Inspiration
We're driven to enhance healthcare by understanding patient survival factors at TD Hospital.
What it does
Our project explores and analyzes patient data to gain insights into survival factors. We leverage machine learning, with Gradient Boosting achieving an impressive 81.02% accuracy.
How we built it
We constructed our project by meticulously preprocessing the data, training various models, and selecting the best-performing one. Our approach combines data science and machine learning techniques.
Challenges we ran into
We encountered challenges in data cleaning, outlier handling, and model selection. Overcoming these hurdles was pivotal in achieving our accuracy goal.
Accomplishments that we're proud of
we take pride in the journey that led us to improve accuracy from 69% to an impressive 81%.
What we learned
We gained invaluable insights into the intricacies of data preprocessing, model selection, and the significance of feature engineering in healthcare analysis.
What's next for TD Hospital Exploration
The next phase involves in-depth clinical validations and collaboration with healthcare experts to refine predictions and introduce our findings into practical patient care.
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