Prediction of House Prices using Machine Learning Techniques

Predicting house prices is a difficult task due to the large number of features and parameters such as area, location, and number of bedrooms (1BHK, 2BHK, 3BHK, 4BHK) that must be analyzed to obtain reliable results. The first and most important phase is data collection and preprocessing. The model is then defined and built in order to implement machine learning algorithms and generate predictions[3].   After applying various regression algorithms to the model, it was concluded that the Decision Tree Regression algorithm performed the best, achieving the highest R² score of approximately 0.95. This indicates that it provided the most accurate predictions, as shown by the Actual vs Predicted graph where both values are closely aligned[7][8][10].   In addition to having the highest R² score, the Decision Tree model also showed lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), indicating that the prediction errors were minimal. Therefore, the developed system is efficient and can be used for accurate house price prediction[17][20].

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