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