Github Link: github.com/MetinUnlu/customer_churn
This study is prepared as a first semester project for the Programming and Databases course. It focuses on predicting customer churn, a critical metric in business analytics, indicating the percentage of customers who end their relationship with a company in a given period.
The primary aim of this project is to develop a predictive model that can accurately identify potential customer churn. This model can assist in strategizing customer retention initiatives effectively.
The dataset for this project is sourced from the BCG Virtual Internship on the Forage platform. It includes features like customer usage patterns, contract details, and demographic information. Key preprocessing steps included handling missing values, feature engineering, and data normalization.
Various models were explored, including Random Forest Classifier and Sequential Neural Networks. The final model choice was based on the balance between accuracy and computational efficiency.
The final model achieved a notable precision score in predicting customer churn. Further details and insights are available in the interactive Streamlit application.
To run this project locally:
pip install -r requirements.txtstreamlit run app.pyThe project can be viewed live at Customer Churn Prediction Project.
For any inquiries or collaboration opportunities, feel free to reach out.