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KNN - K Nearest Neighbors (Standardize the variables (from sklearn…
KNN - K Nearest Neighbors
Standardize the variables
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(df,drop('TARGET CLASS', axis = 1)
scaled_features = scaler.transform(df.drop('TARGET CLASS', axis = 1)
df
new = pd.DataFrame(scaled
features, columns = df.columns[:-1])
Train Test Split
from sklearn.model_selection import train_test_split
X_train, X_test... = train_test_split(X, y)
KNN
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = k)
knn.fit(X_train, y_train)
pred = knn.predict(X_test)
Choosing K
for i in range(1, 40):
k set to i
error_rate.append(np.mean(pred_i != y_test)
Evaluations
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, pred)
print(classification_report(y_test, pred)