Customer churn is one of the biggest challenges faced by subscription-based businesses.
This project builds a machine learning model to predict whether a customer is likely to churn, enabling businesses to take proactive, data-driven retention decisions.

The project demonstrates an end-to-end machine learning workflow, from data preprocessing to model training, evaluation, and saving deployment-ready artifacts.

Businesses often lose customers without early warning signs.
The objective of this project is to identify customers who are at high risk of leaving using historical customer data.

The project follows a structured machine learning pipeline:

1. Data Cleaning and Preprocessing
2. Exploratory Data Analysis (EDA)
3. Feature Engineering and Encoding
4. Model Training and Evaluation
5. Model Persistence for Reusability

• Contains customer demographic and service usage information
• Includes both numerical and categorical features
• Target variable: Churn (Yes / No)

• Algorithms Used: Logistic Regression / Random Forest
• Evaluation Metrics:
  • Accuracy
  • Precision
  • Recall
  • F1-Score

The trained model and encoders are saved as .pkl files to support reuse and deployment.

• Handling categorical variables using encoding techniques
• Building an end-to-end machine learning pipeline
• Saving trained models and preprocessing objects
• Translating business problems into ML solutions

• Programming Language: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Tools: Jupyter Notebook, Git, GitHub

# Clone the repository
git clone https://github.com/aditya-datahub/customer-churn-prediction-project.git

# Install dependencies
pip install -r requirements.txt

# Run the notebook
jupyter notebook notebooks/main.ipynb

customer-churn-prediction-project/
│
├── datasets/
│   └── customer churn dataset.csv
│
├── notebooks/
│   └── main.ipynb
│
├── models/
│   ├── customer_churn_model.pkl
│   └── encoders.pkl
│
├── requirements.txt
├── README.md
└── LICENSE

This project is licensed under the MIT License.
You are free to use, modify, and distribute this project with proper attribution.