text: import os
  import matplotlib.pyplot as plt
  import pandas as pd
  import seaborn as sns
  from sklearn.preprocessing import MinMaxScaler
  
  diabetes = pd.read_csv(filepath_or_buffer='diabetes.data', sep ='\t', header=0)
  
  scaler = MinMaxScaler()
  
  diabetes[diabetes.columns] = scaler.fit_transform(diabetes[diabetes.columns])
  
  os.makedirs('Scatter_plots', exist_ok=True)
  
  plt.style.use("ggplot")
  
  for col1_idx, column1 in enumerate(diabetes.columns):
      if column1 != "Y" and column1 != "SEX":
          for col2_idx, column2 in enumerate(diabetes.columns):
              if col1_idx < col2_idx and column2 != "Y" and column2 != "SEX":
                  for col3_idx, column3 in enumerate(diabetes.columns):
                      if col1_idx < col3_idx and col2_idx < col3_idx and column3 != "Y" and column3 != "SEX":
                          fig, axes = plt.subplots(1, 1, figsize=(5, 5))
                          axes.grid(axis='y', alpha=0.5)
                          axes.scatter(diabetes[column1], diabetes["Y"], marker="1", color='blue')
                          axes.scatter(diabetes[column2], diabetes["Y"], marker="*", color='orange')
                          axes.scatter(diabetes[column3], diabetes["Y"], marker=".", color='green')
                          axes.set_title(f'Diabetes comparisons')
                          axes.set_ylabel('Diabetes Progression Indicator')
                          axes.set_xlabel('Feature Levels')
                          axes.legend((column1, column2, column3))
                          plt.savefig(f'Scatter_plots/diabetesProgression_to_'+str(column1)+str(column2)+str(column3)+'.png', dpi=300)
                          plt.clf()
                          plt.close()
  
  os.makedirs('other_plots', exist_ok=True)
  
  plt.figure(figsize=(12, 10), dpi=80)
  sns.heatmap(diabetes.corr(), xticklabels=diabetes.corr().columns, yticklabels=diabetes.corr().columns, cmap='RdYlGn', center=0, annot=True)
  plt.title('Diabetes Heatmap', fontsize=22)
  plt.xticks(fontsize=12)
  plt.yticks(fontsize=12)
  plt.savefig(f'other_plots/correlogram_diabetes.png', format='png')
  
  diabetes_unaltered = pd.read_csv(filepath_or_buffer='diabetes.data', sep ='\t', header=0)
  
  fig, axes = plt.subplots(ncols=5, nrows=2, figsize=(10, 5))
  for indx, col in enumerate(diabetes_unaltered.columns):
      outliers = dict(markerfacecolor='r', marker='D')
      if col != 'SEX':
          if indx == 0:
              axes[0][indx].boxplot(diabetes_unaltered[col], flierprops=outliers)
              axes[0][indx].set_ylabel(col)
          elif indx <= 5:
              axes[0][6-indx].boxplot(diabetes_unaltered[col], flierprops=outliers)
              axes[0][6-indx].set_ylabel(col)
          else:
              axes[1][10-indx].boxplot(diabetes_unaltered[col], flierprops=outliers)
              axes[1][10-indx].set_ylabel(col)
  
  fig.suptitle("Boxplots for Diabetes Features")
  fig.tight_layout()
  fig.subplots_adjust(top=0.88)
  plt.savefig(f'other_plots/Boxplots_features')
  plt.clf()
  
  diabetes['SEX'] = diabetes['SEX'].astype(int)
  
  fig, axes = plt.subplots(ncols=5, nrows=2)
  for idx, column in enumerate(diabetes_unaltered.columns):
      if column != 'SEX':
          if idx == 0:
              sns.violinplot('SEX', column, data=diabetes_unaltered, palette="Set2", ax=axes[0][idx])
          elif idx <= 5:
              sns.violinplot('SEX', column, data=diabetes_unaltered, palette="Set2", ax=axes[0][6 - idx])
          else:
              sns.violinplot('SEX', column, data=diabetes_unaltered, palette="Set2", ax=axes[1][10 - idx])
  
  fig.suptitle("Violin Plot for Features versus Sex")
  fig.tight_layout()
  fig.subplots_adjust(top=0.88)
  plt.savefig(f'other_plots/ViolinPlots_Sex_to_features.png')
  plt.clf()
  plt.close()
  
  
  