Repo for 2013 General Assembly Data Science class.
Data Science class covers
Hw1 - Linear Regression and Ridge Regression Added: Stepwise Regression for feature selection
The purpose of ridge regression is to correct for multicollinearity between variables.
Hw2 - K-nearest neighbors (KNN) and N-folds Cross Validation (CV)
KNN is a classification algorithm for identifying which group unseen examples blong to. N-folds CV is a method for validating your model using folds of the data. The model trains on each section without learning from the previous section. This is more robust then just using a straight test/train setup to improve model generalization.
Hw4 - Logistic Regression
Classification Algorithm for linearly separable classes.