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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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from sklearn.ensemble import RandomForestClassifier |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.tree import DecisionTreeClassifier |
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score |
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from xgboost import XGBClassifier |
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import logging |
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import joblib |
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from config.config import REPORTS_DIR,ARTIFACTS_DIR |
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logging.basicConfig( |
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filename='/home/sarath_kumar/customer_chrun_prediction/training_log.log', |
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level=logging.INFO, |
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format='%(asctime)s - %(levelname)s - %(message)s' |
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) |
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logging.info("Starting training script...") |
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try: |
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data = pd.read_csv("/home/sarath_kumar/customer_chrun_prediction/processed_data/processed_data.csv") |
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logging.info("Dataset loaded successfully.") |
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X = data.drop('Churn', axis=1) |
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y = data['Churn'] |
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logging.info("Data split into features and target.") |
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models = { |
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"Logistic Regression": LogisticRegression(max_iter=500,solver='saga'), |
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"Random Forest": RandomForestClassifier(), |
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"Decision Tree": DecisionTreeClassifier(), |
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"XGBoost": XGBClassifier(), |
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} |
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metrics_list = [] |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) |
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logging.info("Data split into training and testing sets.") |
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for model_name, model in models.items(): |
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logging.info(f"Training {model_name}...") |
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model.fit(X_train, y_train) |
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logging.info(f"{model_name} training completed.") |
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y_pred = model.predict(X_test) |
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logging.info(f"{model_name} prediction completed.") |
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accuracy = accuracy_score(y_test, y_pred) |
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precision = precision_score(y_test, y_pred, average='weighted') |
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recall = recall_score(y_test, y_pred, average='weighted') |
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f1 = f1_score(y_test, y_pred, average='weighted') |
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logging.info(f"{model_name} evaluation metrics calculated.") |
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metrics_list.append({ |
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"Model": model_name, |
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"Accuracy": accuracy, |
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"Precision": precision, |
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"Recall": recall, |
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"F1 Score": f1 |
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}) |
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metrics_df = pd.DataFrame(metrics_list) |
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logging.info("Metrics DataFrame created.") |
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metrics_df.to_csv(REPORTS_DIR / "model_metrics.csv", index=False) |
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logging.info("Metrics saved to CSV successfully.") |
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for model_name, model in models.items(): |
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joblib.dump(model, ARTIFACTS_DIR/ f"{model_name}.pkl") |
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logging.info(f"{model_name} saved to file.") |
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logging.info("Training script completed successfully.") |
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except Exception as e: |
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logging.error(f"An error occurred: {e}") |
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raise |
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