main.py

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

# loading dataset
diabetes_dataset = pd.read_csv('/content/diabetes.csv')

#first 5 rows of the dataset
diabetes_dataset.head()

# number of rows and Columns in this dataset
diabetes_dataset.shape

# statistical measures
diabetes_dataset.describe()

diabetes_dataset['Outcome'].value_counts()
diabetes_dataset.groupby('Outcome').mean()

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

scaler = StandardScaler()
scaler.fit(X)
standardized_data = scaler.transform(X)
X = standardized_data

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

# the model
classifier = svm.SVC(kernel='linear')

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

# Model Evaluation

# 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 test 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)
# input_data to numpy array
input_data_as_numpy_array = np.asarray(input_data)
# reshaping the array
input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
# standardize the input data
std_data = scaler.transform(input_data_reshaped)
prediction = classifier.predict(std_data)

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

# Link to the Colab notebook: https://colab.research.google.com/drive/1MuWwE3D3Vp5sHcJSLRCPBgDu44yWjcS0#scrollTo=v08NjLj73waP