House Price Prediction Using Machine Learning

This study presents a machine learning algorithm aimed at predicting home values in the housing market. By utilizing a comprehensive dataset that includes details such as location, size, number of bedrooms, and bathrooms for each house, the model goes through several preprocessing steps. These steps include handling missing values, encoding categorical variables, and managing outliers. Feature selection techniques are applied to identify the most significant attributes. To ensure robust model evaluation, the dataset is divided into training and testing subsets. A comparative analysis of various machine learning algorithms is conducted, including Linear Regression, Ridge Regression, Lasso Regression, ElasticNet, SVR, Decision Tree, Gradient Boosting Regressor, Extra Trees Regressor, K Neighbors Regressor, Dense Deep Learning, Regularization, Deep Learning, Random Forest Regressor, XGBoost Regressor, and Polynomial Regression (Degree=2). Evaluation metrics such as mean squared error, mean absolute error, and R-squared are used to assess model performance. Ensemble techniques, particularly Random Forests and Gradient Boosting, consistently demonstrate strong predictive capabilities, outperforming simpler models like Linear Regression with lower prediction errors and higher R-squared values. Neural networks, known for their ability to learn intricate patterns, also show success, especially in handling complex datasets. The gradient boosting model is further optimized through hyperparameter tuning, highlighting its capability to capture complex correlations within the data and generate accurate housing price predictions.