This project implements a Linear Regression model to predict house prices using square footage (GrLivArea), number of bedrooms (BedroomAbvGr), and number of bathrooms (FullBath) as predictors. After importing necessary libraries (NumPy, Pandas, scikit-learn), relevant features were extracted and data was preprocessed to check for null values and split into training and testing sets. The model was trained and evaluated using Mean Squared Error (MSE) on both sets.. Additionally, the model successfully predicted a house price for new input data, indicating its potential reliability.
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This repository includes a report about implementing a Linear Regression model to predict house prices using square footage, number of bedrooms, and number of bathrooms. The model demonstrated reliable performance and successfully predicted house prices for new input data.
AtikaAnjum/PRODIGY_ML_01
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This repository includes a report about implementing a Linear Regression model to predict house prices using square footage, number of bedrooms, and number of bathrooms. The model demonstrated reliable performance and successfully predicted house prices for new input data.
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