📊 Customer Churn Prediction

This project predicts customer churn using machine learning models. It includes data cleaning, feature encoding, model training, evaluation, and visual analysis.

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🗂 Dataset

• Source: /content/drive/MyDrive/paper/archive (2).zip
• Target: Churn

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⚙️ Workflow

1. Data Preprocessing:

   • Handle missing values
   • Encode categorical variables
   • Drop irrelevant columns (customerID)
   • Scale features

2. Models Used:

   • Logistic Regression
   • Random Forest
   • XGBoost

3. Evaluation Metrics:

   • Accuracy, Precision, Recall, ROC-AUC, Classification Report

4. Feature Importance:

   • Visualized using Random Forest

5. EDA Visuals:

   • Churn distribution
   • Contract type vs churn
   • Monthly charges, tenure, and customer service calls vs churn

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pdf link : https://drive.google.com/file/d/1OoSteePkrppLvPvC8B79myYiCFpfWMu2/view?usp=drive_link

📦 Requirements

pip install pandas numpy seaborn matplotlib scikit-learn xgboost

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✅ Run the Code

Use Jupyter Notebook or any Python IDE to execute the script.

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🔍 Insight

Month-to-month contracts, low tenure, and high service calls are key churn indicators.

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