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['essay', 'soil']

[1, 2]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['essay', 'soil'] values = [1, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_0.png

['race', 'cheek', 'land', 'wage', 'cotton', 'move']

[20, 30, 70, 60, 10, 60]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['race', 'cheek', 'land', 'wage', 'cotton', 'move'] values = [20, 30, 70, 60, 10, 60] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1.png

['world', 'battle', 'drift', 'defeat', 'knife']

[7, 8, 1, 5, 7]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['world', 'battle', 'drift', 'defeat', 'knife'] values = [7, 8, 1, 5, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_10.png

['god', 'living', 'back', 'gain', 'drop', 'key']

[40, 40, 20, 30, 60, 30]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['god', 'living', 'back', 'gain', 'drop', 'key'] values = [40, 40, 20, 30, 60, 30] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_100.png

['figure', 'couple', 'year', 'crop']

[40, 50, 40, 40]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['figure', 'couple', 'year', 'crop'] values = [40, 50, 40, 40] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1000.png

['west', 'faith', 'cancer', 'fan']

[1, 3, 2, 6]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['west', 'faith', 'cancer', 'fan'] values = [1, 3, 2, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1001.png

['dinner', 'reply']

[3, 4]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['dinner', 'reply'] values = [3, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1002.png

['plane', 'source', 'effort', 'march', 'coast', 'actor']

[80, 50, 60, 10, 20, 40]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['plane', 'source', 'effort', 'march', 'coast', 'actor'] values = [80, 50, 60, 10, 20, 40] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1003.png

['bit', 'joke']

[8, 5]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['bit', 'joke'] values = [8, 5] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1004.png

['merit', 'trail', 'cast']

[3, 7, 8]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['merit', 'trail', 'cast'] values = [3, 7, 8] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1005.png

['sphere', 'gang', 'store', 'piano', 'party', 'foil', 'attack', 'tray']

[80, 90, 80, 50, 50, 90, 50, 50]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['sphere', 'gang', 'store', 'piano', 'party', 'foil', 'attack', 'tray'] values = [80, 90, 80, 50, 50, 90, 50, 50] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1006.png

['rage', 'bond']

[60, 50]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['rage', 'bond'] values = [60, 50] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1007.png

['side', 'belt', 'course', 'mile', 'dust', 'shop', 'supply']

[70, 30, 20, 10, 40, 60, 80]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['side', 'belt', 'course', 'mile', 'dust', 'shop', 'supply'] values = [70, 30, 20, 10, 40, 60, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1008.png

['entry', 'bottom', 'gate', 'text', 'stress', 'bread', 'denial', 'flight']

[4, 9, 6, 4, 4, 8, 2, 5]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['entry', 'bottom', 'gate', 'text', 'stress', 'bread', 'denial', 'flight'] values = [4, 9, 6, 4, 4, 8, 2, 5] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1009.png

['prison', 'fault', 'mirror', 'cellar', 'tree']

[1, 4, 8, 3, 6]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['prison', 'fault', 'mirror', 'cellar', 'tree'] values = [1, 4, 8, 3, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_101.png

['tongue', 'income', 'drunk', 'sky']

[4, 3, 2, 6]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['tongue', 'income', 'drunk', 'sky'] values = [4, 3, 2, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1010.png

['atom', 'arc', 'match', 'meat', 'head']

[9, 1, 7, 6, 7]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['atom', 'arc', 'match', 'meat', 'head'] values = [9, 1, 7, 6, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1011.png

['notion', 'edge', 'range']

[8, 5, 6]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['notion', 'edge', 'range'] values = [8, 5, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1012.png

['aid', 'adult', 'right', 'mirror', 'light', 'mean']

[10, 20, 80, 20, 50, 10]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['aid', 'adult', 'right', 'mirror', 'light', 'mean'] values = [10, 20, 80, 20, 50, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1013.png

['myth', 'issue']

[1, 2]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['myth', 'issue'] values = [1, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1014.png

['herd', 'moment', 'top', 'height', 'copy', 'flood', 'judge', 'threat']

[8, 8, 6, 1, -7, 4, 3, 1]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['herd', 'moment', 'top', 'height', 'copy', 'flood', 'judge', 'threat'] values = [8, 8, 6, 1, -7, 4, 3, 1] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1015.png

['ice', 'screen', 'piece']

[9, 4, 9]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['ice', 'screen', 'piece'] values = [9, 4, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1016.png

['truth', 'award', 'foot']

[7, 6, 3]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['truth', 'award', 'foot'] values = [7, 6, 3] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1017.png

['pink', 'cabin', 'autumn', 'smoke', 'gun', 'stake', 'fog', 'court', 'board']

[70, 60, 70, 40, 70, 10, 70, 60, 80]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['pink', 'cabin', 'autumn', 'smoke', 'gun', 'stake', 'fog', 'court', 'board'] values = [70, 60, 70, 40, 70, 10, 70, 60, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1018.png

['blow', 'bone', 'camp']

[20, 50, 30]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['blow', 'bone', 'camp'] values = [20, 50, 30] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1019.png

['yield', 'patent', 'pastor']

[70, 50, 30]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['yield', 'patent', 'pastor'] values = [70, 50, 30] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_102.png

['hand', 'craft', 'male', 'lay', 'team', 'string', 'mercy']

[50, 30, 40, 20, 90, 30, 50]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['hand', 'craft', 'male', 'lay', 'team', 'string', 'mercy'] values = [50, 30, 40, 20, 90, 30, 50] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1020.png

['crisis', 'belief', 'drink', 'night']

[4, 4, 5, 9]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['crisis', 'belief', 'drink', 'night'] values = [4, 4, 5, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1021.png

['vision', 'way', 'girl', 'maid']

[1, 4, 9, 3]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['vision', 'way', 'girl', 'maid'] values = [1, 4, 9, 3] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1022.png

['day', 'drive']

[4, 6]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['day', 'drive'] values = [4, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1023.png

['vector', 'diet', 'meat', 'artery', 'atom']

[9, 5, 9, 1, 9]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['vector', 'diet', 'meat', 'artery', 'atom'] values = [9, 5, 9, 1, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1024.png

['graph', 'smell']

[6, 6]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['graph', 'smell'] values = [6, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1025.png

['engine', 'soil']

[2, 7]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['engine', 'soil'] values = [2, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1026.png

['right', 'trade']

[4, 2]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['right', 'trade'] values = [4, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1027.png

['blind', 'knife', 'trail', 'ring', 'cut', 'virtue']

[6, 6, 4, 9, 9, 5]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['blind', 'knife', 'trail', 'ring', 'cut', 'virtue'] values = [6, 6, 4, 9, 9, 5] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1028.png

['light', 'hell', 'result', 'driver']

[4, 8, 7, 9]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['light', 'hell', 'result', 'driver'] values = [4, 8, 7, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1029.png

['evil', 'movie', 'trip', 'horror']

[7, 5, 4, 4]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['evil', 'movie', 'trip', 'horror'] values = [7, 5, 4, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_103.png

['fluid', 'sort']

[8, 2]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['fluid', 'sort'] values = [8, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1030.png

['savage', 'dinner', 'barrel', 'pause', 'pipe', 'food', 'wit']

[90, 30, 60, 50, 10, 50, 40]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['savage', 'dinner', 'barrel', 'pause', 'pipe', 'food', 'wit'] values = [90, 30, 60, 50, 10, 50, 40] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1031.png

['cut', 'sauce', 'cotton', 'drunk', 'mark']

[6, 9, 1, 8, 7]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['cut', 'sauce', 'cotton', 'drunk', 'mark'] values = [6, 9, 1, 8, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1032.png

['pure', 'walk', 'ground', 'close']

[7, 5, 4, 9]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['pure', 'walk', 'ground', 'close'] values = [7, 5, 4, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1033.png

['uncle', 'iodine']

[7, 8]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['uncle', 'iodine'] values = [7, 8] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1034.png

['skirt', 'salary', 'doubt', 'fuel', 'realm']

[90, 80, 80, 80, 0]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['skirt', 'salary', 'doubt', 'fuel', 'realm'] values = [90, 80, 80, 80, 0] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1035.png

['crew', 'rain', 'lay']

[1, 5, 3]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['crew', 'rain', 'lay'] values = [1, 5, 3] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1036.png

['length', 'artery', 'wound']

[2, 8, 5]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['length', 'artery', 'wound'] values = [2, 8, 5] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1037.png

['help', 'heard', 'verse', 'victim', 'pass', 'floor']

[30, 90, 60, 10, 30, 40]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['help', 'heard', 'verse', 'victim', 'pass', 'floor'] values = [30, 90, 60, 10, 30, 40] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1038.png

['head', 'site', 'combat', 'lock', 'cure']

[100, 100000, 100000000, 10000000, 10]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['head', 'site', 'combat', 'lock', 'cure'] values = [100, 100000, 100000000, 10000000, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1039.png

['stress', 'break', 'trap', 'cause', 'load', 'bulk']

[3, 8, 3, 0, 1, 4]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['stress', 'break', 'trap', 'cause', 'load', 'bulk'] values = [3, 8, 3, 0, 1, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_104.png

['parlor', 'bottle', 'talk', 'well', 'laugh']

[90, 20, 20, 20, 80]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['parlor', 'bottle', 'talk', 'well', 'laugh'] values = [90, 20, 20, 20, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1040.png

['mine', 'race', 'speed', 'change', 'mold']

[20, 90, 50, 90, 80]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['mine', 'race', 'speed', 'change', 'mold'] values = [20, 90, 50, 90, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1041.png

['town', 'kid', 'snake', 'eye', 'person', 'error', 'status', 'season']

[8, 7, 2, 9, 8, 6, 3, 5]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['town', 'kid', 'snake', 'eye', 'person', 'error', 'status', 'season'] values = [8, 7, 2, 9, 8, 6, 3, 5] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1042.png

['form', 'water', 'action', 'figure', 'run']

[4, 4, 1, 4, 4]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['form', 'water', 'action', 'figure', 'run'] values = [4, 4, 1, 4, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1043.png

['beef', 'snake', 'draft', 'close']

[1, 8, 5, 9]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['beef', 'snake', 'draft', 'close'] values = [1, 8, 5, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1044.png

['chair', 'barn']

[4, 4]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['chair', 'barn'] values = [4, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1045.png

['sake', 'light', 'award', 'bottle', 'slave', 'lumber', 'farm', 'death']

[3, 4, 5, 9, 8, 7, 6, 4]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['sake', 'light', 'award', 'bottle', 'slave', 'lumber', 'farm', 'death'] values = [3, 4, 5, 9, 8, 7, 6, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1046.png

['drive', 'random', 'luck', 'battle']

[4, 9, 8, 6]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['drive', 'random', 'luck', 'battle'] values = [4, 9, 8, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1047.png

['bar', 'valley', 'chain', 'south', 'muscle']

[0, 5, 1, 5, 2]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['bar', 'valley', 'chain', 'south', 'muscle'] values = [0, 5, 1, 5, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1048.png

['bag', 'role', 'volume', 'effect', 'reply']

[10, 40, 60, 30, 20]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['bag', 'role', 'volume', 'effect', 'reply'] values = [10, 40, 60, 30, 20] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1049.png

['file', 'camp', 'law', 'street']

[9, 5, 2, 6]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['file', 'camp', 'law', 'street'] values = [9, 5, 2, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_105.png

['shift', 'march', 'union', 'victim', 'good', 'week']

[90, 90, 80, 40, 10, 30]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['shift', 'march', 'union', 'victim', 'good', 'week'] values = [90, 90, 80, 40, 10, 30] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1050.png

['loan', 'factor', 'truck', 'hair', 'sin', 'moon', 'farm']

[40, 10, 50, 10, 70, 40, 10]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['loan', 'factor', 'truck', 'hair', 'sin', 'moon', 'farm'] values = [40, 10, 50, 10, 70, 40, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1051.png

['index', 'favor', 'bench']

[40, 50, 10]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['index', 'favor', 'bench'] values = [40, 50, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1052.png

['cut', 'wheel', 'nation']

[4, 2, 9]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['cut', 'wheel', 'nation'] values = [4, 2, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1053.png

['item', 'age', 'tax', 'holder']

[3, 2, 5, 4]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['item', 'age', 'tax', 'holder'] values = [3, 2, 5, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1054.png

['floor', 'neck', 'plot', 'wind', 'bit', 'wonder', 'farm', 'cause']

[9, 9, 8, 8, 3, 2, 3, 9]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['floor', 'neck', 'plot', 'wind', 'bit', 'wonder', 'farm', 'cause'] values = [9, 9, 8, 8, 3, 2, 3, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1055.png

['rush', 'credit', 'code', 'savage']

[6, 7, 5, 1]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['rush', 'credit', 'code', 'savage'] values = [6, 7, 5, 1] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1056.png

['force', 'pocket', 'impact', 'tube', 'term']

[50, 40, 10, 80, 80]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['force', 'pocket', 'impact', 'tube', 'term'] values = [50, 40, 10, 80, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1057.png

['twenty', 'engine', 'income', 'aspect', 'image', 'wave', 'steel']

[90, 40, 70, 20, 80, 90, 80]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['twenty', 'engine', 'income', 'aspect', 'image', 'wave', 'steel'] values = [90, 40, 70, 20, 80, 90, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1058.png

['stick', 'hair', 'growth']

[6, 1, 8]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['stick', 'hair', 'growth'] values = [6, 1, 8] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1059.png

['genius', 'bag', 'sale', 'glass']

[0, 2, 9, 1]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['genius', 'bag', 'sale', 'glass'] values = [0, 2, 9, 1] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_106.png

['pistol', 'light', 'gun', 'dance', 'coat', 'mad', 'member']

[3, 5, 8, 5, 2, 2, 3]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['pistol', 'light', 'gun', 'dance', 'coat', 'mad', 'member'] values = [3, 5, 8, 5, 2, 2, 3] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1060.png

['vein', 'foot', 'mad']

[6, 4, 6]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['vein', 'foot', 'mad'] values = [6, 4, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1061.png

['group', 'bridge', 'mud', 'fund']

[4, 8, 5, 6]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['group', 'bridge', 'mud', 'fund'] values = [4, 8, 5, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1062.png

['valley', 'foam', 'lip']

[2, 9, 5]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['valley', 'foam', 'lip'] values = [2, 9, 5] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1063.png

['garden', 'writer', 'shop', 'spot', 'today', 'driver', 'beat', 'idea']

[10000000, 1000, 100000, 10000000, 10000, 1000, 100, 1000]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['garden', 'writer', 'shop', 'spot', 'today', 'driver', 'beat', 'idea'] values = [10000000, 1000, 100000, 10000000, 10000, 1000, 100, 1000] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1064.png

['bar', 'combat', 'gas', 'corn', 'hero']

[70, 20, 60, 20, 10]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['bar', 'combat', 'gas', 'corn', 'hero'] values = [70, 20, 60, 20, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1065.png

['date', 'whole', 'impact']

[70, 10, 60]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['date', 'whole', 'impact'] values = [70, 10, 60] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1066.png

['face', 'autumn', 'rain', 'jungle', 'cover']

[40, 90, 60, 80, 10]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['face', 'autumn', 'rain', 'jungle', 'cover'] values = [40, 90, 60, 80, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1067.png

['get', 'ward', 'excuse', 'woman', 'type']

[5, 7, 3, 8, 9]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['get', 'ward', 'excuse', 'woman', 'type'] values = [5, 7, 3, 8, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1068.png

['round', 'silver', 'plate', 'end', 'pencil']

[3, 8, 7, 3, 9]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['round', 'silver', 'plate', 'end', 'pencil'] values = [3, 8, 7, 3, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1069.png

['middle', 'sphere', 'wealth', 'laugh', 'smell', 'campus', 'grip']

[1, 8, 9, 2, 7, 9, 4]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['middle', 'sphere', 'wealth', 'laugh', 'smell', 'campus', 'grip'] values = [1, 8, 9, 2, 7, 9, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_107.png

['danger', 'motel', 'dawn', 'ring', 'risk', 'bone', 'couple', 'detail']

[1, 3, -5, 8, -4, 9, -7, 1]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['danger', 'motel', 'dawn', 'ring', 'risk', 'bone', 'couple', 'detail'] values = [1, 3, -5, 8, -4, 9, -7, 1] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1070.png

['wisdom', 'essay', 'dollar']

[80, 50, 20]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['wisdom', 'essay', 'dollar'] values = [80, 50, 20] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1071.png

['lady', 'feed', 'item', 'brief', 'savage', 'doubt']

[7, 6, 7, 2, 6, 7]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['lady', 'feed', 'item', 'brief', 'savage', 'doubt'] values = [7, 6, 7, 2, 6, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1072.png

['flight', 'grace', 'shop', 'marine', 'suite', 'paint', 'fuel']

[6, 5, 2, 7, 3, 5, 7]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['flight', 'grace', 'shop', 'marine', 'suite', 'paint', 'fuel'] values = [6, 5, 2, 7, 3, 5, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1073.png

['skill', 'hat', 'trap']

[6, 2, 4]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['skill', 'hat', 'trap'] values = [6, 2, 4] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1074.png

['profit', 'past', 'driver']

[1, 7, 6]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['profit', 'past', 'driver'] values = [1, 7, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1075.png

['vein', 'myth', 'holder', 'cheek', 'lap']

[40, 20, 50, 90, 70]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['vein', 'myth', 'holder', 'cheek', 'lap'] values = [40, 20, 50, 90, 70] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1076.png

['hill', 'belt', 'disk', 'garage']

[1, 1, 2, 2]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['hill', 'belt', 'disk', 'garage'] values = [1, 1, 2, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1077.png

['list', 'couple', 'pistol', 'hole', 'lobby', 'jet']

[2, 8, 4, 1, 8, 2]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['list', 'couple', 'pistol', 'hole', 'lobby', 'jet'] values = [2, 8, 4, 1, 8, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1078.png

['coffee', 'middle', 'joy']

[5, 1, 3]

Most preferred objects

Number of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['coffee', 'middle', 'joy'] values = [5, 1, 3] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Number of People') # Label for the y-axis # Display the chart plt.show()

figure_1079.png

['price', 'skirt', 'bank', 'rise']

[4, 2, 4, 2]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['price', 'skirt', 'bank', 'rise'] values = [4, 2, 4, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_108.png

['bride', 'battle', 'union', 'barrel', 'crowd', 'car', 'touch']

[30, 90, 10, 90, 30, 50, 10]

Most preferred objects

Percent of People

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['bride', 'battle', 'union', 'barrel', 'crowd', 'car', 'touch'] values = [30, 90, 10, 90, 30, 50, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Most preferred objects') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Percent of People') # Label for the y-axis # Display the chart plt.show()

figure_1080.png

['flood', 'barn']

[10000000, 10]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['flood', 'barn'] values = [10000000, 10] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1081.png

['widow', 'bay', 'ladder', 'cloud', 'light', 'tax', 'shock', 'safety', 'opera']

[8, 4, 5, 8, 3, 3, 6, 7, 2]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['widow', 'bay', 'ladder', 'cloud', 'light', 'tax', 'shock', 'safety', 'opera'] values = [8, 4, 5, 8, 3, 3, 6, 7, 2] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1082.png

['vice', 'none', 'savage', 'gate', 'walk']

[4, 6, 9, 2, 7]

Sales statistics for different items

Units sold

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['vice', 'none', 'savage', 'gate', 'walk'] values = [4, 6, 9, 2, 7] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Sales statistics for different items') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Units sold') # Label for the y-axis # Display the chart plt.show()

figure_1083.png

['strip', 'bare', 'valley', 'iron']

[3, 3, 9, 6]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['strip', 'bare', 'valley', 'iron'] values = [3, 3, 9, 6] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1084.png

['mother', 'plan', 'reply', 'horror']

[40, 70, 20, 30]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['mother', 'plan', 'reply', 'horror'] values = [40, 70, 20, 30] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1085.png

['lay', 'clouds', 'poem', 'record', 'island', 'pink']

[10, 60, 20, 20, 90, 80]

Title

Values

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['lay', 'clouds', 'poem', 'record', 'island', 'pink'] values = [10, 60, 20, 20, 90, 80] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Title') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Values') # Label for the y-axis # Display the chart plt.show()

figure_1086.png

['animal', 'notice', 'space', 'tube', 'belt', 'list']

[3, 2, 1, 8, 3, 9]

Accuracy of different algorithms

Accuracy

import matplotlib.pyplot as plt # Categories and their corresponding values categories = ['animal', 'notice', 'space', 'tube', 'belt', 'list'] values = [3, 2, 1, 8, 3, 9] # Creating the bar chart plt.figure(figsize=(8, 5)) # Set the figure size (optional) plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color # Adding title and labels plt.title('Accuracy of different algorithms') # Add a title to the chart plt.xlabel('Categories') # Label for the x-axis plt.ylabel('Accuracy') # Label for the y-axis # Display the chart plt.show()

figure_1087.png