luciferreeves / iris

Different ML Techniques on the Iris dataset.

Iris

Different ML Techniques on the Iris dataset. For more information, see this Wikipedia article.

Data

About the data

Iris dataset is a multivariate data set with 150 samples and 4 features. The data set is named after the iris plant. The data set is loaded from this link. It comes pre-installed with the Python package sklearn. We have obtained the data set from this Kaggle link.

Features

The data set contains the following features:

Feature	Description
Id	The id of the sample
SepalLengthCm	the length of the sepals
SepalWidthCm	the width of the sepals
PetalLengthCm	the length of the petals
PetalWidthCm	the width of the petals
Species	the species of the iris plant

ML Techniques

Tentative ML techniques:

• K-Nearest Neighbors
• Logistic Regression
• Decision Tree
• Random Forest
• Naive Bayes
• SVM
• K-Means
• Linear Discriminant Analysis
• Quadratic Discriminant Analysis
• Gaussian Process
• Gradient Boosting
• Bagging
• AdaBoost
• Extra Trees
• Voting Classifier
• Stacking Classifier
• Bagging Classifier
• Extra Trees Classifier
• Gradient Boosting Classifier
• Gaussian Process Classifier
• Random Forest Classifier
• Voting
• Stacking
• Logistic Regression
• Perceptron
• Passive-Aggressive
• Ridge
• SGD
• SVC
• Linear SVC
• NuSVC
• One-Class SVM