Build Regression (Linear, Ridge, Lasso) Models in NumPy Python

Using Linear Regression, Lasso Regression, and Ridge Regression models, the project successfully was able to predict laptop prices.

Lasso and Ridge regression performed better because they have good regularization properties, which handle overfitting well.

Key metrics such as MAE, MSE, R2, and RMSE were used to evaluate the models and a clear performance comparison was made.

Since the data needed to be processed effectively, data preprocessing steps such as scaling and encoding were performed.

We showed how the data could be fed to regression analysis to predict prices in various domains.

The performance of the model can be enhanced in situations of real estate pricing or the prediction of the stock market.

The project provided a clear way to evaluate model performance using the R2 and RMSE metrics: these are essential to making data-driven business decisions such as product pricing.

These techniques can be used in other predictive models in e-commerce, such as demand forecasting or Marketing campaigns. For example "Customer churn prediction."

Fields like insurance pricing, financial forecasting, and healthcare cost estimation have wide applications of being able to predict continuous values using regression models.