Can someone explain to me step by step what this code does? I need to create a flowchart for it, and am not sure what the steps for it really are. I dont need an output, just an explanation of how the code works.The train.csv is a massive file that I have shown part of as a picture. EmotionRecognition.py:'''Data set introductionThe data consists of 48x48 pixel grayscale images of faces0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=NeutralThe faces have been automatically registered so that the face is more or less centeredand occupies about the same amount of space in each image'''import numpy as npimport pandas as pdimport matplotlib.pyplot as plt''' ### Read csv data '''df = pd.read_csv('train.csv')print("There are total ", len(df), " sample in the loaded dataset.")print("The size of the dataset is: ", df.shape)# get a subset of the whole data for nowdf = df.sample(frac=0.1, random_state=46)print("The size of the dataset is: ", df.shape)''' Extract images and label from the dataframe df '''width, height = 48, 48images = df['pixels'].tolist()faces = []for sample in images:face = [int(pixel) for pixel in sample.split(' ')] # Splitting the string by space character as a listface = np.asarray(face).reshape(width*height) # convert pixels to images and # Resizing the imagefaces.append(face.astype('float32') / 255.0) # Normalizationfaces = np.asarray(faces)# Get labelsy = df['emotion'].valuesclass_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']# Visualization a few sample imagesplt.figure(figsize=(5, 5))for i in range(6):plt.subplot(2, 3, i+1)plt.xticks([])plt.yticks([])plt.grid(False)plt.imshow(np.squeeze(faces[i].reshape(width, height)), cmap='gray')plt.xlabel(class_names[y[i]])plt.show()## Split data into training and test setsfrom sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(faces, y, test_size=0.1, random_state=46)print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)from sklearn.svm import SVCsvclassifier = SVC(kernel='linear')svclassifier.fit(X_train, y_train)# Now that our classifier has been trained, let's make predictions on the test data. To make predictions, the predict method of the DecisionTreeClassifier class is used.y_pred = svclassifier.predict(X_test)# For classification tasks some commonly used metrics are confusion matrix, precision, recall, and F1 score.# These are calculated by using sklearn's metrics library contains the classification_report and confusion_matrix methodsfrom sklearn.metrics import classification_report, confusion_matrixprint(confusion_matrix(y_test, y_pred))print(classification_report(y_test, y_pred)) train - ExcelMichael KochFileHomeInsertPage LayoutFormulasDataReviewViewHelpO Tell me what you want to do요 ShareA1feemotionA.B1 emotion pixels20 70 80 82 72 58 58 60 63 54 58 60 48 89 115 121 119 115 110 98 91 84 84 90 99 110 126 143 153 158 171 169 172 169 165 129 110 113 107 95 79 66 62 56 57 61 52 43 41 65 61 58 57 56 69 75 70 65 56 54 105 146 154 151 151O 151 150 147 155 148 133 111 140 170 174 182 154 153 164 173 178 185 185 189 187 186 193 194 185 183 186 180 173 166 161 147 133 172 151 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Emotion recognition means identifying the human emotion from images or videos The data set contains…

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Computer Networking: A Top-Down Approach (7th Edition)

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Author:James Kurose, Keith Ross

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Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Can someone explain to me step by step what this code does? I need to create a flowchart for it, and am not sure what the steps for it really are. I dont need an output, just an explanation of how the code works.

The train.csv is a massive file that I have shown part of as a picture.

EmotionRecognition.py:

'''
Data set introduction
The data consists of 48x48 pixel grayscale images of faces
0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral
The faces have been automatically registered so that the face is more or less centered
and occupies about the same amount of space in each image
'''

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

''' ### Read csv data '''
df = pd.read_csv('train.csv')
print("There are total ", len(df), " sample in the loaded dataset.")
print("The size of the dataset is: ", df.shape)
# get a subset of the whole data for now
df = df.sample(frac=0.1, random_state=46)
print("The size of the dataset is: ", df.shape)

''' Extract images and label from the dataframe df '''
width, height = 48, 48
images = df['pixels'].tolist()
faces = []
for sample in images:
face = [int(pixel) for pixel in sample.split(' ')] # Splitting the string by space character as a list
face = np.asarray(face).reshape(width*height) # convert pixels to images and # Resizing the image
faces.append(face.astype('float32') / 255.0) # Normalization
faces = np.asarray(faces)

# Get labels
y = df['emotion'].values

class_names = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
# Visualization a few sample images
plt.figure(figsize=(5, 5))
for i in range(6):
plt.subplot(2, 3, i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(np.squeeze(faces[i].reshape(width, height)), cmap='gray')
plt.xlabel(class_names[y[i]])
plt.show()

## Split data into training and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(faces, y, test_size=0.1, random_state=46)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)

from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)

# Now that our classifier has been trained, let's make predictions on the test data. To make predictions, the predict method of the DecisionTreeClassifier class is used.
y_pred = svclassifier.predict(X_test)

# For classification tasks some commonly used metrics are confusion matrix, precision, recall, and F1 score.
# These are calculated by using sklearn's metrics library contains the classification_report and confusion_matrix methods
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))