HOUSE PRICE PREDICTION MODEL USING MACHINE LEARNING: A COMPARATIVE ANALYSIS

Not Approved

Relevance and Originality

The study addresses a pressing need in the real estate market, especially in India, where accurate house price predictions are crucial due to rising real estate values. The use of machine learning to predict house prices based on features like the number of bedrooms, house age, and proximity to amenities is highly relevant. The originality lies in employing multiple machine learning algorithms—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—to provide a comprehensive approach to house price prediction, offering potential buyers and sellers better tools for decision-making.

Methodology

The study applies various machine learning algorithms to predict house prices, incorporating features such as the number of bedrooms, age of the house, and proximity to amenities. To enhance the methodology section, the article should detail how each algorithm was implemented, including data preprocessing, feature selection, model training, and validation processes. Information on how the performance of these algorithms was measured and compared—such as through cross-validation, metrics like RMSE (Root Mean Squared Error), or MAE (Mean Absolute Error)—will provide a clearer understanding of their effectiveness.

Validity & Reliability

To establish validity and reliability, the article should include performance metrics for each machine learning model, such as accuracy, RMSE, or R² scores. It should discuss how the models were validated, including any techniques used to ensure generalizability, such as cross-validation or hold-out testing. Addressing potential biases in the data and methods for handling missing or outlier values will also be important for assessing the reliability of the predictions.

Clarity and Structure

The article should be well-organized, starting with an introduction that outlines the importance of house price prediction and the objectives of the study. The methodology section needs to clearly describe the machine learning algorithms used, the features considered, and the evaluation metrics. The results section should present the performance of each model and their implications for buyers and sellers. A clear, logical structure and detailed explanations will improve the readability and impact of the research.

Result Analysis

The results should provide a thorough analysis of how each machine learning model performed in predicting house prices. This includes comparing the effectiveness of Linear Regression, Decision Tree, Random Forest, and Support Vector Regression. The analysis should highlight which models performed best and discuss the implications for real estate transactions, including how accurate predictions can help buyers and sellers negotiate more effectively and minimize financial losses. Providing practical examples or case studies demonstrating the impact of these predictions on real estate decisions will enhance the relevance of the findings.