This repository contains solutions to a series of problems relating to the famous Iris data set. The data set contains samples of 3 different types of Iris plant.

• Setosa
• Versicolor
• Virginica

The data samples contain 50 samples for each of the different types of species. Each sample is composed of 4 distinct features of the plants including:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

The original Problem Set can be found HERE!

All of the Python code relating to the Problem Set linked above can be found in the juptyer notebook housed in this repository, which also contains some markdown cells relevant to the tasks being carried out.

Iris Data Jupyter Notebook

Search online for Fisher’s iris data set, find a copy of the data, download it and save it to your repository. If it is not in CSV format, use whatever means (Excel, notepad++, visual studio code, python) to convert it to CSV and save the CSV version to your repository also. Open your Jupyter notebook for this problem sheet, creating a new one if needed, and load the CSV file into a numpy array.

In a markdown cell at the start of your notebook, write a short description of the iris data set, complete with references.

The dataset contains five variables: sepal length, sepal width, petal length, petal width, and species. Use pyplot to create a scatter plot of sepal length on the x-axis versus sepal width on the y-axis. Add axis labels and a title to the plot.

Re-create the above plot, but this time plot the setosa data points in red, the versicolor data point in green, and the virginica data points in blue. Setosa, versicolor, and virginica are the three possible values of the species variable. Add a legend to the plot showing which species is in which colour.

Use the seaborn library to create a scatterplot matrix of all five variables.

Fit a straight line to the variables petal length and petal width for the whole data set. Plot the data points in a scatter plot with the best fit line shown.

Calculate the R-squared value for your line above.

Use numpy to select only the data points where species is setosa. Fit a straight line to the variables petal length and petal width. Plot the data points in a scatter plot with the best fit line shown.

Calculate the R-squared value for your line above.

Use gradient descent to approximate the best fit line for the petal length and petal width setosa values. Compare the outputs to your calculations above.