This project involves the development of a decision tree classifier to forecast customer purchasing behavior. The classifier utilizes demographic and behavioral data to predict whether a customer is likely to purchase a particular product or service.
src/: Contains the source code for the decision tree classifier implementation. data/: Placeholder for the dataset; you may replace it with the actual dataset or provide instructions on how to obtain it. notebooks/: Jupyter notebooks for data exploration, model development, and analysis. docs/: Project documentation, including this README file.
The project uses the Bank Marketing dataset from the UCI Machine Learning Repository. The decision tree classifier implementation is based on Scikit-learn library.
This project holds significant importance for businesses, particularly in the marketing and sales sectors. The predictive model aids in targeting potential customers more efficiently, allowing businesses to optimize their marketing strategies, allocate resources effectively, and ultimately improve conversion rates and revenue.