Machine Learning implementation with Sci-Kit Learn (Supervised)

These notebooks are templates to be used for quick implementation of baseline ML algorithms (unsupervised) and to help students further understand how to apply these algorithms to a real world scenario

Models/ libraries implemented:

1. Linear Regression
2. K-Nearest Neighbors
3. Logistic Regression_Imputation_OneHotEncoding
4. Random Forests vs Decision Trees
5. Support Vector Classifier
6. Ridge model - L2 Regularization
7. Ensemble Modelling - Voting Classifier