.  ,\n",
              "       1.  , 0.  , 1.  , 1.  , 2.  , 0.  , 2.  , 1.  , 0.  , 1.  , 1.  ,\n",
              "       0.  , 1.  , 1.  , 0.  , 2.  , 2.  , 1.  , 2.  , 0.  , 2.  , 1.  ,\n",
              "       0.  , 0.  , 1.  , 2.  , 2.  , 1.  , 2.  , 2.  , 0.  , 1.  , 2.  ,\n",
              "       1.  , 2.  , 0.  , 1.  , 0.  , 2.  , 1.  , 2.  , 1.  , 0.  , 2.  ,\n",
              "       0.  , 0.  , 2.  , 0.  , 1.  , 2.  , 2.  , 1.  , 0.  , 2.  , 1.  ,\n",
              "       1.  , 0.  , 1.  , 0.  , 2.  , 0.  , 1.  , 1.  , 2.  , 0.  , 2.  ,\n",
              "       0.  , 0.  , 2.  , 2.  , 1.  , 2.  , 0.01, 2.  , 0.  , 0.  , 1.  ,\n",
              "       2.  , 0.01, 1.  , 1.  , 2.  , 1.  , 0.  , 0.  , 1.  , 1.  , 2.  ,\n",
              "       2.  , 2.  , 1.  , 1.  , 1.  , 1.  , 0.  , 0.  , 2.  , 0.  , 0.  ,\n",
              "       2.  , 0.  , 2.  , 1.  , 0.  , 0.  , 0.  , 2.  , 1.  , 1.  , 2.  ,\n",
              "       0.  , 0.  , 0.  , 2.  , 1.  , 0.01, 1.  , 0.  , 2.  , 0.  , 2.  ,\n",
              "       2.  , 2.  , 2.  , 2.  , 2.  , 0.  , 2.  , 1.  , 0.  , 1.  , 0.  ,\n",
              "       0.  , 2.  , 0.  ])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Rounding off the values since the required values are discrete and not Continous. "
      ],
      "metadata": {
        "id": "GeccxfKC8wfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=np.abs(np.round(Y_pred))"
      ],
      "metadata": {
        "id": "TKCmjoCVNUj8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Computes confusion matrix to evaluate the accuracy of a classification."
      ],
      "metadata": {
        "id": "9X8MaCX-81qv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEhrGGdJCDiy",
        "outputId": "4cf2a846-e9a3-499c-d34a-9dce2ffa9fac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 95,   0,   0],\n",
              "       [  0, 110,   0],\n",
              "       [  0,   0,  95]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Build a text report showing the main classification metrics such has precision,f1-score and recall values for the actual and predicted values."
      ],
      "metadata": {
        "id": "U_joynmh86WW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uKhGEXmCDiy",
        "outputId": "433be642-cb0a-4b1b-ff88-8dde3e4bc896"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        95\n",
            "           1       1.00      1.00      1.00       110\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           1.00       300\n",
            "   macro avg       1.00      1.00      1.00       300\n",
            "weighted avg       1.00      1.00      1.00       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting a heatmap of the confusion matrix with flags annot=True which plots with numbers of strength and fmt='g' signifies the values to be displayed as integer."
      ],
      "metadata": {
        "id": "C9BwAs269Egn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "wauNN2g4CDiy",
        "outputId": "5a766859-e488-498c-b4e4-8c2fb22ac81a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(model).__name__,': ',getMSEForModel(type(model).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(model,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(model).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGC_0fhOJnZs",
        "outputId": "b7796fcf-f3f6-4dc8-d6e5-4b4a29cb445a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  RandomForestRegressor :  0.0\n",
            "\n",
            "Accuracy of  RandomForestRegressor  model when K Fold=  10 : \n",
            "Min Accuracy 0.9999259153318077\n",
            "Max Accuracy 1.0\n",
            "Mean Accuracy 0.9999838611629587\n",
            "\n",
            "Accuracy of  RandomForestRegressor  model:  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Support Vector Regressor"
      ],
      "metadata": {
        "id": "9y2HCR7kCJCf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Support Vector Regression is a supervised learning algorithm that is used to predict discrete values. Support Vector Regression uses the same principle as the SVMs. The basic idea behind SVR is to find the best fit line."
      ],
      "metadata": {
        "id": "vSqR939G9NfQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVR"
      ],
      "metadata": {
        "id": "Gc4HO3jkCJCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html"
      ],
      "metadata": {
        "id": "PqmbaWzs9Z1I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SVR()\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mAouD8LCJCi",
        "outputId": "fd72fa78-1618-4245-f577-592feea611ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR()"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the next line, the trained model is tested on the test data to predict the outcome based on the input."
      ],
      "metadata": {
        "id": "jkDXOvUe9n2Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kc5e-N4iCJCi",
        "outputId": "7b123cc1-1ba1-4e79-ac17-747d1cd14847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.10018962,  1.08932667,  0.14031015,  1.93158903,  1.90009331,\n",
              "        1.93532798,  1.08242566,  0.9005558 ,  0.92461188,  1.0928405 ,\n",
              "        0.90032584,  0.07905234,  0.96750557,  0.10011419,  1.10007035,\n",
              "        1.07199126,  1.90009331,  0.01662455,  0.90023417,  0.09981244,\n",
              "       -0.01876518,  0.10010542,  1.92797392,  0.90004423,  1.08932667,\n",
              "        1.099955  ,  1.04011925,  1.099955  ,  0.96750557,  0.05290534,\n",
              "        0.88662853,  0.0860155 ,  1.97875316,  1.90028378,  1.09983073,\n",
              "        0.0489168 ,  0.10011419,  0.10018765,  0.09999971,  1.08932667,\n",
              "        1.94518243,  1.89975598,  0.9105472 ,  0.9105472 ,  1.92244098,\n",
              "        1.90028378,  0.99978964,  1.90010111,  1.06944686,  1.04370216,\n",
              "        0.01662455,  1.94518243,  0.10019488,  1.99713504,  1.1004002 ,\n",
              "        2.09118349,  0.09999166,  1.0928405 ,  0.07347262,  1.0928405 ,\n",
              "        1.99713504,  0.01662455,  0.10018765,  1.93649293,  1.90714511,\n",
              "        0.05290534,  0.31205495,  1.099955  ,  1.04011925,  1.89977144,\n",
              "        1.93534085,  1.97875316,  1.03739222, -0.02843562,  2.03024129,\n",
              "        0.90004423,  1.93870322, -0.01876518,  1.90003469,  0.90032584,\n",
              "        0.10018962,  0.90004423,  0.10012645,  0.10018796,  0.09962739,\n",
              "        0.95591005,  0.99978964,  0.10018765,  1.89981474,  1.09983073,\n",
              "        2.03055013,  0.91850437,  0.10018962,  1.90034223,  2.09118349,\n",
              "        0.96750557,  0.90664381,  1.91398722,  1.08242566,  1.90003469,\n",
              "        0.90002191,  0.07905234,  1.10001214,  0.01917503,  0.10012795,\n",
              "        0.05290534,  1.91394825,  0.1001454 ,  1.90007035,  0.9105472 ,\n",
              "        1.0928405 ,  0.10018962,  1.89991963,  1.93649293,  1.07199126,\n",
              "        1.04011925,  1.06944686,  1.10007035,  0.10019488,  0.10018765,\n",
              "        0.9105472 ,  1.90917666,  1.89991963,  0.99978964,  0.10012645,\n",
              "        0.89970356,  0.9447414 ,  1.90028378,  0.01917503,  1.04011925,\n",
              "        0.9105472 ,  0.89960107,  1.94247446,  0.09962739,  1.90028378,\n",
              "        0.09999166, -0.03002989,  1.93534085,  2.03055013,  1.1004002 ,\n",
              "        1.89965756,  0.09962739,  0.90014771,  0.10010542,  1.89964339,\n",
              "        1.06944686,  1.0349675 ,  0.89962591,  1.90714511,  1.89975598,\n",
              "        2.02659567,  0.01662455,  1.90003469,  1.93649293,  0.10012795,\n",
              "        0.1001454 ,  0.90664381,  1.07199126,  1.0998813 ,  1.90007035,\n",
              "        0.90032584,  1.06944686,  1.1004002 ,  0.89962591,  1.0928405 ,\n",
              "        0.92461188,  0.10031122,  1.06944686,  0.90014771,  1.89991963,\n",
              "        0.10018765,  1.89977144,  1.0998813 , -0.03002989,  0.90004423,\n",
              "        1.099955  ,  0.1326066 ,  1.08242566,  0.9105472 , -0.02843562,\n",
              "        1.90028378,  1.90007035,  0.90014771,  1.91398722,  0.09981244,\n",
              "        1.89964339,  0.92461188,  0.10010542,  0.0489168 ,  0.96750557,\n",
              "        2.0871718 ,  1.92797392,  0.96824509,  1.90032222,  1.91398722,\n",
              "        0.10019488,  1.10001214,  1.93534085,  1.07199126,  2.09118349,\n",
              "       -0.03002989,  0.90664381,  0.0860155 ,  1.83411253,  0.90198949,\n",
              "        2.02659567,  0.99978964,  0.10012645,  1.89964339,  0.10019488,\n",
              "        0.10019488,  1.90010111,  0.09999971,  1.0349675 ,  1.92465348,\n",
              "        1.90010111,  1.10007035,  0.10010542,  1.93532798,  0.91850437,\n",
              "        0.92461188,  0.10018962,  0.90002191,  0.07860688,  1.90010111,\n",
              "        0.01662455,  0.89847541,  0.95591005,  1.93532798,  0.10018962,\n",
              "        1.89977144,  0.10010542,  0.10018796,  1.89991963,  1.89981474,\n",
              "        0.89960107,  1.89975598,  0.1001454 ,  1.94518243,  0.09999166,\n",
              "        0.10018796,  1.07199126,  1.93318298,  0.1001454 ,  0.96750557,\n",
              "        1.09983073,  1.90009331,  1.099955  ,  0.16465382,  0.01662455,\n",
              "        0.90004423,  0.89053864,  2.0871718 ,  1.93649293,  1.91875472,\n",
              "        1.1004002 ,  1.04011925,  1.08242566,  0.89962591,  0.0860155 ,\n",
              "        0.0998124 ,  1.89964339,  0.10018796,  0.0489168 ,  2.09118349,\n",
              "       -0.02843562,  1.93318298,  1.10007035,  0.09962739, -0.02272222,\n",
              "        0.09962739,  1.93534085,  1.0998813 ,  0.99978964,  1.94247446,\n",
              "        0.10031122,  0.0998124 ,  0.10018765,  1.89965756,  0.90198949,\n",
              "        0.1001454 ,  1.0349675 ,  0.10012795,  1.90714511,  0.07159846,\n",
              "        1.89965756,  1.96081133,  1.90034223,  1.93318298,  1.89965756,\n",
              "        1.93318298, -0.02272222,  1.91875472,  0.90112664,  0.10031122,\n",
              "        0.91850437,  0.01662455,  0.07905234,  1.94247446,  0.01917503])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=np.abs(np.round(Y_pred))"
      ],
      "metadata": {
        "id": "Vd6iDpfIN3TP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Computes confusion matrix to evaluate the accuracy of a classification."
      ],
      "metadata": {
        "id": "Qv6vhE_e9utp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPauRo8kCJCj",
        "outputId": "db32c0a8-b42a-482c-86df-c1f96b65f606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 95,   0,   0],\n",
              "       [  0, 110,   0],\n",
              "       [  0,   0,  95]])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Build a text report showing the main classification metrics such has precision,f1-score and recall values for the actual and predicted values."
      ],
      "metadata": {
        "id": "M1OG0o8a9zSg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9lUej06jCJCj",
        "outputId": "3001ffe7-a5e5-4eb9-fe11-914ea986b3bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        95\n",
            "           1       1.00      1.00      1.00       110\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           1.00       300\n",
            "   macro avg       1.00      1.00      1.00       300\n",
            "weighted avg       1.00      1.00      1.00       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting a heatmap of the confusion matrix with flags annot=True which plots with numbers of strength and fmt='g' signifies the values to be displayed as integer."
      ],
      "metadata": {
        "id": "_iD0LZoj92Ug"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "33ba6C2-CJCj",
        "outputId": "47d694da-26e2-4e30-c167-6d2c39d58baf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(model).__name__,': ',getMSEForModel(type(model).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(model,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(model).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0GkVa9DJxkm",
        "outputId": "b8e1c1b9-47e9-46d6-f682-8577c9be9f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  SVR :  0.0\n",
            "\n",
            "Accuracy of  SVR  model when K Fold=  10 : \n",
            "Min Accuracy 0.9876571941146587\n",
            "Max Accuracy 0.9910158456963087\n",
            "Mean Accuracy 0.9890937885012787\n",
            "\n",
            "Accuracy of  SVR  model:  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Stochastic Gradient Descent"
      ],
      "metadata": {
        "id": "q0WhozVACOck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- SGD stands for Stochastic Gradient Descent: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate)."
      ],
      "metadata": {
        "id": "YNKXXTYT98cb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDRegressor"
      ],
      "metadata": {
        "id": "LvCnYmFgCOcm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net).\n",
        "- n_iter_no_changeint, default=5\n",
        "Number of iterations with no improvement to wait before stopping fitting.\n",
        "- The ‘squared_error’ refers to the ordinary least squares fit."
      ],
      "metadata": {
        "id": "r8cEx-DP-DJy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = SGDRegressor(loss='squared_error', max_iter=10000, random_state=42)\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NkEsq3wzCOco",
        "outputId": "02b59ccc-7a9d-44ae-b83d-a8addea80e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDRegressor(max_iter=10000, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ezv3NMCcCOcp",
        "outputId": "4fa9c777-6e17-4a01-d063-86871de6fd5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.08988378,  1.29591088,  0.40685769,  1.81255102,  1.83399355,\n",
              "        1.89251739,  1.03008872,  0.77855454,  0.91987363,  0.95234243,\n",
              "        1.00428016,  0.00547725,  1.06156542,  0.13320124,  1.0838803 ,\n",
              "        1.02453851,  1.83399355,  0.45561617,  0.97660386, -0.04786942,\n",
              "        0.11377569,  0.92369319,  2.0519667 ,  0.93564295,  1.29591088,\n",
              "        0.9540075 ,  0.93735687,  0.9540075 ,  1.06156542,  0.11858185,\n",
              "        1.01205045, -0.13715047,  2.03078685,  1.77289789,  0.96954951,\n",
              "       -0.1488927 ,  0.13320124,  0.1209944 ,  0.15597001,  1.29591088,\n",
              "        1.76401756,  1.925892  ,  0.71893105,  0.71893105,  2.00792838,\n",
              "        1.77289789,  1.07044575,  1.61338786,  0.94679223,  0.93902194,\n",
              "        0.45561617,  1.76401756, -0.32115114,  2.11852351,  1.43831523,\n",
              "        2.15867167,  0.15177131,  0.95234243,  0.15651989,  0.95234243,\n",
              "        2.11852351,  0.45561617,  0.1209944 ,  1.84376888,  1.89951762,\n",
              "        0.11858185,  0.7987223 ,  0.9540075 ,  0.93735687,  1.59729226,\n",
              "        1.80700081,  2.03078685,  0.93569181, -0.5532062 ,  2.38840987,\n",
              "        0.93564295,  1.8092209 ,  0.11377569,  1.79855719,  1.00428016,\n",
              "        0.08988378,  0.93564295, -0.10800267,  0.88423888, -0.04400582,\n",
              "        0.91210334,  1.07044575,  0.1209944 ,  2.13748327,  0.96954951,\n",
              "        2.38896489,  0.80888779,  0.08988378,  2.04799249,  2.15867167,\n",
              "        1.06156542,  0.80500264,  1.89365732,  1.03008872,  1.79855719,\n",
              "        0.64752235,  0.00547725,  1.06283638,  0.14541947,  0.08416383,\n",
              "        0.11858185,  1.89840757,  0.23689587,  1.94374159,  0.71893105,\n",
              "        0.95234243,  0.08988378,  1.90250776,  1.84376888,  1.02453851,\n",
              "        0.93735687,  0.94679223,  1.0838803 , -0.32115114,  0.1209944 ,\n",
              "        0.71893105,  1.8041074 ,  1.90250776,  1.07044575, -0.10800267,\n",
              "        0.96041904,  0.91432342,  1.77289789,  0.14541947,  0.93735687,\n",
              "        0.71893105,  0.87490247,  1.92318023, -0.04400582,  1.77289789,\n",
              "        0.15177131, -0.06422648,  1.80700081,  2.38896489,  1.43831523,\n",
              "        2.02301656, -0.04400582,  0.8011175 ,  0.92369319,  1.88518381,\n",
              "        0.94679223,  0.9301416 ,  0.8534565 ,  1.89951762,  1.925892  ,\n",
              "        2.38674481,  0.45561617,  1.79855719,  1.84376888,  0.08416383,\n",
              "        0.23689587,  0.80500264,  1.02453851,  1.05284601,  1.94374159,\n",
              "        1.00428016,  0.94679223,  1.43831523,  0.8534565 ,  0.95234243,\n",
              "        0.91987363,  0.31531731,  0.94679223,  0.8011175 ,  1.90250776,\n",
              "        0.1209944 ,  1.59729226,  1.05284601, -0.06422648,  0.93564295,\n",
              "        0.9540075 ,  0.23728788,  1.03008872,  0.71893105, -0.5532062 ,\n",
              "        1.77289789,  1.94374159,  0.8011175 ,  1.89365732, -0.04786942,\n",
              "        1.88518381,  0.91987363,  0.92369319, -0.1488927 ,  1.06156542,\n",
              "        2.16533192,  2.0519667 ,  1.06212044,  2.00348821,  1.89365732,\n",
              "       -0.32115114,  1.06283638,  1.80700081,  1.02453851,  2.15867167,\n",
              "       -0.06422648,  0.80500264, -0.13715047,  1.61893807,  0.93453291,\n",
              "        2.38674481,  1.07044575, -0.10800267,  1.88518381, -0.32115114,\n",
              "       -0.32115114,  1.61338786,  0.15597001,  0.9301416 ,  2.00903842,\n",
              "        1.61338786,  1.0838803 ,  0.92369319,  1.89251739,  0.80888779,\n",
              "        0.91987363,  0.08988378,  0.64752235,  0.09154884,  1.61338786,\n",
              "        0.45561617,  0.94008311,  0.91210334,  1.89251739,  0.08988378,\n",
              "        1.59729226,  0.92369319,  0.88423888,  1.90250776,  2.13748327,\n",
              "        0.87490247,  1.925892  ,  0.23689587,  1.76401756,  0.15177131,\n",
              "        0.88423888,  1.02453851,  1.90253766,  0.23689587,  1.06156542,\n",
              "        0.96954951,  1.83399355,  0.9540075 ,  0.04990837,  0.45561617,\n",
              "        0.93564295,  1.01538057,  2.16533192,  1.84376888,  1.88918727,\n",
              "        1.43831523,  0.93735687,  1.03008872,  0.8534565 , -0.13715047,\n",
              "        0.07978672,  1.88518381,  0.88423888, -0.1488927 ,  2.15867167,\n",
              "       -0.5532062 ,  1.90253766,  1.0838803 , -0.04400582, -0.29695069,\n",
              "       -0.04400582,  1.80700081,  1.05284601,  1.07044575,  1.92318023,\n",
              "        0.31531731,  0.07978672,  0.1209944 ,  2.02301656,  0.93453291,\n",
              "        0.23689587,  0.9301416 ,  0.08416383,  1.89951762,  0.40555783,\n",
              "        2.02301656,  1.84564899,  2.04799249,  1.90253766,  2.02301656,\n",
              "        1.90253766, -0.29695069,  1.88918727,  0.71560092,  0.31531731,\n",
              "        0.80888779,  0.45561617,  0.00547725,  1.92318023,  0.14541947])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=np.abs(np.round(Y_pred))"
      ],
      "metadata": {
        "id": "mrz_dmm5PVUq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XlXaIZW3COcp",
        "outputId": "ff61c87e-027a-4a0b-89c5-9edc6bec579b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 82,  13,   0],\n",
              "       [  0, 110,   0],\n",
              "       [  0,   0,  95]])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anI3ps4sCOcp",
        "outputId": "127b84fd-6210-4ff4-8763-13f162747646"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.86      0.93        95\n",
            "           1       0.89      1.00      0.94       110\n",
            "           2       1.00      1.00      1.00        95\n",
            "\n",
            "    accuracy                           0.96       300\n",
            "   macro avg       0.96      0.95      0.96       300\n",
            "weighted avg       0.96      0.96      0.96       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting a heatmap of the confusion matrix with flags annot=True which plots with numbers of strength and fmt='g' signifies the values to be displayed as integer."
      ],
      "metadata": {
        "id": "uFXabhCo-gei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "NtqUCD3ECOcq",
        "outputId": "d1699dfe-473d-42a8-be2a-bd60c670cd81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(model).__name__,': ',getMSEForModel(type(model).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(model,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(model).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLgoDYaQJ1i7",
        "outputId": "d6f5dba4-1db7-4b81-b46a-95bbd291093e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  SGDRegressor :  0.043333333333333335\n",
            "\n",
            "Accuracy of  SGDRegressor  model when K Fold=  10 : \n",
            "Min Accuracy 0.8679346670449447\n",
            "Max Accuracy 0.9367918066392555\n",
            "Mean Accuracy 0.9194975013048818\n",
            "\n",
            "Accuracy of  SGDRegressor  model:  0.9566666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Ridge"
      ],
      "metadata": {
        "id": "BU-qUxcACTDJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Linear least squares with l2 regularization."
      ],
      "metadata": {
        "id": "HYiMu7G7-pkq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge"
      ],
      "metadata": {
        "id": "HYZzWgK0CTDL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Minimizes the objective function:\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "||y - Xw||^2_2 + alpha * ||w||^2_2\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "c4q-cLIF-sAR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Ridge(alpha=0.5, solver='sag', random_state=42)\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3Mp2qQtCTDM",
        "outputId": "fcd18e66-7de9-4ed7-fc5e-07dc853712b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, random_state=42, solver='sag')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm."
      ],
      "metadata": {
        "id": "392KLfWn-5gp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)\n",
        "Y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AVXR5BJ0CTDN",
        "outputId": "6897c688-80ab-497f-f94c-ffe511253300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.00581864,  1.35873741,  0.33065218,  1.8314894 ,  1.85715113,\n",
              "        1.91379917,  1.03032221,  1.00455229,  0.9509293 ,  0.95241128,\n",
              "        1.04203674, -0.09692608,  0.99480379,  0.01797802,  1.11387621,\n",
              "        1.01932511,  1.85715113,  0.42667349,  0.90259664, -0.09941055,\n",
              "        0.12781682,  0.94319452,  2.02093484,  0.92142847,  1.35873741,\n",
              "        0.9557104 ,  0.92271913,  0.9557104 ,  0.99480379,  0.32525941,\n",
              "        1.05743267, -0.09432221,  2.00453046,  1.81223148,  0.91072046,\n",
              "       -0.07882425,  0.01797802,  0.11451344,  0.26272794,  1.35873741,\n",
              "        1.79463613,  1.83026847,  0.77537912,  0.77537912,  1.96309766,\n",
              "        1.81223148,  1.01239914,  1.50790125,  0.94141418,  0.92601825,\n",
              "        0.42667349,  1.79463613, -0.41852904,  2.10223615,  1.32419529,\n",
              "        2.23271847,  0.10887146,  0.95241128,  0.14860465,  0.95241128,\n",
              "        2.10223615,  0.42667349,  0.11451344,  1.89381487,  1.94496436,\n",
              "        0.32525941,  0.64188121,  0.9557104 ,  0.92271913,  1.47600968,\n",
              "        1.82049231,  2.00453046,  0.91942   , -0.5720469 ,  2.34498739,\n",
              "        0.92142847,  1.82489114,  0.12781682,  1.68352326,  1.04203674,\n",
              "       -0.00581864,  0.92142847, -0.05012635,  0.73518807, -0.14752142,\n",
              "        0.93553337,  1.01239914,  0.11451344,  2.1142417 ,  0.91072046,\n",
              "        2.3460871 ,  0.87748365, -0.00581864,  2.03862144,  2.23271847,\n",
              "        0.99480379,  0.86978569,  1.92218221,  1.03032221,  1.68352326,\n",
              "        0.821801  , -0.09692608,  1.01942325,  0.12661047,  0.06236989,\n",
              "        0.32525941,  1.94276494,  0.1918051 ,  2.02966618,  0.77537912,\n",
              "        0.95241128, -0.00581864,  1.93359393,  1.89381487,  1.01932511,\n",
              "        0.92271913,  0.94141418,  1.11387621, -0.41852904,  0.11451344,\n",
              "        0.77537912,  1.69452035,  1.93359393,  1.01239914, -0.05012635,\n",
              "        0.93325993,  0.93993221,  1.81223148,  0.12661047,  0.92271913,\n",
              "        0.77537912,  0.83995307,  1.97502492, -0.14752142,  1.81223148,\n",
              "        0.10887146,  0.03795576,  1.82049231,  2.3460871 ,  1.32419529,\n",
              "        1.98913453, -0.14752142,  0.86208772,  0.94319452,  1.77902954,\n",
              "        0.94141418,  0.90842291,  0.83471986,  1.94496436,  1.83026847,\n",
              "        2.34168827,  0.42667349,  1.68352326,  1.89381487,  0.06236989,\n",
              "        0.1918051 ,  0.86978569,  1.01932511,  0.99962848,  2.02966618,\n",
              "        1.04203674,  0.94141418,  1.32419529,  0.83471986,  0.95241128,\n",
              "        0.9509293 ,  0.31824666,  0.94141418,  0.86208772,  1.93359393,\n",
              "        0.11451344,  1.47600968,  0.99962848,  0.03795576,  0.92142847,\n",
              "        0.9557104 ,  0.20217832,  1.03032221,  0.77537912, -0.5720469 ,\n",
              "        1.81223148,  2.02966618,  0.86208772,  1.92218221, -0.09941055,\n",
              "        1.77902954,  0.9509293 ,  0.94319452, -0.07882425,  0.99480379,\n",
              "        2.24591498,  2.02093484,  0.9959035 ,  1.95429998,  1.92218221,\n",
              "       -0.41852904,  1.01942325,  1.82049231,  1.01932511,  2.23271847,\n",
              "        0.03795576,  0.86978569, -0.09432221,  1.51889834,  0.91922905,\n",
              "        2.34168827,  1.01239914, -0.05012635,  1.77902954, -0.41852904,\n",
              "       -0.41852904,  1.50790125,  0.26272794,  0.90842291,  1.96529707,\n",
              "        1.50790125,  1.11387621,  0.94319452,  1.91379917,  0.87748365,\n",
              "        0.9509293 , -0.00581864,  0.821801  , -0.00251951,  1.50790125,\n",
              "        0.42667349,  0.93022614,  0.93553337,  1.91379917, -0.00581864,\n",
              "        1.47600968,  0.94319452,  0.73518807,  1.93359393,  2.1142417 ,\n",
              "        0.83995307,  1.83026847,  0.1918051 ,  1.79463613,  0.10887146,\n",
              "        0.73518807,  1.01932511,  1.93977756,  0.1918051 ,  0.99480379,\n",
              "        0.91072046,  1.85715113,  0.9557104 ,  0.04979902,  0.42667349,\n",
              "        0.92142847,  1.06403093,  2.24591498,  1.89381487,  1.90720091,\n",
              "        1.32419529,  0.92271913,  1.03032221,  0.83471986, -0.09432221,\n",
              "        0.02807992,  1.77902954,  0.73518807, -0.07882425,  2.23271847,\n",
              "       -0.5720469 ,  1.93977756,  1.11387621, -0.14752142, -0.1801723 ,\n",
              "       -0.14752142,  1.82049231,  0.99962848,  1.01239914,  1.97502492,\n",
              "        0.31824666,  0.02807992,  0.11451344,  1.98913453,  0.91922905,\n",
              "        0.1918051 ,  0.90842291,  0.06236989,  1.94496436,  0.21812382,\n",
              "        1.98913453,  1.88024502,  2.03862144,  1.93977756,  1.98913453,\n",
              "        1.93977756, -0.1801723 ,  1.90720091,  0.76878087,  0.31824666,\n",
              "        0.87748365,  0.42667349, -0.09692608,  1.97502492,  0.12661047])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred=np.abs(np.round(Y_pred))"
      ],
      "metadata": {
        "id": "jh_Fmn49PtNj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Computes confusion matrix to evaluate the accuracy of a classification."
      ],
      "metadata": {
        "id": "U1gPScOxAJEk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(Y_test, Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEssbGeZCTDN",
        "outputId": "f64b8287-38e1-4960-bded-99dbbf44b3a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 82,  13,   0],\n",
              "       [  0, 110,   0],\n",
              "       [  0,   3,  92]])"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjS6cq9hCTDO",
        "outputId": "b7babfa5-8ade-40da-d65b-991ea1c9e008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.86      0.93        95\n",
            "           1       0.87      1.00      0.93       110\n",
            "           2       1.00      0.97      0.98        95\n",
            "\n",
            "    accuracy                           0.95       300\n",
            "   macro avg       0.96      0.94      0.95       300\n",
            "weighted avg       0.95      0.95      0.95       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting a heatmap of the confusion matrix with flags annot=True which plots with numbers of strength and fmt='g' signifies the values to be displayed as integer."
      ],
      "metadata": {
        "id": "BhBO1NxsAMF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(Y_test, Y_pred),annot=True,fmt='g')\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.ylabel(\"Actual\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LVk0Y0CdCTDO",
        "outputId": "6e51e0cf-3ca5-4665-ea83-0ebdf4ceefe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(33.0, 0.5, 'Actual')"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean squared error for ',type(model).__name__,': ',getMSEForModel(type(model).__name__,Y_test, Y_pred))\n",
        "Accuracy=getAccuracyForModel(model,Y_test,Y_pred)\n",
        "print('Accuracy of ',type(model).__name__,' model: ',Accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJz3EcobJ49t",
        "outputId": "5f76e62e-a1ea-488f-af2b-2b13e97687a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean squared error for  Ridge :  0.05333333333333334\n",
            "\n",
            "Accuracy of  Ridge  model when K Fold=  10 : \n",
            "Min Accuracy 0.8815834856707034\n",
            "Max Accuracy 0.9484677209661919\n",
            "Mean Accuracy 0.9283184444840555\n",
            "\n",
            "Accuracy of  Ridge  model:  0.9466666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### DEEP LEARNING"
      ],
      "metadata": {
        "id": "JrhfzzDVC0dk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building Neural Network Model"
      ],
      "metadata": {
        "id": "z5YW0VoZ_lHN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop('Level',axis=1)\n",
        "Y=pd.get_dummies(df['Level'])"
      ],
      "metadata": {
        "id": "HLNnSv-0_1H1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing libs for deep learning model"
      ],
      "metadata": {
        "id": "fD9Yf_CAAOSV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation"
      ],
      "metadata": {
        "id": "qgvwr-QN_oxl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sequential model to initialise our ann and dense module to build the layers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "Fi-NKtiuAaVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting dataset in to train and test"
      ],
      "metadata": {
        "id": "--WTL_mNAUBX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,stratify=Y,random_state=12)"
      ],
      "metadata": {
        "id": "HABlftY3__q4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape,Y_train.shape"
      ],
      "metadata": {
        "id": "BgwtInPSAMbI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fa4db09-288b-4721-f8ed-a7022dfd2085"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((700, 23), (700, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape[1]"
      ],
      "metadata": {
        "id": "shvfT_iqBdSl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b05104b-77e6-44f0-8139-a80edc334c4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dlModel = Sequential()\n",
        "# Adding the input layer and the first hidden layer\n",
        "dlModel.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu', input_dim = X.shape[1]))\n",
        "\n",
        "# Adding the second hidden layer\n",
        "dlModel.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu'))\n",
        "dlModel.add(Dense(units = 8, activation = \"relu\"))\n",
        "\n",
        "# Adding the output layer\n",
        "dlModel.add(Dense(units = 3, kernel_initializer = 'uniform', activation = 'softmax'))\n",
        "\n",
        "# Compiling the ANN | means applying SGD on the whole ANN\n",
        "dlModel.compile(optimizer = 'adam', loss = 'categorical_crossentropy', \n",
        "                   metrics = ['accuracy'])\n"
      ],
      "metadata": {
        "id": "yW95axi4YHvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = dlModel.fit(X_train, Y_train, epochs = 50, validation_split = 0.3)"
      ],
      "metadata": {
        "id": "VpuIiLRe_6yZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cdeddbd-8be3-4fba-a8fe-28355b66a7da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "16/16 [==============================] - 1s 41ms/step - loss: 1.0973 - accuracy: 0.3640 - val_loss: 1.0956 - val_accuracy: 0.3697\n",
            "Epoch 2/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 1.0929 - accuracy: 0.3640 - val_loss: 1.0891 - val_accuracy: 0.3697\n",
            "Epoch 3/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 1.0816 - accuracy: 0.3640 - val_loss: 1.0736 - val_accuracy: 0.3697\n",
            "Epoch 4/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 1.0593 - accuracy: 0.3640 - val_loss: 1.0443 - val_accuracy: 0.3697\n",
            "Epoch 5/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 1.0195 - accuracy: 0.3640 - val_loss: 0.9990 - val_accuracy: 0.3744\n",
            "Epoch 6/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.9584 - accuracy: 0.4274 - val_loss: 0.9220 - val_accuracy: 0.5450\n",
            "Epoch 7/50\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.8759 - accuracy: 0.5746 - val_loss: 0.8403 - val_accuracy: 0.5782\n",
            "Epoch 8/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.7959 - accuracy: 0.6074 - val_loss: 0.7899 - val_accuracy: 0.5924\n",
            "Epoch 9/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.7374 - accuracy: 0.6217 - val_loss: 0.7426 - val_accuracy: 0.6493\n",
            "Epoch 10/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6938 - accuracy: 0.6687 - val_loss: 0.7151 - val_accuracy: 0.6588\n",
            "Epoch 11/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6585 - accuracy: 0.6912 - val_loss: 0.7041 - val_accuracy: 0.6919\n",
            "Epoch 12/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.6333 - accuracy: 0.6973 - val_loss: 0.6600 - val_accuracy: 0.6635\n",
            "Epoch 13/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.6103 - accuracy: 0.7239 - val_loss: 0.6387 - val_accuracy: 0.6682\n",
            "Epoch 14/50\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.5926 - accuracy: 0.7178 - val_loss: 0.6241 - val_accuracy: 0.7062\n",
            "Epoch 15/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.5772 - accuracy: 0.7219 - val_loss: 0.6098 - val_accuracy: 0.7441\n",
            "Epoch 16/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.5566 - accuracy: 0.7382 - val_loss: 0.5994 - val_accuracy: 0.6777\n",
            "Epoch 17/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.5412 - accuracy: 0.7423 - val_loss: 0.5769 - val_accuracy: 0.7393\n",
            "Epoch 18/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.5280 - accuracy: 0.7423 - val_loss: 0.5696 - val_accuracy: 0.7014\n",
            "Epoch 19/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.5180 - accuracy: 0.7710 - val_loss: 0.5902 - val_accuracy: 0.6825\n",
            "Epoch 20/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.5107 - accuracy: 0.7853 - val_loss: 0.5800 - val_accuracy: 0.7014\n",
            "Epoch 21/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4948 - accuracy: 0.7996 - val_loss: 0.5329 - val_accuracy: 0.7725\n",
            "Epoch 22/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4835 - accuracy: 0.7873 - val_loss: 0.5605 - val_accuracy: 0.7251\n",
            "Epoch 23/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4768 - accuracy: 0.7955 - val_loss: 0.5375 - val_accuracy: 0.7393\n",
            "Epoch 24/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4588 - accuracy: 0.8160 - val_loss: 0.5130 - val_accuracy: 0.7725\n",
            "Epoch 25/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4480 - accuracy: 0.8364 - val_loss: 0.5018 - val_accuracy: 0.8009\n",
            "Epoch 26/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4333 - accuracy: 0.8528 - val_loss: 0.4884 - val_accuracy: 0.8246\n",
            "Epoch 27/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4244 - accuracy: 0.8405 - val_loss: 0.4715 - val_accuracy: 0.8104\n",
            "Epoch 28/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.4125 - accuracy: 0.8425 - val_loss: 0.4749 - val_accuracy: 0.8246\n",
            "Epoch 29/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3954 - accuracy: 0.8609 - val_loss: 0.4682 - val_accuracy: 0.8199\n",
            "Epoch 30/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.4069 - accuracy: 0.8364 - val_loss: 0.4415 - val_accuracy: 0.8341\n",
            "Epoch 31/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3845 - accuracy: 0.8548 - val_loss: 0.4738 - val_accuracy: 0.8057\n",
            "Epoch 32/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3820 - accuracy: 0.8569 - val_loss: 0.4269 - val_accuracy: 0.8389\n",
            "Epoch 33/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3715 - accuracy: 0.8589 - val_loss: 0.4210 - val_accuracy: 0.8389\n",
            "Epoch 34/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3698 - accuracy: 0.8548 - val_loss: 0.4294 - val_accuracy: 0.8389\n",
            "Epoch 35/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3552 - accuracy: 0.8671 - val_loss: 0.4049 - val_accuracy: 0.8483\n",
            "Epoch 36/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3441 - accuracy: 0.8671 - val_loss: 0.3974 - val_accuracy: 0.8483\n",
            "Epoch 37/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3319 - accuracy: 0.8609 - val_loss: 0.3916 - val_accuracy: 0.8483\n",
            "Epoch 38/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3291 - accuracy: 0.8732 - val_loss: 0.3834 - val_accuracy: 0.8626\n",
            "Epoch 39/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3151 - accuracy: 0.8773 - val_loss: 0.3837 - val_accuracy: 0.8389\n",
            "Epoch 40/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3048 - accuracy: 0.8814 - val_loss: 0.3871 - val_accuracy: 0.8294\n",
            "Epoch 41/50\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3185 - accuracy: 0.8834 - val_loss: 0.4486 - val_accuracy: 0.8104\n",
            "Epoch 42/50\n",
            "16/16 [==============================] - 0s 6ms/step - loss: 0.3134 - accuracy: 0.8793 - val_loss: 0.3875 - val_accuracy: 0.8389\n",
            "Epoch 43/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2996 - accuracy: 0.8753 - val_loss: 0.3540 - val_accuracy: 0.8246\n",
            "Epoch 44/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2791 - accuracy: 0.9059 - val_loss: 0.3869 - val_accuracy: 0.8341\n",
            "Epoch 45/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2725 - accuracy: 0.9039 - val_loss: 0.3653 - val_accuracy: 0.8483\n",
            "Epoch 46/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2708 - accuracy: 0.8896 - val_loss: 0.3344 - val_accuracy: 0.8483\n",
            "Epoch 47/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2594 - accuracy: 0.8957 - val_loss: 0.3374 - val_accuracy: 0.8626\n",
            "Epoch 48/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2567 - accuracy: 0.9080 - val_loss: 0.3226 - val_accuracy: 0.8720\n",
            "Epoch 49/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2410 - accuracy: 0.9018 - val_loss: 0.3238 - val_accuracy: 0.9052\n",
            "Epoch 50/50\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 0.2532 - accuracy: 0.9223 - val_loss: 0.3036 - val_accuracy: 0.8957\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8,5))\n",
        "plt.plot(history.history[\"accuracy\"], label= \"Training loss\")\n",
        "plt.plot(history.history[\"val_accuracy\"], label= \"Validation loss\")\n",
        "plt.title(\"Training VS Validation loss\", fontsize = 20)\n",
        "plt.xlabel(\"No. of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "xcgHuyHKLzUS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "e9f330eb-31b0-4eab-d67a-1b486485ab88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (8,5))\n",
        "plt.plot(history.history[\"loss\"], label= \"Training loss\")\n",
        "plt.plot(history.history[\"val_loss\"], label= \"Validation loss\")\n",
        "plt.title(\"Training VS Validation loss\", fontsize = 20)\n",
        "plt.xlabel(\"No. of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "4Z9uIIEw17tr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "07682ae3-3883-482d-aae1-ac6c4a8870b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score, acc = dlModel.evaluate(X_train, Y_train,batch_size=10)\n",
        "print('Train score:', score)\n",
        "print('Train accuracy:', acc)"
      ],
      "metadata": {
        "id": "nLj74VCB2xHR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2a19f99-7f3e-4c41-da80-828a4f2bb319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "70/70 [==============================] - 0s 2ms/step - loss: 0.2560 - accuracy: 0.9200\n",
            "Train score: 0.25604990124702454\n",
            "Train accuracy: 0.9200000166893005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting the Test set results\n",
        "y_pred = dlModel.predict(X_test)"
      ],
      "metadata": {
        "id": "3ebQ93lu2xAY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f844e54-200a-4c98-f039-698b362473dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score, acc = dlModel.evaluate(X_test, Y_test,batch_size=10)\n",
        "\n",
        "print('Test score:', score)\n",
        "print('Test accuracy:', acc)"
      ],
      "metadata": {
        "id": "yK5ejASO2w9J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c575197-2458-41ea-fe8c-d2db2d8e1615"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "30/30 [==============================] - 0s 1ms/step - loss: 0.2394 - accuracy: 0.9200\n",
            "Test score: 0.2394431084394455\n",
            "Test accuracy: 0.9200000166893005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(Y_test.values.argmax(axis=1), y_pred.argmax(axis=1))\n",
        "print(cm)"
      ],
      "metadata": {
        "id": "q324_WZT2SiE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94b5f037-1dc4-4f00-f978-7332212be4bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 87   4   0]\n",
            " [ 16  81   3]\n",
            " [  0   1 108]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p = sns.heatmap(pd.DataFrame(cm), annot=True ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "6iECWDL73AZO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "dfffda99-478f-4bb2-af9a-f3cd34ae9632"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(Y_test.values.argmax(axis=1), y_pred.argmax(axis=1)))"
      ],
      "metadata": {
        "id": "SrwrTvwU3M0Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4add3443-e3d1-4ec7-f816-751856962e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.96      0.90        91\n",
            "           1       0.94      0.81      0.87       100\n",
            "           2       0.97      0.99      0.98       109\n",
            "\n",
            "    accuracy                           0.92       300\n",
            "   macro avg       0.92      0.92      0.92       300\n",
            "weighted avg       0.92      0.92      0.92       300\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Analysis"
      ],
      "metadata": {
        "id": "6X5hswlfQUa_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuray of every model on this dataset')\n",
        "accuracyTracker"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Blh9xOrEQWM9",
        "outputId": "5e654353-488a-4884-a49a-56c664ba1637"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuray of every model on this dataset\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LogisticRegression': 0.98,\n",
              " 'LinearRegression': 0.9466666666666667,\n",
              " 'GaussianNB': 0.8733333333333333,\n",
              " 'BernoulliNB': 0.88,\n",
              " 'RandomForestRegressor': 1.0,\n",
              " 'SVR': 1.0,\n",
              " 'SGDRegressor': 0.9566666666666667,\n",
              " 'Ridge': 0.9466666666666667}"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('MSE of every model on this dataset')\n",
        "mseTracker"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhdyTUuuQevf",
        "outputId": "6b810505-d908-41d5-8a25-2fba49942c62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE of every model on this dataset\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LogisticRegression': 0.02,\n",
              " 'LinearRegression': 0.05333333333333334,\n",
              " 'GaussianNB': 0.17666666666666667,\n",
              " 'BernoulliNB': 0.17,\n",
              " 'RandomForestRegressor': 0.0,\n",
              " 'SVR': 0.0,\n",
              " 'SGDRegressor': 0.043333333333333335,\n",
              " 'Ridge': 0.05333333333333334}"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Min, Max and Mean accuraies of all models obtained by K Fold =10')\n",
        "KFoldAccuries"
      ],
      "metadata": {
        "id": "WsPJfu_4QhY6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "cf229c21-6e81-450c-fb38-4eb7ef36dd54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min, Max and Mean accuraies of all models obtained by K Fold =10\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Model  Min Accuracy  Mean Accuracy  Max Accuracy\n",
              "0     LogisticRegression      0.942857       0.977143      1.000000\n",
              "1       LinearRegression      0.881773       0.928326      0.948382\n",
              "2             GaussianNB      0.857143       0.900000      0.971429\n",
              "3            BernoulliNB      0.828571       0.894286      0.942857\n",
              "4  RandomForestRegressor      0.999926       0.999984      1.000000\n",
              "5                    SVR      0.987657       0.989094      0.991016\n",
              "6           SGDRegressor      0.867935       0.919498      0.936792\n",
              "7                  Ridge      0.881583       0.928318      0.948468"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9554a49b-008e-4876-900f-ffd2351f4ec8\">\n",
              "    <div 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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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Min Accuracy</th>\n",
              "      <th>Mean Accuracy</th>\n",
              "      <th>Max Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.942857</td>\n",
              "      <td>0.977143</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>0.881773</td>\n",
              "      <td>0.928326</td>\n",
              "      <td>0.948382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.971429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BernoulliNB</td>\n",
              "      <td>0.828571</td>\n",
              "      <td>0.894286</td>\n",
              "      <td>0.942857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RandomForestRegressor</td>\n",
              "      <td>0.999926</td>\n",
              "      <td>0.999984</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SVR</td>\n",
              "      <td>0.987657</td>\n",
              "      <td>0.989094</td>\n",
              "      <td>0.991016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SGDRegressor</td>\n",
              "      <td>0.867935</td>\n",
              "      <td>0.919498</td>\n",
              "      <td>0.936792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ridge</td>\n",
              "      <td>0.881583</td>\n",
              "      <td>0.928318</td>\n",
              "      <td>0.948468</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9554a49b-008e-4876-900f-ffd2351f4ec8')\"\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",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RESULT=KFoldAccuries.copy()\n",
        "B=pd.DataFrame(mseTracker.items(), columns=['Model', 'MSE'])\n",
        "B['Accuracy'] = B['Model'].map(accuracyTracker)\n",
        "B=pd.merge(B,scoreTracker,on='Model')\n",
        "RESULT=pd.merge(RESULT,B,on='Model')"
      ],
      "metadata": {
        "id": "sKF3L4Pf7Q5C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consolidated results of all metrics amoung different models"
      ],
      "metadata": {
        "id": "21N1hscsAwPZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "RESULT"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "yaE-oWlYAzAL",
        "outputId": "a973ba89-38ba-402b-fd73-553aaf6738ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Model  Min Accuracy  Mean Accuracy  Max Accuracy       MSE  \\\n",
              "0     LogisticRegression      0.942857       0.977143      1.000000  0.020000   \n",
              "1       LinearRegression      0.881773       0.928326      0.948382  0.053333   \n",
              "2             GaussianNB      0.857143       0.900000      0.971429  0.176667   \n",
              "3            BernoulliNB      0.828571       0.894286      0.942857  0.170000   \n",
              "4  RandomForestRegressor      0.999926       0.999984      1.000000  0.000000   \n",
              "5                    SVR      0.987657       0.989094      0.991016  0.000000   \n",
              "6           SGDRegressor      0.867935       0.919498      0.936792  0.043333   \n",
              "7                  Ridge      0.881583       0.928318      0.948468  0.053333   \n",
              "\n",
              "   Accuracy  Precision    Recall  F1-score  \n",
              "0  0.980000   0.980020  0.981340  0.980431  \n",
              "1  0.946667   0.957672  0.943860  0.947571  \n",
              "2  0.873333   0.887858  0.877671  0.876326  \n",
              "3  0.880000   0.894029  0.886124  0.881581  \n",
              "4  1.000000   1.000000  1.000000  1.000000  \n",
              "5  1.000000   1.000000  1.000000  1.000000  \n",
              "6  0.956667   0.964770  0.954386  0.956920  \n",
              "7  0.946667   0.957672  0.943860  0.947571  "
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Min Accuracy</th>\n",
              "      <th>Mean Accuracy</th>\n",
              "      <th>Max Accuracy</th>\n",
              "      <th>MSE</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.942857</td>\n",
              "      <td>0.977143</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>0.980020</td>\n",
              "      <td>0.981340</td>\n",
              "      <td>0.980431</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LinearRegression</td>\n",
              "      <td>0.881773</td>\n",
              "      <td>0.928326</td>\n",
              "      <td>0.948382</td>\n",
              "      <td>0.053333</td>\n",
              "      <td>0.946667</td>\n",
              "      <td>0.957672</td>\n",
              "      <td>0.943860</td>\n",
              "      <td>0.947571</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GaussianNB</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.900000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BernoulliNB</td>\n",
              "      <td>0.828571</td>\n",
              "      <td>0.894286</td>\n",
              "      <td>0.942857</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.894029</td>\n",
              "      <td>0.886124</td>\n",
              "      <td>0.881581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RandomForestRegressor</td>\n",
              "      <td>0.999926</td>\n",
              "      <td>0.999984</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SVR</td>\n",
              "      <td>0.987657</td>\n",
              "      <td>0.989094</td>\n",
              "      <td>0.991016</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SGDRegressor</td>\n",
              "      <td>0.867935</td>\n",
              "      <td>0.919498</td>\n",
              "      <td>0.936792</td>\n",
              "      <td>0.043333</td>\n",
              "      <td>0.956667</td>\n",
              "      <td>0.964770</td>\n",
              "      <td>0.954386</td>\n",
              "      <td>0.956920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ridge</td>\n",
              "      <td>0.881583</td>\n",
              "      <td>0.928318</td>\n",
              "      <td>0.948468</td>\n",
              "      <td>0.053333</td>\n",
              "      <td>0.946667</td>\n",
              "      <td>0.957672</td>\n",
              "      <td>0.943860</td>\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-3da43633-e2dd-46b5-a595-9ecf2633faee');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Result: \n",
        "- Random Forest Regressor is giving the best result. The deep learning model is an optimum model as the train and test accuraies are good.\n",
        "- We have used Models like Logistic Regression, SVM, Naïve Bayes, Random Forest. By evaluating the model, we found that the best models are Random Forest and SVM for this dataset.\n",
        "- As a future work we would like to collect more data from many real-world sources, labs and hospitals across the globe and develop the machine learning model to achieve highest accuracy and prevent overfitting of data."
      ],
      "metadata": {
        "id": "wUytoGZPA3mQ"
      }
    }
  ]
}