Using Bayesian, KNN, Logistic Regression to classify spam and non-spam.

Make Sure the dataset file "spamData.mat" is in the folder

1. Environment: Python --version >= 3.7 Third Party: numpy, matplotlib, math, scipy.io

2. Interactive: (1) Open Anaconda Prompt or Python environment prompt (2) Navigate to ..\spam\src\

3. Description: This code implemented classifiers for Spam classification using four methods and then find the error rates: Q1 Beta-Binomial Naïve Bayes, Q2 Gaussian Naïve Bayes, Q3 Logistic Regression with L2 regularization, Q4 K-Nearest Neighbors.