Epilepsy_time_domain_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UVdDme52NBil"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from keras.models import Sequential\n",
        "from keras.utils import np_utils\n",
        "from keras.layers import Dense, Activation , LSTM , Dropout , AveragePooling3D\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.utils import shuffle\n",
        "from lazypredict.Supervised import LazyClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('data.csv')\n",
        "# Getting only X part and removing target column\n",
        "X = df.iloc[:,1:-1]\n",
        "\n",
        "print(\"General info about colums,rows etc.\")\n",
        "df.info()\n",
        "print(\"\\nTarget variables value counts\\n\",df[\"y\"].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ACeBV4VDNXqP",
        "outputId": "38aad941-722b-4a51-d165-0e70ff1a7742"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "General info about colums,rows etc.\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11500 entries, 0 to 11499\n",
            "Columns: 180 entries, column_a to y\n",
            "dtypes: int64(179), object(1)\n",
            "memory usage: 15.8+ MB\n",
            "\n",
            "Target variables value counts\n",
            " 4    2300\n",
            "1    2300\n",
            "5    2300\n",
            "2    2300\n",
            "3    2300\n",
            "Name: y, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def hist(df,plt):\n",
        "  plt.hist(df[df[\"y\"]==1][\"y\"],label=\"epileptic seizure activity\")\n",
        "  plt.hist(df[df[\"y\"]!=1][\"y\"],label=\"not a seizure\")\n",
        "  plt.legend(loc='lower right')\n",
        "  plt.show()\n",
        "\n",
        "hist(df,plt)\n",
        "\n",
        "y = df.iloc[:,0]\n",
        "df[\"column_a\"].value_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "_WbJp48hNXsq",
        "outputId": "20f65661-6c05-4440-9d02-adbf725fd469"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method IndexOpsMixin.value_counts of 0        X21.V1.791\n",
              "1        X15.V1.924\n",
              "2           X8.V1.1\n",
              "3         X16.V1.60\n",
              "4         X20.V1.54\n",
              "            ...    \n",
              "11495    X22.V1.114\n",
              "11496    X19.V1.354\n",
              "11497      X8.V1.28\n",
              "11498    X10.V1.932\n",
              "11499    X16.V1.210\n",
              "Name: column_a, Length: 11500, dtype: object>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#This method drop the unnecessary column (Unnamed: 0) and transform the target variable\n",
        "def prepareDataTime(df):\n",
        "  df[\"y\"]=[1 if df[\"y\"][i]==1 else 0 for i in range(len(df[\"y\"]))]\n",
        "  target=df[\"y\"]\n",
        "  df_copy=df.drop([\"column_a\"],axis=1)\n",
        "  return df_copy,target\n",
        "\n",
        "df_time, target_time = prepareDataTime(df)\n",
        "df_time.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "2bKFTx7vNXvV",
        "outputId": "d0effc3a-ffbc-47a5-bf87-d0e2409e6ec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    x1   x2   x3   x4   x5   x6   x7   x8   x9  x10  ...  x170  x171  x172  \\\n",
              "0  135  190  229  223  192  125   55   -9  -33  -38  ...   -17   -15   -31   \n",
              "1  386  382  356  331  320  315  307  272  244  232  ...   164   150   146   \n",
              "2  -32  -39  -47  -37  -32  -36  -57  -73  -85  -94  ...    57    64    48   \n",
              "3 -105 -101  -96  -92  -89  -95 -102 -100  -87  -79  ...   -82   -81   -80   \n",
              "4   -9  -65  -98 -102  -78  -48  -16    0  -21  -59  ...     4     2   -12   \n",
              "\n",
              "   x173  x174  x175  x176  x177  x178  y  \n",
              "0   -77  -103  -127  -116   -83   -51  0  \n",
              "1   152   157   156   154   143   129  1  \n",
              "2    19   -12   -30   -35   -35   -36  0  \n",
              "3   -77   -85   -77   -72   -69   -65  0  \n",
              "4   -32   -41   -65   -83   -89   -73  0  \n",
              "\n",
              "[5 rows x 179 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e5b83bd7-5a8a-4818-a6e0-ed838ee18a64\">\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>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>x3</th>\n",
              "      <th>x4</th>\n",
              "      <th>x5</th>\n",
              "      <th>x6</th>\n",
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              "      <th>x8</th>\n",
              "      <th>x9</th>\n",
              "      <th>x10</th>\n",
              "      <th>...</th>\n",
              "      <th>x170</th>\n",
              "      <th>x171</th>\n",
              "      <th>x172</th>\n",
              "      <th>x173</th>\n",
              "      <th>x174</th>\n",
              "      <th>x175</th>\n",
              "      <th>x176</th>\n",
              "      <th>x177</th>\n",
              "      <th>x178</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>135</td>\n",
              "      <td>190</td>\n",
              "      <td>229</td>\n",
              "      <td>223</td>\n",
              "      <td>192</td>\n",
              "      <td>125</td>\n",
              "      <td>55</td>\n",
              "      <td>-9</td>\n",
              "      <td>-33</td>\n",
              "      <td>-38</td>\n",
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              "      <td>-17</td>\n",
              "      <td>-15</td>\n",
              "      <td>-31</td>\n",
              "      <td>-77</td>\n",
              "      <td>-103</td>\n",
              "      <td>-127</td>\n",
              "      <td>-116</td>\n",
              "      <td>-83</td>\n",
              "      <td>-51</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>386</td>\n",
              "      <td>382</td>\n",
              "      <td>356</td>\n",
              "      <td>331</td>\n",
              "      <td>320</td>\n",
              "      <td>315</td>\n",
              "      <td>307</td>\n",
              "      <td>272</td>\n",
              "      <td>244</td>\n",
              "      <td>232</td>\n",
              "      <td>...</td>\n",
              "      <td>164</td>\n",
              "      <td>150</td>\n",
              "      <td>146</td>\n",
              "      <td>152</td>\n",
              "      <td>157</td>\n",
              "      <td>156</td>\n",
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              "      <td>143</td>\n",
              "      <td>129</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-32</td>\n",
              "      <td>-39</td>\n",
              "      <td>-47</td>\n",
              "      <td>-37</td>\n",
              "      <td>-32</td>\n",
              "      <td>-36</td>\n",
              "      <td>-57</td>\n",
              "      <td>-73</td>\n",
              "      <td>-85</td>\n",
              "      <td>-94</td>\n",
              "      <td>...</td>\n",
              "      <td>57</td>\n",
              "      <td>64</td>\n",
              "      <td>48</td>\n",
              "      <td>19</td>\n",
              "      <td>-12</td>\n",
              "      <td>-30</td>\n",
              "      <td>-35</td>\n",
              "      <td>-35</td>\n",
              "      <td>-36</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-105</td>\n",
              "      <td>-101</td>\n",
              "      <td>-96</td>\n",
              "      <td>-92</td>\n",
              "      <td>-89</td>\n",
              "      <td>-95</td>\n",
              "      <td>-102</td>\n",
              "      <td>-100</td>\n",
              "      <td>-87</td>\n",
              "      <td>-79</td>\n",
              "      <td>...</td>\n",
              "      <td>-82</td>\n",
              "      <td>-81</td>\n",
              "      <td>-80</td>\n",
              "      <td>-77</td>\n",
              "      <td>-85</td>\n",
              "      <td>-77</td>\n",
              "      <td>-72</td>\n",
              "      <td>-69</td>\n",
              "      <td>-65</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-9</td>\n",
              "      <td>-65</td>\n",
              "      <td>-98</td>\n",
              "      <td>-102</td>\n",
              "      <td>-78</td>\n",
              "      <td>-48</td>\n",
              "      <td>-16</td>\n",
              "      <td>0</td>\n",
              "      <td>-21</td>\n",
              "      <td>-59</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>-12</td>\n",
              "      <td>-32</td>\n",
              "      <td>-41</td>\n",
              "      <td>-65</td>\n",
              "      <td>-83</td>\n",
              "      <td>-89</td>\n",
              "      <td>-73</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 179 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5b83bd7-5a8a-4818-a6e0-ed838ee18a64')\"\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-e5b83bd7-5a8a-4818-a6e0-ed838ee18a64 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-e5b83bd7-5a8a-4818-a6e0-ed838ee18a64');\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": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(nrows=5, ncols=2, figsize=(10, 10))\n",
        "for i, ax in enumerate(axes.flatten()):\n",
        "    ax.plot(df_time.iloc[i, :-1])\n",
        "    ax.set_title('EEG Signal {}'.format(i+1))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fcX9H63d5LgA",
        "outputId": "30b392be-c8f9-479f-ccbe-1145195e23e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "\n",
        "for i in range(10):\n",
        "    ax.plot(df_time.iloc[i+20, :-1], label='EEG Signal {}'.format(i+20+1))\n",
        "\n",
        "ax.legend(loc='upper right')\n",
        "ax.set_xlabel('Time')\n",
        "ax.set_ylabel('Amplitude')\n",
        "ax.set_title('EEG Signals')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "uT_i49vz6eTL",
        "outputId": "69438c80-d2ff-4d16-f526-d6018050143c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def createBalancedDataset(data,random_state):\n",
        "  #shuffling for random sampling\n",
        "  X = shuffle(data,random_state=random_state)\n",
        "  #getting first 6500 value\n",
        "  return X.sort_values(by='y', ascending=False).iloc[:6500].index\n",
        "\n",
        "v=createBalancedDataset(df_time,42)\n",
        "plt.hist((df_time.iloc[v])['y'])\n",
        "(df_time.iloc[v]['y']).value_counts() #more balanced dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "KGo-bnsaNXxV",
        "outputId": "d7486aca-d399-46de-d039-9cd1c030775b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4200\n",
              "1    2300\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#normalizing dataset\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(df_time.drop(['y'],axis=1))\n",
        "n_df_fea=pd.DataFrame(scaler.transform(df_time.drop(['y'],axis=1)))"
      ],
      "metadata": {
        "id": "IkVCbyF7NXz9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_trainr, X_testr, y_trainr, y_testr = train_test_split(n_df_fea.iloc[v], target_time.iloc[v], test_size=0.30, random_state=42)"
      ],
      "metadata": {
        "id": "XK4sQFRCNX2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_random_sampling, X_test_random_sampling, y_train_random_sampling, y_test_random_sampling = train_test_split(X_trainr, y_trainr, test_size=0.30, random_state=42)\n",
        "print(X_train_random_sampling.shape, X_test_random_sampling.shape, y_train_random_sampling.shape, y_test_random_sampling.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bY_89EsP0DP3",
        "outputId": "4b77699f-1dfc-432d-e411-7501071fbf20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3185, 178) (1365, 178) (3185,) (1365,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This is done to just test the accuracy in case of SVM classifier\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "#I will explain this model in model part in the notebook\n",
        "clf = svm.SVC(kernel=\"linear\")\n",
        "clf.fit(X_trainr, y_trainr)\n",
        "#cross validation is 10\n",
        "y_pred = cross_val_predict(clf,X_testr,y_testr,cv=10)\n",
        "print(\"All features are inclueded\\n\",classification_report(y_testr, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L2h-gKhcNX4x",
        "outputId": "99f261d0-f915-4a4d-db1d-13ab7b7fa99f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All features are inclueded\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      1.00      0.80      1252\n",
            "           1       0.98      0.11      0.20       698\n",
            "\n",
            "    accuracy                           0.68      1950\n",
            "   macro avg       0.82      0.56      0.50      1950\n",
            "weighted avg       0.78      0.68      0.59      1950\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selecting most important 20 features with Random Forest"
      ],
      "metadata": {
        "id": "u9qWDHAjyCW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Firstly I used grid Search for getting best hyperparameter for random-forest\n",
        "np.random.seed(42)\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n_estimators=50, oob_score = True) \n",
        "\n",
        "param_grid = { \n",
        "    'max_depth': [2,5],\n",
        "    'min_samples_split':[2,5,10],\n",
        "    'n_estimators': [100,150],\n",
        "    'max_features': ['auto', 'sqrt', 'log2']\n",
        "}\n",
        "\n",
        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv=5)\n",
        "CV_rfc.fit(X_trainr, y_trainr)\n",
        "print (CV_rfc.best_params_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mEP6nWdtNX70",
        "outputId": "4b1b580c-34a7-4419-d82e-7283246c80f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'max_depth': 5, 'max_features': 'sqrt', 'min_samples_split': 2, 'n_estimators': 150}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(random_state=42,max_depth=5,max_features='sqrt',min_samples_split=2,n_estimators=150)\n",
        "clf.fit(X_trainr, y_trainr)\n",
        "#I also get the importance rates and sort in a desending order and create a dataframe\n",
        "zipped=pd.DataFrame(zip(X_trainr.columns,clf.feature_importances_),columns=[\"column\",\"importance\"]).sort_values(by=\"importance\", ascending=False)\n",
        "y_pred2 = cross_val_predict(clf,X_testr,y_testr,cv=10)\n",
        "print(\"All featuares are included\\n\",classification_report(y_testr, y_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FW_tqdEtNX_B",
        "outputId": "5a476db6-364e-4860-c08b-d47f868dc3bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All featuares are included\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.98      0.95      1373\n",
            "           1       0.97      0.84      0.90       772\n",
            "\n",
            "    accuracy                           0.93      2145\n",
            "   macro avg       0.94      0.91      0.92      2145\n",
            "weighted avg       0.93      0.93      0.93      2145\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zipped.head(20) #The most important 20 values according to random forest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "E6ut2yA6NYAl",
        "outputId": "ca298364-d3b4-4c51-a9de-96a9ee92a37f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     column  importance\n",
              "12       12        0.03\n",
              "159     159        0.03\n",
              "127     127        0.02\n",
              "10       10        0.02\n",
              "160     160        0.02\n",
              "36       36        0.02\n",
              "157     157        0.02\n",
              "169     169        0.02\n",
              "126     126        0.01\n",
              "11       11        0.01\n",
              "13       13        0.01\n",
              "1         1        0.01\n",
              "19       19        0.01\n",
              "158     158        0.01\n",
              "44       44        0.01\n",
              "155     155        0.01\n",
              "38       38        0.01\n",
              "18       18        0.01\n",
              "0         0        0.01\n",
              "105     105        0.01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7c4352af-cb4f-4c15-9f08-b9efe052ea71\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>column</th>\n",
              "      <th>importance</th>\n",
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              "  <tbody>\n",
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              "      <th>12</th>\n",
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              "      <td>0.01</td>\n",
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              "      <td>13</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>19</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>158</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
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              "      <th>44</th>\n",
              "      <td>44</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
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              "      <td>0.01</td>\n",
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              "      <th>105</th>\n",
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              "      <td>0.01</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "        buttonEl.style.display =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#F-scores are decreased\n",
        "clf = svm.SVC(kernel=\"linear\")\n",
        "clf.fit(X_trainr[zipped.iloc[:20].index], y_trainr)\n",
        "y_pred = cross_val_predict(clf,X_testr[zipped.iloc[:20].index],y_testr,cv=10)\n",
        "print(\"Only random forest's features are inclueded\\n\",classification_report(y_testr, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5VGaVX3NYCf",
        "outputId": "ce1f7eac-e1ac-45c6-db11-1043b494f22d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Only random forest's features are inclueded\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      1.00      0.78      1373\n",
            "           1       0.91      0.01      0.03       772\n",
            "\n",
            "    accuracy                           0.64      2145\n",
            "   macro avg       0.78      0.51      0.40      2145\n",
            "weighted avg       0.74      0.64      0.51      2145\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tried LSTM implementation\n",
        "#LSTM\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.layers import LSTM\n",
        "# When i designed the network, Basically i used heuristic approach\n",
        "X_trainrr=np.array(X_trainr).reshape(X_trainr.shape[0],X_trainr.shape[1],1)\n",
        "X_testrr=np.array(X_testr).reshape(X_testr.shape[0],X_testr.shape[1],1)\n",
        "model = Sequential()\n",
        "model.add(LSTM(50, input_shape=(X_trainrr.shape[1], X_trainrr.shape[2])))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['accuracy'])\n",
        "# fit network\n",
        "history = model.fit(X_trainrr, y_trainr, epochs=50, batch_size=72, validation_data=(X_testrr, y_testr), verbose=2, shuffle=False)\n",
        "# plot history\n",
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oGwNJtFmNYFO",
        "outputId": "fb667e10-23a5-4bb4-87b4-1501ff3e6f05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "61/61 - 23s - loss: 0.6505 - accuracy: 0.6464 - val_loss: 0.6572 - val_accuracy: 0.6401 - 23s/epoch - 372ms/step\n",
            "Epoch 2/50\n",
            "61/61 - 6s - loss: 0.6488 - accuracy: 0.6491 - val_loss: 0.6570 - val_accuracy: 0.6401 - 6s/epoch - 97ms/step\n",
            "Epoch 3/50\n",
            "61/61 - 7s - loss: 0.6499 - accuracy: 0.6491 - val_loss: 0.6560 - val_accuracy: 0.6401 - 7s/epoch - 122ms/step\n",
            "Epoch 4/50\n",
            "61/61 - 6s - loss: 0.6490 - accuracy: 0.6491 - val_loss: 0.6564 - val_accuracy: 0.6401 - 6s/epoch - 101ms/step\n",
            "Epoch 5/50\n",
            "61/61 - 8s - loss: 0.6489 - accuracy: 0.6491 - val_loss: 0.6566 - val_accuracy: 0.6401 - 8s/epoch - 136ms/step\n",
            "Epoch 6/50\n",
            "61/61 - 6s - loss: 0.6487 - accuracy: 0.6491 - val_loss: 0.6565 - val_accuracy: 0.6401 - 6s/epoch - 104ms/step\n",
            "Epoch 7/50\n",
            "61/61 - 9s - loss: 0.6486 - accuracy: 0.6491 - val_loss: 0.6565 - val_accuracy: 0.6401 - 9s/epoch - 145ms/step\n",
            "Epoch 8/50\n",
            "61/61 - 6s - loss: 0.6486 - accuracy: 0.6491 - val_loss: 0.6561 - val_accuracy: 0.6401 - 6s/epoch - 103ms/step\n",
            "Epoch 9/50\n",
            "61/61 - 8s - loss: 0.6481 - accuracy: 0.6491 - val_loss: 0.6564 - val_accuracy: 0.6401 - 8s/epoch - 132ms/step\n",
            "Epoch 10/50\n",
            "61/61 - 6s - loss: 0.6485 - accuracy: 0.6491 - val_loss: 0.6558 - val_accuracy: 0.6401 - 6s/epoch - 104ms/step\n",
            "Epoch 11/50\n",
            "61/61 - 8s - loss: 0.6487 - accuracy: 0.6491 - val_loss: 0.6554 - val_accuracy: 0.6401 - 8s/epoch - 139ms/step\n",
            "Epoch 12/50\n",
            "61/61 - 10s - loss: 0.6484 - accuracy: 0.6491 - val_loss: 0.6558 - val_accuracy: 0.6401 - 10s/epoch - 158ms/step\n",
            "Epoch 13/50\n",
            "61/61 - 6s - loss: 0.6480 - accuracy: 0.6491 - val_loss: 0.6556 - val_accuracy: 0.6401 - 6s/epoch - 99ms/step\n",
            "Epoch 14/50\n",
            "61/61 - 8s - loss: 0.6483 - accuracy: 0.6491 - val_loss: 0.6552 - val_accuracy: 0.6401 - 8s/epoch - 136ms/step\n",
            "Epoch 15/50\n",
            "61/61 - 6s - loss: 0.6481 - accuracy: 0.6491 - val_loss: 0.6554 - val_accuracy: 0.6401 - 6s/epoch - 106ms/step\n",
            "Epoch 16/50\n",
            "61/61 - 8s - loss: 0.6477 - accuracy: 0.6491 - val_loss: 0.6553 - val_accuracy: 0.6401 - 8s/epoch - 125ms/step\n",
            "Epoch 17/50\n",
            "61/61 - 6s - loss: 0.6477 - accuracy: 0.6491 - val_loss: 0.6553 - val_accuracy: 0.6401 - 6s/epoch - 102ms/step\n",
            "Epoch 18/50\n",
            "61/61 - 8s - loss: 0.6477 - accuracy: 0.6491 - val_loss: 0.6552 - val_accuracy: 0.6401 - 8s/epoch - 132ms/step\n",
            "Epoch 19/50\n",
            "61/61 - 6s - loss: 0.6478 - accuracy: 0.6491 - val_loss: 0.6551 - val_accuracy: 0.6401 - 6s/epoch - 98ms/step\n",
            "Epoch 20/50\n",
            "61/61 - 8s - loss: 0.6475 - accuracy: 0.6491 - val_loss: 0.6553 - val_accuracy: 0.6401 - 8s/epoch - 125ms/step\n",
            "Epoch 21/50\n",
            "61/61 - 6s - loss: 0.6472 - accuracy: 0.6494 - val_loss: 0.6548 - val_accuracy: 0.6401 - 6s/epoch - 103ms/step\n",
            "Epoch 22/50\n",
            "61/61 - 8s - loss: 0.6466 - accuracy: 0.6514 - val_loss: 0.6546 - val_accuracy: 0.6406 - 8s/epoch - 124ms/step\n",
            "Epoch 23/50\n",
            "61/61 - 7s - loss: 0.6402 - accuracy: 0.6629 - val_loss: 0.6480 - val_accuracy: 0.6443 - 7s/epoch - 114ms/step\n",
            "Epoch 24/50\n",
            "61/61 - 7s - loss: 0.6415 - accuracy: 0.6560 - val_loss: 0.6487 - val_accuracy: 0.6429 - 7s/epoch - 109ms/step\n",
            "Epoch 25/50\n",
            "61/61 - 8s - loss: 0.6318 - accuracy: 0.6627 - val_loss: 0.6130 - val_accuracy: 0.6671 - 8s/epoch - 128ms/step\n",
            "Epoch 26/50\n",
            "61/61 - 6s - loss: 0.6517 - accuracy: 0.6519 - val_loss: 0.6721 - val_accuracy: 0.6559 - 6s/epoch - 100ms/step\n",
            "Epoch 27/50\n",
            "61/61 - 9s - loss: 0.6386 - accuracy: 0.6583 - val_loss: 0.6472 - val_accuracy: 0.6490 - 9s/epoch - 143ms/step\n",
            "Epoch 28/50\n",
            "61/61 - 6s - loss: 1.0510 - accuracy: 0.5330 - val_loss: 1.4644 - val_accuracy: 0.3529 - 6s/epoch - 104ms/step\n",
            "Epoch 29/50\n",
            "61/61 - 7s - loss: 0.6875 - accuracy: 0.6455 - val_loss: 1.1434 - val_accuracy: 0.2774 - 7s/epoch - 123ms/step\n",
            "Epoch 30/50\n",
            "61/61 - 7s - loss: 0.7238 - accuracy: 0.6034 - val_loss: 0.6417 - val_accuracy: 0.7310 - 7s/epoch - 108ms/step\n",
            "Epoch 31/50\n",
            "61/61 - 8s - loss: 0.6263 - accuracy: 0.6657 - val_loss: 0.5576 - val_accuracy: 0.7608 - 8s/epoch - 128ms/step\n",
            "Epoch 32/50\n",
            "61/61 - 7s - loss: 0.5950 - accuracy: 0.6863 - val_loss: 0.5260 - val_accuracy: 0.7501 - 7s/epoch - 109ms/step\n",
            "Epoch 33/50\n",
            "61/61 - 8s - loss: 0.5813 - accuracy: 0.7123 - val_loss: 0.7374 - val_accuracy: 0.4019 - 8s/epoch - 126ms/step\n",
            "Epoch 34/50\n",
            "61/61 - 6s - loss: 0.5656 - accuracy: 0.7348 - val_loss: 0.6149 - val_accuracy: 0.7193 - 6s/epoch - 104ms/step\n",
            "Epoch 35/50\n",
            "61/61 - 8s - loss: 0.5590 - accuracy: 0.7336 - val_loss: 0.5506 - val_accuracy: 0.7408 - 8s/epoch - 123ms/step\n",
            "Epoch 36/50\n",
            "61/61 - 7s - loss: 0.5484 - accuracy: 0.7493 - val_loss: 0.6348 - val_accuracy: 0.7217 - 7s/epoch - 117ms/step\n",
            "Epoch 37/50\n",
            "61/61 - 7s - loss: 0.5350 - accuracy: 0.7637 - val_loss: 0.6627 - val_accuracy: 0.6979 - 7s/epoch - 112ms/step\n",
            "Epoch 38/50\n",
            "61/61 - 7s - loss: 0.5170 - accuracy: 0.7701 - val_loss: 0.6497 - val_accuracy: 0.7124 - 7s/epoch - 117ms/step\n",
            "Epoch 39/50\n",
            "61/61 - 7s - loss: 0.6090 - accuracy: 0.7123 - val_loss: 0.6408 - val_accuracy: 0.6639 - 7s/epoch - 109ms/step\n",
            "Epoch 40/50\n",
            "61/61 - 7s - loss: 0.6231 - accuracy: 0.7148 - val_loss: 0.5106 - val_accuracy: 0.8140 - 7s/epoch - 119ms/step\n",
            "Epoch 41/50\n",
            "61/61 - 7s - loss: 0.4708 - accuracy: 0.8078 - val_loss: 0.5714 - val_accuracy: 0.7310 - 7s/epoch - 107ms/step\n",
            "Epoch 42/50\n",
            "61/61 - 8s - loss: 0.4275 - accuracy: 0.8459 - val_loss: 0.4894 - val_accuracy: 0.7893 - 8s/epoch - 126ms/step\n",
            "Epoch 43/50\n",
            "61/61 - 6s - loss: 0.4643 - accuracy: 0.8202 - val_loss: 0.4223 - val_accuracy: 0.8378 - 6s/epoch - 104ms/step\n",
            "Epoch 44/50\n",
            "61/61 - 7s - loss: 0.4430 - accuracy: 0.8321 - val_loss: 0.3211 - val_accuracy: 0.9133 - 7s/epoch - 122ms/step\n",
            "Epoch 45/50\n",
            "61/61 - 7s - loss: 0.4229 - accuracy: 0.8558 - val_loss: 0.5357 - val_accuracy: 0.7655 - 7s/epoch - 113ms/step\n",
            "Epoch 46/50\n",
            "61/61 - 7s - loss: 0.5433 - accuracy: 0.7385 - val_loss: 0.6071 - val_accuracy: 0.7119 - 7s/epoch - 123ms/step\n",
            "Epoch 47/50\n",
            "61/61 - 6s - loss: 0.5481 - accuracy: 0.7318 - val_loss: 0.5096 - val_accuracy: 0.7650 - 6s/epoch - 104ms/step\n",
            "Epoch 48/50\n",
            "61/61 - 8s - loss: 0.4070 - accuracy: 0.8524 - val_loss: 0.3750 - val_accuracy: 0.8564 - 8s/epoch - 137ms/step\n",
            "Epoch 49/50\n",
            "61/61 - 7s - loss: 0.4411 - accuracy: 0.8367 - val_loss: 1.8532 - val_accuracy: 0.3594 - 7s/epoch - 122ms/step\n",
            "Epoch 50/50\n",
            "61/61 - 7s - loss: 0.6255 - accuracy: 0.6606 - val_loss: 0.4042 - val_accuracy: 0.8163 - 7s/epoch - 108ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "###################################################################################### Using Lazy predict classfier ########################################################################################333"
      ],
      "metadata": {
        "id": "ykCyYTywx5mV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "models,predictions = clf.fit(X_trainr, X_testr, y_trainr, y_testr)\n",
        "models"
      ],
      "metadata": {
        "id": "gJRfSUrYNYIH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "a310caa3-20ff-4013-95c1-75375be0311f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:54<00:00,  1.88s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
              "Model                                                                           \n",
              "LGBMClassifier                     0.97               0.96     0.96      0.97   \n",
              "ExtraTreesClassifier               0.97               0.96     0.96      0.97   \n",
              "RandomForestClassifier             0.96               0.96     0.96      0.96   \n",
              "XGBClassifier                      0.96               0.95     0.95      0.96   \n",
              "SVC                                0.96               0.95     0.95      0.96   \n",
              "BaggingClassifier                  0.95               0.95     0.95      0.95   \n",
              "GaussianNB                         0.95               0.93     0.93      0.95   \n",
              "AdaBoostClassifier                 0.93               0.92     0.92      0.93   \n",
              "QuadraticDiscriminantAnalysis      0.91               0.90     0.90      0.91   \n",
              "ExtraTreeClassifier                0.91               0.90     0.90      0.91   \n",
              "DecisionTreeClassifier             0.90               0.88     0.88      0.90   \n",
              "NuSVC                              0.90               0.86     0.86      0.89   \n",
              "KNeighborsClassifier               0.87               0.83     0.83      0.87   \n",
              "PassiveAggressiveClassifier        0.73               0.62     0.62      0.67   \n",
              "SGDClassifier                      0.66               0.60     0.60      0.64   \n",
              "LinearDiscriminantAnalysis         0.70               0.58     0.58      0.62   \n",
              "LogisticRegression                 0.70               0.58     0.58      0.62   \n",
              "RidgeClassifierCV                  0.70               0.58     0.58      0.61   \n",
              "RidgeClassifier                    0.69               0.57     0.57      0.61   \n",
              "LinearSVC                          0.69               0.57     0.57      0.61   \n",
              "NearestCentroid                    0.60               0.57     0.57      0.60   \n",
              "BernoulliNB                        0.60               0.56     0.56      0.60   \n",
              "Perceptron                         0.57               0.54     0.54      0.57   \n",
              "LabelSpreading                     0.67               0.54     0.54      0.56   \n",
              "LabelPropagation                   0.67               0.54     0.54      0.56   \n",
              "CalibratedClassifierCV             0.64               0.50     0.50      0.50   \n",
              "DummyClassifier                    0.64               0.50     0.50      0.50   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "LGBMClassifier                       2.58  \n",
              "ExtraTreesClassifier                 1.27  \n",
              "RandomForestClassifier               6.33  \n",
              "XGBClassifier                        7.32  \n",
              "SVC                                  0.72  \n",
              "BaggingClassifier                    7.85  \n",
              "GaussianNB                           0.06  \n",
              "AdaBoostClassifier                   3.93  \n",
              "QuadraticDiscriminantAnalysis        0.19  \n",
              "ExtraTreeClassifier                  0.06  \n",
              "DecisionTreeClassifier               1.29  \n",
              "NuSVC                                3.91  \n",
              "KNeighborsClassifier                 0.22  \n",
              "PassiveAggressiveClassifier          0.08  \n",
              "SGDClassifier                        0.33  \n",
              "LinearDiscriminantAnalysis           0.33  \n",
              "LogisticRegression                   0.32  \n",
              "RidgeClassifierCV                    0.24  \n",
              "RidgeClassifier                      0.07  \n",
              "LinearSVC                            2.83  \n",
              "NearestCentroid                      0.10  \n",
              "BernoulliNB                          0.07  \n",
              "Perceptron                           0.09  \n",
              "LabelSpreading                       1.63  \n",
              "LabelPropagation                     1.34  \n",
              "CalibratedClassifierCV              11.22  \n",
              "DummyClassifier                      0.05  "
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              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
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              "      <th>Model</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>2.58</td>\n",
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              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.97</td>\n",
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              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>6.33</td>\n",
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              "      <th>XGBClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>7.32</td>\n",
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              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.72</td>\n",
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              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>7.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.06</td>\n",
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              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.93</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.93</td>\n",
              "      <td>3.93</td>\n",
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              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.19</td>\n",
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              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.06</td>\n",
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              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.29</td>\n",
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              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.89</td>\n",
              "      <td>3.91</td>\n",
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              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.87</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.33</td>\n",
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              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.33</td>\n",
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              "      <th>LogisticRegression</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.32</td>\n",
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              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.24</td>\n",
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              "      <th>RidgeClassifier</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.07</td>\n",
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              "      <th>LinearSVC</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>2.83</td>\n",
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              "      <th>NearestCentroid</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.10</td>\n",
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              "      <th>BernoulliNB</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>11.22</td>\n",
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              "      <th>DummyClassifier</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
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          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_random_sampling = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "models_random_sampling,predictions_random_sampling = clf_random_sampling.fit(X_train_random_sampling, X_test_random_sampling, y_train_random_sampling, y_test_random_sampling)\n",
        "models_random_sampling"
      ],
      "metadata": {
        "id": "m-PzV5jmNYLf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "0e6ca739-857e-4769-b37b-1681e5b6d8f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:34<00:00,  1.19s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
              "Model                                                                           \n",
              "LGBMClassifier                     0.97               0.96     0.96      0.97   \n",
              "RandomForestClassifier             0.96               0.96     0.96      0.96   \n",
              "ExtraTreesClassifier               0.96               0.96     0.96      0.96   \n",
              "XGBClassifier                      0.95               0.94     0.94      0.95   \n",
              "SVC                                0.95               0.94     0.94      0.95   \n",
              "BaggingClassifier                  0.95               0.94     0.94      0.95   \n",
              "GaussianNB                         0.94               0.92     0.92      0.94   \n",
              "DecisionTreeClassifier             0.91               0.91     0.91      0.91   \n",
              "AdaBoostClassifier                 0.92               0.90     0.90      0.92   \n",
              "QuadraticDiscriminantAnalysis      0.90               0.89     0.89      0.90   \n",
              "ExtraTreeClassifier                0.90               0.88     0.88      0.90   \n",
              "NuSVC                              0.89               0.85     0.85      0.89   \n",
              "KNeighborsClassifier               0.86               0.80     0.80      0.85   \n",
              "PassiveAggressiveClassifier        0.76               0.65     0.65      0.71   \n",
              "SGDClassifier                      0.72               0.65     0.65      0.70   \n",
              "LinearDiscriminantAnalysis         0.72               0.60     0.60      0.66   \n",
              "LogisticRegression                 0.72               0.60     0.60      0.66   \n",
              "RidgeClassifier                    0.72               0.60     0.60      0.65   \n",
              "Perceptron                         0.64               0.60     0.60      0.64   \n",
              "RidgeClassifierCV                  0.71               0.58     0.58      0.64   \n",
              "NearestCentroid                    0.63               0.58     0.58      0.63   \n",
              "LinearSVC                          0.71               0.58     0.58      0.64   \n",
              "BernoulliNB                        0.60               0.55     0.55      0.60   \n",
              "LabelPropagation                   0.68               0.53     0.53      0.57   \n",
              "LabelSpreading                     0.68               0.53     0.53      0.57   \n",
              "DummyClassifier                    0.66               0.50     0.50      0.52   \n",
              "CalibratedClassifierCV             0.66               0.50     0.50      0.52   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "LGBMClassifier                       2.61  \n",
              "RandomForestClassifier               4.02  \n",
              "ExtraTreesClassifier                 0.84  \n",
              "XGBClassifier                        3.84  \n",
              "SVC                                  0.38  \n",
              "BaggingClassifier                    4.78  \n",
              "GaussianNB                           0.04  \n",
              "DecisionTreeClassifier               0.84  \n",
              "AdaBoostClassifier                   2.68  \n",
              "QuadraticDiscriminantAnalysis        0.26  \n",
              "ExtraTreeClassifier                  0.04  \n",
              "NuSVC                                1.91  \n",
              "KNeighborsClassifier                 0.14  \n",
              "PassiveAggressiveClassifier          0.09  \n",
              "SGDClassifier                        0.23  \n",
              "LinearDiscriminantAnalysis           0.24  \n",
              "LogisticRegression                   0.15  \n",
              "RidgeClassifier                      0.06  \n",
              "Perceptron                           0.12  \n",
              "RidgeClassifierCV                    0.16  \n",
              "NearestCentroid                      0.05  \n",
              "LinearSVC                            1.75  \n",
              "BernoulliNB                          0.05  \n",
              "LabelPropagation                     0.69  \n",
              "LabelSpreading                       0.85  \n",
              "DummyClassifier                      0.04  \n",
              "CalibratedClassifierCV               7.63  "
            ],
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              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
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              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>LGBMClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
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              "      <td>2.61</td>\n",
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              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.96</td>\n",
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              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
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              "      <td>0.84</td>\n",
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              "      <td>0.94</td>\n",
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              "      <td>3.84</td>\n",
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              "      <th>SVC</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.38</td>\n",
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              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>4.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.92</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.89</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.64</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.52</td>\n",
              "      <td>7.63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "########################################################################### Trying out after doing feature extraction ################################################################"
      ],
      "metadata": {
        "id": "vEmHPJVMmxs9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(random_state=42,max_depth=5,max_features='sqrt',min_samples_split=2,n_estimators=150)\n",
        "clf.fit(X_trainr, y_trainr)\n",
        "#I also get the importance rates and sort in a desending order and create a dataframe\n",
        "zipped=pd.DataFrame(zip(X_trainr.columns,clf.feature_importances_),columns=[\"column\",\"importance\"]).sort_values(by=\"importance\", ascending=False)"
      ],
      "metadata": {
        "id": "DotIBmG1hx01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_trainr_FE = X_trainr[zipped.iloc[:,:20].index]\n",
        "y_trainr_FE = y_trainr\n",
        "X_testr_FE = X_testr[zipped.iloc[:,:20].index]\n",
        "y_testr_FE = y_testr\n",
        "##############################################################################\n",
        "X_train_random_sampling_FE = X_train_random_sampling[zipped.iloc[:,:20].index]\n",
        "y_train_random_sampling_FE = y_train_random_sampling\n",
        "X_test_random_sampling_FE = X_test_random_sampling[zipped.iloc[:,:20].index]\n",
        "y_test_random_sampling_FE = y_test_random_sampling"
      ],
      "metadata": {
        "id": "Jzn0-E_Am6bt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_trainr_FE.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWa-TWBzpkRa",
        "outputId": "be3e577d-9178-437f-9934-368826e7bfd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7906    0\n",
              "1927    0\n",
              "8234    0\n",
              "1630    0\n",
              "5774    0\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_FE = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "models_FE,predictions_FE = clf_FE.fit(X_trainr_FE, X_testr_FE, y_trainr_FE, y_testr_FE)\n",
        "models_FE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "UUUE2TyFnpMT",
        "outputId": "e2225d09-4732-4fea-cae0-a632b3f114b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:55<00:00,  1.91s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
              "Model                                                                           \n",
              "RandomForestClassifier             0.97               0.97     0.97      0.97   \n",
              "LGBMClassifier                     0.97               0.96     0.96      0.97   \n",
              "ExtraTreesClassifier               0.96               0.96     0.96      0.96   \n",
              "XGBClassifier                      0.96               0.95     0.95      0.96   \n",
              "SVC                                0.96               0.95     0.95      0.96   \n",
              "BaggingClassifier                  0.95               0.95     0.95      0.95   \n",
              "GaussianNB                         0.95               0.93     0.93      0.95   \n",
              "AdaBoostClassifier                 0.93               0.92     0.92      0.93   \n",
              "ExtraTreeClassifier                0.92               0.91     0.91      0.92   \n",
              "QuadraticDiscriminantAnalysis      0.91               0.90     0.90      0.91   \n",
              "DecisionTreeClassifier             0.91               0.89     0.89      0.90   \n",
              "NuSVC                              0.90               0.86     0.86      0.89   \n",
              "KNeighborsClassifier               0.87               0.83     0.83      0.87   \n",
              "PassiveAggressiveClassifier        0.73               0.62     0.62      0.67   \n",
              "SGDClassifier                      0.66               0.60     0.60      0.64   \n",
              "LinearDiscriminantAnalysis         0.70               0.58     0.58      0.62   \n",
              "LogisticRegression                 0.70               0.58     0.58      0.62   \n",
              "RidgeClassifierCV                  0.70               0.58     0.58      0.61   \n",
              "RidgeClassifier                    0.69               0.57     0.57      0.61   \n",
              "LinearSVC                          0.69               0.57     0.57      0.61   \n",
              "NearestCentroid                    0.60               0.57     0.57      0.60   \n",
              "BernoulliNB                        0.60               0.56     0.56      0.60   \n",
              "Perceptron                         0.57               0.54     0.54      0.57   \n",
              "LabelSpreading                     0.67               0.54     0.54      0.56   \n",
              "LabelPropagation                   0.67               0.54     0.54      0.56   \n",
              "CalibratedClassifierCV             0.64               0.50     0.50      0.50   \n",
              "DummyClassifier                    0.64               0.50     0.50      0.50   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "RandomForestClassifier               7.04  \n",
              "LGBMClassifier                       4.72  \n",
              "ExtraTreesClassifier                 1.37  \n",
              "XGBClassifier                        5.69  \n",
              "SVC                                  0.72  \n",
              "BaggingClassifier                    7.33  \n",
              "GaussianNB                           0.08  \n",
              "AdaBoostClassifier                   4.50  \n",
              "ExtraTreeClassifier                  0.06  \n",
              "QuadraticDiscriminantAnalysis        0.35  \n",
              "DecisionTreeClassifier               1.30  \n",
              "NuSVC                                2.66  \n",
              "KNeighborsClassifier                 0.53  \n",
              "PassiveAggressiveClassifier          0.13  \n",
              "SGDClassifier                        0.30  \n",
              "LinearDiscriminantAnalysis           0.28  \n",
              "LogisticRegression                   0.19  \n",
              "RidgeClassifierCV                    0.23  \n",
              "RidgeClassifier                      0.07  \n",
              "LinearSVC                            2.84  \n",
              "NearestCentroid                      0.06  \n",
              "BernoulliNB                          0.08  \n",
              "Perceptron                           0.15  \n",
              "LabelSpreading                       1.64  \n",
              "LabelPropagation                     2.00  \n",
              "CalibratedClassifierCV              10.97  \n",
              "DummyClassifier                      0.05  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>7.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.97</td>\n",
              "      <td>4.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>1.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>5.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>7.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.93</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.93</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.92</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.89</td>\n",
              "      <td>2.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.87</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>2.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.67</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.56</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>10.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_random_sampling_FE = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "models_random_sampling_FE,predictions_random_sampling_FE = clf_random_sampling_FE.fit(X_train_random_sampling_FE, X_test_random_sampling_FE, y_train_random_sampling_FE, y_test_random_sampling_FE)\n",
        "models_random_sampling_FE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "RmDl3ufJnpPA",
        "outputId": "b2e824ed-f598-480f-8088-cfb53c44938a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:38<00:00,  1.34s/it]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
              "Model                                                                           \n",
              "LGBMClassifier                     0.96               0.96     0.96      0.96   \n",
              "RandomForestClassifier             0.96               0.96     0.96      0.96   \n",
              "ExtraTreesClassifier               0.96               0.96     0.96      0.96   \n",
              "XGBClassifier                      0.96               0.95     0.95      0.96   \n",
              "SVC                                0.95               0.94     0.94      0.95   \n",
              "BaggingClassifier                  0.95               0.94     0.94      0.95   \n",
              "GaussianNB                         0.94               0.92     0.92      0.94   \n",
              "AdaBoostClassifier                 0.92               0.90     0.90      0.92   \n",
              "DecisionTreeClassifier             0.91               0.90     0.90      0.91   \n",
              "QuadraticDiscriminantAnalysis      0.90               0.89     0.89      0.90   \n",
              "ExtraTreeClassifier                0.89               0.88     0.88      0.89   \n",
              "NuSVC                              0.89               0.85     0.85      0.89   \n",
              "KNeighborsClassifier               0.86               0.80     0.80      0.85   \n",
              "PassiveAggressiveClassifier        0.76               0.65     0.65      0.71   \n",
              "SGDClassifier                      0.72               0.65     0.65      0.70   \n",
              "LinearDiscriminantAnalysis         0.72               0.60     0.60      0.66   \n",
              "LogisticRegression                 0.72               0.60     0.60      0.66   \n",
              "RidgeClassifier                    0.72               0.60     0.60      0.65   \n",
              "Perceptron                         0.64               0.60     0.60      0.64   \n",
              "RidgeClassifierCV                  0.71               0.58     0.58      0.64   \n",
              "NearestCentroid                    0.63               0.58     0.58      0.63   \n",
              "LinearSVC                          0.71               0.58     0.58      0.64   \n",
              "BernoulliNB                        0.60               0.55     0.55      0.60   \n",
              "LabelSpreading                     0.68               0.53     0.53      0.57   \n",
              "LabelPropagation                   0.68               0.53     0.53      0.57   \n",
              "DummyClassifier                    0.66               0.50     0.50      0.52   \n",
              "CalibratedClassifierCV             0.66               0.50     0.50      0.52   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "LGBMClassifier                       2.28  \n",
              "RandomForestClassifier               3.95  \n",
              "ExtraTreesClassifier                 0.91  \n",
              "XGBClassifier                        5.66  \n",
              "SVC                                  0.39  \n",
              "BaggingClassifier                    4.53  \n",
              "GaussianNB                           0.05  \n",
              "AdaBoostClassifier                   2.68  \n",
              "DecisionTreeClassifier               0.91  \n",
              "QuadraticDiscriminantAnalysis        0.16  \n",
              "ExtraTreeClassifier                  0.05  \n",
              "NuSVC                                1.34  \n",
              "KNeighborsClassifier                 0.15  \n",
              "PassiveAggressiveClassifier          0.07  \n",
              "SGDClassifier                        0.24  \n",
              "LinearDiscriminantAnalysis           0.47  \n",
              "LogisticRegression                   0.15  \n",
              "RidgeClassifier                      0.08  \n",
              "Perceptron                           0.08  \n",
              "RidgeClassifierCV                    0.22  \n",
              "NearestCentroid                      0.06  \n",
              "LinearSVC                            3.57  \n",
              "BernoulliNB                          0.06  \n",
              "LabelSpreading                       1.58  \n",
              "LabelPropagation                     0.82  \n",
              "DummyClassifier                      0.04  \n",
              "CalibratedClassifierCV               8.41  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3571b654-caf3-4b63-9177-722e7163f86b\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
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              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>2.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>3.95</td>\n",
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              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.91</td>\n",
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              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.96</td>\n",
              "      <td>5.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>4.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.92</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.89</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.89</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.64</td>\n",
              "      <td>3.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.52</td>\n",
              "      <td>8.41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3571b654-caf3-4b63-9177-722e7163f86b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-3571b654-caf3-4b63-9177-722e7163f86b 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-3571b654-caf3-4b63-9177-722e7163f86b');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
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    {
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      "metadata": {
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      "outputs": []
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      "execution_count": null,
      "outputs": []
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    {
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      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
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      "execution_count": null,
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}