Homework6_2023.ipynb

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        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/Homework6_2023.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "execution_count": 1,
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        "id": "F0fOzKP-43mA",
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          "base_uri": "https://localhost:8080/"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9240 entries, 0 to 9239\n",
            "Data columns (total 37 columns):\n",
            " #   Column                                         Non-Null Count  Dtype  \n",
            "---  ------                                         --------------  -----  \n",
            " 0   Prospect ID                                    9240 non-null   object \n",
            " 1   Lead Number                                    9240 non-null   int64  \n",
            " 2   Lead Origin                                    9240 non-null   object \n",
            " 3   Lead Source                                    9204 non-null   object \n",
            " 4   Do Not Email                                   9240 non-null   object \n",
            " 5   Do Not Call                                    9240 non-null   object \n",
            " 6   Converted                                      9240 non-null   int64  \n",
            " 7   TotalVisits                                    9103 non-null   float64\n",
            " 8   Total Time Spent on Website                    9240 non-null   int64  \n",
            " 9   Page Views Per Visit                           9103 non-null   float64\n",
            " 10  Last Activity                                  9137 non-null   object \n",
            " 11  Country                                        6779 non-null   object \n",
            " 12  Specialization                                 7802 non-null   object \n",
            " 13  How did you hear about X Education             7033 non-null   object \n",
            " 14  What is your current occupation                6550 non-null   object \n",
            " 15  What matters most to you in choosing a course  6531 non-null   object \n",
            " 16  Search                                         9240 non-null   object \n",
            " 17  Magazine                                       9240 non-null   object \n",
            " 18  Newspaper Article                              9240 non-null   object \n",
            " 19  X Education Forums                             9240 non-null   object \n",
            " 20  Newspaper                                      9240 non-null   object \n",
            " 21  Digital Advertisement                          9240 non-null   object \n",
            " 22  Through Recommendations                        9240 non-null   object \n",
            " 23  Receive More Updates About Our Courses         9240 non-null   object \n",
            " 24  Tags                                           5887 non-null   object \n",
            " 25  Lead Quality                                   4473 non-null   object \n",
            " 26  Update me on Supply Chain Content              9240 non-null   object \n",
            " 27  Get updates on DM Content                      9240 non-null   object \n",
            " 28  Lead Profile                                   6531 non-null   object \n",
            " 29  City                                           7820 non-null   object \n",
            " 30  Asymmetrique Activity Index                    5022 non-null   object \n",
            " 31  Asymmetrique Profile Index                     5022 non-null   object \n",
            " 32  Asymmetrique Activity Score                    5022 non-null   float64\n",
            " 33  Asymmetrique Profile Score                     5022 non-null   float64\n",
            " 34  I agree to pay the amount through cheque       9240 non-null   object \n",
            " 35  A free copy of Mastering The Interview         9240 non-null   object \n",
            " 36  Last Notable Activity                          9240 non-null   object \n",
            "dtypes: float64(4), int64(3), object(30)\n",
            "memory usage: 2.6+ MB\n"
          ]
        }
      ],
      "source": [
        "# Data description is provided below\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/ogut77/DataScience/main/data/Leads.csv')\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #There are missing values in the Lead data .\n",
        "# Replace missing variables with the mean  if variable is numeric or mode if variable is categorical.\n",
        "for col in df.columns:\n",
        "    if ((df[col].dtype == 'float64') or (df[col].dtype == 'float64')) :\n",
        "        df[col] = df[col].fillna(df[col].mean())\n",
        "    else:\n",
        "        df[col] = df[col].fillna(df[col].mode()[0])\n"
      ],
      "metadata": {
        "id": "ihog3y2T9uKN"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check that there are no missing data\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "MekOuGEPHK0x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "315370d6-6707-47b7-8845-65086ef62f8e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prospect ID                                      0\n",
              "Lead Number                                      0\n",
              "Lead Origin                                      0\n",
              "Lead Source                                      0\n",
              "Do Not Email                                     0\n",
              "Do Not Call                                      0\n",
              "Converted                                        0\n",
              "TotalVisits                                      0\n",
              "Total Time Spent on Website                      0\n",
              "Page Views Per Visit                             0\n",
              "Last Activity                                    0\n",
              "Country                                          0\n",
              "Specialization                                   0\n",
              "How did you hear about X Education               0\n",
              "What is your current occupation                  0\n",
              "What matters most to you in choosing a course    0\n",
              "Search                                           0\n",
              "Magazine                                         0\n",
              "Newspaper Article                                0\n",
              "X Education Forums                               0\n",
              "Newspaper                                        0\n",
              "Digital Advertisement                            0\n",
              "Through Recommendations                          0\n",
              "Receive More Updates About Our Courses           0\n",
              "Tags                                             0\n",
              "Lead Quality                                     0\n",
              "Update me on Supply Chain Content                0\n",
              "Get updates on DM Content                        0\n",
              "Lead Profile                                     0\n",
              "City                                             0\n",
              "Asymmetrique Activity Index                      0\n",
              "Asymmetrique Profile Index                       0\n",
              "Asymmetrique Activity Score                      0\n",
              "Asymmetrique Profile Score                       0\n",
              "I agree to pay the amount through cheque         0\n",
              "A free copy of Mastering The Interview           0\n",
              "Last Notable Activity                            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "le = LabelEncoder()\n",
        "for col in df.columns:\n",
        "    if df[col].dtype == 'object':\n",
        "        df[col] = le.fit_transform(df[col])\n",
        "\n",
        "df = df.drop(columns=['Prospect ID','Lead Number'])\n",
        "y = df['Converted']\n",
        "X = df.drop(columns=['Converted'])\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
      ],
      "metadata": {
        "id": "DKJ3NU0X_Gch"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval(yact,ypred):\n",
        " from sklearn import metrics\n",
        " print(\"Accuracy:\",metrics.accuracy_score(yact, ypred))\n",
        " print(\"Precision:\",metrics.precision_score(yact, ypred))\n",
        " print(\"Recall:\",metrics.recall_score(yact, ypred))\n",
        " \n",
        " cnf_matrix = metrics.confusion_matrix(yact, ypred)\n",
        " import numpy as np\n",
        " import matplotlib.pyplot as plt\n",
        " import seaborn as sns\n",
        " %matplotlib inline\n",
        " class_names=[0,1] # name  of classes\n",
        " fig, ax = plt.subplots()\n",
        " tick_marks = np.arange(len(class_names))\n",
        " plt.xticks(tick_marks, class_names)\n",
        " plt.yticks(tick_marks, class_names)\n",
        " # create heatmap\n",
        " sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        " ax.xaxis.set_label_position(\"top\")\n",
        " plt.tight_layout()\n",
        " plt.title('Confusion matrix', y=1.1)\n",
        " plt.ylabel('Actual label')\n",
        " plt.xlabel('Predicted label')\n"
      ],
      "metadata": {
        "id": "qyiN6tHuE4Zl"
      },
      "execution_count": 5,
      "outputs": []
    },
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#1)With optuna method and using train data  choose best method among Random Forest\",\"XGBoost\", \"LightGBM\",\"GradientBoostingClassifier\"\n",
        "#and optimeze these method based on following parameters.\n",
        "#max_depth = trial.suggest_int(\"max_depth\", 2,X_train.shape[1])\n",
        "#max_features = trial.suggest_int(\"max_features\", 2,X_train.shape[1])\n",
        "#Evaluate performance of the best method on test data using eval function defined above.\n",
        "#Note: This should be similar to what we have done in the exercises in class. \n",
        "\n",
        "\n",
        "import optuna\n",
        "\n",
        "import sklearn.datasets\n",
        "import sklearn.ensemble\n",
        "import sklearn.model_selection\n",
        "import sklearn.svm\n",
        "\n",
        "def objective(trial):\n",
        "    x, y = X_train,y_train\n",
        "\n",
        "    classifier_name = trial.suggest_categorical(\"classifier\", [\"Random Forest\",\"XGBoost\", \"LightGBM\",\"GradientBoostingClassifier\" ])\n",
        "    if classifier_name == \"Random Forest\":\n",
        "         from sklearn.ensemble import RandomForestClassifier\n",
        "         max_depth = trial.suggest_int(\"max_depth\", 2,X_train.shape[1])\n",
        "         max_features = trial.suggest_int(\"max_features\", 2,X_train.shape[1])\n",
        "         classifier_obj = RandomForestClassifier(random_state=17,  max_depth=max_depth, max_features=max_features )\n",
        "        \n",
        "         \n",
        "\n",
        "    elif classifier_name == \"XGBoost\":\n",
        "         from xgboost import XGBClassifier\n",
        "         max_depth = trial.suggest_int(\"max_depth\", 2,X_train.shape[1])\n",
        "         max_features = trial.suggest_int(\"max_features\", 2,X_train.shape[1])\n",
        "         classifier_obj = XGBClassifier(random_state=17,  max_depth=max_depth, max_features=max_features )\n",
        "        \n",
        "         \n",
        "\n",
        "    elif classifier_name == \"LightGBM\":\n",
        "         import lightgbm as lgb\n",
        "         max_depth = trial.suggest_int(\"max_depth\", 2,X_train.shape[1])\n",
        "         max_features = trial.suggest_int(\"max_features\",2,X_train.shape[1])\n",
        "         classifier_obj = lgb.LGBMClassifier(random_state=17,  max_depth=max_depth, max_features=max_features )\n",
        "        \n",
        "       \n",
        "       \n",
        "    else:\n",
        "         max_depth = trial.suggest_int(\"max_depth\", 2,X_train.shape[1])\n",
        "         max_features = trial.suggest_int(\"max_features\", 2,X_train.shape[1])\n",
        "         from sklearn.ensemble import GradientBoostingClassifier\n",
        "         classifier_obj = GradientBoostingClassifier(random_state=17,  max_depth=max_depth, max_features=max_features )\n",
        "        \n",
        "         \n",
        "\n",
        "    accuracy=sklearn.model_selection.cross_val_score(classifier_obj, x, y, n_jobs=-1, cv=3).mean()\n",
        "   \n",
        "    return accuracy\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    study = optuna.create_study(direction=\"maximize\")\n",
        "    study.optimize(objective, n_trials=100)\n",
        "    print(study.best_trial)"
      ],
      "metadata": {
        "id": "g2gxBq6kCdWa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "139549cf-3f43-4182-ff30-2ca0526b873b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2023-04-14 17:17:10,547]\u001b[0m A new study created in memory with name: no-name-7f5d2377-6daf-44c2-a9be-425701c53de0\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:16,762]\u001b[0m Trial 0 finished with value: 0.935064935064935 and parameters: {'classifier': 'LightGBM', 'max_depth': 9, 'max_features': 28}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:19,359]\u001b[0m Trial 1 finished with value: 0.932591218305504 and parameters: {'classifier': 'XGBoost', 'max_depth': 6, 'max_features': 8}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:21,009]\u001b[0m Trial 2 finished with value: 0.9174397031539888 and parameters: {'classifier': 'Random Forest', 'max_depth': 11, 'max_features': 14}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:29,831]\u001b[0m Trial 3 finished with value: 0.9094001236858379 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 32, 'max_features': 5}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:30,233]\u001b[0m Trial 4 finished with value: 0.935064935064935 and parameters: {'classifier': 'LightGBM', 'max_depth': 9, 'max_features': 25}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:33,493]\u001b[0m Trial 5 finished with value: 0.9296536796536796 and parameters: {'classifier': 'XGBoost', 'max_depth': 11, 'max_features': 28}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:36,199]\u001b[0m Trial 6 finished with value: 0.9302721088435373 and parameters: {'classifier': 'XGBoost', 'max_depth': 9, 'max_features': 34}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:49,111]\u001b[0m Trial 7 finished with value: 0.9250154607297465 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 13, 'max_features': 27}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:53,908]\u001b[0m Trial 8 finished with value: 0.9324366110080397 and parameters: {'classifier': 'XGBoost', 'max_depth': 33, 'max_features': 5}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:54,203]\u001b[0m Trial 9 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 9}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:54,529]\u001b[0m Trial 10 finished with value: 0.932591218305504 and parameters: {'classifier': 'LightGBM', 'max_depth': 21, 'max_features': 20}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:54,946]\u001b[0m Trial 11 finished with value: 0.9332096474953618 and parameters: {'classifier': 'LightGBM', 'max_depth': 18, 'max_features': 23}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:55,203]\u001b[0m Trial 12 finished with value: 0.9056895485466914 and parameters: {'classifier': 'LightGBM', 'max_depth': 2, 'max_features': 34}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:55,634]\u001b[0m Trial 13 finished with value: 0.9332096474953618 and parameters: {'classifier': 'LightGBM', 'max_depth': 17, 'max_features': 26}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:57,611]\u001b[0m Trial 14 finished with value: 0.920995670995671 and parameters: {'classifier': 'Random Forest', 'max_depth': 23, 'max_features': 16}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:58,001]\u001b[0m Trial 15 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 30}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:58,419]\u001b[0m Trial 16 finished with value: 0.9329004329004329 and parameters: {'classifier': 'LightGBM', 'max_depth': 15, 'max_features': 22}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:17:58,835]\u001b[0m Trial 17 finished with value: 0.932591218305504 and parameters: {'classifier': 'LightGBM', 'max_depth': 26, 'max_features': 31}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:00,953]\u001b[0m Trial 18 finished with value: 0.9155844155844156 and parameters: {'classifier': 'Random Forest', 'max_depth': 9, 'max_features': 25}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:02,573]\u001b[0m Trial 19 finished with value: 0.9336734693877551 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 5, 'max_features': 19}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:02,990]\u001b[0m Trial 20 finished with value: 0.9336734693877551 and parameters: {'classifier': 'LightGBM', 'max_depth': 14, 'max_features': 14}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:03,390]\u001b[0m Trial 21 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 30}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:03,802]\u001b[0m Trial 22 finished with value: 0.9333642547928261 and parameters: {'classifier': 'LightGBM', 'max_depth': 8, 'max_features': 30}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:04,136]\u001b[0m Trial 23 finished with value: 0.9265615337043909 and parameters: {'classifier': 'LightGBM', 'max_depth': 3, 'max_features': 23}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:04,786]\u001b[0m Trial 24 finished with value: 0.9344465058750773 and parameters: {'classifier': 'LightGBM', 'max_depth': 11, 'max_features': 32}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:05,296]\u001b[0m Trial 25 finished with value: 0.9342918985776129 and parameters: {'classifier': 'LightGBM', 'max_depth': 6, 'max_features': 28}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:05,906]\u001b[0m Trial 26 finished with value: 0.9325912183055042 and parameters: {'classifier': 'LightGBM', 'max_depth': 16, 'max_features': 25}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:14,216]\u001b[0m Trial 27 finished with value: 0.9251700680272109 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 12, 'max_features': 21}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:15,172]\u001b[0m Trial 28 finished with value: 0.8031849103277674 and parameters: {'classifier': 'Random Forest', 'max_depth': 2, 'max_features': 28}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:20,357]\u001b[0m Trial 29 finished with value: 0.932591218305504 and parameters: {'classifier': 'XGBoost', 'max_depth': 20, 'max_features': 24}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:20,750]\u001b[0m Trial 30 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 33}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:21,136]\u001b[0m Trial 31 finished with value: 0.9342918985776129 and parameters: {'classifier': 'LightGBM', 'max_depth': 6, 'max_features': 30}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:21,554]\u001b[0m Trial 32 finished with value: 0.935064935064935 and parameters: {'classifier': 'LightGBM', 'max_depth': 9, 'max_features': 30}. Best is trial 0 with value: 0.935064935064935.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:21,972]\u001b[0m Trial 33 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 29}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:22,391]\u001b[0m Trial 34 finished with value: 0.9344465058750773 and parameters: {'classifier': 'LightGBM', 'max_depth': 11, 'max_features': 27}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:24,123]\u001b[0m Trial 35 finished with value: 0.9166666666666666 and parameters: {'classifier': 'Random Forest', 'max_depth': 10, 'max_features': 17}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:37,763]\u001b[0m Trial 36 finished with value: 0.9223871366728509 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 13, 'max_features': 28}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:40,509]\u001b[0m Trial 37 finished with value: 0.9302721088435373 and parameters: {'classifier': 'XGBoost', 'max_depth': 9, 'max_features': 32}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:40,824]\u001b[0m Trial 38 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 11}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:46,052]\u001b[0m Trial 39 finished with value: 0.9324366110080397 and parameters: {'classifier': 'XGBoost', 'max_depth': 29, 'max_features': 26}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:46,481]\u001b[0m Trial 40 finished with value: 0.9336734693877551 and parameters: {'classifier': 'LightGBM', 'max_depth': 14, 'max_features': 29}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:46,887]\u001b[0m Trial 41 finished with value: 0.9333642547928261 and parameters: {'classifier': 'LightGBM', 'max_depth': 8, 'max_features': 32}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:47,306]\u001b[0m Trial 42 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 12, 'max_features': 3}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:47,730]\u001b[0m Trial 43 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 34}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:48,142]\u001b[0m Trial 44 finished with value: 0.935064935064935 and parameters: {'classifier': 'LightGBM', 'max_depth': 9, 'max_features': 34}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:48,568]\u001b[0m Trial 45 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 34}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:18:59,934]\u001b[0m Trial 46 finished with value: 0.897804576376005 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 18, 'max_features': 34}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:00,345]\u001b[0m Trial 47 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 12, 'max_features': 32}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:00,764]\u001b[0m Trial 48 finished with value: 0.9325912183055042 and parameters: {'classifier': 'LightGBM', 'max_depth': 16, 'max_features': 33}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:02,267]\u001b[0m Trial 49 finished with value: 0.9021335807050092 and parameters: {'classifier': 'Random Forest', 'max_depth': 5, 'max_features': 26}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:02,689]\u001b[0m Trial 50 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 31}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:03,109]\u001b[0m Trial 51 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 31}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:03,526]\u001b[0m Trial 52 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 31}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:03,947]\u001b[0m Trial 53 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 31}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:04,371]\u001b[0m Trial 54 finished with value: 0.9336734693877551 and parameters: {'classifier': 'LightGBM', 'max_depth': 14, 'max_features': 33}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:04,785]\u001b[0m Trial 55 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 31}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:05,206]\u001b[0m Trial 56 finished with value: 0.9352195423623995 and parameters: {'classifier': 'LightGBM', 'max_depth': 13, 'max_features': 29}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:05,625]\u001b[0m Trial 57 finished with value: 0.9333642547928261 and parameters: {'classifier': 'LightGBM', 'max_depth': 8, 'max_features': 34}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:06,071]\u001b[0m Trial 58 finished with value: 0.9344465058750773 and parameters: {'classifier': 'LightGBM', 'max_depth': 11, 'max_features': 29}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:11,614]\u001b[0m Trial 59 finished with value: 0.930890538033395 and parameters: {'classifier': 'XGBoost', 'max_depth': 15, 'max_features': 31}. Best is trial 33 with value: 0.9356833642547927.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:12,086]\u001b[0m Trial 60 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:12,552]\u001b[0m Trial 61 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:13,208]\u001b[0m Trial 62 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 32}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:13,823]\u001b[0m Trial 63 finished with value: 0.9342918985776129 and parameters: {'classifier': 'LightGBM', 'max_depth': 6, 'max_features': 34}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:14,411]\u001b[0m Trial 64 finished with value: 0.9356833642547927 and parameters: {'classifier': 'LightGBM', 'max_depth': 10, 'max_features': 31}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:15,000]\u001b[0m Trial 65 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 29}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:15,548]\u001b[0m Trial 66 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 29}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:17,443]\u001b[0m Trial 67 finished with value: 0.9254792826221397 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 3, 'max_features': 27}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:18,754]\u001b[0m Trial 68 finished with value: 0.8299319727891157 and parameters: {'classifier': 'Random Forest', 'max_depth': 3, 'max_features': 29}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:19,602]\u001b[0m Trial 69 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:20,647]\u001b[0m Trial 70 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:21,397]\u001b[0m Trial 71 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:21,886]\u001b[0m Trial 72 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:22,382]\u001b[0m Trial 73 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:23,113]\u001b[0m Trial 74 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:23,549]\u001b[0m Trial 75 finished with value: 0.9056895485466914 and parameters: {'classifier': 'LightGBM', 'max_depth': 2, 'max_features': 32}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:24,268]\u001b[0m Trial 76 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:24,848]\u001b[0m Trial 77 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 30}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:25,873]\u001b[0m Trial 78 finished with value: 0.9291898577612864 and parameters: {'classifier': 'XGBoost', 'max_depth': 2, 'max_features': 32}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:26,435]\u001b[0m Trial 79 finished with value: 0.9342918985776129 and parameters: {'classifier': 'LightGBM', 'max_depth': 6, 'max_features': 7}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:26,819]\u001b[0m Trial 80 finished with value: 0.9265615337043909 and parameters: {'classifier': 'LightGBM', 'max_depth': 3, 'max_features': 14}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:27,534]\u001b[0m Trial 81 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:27,933]\u001b[0m Trial 82 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:28,322]\u001b[0m Trial 83 finished with value: 0.9342918985776129 and parameters: {'classifier': 'LightGBM', 'max_depth': 6, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:28,643]\u001b[0m Trial 84 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 30}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:30,927]\u001b[0m Trial 85 finished with value: 0.9333642547928261 and parameters: {'classifier': 'GradientBoostingClassifier', 'max_depth': 5, 'max_features': 32}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:31,448]\u001b[0m Trial 86 finished with value: 0.9333642547928261 and parameters: {'classifier': 'LightGBM', 'max_depth': 8, 'max_features': 34}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:33,839]\u001b[0m Trial 87 finished with value: 0.9019789734075448 and parameters: {'classifier': 'Random Forest', 'max_depth': 5, 'max_features': 27}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:34,256]\u001b[0m Trial 88 finished with value: 0.932591218305504 and parameters: {'classifier': 'LightGBM', 'max_depth': 24, 'max_features': 28}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:34,661]\u001b[0m Trial 89 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 19}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:34,971]\u001b[0m Trial 90 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 24}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:35,354]\u001b[0m Trial 91 finished with value: 0.9342918985776129 and parameters: {'classifier': 'LightGBM', 'max_depth': 6, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:35,644]\u001b[0m Trial 92 finished with value: 0.9265615337043909 and parameters: {'classifier': 'LightGBM', 'max_depth': 3, 'max_features': 33}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:35,990]\u001b[0m Trial 93 finished with value: 0.9370748299319729 and parameters: {'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 32}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:36,300]\u001b[0m Trial 94 finished with value: 0.9335188620902907 and parameters: {'classifier': 'LightGBM', 'max_depth': 4, 'max_features': 12}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:36,714]\u001b[0m Trial 95 finished with value: 0.9333642547928261 and parameters: {'classifier': 'LightGBM', 'max_depth': 8, 'max_features': 30}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:36,968]\u001b[0m Trial 96 finished with value: 0.9056895485466914 and parameters: {'classifier': 'LightGBM', 'max_depth': 2, 'max_features': 34}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:37,362]\u001b[0m Trial 97 finished with value: 0.9346011131725418 and parameters: {'classifier': 'LightGBM', 'max_depth': 7, 'max_features': 30}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:38,918]\u001b[0m Trial 98 finished with value: 0.935064935064935 and parameters: {'classifier': 'XGBoost', 'max_depth': 5, 'max_features': 34}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n",
            "\u001b[32m[I 2023-04-14 17:19:39,199]\u001b[0m Trial 99 finished with value: 0.9265615337043909 and parameters: {'classifier': 'LightGBM', 'max_depth': 3, 'max_features': 31}. Best is trial 60 with value: 0.9370748299319729.\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FrozenTrial(number=60, state=TrialState.COMPLETE, values=[0.9370748299319729], datetime_start=datetime.datetime(2023, 4, 14, 17, 19, 11, 618833), datetime_complete=datetime.datetime(2023, 4, 14, 17, 19, 12, 86589), params={'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'classifier': CategoricalDistribution(choices=('Random Forest', 'XGBoost', 'LightGBM', 'GradientBoostingClassifier')), 'max_depth': IntDistribution(high=34, log=False, low=2, step=1), 'max_features': IntDistribution(high=34, log=False, low=2, step=1)}, trial_id=60, value=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "study.best_params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vNeEHoYoxd2g",
        "outputId": "229a7415-36e8-42de-870d-1987ad68d87b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'classifier': 'LightGBM', 'max_depth': 5, 'max_features': 33}"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "lgb_model = lgb.LGBMClassifier(random_state=17,max_depth=5,max_features=33)\n",
        "lgb_model.fit(X_train, y_train)\n",
        "\n",
        "print(eval(y_test, lgb_model.predict(X_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "jHMsSXePxy1D",
        "outputId": "d7222ddf-91c5-4ffb-bdba-5bddfeb1b914"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Warning] Unknown parameter: max_features\n",
            "Accuracy: 0.9296536796536796\n",
            "Precision: 0.9176470588235294\n",
            "Recall: 0.9069767441860465\n",
            "None\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#2)Using pycaret AutoML tools,make prediction on test data and evaluate performance of prediction using eval function defined above.\n",
        "\n",
        "!pip install pycaret"
      ],
      "metadata": {
        "id": "vTf67JtDK-dP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "af49d88f-d4e6-45d3-8b6e-e1217db16579"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pycaret\n",
            "  Downloading pycaret-3.0.0-py3-none-any.whl (481 kB)\n",
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            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.9/dist-packages (from Flask>=1.0.4->dash<3.0.0,>=2.2.0->plotly-resampler>=0.8.3.1->pycaret) (8.1.3)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.9/dist-packages (from Flask>=1.0.4->dash<3.0.0,>=2.2.0->plotly-resampler>=0.8.3.1->pycaret) (2.1.2)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.9/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.5.4)\n",
            "Requirement already satisfied: pyzmq>=17 in /usr/local/lib/python3.9/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (23.2.1)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.9/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.17.1)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.9/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.16.0)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.9/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (21.3.0)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.9/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.8.0)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.9/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (21.2.0)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.5.0)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.2.2)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.0.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.1)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.8.4)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.3)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.9.2)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.11.2)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.4)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.15.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.9/dist-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.9/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.9/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.21)\n",
            "Building wheels for collected packages: plotly-resampler, pyod\n",
            "  Building wheel for plotly-resampler (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for plotly-resampler: filename=plotly_resampler-0.8.3.2-cp39-cp39-manylinux_2_31_x86_64.whl size=75056 sha256=36f3d949ef2b6e4cd257db0e000a18b533074bfa0ded3719e520d20dc01b5561\n",
            "  Stored in directory: /root/.cache/pip/wheels/9e/36/06/4c11e300918011376af149098621ec7ebe06d8256566d43d51\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.0.9-py3-none-any.whl size=184112 sha256=679d454ecb1b39fc122b06d85b12a1601c0e8392e1152b157ca0744d51e5d3cc\n",
            "  Stored in directory: /root/.cache/pip/wheels/1b/9c/b8/9759d7cc64a1e01bb9872ade80cb7db445ccf506e083325106\n",
            "Successfully built plotly-resampler pyod\n",
            "Installing collected packages: trace-updater, kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, schemdraw, retrying, orjson, jedi, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, jupyter-dash, category-encoders, tbats, plotly-resampler, pycaret\n",
            "Successfully installed ansi2html-1.8.0 category-encoders-2.6.0 dash-2.9.3 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecated-1.2.13 deprecation-2.1.0 jedi-0.18.2 jupyter-dash-0.4.2 kaleido-0.2.1 orjson-3.8.10 plotly-resampler-0.8.3.2 pmdarima-2.0.3 pycaret-3.0.0 pyod-1.0.9 retrying-1.3.4 schemdraw-0.16 scikit-plot-0.3.7 sktime-0.17.1 tbats-1.1.2 trace-updater-0.0.9.1 wurlitzer-3.0.3 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "# import pycaret\n",
        "\n",
        "clf = setup(df, target='Converted')\n",
        "\n",
        "best_model = compare_models()\n",
        "\n",
        "train_data = get_config('X_train')\n",
        "train_target = get_config('y_train')\n",
        "test_data = get_config('X_test')\n",
        "test_target = get_config('y_test')\n",
        "\n",
        "predictions = predict_model(best_model, data=test_data)\n",
        "\n",
        "performance = eval(test_target, predictions['prediction_label'])\n",
        "\n",
        "print(performance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "87926c080d224687b31dba41cdddc640",
            "95515dfb446d45caa3d4beb6da46e4a7",
            "9fcecc6e7ba24992bd1eb665251266d8",
            "14da9086e9a1483da39d364426323aed",
            "79108580944f4d9b950c88aa52aba85a",
            "cefc6b8f67c3486eb0ee56da1b7b8bce",
            "19c4ba243dee4e7585e43caa96269492",
            "7ad50576e5d44d6dbf95b646f34adfca",
            "6d02d9a406c649f78ab8ff83329dac6a",
            "c2b2a76869bd45e88ac1a0c198eaf5e4",
            "c055b61d827f4bc29bf8ba6b31cfe6e6"
          ]
        },
        "id": "Ug7EGYQDzhkV",
        "outputId": "a4db76c9-ae8f-4d28-cdb6-6a13b37ef5b9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f7827a9acd0>"
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              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_f41d1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_f41d1_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_f41d1_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_f41d1_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_f41d1_row0_col1\" class=\"data row0 col1\" >1693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_f41d1_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_f41d1_row1_col1\" class=\"data row1 col1\" >Converted</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_f41d1_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_f41d1_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_f41d1_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_f41d1_row3_col1\" class=\"data row3 col1\" >(9240, 35)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_f41d1_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_f41d1_row4_col1\" class=\"data row4 col1\" >(9240, 35)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_f41d1_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_f41d1_row5_col1\" class=\"data row5 col1\" >(6468, 35)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_f41d1_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_f41d1_row6_col1\" class=\"data row6 col1\" >(2772, 35)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_f41d1_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_f41d1_row7_col1\" class=\"data row7 col1\" >34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_f41d1_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_f41d1_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_f41d1_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_f41d1_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_f41d1_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_f41d1_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_f41d1_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_f41d1_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_f41d1_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_f41d1_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_f41d1_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
              "      <td id=\"T_f41d1_row13_col1\" class=\"data row13 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_f41d1_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_f41d1_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_f41d1_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
              "      <td id=\"T_f41d1_row15_col1\" class=\"data row15 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_f41d1_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_f41d1_row16_col1\" class=\"data row16 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_f41d1_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_f41d1_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_f41d1_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_f41d1_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
              "      <td id=\"T_f41d1_row18_col1\" class=\"data row18 col1\" >75c7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
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              "  text-align: left;\n",
              "}\n",
              "#T_e4a80_row0_col0, #T_e4a80_row0_col4, #T_e4a80_row1_col0, #T_e4a80_row1_col1, #T_e4a80_row1_col2, #T_e4a80_row1_col3, #T_e4a80_row1_col4, #T_e4a80_row1_col5, #T_e4a80_row1_col6, #T_e4a80_row1_col7, #T_e4a80_row2_col0, #T_e4a80_row2_col1, #T_e4a80_row2_col2, #T_e4a80_row2_col3, #T_e4a80_row2_col5, #T_e4a80_row2_col6, #T_e4a80_row2_col7, #T_e4a80_row3_col0, #T_e4a80_row3_col1, #T_e4a80_row3_col2, #T_e4a80_row3_col3, #T_e4a80_row3_col4, #T_e4a80_row3_col5, #T_e4a80_row3_col6, #T_e4a80_row3_col7, #T_e4a80_row4_col0, #T_e4a80_row4_col1, #T_e4a80_row4_col2, #T_e4a80_row4_col3, #T_e4a80_row4_col4, #T_e4a80_row4_col5, #T_e4a80_row4_col6, #T_e4a80_row4_col7, #T_e4a80_row5_col0, #T_e4a80_row5_col1, #T_e4a80_row5_col2, #T_e4a80_row5_col3, #T_e4a80_row5_col4, #T_e4a80_row5_col5, #T_e4a80_row5_col6, #T_e4a80_row5_col7, #T_e4a80_row6_col0, #T_e4a80_row6_col1, #T_e4a80_row6_col2, #T_e4a80_row6_col3, #T_e4a80_row6_col4, #T_e4a80_row6_col5, #T_e4a80_row6_col6, #T_e4a80_row6_col7, #T_e4a80_row7_col0, #T_e4a80_row7_col1, #T_e4a80_row7_col2, #T_e4a80_row7_col3, #T_e4a80_row7_col4, #T_e4a80_row7_col5, #T_e4a80_row7_col6, #T_e4a80_row7_col7, #T_e4a80_row8_col0, #T_e4a80_row8_col1, #T_e4a80_row8_col2, #T_e4a80_row8_col3, #T_e4a80_row8_col4, #T_e4a80_row8_col5, #T_e4a80_row8_col6, #T_e4a80_row8_col7, #T_e4a80_row9_col0, #T_e4a80_row9_col1, #T_e4a80_row9_col2, #T_e4a80_row9_col3, #T_e4a80_row9_col4, #T_e4a80_row9_col5, #T_e4a80_row9_col6, #T_e4a80_row9_col7, #T_e4a80_row10_col0, #T_e4a80_row10_col1, #T_e4a80_row10_col2, #T_e4a80_row10_col3, #T_e4a80_row10_col4, #T_e4a80_row10_col5, #T_e4a80_row10_col6, #T_e4a80_row10_col7, #T_e4a80_row11_col0, #T_e4a80_row11_col1, #T_e4a80_row11_col2, #T_e4a80_row11_col3, #T_e4a80_row11_col4, #T_e4a80_row11_col5, #T_e4a80_row11_col6, #T_e4a80_row11_col7, #T_e4a80_row12_col0, #T_e4a80_row12_col1, #T_e4a80_row12_col2, #T_e4a80_row12_col3, #T_e4a80_row12_col4, #T_e4a80_row12_col5, #T_e4a80_row12_col6, #T_e4a80_row12_col7, #T_e4a80_row13_col0, #T_e4a80_row13_col1, #T_e4a80_row13_col2, #T_e4a80_row13_col3, #T_e4a80_row13_col4, #T_e4a80_row13_col5, #T_e4a80_row13_col6, #T_e4a80_row13_col7, #T_e4a80_row14_col0, #T_e4a80_row14_col1, #T_e4a80_row14_col2, #T_e4a80_row14_col3, #T_e4a80_row14_col4, #T_e4a80_row14_col5, #T_e4a80_row14_col6, #T_e4a80_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_e4a80_row0_col1, #T_e4a80_row0_col2, #T_e4a80_row0_col3, #T_e4a80_row0_col5, #T_e4a80_row0_col6, #T_e4a80_row0_col7, #T_e4a80_row2_col4 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_e4a80_row0_col8, #T_e4a80_row1_col8, #T_e4a80_row2_col8, #T_e4a80_row3_col8, #T_e4a80_row4_col8, #T_e4a80_row5_col8, #T_e4a80_row6_col8, #T_e4a80_row7_col8, #T_e4a80_row8_col8, #T_e4a80_row9_col8, #T_e4a80_row10_col8, #T_e4a80_row11_col8, #T_e4a80_row12_col8, #T_e4a80_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_e4a80_row13_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_e4a80\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_e4a80_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_e4a80_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_e4a80_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_e4a80_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_e4a80_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_e4a80_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_e4a80_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_e4a80_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_e4a80_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n",
              "      <td id=\"T_e4a80_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_e4a80_row0_col1\" class=\"data row0 col1\" >0.9368</td>\n",
              "      <td id=\"T_e4a80_row0_col2\" class=\"data row0 col2\" >0.9799</td>\n",
              "      <td id=\"T_e4a80_row0_col3\" class=\"data row0 col3\" >0.9129</td>\n",
              "      <td id=\"T_e4a80_row0_col4\" class=\"data row0 col4\" >0.9225</td>\n",
              "      <td id=\"T_e4a80_row0_col5\" class=\"data row0 col5\" >0.9175</td>\n",
              "      <td id=\"T_e4a80_row0_col6\" class=\"data row0 col6\" >0.8663</td>\n",
              "      <td id=\"T_e4a80_row0_col7\" class=\"data row0 col7\" >0.8665</td>\n",
              "      <td id=\"T_e4a80_row0_col8\" class=\"data row0 col8\" >0.4350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row1\" class=\"row_heading level0 row1\" >xgboost</th>\n",
              "      <td id=\"T_e4a80_row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_e4a80_row1_col1\" class=\"data row1 col1\" >0.9323</td>\n",
              "      <td id=\"T_e4a80_row1_col2\" class=\"data row1 col2\" >0.9785</td>\n",
              "      <td id=\"T_e4a80_row1_col3\" class=\"data row1 col3\" >0.9045</td>\n",
              "      <td id=\"T_e4a80_row1_col4\" class=\"data row1 col4\" >0.9187</td>\n",
              "      <td id=\"T_e4a80_row1_col5\" class=\"data row1 col5\" >0.9114</td>\n",
              "      <td id=\"T_e4a80_row1_col6\" class=\"data row1 col6\" >0.8566</td>\n",
              "      <td id=\"T_e4a80_row1_col7\" class=\"data row1 col7\" >0.8569</td>\n",
              "      <td id=\"T_e4a80_row1_col8\" class=\"data row1 col8\" >0.8300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row2\" class=\"row_heading level0 row2\" >gbc</th>\n",
              "      <td id=\"T_e4a80_row2_col0\" class=\"data row2 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_e4a80_row2_col1\" class=\"data row2 col1\" >0.9269</td>\n",
              "      <td id=\"T_e4a80_row2_col2\" class=\"data row2 col2\" >0.9771</td>\n",
              "      <td id=\"T_e4a80_row2_col3\" class=\"data row2 col3\" >0.8825</td>\n",
              "      <td id=\"T_e4a80_row2_col4\" class=\"data row2 col4\" >0.9248</td>\n",
              "      <td id=\"T_e4a80_row2_col5\" class=\"data row2 col5\" >0.9028</td>\n",
              "      <td id=\"T_e4a80_row2_col6\" class=\"data row2 col6\" >0.8443</td>\n",
              "      <td id=\"T_e4a80_row2_col7\" class=\"data row2 col7\" >0.8452</td>\n",
              "      <td id=\"T_e4a80_row2_col8\" class=\"data row2 col8\" >1.1630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row3\" class=\"row_heading level0 row3\" >ada</th>\n",
              "      <td id=\"T_e4a80_row3_col0\" class=\"data row3 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_e4a80_row3_col1\" class=\"data row3 col1\" >0.9147</td>\n",
              "      <td id=\"T_e4a80_row3_col2\" class=\"data row3 col2\" >0.9696</td>\n",
              "      <td id=\"T_e4a80_row3_col3\" class=\"data row3 col3\" >0.8720</td>\n",
              "      <td id=\"T_e4a80_row3_col4\" class=\"data row3 col4\" >0.9037</td>\n",
              "      <td id=\"T_e4a80_row3_col5\" class=\"data row3 col5\" >0.8872</td>\n",
              "      <td id=\"T_e4a80_row3_col6\" class=\"data row3 col6\" >0.8186</td>\n",
              "      <td id=\"T_e4a80_row3_col7\" class=\"data row3 col7\" >0.8194</td>\n",
              "      <td id=\"T_e4a80_row3_col8\" class=\"data row3 col8\" >0.4840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row4\" class=\"row_heading level0 row4\" >rf</th>\n",
              "      <td id=\"T_e4a80_row4_col0\" class=\"data row4 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_e4a80_row4_col1\" class=\"data row4 col1\" >0.9120</td>\n",
              "      <td id=\"T_e4a80_row4_col2\" class=\"data row4 col2\" >0.9676</td>\n",
              "      <td id=\"T_e4a80_row4_col3\" class=\"data row4 col3\" >0.8632</td>\n",
              "      <td id=\"T_e4a80_row4_col4\" class=\"data row4 col4\" >0.9045</td>\n",
              "      <td id=\"T_e4a80_row4_col5\" class=\"data row4 col5\" >0.8831</td>\n",
              "      <td id=\"T_e4a80_row4_col6\" class=\"data row4 col6\" >0.8127</td>\n",
              "      <td id=\"T_e4a80_row4_col7\" class=\"data row4 col7\" >0.8135</td>\n",
              "      <td id=\"T_e4a80_row4_col8\" class=\"data row4 col8\" >0.8160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row5\" class=\"row_heading level0 row5\" >et</th>\n",
              "      <td id=\"T_e4a80_row5_col0\" class=\"data row5 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_e4a80_row5_col1\" class=\"data row5 col1\" >0.9074</td>\n",
              "      <td id=\"T_e4a80_row5_col2\" class=\"data row5 col2\" >0.9642</td>\n",
              "      <td id=\"T_e4a80_row5_col3\" class=\"data row5 col3\" >0.8528</td>\n",
              "      <td id=\"T_e4a80_row5_col4\" class=\"data row5 col4\" >0.9020</td>\n",
              "      <td id=\"T_e4a80_row5_col5\" class=\"data row5 col5\" >0.8764</td>\n",
              "      <td id=\"T_e4a80_row5_col6\" class=\"data row5 col6\" >0.8025</td>\n",
              "      <td id=\"T_e4a80_row5_col7\" class=\"data row5 col7\" >0.8036</td>\n",
              "      <td id=\"T_e4a80_row5_col8\" class=\"data row5 col8\" >1.4210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row6\" class=\"row_heading level0 row6\" >dt</th>\n",
              "      <td id=\"T_e4a80_row6_col0\" class=\"data row6 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_e4a80_row6_col1\" class=\"data row6 col1\" >0.8868</td>\n",
              "      <td id=\"T_e4a80_row6_col2\" class=\"data row6 col2\" >0.8826</td>\n",
              "      <td id=\"T_e4a80_row6_col3\" class=\"data row6 col3\" >0.8592</td>\n",
              "      <td id=\"T_e4a80_row6_col4\" class=\"data row6 col4\" >0.8495</td>\n",
              "      <td id=\"T_e4a80_row6_col5\" class=\"data row6 col5\" >0.8541</td>\n",
              "      <td id=\"T_e4a80_row6_col6\" class=\"data row6 col6\" >0.7617</td>\n",
              "      <td id=\"T_e4a80_row6_col7\" class=\"data row6 col7\" >0.7620</td>\n",
              "      <td id=\"T_e4a80_row6_col8\" class=\"data row6 col8\" >0.1800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row7\" class=\"row_heading level0 row7\" >lda</th>\n",
              "      <td id=\"T_e4a80_row7_col0\" class=\"data row7 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_e4a80_row7_col1\" class=\"data row7 col1\" >0.8344</td>\n",
              "      <td id=\"T_e4a80_row7_col2\" class=\"data row7 col2\" >0.9018</td>\n",
              "      <td id=\"T_e4a80_row7_col3\" class=\"data row7 col3\" >0.7200</td>\n",
              "      <td id=\"T_e4a80_row7_col4\" class=\"data row7 col4\" >0.8284</td>\n",
              "      <td id=\"T_e4a80_row7_col5\" class=\"data row7 col5\" >0.7701</td>\n",
              "      <td id=\"T_e4a80_row7_col6\" class=\"data row7 col6\" >0.6417</td>\n",
              "      <td id=\"T_e4a80_row7_col7\" class=\"data row7 col7\" >0.6459</td>\n",
              "      <td id=\"T_e4a80_row7_col8\" class=\"data row7 col8\" >0.1410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row8\" class=\"row_heading level0 row8\" >ridge</th>\n",
              "      <td id=\"T_e4a80_row8_col0\" class=\"data row8 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_e4a80_row8_col1\" class=\"data row8 col1\" >0.8336</td>\n",
              "      <td id=\"T_e4a80_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row8_col3\" class=\"data row8 col3\" >0.7164</td>\n",
              "      <td id=\"T_e4a80_row8_col4\" class=\"data row8 col4\" >0.8292</td>\n",
              "      <td id=\"T_e4a80_row8_col5\" class=\"data row8 col5\" >0.7684</td>\n",
              "      <td id=\"T_e4a80_row8_col6\" class=\"data row8 col6\" >0.6397</td>\n",
              "      <td id=\"T_e4a80_row8_col7\" class=\"data row8 col7\" >0.6441</td>\n",
              "      <td id=\"T_e4a80_row8_col8\" class=\"data row8 col8\" >0.1320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row9\" class=\"row_heading level0 row9\" >lr</th>\n",
              "      <td id=\"T_e4a80_row9_col0\" class=\"data row9 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_e4a80_row9_col1\" class=\"data row9 col1\" >0.8284</td>\n",
              "      <td id=\"T_e4a80_row9_col2\" class=\"data row9 col2\" >0.8953</td>\n",
              "      <td id=\"T_e4a80_row9_col3\" class=\"data row9 col3\" >0.7220</td>\n",
              "      <td id=\"T_e4a80_row9_col4\" class=\"data row9 col4\" >0.8123</td>\n",
              "      <td id=\"T_e4a80_row9_col5\" class=\"data row9 col5\" >0.7642</td>\n",
              "      <td id=\"T_e4a80_row9_col6\" class=\"data row9 col6\" >0.6301</td>\n",
              "      <td id=\"T_e4a80_row9_col7\" class=\"data row9 col7\" >0.6331</td>\n",
              "      <td id=\"T_e4a80_row9_col8\" class=\"data row9 col8\" >0.8570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row10\" class=\"row_heading level0 row10\" >knn</th>\n",
              "      <td id=\"T_e4a80_row10_col0\" class=\"data row10 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_e4a80_row10_col1\" class=\"data row10 col1\" >0.7965</td>\n",
              "      <td id=\"T_e4a80_row10_col2\" class=\"data row10 col2\" >0.8487</td>\n",
              "      <td id=\"T_e4a80_row10_col3\" class=\"data row10 col3\" >0.7332</td>\n",
              "      <td id=\"T_e4a80_row10_col4\" class=\"data row10 col4\" >0.7388</td>\n",
              "      <td id=\"T_e4a80_row10_col5\" class=\"data row10 col5\" >0.7354</td>\n",
              "      <td id=\"T_e4a80_row10_col6\" class=\"data row10 col6\" >0.5702</td>\n",
              "      <td id=\"T_e4a80_row10_col7\" class=\"data row10 col7\" >0.5708</td>\n",
              "      <td id=\"T_e4a80_row10_col8\" class=\"data row10 col8\" >0.1470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row11\" class=\"row_heading level0 row11\" >nb</th>\n",
              "      <td id=\"T_e4a80_row11_col0\" class=\"data row11 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_e4a80_row11_col1\" class=\"data row11 col1\" >0.7900</td>\n",
              "      <td id=\"T_e4a80_row11_col2\" class=\"data row11 col2\" >0.8455</td>\n",
              "      <td id=\"T_e4a80_row11_col3\" class=\"data row11 col3\" >0.7320</td>\n",
              "      <td id=\"T_e4a80_row11_col4\" class=\"data row11 col4\" >0.7298</td>\n",
              "      <td id=\"T_e4a80_row11_col5\" class=\"data row11 col5\" >0.7284</td>\n",
              "      <td id=\"T_e4a80_row11_col6\" class=\"data row11 col6\" >0.5576</td>\n",
              "      <td id=\"T_e4a80_row11_col7\" class=\"data row11 col7\" >0.5602</td>\n",
              "      <td id=\"T_e4a80_row11_col8\" class=\"data row11 col8\" >0.1490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row12\" class=\"row_heading level0 row12\" >svm</th>\n",
              "      <td id=\"T_e4a80_row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_e4a80_row12_col1\" class=\"data row12 col1\" >0.7441</td>\n",
              "      <td id=\"T_e4a80_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row12_col3\" class=\"data row12 col3\" >0.7310</td>\n",
              "      <td id=\"T_e4a80_row12_col4\" class=\"data row12 col4\" >0.6898</td>\n",
              "      <td id=\"T_e4a80_row12_col5\" class=\"data row12 col5\" >0.6664</td>\n",
              "      <td id=\"T_e4a80_row12_col6\" class=\"data row12 col6\" >0.4679</td>\n",
              "      <td id=\"T_e4a80_row12_col7\" class=\"data row12 col7\" >0.4998</td>\n",
              "      <td id=\"T_e4a80_row12_col8\" class=\"data row12 col8\" >0.2540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
              "      <td id=\"T_e4a80_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_e4a80_row13_col1\" class=\"data row13 col1\" >0.6540</td>\n",
              "      <td id=\"T_e4a80_row13_col2\" class=\"data row13 col2\" >0.8823</td>\n",
              "      <td id=\"T_e4a80_row13_col3\" class=\"data row13 col3\" >0.4449</td>\n",
              "      <td id=\"T_e4a80_row13_col4\" class=\"data row13 col4\" >0.7220</td>\n",
              "      <td id=\"T_e4a80_row13_col5\" class=\"data row13 col5\" >0.4344</td>\n",
              "      <td id=\"T_e4a80_row13_col6\" class=\"data row13 col6\" >0.2513</td>\n",
              "      <td id=\"T_e4a80_row13_col7\" class=\"data row13 col7\" >0.2961</td>\n",
              "      <td id=\"T_e4a80_row13_col8\" class=\"data row13 col8\" >0.1040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_e4a80_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_e4a80_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_e4a80_row14_col1\" class=\"data row14 col1\" >0.6146</td>\n",
              "      <td id=\"T_e4a80_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_e4a80_row14_col3\" class=\"data row14 col3\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row14_col4\" class=\"data row14 col4\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_e4a80_row14_col8\" class=\"data row14 col8\" >0.2210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/65 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "87926c080d224687b31dba41cdddc640"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9361471861471862\n",
            "Precision: 0.9183098591549296\n",
            "Recall: 0.9157303370786517\n",
            "None\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3) Using Autogluon  AutoML tools,make prediction on test data and evaluate performance of prediction using eval function defined above.\n",
        "\n",
        "!pip install autogluon"
      ],
      "metadata": {
        "id": "R1SvLYWmL8AM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ab5f266-b557-418c-b430-1e80c740d453"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting autogluon\n",
            "  Downloading autogluon-0.7.0-py3-none-any.whl (9.7 kB)\n",
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            "\u001b[?25hCollecting autogluon.features==0.7.0\n",
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            "\u001b[?25hCollecting autogluon.timeseries[all]==0.7.0\n",
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            "\u001b[?25hCollecting autogluon.core[all]==0.7.0\n",
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            "Building wheels for collected packages: fairscale, antlr4-python3-runtime, seqeval\n",
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            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=6485b15840ba47a0164e1eee11e77fb8744c8c5d01840e2062a02933c5c15aa8\n",
            "  Stored in directory: /root/.cache/pip/wheels/e2/a5/92/2c80d1928733611c2747a9820e1324a6835524d9411510c142\n",
            "Successfully built fairscale antlr4-python3-runtime seqeval\n",
            "Installing collected packages: tokenizers, sentencepiece, distlib, antlr4-python3-runtime, virtualenv, ujson, tensorboardX, pyDeprecate, Pillow, ordered-set, omegaconf, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cublas-cu11, nptyping, networkx, multidict, lightning-utilities, jsonschema, jmespath, frozenlist, dill, colorama, async-timeout, yarl, responses, pytesseract, nvidia-cudnn-cu11, multiprocess, huggingface-hub, botocore, aiosignal, transformers, torch, seqeval, s3transfer, ray, model-index, gluonts, catboost, aiohttp, torchvision, torchmetrics, statsforecast, sktime, pytorch-metric-learning, pmdarima, openmim, nlpaug, fairscale, boto3, accelerate, timm, pytorch-lightning, datasets, autogluon.common, evaluate, autogluon.features, autogluon.core, autogluon.tabular, autogluon.multimodal, autogluon.timeseries, autogluon\n",
            "  Attempting uninstall: Pillow\n",
            "    Found existing installation: Pillow 8.4.0\n",
            "    Uninstalling Pillow-8.4.0:\n",
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            "  Attempting uninstall: jsonschema\n",
            "    Found existing installation: jsonschema 4.3.3\n",
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            "      Successfully uninstalled jsonschema-4.3.3\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.0+cu118\n",
            "    Uninstalling torch-2.0.0+cu118:\n",
            "      Successfully uninstalled torch-2.0.0+cu118\n",
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            "    Found existing installation: torchvision 0.15.1+cu118\n",
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            "  Attempting uninstall: sktime\n",
            "    Found existing installation: sktime 0.17.1\n",
            "    Uninstalling sktime-0.17.1:\n",
            "      Successfully uninstalled sktime-0.17.1\n",
            "  Attempting uninstall: pmdarima\n",
            "    Found existing installation: pmdarima 2.0.3\n",
            "    Uninstalling pmdarima-2.0.3:\n",
            "      Successfully uninstalled pmdarima-2.0.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.15.1 requires torch==2.0.0, but you have torch 1.13.1 which is incompatible.\n",
            "torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.13.1 which is incompatible.\n",
            "torchaudio 2.0.1+cu118 requires torch==2.0.0, but you have torch 1.13.1 which is incompatible.\n",
            "pycaret 3.0.0 requires sktime>=0.16.1, but you have sktime 0.15.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Pillow-9.5.0 accelerate-0.16.0 aiohttp-3.8.4 aiosignal-1.3.1 antlr4-python3-runtime-4.9.3 async-timeout-4.0.2 autogluon-0.7.0 autogluon.common-0.7.0 autogluon.core-0.7.0 autogluon.features-0.7.0 autogluon.multimodal-0.7.0 autogluon.tabular-0.7.0 autogluon.timeseries-0.7.0 boto3-1.26.113 botocore-1.29.113 catboost-1.1.1 colorama-0.4.6 datasets-2.11.0 dill-0.3.6 distlib-0.3.6 evaluate-0.3.0 fairscale-0.4.13 frozenlist-1.3.3 gluonts-0.12.6 huggingface-hub-0.13.4 jmespath-1.0.1 jsonschema-4.17.3 lightning-utilities-0.8.0 model-index-0.1.11 multidict-6.0.4 multiprocess-0.70.14 networkx-2.8.8 nlpaug-1.1.11 nptyping-2.4.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 omegaconf-2.2.3 openmim-0.3.7 ordered-set-4.1.0 pmdarima-1.8.5 pyDeprecate-0.3.2 pytesseract-0.3.10 pytorch-lightning-1.9.5 pytorch-metric-learning-1.7.3 ray-2.2.0 responses-0.18.0 s3transfer-0.6.0 sentencepiece-0.1.98 seqeval-1.2.2 sktime-0.15.1 statsforecast-1.4.0 tensorboardX-2.6 timm-0.6.13 tokenizers-0.13.3 torch-1.13.1 torchmetrics-0.8.2 torchvision-0.14.1 transformers-4.26.1 ujson-5.7.0 virtualenv-20.21.0 yarl-1.8.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from autogluon.tabular import TabularPredictor\n",
        "\n",
        "# df_train,df_test=train_test_split(df,test_size=0.33,random_state=1)\n",
        "\n",
        "train_df = pd.concat([X_train, y_train], axis=1)\n",
        "\n",
        "# predictor = TabularPredictor(label='Converted')\n",
        "# predictor.fit(train_data)\n",
        "\n",
        "predictor= TabularPredictor(label ='Converted').fit(train_data = train_df, verbosity = 2, presets='best_quality')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MsPMwjZl03XE",
        "outputId": "a700b392-4969-4676-b203-5e8cdd3972f9"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No path specified. Models will be saved in: \"AutogluonModels/ag-20230414_175612/\"\n",
            "Presets specified: ['best_quality']\n",
            "Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=8, num_bag_sets=1\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"AutogluonModels/ag-20230414_175612/\"\n",
            "AutoGluon Version:  0.7.0\n",
            "Python Version:     3.9.16\n",
            "Operating System:   Linux\n",
            "Platform Machine:   x86_64\n",
            "Platform Version:   #1 SMP Sat Dec 10 16:00:40 UTC 2022\n",
            "Train Data Rows:    6468\n",
            "Train Data Columns: 34\n",
            "Label Column: Converted\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).\n",
            "\t2 unique label values:  [0, 1]\n",
            "\tIf 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Selected class <--> label mapping:  class 1 = 1, class 0 = 0\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    10235.33 MB\n",
            "\tTrain Data (Original)  Memory Usage: 1.76 MB (0.0% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\t\t\tNote: Converting 8 features to boolean dtype as they only contain 2 unique values.\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tUseless Original Features (Count: 6): ['Magazine', 'Newspaper', 'Receive More Updates About Our Courses', 'Update me on Supply Chain Content', 'Get updates on DM Content', 'I agree to pay the amount through cheque']\n",
            "\t\tThese features carry no predictive signal and should be manually investigated.\n",
            "\t\tThis is typically a feature which has the same value for all rows.\n",
            "\t\tThese features do not need to be present at inference time.\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', []) :  4 | ['TotalVisits', 'Page Views Per Visit', 'Asymmetrique Activity Score', 'Asymmetrique Profile Score']\n",
            "\t\t('int', [])   : 24 | ['Lead Origin', 'Lead Source', 'Do Not Email', 'Do Not Call', 'Total Time Spent on Website', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('float', [])     :  4 | ['TotalVisits', 'Page Views Per Visit', 'Asymmetrique Activity Score', 'Asymmetrique Profile Score']\n",
            "\t\t('int', [])       : 16 | ['Lead Origin', 'Lead Source', 'Total Time Spent on Website', 'Last Activity', 'Country', ...]\n",
            "\t\t('int', ['bool']) :  8 | ['Do Not Email', 'Do Not Call', 'Search', 'Newspaper Article', 'X Education Forums', ...]\n",
            "\t0.1s = Fit runtime\n",
            "\t28 features in original data used to generate 28 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 1.09 MB (0.0% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.18s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'\n",
            "\tTo change this, specify the eval_metric parameter of Predictor()\n",
            "Fitting 13 L1 models ...\n",
            "Fitting model: KNeighborsUnif_BAG_L1 ...\n",
            "\t0.7984\t = Validation score   (accuracy)\n",
            "\t0.01s\t = Training   runtime\n",
            "\t0.48s\t = Validation runtime\n",
            "Fitting model: KNeighborsDist_BAG_L1 ...\n",
            "\t0.8026\t = Validation score   (accuracy)\n",
            "\t0.01s\t = Training   runtime\n",
            "\t0.41s\t = Validation runtime\n",
            "Fitting model: LightGBMXT_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9348\t = Validation score   (accuracy)\n",
            "\t36.4s\t = Training   runtime\n",
            "\t0.34s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9408\t = Validation score   (accuracy)\n",
            "\t22.41s\t = Training   runtime\n",
            "\t0.15s\t = Validation runtime\n",
            "Fitting model: RandomForestGini_BAG_L1 ...\n",
            "\t0.9215\t = Validation score   (accuracy)\n",
            "\t2.0s\t = Training   runtime\n",
            "\t0.29s\t = Validation runtime\n",
            "Fitting model: RandomForestEntr_BAG_L1 ...\n",
            "\t0.9227\t = Validation score   (accuracy)\n",
            "\t1.62s\t = Training   runtime\n",
            "\t0.28s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9397\t = Validation score   (accuracy)\n",
            "\t34.17s\t = Training   runtime\n",
            "\t0.07s\t = Validation runtime\n",
            "Fitting model: ExtraTreesGini_BAG_L1 ...\n",
            "\t0.9102\t = Validation score   (accuracy)\n",
            "\t1.68s\t = Training   runtime\n",
            "\t0.35s\t = Validation runtime\n",
            "Fitting model: ExtraTreesEntr_BAG_L1 ...\n",
            "\t0.9114\t = Validation score   (accuracy)\n",
            "\t1.33s\t = Training   runtime\n",
            "\t0.32s\t = Validation runtime\n",
            "Fitting model: NeuralNetFastAI_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9111\t = Validation score   (accuracy)\n",
            "\t104.03s\t = Training   runtime\n",
            "\t0.78s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9411\t = Validation score   (accuracy)\n",
            "\t21.22s\t = Training   runtime\n",
            "\t0.16s\t = Validation runtime\n",
            "Fitting model: NeuralNetTorch_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9113\t = Validation score   (accuracy)\n",
            "\t193.52s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ...\n",
            "\tFitting 8 child models (S1F1 - S1F8) | Fitting with ParallelLocalFoldFittingStrategy\n",
            "\t0.9402\t = Validation score   (accuracy)\n",
            "\t31.56s\t = Training   runtime\n",
            "\t0.37s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t0.9414\t = Validation score   (accuracy)\n",
            "\t3.68s\t = Training   runtime\n",
            "\t0.02s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 600.95s ... Best model: \"WeightedEnsemble_L2\"\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"AutogluonModels/ag-20230414_175612/\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictor.fit_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZx7XFoW5RJc",
        "outputId": "26888b7b-c480-4af8-ba4a-0566acc1e7a1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                      model  score_val  pred_time_val    fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0       WeightedEnsemble_L2   0.941404       0.589224   60.743481                0.015544           3.675826            2       True         14\n",
            "1            XGBoost_BAG_L1   0.941095       0.159167   21.218668                0.159167          21.218668            1       True         11\n",
            "2           LightGBM_BAG_L1   0.940785       0.149851   22.413463                0.149851          22.413463            1       True          4\n",
            "3      LightGBMLarge_BAG_L1   0.940167       0.367187   31.558282                0.367187          31.558282            1       True         13\n",
            "4           CatBoost_BAG_L1   0.939703       0.069375   34.165045                0.069375          34.165045            1       True          7\n",
            "5         LightGBMXT_BAG_L1   0.934756       0.337643   36.401113                0.337643          36.401113            1       True          3\n",
            "6   RandomForestEntr_BAG_L1   0.922696       0.280929    1.622883                0.280929           1.622883            1       True          6\n",
            "7   RandomForestGini_BAG_L1   0.921459       0.286786    1.999645                0.286786           1.999645            1       True          5\n",
            "8     ExtraTreesEntr_BAG_L1   0.911410       0.323339    1.326080                0.323339           1.326080            1       True          9\n",
            "9     NeuralNetTorch_BAG_L1   0.911255       0.374825  193.521357                0.374825         193.521357            1       True         12\n",
            "10   NeuralNetFastAI_BAG_L1   0.911101       0.778924  104.025413                0.778924         104.025413            1       True         10\n",
            "11    ExtraTreesGini_BAG_L1   0.910173       0.345138    1.683941                0.345138           1.683941            1       True          8\n",
            "12    KNeighborsDist_BAG_L1   0.802566       0.410903    0.011949                0.410903           0.011949            1       True          2\n",
            "13    KNeighborsUnif_BAG_L1   0.798392       0.478955    0.009305                0.478955           0.009305            1       True          1\n",
            "Number of models trained: 14\n",
            "Types of models trained:\n",
            "{'WeightedEnsembleModel', 'StackerEnsembleModel_NNFastAiTabular', 'StackerEnsembleModel_RF', 'StackerEnsembleModel_TabularNeuralNetTorch', 'StackerEnsembleModel_KNN', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_LGB', 'StackerEnsembleModel_XGBoost'}\n",
            "Bagging used: True  (with 8 folds)\n",
            "Multi-layer stack-ensembling used: False \n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('float', [])     :  4 | ['TotalVisits', 'Page Views Per Visit', 'Asymmetrique Activity Score', 'Asymmetrique Profile Score']\n",
            "('int', [])       : 16 | ['Lead Origin', 'Lead Source', 'Total Time Spent on Website', 'Last Activity', 'Country', ...]\n",
            "('int', ['bool']) :  8 | ['Do Not Email', 'Do Not Call', 'Search', 'Newspaper Article', 'X Education Forums', ...]\n",
            "Plot summary of models saved to file: AutogluonModels/ag-20230414_175612/SummaryOfModels.html\n",
            "*** End of fit() summary ***\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_types': {'KNeighborsUnif_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'KNeighborsDist_BAG_L1': 'StackerEnsembleModel_KNN',\n",
              "  'LightGBMXT_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'LightGBM_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'RandomForestGini_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'RandomForestEntr_BAG_L1': 'StackerEnsembleModel_RF',\n",
              "  'CatBoost_BAG_L1': 'StackerEnsembleModel_CatBoost',\n",
              "  'ExtraTreesGini_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'ExtraTreesEntr_BAG_L1': 'StackerEnsembleModel_XT',\n",
              "  'NeuralNetFastAI_BAG_L1': 'StackerEnsembleModel_NNFastAiTabular',\n",
              "  'XGBoost_BAG_L1': 'StackerEnsembleModel_XGBoost',\n",
              "  'NeuralNetTorch_BAG_L1': 'StackerEnsembleModel_TabularNeuralNetTorch',\n",
              "  'LightGBMLarge_BAG_L1': 'StackerEnsembleModel_LGB',\n",
              "  'WeightedEnsemble_L2': 'WeightedEnsembleModel'},\n",
              " 'model_performance': {'KNeighborsUnif_BAG_L1': 0.7983920841063699,\n",
              "  'KNeighborsDist_BAG_L1': 0.8025664811379097,\n",
              "  'LightGBMXT_BAG_L1': 0.9347557204700062,\n",
              "  'LightGBM_BAG_L1': 0.9407854050711194,\n",
              "  'RandomForestGini_BAG_L1': 0.9214594928880643,\n",
              "  'RandomForestEntr_BAG_L1': 0.9226963512677798,\n",
              "  'CatBoost_BAG_L1': 0.9397031539888683,\n",
              "  'ExtraTreesGini_BAG_L1': 0.9101731601731602,\n",
              "  'ExtraTreesEntr_BAG_L1': 0.9114100185528757,\n",
              "  'NeuralNetFastAI_BAG_L1': 0.9111008039579468,\n",
              "  'XGBoost_BAG_L1': 0.9410946196660482,\n",
              "  'NeuralNetTorch_BAG_L1': 0.9112554112554112,\n",
              "  'LightGBMLarge_BAG_L1': 0.9401669758812616,\n",
              "  'WeightedEnsemble_L2': 0.9414038342609771},\n",
              " 'model_best': 'WeightedEnsemble_L2',\n",
              " 'model_paths': {'KNeighborsUnif_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/KNeighborsUnif_BAG_L1/',\n",
              "  'KNeighborsDist_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/KNeighborsDist_BAG_L1/',\n",
              "  'LightGBMXT_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/LightGBMXT_BAG_L1/',\n",
              "  'LightGBM_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/LightGBM_BAG_L1/',\n",
              "  'RandomForestGini_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/RandomForestGini_BAG_L1/',\n",
              "  'RandomForestEntr_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/RandomForestEntr_BAG_L1/',\n",
              "  'CatBoost_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/CatBoost_BAG_L1/',\n",
              "  'ExtraTreesGini_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/ExtraTreesGini_BAG_L1/',\n",
              "  'ExtraTreesEntr_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/ExtraTreesEntr_BAG_L1/',\n",
              "  'NeuralNetFastAI_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/NeuralNetFastAI_BAG_L1/',\n",
              "  'XGBoost_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/XGBoost_BAG_L1/',\n",
              "  'NeuralNetTorch_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/NeuralNetTorch_BAG_L1/',\n",
              "  'LightGBMLarge_BAG_L1': 'AutogluonModels/ag-20230414_175612/models/LightGBMLarge_BAG_L1/',\n",
              "  'WeightedEnsemble_L2': 'AutogluonModels/ag-20230414_175612/models/WeightedEnsemble_L2/'},\n",
              " 'model_fit_times': {'KNeighborsUnif_BAG_L1': 0.009305238723754883,\n",
              "  'KNeighborsDist_BAG_L1': 0.011948585510253906,\n",
              "  'LightGBMXT_BAG_L1': 36.40111255645752,\n",
              "  'LightGBM_BAG_L1': 22.413463354110718,\n",
              "  'RandomForestGini_BAG_L1': 1.9996445178985596,\n",
              "  'RandomForestEntr_BAG_L1': 1.622882604598999,\n",
              "  'CatBoost_BAG_L1': 34.165045499801636,\n",
              "  'ExtraTreesGini_BAG_L1': 1.6839408874511719,\n",
              "  'ExtraTreesEntr_BAG_L1': 1.326080322265625,\n",
              "  'NeuralNetFastAI_BAG_L1': 104.02541279792786,\n",
              "  'XGBoost_BAG_L1': 21.218668460845947,\n",
              "  'NeuralNetTorch_BAG_L1': 193.5213565826416,\n",
              "  'LightGBMLarge_BAG_L1': 31.558282136917114,\n",
              "  'WeightedEnsemble_L2': 3.675825834274292},\n",
              " 'model_pred_times': {'KNeighborsUnif_BAG_L1': 0.4789547920227051,\n",
              "  'KNeighborsDist_BAG_L1': 0.4109029769897461,\n",
              "  'LightGBMXT_BAG_L1': 0.3376429080963135,\n",
              "  'LightGBM_BAG_L1': 0.14985108375549316,\n",
              "  'RandomForestGini_BAG_L1': 0.2867863178253174,\n",
              "  'RandomForestEntr_BAG_L1': 0.2809293270111084,\n",
              "  'CatBoost_BAG_L1': 0.06937503814697266,\n",
              "  'ExtraTreesGini_BAG_L1': 0.3451375961303711,\n",
              "  'ExtraTreesEntr_BAG_L1': 0.32333874702453613,\n",
              "  'NeuralNetFastAI_BAG_L1': 0.778923749923706,\n",
              "  'XGBoost_BAG_L1': 0.15916705131530762,\n",
              "  'NeuralNetTorch_BAG_L1': 0.37482547760009766,\n",
              "  'LightGBMLarge_BAG_L1': 0.3671867847442627,\n",
              "  'WeightedEnsemble_L2': 0.01554417610168457},\n",
              " 'num_bag_folds': 8,\n",
              " 'max_stack_level': 2,\n",
              " 'num_classes': 2,\n",
              " 'model_hyperparams': {'KNeighborsUnif_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'KNeighborsDist_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'LightGBMXT_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'LightGBM_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'RandomForestGini_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'RandomForestEntr_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'CatBoost_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'ExtraTreesGini_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'ExtraTreesEntr_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True,\n",
              "   'use_child_oof': True},\n",
              "  'NeuralNetFastAI_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'XGBoost_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'NeuralNetTorch_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'LightGBMLarge_BAG_L1': {'use_orig_features': True,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True},\n",
              "  'WeightedEnsemble_L2': {'use_orig_features': False,\n",
              "   'max_base_models': 25,\n",
              "   'max_base_models_per_type': 5,\n",
              "   'save_bag_folds': True}},\n",
              " 'leaderboard':                       model  score_val  pred_time_val    fit_time  \\\n",
              " 0       WeightedEnsemble_L2   0.941404       0.589224   60.743481   \n",
              " 1            XGBoost_BAG_L1   0.941095       0.159167   21.218668   \n",
              " 2           LightGBM_BAG_L1   0.940785       0.149851   22.413463   \n",
              " 3      LightGBMLarge_BAG_L1   0.940167       0.367187   31.558282   \n",
              " 4           CatBoost_BAG_L1   0.939703       0.069375   34.165045   \n",
              " 5         LightGBMXT_BAG_L1   0.934756       0.337643   36.401113   \n",
              " 6   RandomForestEntr_BAG_L1   0.922696       0.280929    1.622883   \n",
              " 7   RandomForestGini_BAG_L1   0.921459       0.286786    1.999645   \n",
              " 8     ExtraTreesEntr_BAG_L1   0.911410       0.323339    1.326080   \n",
              " 9     NeuralNetTorch_BAG_L1   0.911255       0.374825  193.521357   \n",
              " 10   NeuralNetFastAI_BAG_L1   0.911101       0.778924  104.025413   \n",
              " 11    ExtraTreesGini_BAG_L1   0.910173       0.345138    1.683941   \n",
              " 12    KNeighborsDist_BAG_L1   0.802566       0.410903    0.011949   \n",
              " 13    KNeighborsUnif_BAG_L1   0.798392       0.478955    0.009305   \n",
              " \n",
              "     pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              " 0                 0.015544           3.675826            2       True   \n",
              " 1                 0.159167          21.218668            1       True   \n",
              " 2                 0.149851          22.413463            1       True   \n",
              " 3                 0.367187          31.558282            1       True   \n",
              " 4                 0.069375          34.165045            1       True   \n",
              " 5                 0.337643          36.401113            1       True   \n",
              " 6                 0.280929           1.622883            1       True   \n",
              " 7                 0.286786           1.999645            1       True   \n",
              " 8                 0.323339           1.326080            1       True   \n",
              " 9                 0.374825         193.521357            1       True   \n",
              " 10                0.778924         104.025413            1       True   \n",
              " 11                0.345138           1.683941            1       True   \n",
              " 12                0.410903           0.011949            1       True   \n",
              " 13                0.478955           0.009305            1       True   \n",
              " \n",
              "     fit_order  \n",
              " 0          14  \n",
              " 1          11  \n",
              " 2           4  \n",
              " 3          13  \n",
              " 4           7  \n",
              " 5           3  \n",
              " 6           6  \n",
              " 7           5  \n",
              " 8           9  \n",
              " 9          12  \n",
              " 10         10  \n",
              " 11          8  \n",
              " 12          2  \n",
              " 13          1  }"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictor.leaderboard(train_df, silent=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "KE4PCA_l5VwS",
        "outputId": "cea74ed0-15ed-4f4a-99f0-6a8d3bb74eb6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      model  score_test  score_val  pred_time_test  \\\n",
              "0   RandomForestEntr_BAG_L1    0.995362   0.922696        0.432375   \n",
              "1   RandomForestGini_BAG_L1    0.995362   0.921459        0.482026   \n",
              "2     ExtraTreesGini_BAG_L1    0.995362   0.910173        0.512805   \n",
              "3     ExtraTreesEntr_BAG_L1    0.995362   0.911410        0.521353   \n",
              "4      LightGBMLarge_BAG_L1    0.995207   0.940167        1.949836   \n",
              "5     KNeighborsDist_BAG_L1    0.994434   0.802566        0.236634   \n",
              "6       WeightedEnsemble_L2    0.969388   0.941404        1.608966   \n",
              "7           LightGBM_BAG_L1    0.967687   0.940785        1.171186   \n",
              "8            XGBoost_BAG_L1    0.966450   0.941095        0.975794   \n",
              "9         LightGBMXT_BAG_L1    0.965832   0.934756        2.181059   \n",
              "10          CatBoost_BAG_L1    0.960730   0.939703        0.116658   \n",
              "11   NeuralNetFastAI_BAG_L1    0.959029   0.911101        1.417020   \n",
              "12    NeuralNetTorch_BAG_L1    0.954082   0.911255        0.764692   \n",
              "13    KNeighborsUnif_BAG_L1    0.854360   0.798392        0.191115   \n",
              "\n",
              "    pred_time_val    fit_time  pred_time_test_marginal  \\\n",
              "0        0.280929    1.622883                 0.432375   \n",
              "1        0.286786    1.999645                 0.482026   \n",
              "2        0.345138    1.683941                 0.512805   \n",
              "3        0.323339    1.326080                 0.521353   \n",
              "4        0.367187   31.558282                 1.949836   \n",
              "5        0.410903    0.011949                 0.236634   \n",
              "6        0.589224   60.743481                 0.003710   \n",
              "7        0.149851   22.413463                 1.171186   \n",
              "8        0.159167   21.218668                 0.975794   \n",
              "9        0.337643   36.401113                 2.181059   \n",
              "10       0.069375   34.165045                 0.116658   \n",
              "11       0.778924  104.025413                 1.417020   \n",
              "12       0.374825  193.521357                 0.764692   \n",
              "13       0.478955    0.009305                 0.191115   \n",
              "\n",
              "    pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  \\\n",
              "0                 0.280929           1.622883            1       True   \n",
              "1                 0.286786           1.999645            1       True   \n",
              "2                 0.345138           1.683941            1       True   \n",
              "3                 0.323339           1.326080            1       True   \n",
              "4                 0.367187          31.558282            1       True   \n",
              "5                 0.410903           0.011949            1       True   \n",
              "6                 0.015544           3.675826            2       True   \n",
              "7                 0.149851          22.413463            1       True   \n",
              "8                 0.159167          21.218668            1       True   \n",
              "9                 0.337643          36.401113            1       True   \n",
              "10                0.069375          34.165045            1       True   \n",
              "11                0.778924         104.025413            1       True   \n",
              "12                0.374825         193.521357            1       True   \n",
              "13                0.478955           0.009305            1       True   \n",
              "\n",
              "    fit_order  \n",
              "0           6  \n",
              "1           5  \n",
              "2           8  \n",
              "3           9  \n",
              "4          13  \n",
              "5           2  \n",
              "6          14  \n",
              "7           4  \n",
              "8          11  \n",
              "9           3  \n",
              "10          7  \n",
              "11         10  \n",
              "12         12  \n",
              "13          1  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>score_test</th>\n",
              "      <th>score_val</th>\n",
              "      <th>pred_time_test</th>\n",
              "      <th>pred_time_val</th>\n",
              "      <th>fit_time</th>\n",
              "      <th>pred_time_test_marginal</th>\n",
              "      <th>pred_time_val_marginal</th>\n",
              "      <th>fit_time_marginal</th>\n",
              "      <th>stack_level</th>\n",
              "      <th>can_infer</th>\n",
              "      <th>fit_order</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RandomForestEntr_BAG_L1</td>\n",
              "      <td>0.995362</td>\n",
              "      <td>0.922696</td>\n",
              "      <td>0.432375</td>\n",
              "      <td>0.280929</td>\n",
              "      <td>1.622883</td>\n",
              "      <td>0.432375</td>\n",
              "      <td>0.280929</td>\n",
              "      <td>1.622883</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RandomForestGini_BAG_L1</td>\n",
              "      <td>0.995362</td>\n",
              "      <td>0.921459</td>\n",
              "      <td>0.482026</td>\n",
              "      <td>0.286786</td>\n",
              "      <td>1.999645</td>\n",
              "      <td>0.482026</td>\n",
              "      <td>0.286786</td>\n",
              "      <td>1.999645</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ExtraTreesGini_BAG_L1</td>\n",
              "      <td>0.995362</td>\n",
              "      <td>0.910173</td>\n",
              "      <td>0.512805</td>\n",
              "      <td>0.345138</td>\n",
              "      <td>1.683941</td>\n",
              "      <td>0.512805</td>\n",
              "      <td>0.345138</td>\n",
              "      <td>1.683941</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ExtraTreesEntr_BAG_L1</td>\n",
              "      <td>0.995362</td>\n",
              "      <td>0.911410</td>\n",
              "      <td>0.521353</td>\n",
              "      <td>0.323339</td>\n",
              "      <td>1.326080</td>\n",
              "      <td>0.521353</td>\n",
              "      <td>0.323339</td>\n",
              "      <td>1.326080</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBMLarge_BAG_L1</td>\n",
              "      <td>0.995207</td>\n",
              "      <td>0.940167</td>\n",
              "      <td>1.949836</td>\n",
              "      <td>0.367187</td>\n",
              "      <td>31.558282</td>\n",
              "      <td>1.949836</td>\n",
              "      <td>0.367187</td>\n",
              "      <td>31.558282</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>KNeighborsDist_BAG_L1</td>\n",
              "      <td>0.994434</td>\n",
              "      <td>0.802566</td>\n",
              "      <td>0.236634</td>\n",
              "      <td>0.410903</td>\n",
              "      <td>0.011949</td>\n",
              "      <td>0.236634</td>\n",
              "      <td>0.410903</td>\n",
              "      <td>0.011949</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>WeightedEnsemble_L2</td>\n",
              "      <td>0.969388</td>\n",
              "      <td>0.941404</td>\n",
              "      <td>1.608966</td>\n",
              "      <td>0.589224</td>\n",
              "      <td>60.743481</td>\n",
              "      <td>0.003710</td>\n",
              "      <td>0.015544</td>\n",
              "      <td>3.675826</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LightGBM_BAG_L1</td>\n",
              "      <td>0.967687</td>\n",
              "      <td>0.940785</td>\n",
              "      <td>1.171186</td>\n",
              "      <td>0.149851</td>\n",
              "      <td>22.413463</td>\n",
              "      <td>1.171186</td>\n",
              "      <td>0.149851</td>\n",
              "      <td>22.413463</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>XGBoost_BAG_L1</td>\n",
              "      <td>0.966450</td>\n",
              "      <td>0.941095</td>\n",
              "      <td>0.975794</td>\n",
              "      <td>0.159167</td>\n",
              "      <td>21.218668</td>\n",
              "      <td>0.975794</td>\n",
              "      <td>0.159167</td>\n",
              "      <td>21.218668</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LightGBMXT_BAG_L1</td>\n",
              "      <td>0.965832</td>\n",
              "      <td>0.934756</td>\n",
              "      <td>2.181059</td>\n",
              "      <td>0.337643</td>\n",
              "      <td>36.401113</td>\n",
              "      <td>2.181059</td>\n",
              "      <td>0.337643</td>\n",
              "      <td>36.401113</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>CatBoost_BAG_L1</td>\n",
              "      <td>0.960730</td>\n",
              "      <td>0.939703</td>\n",
              "      <td>0.116658</td>\n",
              "      <td>0.069375</td>\n",
              "      <td>34.165045</td>\n",
              "      <td>0.116658</td>\n",
              "      <td>0.069375</td>\n",
              "      <td>34.165045</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>NeuralNetFastAI_BAG_L1</td>\n",
              "      <td>0.959029</td>\n",
              "      <td>0.911101</td>\n",
              "      <td>1.417020</td>\n",
              "      <td>0.778924</td>\n",
              "      <td>104.025413</td>\n",
              "      <td>1.417020</td>\n",
              "      <td>0.778924</td>\n",
              "      <td>104.025413</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>NeuralNetTorch_BAG_L1</td>\n",
              "      <td>0.954082</td>\n",
              "      <td>0.911255</td>\n",
              "      <td>0.764692</td>\n",
              "      <td>0.374825</td>\n",
              "      <td>193.521357</td>\n",
              "      <td>0.764692</td>\n",
              "      <td>0.374825</td>\n",
              "      <td>193.521357</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>KNeighborsUnif_BAG_L1</td>\n",
              "      <td>0.854360</td>\n",
              "      <td>0.798392</td>\n",
              "      <td>0.191115</td>\n",
              "      <td>0.478955</td>\n",
              "      <td>0.009305</td>\n",
              "      <td>0.191115</td>\n",
              "      <td>0.478955</td>\n",
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              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-660fab96-8d92-4919-8c3f-40b43cbd909e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = predictor.predict(X_test)\n",
        "\n",
        "performance = eval(y_test,y_pred)\n",
        "\n",
        "print(performance)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "Ua32Ri5q5W5o",
        "outputId": "c272381f-ec45-439f-9ff5-5156ffa72ee1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9307359307359307\n",
            "Precision: 0.9287037037037037\n",
            "Recall: 0.8971377459749553\n",
            "None\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}