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