Streamlit app link: Heart failure risk predictor
Google Colab link: Heart-failure-risk-predictor-Colab.
(Download the dataset and upload it to the above google colab before running the program)
Github link: Heart-failure-risk-predictor-Github.
This is a machine learning model that can predict if a person is in a risk of heart failure or not based on the medical data of that person. This is a binary classification model built using the linear kernel in the SVC of Support Vector Machine (SVM). My choice for this model is based on the fact that the dataset used is relatively small but has a lot of feature sets to be processed and this model works well with higher dimensional data. This model has been trained on the recorded data from about 900 people and predicts results with 86% accuracy.
The dataset for this project was obtained from heart_data.
P.S. This is my first machine learning model using numpy, pandas and sklearn. Looking forward to learning more and developing advanced machine learning models in the future.