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| # -*- coding: utf-8 -*- | |
| """Diabetes.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/15IbzL0ARqBYPhh4fx4KN2rJ62USEmIO2 | |
| Importing the Dependencies | |
| """ | |
| #pip install -U scikit-learn | |
| 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') | |
| # 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) | |
| """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""" | |
| def predict(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age): | |
| #input_data = (5,166,72,19,175,25.8,0.587,51) | |
| input_data = (Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age) | |
| # 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') | |
| return prediction | |
| predict(4,136,64,20,175,25.6,0.597,50) | |
| import gradio as gr | |
| def dibetis_predict(Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age): | |
| #input_data = (5,166,72,19,175,25.8,0.587,51) | |
| input_data = (Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age) | |
| # 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) | |
| if (prediction[0] == 0): | |
| print('The person is not diabetic') | |
| return 'The person is not diabetic' | |
| else: | |
| print('The person is diabetic') | |
| return 'The person is diabetic' | |
| demo = gr.Interface( | |
| fn=dibetis_predict, | |
| inputs = [ | |
| gr.Slider(0, 20, value=4, label="Pregnancies", info="Choose between 0 and 20"), | |
| gr.Slider(1, 200, value=136, label="Glucose", info="Choose between 1 and 200"), | |
| gr.Slider(1, 100, value=64, label="BloodPressure", info="Choose between 1 and 100"), | |
| gr.Slider(1, 50, value=20, label="SkinThickness", info="Choose between 1 and 50"), | |
| gr.Slider(1, 200, value=175, label="Insulin", info="Choose between 1 and 200"), | |
| gr.Slider(1, 100, value=25.5, label="BMI", info="Choose between 1 and 100"), | |
| gr.Slider(0, 1.0, value=0.549, label="DiabetesPedigreeFunction", info="Choose between 0.0 and 1.0"), | |
| gr.Slider(1, 100, value=50, label="Age", info="Choose between 1 and 100"), | |
| ], | |
| #description="Diabetes Prediction Model By Yash Rawal" | |
| #Markdown("""Dibetese prediction system by Yash Rawal""") | |
| outputs = "text", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |