Create ANN-Saloni

ANN-Saloni

@@ -0,0 +1,39 @@
+import os
+from numpy.distutils.system_info import p
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn import metrics
+import numpy as np
+import pandas as pd
+
+
+print(os.getcwd())
+rd = pd.read_excel('')
+
+ds = pd.read_csv(r"C:\Users\salon\PycharmProjects\pythonProject")
+filename = 'data.csv'
+raw_data = open('data.csv')
+df = pd.read_csv('data.csv')
+ds = ds.dropna()
+for i in ['age', 'bmi', 'avg_glucose_level']:
+ ds[1] = ds[1].replace(0, np.NaN)
+ mean = int(ds[1].mean(skipna=True))
+ ds[1] = ds[1].replace(np.NaN, mean)
+
+X = ds.iloc[:, [2, 8, 9]]
+Y = ds.iloc[:, 11]
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
+K_range = range(1, 30)
+score = {}
+score_list = []
+for k in K_range:
+ Knn = KNeighborsClassifier(n_neighbors=k)
+ Knn.fit(X_train, y_train)
+ Pred = Knn.predict(X_test)
+ score_list.append(metrics.accuracy_score(y_test, Pred))
+
+p.plot(K_range, score_list)
+p.xlabel("value of k for ANN")
+p.ylabel("Testing Accuracy")
+p.show()