• Lokesh Babu P N
• Sachith S Pai
• Laxmi V

House prices have a big impact on the economy of city, hence customers and real estate agents are quite concerned about the price ranges. Users can input their preferences and get precise predictions and recommendations in real-time. The user interface and the machine learning algorithms in the backend work together to process data quickly and efficiently. K means and random forest are two examples of machine learning techniques that can be used to predict house prices with accuracy in light of variables like location, size, and neighborhood features. Metrics like mean squared error and accuracy are used to measure the system's performance, assuring accurate and dependable forecasts. The machine learning models are trained and tested using a big dataset of past home prices. Different Methods are used to draw out relevant data from the dataset and raise the predictive accuracy. To identify the greatest fit for the training data, the models are trained using a variety of optimization techniques, improving their predictive power.

Random Forest Regressor,K-means, Mean Absolute Error, Mean squared error, Absolute median error.

"House Price Predication & Recommendation System using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.j285-j290, May-2023, Available :http://www.jetir.org/papers/JETIR2305988.pdf

"House Price Predication & Recommendation System using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppj285-j290, May-2023, Available at : http://www.jetir.org/papers/JETIR2305988.pdf