The Health Insurance Cross Sell Prediction project predicts customer likelihood of purchasing vehicle insurance using machine learning models like Logistic Regression and Random Forest. The project involves data preprocessing, feature engineering, and model fine-tuning to enhance predictive accuracy and inform strategic decision-making.

This Python project aims to examine and understand how customer interact with a business, product or a service. This analysis helps organizations make informed decisions, tailor their strategies and enhance customer experiences.

As the name suggests, this application helps banks decide whether a loan should be sanctioned by assessing various factors from the borrower's profile.

Case studies using Kaggle Datasets