House Price Prediction
This project aims to predict house prices based on various features using different Machine Learning models. The objective is to identify the best-performing model by evaluating multiple regression algorithms and assessing their performance using common error metrics.
Tech Stack
Dataset
The dataset contains multiple features of houses, which serve as predictors for estimating house prices. The dataset is preprocessed to handle missing values, normalize numerical variables, and encode categorical features where necessary.
Machine Learning Models Tested
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Linear Regression
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Decision Tree Regressor
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Random Forest Regressor
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Gradient Boosting Regressor
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Support Vector Regressor (SVR)
Final Model Performance Metrics
The best-performing model achieved the following metrics:
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Mean Absolute Error (MAE): 0.0280
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Mean Squared Error (MSE): 0.0024
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Root Mean Squared Error (RMSE): 0.0489
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R-squared (R2): 0.5388
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Adjusted R-squared: 0.2377