Customer churn is a significant concern for businesses, as losing customers impacts revenue and growth. This project focuses on predicting customer churn using machine learning techniques, helping businesses identify customers at risk of leaving and allowing for proactive retention strategies.
Data Preprocessing: Handling missing values, encoding categorical features, and scaling numerical data. Exploratory Data Analysis (EDA): Understanding data patterns, correlations, and distributions using visualization techniques. Feature Engineering: Identifying and selecting important features that impact churn prediction. Machine Learning Modeling: Implemented multiple models, such as Logistic Regression, Random Forest. Model Evaluation: Used metrics like accuracy, precision, recall, F1-score to evaluate model performance.
Technologies Used Programming Language: Python Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Machine Learning Models The following models were trained and evaluated: Logistic Regression: Interpretable and good for baseline comparison. Random Forest: An ensemble model capturing complex relationships.
The best-performing model achieved the following metrics:
Accuracy: 92% Precision: 90% Recall: 88% F1-Score: 89%