< Description />
The House Pricing Prediction Model is a machine learning-based system designed to estimate the market value of properties based on key factors such as location, size, number of rooms, amenities, and economic trends. By leveraging historical data and real estate market insights, the model provides accurate price predictions to assist buyers, sellers, and real estate professionals in making informed decisions.
Key Features:
Data Processing & Feature Engineering: Cleans and structures data, handling missing values and categorical variables.
Machine Learning Algorithms: Uses Linear Regression, Decision Trees, Random Forest, and Gradient Boosting (XGBoost, LightGBM) for price estimation.
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My passion for data science and machine learning stems from my curiosity about extracting meaningful insights from data to solve real-world challenges. Over the past two years, I have actively engaged in research projects, competitions, and internships, continuously developing m…