Diabetes-Prediction

Importing the Dependencies

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

Diabetic Dataset

diabetes_dataset=pd.read_csv('/content/diabetes.csv') # copy path of csv file

printing first 5 rows of dataset

diabetes_dataset.head()

number of rows and columns in the dataset

diabetes_dataset.shape

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

scalar = StandardScaler()

scalar.fit(X)

standardized_data=scalar.transform(X)

print(standardized_data)

X = standardized_data Y=diabetes_dataset['Outcome']

print(X,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')

classifier.fit(X_train, Y_train)

Model Evaluation

Accuracy Score

X_train_prediction = classifier.predict(X_train) training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

print("Accuracy score of training data: ",training_data_accuracy)

X_test_prediction = classifier.predict(X_test) test_data_accuracy = accuracy_score(X_test_prediction,Y_test)

print("Accuracy score of test data: ",test_data_accuracy)

Making a Predictive System

input_data= (10,168,74,0,0,38,0.537,34)

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

standard_data = scalar.transform(input_data_reshaped) print(standard_data) prediction= classifier.predict(standard_data) print(prediction) if (prediction[0] == 0): print("The person is not diabetic") else: print("The person is diabetic")