House Rent predict

In this project, I developed a complete machine learning pipeline to predict house rental prices based on real-world housing data. The model was built to estimate the expected rent of residential properties by analyzing multiple influential features such as location, size, number of rooms, area type, and furnishing status. The aim was to provide a reliable predictive tool that can support landlords, tenants, and real estate professionals in making data-driven decisions.

The workflow included thorough data preprocessing, handling missing values, and encoding categorical variables to prepare the dataset for modeling. I conducted exploratory data analysis (EDA) to understand feature distributions and relationships, and applied visualizations to reveal key insights. Multiple regression algorithms were tested and evaluated to select the best-performing model, using performance metrics such as R² Score and Mean Squared Error (MSE) to assess predictive quality.

This project demonstrates practical application of regression modeling techniques to solve real estate prediction tasks, with a focus on clean data preparation, strong model performance, and actionable analytical insights.

Tools & Technologies: Python, Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn, Machine Learning Regression