A comprehensive R-based data analysis project that examines housing rental patterns across multiple cities, utilizing statistical methods and visualization techniques to analyze 4,746 properties' data points including rent prices, locations, and amenities. The project employs various R libraries to clean, process, and visualize rental market trends

An end-to-end Machine Learning web application to predict house rentals using Random Forest, featuring interactive map visualizations and market insights.

House Rent Prediction using Multiple Linear Regression with EDA, feature engineering, and regression assumption validation.

A complete end-to-end machine learning project to predict monthly house rent in Mumbai using data cleaning, outlier handling, feature encoding, and regression models (Linear Regression, Decision Tree, Random Forest). The best model is deployed as a Streamlit web app for real-time rent prediction.