--- a
+++ b/users/utility/MyClassifier.py
@@ -0,0 +1,114 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn import metrics
+from django.conf import settings
+filepath = settings.MEDIA_ROOT + "\\" + 'stress_data.xlsx'
+df = pd.read_excel(filepath, header=None)
+
+df.columns=['Target', 'ECG(mV)', 'EMG(mV)','Foot GSR(mV)','Hand GSR(mV)', 'HR(bpm)','RESP(mV)']
+X_train, X_test, y_train, y_test = train_test_split(df[['ECG(mV)', 'EMG(mV)','Foot GSR(mV)','Hand GSR(mV)', 'HR(bpm)','RESP(mV)']], df['Target'],
+    test_size=0.30, random_state=12345)
+
+# Min-Max Scaling
+
+minmax_scale = preprocessing.MinMaxScaler().fit(df[['ECG(mV)', 'EMG(mV)','Foot GSR(mV)','Hand GSR(mV)', 'HR(bpm)','RESP(mV)']])
+df_minmax = minmax_scale.transform(df[['ECG(mV)', 'EMG(mV)','Foot GSR(mV)','Hand GSR(mV)', 'HR(bpm)','RESP(mV)']])
+X_train_norm, X_test_norm, y_train_norm, y_test_norm = train_test_split(df_minmax, df['Target'],
+    test_size=0.30, random_state=12345)
+def plot():
+    plt.figure(figsize=(8,6))
+
+    plt.scatter(df['Hand GSR(mV)'], df['HR(bpm)'],
+            color='green', label='input scale', alpha=0.5)
+
+    plt.scatter(df_minmax[:,0], df_minmax[:,1],
+            color='blue', label='min-max scaled [min=0, max=1]', alpha=0.3)
+
+    plt.title('Hand GSR and HR content of the physiological dataset')
+    plt.xlabel('Hand GSR')
+    plt.ylabel('HR')
+    plt.legend(loc='upper left')
+    plt.grid()
+
+    plt.tight_layout()
+
+class KNNclassifier:
+    def getKnnResults(self):
+        # filepath = settings.MEDIA_ROOT + "\\" + 'stress_data.xlsx'
+        print("Started works")
+        knn = KNeighborsClassifier(n_neighbors=5)
+        fit = knn.fit(X_train, y_train)
+
+        # on normalized data
+        knn_norm = KNeighborsClassifier(n_neighbors=5)
+        fit_norm = knn_norm.fit(X_train_norm, y_train)
+
+        pred_train = knn.predict(X_train)
+        pred_test = knn.predict(X_test)
+
+        # Accuracy measure for datasets
+
+        print('Accuracy measure for dataset:- ', '{:.2%}\n'.format(metrics.accuracy_score(y_test, pred_test)))
+
+        pred_test_norm = knn_norm.predict(X_test_norm)
+        print('Accuracy measure for normalized dataset:- ',
+              '{:.2%}\n'.format(metrics.accuracy_score(y_test, pred_test_norm)))
+
+        # comparing the true and predicted responses
+
+        print('True target values: ', y_test.values[0:25])
+        print('Predicted target values: ', pred_test_norm[0:25])
+
+        # Confusion Matrix
+        print(metrics.confusion_matrix(y_test, pred_test_norm))
+        print('True target values: ', y_test.values[0:25])
+        print('Predicted target values: ', pred_test_norm[0:25])
+        print()
+        confusion = metrics.confusion_matrix(y_test, pred_test_norm)
+        TP = confusion[1, 1]
+        TN = confusion[0, 0]
+        FP = confusion[0, 1]
+        FN = confusion[1, 0]
+
+        # Metrics calclulation using confusion matrix
+
+        print()
+        # Classsification accuracy:- how often is the classifier correct
+        accuracy = metrics.accuracy_score(y_test, pred_test_norm)
+        print('Classification Accuracy:- ', accuracy)
+
+        # Classification error/Misclassification rate:- how often is the classifier is incorrect
+        classificationerror = 1 - metrics.accuracy_score(y_test, pred_test_norm)
+        print('Classification Error:- ',classificationerror )
+
+        # Sensitivity :- when the actual value is positive , how often is the prediction correct?
+        sensitivity = metrics.recall_score(y_test, pred_test_norm)
+        print('Sensitivity:- ',sensitivity )
+
+        # Specificity:- when the actual value is negative ,how often the prediction is the correct?
+        Specificity =  TN / float(TN + FP)
+        print('Specificity:- ',Specificity )
+
+        # False positive rate:- when the actual value is negative ,how often the prediction is the incorrect?
+        fsp =  FP / float(TN + FP)
+        print('False positive rate:- ', fsp)
+
+        # Precision:- when a positive value is predicted , how often is the prediction correct?
+        precision = metrics.precision_score(y_test, pred_test_norm)
+        print('Precision:- ', precision)
+
+        # Prediction of stress/no stress class on new dataset
+        print()
+        pred_data_norm = minmax_scale.transform([[-0.005, 0.49, 8.257, 5.853, 66.142, 45.998]])
+        pred = knn_norm.predict(pred_data_norm)
+        print('Predicted class for dataset [-0.005,0.49,8.257,5.853,66.142,45.998]:- ', pred)
+
+        pred_data_norm = minmax_scale.transform([[0.001, 0.931, 5.91, 19.773, 99.065, 35.59]])
+        pred = knn_norm.predict(pred_data_norm)
+        print('Predicted class for dataset [0.001,0.931,5.91,19.773,99.065,35.59]:- ', pred)
+
+        return df,accuracy,classificationerror,sensitivity,Specificity,fsp,precision
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