PriceWise : Predicting House Prices with Regression Models
Project Overview
In this project, students built a machine learning model to predict house prices based on features such as square footage, number of bedrooms, location, and more. This real estate-focused problem teaches how regression models can be applied to continuous value predictions.
Objective
To develop a regression-based ML model that estimates property prices using housing dataset features, supporting informed real estate decisions.
Key Skills Applied
- Data Preprocessing (handling missing values, scaling)
- Feature Engineering (location encoding, area categorization)
- Regression Techniques (Linear Regression, Ridge, Lasso, XGBoost)
- Model Evaluation (MAE, RMSE, R² Score)
Tools & Libraries Used
Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
Learning Outcomes
- Applied regression models to a classic real-world dataset (e.g., Boston or Kaggle housing datasets)
- Gained insight into what drives real estate prices
- Understood importance of feature correlation and multicollinearity
- Learned to visualize predictions and evaluate model accuracy
Result
Built a regression model with an R² score of [e.g., 0.89], effectively predicting home prices within a reasonable margin for residential properties.