Install

This project requires Python 3.1 and the following Python libraries installed:

• NumPy
• Pandas
• Scikit-Learn
• Matplotlib
• Seaborn

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

Run

In a terminal or command window, navigate to the top-level project directory iris-dataset/ (that contains this README) and run one of the following commands:

ipython iris_SVM.ipynb

or

jupyter notebook irisAnalysis_kNN.ipynb

This will open the Jupyter Notebook software and project file in your browser.

Data

The dataset used in this project is included as iris.csv. This dataset is a freely available on the UCI Machine Learning Repository. This dataset has the following attributes:

Features

1. Features: SepalLengthCm , SepalWidthCm, PetalLengthCm, PetalWidthCm

Target Variable

4. Target: Species

Outcome

We basically used 3 types of classifiers to predict the outcome of the IRIS types.

kNN The accuracy score turned out to be 1 . This might be due to the less number of data in the datasets since in the real life situations the datasets we deal with has data over 100000 entries.

SVM The accuracy of SVM classifier turned out to be 0.93999999999999995.

NaiveBayes The accuracy of Naive Bayes classifier turned out to be 0.973684210526.