Learn to Build a Polynomial Regression Model from Scratch

Improved ability to model nonlinear relationships in data by using polynomial regression techniques.

Imputed missing values using LightGBM and then further improved the data completeness.

Improved the model accuracy by finding the right polynomial degree with cross-validation.

Improved knowledge of new feature importance and how they influence the target variable nonlinearly.

It helps to reduce overfitting risks by balancing polynomial degree complexity and model performance.

Ideal metrics such as R², RMSE, and MAE were successfully identified for good model evaluation.

Application for improving predictive accuracy in finance through capturing more complex market trends in polynomial regression.

Modeling nonlinear relationships in patient health metrics to enhance medical diagnosis tools.

More accurate predictions allow greater sales forecasting efficiency through accurate predictions of seasonal and cyclical patterns.

Environmental modeling of pollution level, weather patterns, and climate change.