imports cleaned

code/machine_learning_models/decision_tree.py

@@ -8,7 +8,7 @@ from sklearn.metrics import classification_report, confusion_matrix
 from sklearn.preprocessing import StandardScaler, LabelEncoder
 from sklearn.tree import DecisionTreeClassifier
 
-from utilities import plot_counts, plot_roc_curve
+from utilities import plot_counts
 from utilities import plot_features, ordinal_encode, normalize, plot_confusion_matrix, print_high_confidence_samples, import_data
 
 warnings.filterwarnings("ignore")

code/machine_learning_models/knn.py

@@ -2,14 +2,13 @@ import warnings
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc
+from sklearn.metrics import classification_report, confusion_matrix
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.preprocessing import StandardScaler, LabelEncoder
 from sklearn.pipeline import Pipeline
 from sklearn.model_selection import train_test_split
 
-from utilities import ordinal_encode, normalize, plot_confusion_matrix, plot_counts, import_data, save_plot, show_plots, \
-    plot_roc_curve
+from utilities import ordinal_encode, normalize, plot_confusion_matrix, plot_counts, import_data, plot_roc_curve
 
 warnings.filterwarnings("ignore")
 

code/machine_learning_models/random_forest.py

 import numpy as np
 import pandas as pd
-from scipy.io import arff
 
 import warnings
 from sklearn.preprocessing import StandardScaler, LabelEncoder
-from sklearn.model_selection import train_test_split, learning_curve
+from sklearn.model_selection import learning_curve
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import classification_report, confusion_matrix, precision_recall_curve