Machine Learning Regression Models
Machine Learning Regression Models
Examples of Machine Learning Regression Models Built in Python and R from academic projects to explore ML and its applications.
Meet the Models
| Template (Direct Link) | Note | Language |
|---|---|---|
| Simple Linear | Statistical Model | Python / R |
| Mutiple Linear | Mulitple Variable | Python / R |
| Polynomial | Relationship: Independent x vs. dependent y | Python |
| Support Vector Machine (SVM) | Features Hyperplane | Python / R |
| Decision Tree | Data Mining | Python / R |
| Random Forest | Ensemble Learning | Python / R |
More about the templates here
- Simple Linear : Statistical model
- Muplie Linear : Multiple linear regression (MLR) or simply multiple regression: statistical technique that uses several explanatory variables to predict the outcome.
- Polynomial : Relationship between the independent variable x and the dependent variable y, modelled as an nth degree of x.
- Support Vector (SVM): More often developed into a machine (SVM), maintains all the main features that characterize the algorithm (maximal margin).
- Decision Tree : Data Mining : Builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller subsets while at the same time an associated decision tree is incrementally developed.
- Random Forest : Supervised Learning algorithm which uses ensemble learning method for classification and regression
Each folder contains a sample data file, mostly a single comma-separated value spreadsheet, and its Python and R machine learning versions.
This is an academic course project with additional changes to use my customized Anaconda environment, as part of SuperDataScience instruction found on Udemy 2019