This project involves predicting house prices based on various factors such as location, size, number of bedrooms, bathrooms, amenities, and more. Using regression models, the project analyzes historical data to establish a relationship between these features and the target variable, which is the price of a house.
Dataset: Utilizes a dataset containing housing attributes and corresponding prices. Data Preprocessing: Handles missing values, outliers, and normalizes data for better model performance. Feature Engineering: Identifies key factors influencing house prices, such as square footage, proximity to schools, and market trends. Model Selection: Implements and compares multiple regression models, including: Linear Regression Ridge and Lasso Regression Polynomial Regression Decision Tree and Random Forest Regressors Gradient Boosting Regressors Evaluation Metrics: Assesses model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²). Visualization: Includes data insights and prediction trends through interactive plots and dashboards. This project serves as a practical application of regression techniques and aims to provide accurate price predictions to aid stakeholders in making informed decisions in the real estate market.