In this repository I will use a dataset of 21,000 properties to determine if real estate prices are influenced more by property size or location. Then I build data visualizations, and examine the relationship between two variables using correlation. Finally I will build a Linear Regression model and Ridge Regression to predict apartment prices in Mexico. Additionally, I also create a data pipeline to impute missing values and encode categorical features, and they improve model performance by reducing overfitting.
This project was inspired by the lesson I learned in WQU
This is a Jupyter notebook. Package requirements are included in requirement.txt. This project uses Python 3.9. Run the following command in terminal to install the required packages. pip3 install -r requirements.txt
The notebook includes all the markdowns which explain the process.
- Linear Regression Linear Regression
- Ridge Ridge Regression
| Model | Baseline MAE | Val MAE |
|---|---|---|
| Linear Regression | 17189.62 | 15200.969 |
| Ridge | 17189.62 | 15200.246 |
- Fork it
- Create your feature branch:
git checkout -b my-new-feature - Commit your changes:
git commit -am 'Add some feature' - Push to the branch:
git push origin my-new-feature - Submit a pull request