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# -*- coding: utf-8 -*-
"""Diabetes Predicition.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1aNMlOsS2sOTF_m50QYOm5pAz-UmbD4_u
Importing the Dependencies
"""
import numpy as np
import pandas as pd
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('/content/diabetes.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)
"""Train Test Split"""
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 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)
# 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)
prediction = classifier.predict(input_data_reshaped)
print(prediction)
if (prediction[0] == 0):
print('The person is not diabetic')
else:
print('The person is diabetic')
"""Saving the trained model"""
import pickle
filename = 'trained_model.sav'
pickle.dump(classifier, open(filename, 'wb'))
# loading the saved model
loaded_model = pickle.load(open('trained_model.sav', 'rb'))
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)
prediction = loaded_model.predict(input_data_reshaped)
print(prediction)
if (prediction[0] == 0):
print('The person is not diabetic')
else:
print('The person is diabetic')
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