Laptop price prediction is an important activity to help consumers make purchasing decisions. Linear regression is a method that can be used to predict laptop prices using features as input. This study aims to predict laptop prices using linear regression models and evaluate the ability of the model to predict laptop prices. In addition, this study also aims to determine which features have the most influence on laptop prices and how much influence each feature has on laptop prices. The data used is laptop data containing information about laptop specifications such as screen size, RAM, hard drive, processor (CPU), and other parameters as input features and laptop prices as targets. The results showed that the linear regression model can predict laptop prices well with an MAE value of 0.1, an MSE value of 0.03, and a coefficient of determination (r²) value of 0.92 and the features that have the most influence on laptop prices are CPU and GPU. This research is expected to provide benefits for consumers in making purchasing decisions and for producers in determining the right pricing strategy.
| Nama | Social |
|---|---|
| Faisal Fiqri | [Github] |
| Riyandi Firman | Github |
| Rony Wahyu | Github |