The main aim of this project is to create a predictive model that helps estimate the price of a house by analyzing historical data and current market features. The system is intended to assist buyers, sellers, and real estate professionals in making informed decisions by providing realistic price predictions. The model uses supervised learning algorithms to identify patterns and relationships between various features like location, area, number of bedrooms and bathrooms, proximity to amenities, and more and the final selling price. By the end of the project, students will have developed a model that can predict house prices with high accuracy and present the results through an interactive interface or dashboard.
Students will start the project by gathering a suitable real estate dataset (e.g., from Kaggle or public government sources). They will perform data cleaning and preprocessing to handle missing values, outliers, and categorical features. Feature engineering and exploratory data analysis (EDA) will be conducted to understand key drivers of house pricing.
Various regression algorithms such as Linear Regression, Decision Trees, Random Forest, and Gradient Boosting will be tested to evaluate their performance. Students will compare these models using performance metrics like RMSE (Root Mean Square Error) and R² score to select the best-performing approach. Visualization tools like Matplotlib or Seaborn may be used to present data insights, and optionally, a front-end interface may be created using Flask or Streamlit for user interaction.
The final phase includes model testing, optimization, documentation, and a team presentation demonstrating the complete solution.
