HOUSE PRICE PREDICTION MODEL USING MACHINE LEARNING: A COMPARATIVE ANALYSIS

Approved

Relevance and Originality

The research article is highly relevant as it tackles the growing need for accurate house price predictions in the context of rising real estate values. By focusing on predicting house prices in India, the study addresses a specific market need, which is crucial for potential buyers and sellers. The originality of the research lies in its application of various machine learning algorithms—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—to real estate data, offering a comprehensive approach to improving prediction accuracy.

Methodology

The study uses a range of machine learning algorithms to predict house prices based on features such as the number of bedrooms, house age, transportation accessibility, proximity to schools, and shopping centers. To strengthen the methodology, the article should provide detailed information on how each algorithm was implemented, including data preprocessing, feature selection, and model training processes. Information on how these models were validated and compared would also be beneficial for assessing the robustness of the approach.

Validity & Reliability

To ensure validity and reliability, the study should present quantitative performance metrics for each machine learning model, such as accuracy, mean squared error, and R-squared values. The research should discuss how the models were tested on different data subsets and any measures taken to handle potential issues such as data bias or overfitting. Addressing these aspects will help confirm the reliability of the predictions and the generalizability of the models to other datasets or regions.

Clarity and Structure

The article should be clearly structured, with an introduction that outlines the significance of accurate house price predictions and the study's objectives. The methodology section needs to detail the machine learning algorithms used and the data features considered. The results section should present the performance of each model and their implications for real estate transactions. Clear explanations and logical organization will enhance the readability and impact of the research.

Result Analysis

The results should include a thorough evaluation of the performance of each machine learning model in predicting house prices. The analysis should compare the effectiveness of different algorithms and discuss how these predictions can help buyers and sellers make informed decisions. Additionally, insights into how the model’s predictions could be used to negotiate better terms and minimize financial and time losses will highlight the practical benefits of the research.