Visualization

Our dataset consists of various rows, each depicting pixel values on a grey scale ranging from 0 to 255. Let's display a few of these images.

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Extract the feature data (images) from the MNIST dataset and store it in variable X.
Loop through the first 7 images in the dataset and visualize each image using seaborn's heatmap function. Also you need to reshape each image to a 2D array of 28x28 pixels.

import matplotlib as mpl

import matplotlib.pyplot as plt

import seaborn as sns

# Extract the feature data (images) from the MNIST dataset

X = mnist['data']

# Create a subplot of 1 row and 7 columns

f, ax = plt.subplots(1, 7, figsize=(13, 2))

# Loop through the first 7 images in the dataset

for n in range(7):

# Use seaborn's heatmap function to visualize each image

sns.heatmap(X[n].reshape(28, 28), cbar=False, cmap='gray_r', ax=ax[n])

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