HOUSE PRICE PREDICTION MODEL USING MACHINE LEARNING: A COMPARATIVE ANALYSIS

Approved

Relevance and Originality

The study is highly relevant, given the growing importance of accurate house price prediction in the context of rising real estate values. By focusing on India’s housing market and employing machine learning techniques, the research addresses a significant practical need. The originality of the work lies in its application of multiple machine learning algorithms—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—to predict house prices, offering a comparative analysis of their effectiveness.

Methodology

The study employs several machine learning algorithms to predict house prices, utilizing features such as the number of bedrooms, age of the house, transportation access, proximity to schools, and shopping centers. To enhance the methodology section, the paper should detail how each algorithm was implemented, including data preprocessing steps, feature selection methods, and model training procedures. It should also describe the dataset used, including its size, sources, and any handling of missing or inconsistent data. Additionally, the methodology should cover how the models were evaluated, including metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or R-squared.

Validity & Reliability

To establish validity and reliability, the paper should present performance metrics for each machine learning model, comparing their predictive accuracy and robustness. It should discuss the validation techniques used, such as cross-validation or train-test splits, and how these methods contribute to the reliability of the results. Addressing potential biases in the data and ensuring that the models generalize well to new, unseen data will be crucial for assessing the validity of the predictions.

Clarity and Structure

The article should be well-organized, beginning with an introduction that outlines the significance of accurate house price prediction and the specific challenges faced in the Indian real estate market. The methodology section needs to clearly explain the machine learning algorithms used, the features considered, and the data handling processes. Results should be presented with clear comparisons among the models, and a discussion should highlight the implications of the findings for buyers and sellers. A clear and logical structure will enhance the readability and impact of the research.

Result Analysis

The results section should provide a detailed analysis of how each machine learning model performed in predicting house prices, including comparative metrics and insights. The paper should discuss which models provided the most accurate predictions and how these models can help buyers and sellers make more informed decisions. The analysis should also explore any limitations of the models and suggest potential improvements or future research directions. Providing practical examples or case studies will illustrate the real-world applicability of the findings and the benefits of using these predictive models.