🔮 Customer Churn Prediction

This project predicts customer churn (likelihood of a customer leaving a bank) using machine learning. It combines data analysis, feature engineering, classification models, and deployment through both Flask API and Streamlit UI.

📊 Features

📈 Exploratory Data Analysis (EDA): heatmaps, distributions, churn insights

🛠 Feature Engineering: log transformations, interaction terms, binning

🤖 Machine Learning Models: Logistic Regression, Decision Tree, KNN

⚡ Model Pipelines: preprocessing + training with scikit-learn

💾 Model Persistence: trained model saved as churn_model.pkl

🌍 Flask API: REST endpoint for churn prediction (/predict)

🎛 Streamlit UI: user-friendly interface to input customer details and predict churn

⚖️ Imbalance Handling: SMOTE + class weighting for better recall

🛠️ Tech Stack

Python 3.9+

Pandas – data handling

Matplotlib , Seaborn – visualization

Scikit-learn – ML models & pipelines

Imbalanced-learn – SMOTE for imbalance

Joblib – model saving

Flask – API deployment

Streamlit – interactive dashboard

🚀 Installation & Setup

Clone the repository

git clone https://github.com/your-username/churn-prediction.git cd churn-prediction

Install dependencies

pip install -r requirements.txt

Run EDA / Training script

python project.py

Run Flask API

python app.py

Visit → http://127.0.0.1:5000

Example API request:

curl -X POST http://127.0.0.1:5000/predict -H "Content-Type: application/json"
-d '{"CreditScore":600,"Geography":"France","Gender":"Male","Age":40,"Tenure":5, "Balance":60000,"NumOfProducts":2,"HasCrCard":1,"IsActiveMember":1,"EstimatedSalary":50000}'

Run Streamlit App

streamlit run app_streamlit.py

Visit → http://localhost:8501

📊 Model Evaluation

Metrics used: Accuracy, AUC, Confusion Matrix, Precision-Recall

Threshold tuning for better balance between precision & recall

Visual outputs: ROC & Precision-Recall curves