The Heart Failure Mortality Prediction project is aimed at developing a machine learning model using Python that predicts mortality risk associated with heart failure using patient data. The primary objective of this project is to identify patients who are at high risk of heart failure mortality early and manage them appropriately, reducing fatalities associated with cardiovascular diseases.
The project involves various stages such as data preprocessing, exploratory data analysis, feature engineering, machine learning model building, and evaluation, and result interpretation. The data preprocessing stage involves cleaning and transforming raw data to make it ready for further analysis. Exploratory data analysis involves understanding and visualizing the data to identify patterns, trends, and insights.
Feature engineering is a crucial stage where new features are created from existing ones to improve model accuracy. The machine learning model building and evaluation stage involves training various models and evaluating their performance using different metrics. The result interpretation stage involves analyzing the model output and drawing meaningful conclusions.
Overall, this project demonstrates the developer's proficiency in data analysis, machine learning, and Python programming. The business value of this project lies in its potential to save lives by identifying patients at high risk of heart failure mortality early and providing timely interventions.