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| # -*- coding: utf-8 -*- | |
| """Heart_Disease_Prediction.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1cqMvzxYU8QVM8IdfByIymr-3YryFBU5p | |
| Importing the Dependencies | |
| """ | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import accuracy_score | |
| """Data Collection and Processing""" | |
| # loading the csv data to a Pandas DataFrame | |
| heart_data = pd.read_csv('heart_disease_data.csv') | |
| # print first 5 rows of the dataset | |
| heart_data.head() | |
| # print last 5 rows of the dataset | |
| heart_data.tail() | |
| # number of rows and columns in the dataset | |
| heart_data.shape | |
| # getting some info about the data | |
| heart_data.info() | |
| # checking for missing values | |
| heart_data.isnull().sum() | |
| # statistical measures about the data | |
| heart_data.describe() | |
| # checking the distribution of Target Variable | |
| heart_data['target'].value_counts() | |
| """1 --> Defective Heart | |
| 0 --> Healthy Heart | |
| Splitting the Features and Target | |
| """ | |
| X = heart_data.drop(columns='target', axis=1) | |
| Y = heart_data['target'] | |
| print(X) | |
| print(Y) | |
| """Splitting the Data into Training data & Test Data""" | |
| 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) | |
| """Model Training | |
| Logistic Regression | |
| """ | |
| model = LogisticRegression() | |
| # training the LogisticRegression model with Training data | |
| model.fit(X_train, Y_train) | |
| """Model Evaluation | |
| Accuracy Score | |
| """ | |
| # accuracy on training data | |
| X_train_prediction = model.predict(X_train) | |
| training_data_accuracy = accuracy_score(X_train_prediction, Y_train) | |
| print('Accuracy on Training data : ', training_data_accuracy) | |
| # accuracy on test data | |
| X_test_prediction = model.predict(X_test) | |
| test_data_accuracy = accuracy_score(X_test_prediction, Y_test) | |
| print('Accuracy on Test data : ', test_data_accuracy) | |
| """Building a Predictive System""" | |
| def heart_predict(age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal): | |
| #input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2) | |
| sex="" | |
| if sex == "male": | |
| sex = 0 | |
| else: | |
| sex = 1 | |
| input_data = (age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal) | |
| # change the input data to a numpy array | |
| input_data_as_numpy_array= np.asarray(input_data) | |
| # reshape the numpy array as we are predicting for only on instance | |
| input_data_reshaped = input_data_as_numpy_array.reshape(1,-1) | |
| prediction = model.predict(input_data_reshaped) | |
| print(prediction) | |
| if (prediction[0]== 0): | |
| print('The Person does not have a Heart Disease') | |
| return 'The Person does not have a Heart Disease' | |
| else: | |
| print('The Person has Heart Disease') | |
| return 'The Person has Heart Disease' | |
| heart_predict(62,"female",0,140,268,0,0,160,0,3.6,0,2,2) | |
| #!pip install gradio | |
| import gradio as gr | |
| def heart_predict(age,sex_R,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal): | |
| #input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2) | |
| sex="" | |
| if sex == "male": | |
| sex = 0 | |
| else: | |
| sex = 1 | |
| input_data = (age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal) | |
| # change the input data to a numpy array | |
| input_data_as_numpy_array= np.asarray(input_data) | |
| # reshape the numpy array as we are predicting for only on instance | |
| input_data_reshaped = input_data_as_numpy_array.reshape(1,-1) | |
| prediction = model.predict(input_data_reshaped) | |
| print(prediction) | |
| if (prediction[0]== 0): | |
| print('The Person does not have a Heart Disease') | |
| return 'The Person does not have a Heart Disease' | |
| else: | |
| print('The Person has Heart Disease') | |
| return 'The Person has Heart Disease' | |
| demo = gr.Interface( | |
| fn=heart_predict, | |
| inputs = [ | |
| gr.Slider(1, 100, value=62, label="age", info="Choose between 1 and 20"), | |
| gr.Radio(["male","female"], value="male", label="sex", info="select"), | |
| gr.Slider(1, 100, value=0, label="cp", info="Choose between 1 and 100"), | |
| gr.Slider(1, 50, value=140, label="trestbps", info="Choose between 1 and 50"), | |
| gr.Slider(1, 200, value=268, label="chol", info="Choose between 1 and 200"), | |
| gr.Slider(1, 100, value=0, label="fbs", info="Choose between 1 and 100"), | |
| gr.Slider(0.0, 2.0, value=0, label="restecg", info="Choose between 0.0 and 1.0"), | |
| gr.Slider(1, 100, value=160, label="thalach", info="Choose between 1 and 100"), | |
| gr.Slider(1, 100, value=0, label="exang", info="Choose between 1 and 100"), | |
| gr.Slider(1, 100, value=3.6, label="oldpeak", info="Choose between 1 and 100"), | |
| gr.Slider(1, 100, value=0, label="slope", info="Choose between 1 and 100"), | |
| gr.Slider(1, 100, value=2, label="ca", info="Choose between 1 and 100"), | |
| gr.Slider(1, 100, value=2, label="thal", info="Choose between 1 and 100"), | |
| ], | |
| outputs = "text", | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |