The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The use of multiple measurements in taxonomic problems" as an example of linear discriminant analysis. This famous iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.
The dataset contains a set of 150 records under 5 attributes -
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- Species: -- Iris Setosa -- Iris Versicolour -- Iris Virginica
Importing the libaries for this project: Pandas, Numpy, Holoviews.
Pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools.
NumPy is the fundamental package for scientific computing with Python
HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple.
Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
I also used the Jupyter Notebook for this project.
import pandas as pd
import numpy as np
import seaborn as sns
Import the iris.csv using the panda library and examine first few rows of data
iris_data = pd.read_csv('assets/iris.csv')
iris_data.columns = ['sepal_length', 'sepal_width' , 'petal_length', 'petal_width', 'species']
#you can specific the number to show here
iris_data.head(10)
Find out what the size of rows and columns in the table
iris_data.shape
iris_data['species'].unique()
print(iris_data.groupby('species').size())
'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'
Min, Max, Mean, Median and Standard Deviation
iris_data.min()
iris_data.max()
iris_data.mean()
iris_data.median()
iris_data.std()
This statistics table is a much nicer, cleaner way to present the data. We can see there is huge range in the size of the Sepal Length and Petal Length. We will use box plots and scatter plots to see if the size is related to the species of Iris.
summary = iris_data.describe()
summary = summary.transpose()
summary.head()
The boxplot is a quick way of visually summarizing one or more groups of numerical data through their quartiles. Comparing the distributions of:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
From the Boxplot, we can see that there are distinct differences between the Petal Length, Petal Width and Sepal Length across the Species.
Here we can use to variables to show that there is distinct difference in sizes between the species. Firstly, we look at the Petal width and Petal length across the species. Is it clear to see that the iris Setosa has a significantly smaller petal width and petal length than the other two species. This difference occurs again for the Petal width and Sepal length. And in both cases we can see that the Iris Viginica is the largest species.
This chart enables us to quickly see the relationships between variables across multiple dimensions usings scatterplots and histograms.
Plotting regression and confidence intervals
Use kernel density estimates for univariate plots
A voilin plot is used to visualise the distribution of the data and its probability density. The thick black bar in the center represents the interquartile range, the thin black line extended from it represents the 95% confidence intervals, and the white dot is the median.
Petal Length
Petal Width
Using the Scikit-learn library we can perform machine learning on the dataset. As this is my first step into machine learning I have heavily relied on the tutorials below for help.
It is a free machine learning library for python. It features various classification, regression and clustering algorithms. Built on Numpy and Scipy. For this project, I will use the powerful classification algorithm, K-Nearest-Neighbors (KNN) to perform supervised learning.
As the dataset is already import into scikit-learn, I will reuse it. Here are the steps:
- Import Data
- Investigate the Data
- Perform supervised Learning with K-Nearest-Neighbors (KNN)
- Fitting the model
- Predict the response
This data is four-dimensional, but we can visualize two of the dimensions at a time using a scatter plot:
Using supervised learning with K-Nearest Neighbours(KNN), we are able to ask the algorithm "Based on these measurements, what is the species?"
Question: What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?
knn.predict([[3, 5, 4, 2]])
answer:['virginica']
A plot of the sepal space and the prediction of the KNN
Background info https://en.wikipedia.org/wiki/Iris_flower_data_set https://archive.ics.uci.edu/ml/datasets/iris
Summary values https://stackoverflow.com/questions/33889310/r-summary-equivalent-in-numpy
R iris project https://rstudio-pubs-static.s3.amazonaws.com/205883_b658730c12d14aa6996fe2f6c612c65f.html
python iris project https://rajritvikblog.wordpress.com/2017/06/29/iris-dataset-analysis-python/
min value http://www.datasciencemadesimple.com/get-minimum-value-column-python-pandas/
A histogram with Iris Dataset: Sora Jin June 21st, 2015 https://rpubs.com/Sora/developing-data-product
Plot 2D views of the iris dataset http://www.scipy-lectures.org/packages/scikit-learn/auto_examples/plot_iris_scatter.html
Statistics in Python http://www.scipy-lectures.org/packages/statistics/index.html#statistics
Python - IRIS Data visualization and explanation https://www.kaggle.com/abhishekkrg/python-iris-data-visualization-and-explanation
Visualization with Seaborn (Python) https://www.kaggle.com/rahulm7/visualization-with-seaborn-python
Iris Data Visualization using Python https://www.kaggle.com/aschakra/iris-data-visualization-using-python
Seaborn Understanding the Weird Parts: pairplot https://www.youtube.com/watch?v=cpZExlOKFH4
Docs https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
http://holoviews.org/gallery/demos/bokeh/boxplot_chart.html
Machine Learning Tutorial http://scikit-learn.org/stable/tutorial/basic/tutorial.html
http://www.scipy-lectures.org/packages/scikit-learn/index.html
https://kevinzakka.github.io/2016/07/13/k-nearest-neighbor/
https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
https://github.com/whatsrupp/iris-classification/blob/master/petal_classifier.py
https://diwashrestha.com/2017/09/18/machine-learning-on-iris/
https://www.youtube.com/watch?v=rNHKCKXZde8
http://seaborn.pydata.org/examples/scatterplot_categorical.html
IRIS DATASET ANALYSIS (PYTHON) http://d4t4.biz/ml-with-scikit-learn/support-vector-machines-project-wip/
Getting started in scikit-learn with the famous iris dataset https://www.youtube.com/watch?v=hd1W4CyPX58 http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machine-learning-first-steps-with-the-iris-dataset/