Predicting bank consumer churn is crucial for financial institutions to maintain profitability and ensure customer satisfaction. Identifying potential churners allows banks to take proactive steps to retain valuable customers and optimize marketing efforts.
Factors such as dissatisfaction with services, competitive offers from other banks, or changes in financial circumstances contribute to customer attrition. By understanding these factors, banks can implement targeted strategies, such as offering personalized incentives, improving customer service, or enhancing product offerings to meet customer needs better.
Reducing customer churn has a significant impact on a bank's bottom line by preserving revenue streams and minimizing the costs associated with acquiring new customers. Accurate churn prediction enables banks to strengthen long-term customer relationships, improve customer loyalty, and drive sustainable business growth.
The objective of this project is to use the Kaggle Consumer Churn Dataset to build a predictive model that distinguishes between customers likely to churn and those who are likely to remain with the bank. By leveraging historical data on customer interactions, transactions, and demographics, we aim to develop an effective churn prediction model.
We will be using the Kaggle Consumer Churn Dataset for this project, which contains information on customer behavior and demographics.
- Data cleaning and preprocessing
- Exploratory data analysis (EDA)
- Feature selection and engineering
- Model building (various machine learning algorithms)
- Model evaluation and selection
- Python (pandas, numpy, scikit-learn, etc.)
- Jupyter Notebook/Google Colab
- Machine Learning algorithms (e.g., logistic regression, random forest, etc.)