This project aims to provide an accurate and reliable model to predict laptop prices using XGBRegressor.
The dataset consist a target variable (Prices_euros) and of 11 features, which are:
| Feature | Description |
|---|---|
| Company | Laptop Manufacturer |
| Product | Brand and Model |
| TypeName | Type (Notebook, Ultrabook, Gaming, etc.) |
| Inches | Screen Size |
| ScreenResolution | Screen Resolution |
| Cpu | Central Processing Unit (CPU) |
| Ram | Laptop RAM |
| Memory | Hard Disk / SSD Memory |
| GPU | Graphics Processing Units (GPU) |
| OpSys | Operating System |
| Weight | Laptop Weight |
Dataset source: here
The results are shown below:
| Metric | Training Score | Testing Score |
|---|---|---|
| R-Squared | 0.963 | 0.906 |
| RMSE | 133.88 | 216.17 |
In order to run the python script and notebook, you will need to have the following packages installed:
- matplotlib
- numpy
- pandas
- scikit-learn
- xgboost