This Jupyter Notebook demonstrates the application of Linear Regression (both Simple and Multiple) to predict house prices using the Housing.csv dataset. The project covers data preprocessing, model training, evaluation, and visualization.
(i) Data Preprocessing: Handles missing values and prepares features for regression.
(ii) Model Training: Implements both Simple and Multiple Linear Regression.
(iii) Evaluation: Uses MAE, MSE, and R² metrics to assess model performance.
(iv) Visualization: Includes plots to compare actual vs. predicted prices.
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Simple Linear Regression: Achieved an R² score of 0.27.
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Multiple Linear Regression: Achieved an R² score of 0.55, showing better performance with additional features.
(i) Python 3.x
(ii) Libraries: pandas, numpy, matplotlib, scikit-learn
The dataset (Housing.csv) contains features like area, bedrooms, bathrooms, and price of houses.