K-Nearest Neighbor Algorithm
Q1 and Q2 include basic Python exercises
Q3 includes the K-Nearest Neighbor Algorithm
Q4 includes a variation of accuracies for processed data
A PDF report has been made to address specificities on each question
Specific Breakdown:
Q1. Numerical Programming: Performed a speed comparison of two types of code, comparing non-vectorized code (for-loop) and a vectorized version (Numpy).
Q2. Visualization Exploration: Exploration of the Iris Dataset
Q3. K-NN Implementation
Q4. K-NN Performance: explore the impact of preprocessing and irrelevant features to predict the quality of a red wine given some of the physicochemical properties including acidity, citric acid, sulphates, and residual. Details of the dataset can be found at https://en.wikipedia.org/wiki/Iris_flower_data_set.