The House Price Prediction System is a machine learning project designed to predict house prices based on various features like location, size, and other attributes. It utilizes several powerful algorithms, including Random Forest Regressor, Extra Trees Regressor, Linear Regression, KMeans Clustering , By combining these techniques, the system aims to deliver highly accurate predictions, making it a valuable tool for real estate analysis.
The development of this system stemmed from my interest in exploring the application of different regression models and clustering techniques in predicting complex real-world outcomes like house prices. Using libraries such as NumPy and Pandas, I was able to preprocess and clean the data, while Matplotlib and Seaborn helped visualize the relationships between features and target variables. These visualizations provided insights into the factors that most influence house prices, helping to refine the prediction models.
Each model in the system brings its own strengths. For example, the Random Forest Regressor and Extra Trees Regressor effectively handle non-linear relationships and prevent overfitting, while Linear Regression serves as a simple, interpretable baseline. KMeans Clustering adds another layer of analysis by grouping similar houses, which enhances prediction accuracy for houses with similar features. The inclusion of XGBoost Regressor allows for fine-tuning through hyperparameter optimization, making the system more robust and reducing the bias-variance trade-off.
