House_price_prediction_using_regression_done_by_Yididiya_Beyene#43
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Yididiya16 wants to merge 1 commit intosoftwareWCU:mainfrom
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House_price_prediction_using_regression_done_by_Yididiya_Beyene#43Yididiya16 wants to merge 1 commit intosoftwareWCU:mainfrom
Yididiya16 wants to merge 1 commit intosoftwareWCU:mainfrom
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This project focuses on predicting house prices using machine learning regression techniques. The Housing Price dataset was preprocessed by converting categorical features into numerical values using binary encoding and one-hot encoding, and by handling missing values. Several regression models—Linear Regression, Multiple Linear Regression, Polynomial Regression, K-Nearest Neighbors Regression, and Decision Tree Regression—were implemented and trained on the dataset. The models were evaluated using standard performance metrics such as MAE, MSE, RMSE, and R² score, and their prediction performances were visualized using actual versus predicted graphs. Based on the evaluation results, the most suitable regression model for house price prediction was identified.
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This project focuses on predicting house prices using machine learning regression techniques. The Housing Price dataset was preprocessed by converting categorical features into numerical values using binary encoding and one-hot encoding, and by handling missing values. Several regression models—Linear Regression, Multiple Linear Regression, Polynomial Regression, K-Nearest Neighbors Regression, and Decision Tree Regression—were implemented and trained on the dataset.
The models were evaluated using standard performance metrics such as MAE, MSE, RMSE, and R² score, and their prediction performances were visualized using actual versus predicted graphs. Based on the evaluation results, the most suitable regression model for house price prediction was identified.