# -*- coding: utf-8 -*-
"""python code.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1mxH3vDVZ4xyhu0NpmolxTWit0ZAkxG3V
"""

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

# Read data from CSV file
csv_file_path = 'datasets.csv'
data = pd.read_csv(csv_file_path)

# Categories (Cryptocurrencies) for x-axis
categories = [
    '1INCHBUSD', 'ADXBUSD', 'ALICEBUSD', 'ANKRBUSD', 'ASTRBUSD', 'AUTOBUSD', 'BANDBUSD', 'BIFIBUSD',
    'BSWBUSD', 'CAKEBUSD', 'CREAMBUSD', 'CKBBUSD', 'CVPBUSD', 'DENTBUSD', 'DOCKBUSD', 'ELFBUSD',
    'FARMBUSD', 'FLOWBUSD', 'GALBUSD', 'GTOBUSD', 'ICXBUSD', 'IOTABUSD', 'LAZIOBUSD', 'LTCBUSD',
    'MATICBUSD', 'MINABUSD', 'MULTIBUSD', 'OCEANBUSD', 'OOKIBUSD', 'PHABUSD', 'POWRBUSD', 'REIBUSD',
    'RVNBUSD', 'SKLBUSD', 'STEEMBUSD', 'SUNBUSD', 'THETABUSD', 'TRIBUSD', 'UMABUSD', 'UNFIUSD',
    'WAVESBUSD', 'WNXMBUSD', 'XVGBUSD', 'YFIBUSD', 'ZILBUSD', 'ADABUSD', 'ANKRBUSD', 'ASTRBUSD',
    'AUTOUSD', 'BATBUSD'
]

# Random example data for bars
cross_over = np.random.randint(0, 21, size=len(categories))
ma44 = np.random.randint(0, 21, size=len(categories))

x = np.arange(len(categories))  # the label locations
width = 0.35  # the width of the bars

fig, ax = plt.subplots(figsize=(14, 8))
rects1 = ax.bar(x - width/2, cross_over, width, label='CrossOver')
rects2 = ax.bar(x + width/2, ma44, width, label='MA44')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_xlabel('Coin')
ax.set_ylabel('Values')
ax.set_title('Distribution of Trading Strategies by Coin')
ax.set_xticks(x)
ax.set_xticklabels(categories, rotation=90)
ax.set_yticks([0, 5, 10, 15, 20])
ax.legend()

fig.tight_layout()

plt.show()

# Simulated coin names based on the image provided
coins = ['GDAXBTC', 'BINAETH', 'BINAADA', 'GDAXETH', 'BINAXRP', 'BINABNB', 'GDAXLTC', 'BINALTC',
         'BINATRX', 'BINAETC', 'GDAXBCH', 'GDAXETC', 'BINAQTUM', 'BINAOMG', 'BINAEOS', 'GDAXZRX',
         'BINABAT', 'GDAXUSDC', 'BINAXLM', 'BINAONT', 'BINAIOTA', 'GDAXALGO', 'BINAATOM', 'BINANEOS',
         'BINANEO', 'BINAVET', 'BINAIOST', 'BINAZEC', 'BINAELF', 'BINALINK', 'BINAVTH', 'GDAXDOGE',
         'BINAANKR', 'BINATWT', 'BINASXP', 'BINASAND', 'GDAXTHETA', 'BINAUNI', 'BINAAAVE', 'BINACOMP',
         'BINADOT', 'BINAALICE', 'BINASOL', 'BINAAVAX', 'GDAXSHIB']

# Generating mock data for CrossOver and MA44 strategies
np.random.seed(0)  # For reproducibility of the random data
cross_over = np.random.randint(5, 25, size=len(coins))
ma44 = np.random.randint(1, 20, size=len(coins))

# Creating DataFrame
df = pd.DataFrame({
    'Coin': coins,
    'CrossOver': cross_over,
    'MA44': ma44
})

# Plotting
fig, ax = plt.subplots(figsize=(10, 5))
bar_width = 0.4
index = np.arange(len(df['Coin']))

# Assigning colors as per the original chart: blue for CrossOver and orange for MA44
bars1 = ax.bar(index - bar_width/2, df['CrossOver'], bar_width, color='blue', label='CrossOver')
bars2 = ax.bar(index + bar_width/2, df['MA44'], bar_width, color='orange', label='MA44')

# Set x and y axis labels, title, and legend, if needed (omitting here for exact match)
ax.set_xticks(index)
ax.set_xticklabels(df['Coin'], rotation=90)
ax.set_ylim(0, 30)  # Setting Y-axis to match the maximum value seen in the image

# Removing titles and labels for an exact visual match
ax.set_title('')
ax.set_xlabel('')
ax.set_ylabel('')

ax.legend()  # Include if the original has a legend

plt.tight_layout()  # Ensure no clipping of tick labels
plt.show()

# Original list of coin names from your input
coins = ["ZILBUSD", "XVGBUSD", "XECBUSD", "WAVESBUSD", "UMABUSD", "TRBBUSD", "THETABUSD",
         "SUNBUSD", "STEEMBUSD", "SKLBUSD", "RVNBUSD", "REIBUSD", "QNTBUSD", "POWRBUSD",
         "PHABUSD", "OOKIBUSD", "OCEANBUSD", "MULTIBUSD", "MINABUSD", "MATICBUSD", "LTCBUSD",
         "LAZIOBUSD", "KAVABUSD", "IOTABUSD", "ICXBUSD", "GTOBUSD", "GALBUSD", "FLOWBUSD",
         "FARMBUSD", "ELFBUSD", "DOCKBUSD", "DENTBUSD", "CVPBUSD", "CREAMBUSD", "CKBBUSD",
         "CAKEBUSD", "BSWBUSD", "BIFIBUSD", "BANDBUSD", "AUTOBUSD", "ASTRBUSD", "ANKRBUSD",
         "ALICEBUSD", "ADXBUSUSD", "1INCHBUSD"]

# Reversing the order of the coins
coins.reverse()

# Simulating random data for 'CrossOver' and 'MA44' strategies
np.random.seed(0)
cross_over_values = np.random.normal(0, 1, size=len(coins))
ma44_values = np.random.normal(0, 1, size=len(coins))

# Creating the line graph
fig, ax = plt.subplots(figsize=(15, 6))  # Adjust the figure size as needed
ax.plot(coins, cross_over_values, label='CrossOver', color='orange')
ax.plot(coins, ma44_values, label='MA44', color='yellow')


ax.set_xticks(np.arange(len(coins)))
ax.set_xticklabels(coins, rotation=90)  # Rotate the labels for better readability
ax.set_ylim(-5, 2)  # Set the Y-axis limit according to your requirements

# Adding a legend
ax.legend()

plt.tight_layout()
plt.show()

# Original list of coin names from your input
coins = ["ZILBUSD", "XVGBUSD", "XECBUSD", "WAVESBUSD", "UMABUSD", "TRBBUSD", "THETABUSD",
         "SUNBUSD", "STEEMBUSD", "SKLBUSD", "RVNBUSD", "REIBUSD", "QNTBUSD", "POWRBUSD",
         "PHABUSD", "OOKIBUSD", "OCEANBUSD", "MULTIBUSD", "MINABUSD", "MATICBUSD", "LTCBUSD",
         "LAZIOBUSD", "KAVABUSD", "IOTABUSD", "ICXBUSD", "GTOBUSD", "GALBUSD", "FLOWBUSD",
         "FARMBUSD", "ELFBUSD", "DOCKBUSD", "DENTBUSD", "CVPBUSD", "CREAMBUSD", "CKBBUSD",
         "CAKEBUSD", "BSWBUSD", "BIFIBUSD", "BANDBUSD", "AUTOBUSD", "ASTRBUSD", "ANKRBUSD",
         "ALICEBUSD", "ADXBUSUSD", "1INCHBUSD"]

# Reversing the order of the coins
coins.reverse()

# Simulating random data for 'CrossOver' and 'MA44' strategies
np.random.seed(0)
cross_over_values = np.random.normal(0, 1, size=len(coins))
ma44_values = np.random.normal(0, 1, size=len(coins))

# Creating the line graph
fig, ax = plt.subplots(figsize=(15, 6))  # Adjust the figure size as needed
ax.plot(coins, cross_over_values, label='CrossOver', color='orange')
ax.plot(coins, ma44_values, label='MA44', color='yellow')


ax.set_xticks(np.arange(len(coins)))
ax.set_xticklabels(coins, rotation=90)  # Rotate the labels for better readability
ax.set_ylim(-5, 2)  # Set the Y-axis limit according to your requirements

# Adding a legend
ax.legend()

plt.tight_layout()
plt.show()

# Assume some sort of time-like sequential data for the X-axis. We need to format these as seen.
# Let's simulate a date range and then format it to look like '1.655T' etc.
dates = pd.date_range(start='2024-01-01', periods=100, freq='T')
formatted_dates = dates.strftime('%H.%M') + 'T'  # Simulate the T label as seen

# Simulate some data for Bollinger Bands and some other strategy ('GS')
np.random.seed(0)
price_data = np.random.normal(loc=0.08, scale=0.01, size=len(dates))
price_data += np.sin(np.linspace(0, 10, len(dates))) * 0.01  # Add some cyclicality

# Create a DataFrame
df = pd.DataFrame({
    'Time': formatted_dates,
    'Price': price_data
})

# Calculate moving average and Bollinger Bands
df['Moving Average'] = df['Price'].rolling(window=20).mean()
df['Upper Band'] = df['Moving Average'] + 2 * df['Price'].rolling(window=20).std()
df['Lower Band'] = df['Moving Average'] - 2 * df['Price'].rolling(window=20).std()

# Plotting
fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(df['Time'], df['Price'], label='Price', color='turquoise')
ax.plot(df['Time'], df['Moving Average'], label='BOLL(close,20)', color='blue')
ax.plot(df['Time'], df['Upper Band'], label='Upper Band', linestyle='--', color='darkblue')
ax.plot(df['Time'], df['Lower Band'], label='Lower Band', linestyle='--', color='darkblue')

# Set graph limits and ticks based on your image
ax.set_xlim('1.655T', '1.657T')  # Adjust as needed to match your exact ticks
ax.set_ylim(0.05, 0.11)

# Customizing tick marks and labels
ax.set_xticks(np.linspace(0, len(df)-1, 5))  # 5 ticks as seen in your graph
ax.set_xticklabels(['1.655T', '1.656T', '1.6565T', '1.657T', '1.6575T'])

ax.set_title('IDEXBUSD Quant Figure')
ax.set_xlabel('Time')
ax.set_ylabel('Price')
ax.legend()
ax.grid(True)

plt.tight_layout()
plt.show()

# Model names
models = ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'Decision Tree',
          'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boost', 'AdaBoost',
          'Histogram Gradient Boost', 'XGBoost', 'Ridge Classifier', 'LDA', 'QDA']

# Randomly generated data for demonstration; replace with your actual data
np.random.seed(0)
accuracy = np.random.uniform(0.5, 1, len(models))  # Uniform distribution between 0.5 and 1
recall = np.random.uniform(0.5, 1, len(models))
f1_score = np.random.uniform(0.5, 1, len(models))

# The x locations for the groups
x = np.arange(len(models))

# Width of the bars
width = 0.25

fig, ax = plt.subplots(figsize=(14, 7))
rects1 = ax.bar(x - width, accuracy, width, label='Accuracy', color='orange')
rects2 = ax.bar(x, recall, width, label='Recall', color='blue')
rects3 = ax.bar(x + width, f1_score, width, label='F1-Score', color='green')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_xticks(x)
ax.set_xticklabels(models, rotation=45, ha="right")
ax.legend()



fig.tight_layout()
plt.show()

# Creating a DataFrame from the provided data
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighbors Classifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoost', 'HistGradientBoost', 'XGBoost', 'Ridge Classifier', 'LDA', 'QDA'],
    'Accuracy': [0.35, 0.65, 0.59, 0.35, 0.66, 0.64, 0.60, 0.66, 0.65, 0.66, 0.67, 0.67, 0.67, 0.68, 0.62, 0.63],
    'Recall': [0.31, 0.69, 0.60, 0.32, 0.59, 0.58, 0.58, 0.67, 0.62, 0.67, 0.64, 0.65, 0.63, 0.65, 0.66, 0.67],
    'F1-Score': [0.34, 0.81, 0.56, 0.28, 0.69, 0.67, 0.67, 0.65, 0.61, 0.62, 0.62, 0.76, 0.64, 0.66, 0.66, 0.65]
}
df = pd.DataFrame(data)

# Set the Model names as the index
df.set_index('Model', inplace=True)

# X values mapped to model indices
x = np.arange(len(df))

# Plotting
fig, ax = plt.subplots(figsize=(14, 7))
ax.plot(x, df['Accuracy'], marker='o', linestyle='-', color='orange', label='Accuracy')
ax.plot(x, df['Recall'], marker='o', linestyle='-', color='green', label='Recall')
ax.plot(x, df['F1-Score'], marker='o', linestyle='-', color='blue', label='F1-Score')


ax.set_xticks(x)
ax.set_xticklabels(df.index, rotation=45, ha="right")
ax.legend()

plt.tight_layout()
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighbors Classifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoost', 'HistGradientBoost', 'XGBoost', 'Ridge Classifier', 'LDA', 'QDA'],
    'Accuracy': [0.57, 0.64, 0.45, 0.36, 0.61, 0.59, 0.61, 0.66, 0.61, 0.65, 0.63, 0.62, 0.64, 0.64, 0.63, 0.51],
    'Recall': [0.57, 0.64, 0.45, 0.36, 0.61, 0.59, 0.61, 0.66, 0.61, 0.65, 0.74, 0.62, 0.64, 0.64, 0.63, 0.51],
    'F1-Score': [0.63, 0.78, 0.35, 0.28, 0.73, 0.69, 0.71, 0.76, 0.70, 0.76, 0.62, 0.74, 0.75, 0.77, 0.76, 0.50]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Bar locations
x = np.arange(len(df['Model']))  # the label locations
width = 0.25  # the width of the bars

# Create stacked bars
rects1 = ax.bar(x, df['Accuracy'], width, label='Accuracy', color='orange')
rects2 = ax.bar(x, df['Recall'], width, bottom=df['Accuracy'], label='Recall', color='blue')
rects3 = ax.bar(x, df['F1-Score'], width, bottom=df['Accuracy'] + df['Recall'], label='F1-Score', color='green')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Bar Chart On Model Performance with 80/20 Ratio')
ax.set_xticks(x)
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.legend()

# Setting Y-axis values as per your requirement
ax.set_yticks([0, 0.5, 1, 1.5, 2])


fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighbors Classifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoost', 'HistGradientBoost', 'XGBoost', 'Ridge Classifier', 'LDA', 'QDA'],
    'Accuracy': [0.57, 0.64, 0.45, 0.36, 0.61, 0.59, 0.61, 0.66, 0.61, 0.65, 0.63, 0.62, 0.64, 0.64, 0.63, 0.51],
    'Recall': [0.57, 0.64, 0.45, 0.36, 0.61, 0.59, 0.61, 0.66, 0.61, 0.65, 0.74, 0.62, 0.64, 0.64, 0.63, 0.51],
    'F1-Score': [0.63, 0.78, 0.35, 0.28, 0.73, 0.69, 0.71, 0.76, 0.70, 0.76, 0.62, 0.74, 0.75, 0.77, 0.76, 0.50]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Line plots for each metric
ax.plot(df['Model'], df['Accuracy'], marker='o', linestyle='-', color='orange', label='Accuracy')
ax.plot(df['Model'], df['Recall'], marker='o', linestyle='-', color='blue', label='Recall')
ax.plot(df['Model'], df['F1-Score'], marker='o', linestyle='-', color='green', label='F1-Score')


ax.set_xticks(np.arange(len(df['Model'])))
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.set_ylim(0.3, 0.8)  # Adjusting y-axis to match provided graph
ax.legend()

fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighborsClassifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoast', 'HistGradientBoast', 'XGBOOST', 'Ridge Classifier', 'LDA', 'QDA'],
    'Accuracy': [0.49, 0.49, 0.51, 0.51, 0.59, 0.72, 0.72, 0.78, 0.70, 0.71, 0.62, 0.75, 0.76, 0.60, 0.59, 0.53],
    'Recall': [0.00, 0.00, 0.35, 1.00, 0.54, 0.63, 0.62, 0.76, 0.58, 0.66, 0.59, 0.71, 0.72, 0.53, 0.55, 0.99],
    'F1-Score': [0.00, 0.01, 0.42, 0.68, 0.57, 0.70, 0.70, 0.78, 0.67, 0.70, 0.61, 0.75, 0.75, 0.57, 0.58, 0.68]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Bar locations
x = np.arange(len(df['Model']))  # the label locations
width = 0.35  # the width of the bars

# Create stacked bars
rects1 = ax.bar(x, df['Accuracy'], width, label='Accuracy', color='orange')
rects2 = ax.bar(x, df['Recall'], width, bottom=df['Accuracy'], label='Recall', color='blue')
rects3 = ax.bar(x, df['F1-Score'], width, bottom=df['Accuracy'] + df['Recall'], label='F1-Score', color='green')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_xticks(x)
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.legend()

# Setting Y-axis values as per your requirement
ax.set_yticks([0, 0.5, 1, 1.5, 2])


fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighborsClassifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoast', 'HistGradientBoast', 'XGBOOST', 'Ridge Classifier', 'Linear Discriminant Analysis(LDA)', 'Quadratic Discriminant Analysis(QDA)'],
    'Accuracy': [0.48, 0.48, 0.48, 0.48, 0.62, 0.71, 0.76, 0.80, 0.70, 0.72, 0.63, 0.78, 0.77, 0.61, 0.61, 0.55],
    'Recall': [0.00, 0.00, 0.20, 1.00, 0.54, 0.62, 0.68, 0.70, 0.60, 0.67, 0.62, 0.75, 0.72, 0.50, 0.50, 0.99],
    'F1-Score': [0.00, 0.01, 0.28, 0.01, 0.59, 0.69, 0.74, 0.78, 0.67, 0.70, 0.64, 0.78, 0.72, 0.57, 0.57, 0.69]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Bar locations
x = np.arange(len(df['Model']))  # the label locations
width = 0.35  # the width of the bars

# Create stacked bars
rects1 = ax.bar(x, df['Accuracy'], width, label='Accuracy', color='orange')
rects2 = ax.bar(x, df['Recall'], width, bottom=df['Accuracy'], label='Recall', color='blue')
rects3 = ax.bar(x, df['F1-Score'], width, bottom=df['Accuracy'] + df['Recall'], label='F1-Score', color='green')


ax.set_xticks(x)
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.legend()

# Setting Y-axis values as per your requirement
ax.set_yticks([0, 0.5, 1, 1.5, 2])



fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighborsClassifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoast', 'HistGradientBoast', 'XGBOOST', 'Ridge Classifier', 'Linear Discriminant Analysis(LDA)', 'Quadratic Discriminant Analysis(QDA)'],
    'Accuracy': [0.48, 0.48, 0.48, 0.48, 0.62, 0.71, 0.76, 0.80, 0.70, 0.72, 0.63, 0.78, 0.77, 0.61, 0.61, 0.55],
    'Recall': [0.00, 0.00, 0.20, 1.00, 0.54, 0.62, 0.68, 0.70, 0.60, 0.67, 0.62, 0.75, 0.72, 0.50, 0.50, 0.99],
    'F1-Score': [0.00, 0.01, 0.28, 0.01, 0.59, 0.69, 0.74, 0.78, 0.67, 0.70, 0.64, 0.78, 0.72, 0.57, 0.57, 0.69]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Line plots for each metric
ax.plot(df['Model'], df['Accuracy'], marker='o', linestyle='-', color='orange', label='Accuracy')
ax.plot(df['Model'], df['Recall'], marker='o', linestyle='-', color='blue', label='Recall')
ax.plot(df['Model'], df['F1-Score'], marker='o', linestyle='-', color='green', label='F1-Score')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Scatter Chart For Balance Dataset With 80/20 Ratio')
ax.set_xticks(np.arange(len(df['Model'])))
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.set_ylim(0, 1)  # Adjusting y-axis to match provided graph
ax.legend()

fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'KNeigboursClassifier', 'Decision Tree', 'Extra Tree',
              'Random Forest', 'Bagging', 'AdaBoast', 'HistGradientBoast', 'XGBOOST'],
    'Accuracy': [0.65, 0.65, 0.57, 0.65, 0.65, 0.64, 0.65, 0.65, 0.63, 0.65],
    'Recall': [1.00, 1.00, 0.79, 1.00, 1.00, 0.99, 1.00, 1.00, 0.88, 1.00],
    'F1-Score': [0.79, 0.79, 0.70, 0.79, 0.79, 0.78, 0.79, 0.79, 0.76, 0.79]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Bar locations
x = np.arange(len(df['Model']))  # the label locations
width = 0.35  # the width of the bars

# Create stacked bars
rects1 = ax.bar(x, df['Accuracy'], width, label='Accuracy', color='orange')
rects2 = ax.bar(x, df['Recall'], width, bottom=df['Accuracy'], label='Recall', color='blue')
rects3 = ax.bar(x, df['F1-Score'], width, bottom=df['Accuracy'] + df['Recall'], label='F1-Score', color='green')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Bar Chart For Hyper-Parametrized Models With 70/30 Ratio')
ax.set_xticks(x)
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.legend()

# Setting Y-axis values as per your requirement
ax.set_yticks([0, 0.5, 1, 1.5, 2, 2.5])



fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'KNeigboursClassifier', 'Decision Tree', 'Extra Tree',
              'Random Forest', 'Bagging', 'AdaBoast', 'HistGradientBoast', 'XGBOOST'],
    'Accuracy': [0.65, 0.65, 0.57, 0.65, 0.65, 0.64, 0.65, 0.65, 0.63, 0.65],
    'Recall': [1.00, 1.00, 0.79, 1.00, 1.00, 0.99, 1.00, 1.00, 0.88, 1.00],
    'F1-Score': [0.79, 0.79, 0.70, 0.79, 0.79, 0.78, 0.79, 0.79, 0.76, 0.79]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))

# Line plots for each metric
ax.plot(df['Model'], df['Accuracy'], marker='o', linestyle='-', color='orange', label='Accuracy')
ax.plot(df['Model'], df['Recall'], marker='o', linestyle='-', color='blue', label='Recall')
ax.plot(df['Model'], df['F1-Score'], marker='o', linestyle='-', color='green', label='F1-Score')


ax.set_xticks(np.arange(len(df['Model'])))
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.set_yticks([0.6, 0.7, 0.8, 0.9, 1.0])  # Setting the y-axis ticks as specified
ax.set_ylim(0.55, 1.05)  # Adjusting y-axis to match provided graph
ax.grid(True)  # Add grid for better visualization
ax.legend()

fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'KNeigboursClassifier', 'Decision Tree', 'Extra Tree',
              'Random Forest', 'Bagging', 'AdaBoast', 'HistGradientBoast', 'XGBOOST'],
    'Accuracy': [0.70, 0.70, 0.63, 0.70, 0.70, 0.69, 0.70, 0.70, 0.63, 0.70],
    'Recall': [0.84, 1.00, 0.80, 1.00, 1.00, 0.97, 1.00, 1.00, 0.88, 1.00],
    'F1-Score': [0.76, 0.82, 0.75, 0.82, 0.82, 0.81, 0.82, 0.82, 0.75, 0.82]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
width = 0.6  # the width of the bars

# Stacked bar chart
ax.bar(df['Model'], df['Accuracy'], width, label='Accuracy', color='orange')
ax.bar(df['Model'], df['Recall'], width, bottom=df['Accuracy'], label='Recall', color='blue')
ax.bar(df['Model'], df['F1-Score'], width, bottom=df['Accuracy'] + df['Recall'], label='F1-Score', color='green')

ax.set_xticks(np.arange(len(df['Model'])))
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.set_yticks([0.5, 1, 1.5, 2, 2.5])
ax.set_ylim(0, 2.5)
ax.legend()

fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()

# Data from your table
data = {
    'Model': ['Logistic Regression', 'SVC', 'SGD', 'Gaussian Naive Bayes', 'KNeighborsClassifier',
              'Decision Tree', 'Extra Tree', 'Random Forest', 'Bagging', 'Gradient Boosting',
              'AdaBoast', 'HistGradientBoast', 'XGBOOST', 'Ridge Classifier', 'LDA', 'QDA'],
    'Accuracy': [0.49, 0.49, 0.51, 0.51, 0.59, 0.72, 0.72, 0.78, 0.70, 0.71, 0.62, 0.75, 0.76, 0.60, 0.59, 0.53],
    'Recall': [0.00, 0.00, 0.35, 1.00, 0.54, 0.63, 0.62, 0.76, 0.58, 0.66, 0.59, 0.71, 0.72, 0.53, 0.55, 0.99],
    'F1-Score': [0.00, 0.01, 0.42, 0.68, 0.57, 0.70, 0.70, 0.78, 0.67, 0.70, 0.61, 0.75, 0.75, 0.57, 0.58, 0.68]
}

# Convert data to DataFrame
df = pd.DataFrame(data)

# Plotting
fig, ax = plt.subplots(figsize=(14, 8))
# Line plots for each metric
ax.plot(df['Model'], df['Accuracy'], marker='o', linestyle='-', color='orange', label='Accuracy')
ax.plot(df['Model'], df['Recall'], marker='o', linestyle='-', color='blue', label='Recall')
ax.plot(df['Model'], df['F1-Score'], marker='o', linestyle='-', color='green', label='F1-Score')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Scatter Chart For Balance Dataset With 70/30 Ratio')
ax.set_xticks(np.arange(len(df['Model'])))
ax.set_xticklabels(df['Model'], rotation=45, ha='right')
ax.set_ylim(0, 1)  # Adjusting y-axis to match provided graph
ax.legend()

fig.tight_layout()  # adjust subplot parameters to give specified padding
plt.show()