Part 5.1: Best_Pipeline_Featurewiz_classification.ipynb

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      "source": [
        "!pip install git+https://github.com/AutoViML/featurewiz.git"
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      "id": "IwS19FR0ukwO",
      "execution_count": null,
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      "id": "2b7bc8ab",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "import pdb\n",
        "from collections import defaultdict\n",
        "### These imports give fit_transform method for free ###\n",
        "from sklearn.base import BaseEstimator, TransformerMixin \n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "from sklearn.preprocessing import MaxAbsScaler, StandardScaler\n",
        "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
        "import pdb\n",
        "from collections import defaultdict\n",
        "import pickle"
      ]
    },
    {
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              "     Unnamed: 0  age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  \\\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "###### train data set for the model #####\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/gulabpatel/Feature_Selection/master/heart.csv')\n",
        "print(df.shape)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33fa35ae",
      "metadata": {
        "id": "33fa35ae",
        "outputId": "ef4255bb-f728-433d-cadb-a508be272fdf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Imported featurewiz. Version = 0.1.22. nrows=None uses all rows. Set nrows=1000 to randomly sample 1000 rows.\n",
            "outputs = featurewiz(dataname, target, corr_limit=0.70, verbose=2, sep=',', \n",
            "\t\theader=0, test_data='',feature_engg='', category_encoders='',\n",
            "\t\tdask_xgboost_flag=False, nrows=None)\n",
            "Create new features via 'feature_engg' flag : ['interactions','groupby','target']\n",
            "                                \n"
          ]
        }
      ],
      "source": [
        "from featurewiz import FeatureWiz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f76f07d9",
      "metadata": {
        "id": "f76f07d9"
      },
      "outputs": [],
      "source": [
        "### Once the SimFormer pipeline is built, it can work with Featurewiz ##\n",
        "#smp = SimFormer(with_model=False, date_to_string=False)\n",
        "FW = FeatureWiz()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4de73af3",
      "metadata": {
        "id": "4de73af3"
      },
      "outputs": [],
      "source": [
        "target = 'target'\n",
        "modeltype = 'Classification'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c4f4c9d",
      "metadata": {
        "id": "5c4f4c9d",
        "outputId": "caf1e0a9-7cb2-488a-84ee-12fbb2cedd97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0      int64\n",
              "age             int64\n",
              "sex             int64\n",
              "cp              int64\n",
              "trestbps        int64\n",
              "chol            int64\n",
              "fbs             int64\n",
              "restecg         int64\n",
              "thalach         int64\n",
              "exang           int64\n",
              "oldpeak       float64\n",
              "slope           int64\n",
              "ca              int64\n",
              "thal            int64\n",
              "target          int64\n",
              "dtype: object"
            ]
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          "metadata": {},
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      ],
      "source": [
        "if isinstance(target, str):\n",
        "    cols = [x for x in list(df) if x not in [target]]\n",
        "else:\n",
        "    cols = [x for x in list(df) if x not in target]\n",
        "X = df[cols]\n",
        "y = df[target]\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "348cd500",
      "metadata": {
        "id": "348cd500",
        "outputId": "0a49b2b3-0b73-47be-936c-c87c0d8675f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((303, 14), (303,))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "###  find the y values that have low samples ####\n",
        "if modeltype != 'Regression':\n",
        "    low_counts = y.value_counts()[(y.value_counts()<=1).values].index\n",
        "    print(len(low_counts))\n",
        "    ## You need to remove those rows that have just one sample ##\n",
        "    X = X[~(y.isin(low_counts))]\n",
        "    y = y[~(y.isin(low_counts))]\n",
        "X.shape, y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "889492e3",
      "metadata": {
        "id": "889492e3",
        "outputId": "bfe6dc60-56e7-4c76-b79d-cf43677d9fef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(272, 14) (31, 14)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "if modeltype == 'Regression':\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.1, random_state=999)\n",
        "else:\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.1,stratify=y, random_state=999)\n",
        "print(X_train.shape, X_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a02f4334",
      "metadata": {
        "scrolled": false,
        "id": "a02f4334"
      },
      "outputs": [],
      "source": [
        "### This returns a transformer + feature selection pipeline once you fit it ###\n",
        "#mlp = smp.fit(X_train, y_train)\n",
        "#FW.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be211dfa",
      "metadata": {
        "id": "be211dfa"
      },
      "outputs": [],
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.base import BaseEstimator, TransformerMixin \n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "from sklearn.preprocessing import MaxAbsScaler, StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "pipe = Pipeline([('fw', FeatureWiz()),('logistic', LogisticRegression())])\n",
        "param_grid = {\"logistic__C\": np.logspace(-4, 4, 4)}\n",
        "search = RandomizedSearchCV(estimator=pipe,\n",
        "                            param_distributions=param_grid,\n",
        "                            scoring='accuracy',\n",
        "                            n_iter=3,\n",
        "                            n_jobs=-1,\n",
        "                            random_state=99,\n",
        "                            cv=3,\n",
        "                            error_score=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f4715800",
      "metadata": {
        "scrolled": false,
        "id": "f4715800",
        "outputId": "8584a463-591d-4e68-9830-869220bc717a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "############################################################################################\n",
            "############       F A S T   F E A T U R E  E N G G    A N D    S E L E C T I O N ! ########\n",
            "# Be judicious with featurewiz. Don't use it to create too many un-interpretable features! #\n",
            "############################################################################################\n",
            "Skipping feature engineering since no feature_engg input...\n",
            "Skipping category encoding since no category encoders specified in input...\n",
            "**INFO: featurewiz can now read feather formatted files. Loading train data...\n",
            "    Shape of your Data Set loaded: (272, 15)\n",
            "     Loaded. Shape = (272, 15)\n",
            "\n",
            "Warning: Removed special characters in names of columns. You must use these new column names for XGBoost.\n",
            "No test data filename given...\n",
            "############## C L A S S I F Y I N G  V A R I A B L E S  ####################\n",
            "Classifying variables in data set...\n",
            "    14 Predictors classified...\n",
            "        1 variable(s) will be ignored since they are ID or low-information variables\n",
            "GPU active on this device\n",
            "    Tuning XGBoost using GPU hyper-parameters. This will take time...\n",
            "Removing 1 columns from further processing since ID or low information variables\n",
            "    columns removed: ['Unnamed:_0']\n",
            "    After removing redundant variables from further processing, features left = 13\n",
            "No interactions created for categorical vars since feature engg does not specify it\n",
            "#### Single_Label Binary_Classification problem ####\n",
            "Searching for highly correlated variables from 13 variables using SULOV method\n",
            "#####  SULOV : Searching for Uncorrelated List Of Variables (takes time...) ############\n",
            "    No highly correlated variables in data set to remove. All selected...\n",
            "Time taken for SULOV method = 0 seconds\n",
            "    Adding 0 categorical variables to reduced numeric variables  of 13\n",
            "Final list of selected vars after SULOV = 13\n",
            "############## F E A T U R E   S E L E C T I O N  ####################\n",
            "    using regular XGBoost\n",
            "Train and Test loaded into Dask dataframes successfully after feature_engg completed\n",
            "Current number of predictors = 13 \n",
            "    XGBoost version: 1.5.2\n",
            "Number of booster rounds = 100\n",
            "        using 13 variables...\n",
            "            Time taken for regular XGBoost feature selection = 1 seconds\n",
            "        using 10 variables...\n",
            "            Time taken for regular XGBoost feature selection = 1 seconds\n",
            "        using 7 variables...\n",
            "            Time taken for regular XGBoost feature selection = 2 seconds\n",
            "        using 4 variables...\n",
            "            Time taken for regular XGBoost feature selection = 2 seconds\n",
            "        using 1 variables...\n",
            "            Time taken for regular XGBoost feature selection = 2 seconds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x1080 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Total time taken for XGBoost feature selection = 3 seconds\n",
            "No ID variables ['Unnamed:_0'] are selected since they are not considered important for modeling\n",
            "Selected 13 important features:\n",
            "['cp', 'thal', 'ca', 'exang', 'slope', 'oldpeak', 'sex', 'age', 'chol', 'thalach', 'trestbps', 'fbs', 'restecg']\n",
            "    Time taken = 3 seconds\n",
            "    Reverted column names to original names given in train dataset\n",
            "Returning list of 13 important features and a dataframe.\n",
            "    Time taken to create entire pipeline = 3 second(s)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3, error_score=1,\n",
              "                   estimator=Pipeline(steps=[('fw', FeatureWiz()),\n",
              "                                             ('logistic',\n",
              "                                              LogisticRegression())]),\n",
              "                   n_iter=3, n_jobs=-1,\n",
              "                   param_distributions={'logistic__C': array([1.00000000e-04, 4.64158883e-02, 2.15443469e+01, 1.00000000e+04])},\n",
              "                   random_state=99, scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "search.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "32dfc242",
      "metadata": {
        "id": "32dfc242",
        "outputId": "221b9882-2fe3-42d8-c008-f7ea39caaa50",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31,)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "y_pred = search.predict(X_test)\n",
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c2bbb9d6",
      "metadata": {
        "id": "c2bbb9d6",
        "outputId": "6ff2a641-fcf1-473e-992b-00ad0eee078c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8697478991596639\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import balanced_accuracy_score, mean_squared_error\n",
        "if modeltype == 'Regression':\n",
        "    print(np.sqrt(mean_squared_error(y_test, y_pred)))\n",
        "else:\n",
        "    print(balanced_accuracy_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "87fd10f4",
      "metadata": {
        "id": "87fd10f4",
        "outputId": "77ae2559-fba7-4efd-8446-2cc6c46625dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('fw', FeatureWiz()), ('logistic', LogisticRegression())])"
            ],
            "text/html": [
              "<style>#sk-1ca513c0-fd5e-4850-8715-78e533868582 {color: black;background-color: white;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 pre{padding: 0;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-toggleable {background-color: white;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-estimator:hover {background-color: #d4ebff;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-item {z-index: 1;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-parallel-item:only-child::after {width: 0;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-1ca513c0-fd5e-4850-8715-78e533868582 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-1ca513c0-fd5e-4850-8715-78e533868582\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;fw&#x27;, FeatureWiz()), (&#x27;logistic&#x27;, LogisticRegression())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c9a617a1-6f0d-4cfc-95c4-c6b045bf9df6\" type=\"checkbox\" ><label for=\"c9a617a1-6f0d-4cfc-95c4-c6b045bf9df6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;fw&#x27;, FeatureWiz()), (&#x27;logistic&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3c034313-826c-414b-ad14-913e106b44c4\" type=\"checkbox\" ><label for=\"3c034313-826c-414b-ad14-913e106b44c4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FeatureWiz</label><div class=\"sk-toggleable__content\"><pre>FeatureWiz()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a67d5532-29d3-4b4a-b31f-78e73769cc26\" type=\"checkbox\" ><label for=\"a67d5532-29d3-4b4a-b31f-78e73769cc26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "from sklearn import set_config\n",
        "#### You can display your pipeline in a nice diagram if it is a Jupyter Notebook ##\n",
        "set_config(display=\"diagram\")\n",
        "pipe"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "Part 5.1: Best_Pipeline_Featurewiz_classification.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}