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Master Data Science step-by-step
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Module 5
Supervised Learning: Regression
1
Simple Linear Regression with scikit-learn
Fit a simple linear regression model using scikit-learn and interpret its coefficients.
2
Evaluating Regression Models: MSE, RMSE, MAE, and R-squared
Evaluate a regression model using key metrics: MSE, RMSE, MAE, and R-squared.
3
Multiple Linear Regression: Model Fitting and Multicollinearity Assessment
Fit a multiple linear regression model and assess the impact of multicollinearity.
Polynomial Regression for Non-Linear Modeling
Implement polynomial regression to model non-linear relationships.
5
Regularization with Ridge and Lasso
Apply regularization using Ridge and Lasso regression to prevent overfitting.
6
Comparing Regression Models
Compare the performance of different regression models on the same dataset.
Previous module
Fundamentals of Model Validation
Next module
Supervised Learning: Classification