This project focuses on developing a machine learning model to predict customer churn. Using data analysis and predictive modeling, the project aims to identify customers likely to discontinue service, enabling businesses to take proactive steps to retain them.
The dataset includes customer demographic information, service usage patterns, and historical account details. This data is crucial in identifying potential churn indicators.
- Python: Primary programming language for analysis and modeling.
- Jupyter Notebook: Used for interactive development and documentation.
- Libraries: Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn are used for data manipulation, visualization, and machine learning tasks.
- Data Preprocessing: Cleaning and formatting data for analysis.
- Exploratory Data Analysis: Analyzing data to understand patterns and trends.
- Feature Engineering: Identifying and selecting key features that impact customer churn.
- Model Building: Applying various machine learning algorithms to develop a predictive model.
- Model Evaluation: Assessing the performance of the model using accuracy, precision, recall, and F1-score.
The churn prediction model demonstrated high accuracy and efficiency in identifying potential churn customers. The findings offer actionable insights for designing effective customer retention strategies.
This project contributed to a publication in the field of data science and machine learning, highlighting the innovative approach and practical implications of the churn prediction model.