Use the iloc() function to extract the first 20 features of the dataframe har_train. Save this new dataframe to first_twenty.

Next, using the seaborn library create a heatmap for the correlation matrix.

First you have to create the correlation matrix from the pandas dataframe (save it in a dataframe called corr) and then plot it using seaborn with these customizations:

• Set the seaborn style to white.
• Generate a mask using np.triu(np.ones_like()) with the dtype as boolean to only show the lower triangle of the correlation matrix. Save it in a variable called mask.
• Set up the figure with matplotlib with figsize=(11,9). Use fig, ax = ...
• Generate a custom diverging colormap for the heatmap with the arguments (220, 10, as_cmap=True). Save it in a variable called cmap.
• Draw the heatmap with the mask and correct aspect ratio, using the arguments corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5}).
• Finally, use fig.tight_layout() just before saving the plot to produce a nicely centered graph.

You can find more information about how to create a heatmap using seaborn here.

 

Save your plot as a png with the name "plot2.png" in the folder "results".

#necessary imports

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

FEATURE_NAMES = './data/features.txt'

TRAIN_DATA='./data/X_train.txt'

TRAIN_LABELS = './data/y_train.txt'

 

feats = pd.read_csv(FEATURE_NAMES, sep='', header=None)

har_train = pd.read_csv(TRAIN_DATA, sep='s+', header = None)

har_train_labels = pd.read_csv(TRAIN_LABELS, sep='', header = None, names = ["label"], squeeze = True)

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Extract the first 20 features of the har train dataframe using the iloc function and save the result View the full answer