Check out my Medium post "Exploring Classifiers with Python Scikit-learn — Iris Dataset" here.

• Demo
• Overview
• Motivation
• Procedures
• Models
• Installation

Check out a simple web demo I created and deployed with Heroku here: https://dehao-iris-clf.herokuapp.com/. This web app displays model prediction results based on user-input parameters.

This is a simple classification task performed on the Iris dataset - one of the best known dataset in the world of data science.

I am definitely not a flower expert, but with the help of a dataset with 150 iris instances with their petal/sepal measurements, can I be one? Also which of the petal/sepal measurements are more useful features to look at?

• Import Libraries and Load Dataset
• Numerical Summary
• Train-Test Split
• Exploratory Data Analysis
• Model Development
• Further Discussion

• Classification Tree
• Gaussian Naive Bayes Classifier
• Linear Discriminant Analysis
• Quadratic Discriminant Analysis
• K Nearest Neighbors
• Logistic Regression
• Support Vector Machine (linear kernel and polynomial kernel)

pip install -r requirements.txt