Diabetes Prediction

import pandas as pd import numpy as np from matplotlib import pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore')

df_Pima = pd.read_csv('diabetes.csv') df_Pima.head()

df_Pima.describe()

df_Pima[['Glucose', 'BloodPressure', 'SkinThickness','Insulin', 'BMI']] = df_Pima[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']].replace(0, np.NaN)

df_Pima.isna().sum()

def find_mean(column): temp = df_Pima[df_Pima[column].notnull()] temp = df_Pima[[column, 'Outcome']].groupby('Outcome')[[column]].mean().reset_index() return temp

print(find_mean('Glucose')) print(find_mean('BloodPressure')) print(find_mean('SkinThickness')) print(find_mean('Insulin')) print(find_mean('BMI'))

   Outcome     Glucose
0        0  110.643863
1        1  142.319549
   Outcome  BloodPressure
0        0      70.877339
1        1      75.321429
   Outcome  SkinThickness
0        0      27.235457
1        1      33.000000
   Outcome     Insulin
0        0  130.287879
1        1  206.846154
   Outcome        BMI
0        0  30.859674
1        1  35.406767

df_Pima.loc[(df_Pima['Outcome'] == 0) & (df_Pima['Glucose'].isnull()), 'Glucose'] = 110.6 df_Pima.loc[(df_Pima['Outcome'] == 1) & (df_Pima['Glucose'].isnull()), 'Glucose'] = 169.5 df_Pima.loc[(df_Pima['Outcome'] == 0) & (df_Pima['BloodPressure'].isnull()), 'BloodPressure'] = 70.8 df_Pima.loc[(df_Pima['Outcome'] == 1) & (df_Pima['BloodPressure'].isnull()), 'BloodPressure'] = 75.3 df_Pima.loc[(df_Pima['Outcome'] == 0) & (df_Pima['SkinThickness'].isnull()), 'SkinThickness'] = 27.2 df_Pima.loc[(df_Pima['Outcome'] == 1) & (df_Pima['SkinThickness'].isnull()), 'SkinThickness'] = 33.0 df_Pima.loc[(df_Pima['Outcome'] == 0) & (df_Pima['Insulin'].isnull()), 'Insulin'] = 130.3 df_Pima.loc[(df_Pima['Outcome'] == 1) & (df_Pima['Insulin'].isnull()), 'Insulin'] = 206.8 df_Pima.loc[(df_Pima['Outcome'] == 0) & (df_Pima['BMI'].isnull()), 'BMI'] = 30.8 df_Pima.loc[(df_Pima['Outcome'] == 1) & (df_Pima['BMI'].isnull()), 'BMI'] = 35.4

df_Pima.isna().sum()

plt.figure(figsize=(10,6)) sns.scatterplot(x='Pregnancies', y='BMI', data=df_Pima, hue='Outcome', alpha= 0.7) plt.title('Comparison of BMI and Pregnancies') plt.show()

plt.figure(figsize=(10,6)) sns.scatterplot(x='Insulin', y='BMI', data=df_Pima, hue='Outcome', alpha= 0.7) plt.title('Comparison of BMI and Insulin') plt.show()

plt.title('Count of Positive / Negative Outcome') sns.countplot(data=df_Pima, y= 'Outcome')

plt.figure(figsize=(10, 12)) plt.title('Age count with Positive / Negative Outcome') sns.histplot(data= df_Pima, x='Age', hue='Outcome', element= 'step', fill=False)

df_Pima.corr()

from sklearn.preprocessing import scale diabetes_labels = df_Pima['Outcome'] diabetes_features = df_Pima[['Pregnancies', 'Glucose', 'SkinThickness', 'Insulin', 'BMI', 'Age']] scaled_features = scale(diabetes_features, axis=0) print(scaled_features)

[[ 0.63994726  0.85945808  0.64570209  0.56083978  0.16790636  1.4259954 ]
 [-0.84488505 -1.20465424 -0.02697724 -0.30067319 -0.85032783 -0.19067191]
 [ 1.23388019  2.00618715  0.42147564  0.56083978 -1.33035252 -0.10558415]
 ...
 [ 0.3429808  -0.02516149 -0.69965657 -0.50676061 -0.90851264 -0.27575966]
 [-0.84488505  0.13865695  0.42147564  0.56083978 -0.34121073  1.17073215]
 [-0.84488505 -0.94254474  0.1972492  -0.30067319 -0.29757212 -0.87137393]]

from sklearn.model_selection import train_test_split train_data, test_data, train_label, test_label = train_test_split(scaled_features, diabetes_labels, test_size=0.2, random_state =1)

from sklearn.neighbors import KNeighborsClassifier classifier = KNeighborsClassifier(n_neighbors = 3) classifier.fit(train_data, train_label) knn_prediction = classifier.predict(test_data) classifier.score(test_data, test_label)

from sklearn.ensemble import RandomForestClassifier forest_classifier = RandomForestClassifier(n_estimators = 100, max_depth=6) forest_classifier.fit(train_data, train_label) forest_prediction = forest_classifier.predict(test_data) forest_classifier.score(test_data, test_label)

from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score print('KNN Accuracey is: ' +str(accuracy_score(test_label, knn_prediction))) print('KNN Precision score is ' + str(precision_score(test_label,knn_prediction))) print('KNN F1 score is ' + str(f1_score(test_label,knn_prediction)))

KNN Accuracey is: 0.8831168831168831
KNN Precision score is 0.8627450980392157
KNN F1 score is 0.8301886792452831

print('Random forest Accuracey is: ' +str(accuracy_score(test_label,forest_prediction))) print('Random forest Precision score is ' + str(precision_score(test_label,forest_prediction))) print('Random forest F1 score is ' + str(f1_score(test_label,forest_prediction)))

Random forest Accuracey is: 0.8961038961038961
Random forest Precision score is 0.8823529411764706
Random forest F1 score is 0.8490566037735848

diabetes_labels2 = df_Pima['Outcome'] diabetes_features2 = df_Pima[['Glucose', 'SkinThickness', 'Insulin', 'BMI']] scaled_features2 = scale(diabetes_features, axis=0)

train_data2, test_data2, train_label2, test_label2 = train_test_split(scaled_features2, diabetes_labels2, test_size=0.2, random_state =1)

classifier2 = KNeighborsClassifier(n_neighbors = 3) classifier2.fit(train_data2, train_label2) classifier2.score(test_data2, test_label2)

knn_prediction2 = classifier2.predict(test_data2)

print('KNN2 Accuracey is: ' +str(accuracy_score(test_label2,knn_prediction2))) print('KNN2 Precision score is ' + str(precision_score(test_label2,knn_prediction2))) print('KNN2 F1 score is ' + str(f1_score(test_label2,knn_prediction2)))

KNN2 Accuracey is: 0.8831168831168831
KNN2 Precision score is 0.8627450980392157
KNN2 F1 score is 0.8301886792452831

forest_classifier2 = RandomForestClassifier(n_estimators = 75, max_depth=6) forest_classifier2.fit(train_data2, train_label2) forest_classifier2.score(test_data2, test_label2)

predictions2 = forest_classifier2.predict(test_data2)

print('Random forest2 Accuracey is: ' +str(accuracy_score(test_label2,predictions2 ))) print('Random forest2 Precision score is ' + str(precision_score(test_label2,predictions2 ))) print('Random forest2 F1 score is ' + str(f1_score(test_label2,predictions2 )))

Random forest2 Accuracey is: 0.8831168831168831
Random forest2 Precision score is 0.8627450980392157
Random forest2 F1 score is 0.8301886792452831