Proyecto 5 : Data Classification using Naive Bayes and kNN

A note on the dataset:

We are working with 27 out of 70 features from the original dataset.

Please, please document(comment) your code and use standard notation for all variables.

Data Preprocessing (James)

• Min-max normalization of the selected 27 features.
• Verifying the integrity of the normalized dataset.

Stratified 10-Fold Cross-Validation (Pending)

• Write a briefing on how the stratified 10-fold cross-validation work.
• Implement this on the dataset.

Naive Bayes Classifier (Pending)

• Implement the Naive Bayes classifier.
• Apply the classifier using the stratified 10-fold cross-validation scheme.
• Calculate the required metrics (ACC, PRE, REC, AUC)

k-Nearest Neighbors Classifier (Nicole)

• Implement the classifier with Euclidean and Manhattan distance measures.
• Optimize k in the interval [1,15] considering only odd numbers.
• Calculate the required metrics (ACC, PRE, REC, AUC)