Mahisa Ajy Kusuma

Diabetes Prediction – Machine Learning Project

mahisaajy
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Dataset link: https://www.dropbox.com/s/uh7o7uyeghqkhoy/diabetes.csv?dl=0

Importing the Depedencies

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score

Data Collection And Analysis

PIMA Diabetes Dataset

# loading the diabetes dataset to a pandas DataFrame
diabetes_dataset = pd.read_csv('diabetes.csv')

pd.read_csv?
# printing the first 5 rows of the dataset
diabetes_dataset.head()
# number of rows and columns in this dataset
diabetes_dataset.shape
# getting the statistical measures of the data
diabetes_dataset.describe()
diabetes_dataset['Outcome'].value_counts()

0 –> Non-Diabetic
1 –> Diabetic

diabetes_dataset.groupby('Outcome').mean()
# separating the data and labels
X = diabetes_dataset.drop(columns = 'Outcome', axis=1)
Y = diabetes_dataset['Outcome']
print(X)
print(Y)

Data Standardization

scaler = StandardScaler()
scaler.fit(X)

standardized_data = scaler.transform(X)
print(standardized_data)
X = standardized_data
Y = diabetes_dataset['Outcome']

print(X)
print(Y)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)

print(X.shape, X_train.shape, X_test.shape)

Training the Model

classifier = svm.SVC(kernel='linear')

# training the support vector machine classifier
classifier.fit(X_train, Y_train)

Model Evaluation : Accuracy Score

# accuracy score on the training data
X_train_prediction = classifier.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

print('Accuracy score of the training data : ', training_data_accuracy)
# accuracy score on the training data
X_test_prediction = classifier.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

print('Accuracy score of the test data :', test_data_accuracy)

Making a Predictive System

input_data = (5, 166, 72, 19, 175, 25.8, 0.587, 51)

# changing the input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)

# reshape the array as we are predicting for one instance
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)


# standardize the input data
std_data = scaler.transform(input_data_reshaped)
print(std_data)

prediction = classifier.predict(std_data)
print(prediction)

if (prediction[0] == 0):
    print('The person is not diabetic')
else:
    print('The person is diabetic')
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mahisaajy

PMG Ahli Pertama, Bidang Manajemen Database at BMKG
Seorang pemuda luar biasa yang mempunyai hobi menulis, membaca, dan bermusik. Tertarik dengan bidang ilmu komputer untuk memecahkan beberapa persoalan. Co-Founder Triglav ID dan Co-Founder METLIGO. Sejak tahun 2018 bekerja di BMKG di bagian Pusat Database.