📊 Customer Churn Prediction

This project aims to predict customer churn using various machine learning models. It leverages classification techniques to help businesses understand and reduce churn rates, which is crucial for customer retention strategies. Multiple ML Algorithms/Models have been employed which was later fed to a voting classifier to determine the best possible outcome. This Churn Prediction in applied on Ensemble Models.

📁 Project Structure

├── Churn.ipynb          – Jupyter notebook containing the full pipeline from data preprocessing to model evaluation.
├── README.md            – Overview and usage guide for the project.
└── WA_Fn-UseC_-Telco-Customer-Churn.csv # Telco Customer Churn Dataset available on Kaggle

🚀 Features

Data Cleaning and Preprocessing

Exploratory Data Analysis (EDA)

Model Building:

CatBoost

Model Evaluation:

Classification report of the final ensemble model

📌 Requirements

Install dependencies using:

pip install -r requirements.txt

Key Libraries Used:

pandas
numpy
catboost
xgboost
scikit-learn

🛠️ How to Run

Clone the repository:

git clone https://github.com/your-username/customer-churn-prediction.git
pip install -r requirements.txt

Run all cells to execute the full pipeline.


## 🧹 TODO

Deploy best model using Flask/FastAPI

Export trained model with joblib or pickle

Add a Streamlit-based interactive UI