Heart_Disease_Prediction

Machine Learning Project
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View Flask app code · Model Building

Heart disease prevention has become more important than ever. In order to ensure that more people may live healthy lives, effective data-driven methods for predicting cardiac problems can enhance the overall research and preventive process. Machine learning is useful in this situation. The heart illnesses are predicted with the use of machine learning, and the forecasts are rather accurate.

In order to determine whether a patient has heart disease, I've employed a range of Machine Learning algorithms that were developed in Python. This classification algorithm predicts whether or not cardiac disease is present by using a binary variable as the goal variable and a range of characteristics as the input features.

• Building a Flask App hosted on Heroku.
• Sklearn for pre-processing and Model Building
• Pandas, Numpy for csv reading, Data Processing, Data Cleaning, Visualization etc.
• Machine Learning algorithms used: Logistic Regression (Scikit-learn)

LINK TO HEROKU APP

• Classification algorithm decided to predict the features Classes from the dataset which is Binary classification (0 = Healthy Heart, 1 = Defective Heart).
• Models used : Logistic Regression.

• Importing the Flask module and creating a Flask web server from the Flask module.
• Create an object app in flask class with __name__ which represents current app.py file.
• Create / route to render default page html.
• Create a route /predict to get user input for Classification.
• Run the flask app with app.run() code.

• Create new repo in Github and push all the data using Git.
• Login to Heroku using heroku login and setup the app in Heroku Web.
• Connect new Github repo in heroku and deploy the app