(Solved): The Fisher's Iris dataset consists of 50 samples from each of three species of Iris (setosa, ...

The Fisher's Iris dataset consists of 50 samples from each of three species of Iris (setosa, virginica and versicolor). To simplify the project, just use the data for setosa and versicolor species. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other. Part 1 -- Data and Visualization ( 50 points) ]: \#include all the modules here ]: \# Import the Iris dataset: \# Create a Pandas DataFrame to store the data \# Show Iris data to check if the data is being uploaded correctly ]: \# Show samples from each species ]: \# Create a dataframe for only setosa and versicolor ]: \# Show pairwise relationships in this dataset using seaborn's pairplot Part 2 -- Draw 2 figures and find the distance between means and show it ( 50 points) 1.petal_length vs. petal_width 2.petal_length and sepal_width : \# draw a subplot for two figures such as: \# 1) petal_length vs. petal_width \# 2) petal_length and sepal_width \# find the means of each specimens in each graph and calculate the distance between the means and draw a line for ea \# with a text showing the distance use plt.text : This time use seaborn's jointplot to show the regression line : \# Use the given regression code in the class to find the regression line for only setosa for petal_length and petal_ Part 3 -- Correlation between features ( 20 points for graduate students and 10 bonus points for undergraduates) : \# This time use all the data for all the species (setosa, versicolor, virginica) \# Find the two features that correlates the most for the iris dataset. \# Use the seaborn correlation matrix and heatmaps to visualize) liris_data.csv iris_data