Inspiration
Our goal is to exceed 85% model accuracy. The model will be chosen if it receives a score higher than 85%.
What it does
Based on Given clinical parameters about a patient, we can predict whether the patient has heart disease or not
How we built it
Pandas & Numpy for Data Analysis and Manipulation Matplotlib and Seaborn for Data Visualization Scikit-Learn for the Modelling and Evaluation
Challenges we ran into
Accomplishments that we're proud of
Based on the various features we can predict whether a person can have heart disease or not.
What we learned
If we can develop an appropriate machine learning method from the information that more properly classifies heart disease, both the health organization and patients would benefit greatly.
What's next for Heart Disease Prediction Using Logistic Regression
The future scope of the paper is the prediction of heart diseases by using advanced techniques and algorithms in less time complexity.
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