This project uses machine learning to predict housing prices in Boston based on various features like the number of rooms, neighborhood status, and pupil-teacher ratios. It covers Exploratory Data Analysis (EDA) and compares Simple vs. Multiple Linear Regression models.
- Data Visualization: Histograms, scatter plots, and correlation heatmaps.
- Simple Linear Regression: Predicting price based on rooms (RM).
- Multiple Linear Regression: Predicting price using multiple factors (RM, LSTAT, PTRATIO).
- Statistical Analysis: Detailed OLS regression summaries.
To run this notebook, you need the following libraries installed:
pip install pandas numpy matplotlib seaborn scikit-learn statsmodels
How to Use
Open the notebook in Google Colab or Jupyter.
Run the cells sequentially to load the data and train the models.
Check the OLS Regression Results for statistical significance.
Results
SLR R-squared: ~0.55
MLR R-squared: ~0.65
License
This project is open-source and available under the MIT License.