import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline

diabetes = pd.read_csv('diabetes.csv') print(diabetes.columns)

diabetes.head()

print("dimension of diabetes data: {}".format(diabetes.shape))

print(diabetes.groupby('Outcome').size())

import seaborn as sns sns.countplot(diabetes['Outcome'],label="Count")

diabetes.info()

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(diabetes.loc[:, diabetes.columns != 'Outcome'], diabetes['Outcome'], stratify=diabetes['Outcome'], random_state=66)

from sklearn.neighbors import KNeighborsClassifier training_accuracy = [] test_accuracy = [] # try n_neighbors from 1 to 10 neighbors_settings = range(1, 11) for n_neighbors in neighbors_settings: # build the model knn = KNeighborsClassifier(n_neighbors=n_neighbors) knn.fit(X_train, y_train) # record training set accuracy training_accuracy.append(knn.score(X_train, y_train)) # record test set accuracy test_accuracy.append(knn.score(X_test, y_test)) plt.plot(neighbors_settings, training_accuracy, label="training accuracy") plt.plot(neighbors_settings, test_accuracy, label="test accuracy") plt.ylabel("Accuracy") plt.xlabel("n_neighbors") plt.legend() plt.savefig('knn_compare_model')

knn = KNeighborsClassifier(n_neighbors=9) knn.fit(X_train, y_train) print('Accuracy of K-NN classifier on training set: {:.2f}'.format(knn.score(X_train, y_train))) print('Accuracy of K-NN classifier on test set: {:.2f}'.format(knn.score(X_test, y_test)))

from sklearn.linear_model import LogisticRegression logreg = LogisticRegression().fit(X_train, y_train) print("Training set accuracy: {:.3f}".format(logreg.score(X_train, y_train))) print("Test set accuracy: {:.3f}".format(logreg.score(X_test, y_test)))

logreg001 = LogisticRegression(C=0.01).fit(X_train, y_train) print("Training set accuracy: {:.3f}".format(logreg001.score(X_train, y_train))) print("Test set accuracy: {:.3f}".format(logreg001.score(X_test, y_test)))

logreg100 = LogisticRegression(C=100).fit(X_train, y_train) print("Training set accuracy: {:.3f}".format(logreg100.score(X_train, y_train))) print("Test set accuracy: {:.3f}".format(logreg100.score(X_test, y_test)))

diabetes_features = [x for i,x in enumerate(diabetes.columns) if i!=8] plt.figure(figsize=(8,6)) plt.plot(logreg.coef_.T, 'o', label="C=1") plt.plot(logreg100.coef_.T, '^', label="C=100") plt.plot(logreg001.coef_.T, 'v', label="C=0.001") plt.xticks(range(diabetes.shape[1]), diabetes_features, rotation=90) plt.hlines(0, 0, diabetes.shape[1]) plt.ylim(-5, 5) plt.xlabel("Feature") plt.ylabel("Coefficient magnitude") plt.legend() plt.savefig('log_coef')

from sklearn.tree import DecisionTreeClassifier tree = DecisionTreeClassifier(random_state=0) tree.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(tree.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(tree.score(X_test, y_test)))

tree = DecisionTreeClassifier(max_depth=3, random_state=0) tree.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(tree.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(tree.score(X_test, y_test)))

print("Feature importances:\n{}".format(tree.feature_importances_))

def plot_feature_importances_diabetes(model): plt.figure(figsize=(8,6)) n_features = 8 plt.barh(range(n_features), model.feature_importances_, align='center') plt.yticks(np.arange(n_features), diabetes_features) plt.xlabel("Feature importance") plt.ylabel("Feature") plt.ylim(-1, n_features) plot_feature_importances_diabetes(tree) plt.savefig('feature_importance')

from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(n_estimators=100, random_state=0) rf.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(rf.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(rf.score(X_test, y_test)))

rf1 = RandomForestClassifier(max_depth=3, n_estimators=100, random_state=0) rf1.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(rf1.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(rf1.score(X_test, y_test)))

plot_feature_importances_diabetes(rf)

from sklearn.ensemble import GradientBoostingClassifier gb = GradientBoostingClassifier(random_state=0) gb.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(gb.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(gb.score(X_test, y_test)))

gb1 = GradientBoostingClassifier(random_state=0, max_depth=1) gb1.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(gb1.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(gb1.score(X_test, y_test)))

gb2 = GradientBoostingClassifier(random_state=0, learning_rate=0.01) gb2.fit(X_train, y_train) print("Accuracy on training set: {:.3f}".format(gb2.score(X_train, y_train))) print("Accuracy on test set: {:.3f}".format(gb2.score(X_test, y_test)))

plot_feature_importances_diabetes(gb1)

from sklearn.svm import SVC svc = SVC() svc.fit(X_train, y_train) print("Accuracy on training set: {:.2f}".format(svc.score(X_train, y_train))) print("Accuracy on test set: {:.2f}".format(svc.score(X_test, y_test)))

from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.fit_transform(X_test) svc = SVC() svc.fit(X_train_scaled, y_train) print("Accuracy on training set: {:.2f}".format(svc.score(X_train_scaled, y_train))) print("Accuracy on test set: {:.2f}".format(svc.score(X_test_scaled, y_test)))

svc = SVC(C=1000) svc.fit(X_train_scaled, y_train) print("Accuracy on training set: {:.3f}".format( svc.score(X_train_scaled, y_train))) print("Accuracy on test set: {:.3f}".format(svc.score(X_test_scaled, y_test)))

from sklearn.neural_network import MLPClassifier mlp = MLPClassifier(random_state=42) mlp.fit(X_train, y_train) print("Accuracy on training set: {:.2f}".format(mlp.score(X_train, y_train))) print("Accuracy on test set: {:.2f}".format(mlp.score(X_test, y_test)))

from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.fit_transform(X_test) mlp = MLPClassifier(random_state=0) mlp.fit(X_train_scaled, y_train) print("Accuracy on training set: {:.3f}".format( mlp.score(X_train_scaled, y_train))) print("Accuracy on test set: {:.3f}".format(mlp.score(X_test_scaled, y_test)))

mlp = MLPClassifier(max_iter=1000, random_state=0) mlp.fit(X_train_scaled, y_train) print("Accuracy on training set: {:.3f}".format( mlp.score(X_train_scaled, y_train))) print("Accuracy on test set: {:.3f}".format(mlp.score(X_test_scaled, y_test)))

mlp = MLPClassifier(max_iter=1000, alpha=1, random_state=0) mlp.fit(X_train_scaled, y_train) print("Accuracy on training set: {:.3f}".format( mlp.score(X_train_scaled, y_train))) print("Accuracy on test set: {:.3f}".format(mlp.score(X_test_scaled, y_test)))

plt.figure(figsize=(20, 5)) plt.imshow(mlp.coefs_[0], interpolation='none', cmap='viridis') plt.yticks(range(8), diabetes_features) plt.xlabel("Columns in weight matrix") plt.ylabel("Input feature") plt.colorbar()