Housing and accommodation are fundamental needs globally, but financial constraints pose challenges in acquiring a home. This project focuses on optimizing house price predictions through various regression techniques, including Random Forest, KNN, Linear Regression, Lasso Regression, Gradient Boosting Regression, Support Vector Regression, and Ridge Regression. The objective is to provide accurate insights for prospective buyers and real estate advisors, aiding in property comparison and informed decision-making. The study considers influential factors like physical conditions, concept, and location.

The research utilizes datasets from Kaggle, a reputable platform known for accurate and comprehensive data. Algorithms such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (LR), Gradient Boosting Regression (GBR), Random Forest Regression (RFR), Support Vector Regression (SVR), and K Nearest Neighbors (KNN) are employed. Performance evaluation metrics include scores, Mean Square Error (MSE), and R-Squared. The implementation is carried out in Python, and the code is available on GitHub at project repository.

The project's data flow and processing intricacies are visualized in Figure 1, encapsulating the essence of the methodology in predicting house prices with precision.

The project builds upon existing research, drawing inspiration from studies like [1], which conducted a comparative analysis of regression techniques for house price prediction. Valuable insights from these works guide the selection of algorithms and evaluation metrics.

• The study reveals that Gradient Boosting Regression showed the highest accuracy in house price predictions based on previous research [1].
• Crucial variables in predicting house prices include square footage, number of bathrooms, and bedrooms, aligning with previous studies [5].
• Locational and structural attributes significantly influence house prices, emphasizing factors like hospital access, schools, and neighborhood attributes [7].
• The Random Forest algorithm is explored and validated as an effective technique for house price prediction, in line with findings by Adetunji et al. [9].

To replicate the study or utilize the predictive models, follow these steps:

1. Clone the repository: git clone https://github.com/pariyaab/House_Prices_Regression_Models.git
2. Install the required dependencies: pip install -r requirements.txt
3. Execute the Python scripts for specific regression models.

This project contributes to the understanding of house price prediction by exploring various regression techniques and considering influential factors. The findings aim to empower individuals in the real estate market to make informed decisions. Collaborations and contributions to enhance the project are welcome.

Feel free to explore the project repository for detailed code and documentation.