This repository presents a comprehensive exploration of customer churn using various machine learning algorithms, including linear regression, logistic regression, decision tree, and random forest. Through this project, we aim to understand and predict customer churn, providing valuable insights for proactive customer retention strategies.

This repository is dedicated to the analysis and visualization of COVID-19 patient data. The primary goal is to understand the impact of the virus, track the trends in infection and recovery rates, and make informed predictions about the future number of cases.

This repository hosts a machine learning model developed to predict restaurant revenue based on key features present in the dataset. Leveraging the power of linear regression, this model aims to provide valuable insights for restaurant owners and managers to forecast revenue based on various factors.