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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K1JbMys043S_",
"outputId": "c60da538-342e-4d5d-b678-0acdcf4f1976"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"source": [
"!pip install gradio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IbZYtX6k45IY",
"outputId": "c9221d56-1854-47ca-e1b9-41e4ee641859"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Collecting uc-micro-py (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio)\n",
" Downloading uc_micro_py-1.0.2-py3-none-any.whl (6.2 kB)\n",
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"Building wheels for collected packages: ffmpy\n",
" Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4694 sha256=833a310a70972f4ea4972633c44808b65fc42559afcf441a716536e3ceddfb1f\n",
" Stored in directory: /root/.cache/pip/wheels/0c/c2/0e/3b9c6845c6a4e35beb90910cc70d9ac9ab5d47402bd62af0df\n",
"Successfully built ffmpy\n",
"Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, markdown-it-py, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, httpx, fastapi, gradio-client, gradio\n",
" Attempting uninstall: markdown-it-py\n",
" Found existing installation: markdown-it-py 3.0.0\n",
" Uninstalling markdown-it-py-3.0.0:\n",
" Successfully uninstalled markdown-it-py-3.0.0\n",
"Successfully installed aiofiles-23.1.0 fastapi-0.100.0 ffmpy-0.3.0 gradio-3.36.1 gradio-client-0.2.7 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.16.4 linkify-it-py-2.0.2 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 orjson-3.9.2 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 uc-micro-py-1.0.2 uvicorn-0.22.0 websockets-11.0.3\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import gradio as gr\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.compose import ColumnTransformer\n",
"from imblearn.over_sampling import ADASYN\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import roc_auc_score\n",
"from sklearn.metrics import roc_curve\n",
"from sklearn.metrics import precision_score, recall_score, f1_score\n",
"import sklearn.metrics as metrics\n",
"\n",
"from joblib import dump\n",
"import os\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"metadata": {
"id": "DuZ7ZW3E5Ge7"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"c_data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Gradio/Telco-Customer-Churn.csv')\n",
"c_data.drop(['customerID','PhoneService','SeniorCitizen','StreamingMovies','StreamingTV'], axis=1, inplace=True)\n",
"c_data.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nDm6h1Pl6tRd",
"outputId": "14740557-c7d4-40fa-9efb-e9882d6c42ed"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7043 entries, 0 to 7042\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 gender 7043 non-null object \n",
" 1 Partner 7043 non-null object \n",
" 2 Dependents 7043 non-null object \n",
" 3 tenure 7043 non-null int64 \n",
" 4 MultipleLines 7043 non-null object \n",
" 5 InternetService 7043 non-null object \n",
" 6 OnlineSecurity 7043 non-null object \n",
" 7 OnlineBackup 7043 non-null object \n",
" 8 DeviceProtection 7043 non-null object \n",
" 9 TechSupport 7043 non-null object \n",
" 10 Contract 7043 non-null object \n",
" 11 PaperlessBilling 7043 non-null object \n",
" 12 PaymentMethod 7043 non-null object \n",
" 13 MonthlyCharges 7043 non-null float64\n",
" 14 TotalCharges 7043 non-null object \n",
" 15 Churn 7043 non-null object \n",
"dtypes: float64(1), int64(1), object(14)\n",
"memory usage: 880.5+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"c_data['TotalCharges'] = pd.to_numeric(c_data['TotalCharges'], errors='coerce')\n",
"c_data.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "w_SwTyvi5GYI",
"outputId": "4e29588c-f81a-4d1e-d626-8a80b3f02ce9"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7043 entries, 0 to 7042\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 gender 7043 non-null object \n",
" 1 Partner 7043 non-null object \n",
" 2 Dependents 7043 non-null object \n",
" 3 tenure 7043 non-null int64 \n",
" 4 MultipleLines 7043 non-null object \n",
" 5 InternetService 7043 non-null object \n",
" 6 OnlineSecurity 7043 non-null object \n",
" 7 OnlineBackup 7043 non-null object \n",
" 8 DeviceProtection 7043 non-null object \n",
" 9 TechSupport 7043 non-null object \n",
" 10 Contract 7043 non-null object \n",
" 11 PaperlessBilling 7043 non-null object \n",
" 12 PaymentMethod 7043 non-null object \n",
" 13 MonthlyCharges 7043 non-null float64\n",
" 14 TotalCharges 7032 non-null float64\n",
" 15 Churn 7043 non-null object \n",
"dtypes: float64(2), int64(1), object(13)\n",
"memory usage: 880.5+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Removing missing values\n",
"c_data.dropna(inplace=True)\n",
"c_data.duplicated().sum()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AuIXmyf_5GEm",
"outputId": "dbe796ae-7870-4cde-c81d-d1abb3da6382"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"26"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"#le = LabelEncoder()\n",
"#c_data['Churn'] = le.fit_transform(c_data['Churn'])\n",
"#c_data.head()"
],
"metadata": {
"id": "EUI4hmnA8rcL"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for col in c_data.columns:\n",
" print(f\"Column '{col}' categories: {c_data[col].unique()}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2cyG_RbM_yX_",
"outputId": "558cfe16-f354-4681-b973-a9809d7bc5e8"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Column 'gender' categories: ['Female' 'Male']\n",
"Column 'Partner' categories: ['Yes' 'No']\n",
"Column 'Dependents' categories: ['No' 'Yes']\n",
"Column 'tenure' categories: [ 1 34 2 45 8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
" 5 46 11 70 63 43 15 60 18 66 9 3 31 50 64 56 7 42 35 48 29 65 38 68\n",
" 32 55 37 36 41 6 4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
"Column 'MultipleLines' categories: ['No phone service' 'No' 'Yes']\n",
"Column 'InternetService' categories: ['DSL' 'Fiber optic' 'No']\n",
"Column 'OnlineSecurity' categories: ['No' 'Yes' 'No internet service']\n",
"Column 'OnlineBackup' categories: ['Yes' 'No' 'No internet service']\n",
"Column 'DeviceProtection' categories: ['No' 'Yes' 'No internet service']\n",
"Column 'TechSupport' categories: ['No' 'Yes' 'No internet service']\n",
"Column 'Contract' categories: ['Month-to-month' 'One year' 'Two year']\n",
"Column 'PaperlessBilling' categories: ['Yes' 'No']\n",
"Column 'PaymentMethod' categories: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
" 'Credit card (automatic)']\n",
"Column 'MonthlyCharges' categories: [29.85 56.95 53.85 ... 63.1 44.2 78.7 ]\n",
"Column 'TotalCharges' categories: [ 29.85 1889.5 108.15 ... 346.45 306.6 6844.5 ]\n",
"Column 'Churn' categories: ['No' 'Yes']\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"y = c_data['Churn'] # Target Variable\n",
"X = c_data.drop('Churn', axis =1) # Independent Variable"
],
"metadata": {
"id": "7pL-2l8T8rU3"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"numeric_transformer = Pipeline(steps = [('imputer',SimpleImputer(strategy = 'mean')),('scaler',StandardScaler())])\n",
"categorical_transformer = Pipeline(steps = [('imputer',SimpleImputer(strategy = 'most_frequent')),('one_hot_encoder',OneHotEncoder(handle_unknown='ignore', categories='auto', sparse=False))])"
],
"metadata": {
"id": "HWA2tbQBJU2e"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"data_numeric =['tenure','MonthlyCharges','TotalCharges']\n",
"data_categorical =['gender','Partner','Dependents','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','Contract','PaperlessBilling','PaymentMethod']\n",
"preprocessor =ColumnTransformer(transformers =[('numeric',numeric_transformer,data_numeric),('categoric',categorical_transformer,data_categorical)])"
],
"metadata": {
"id": "ObJQIJ8RJxXl"
},
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Identify numeric and non-numeric columns\n",
"#num_cols = X.select_dtypes(include=[np.number]).columns.tolist()\n",
"#cat_cols = X.select_dtypes(exclude=[np.number]).columns.tolist()\n",
"\n",
"#X_cat = X[cat_cols].copy()\n",
"#X_num = X[num_cols].copy()\n"
],
"metadata": {
"id": "7T37-Hq78q95"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Creating imputer variables\n",
"#numerical_imputer = SimpleImputer(strategy = \"mean\")\n",
"#categorical_imputer = SimpleImputer(strategy = \"most_frequent\")\n",
"\n",
"\n",
"\n",
"# Define the column transformer\n",
"#categorical_features = cat_cols\n",
"#categorical_transformer = Pipeline(steps=[('ohc', OneHotEncoder(handle_unknown='ignore', categories='auto', sparse=False))])\n",
"#preprocessor = ColumnTransformer(transformers=[('cat', categorical_transformer, cat_cols)])\n"
],
"metadata": {
"id": "Hx-SIs2K8q2M"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Fitting the Imputer\n",
"#X_cat_imputed = categorical_imputer.fit_transform(X_cat)\n",
"#X_num_imputed = numerical_imputer.fit_transform(X_num)"
],
"metadata": {
"id": "LhfvzFX-KnCe"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#ohe = OneHotEncoder(handle_unknown='ignore')\n",
"#X_cat_encoded = ohe.fit(X_cat_imputed)\n",
"#X_cat_encoded = pd.DataFrame(ohe.transform(X_cat_imputed).toarray(),\n",
"# columns=ohe.get_feature_names_out(cat_cols))\n",
"\n"
],
"metadata": {
"id": "6N8dMlRuKm7k"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#scaler = StandardScaler()\n",
"#X_num_scaled = scaler.fit_transform(X_num_imputed)\n",
"#X_num_sc = pd.DataFrame(X_num_scaled, columns = num_cols)\n",
"\n"
],
"metadata": {
"id": "EtrHYrNcOvHM"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#X_df = pd.concat([X_num_sc,X_cat_encoded], axis =1)"
],
"metadata": {
"id": "aATpxlXiTA83"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=30)"
],
"metadata": {
"id": "cxIt0C7TZ91X"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"gbc = GradientBoostingClassifier(learning_rate = 0.1, max_depth = 5, n_estimators=50, random_state=30)\n",
"gbc = Pipeline(steps =[('processor',preprocessor),('estimator',gbc)])\n",
"model = gbc.fit(X_train,y_train)\n",
"\n",
"# make predictions on the test data\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# generate the classification report\n",
"report = classification_report(y_test, y_pred)\n",
"name = 'Gradient Boosting Classifier'\n",
"# print the classification report\n",
"print(f'{name} classification report:\\n{report}\\n')\n"
],
"metadata": {
"id": "DRT-nIuvOu9y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ca6a84f9-cf35-4bdb-aaa4-c0155128580c"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Gradient Boosting Classifier classification report:\n",
" precision recall f1-score support\n",
"\n",
" No 0.83 0.89 0.86 1031\n",
" Yes 0.64 0.51 0.57 376\n",
"\n",
" accuracy 0.79 1407\n",
" macro avg 0.74 0.70 0.72 1407\n",
"weighted avg 0.78 0.79 0.78 1407\n",
"\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
" # create a dictionary of a model to fit\n",
" #models = {'Gradient Boosting Classifier': GradientBoostingClassifier(learning_rate = 0.1, max_depth = 5, n_estimators=50, random_state=30),}\n",
" # iterate over the models and fit each one to the resampled training data\n",
" #for name, model in models.items():\n",
" # model.fit(X_train, y_train)\n",
"\n",
" # evaluate each model using cross-validation based on ROC-AUC\n",
" #roc_auc_scores = {}\n",
" #for name, model in models.items():\n",
" # scores = cross_val_score(model, X_train, y_train, cv=5, scoring='roc_auc')\n",
" # roc_auc_scores[name] = scores.mean()\n",
"\n",
" # print the ROC-AUC scores for each model\n",
" #for name, score in roc_auc_scores.items():\n",
" # print(f'{name}: {score}')\n",
"\n",
" # choose the model with the highest ROC-AUC score\n",
" #best_model_name = max(roc_auc_scores, key=roc_auc_scores.get)\n",
" #best_model = models[best_model_name]\n",
" #print(f'Best model: {best_model_name}')\n",
"\n",
"\n",
" # iterate over the models and make predictions on the test data for each one\n",
" #for name, model in models.items():\n",
" # fit the model to the resampled training data\n",
" # model.fit(X_train, y_train)\n",
" # make predictions on the test data\n",
" # y_pred = model.predict(X_test)\n",
" # generate the classification report\n",
" # report = classification_report(y_test, y_pred)\n",
" # print the classification report\n",
" # print(f'{name} classification report:\\n{report}\\n')\n",
"\n",
"\n"
],
"metadata": {
"id": "XW_eIe9ROu2s"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# set the destination path to the \"export\" directory\n",
"destination = \".\"\n",
"\n",
"#best_model = gbc\n",
"models = {\"best_model\": gbc}\n",
"\n",
"# loop through the models and save them using joblib.dump()\n",
"for name, model in models.items():\n",
" dump(model, os.path.join(destination, f\"{name}.joblib\"))\n"
],
"metadata": {
"id": "cVzILcaoOusD"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip freeze > requirements.txt"
],
"metadata": {
"id": "HrsFQmUROufW"
},
"execution_count": 22,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#for name, model in models.items():\n",
"# dump(model, os.path.join(destination, f\"{name}.joblib\"))\n",
"# if os.path.exists(os.path.join(destination, f\"{name}.joblib\")):\n",
"# print(f\"{name} saved successfully!\")\n",
"# else:\n",
"# print(f\"{name} failed to save.\")"
],
"metadata": {
"id": "xsrbuX4JKmnR"
},
"execution_count": 23,
"outputs": []
}
]
} |