This repository contains the predictive models for identifying customers likely to purchase in a year-end sale campaign. The goal of this project is to reduce the cost of the campaign by accurately targeting customers who are predicted to respond positively based on data from last year's campaign.
We explored several machine learning models to classify potential customers:
- Logistic Regression
- Support Vector Machine (SVM)
- Naive Bayes
- Decision Tree
- K-Nearest Neighbors (KNN)
This project is implemented using Python and several libraries essential for data analysis, preprocessing, and machine learning:
- Pandas
- Scikit-Learn
- Matplotlib
- Seaborn
- NumPy