Classifying Iris dataset found at https://www.kaggle.com/uciml/iris with models in Python and R.

Performed K-nearest neighbors, linear discriminant analysis, and quadratic discriminant analysis on iris data

1. K-NN (KNN_iris.R)

2. LDA and QDA (LDA-QDA_iris.R)

Since binary classifier, performed log-reg out of curiosity to predict versicolor vs. setosa using Sepal.Width

RESULTS:

All predictors -> LDA outperformed other methods LDA: 96% accuracy, K-NN: 95.7% accuracy, QDA: 94.67% accuracy

Sepal.Width and Sepal.Length -> LDA and QDA performed identically LDA: 81.33% accuracy, K-NN: 77.3% accuracy, QDA: 81.33% accuracy

Petal.Width and Petal.Length -> KNN and QDA performed identically LDA: 93.33% accuracy, K-NN: 97.33% accuracy, QDA: 97.33% accuracy

Only performed log-reg in Python using Professor Ng's equations from Stanford's Machine Learning course.

1. Logic Approach (log_Unvectorized.py)

2. Matrix Approach (iris_Vectorized.py)

Gradient Descent algorithm managed to converge on iris data. Coefficient for Sepal.Width (only predictor used in log reg for testing in R) was identical at ~2.5.

Languages: Python, R

Libraries: Pandas, Numpy, scipy, caret