The Customer Churn Prediction System is a machine learning project designed to analyze customer behavior and predict the likelihood of customer churn. It helps businesses understand customer retention risks and make data-driven decisions to improve customer loyalty and reduce revenue loss.
Customer churn refers to the situation where customers stop using a company’s products or services over a given period. High churn rates can indicate customer dissatisfaction, strong competition, or poor customer experience. Predicting churn allows businesses to proactively retain customers by improving services, offering personalized incentives, and strengthening customer relationships.
- Identify customers at risk of leaving
- Analyze key factors contributing to churn
- Build predictive models to support business decision-making
- Improve customer retention strategies through actionable insights
- Data preprocessing and cleaning
- Exploratory Data Analysis (EDA) with visual insights
- Feature engineering and selection
- Machine learning model training and evaluation
- Performance measurement using industry-standard metrics
- Logistic Regression
- Decision Tree
- Random Forest
- Support Vector Machine (SVM)
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
Email: apekshyasharma2308@gmail.com
- Clone the repository:
git clone https://github.com/your-username/Customer-Churn-Prediction.git