iris_classification_ml.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tanjinadnanabir/qbosons-hackathon-qiskit-fall-fest-kolkata-2022/blob/main/iris_classification_ml.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "j_xGpUA5Uw1x"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wvb4Ts4o-wl9"
      },
      "outputs": [],
      "source": [
        "# importing libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lzanDLd6-7D1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bf0858e-2250-4f03-a763-e380193bf633"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MMGUq1Re_C5A"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m7Lm0lpT_FaE",
        "colab": {
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          "height": 424
        },
        "outputId": "251b9133-8f64-4d2d-b668-cf03d5f6e65c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "0       Iris-setosa  \n",
              "1       Iris-setosa  \n",
              "2       Iris-setosa  \n",
              "3       Iris-setosa  \n",
              "4       Iris-setosa  \n",
              "..              ...  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  \n",
              "\n",
              "[150 rows x 6 columns]"
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
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              "          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",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/iris/Iris.csv')\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# shape checking\n",
        "dataset.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to display stats about data\n",
        "dataset.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "0GUGD75lnoiE",
        "outputId": "23e28f4d-2294-495b-872a-0502ebc47564"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ],
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              "      <th>PetalWidthCm</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
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              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7e92101d-7579-4039-9eec-d43adc4e9a80');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to basic info about datatype\n",
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPQR7wIEnsNq",
        "outputId": "637ee440-7c82-483f-97aa-399eca8bc346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to display no. of samples on each class\n",
        "dataset['Species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToLjmQ0ynwdX",
        "outputId": "239abe61-46bc-4a9d-bb99-b8e0e84f889a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-axDeV_lAlid"
      },
      "outputs": [],
      "source": [
        "# Null/ missing values and percentage checking\n",
        "def nullValueCheck(n):\n",
        "  nullValues = n.isnull().sum()\n",
        "  percentage = nullValues * 100 / len(n)\n",
        "  print(percentage)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nullValueCheck(dataset)"
      ],
      "metadata": {
        "id": "zyAuxWew-v1z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a71b1eac-b404-41b8-c00b-ff6d6bfd8cfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Id               0.0\n",
            "SepalLengthCm    0.0\n",
            "SepalWidthCm     0.0\n",
            "PetalLengthCm    0.0\n",
            "PetalWidthCm     0.0\n",
            "Species          0.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VjmUSSHIYLeA"
      },
      "outputs": [],
      "source": [
        "# droping columns with higher missing values (above 70%) || Same values || from correlation matrix\n",
        "dataset = dataset.drop('Id', 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to display stats about data\n",
        "dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "be245aa1-b77d-40a8-a557-d047663188ac",
        "id": "1E6AUyil_laB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count     150.000000    150.000000     150.000000    150.000000\n",
              "mean        5.843333      3.054000       3.758667      1.198667\n",
              "std         0.828066      0.433594       1.764420      0.763161\n",
              "min         4.300000      2.000000       1.000000      0.100000\n",
              "25%         5.100000      2.800000       1.600000      0.300000\n",
              "50%         5.800000      3.000000       4.350000      1.300000\n",
              "75%         6.400000      3.300000       5.100000      1.800000\n",
              "max         7.900000      4.400000       6.900000      2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08a8dfed-a7e1-461b-873d-2da895a7ed17\">\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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08a8dfed-a7e1-461b-873d-2da895a7ed17')\"\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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              "  </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",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    .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",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08a8dfed-a7e1-461b-873d-2da895a7ed17 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-08a8dfed-a7e1-461b-873d-2da895a7ed17');\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to basic info about datatype\n",
        "dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5de2b7cc-8e8d-48e9-d534-010603141c67",
        "id": "VDp_Oaxh_laC"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   SepalLengthCm  150 non-null    float64\n",
            " 1   SepalWidthCm   150 non-null    float64\n",
            " 2   PetalLengthCm  150 non-null    float64\n",
            " 3   PetalWidthCm   150 non-null    float64\n",
            " 4   Species        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to display no. of samples on each class\n",
        "dataset['Species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f6e2e83-c652-4c8c-c4de-59b657f2a80f",
        "id": "hLH6AUXY_laC"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "id": "KrOZLnpy13tU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0285e7e4-5714-4f81-e58e-7ff9f4f13705"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qssxef4NAqmz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ddd6533-9078-4ca4-d754-dacb07d90791"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SepalLengthCm\n",
            "SepalWidthCm\n",
            "PetalLengthCm\n",
            "PetalWidthCm\n",
            "Species\n"
          ]
        }
      ],
      "source": [
        "# iterating the columns \n",
        "for col in dataset.columns: \n",
        "    print(col)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXltmRtTaVpb"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "05gEPHFXaVpd",
        "outputId": "b5e42c0a-130c-458c-88bc-e2bee303704b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdeb4578610>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# histograms\n",
        "dataset['SepalLengthCm'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "78WfABy2aVpe",
        "outputId": "aac1bd31-32fe-48cd-ea60-d207080244cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdeab595c50>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "dataset['SepalWidthCm'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MHphAa3qaVpf",
        "outputId": "7821b820-5a80-49a6-8cdb-c8d3f9949c61"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdeaaa40490>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "dataset['PetalLengthCm'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "PtrnN1pVaVph",
        "outputId": "d4772eaf-a6fd-4cde-d762-df34c50e3a99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdeaa4897d0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "dataset['PetalWidthCm'].hist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1l383A8oaVpi"
      },
      "outputs": [],
      "source": [
        "# scatterplot\n",
        "colors = ['red', 'orange', 'blue']\n",
        "species = ['Iris-virginica','Iris-versicolor','Iris-setosa']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "dPRES8fnaVpj",
        "outputId": "d41b7909-608f-41bf-d3e9-6cd0c57e55c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeaa395590>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for i in range(3):\n",
        "    x = dataset[dataset['Species'] == species[i]]\n",
        "    plt.scatter(x['SepalLengthCm'], x['SepalWidthCm'], c = colors[i], label=species[i])\n",
        "plt.xlabel(\"Sepal Length\")\n",
        "plt.ylabel(\"Sepal Width\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_3Hmpx56aVpk",
        "outputId": "a9151d3b-fb77-48e8-cc97-daada7397fcb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeaa3d8f50>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for i in range(3):\n",
        "    x = dataset[dataset['Species'] == species[i]]\n",
        "    plt.scatter(x['PetalLengthCm'], x['PetalWidthCm'], c = colors[i], label=species[i])\n",
        "plt.xlabel(\"Petal Length\")\n",
        "plt.ylabel(\"Petal Width\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "1ix2WrseaVpm",
        "outputId": "6ae51829-f69e-48de-b43a-c435a5dc326b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeaa2e1510>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for i in range(3):\n",
        "    x = dataset[dataset['Species'] == species[i]]\n",
        "    plt.scatter(x['SepalLengthCm'], x['PetalLengthCm'], c = colors[i], label=species[i])\n",
        "plt.xlabel(\"Sepal Length\")\n",
        "plt.ylabel(\"Petal Length\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "NWSj_w1NaVpn",
        "outputId": "d409c6e1-c896-4e35-d4f9-2f8147bccfcd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdeaa22fd90>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for i in range(3):\n",
        "    x = dataset[dataset['Species'] == species[i]]\n",
        "    plt.scatter(x['SepalWidthCm'], x['PetalWidthCm'], c = colors[i], label=species[i])\n",
        "plt.xlabel(\"Sepal Width\")\n",
        "plt.ylabel(\"Petal Width\")\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature correlation\n",
        "def featureCorrelation(s):\n",
        "  import seaborn as sn\n",
        "  import matplotlib.pyplot as plt\n",
        "  sn.set(font_scale=1.3)\n",
        "  sn.set_style(\"darkgrid\")\n",
        "  fig_dims = (10, 4)\n",
        "  fig, ax = plt.subplots(figsize=fig_dims)\n",
        "  sn.heatmap(s.corr(), annot=True, ax=ax)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "ky3mrwCg_3Tj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "featureCorrelation(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "425b53da-8a87-4e21-cddf-85a9f090837b",
        "id": "X44hWFSx_3Tj"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Label Encoder"
      ],
      "metadata": {
        "id": "u6dl9qjwovdf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d_rJMGJiaVpu"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NDBcN7DRaVpv",
        "outputId": "1a74105e-4824-42b7-8587-2b11493b418c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0            5.1           3.5            1.4           0.2        0\n",
              "1            4.9           3.0            1.4           0.2        0\n",
              "2            4.7           3.2            1.3           0.2        0\n",
              "3            4.6           3.1            1.5           0.2        0\n",
              "4            5.0           3.6            1.4           0.2        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c299eea-a5f9-4a32-b707-e6a57f357447\">\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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c299eea-a5f9-4a32-b707-e6a57f357447')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c299eea-a5f9-4a32-b707-e6a57f357447 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-8c299eea-a5f9-4a32-b707-e6a57f357447');\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": 31
        }
      ],
      "source": [
        "dataset['Species'] = le.fit_transform(dataset['Species'])\n",
        "dataset.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "featureCorrelation(dataset)"
      ],
      "metadata": {
        "id": "W4_LsZdHVE3i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "9f017946-d35f-4a33-b4bb-ca788d95cf7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cor = dataset.corr()"
      ],
      "metadata": {
        "id": "0eCkfBUJOgHQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# cor.to_csv(\"/content/drive/MyDrive/Colab Notebooks/iris/corr-mat.csv\")"
      ],
      "metadata": {
        "id": "Y-EiqDWLN7-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GoJjON5iEUfH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06a9e214-b683-4d9f-d1aa-02c2c449e804"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X shape: (150, 4)\n",
            "y shape: (150, 1)\n"
          ]
        }
      ],
      "source": [
        "# creating X Y variables\n",
        "max_len = len(dataset.columns)-1\n",
        "min_len = 0\n",
        "max = len(dataset.columns)\n",
        "\n",
        "X = dataset.iloc[:,min_len:max_len].values\n",
        "x = dataset.iloc[:,min_len:max_len]\n",
        "\n",
        "y = dataset.iloc[:,max_len].values\n",
        "y_name = dataset.iloc[:, max_len:max]\n",
        "\n",
        "print(\"X shape:\",x.shape)\n",
        "print(\"y shape:\",y_name.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zmGAO950Jmtx"
      },
      "outputs": [],
      "source": [
        "# iterating the columns \n",
        "def iteratingColumn(n):\n",
        "  print(\"Attributes:\\n\")\n",
        "  for col in x.columns: \n",
        "      print(col)\n",
        "\n",
        "  print(\"\\n\")\n",
        "  print(\"Class:\\n\")\n",
        "  for col in y_name.columns: \n",
        "      print(col)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iteratingColumn(dataset)"
      ],
      "metadata": {
        "id": "PYSt9CEIxQi-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84be40e9-a21b-4ed7-c2f0-4c8211036262"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attributes:\n",
            "\n",
            "SepalLengthCm\n",
            "SepalWidthCm\n",
            "PetalLengthCm\n",
            "PetalWidthCm\n",
            "\n",
            "\n",
            "Class:\n",
            "\n",
            "Species\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlBbtgnYNVoI"
      },
      "source": [
        "#  Spliting the Dataset into Training set and Test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "23rhGuYeNe1m"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnWyJVDfgf8n",
        "outputId": "b47d4c9c-1d88-4f2f-f7e5-21f5eb519807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[5.  2.  3.5 1. ]\n",
            " [6.5 3.  5.5 1.8]\n",
            " [6.7 3.3 5.7 2.5]\n",
            " [6.  2.2 5.  1.5]\n",
            " [6.7 2.5 5.8 1.8]\n",
            " [5.6 2.5 3.9 1.1]\n",
            " [7.7 3.  6.1 2.3]\n",
            " [6.3 3.3 4.7 1.6]\n",
            " [5.5 2.4 3.8 1.1]\n",
            " [6.3 2.7 4.9 1.8]\n",
            " [6.3 2.8 5.1 1.5]\n",
            " [4.9 2.5 4.5 1.7]\n",
            " [6.3 2.5 5.  1.9]\n",
            " [7.  3.2 4.7 1.4]\n",
            " [6.5 3.  5.2 2. ]\n",
            " [6.  3.4 4.5 1.6]\n",
            " [4.8 3.1 1.6 0.2]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [5.6 2.7 4.2 1.3]\n",
            " [5.6 2.9 3.6 1.3]\n",
            " [5.5 2.5 4.  1.3]\n",
            " [6.1 3.  4.6 1.4]\n",
            " [7.2 3.2 6.  1.8]\n",
            " [5.3 3.7 1.5 0.2]\n",
            " [4.3 3.  1.1 0.1]\n",
            " [6.4 2.7 5.3 1.9]\n",
            " [5.7 3.  4.2 1.2]\n",
            " [5.4 3.4 1.7 0.2]\n",
            " [5.7 4.4 1.5 0.4]\n",
            " [6.9 3.1 4.9 1.5]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.9 3.  5.1 1.8]\n",
            " [5.1 2.5 3.  1.1]\n",
            " [4.6 3.4 1.4 0.3]\n",
            " [6.2 2.2 4.5 1.5]\n",
            " [7.2 3.6 6.1 2.5]\n",
            " [5.7 2.9 4.2 1.3]\n",
            " [4.8 3.  1.4 0.1]\n",
            " [7.1 3.  5.9 2.1]\n",
            " [6.9 3.2 5.7 2.3]\n",
            " [6.5 3.  5.8 2.2]\n",
            " [6.4 2.8 5.6 2.1]\n",
            " [5.1 3.8 1.6 0.2]\n",
            " [4.8 3.4 1.6 0.2]\n",
            " [6.5 3.2 5.1 2. ]\n",
            " [6.7 3.3 5.7 2.1]\n",
            " [4.5 2.3 1.3 0.3]\n",
            " [6.2 3.4 5.4 2.3]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [5.7 2.5 5.  2. ]\n",
            " [6.9 3.1 5.4 2.1]\n",
            " [4.4 3.2 1.3 0.2]\n",
            " [5.  3.6 1.4 0.2]\n",
            " [7.2 3.  5.8 1.6]\n",
            " [5.1 3.5 1.4 0.3]\n",
            " [4.4 3.  1.3 0.2]\n",
            " [5.4 3.9 1.7 0.4]\n",
            " [5.5 2.3 4.  1.3]\n",
            " [6.8 3.2 5.9 2.3]\n",
            " [7.6 3.  6.6 2.1]\n",
            " [5.1 3.5 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.2 3.4 1.4 0.2]\n",
            " [5.7 2.8 4.5 1.3]\n",
            " [6.6 3.  4.4 1.4]\n",
            " [5.  3.2 1.2 0.2]\n",
            " [5.1 3.3 1.7 0.5]\n",
            " [6.4 2.9 4.3 1.3]\n",
            " [5.4 3.4 1.5 0.4]\n",
            " [7.7 2.6 6.9 2.3]\n",
            " [4.9 2.4 3.3 1. ]\n",
            " [7.9 3.8 6.4 2. ]\n",
            " [6.7 3.1 4.4 1.4]\n",
            " [5.2 4.1 1.5 0.1]\n",
            " [6.  3.  4.8 1.8]\n",
            " [5.8 4.  1.2 0.2]\n",
            " [7.7 2.8 6.7 2. ]\n",
            " [5.1 3.8 1.5 0.3]\n",
            " [4.7 3.2 1.6 0.2]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [5.  3.3 1.4 0.2]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [5.7 2.8 4.1 1.3]\n",
            " [5.8 2.7 3.9 1.2]\n",
            " [5.7 2.6 3.5 1. ]\n",
            " [6.4 3.2 5.3 2.3]\n",
            " [6.7 3.  5.2 2.3]\n",
            " [6.3 2.5 4.9 1.5]\n",
            " [6.7 3.  5.  1.7]\n",
            " [5.  3.  1.6 0.2]\n",
            " [5.5 2.4 3.7 1. ]\n",
            " [6.7 3.1 5.6 2.4]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [5.1 3.4 1.5 0.2]\n",
            " [6.6 2.9 4.6 1.3]\n",
            " [5.6 3.  4.1 1.3]\n",
            " [5.9 3.2 4.8 1.8]\n",
            " [6.3 2.3 4.4 1.3]\n",
            " [5.5 3.5 1.3 0.2]\n",
            " [5.1 3.7 1.5 0.4]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [6.3 2.9 5.6 1.8]\n",
            " [5.8 2.7 4.1 1. ]\n",
            " [7.7 3.8 6.7 2.2]\n",
            " [4.6 3.2 1.4 0.2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lk2tW5JSTXei",
        "outputId": "c047142f-f403-476a-8cd4-256b00f46758"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 2 2 2 1 2 1 1 2 2 2 2 1 2 1 0 2 1 1 1 1 2 0 0 2 1 0 0 1 0 2 1 0 1 2 1\n",
            " 0 2 2 2 2 0 0 2 2 0 2 0 2 2 0 0 2 0 0 0 1 2 2 0 0 0 1 1 0 0 1 0 2 1 2 1 0\n",
            " 2 0 2 0 0 2 0 2 1 1 1 2 2 1 1 0 1 2 2 0 1 1 1 1 0 0 0 2 1 2 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tUjFHF_WpYUv",
        "outputId": "57fc588d-1990-4ec1-c0d5-1af564ce669f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0              5.1           3.5            1.4           0.2        0\n",
              "1              4.9           3.0            1.4           0.2        0\n",
              "2              4.7           3.2            1.3           0.2        0\n",
              "3              4.6           3.1            1.5           0.2        0\n",
              "4              5.0           3.6            1.4           0.2        0\n",
              "..             ...           ...            ...           ...      ...\n",
              "145            6.7           3.0            5.2           2.3        2\n",
              "146            6.3           2.5            5.0           1.9        2\n",
              "147            6.5           3.0            5.2           2.0        2\n",
              "148            6.2           3.4            5.4           2.3        2\n",
              "149            5.9           3.0            5.1           1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e65c58f-5d46-4523-939a-76f811ae9977\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e65c58f-5d46-4523-939a-76f811ae9977')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e65c58f-5d46-4523-939a-76f811ae9977 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-0e65c58f-5d46-4523-939a-76f811ae9977');\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": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pReqEiW4Nicn"
      },
      "source": [
        "# Feature Scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FQrS49O4NlkT"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "plQoiniKqSgS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b268d39-9671-4094-9605-fa111b029c0a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 150)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "len(X), len(y)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WffHR8zY30GA",
        "outputId": "c81c4381-9fa2-4111-c9f4-ccc4d0025977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((150, 4), (150,))"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "oj7hZOOi4GPF",
        "outputId": "36759675-2331-40a4-f372-7b95c630c6dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0              5.1           3.5            1.4           0.2        0\n",
              "1              4.9           3.0            1.4           0.2        0\n",
              "2              4.7           3.2            1.3           0.2        0\n",
              "3              4.6           3.1            1.5           0.2        0\n",
              "4              5.0           3.6            1.4           0.2        0\n",
              "..             ...           ...            ...           ...      ...\n",
              "145            6.7           3.0            5.2           2.3        2\n",
              "146            6.3           2.5            5.0           1.9        2\n",
              "147            6.5           3.0            5.2           2.0        2\n",
              "148            6.2           3.4            5.4           2.3        2\n",
              "149            5.9           3.0            5.1           1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd3e1c53-f9db-4238-a3d8-940f9a89bb84\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd3e1c53-f9db-4238-a3d8-940f9a89bb84')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bd3e1c53-f9db-4238-a3d8-940f9a89bb84 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-bd3e1c53-f9db-4238-a3d8-940f9a89bb84');\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": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LightGBM"
      ],
      "metadata": {
        "id": "WyoKCmMmAwKL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "import lightgbm as lgbm\n",
        "param_grid = {\n",
        "              'colsample_bytree' : [0.1,0.2,0.3,0.4,0.5,1.0],\n",
        "              'max_depth' : [1,2,3,4,5], \n",
        "              'min_split_gain' : [0.1,0.2,0.3,0.4,0.5],\n",
        "              'n_estimators' : [1,10,100],\n",
        "              'num_leaves' :  [100,200],\n",
        "              'subsample_freq' : [0,5,10,15,20]\n",
        "              }"
      ],
      "metadata": {
        "id": "EJmj9HmGDhFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88d69439-653e-4714-888f-ac4fa39f0f2e",
        "id": "JaIEE_fgDhFm"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'colsample_bytree': [0.1, 0.2, 0.3, 0.4, 0.5, 1.0], 'max_depth': [1, 2, 3, 4, 5], 'min_split_gain': [0.1, 0.2, 0.3, 0.4, 0.5], 'n_estimators': [1, 10, 100], 'num_leaves': [100, 200], 'subsample_freq': [0, 5, 10, 15, 20]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_clf = lgbm.LGBMClassifier()\n",
        "lgbm_clf_cv = GridSearchCV(lgbm_clf, param_grid, cv=10, verbose=2, n_jobs=-1)\n",
        "print(lgbm_clf_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d517a30-e8aa-4111-bcf9-b4e6349d0a29",
        "id": "KlY0anMZDhFn"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=LGBMClassifier(), n_jobs=-1,\n",
            "             param_grid={'colsample_bytree': [0.1, 0.2, 0.3, 0.4, 0.5, 1.0],\n",
            "                         'max_depth': [1, 2, 3, 4, 5],\n",
            "                         'min_split_gain': [0.1, 0.2, 0.3, 0.4, 0.5],\n",
            "                         'n_estimators': [1, 10, 100], 'num_leaves': [100, 200],\n",
            "                         'subsample_freq': [0, 5, 10, 15, 20]},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_clf_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",lgbm_clf_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",lgbm_clf_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca819bf7-b1f5-4935-c405-0e36883255b0",
        "id": "vwVlAcTGDhFn"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 4500 candidates, totalling 45000 fits\n",
            "tuned hpyerparameters :(best parameters)  {'colsample_bytree': 0.5, 'max_depth': 2, 'min_split_gain': 0.4, 'n_estimators': 100, 'num_leaves': 100, 'subsample_freq': 0}\n",
            "\n",
            "\n",
            "accuracy : 0.9618181818181819\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljrLORVWDhFn"
      },
      "source": [
        "lgbm_clf = lgbm.LGBMClassifier(colsample_bytree = 1.0, max_depth = 4, min_split_gain = 0.3, n_estimators = 100, num_leaves = 100, subsample_freq = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SgjPnnxA-xV",
        "outputId": "e9cb364f-035c-4939-bd69-a9c71ec324a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(max_depth=4, min_split_gain=0.3, num_leaves=100)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lgbm = lgbm_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "dZFmv-pxBdPt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "662cba25-12a7-4f9e-a2d1-8c8795049570",
        "id": "3f_Cg9xZCAAj"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_svm = confusion_matrix(y_test, y_pred_lgbm)\n",
        "print(cm_svm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaP9WkIGCAAk"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_lgbm)\n",
        "pre = precision_score(y_test, y_pred_lgbm, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_lgbm, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_lgbm, average = 'micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "844aa80c-cb2e-4aa2-804e-07ced93e07c6",
        "id": "SNqp4XABCAAl"
      },
      "source": [
        "print(\"Summary of LGBM\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of LGBM\n",
            "Accuracy    = 0.9777777777777777\n",
            "Precison    = 0.9777777777777777\n",
            "Recall      = 0.9777777777777777\n",
            "f1 score    = 0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GradientBoostingClassifier"
      ],
      "metadata": {
        "id": "MhP05mvw58Ul"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_hastie_10_2\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {'criterion' : ['friedman_mse', 'squared_error', 'mse'],\n",
        "              'learning_rate' : [0.1,0.2,0.3,0.4,0.5,1.0],\n",
        "              'loss' : ['log_loss', 'deviance', 'exponential'],\n",
        "              'max_depth' : [1,2,3,4,5],\n",
        "              'max_features' : ['auto', 'sqrt', 'log2'],\n",
        "              'n_estimators' : [1,10,100]\n",
        "              }"
      ],
      "metadata": {
        "id": "ESUphvkR570_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "id": "Khmus-_qfcGj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "278addc7-937c-44a9-be2d-ddae61939936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'criterion': ['friedman_mse', 'squared_error', 'mse'], 'learning_rate': [0.1, 0.2, 0.3, 0.4, 0.5, 1.0], 'loss': ['log_loss', 'deviance', 'exponential'], 'max_depth': [1, 2, 3, 4, 5], 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': [1, 10, 100]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grad_clf = GradientBoostingClassifier()\n",
        "grad_clf_cv = GridSearchCV(grad_clf, param_grid, cv = 10, verbose = 2, n_jobs = -1)\n",
        "print(grad_clf_cv)"
      ],
      "metadata": {
        "id": "nRvPZLHSfeyA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d76bb1eb-ff25-409a-a213-515afffc8cb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_jobs=-1,\n",
            "             param_grid={'criterion': ['friedman_mse', 'squared_error', 'mse'],\n",
            "                         'learning_rate': [0.1, 0.2, 0.3, 0.4, 0.5, 1.0],\n",
            "                         'loss': ['log_loss', 'deviance', 'exponential'],\n",
            "                         'max_depth': [1, 2, 3, 4, 5],\n",
            "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
            "                         'n_estimators': [1, 10, 100]},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grad_clf_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \", grad_clf_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",grad_clf_cv.best_score_)"
      ],
      "metadata": {
        "id": "JoJpbPnWf5Fs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5aa95995-7905-4335-f467-0fd433499819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 2430 candidates, totalling 24300 fits\n",
            "tuned hpyerparameters :(best parameters)  {'criterion': 'friedman_mse', 'learning_rate': 0.1, 'loss': 'deviance', 'max_depth': 3, 'max_features': 'auto', 'n_estimators': 1}\n",
            "\n",
            "\n",
            "accuracy : 0.9709090909090909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grad_clf = GradientBoostingClassifier(criterion = 'friedman_mse', n_estimators = 1, learning_rate = 0.1, loss = 'deviance', max_depth = 3, max_features = 'auto')\n",
        "grad_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "-tc1TMQP6AH8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d96c097e-6d95-4651-f4a7-754bc6fe9645"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(max_features='auto', n_estimators=1)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_grad = grad_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "gFijf22g6knj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-CklsYhd6896",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca938391-da34-4464-8b7a-40893cb9a7b1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        16\n",
            "           1       1.00      0.94      0.97        18\n",
            "           2       0.92      1.00      0.96        11\n",
            "\n",
            "    accuracy                           0.98        45\n",
            "   macro avg       0.97      0.98      0.98        45\n",
            "weighted avg       0.98      0.98      0.98        45\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "print(confusion_matrix(y_test,y_pred_grad))\n",
        "print(classification_report(y_test,y_pred_grad))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhhjAltdCfyg"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_grad)\n",
        "pre = precision_score(y_test, y_pred_grad, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_grad, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_grad, average = 'micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "512c4934-abfa-40c2-8a5e-c41aeb997fb4",
        "id": "j6eWXNPTCfyg"
      },
      "source": [
        "print(\"Summary of GBC\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of GBC\n",
            "Accuracy    = 0.9777777777777777\n",
            "Precison    = 0.9777777777777777\n",
            "Recall      = 0.9777777777777777\n",
            "f1 score    = 0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AdaBoostClassifier"
      ],
      "metadata": {
        "id": "qiZ5WyVg78ss"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {'learning_rate' : [0.1,0.2,0.3,0.4,0.5,1.0],\n",
        "              'n_estimators' : [50,100],\n",
        "              'algorithm' : ['SAMME', 'SAMME.R'],\n",
        "              'random_state' : [0]\n",
        "              }"
      ],
      "metadata": {
        "id": "kIyIVJDi7y3l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97fd397e-c751-45c2-b2e7-6f37a966f04b",
        "id": "8GCLJlRF9Z4M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'learning_rate': [0.1, 0.2, 0.3, 0.4, 0.5, 1.0], 'n_estimators': [50, 100], 'algorithm': ['SAMME', 'SAMME.R'], 'random_state': [0]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_clf = AdaBoostClassifier()\n",
        "ada_clf_cv = GridSearchCV(ada_clf,param_grid,cv=10,verbose=2,n_jobs=-1)\n",
        "print(ada_clf_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1da81b8f-c912-465e-d1a8-6228f384481f",
        "id": "fgLGQJd39Z4N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=AdaBoostClassifier(), n_jobs=-1,\n",
            "             param_grid={'algorithm': ['SAMME', 'SAMME.R'],\n",
            "                         'learning_rate': [0.1, 0.2, 0.3, 0.4, 0.5, 1.0],\n",
            "                         'n_estimators': [50, 100], 'random_state': [0]},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_clf_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",ada_clf_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",ada_clf_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10b44e00-33d2-403a-c920-17effa7ed3f7",
        "id": "kODbh9Io9Z4O"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 24 candidates, totalling 240 fits\n",
            "tuned hpyerparameters :(best parameters)  {'algorithm': 'SAMME.R', 'learning_rate': 0.4, 'n_estimators': 100, 'random_state': 0}\n",
            "\n",
            "\n",
            "accuracy : 0.9542041248016921\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ada_clf = AdaBoostClassifier(algorithm = 'SAMME.R', learning_rate = 0.5, n_estimators = 100, random_state=0)\n",
        "ada_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "mCytHAcG7-e6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a855e35c-c909-48be-ca19-e1b7ebe43cbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(learning_rate=0.5, n_estimators=100, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_ada = ada_clf.predict(X_test)"
      ],
      "metadata": {
        "id": "43DV5BZx8ZmE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZyi3X2k8ZmF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f55dbc61-7ab9-4593-d89b-17dd8e953948"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         1\n",
            "\n",
            "    accuracy                           1.00         1\n",
            "   macro avg       1.00      1.00      1.00         1\n",
            "weighted avg       1.00      1.00      1.00         1\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "print(confusion_matrix(y_test,y_pred_ada))\n",
        "print(classification_report(y_test,y_pred_ada))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LnPEKlZJ8ZmG"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_ada)\n",
        "pre = precision_score(y_test, y_pred_ada, average = 'macro')\n",
        "re  = recall_score(y_test, y_pred_ada, average = 'macro')\n",
        "f1  = f1_score(y_test, y_pred_ada, average = 'macro')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iYVepirs8ZmH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "070bab13-5436-4508-b808-b7d8ce442713"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of AdaBC\n",
            "Accuracy    = 0.9512195121951219\n",
            "Precison    = 0.9519893899204244\n",
            "Recall      = 0.9507936507936507\n",
            "f1 score    = 0.9511387711864407\n"
          ]
        }
      ],
      "source": [
        "print(\"Summary of AdaBC\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN\n",
        "\n"
      ],
      "metadata": {
        "id": "D8X5rtMONEmy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "param_grid = {'n_neighbors': [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],\n",
        "              \"weights\": [\"uniform\", \"distance\"],\n",
        "              \"metric\":[\"minkowski\",\"euclidean\", \"manhattan\"],\n",
        "              'p':[1,2]\n",
        "              }"
      ],
      "metadata": {
        "id": "Q5tvSKhxA7Db"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVgAsnkoF251",
        "outputId": "519b3bbd-8e23-4289-9389-9883207c2b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], 'weights': ['uniform', 'distance'], 'metric': ['minkowski', 'euclidean', 'manhattan'], 'p': [1, 2]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_knn    = KNeighborsClassifier()\n",
        "classifier_knn_cv = GridSearchCV(classifier_knn,param_grid,cv=10,verbose=2,n_jobs=-1)\n",
        "print(classifier_knn_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pINn_CPpF0_H",
        "outputId": "fee3d54a-5ee7-4d4f-ae95-efeb26046cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=KNeighborsClassifier(), n_jobs=-1,\n",
            "             param_grid={'metric': ['minkowski', 'euclidean', 'manhattan'],\n",
            "                         'n_neighbors': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n",
            "                                         14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n",
            "                                         24, 25, 26, 27, 28, 29, 30],\n",
            "                         'p': [1, 2], 'weights': ['uniform', 'distance']},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_knn_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",classifier_knn_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",classifier_knn_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33qEK6UuBZIw",
        "outputId": "6accae80-43df-495f-9c39-a96caa7b56a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 336 candidates, totalling 3360 fits\n",
            "tuned hpyerparameters :(best parameters)  {'metric': 'minkowski', 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}\n",
            "\n",
            "\n",
            "accuracy : 0.9627272727272727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twpr9BpGCfye"
      },
      "source": [
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier_knn = KNeighborsClassifier(metric= 'minkowski', n_neighbors= 5, p= 2, weights='uniform')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33ee4e22-e774-4b79-cb06-68f64397876d",
        "id": "BsYabKjxCfyf"
      },
      "source": [
        "classifier_knn.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fytc_PNDCfyg"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred_knn = classifier_knn.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd8872bf-2b21-410b-ebfc-f5bdd9603556",
        "id": "m8Ms9h5JCfyg"
      },
      "source": [
        "print(y_pred_knn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2 1 1 2 0 2 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5348400-07dd-4483-f723-9aa56ed5a1cd",
        "id": "sHAAkNR6Cfyg"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
        "print(cm_knn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7pK8CIgsesr"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_knn)\n",
        "pre = precision_score(y_test, y_pred_knn, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_knn, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_knn, average = 'micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a97a94c-954f-4cff-98fe-1b2d6a8d4b40",
        "id": "ENbG4Nydsess"
      },
      "source": [
        "print(\"Summary of KNN\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of KNN\n",
            "Accuracy    = 0.9777777777777777\n",
            "Precison    = 0.9777777777777777\n",
            "Recall      = 0.9777777777777777\n",
            "f1 score    = 0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SVM\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RqCuwjeYsu65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "param_grid = {'C': [1,2,3,4,5],\n",
        "              \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n",
        "              'degree': [1,2,3,4,5],\n",
        "              \"probability\" : [True]\n",
        "              }"
      ],
      "metadata": {
        "id": "IMvsTg_Vsu65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9ec5a57-c89c-4989-bb1f-b5c09d0f6eb6",
        "id": "BVIMIbhMsu66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': [1, 2, 3, 4, 5], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'degree': [1, 2, 3, 4, 5], 'probability': [True]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_svm    = SVC()\n",
        "classifier_svm_cv = GridSearchCV(classifier_svm,param_grid,cv=10,verbose=2,n_jobs=-1)\n",
        "print(classifier_svm_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea7e06d9-fcae-40ed-92c5-1b8d82837924",
        "id": "pGsc666Csu66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=SVC(), n_jobs=-1,\n",
            "             param_grid={'C': [1, 2, 3, 4, 5], 'degree': [1, 2, 3, 4, 5],\n",
            "                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n",
            "                         'probability': [True]},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_svm_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",classifier_svm_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",classifier_svm_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96c551f3-6ff7-4b35-c847-ef379d15e93c",
        "id": "jL7COTx6su67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n",
            "tuned hpyerparameters :(best parameters)  {'C': 3, 'degree': 3, 'kernel': 'poly', 'probability': True}\n",
            "\n",
            "\n",
            "accuracy : 0.9536363636363635\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VbyS4mosu67"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier_svm = SVC(C = 3, degree = 1, kernel = 'poly', probability=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16745180-ba1f-424f-897d-96e9c9d7fade",
        "id": "62JAnLXwsu67"
      },
      "source": [
        "classifier_svm.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=3, degree=1, kernel='poly', probability=True)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sS0rjfWisu68"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred_svm = classifier_svm.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d7da3ec-7c82-4d4a-f775-391ec2326550",
        "id": "_OaBWCHEsu68"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
        "print(cm_svm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k13whmtsu69"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_svm)\n",
        "pre = precision_score(y_test, y_pred_svm, average='micro')\n",
        "re  = recall_score(y_test, y_pred_svm, average='micro')\n",
        "f1  = f1_score(y_test, y_pred_svm, average='micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a0be3b6-d831-4da4-ffb3-a998a0c1d261",
        "id": "m2L8QlZ3su69"
      },
      "source": [
        "print(\"Summary of SVM\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of SVM\n",
            "Accuracy    = 0.9777777777777777\n",
            "Precison    = 0.9777777777777777\n",
            "Recall      = 0.9777777777777777\n",
            "f1 score    = 0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RpExvXuxtE5q"
      },
      "source": [
        "# LR\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vcdQNGP-tE5q"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "param_grid = {\n",
        "              'random_state' : [0,1,2,3,4,5],\n",
        "              'C': [0.001,0.1,0.2,0.3,0.4,0.5,1],\n",
        "              \"penalty\" : [\"l1\", \"l2\", \"elasticnet\", \"none\"],\n",
        "              \"dual\" : [True],\n",
        "              \"fit_intercept\" : [True],\n",
        "              \"solver\" : [\"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\", \"saga\"],\n",
        "              \"multi_class\" : [\"auto\", \"ovr\", \"multinomial\"],\n",
        "              }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a7336b9-5f8f-4ee1-fedc-7cb7deddfa39",
        "id": "MDSokEzetE5q"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'random_state': [0, 1, 2, 3, 4, 5], 'C': [0.001, 0.1, 0.2, 0.3, 0.4, 0.5, 1], 'penalty': ['l1', 'l2', 'elasticnet', 'none'], 'dual': [True], 'fit_intercept': [True], 'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'], 'multi_class': ['auto', 'ovr', 'multinomial']}\n"
          ]
        }
      ],
      "source": [
        "print(param_grid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "278208c1-6e72-4272-b12a-79e0fa453259",
        "id": "JCahCsKctE5r"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=LogisticRegression(), n_jobs=-1,\n",
            "             param_grid={'C': [0.001, 0.1, 0.2, 0.3, 0.4, 0.5, 1],\n",
            "                         'dual': [True], 'fit_intercept': [True],\n",
            "                         'multi_class': ['auto', 'ovr', 'multinomial'],\n",
            "                         'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
            "                         'random_state': [0, 1, 2, 3, 4, 5],\n",
            "                         'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag',\n",
            "                                    'saga']},\n",
            "             verbose=2)\n"
          ]
        }
      ],
      "source": [
        "classifier_log = LogisticRegression()\n",
        "classifier_log_cv = GridSearchCV(classifier_log,param_grid,cv=10,verbose=2,n_jobs=-1)\n",
        "print(classifier_log_cv)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c72733b2-4bb7-4940-e52b-15000c7db5bf",
        "id": "Tvt_yHmitE5r"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 2520 candidates, totalling 25200 fits\n",
            "tuned hpyerparameters :(best parameters)  {'C': 0.5, 'dual': True, 'fit_intercept': True, 'multi_class': 'auto', 'penalty': 'l2', 'random_state': 0, 'solver': 'liblinear'}\n",
            "\n",
            "\n",
            "accuracy : 0.8872727272727273\n"
          ]
        }
      ],
      "source": [
        "classifier_log_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",classifier_log_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",classifier_log_cv.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mCZpb10dtE5s"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier_log = LogisticRegression(C = 1, random_state=0, dual = True, fit_intercept=True, multi_class= 'auto', penalty = 'l2', solver = 'liblinear')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ff10790-5cf2-478e-9fdb-f84a41bf689a",
        "id": "nWS5aO-OtE5s"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1, dual=True, random_state=0, solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "classifier_log.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KSOP3D99tE5s"
      },
      "outputs": [],
      "source": [
        "# Predicting the Test set results\n",
        "y_pred_log = classifier_log.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39f5c9aa-2bbb-4904-b0aa-6aad99efef68",
        "id": "jrmrj9bitE5s"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 2 0 0 2 0 0 1 1 0 2 2 0 2 2 1 0\n",
            " 2 1 1 2 0 2 0 0]\n"
          ]
        }
      ],
      "source": [
        "print(y_pred_log)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6605fcc7-ecec-4bf0-888c-39426310b870",
        "id": "xIzO49bitE5t"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 15  3]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ],
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_log = confusion_matrix(y_test, y_pred_log)\n",
        "print(cm_log)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c2aPrkv_tE5t"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_log)\n",
        "pre = precision_score(y_test, y_pred_log, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_log, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_log, average = 'micro')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50300383-79e3-4298-910f-22a86376d383",
        "id": "BVILUcLRtE5t"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of LR\n",
            "Accuracy    = 0.9512195121951219\n",
            "Precison    = 0.9655172413793104\n",
            "Recall      = 0.9333333333333333\n",
            "f1 score    = 0.9491525423728815\n"
          ]
        }
      ],
      "source": [
        "print(\"Summary of LR\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DT\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Yee_kXSnti6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "param_grid = {\n",
        "              \"criterion\" : [\"gini\", \"entropy\"],\n",
        "              \"splitter\" : [\"best\", \"random\"],\n",
        "              'max_depth' : [0,1,2,3,4,5,6,7,8,9,10],\n",
        "              \"max_features\" : [\"auto\", \"sqrt\", \"log2\"],\n",
        "              'max_leaf_nodes' : [0,1,2,3,4,5,6,7,8,9,10,11,12,13],\n",
        "              'min_samples_split' : [0,1,2,3,4,5,6,7,8,9]\n",
        "              }"
      ],
      "metadata": {
        "id": "zsQVcfzHti6j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01ce6ee8-20e0-4da8-c51b-90831362445f",
        "id": "FB7G7ZLUti6j"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random'], 'max_depth': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'max_features': ['auto', 'sqrt', 'log2'], 'max_leaf_nodes': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], 'min_samples_split': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_dt = DecisionTreeClassifier()\n",
        "classifier_dt_cv = GridSearchCV(classifier_dt,param_grid,cv=10,verbose=2,n_jobs=-1)\n",
        "print(classifier_dt_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35c86564-2311-431a-83e9-1e61b18cb46f",
        "id": "hcA0CBIIti6j"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_jobs=-1,\n",
            "             param_grid={'criterion': ['gini', 'entropy'],\n",
            "                         'max_depth': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
            "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
            "                         'max_leaf_nodes': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n",
            "                                            11, 12, 13],\n",
            "                         'min_samples_split': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
            "                         'splitter': ['best', 'random']},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_dt_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",classifier_dt_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",classifier_dt_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df901a8b-2ed4-49d4-d6fa-fac7fd1402a2",
        "id": "rE-sLQfbti6k"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 18480 candidates, totalling 184800 fits\n",
            "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'max_leaf_nodes': 11, 'min_samples_split': 9, 'splitter': 'best'}\n",
            "\n",
            "\n",
            "accuracy : 0.8957482993197278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apuCYw6jti6k"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier_dt = DecisionTreeClassifier(criterion = 'gini', max_depth = 10, max_features = 'sqrt', max_leaf_nodes = 12, min_samples_split = 3, splitter = 'best')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30fafd36-abaa-4769-e280-7a3a1b8f26ec",
        "id": "vmtRtwdati6k"
      },
      "source": [
        "classifier_dt.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=10, max_features='sqrt', max_leaf_nodes=12,\n",
              "                       min_samples_split=3)"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBiEnYvlti6l"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred_dt = classifier_dt.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24218c52-f649-46f5-9fa3-c7aa7dff8817",
        "id": "ulrS0kC-ti6l"
      },
      "source": [
        "print(y_pred_dt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 1.\n",
            " 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1.\n",
            " 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0. 1. 1. 1.\n",
            " 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 1.\n",
            " 0. 0. 1. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1.\n",
            " 0. 1. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afc09cd9-85e8-47c5-c5e0-b5ed2c672db1",
        "id": "GygZSGw-ti6l"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_svm = confusion_matrix(y_test, y_pred_dt)\n",
        "print(cm_svm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[59  4]\n",
            " [ 7 53]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTGMR5GLti6m"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_dt)\n",
        "pre = precision_score(y_test, y_pred_dt, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_dt, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_dt, average = 'micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f67d2f28-f163-4d17-89f7-377f276c701d",
        "id": "1UbJVqjOti6m"
      },
      "source": [
        "print(\"Summary of DT\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of DT\n",
            "Accuracy    = 0.9105691056910569\n",
            "Precison    = 0.9298245614035088\n",
            "Recall      = 0.8833333333333333\n",
            "f1 score    = 0.905982905982906\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RF\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "HdXV7Remt1MN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "param_grid = {\n",
        "              'criterion' : ['gini', 'entropy', 'log_loss'],\n",
        "              'max_depth' : [0,1,2,3,4,5,6,7,8,9,10],\n",
        "              'max_features' : ['sqrt', 'log2'],\n",
        "              'random_state' : [0,1,2,3,4,5],\n",
        "              'n_estimators' : [1,2,3,4,5,6,7,8,9,10],\n",
        "              }"
      ],
      "metadata": {
        "id": "n1y6aqMlt1MO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b51e76a2-e24d-4aec-868a-713b0a395ccd",
        "id": "PVD14fpSt1MP"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'criterion': ['gini', 'entropy', 'log_loss'], 'max_depth': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'max_features': ['sqrt', 'log2'], 'random_state': [0, 1, 2, 3, 4, 5], 'n_estimators': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_rf = RandomForestClassifier()\n",
        "classifier_rf_cv = GridSearchCV(classifier_rf,param_grid,cv=10,verbose=2,n_jobs=-1)\n",
        "print(classifier_rf_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcbcc8d6-35cf-4bd9-9050-74f1ffc98d31",
        "id": "SXsA-f8Jt1MP"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=RandomForestClassifier(), n_jobs=-1,\n",
            "             param_grid={'criterion': ['gini', 'entropy', 'log_loss'],\n",
            "                         'max_depth': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
            "                         'max_features': ['sqrt', 'log2'],\n",
            "                         'n_estimators': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
            "                         'random_state': [0, 1, 2, 3, 4, 5]},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier_rf_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",classifier_rf_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",classifier_rf_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10128bb3-5838-49ee-cd7c-06b89ba1599b",
        "id": "KzKYiX1It1MQ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 3960 candidates, totalling 39600 fits\n",
            "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 9, 'max_features': 'sqrt', 'n_estimators': 10, 'random_state': 3}\n",
            "\n",
            "\n",
            "accuracy : 0.9611394557823131\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVSTQpSLt1MQ"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier_rf = RandomForestClassifier(criterion = 'gini', max_depth = 9, max_features = 'sqrt', n_estimators = 10, random_state = 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73b5d07e-0e43-4592-b433-a0574aec9caf",
        "id": "_3sB7fHft1MQ"
      },
      "source": [
        "classifier_rf.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=9, max_features='sqrt', n_estimators=10,\n",
              "                       random_state=3)"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJ4I8w7It1MR"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred_rf = classifier_rf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16c17dab-6175-46b8-9f23-e523ecb813db",
        "id": "djQlekW1t1MR"
      },
      "source": [
        "print(y_pred_rf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1. 0. 1. 1. 1. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 1.\n",
            " 0. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 0. 1.\n",
            " 1. 0. 1. 0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 1. 0. 1. 0. 0. 0. 1. 1. 1.\n",
            " 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 1. 1.\n",
            " 1. 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 1. 1. 0. 0. 0. 0. 1.\n",
            " 1. 1. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e09d7982-c70c-435d-8f06-bc89b977d9fa",
        "id": "r15eOPeEt1MS"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
        "print(cm_rf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[61  2]\n",
            " [ 2 58]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImFVWp3It1MS"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_rf)\n",
        "pre = precision_score(y_test, y_pred_rf, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_rf, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_rf, average = 'micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b93eaf1-4e3b-418d-f11c-08516c48e688",
        "id": "in-P4BFMt1MS"
      },
      "source": [
        "print(\"Summary of RF\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of RF\n",
            "Accuracy    = 0.967479674796748\n",
            "Precison    = 0.9666666666666667\n",
            "Recall      = 0.9666666666666667\n",
            "f1 score    = 0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMRYpoh8FKO5"
      },
      "source": [
        "# MLP"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "param_grid = {\n",
        "              'hidden_layer_sizes' : [(10, 10, 10)], \n",
        "              \"activation\" : [\"identity\", \"logistic\", \"tanh\", \"relu\"],\n",
        "              \"solver\" : [\"lbfgs\", \"sgd\", \"adam\"],\n",
        "              'alpha' : [0.0001, 0.05],\n",
        "              \"learning_rate\" : [\"constant\", \"invscaling\", \"adaptive\"]\n",
        "              }"
      ],
      "metadata": {
        "id": "VkTEQbEW-0qk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(param_grid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39b1c57f-7e14-4ed7-f772-28b843af2807",
        "id": "NyLi93Qr-0qk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'hidden_layer_sizes': [(10, 10, 10)], 'activation': ['identity', 'logistic', 'tanh', 'relu'], 'solver': ['lbfgs', 'sgd', 'adam'], 'alpha': [0.0001, 0.05], 'learning_rate': ['constant', 'invscaling', 'adaptive']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp = MLPClassifier()\n",
        "mlp_cv = GridSearchCV(mlp, param_grid, cv=10, verbose=2, n_jobs=-1)\n",
        "print(mlp_cv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ea58ad6-f0b9-4ce9-c4b1-cdf6b440378d",
        "id": "ad5S_tYp-0qk"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV(cv=10, estimator=MLPClassifier(), n_jobs=-1,\n",
            "             param_grid={'activation': ['identity', 'logistic', 'tanh', 'relu'],\n",
            "                         'alpha': [0.0001, 0.05],\n",
            "                         'hidden_layer_sizes': [(10, 10, 10)],\n",
            "                         'learning_rate': ['constant', 'invscaling',\n",
            "                                           'adaptive'],\n",
            "                         'solver': ['lbfgs', 'sgd', 'adam']},\n",
            "             verbose=2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlp_cv.fit(X_train, y_train)\n",
        "print(\"tuned hpyerparameters :(best parameters) \",mlp_cv.best_params_)\n",
        "print(\"\\n\")\n",
        "print(\"accuracy :\",mlp_cv.best_score_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5d8050a-3f44-49ff-c078-7c5172ed1a7d",
        "id": "qDZhzItf-0ql"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 72 candidates, totalling 720 fits\n",
            "tuned hpyerparameters :(best parameters)  {'activation': 'identity', 'alpha': 0.05, 'hidden_layer_sizes': (10, 10, 10), 'learning_rate': 'adaptive', 'solver': 'adam'}\n",
            "\n",
            "\n",
            "accuracy : 0.9709090909090909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79DFy7-p-0ql"
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "mlp = MLPClassifier(activation = 'identity', alpha = 0.05, hidden_layer_sizes = (10, 10, 10), learning_rate = 'adaptive', solver = 'adam') \n",
        "# hidden_layer_sizes=(10, 10, 10), max_iter=1000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f1715b0-44b0-4f66-a422-a808d4b7e8ba",
        "id": "NwI8VNev-0qm"
      },
      "source": [
        "mlp.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='identity', alpha=0.05,\n",
              "              hidden_layer_sizes=(10, 10, 10), learning_rate='adaptive')"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2VbbUxI-0qm"
      },
      "source": [
        "# Predicting the Test set results\n",
        "y_pred_mlp = mlp.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3ed8f81-fbab-4a96-af9e-c5a163fb5b33",
        "id": "3sDwgwqV-0qm"
      },
      "source": [
        "print(y_pred_mlp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
            " 2 1 1 2 0 2 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b687ef56-7715-4d39-a2b6-a59691208b08",
        "id": "r9xsTb2T-0qn"
      },
      "source": [
        "# Making the Confusion Matrix\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm_mlp = confusion_matrix(y_test, y_pred_mlp)\n",
        "print(cm_mlp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[16  0  0]\n",
            " [ 0 17  1]\n",
            " [ 0  0 11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXsxauoF-0qn"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,  f1_score\n",
        "ac  = accuracy_score(y_test, y_pred_mlp)\n",
        "pre = precision_score(y_test, y_pred_mlp, average = 'micro')\n",
        "re  = recall_score(y_test, y_pred_mlp, average = 'micro')\n",
        "f1  = f1_score(y_test, y_pred_mlp, average = 'micro')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc2a3114-2328-484b-a234-263d0b71b8bd",
        "id": "VzHQcd7Y-0qo"
      },
      "source": [
        "print(\"Summary of MLP\")\n",
        "print(\"Accuracy    =\", ac)\n",
        "print(\"Precison    =\",pre)\n",
        "print(\"Recall      =\", re)\n",
        "print(\"f1 score    =\", f1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of MLP\n",
            "Accuracy    = 0.9777777777777777\n",
            "Precison    = 0.9777777777777777\n",
            "Recall      = 0.9777777777777777\n",
            "f1 score    = 0.9777777777777777\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}