Inspiration
My motivation for joining this hackathon was to enhance my skills in data science. The problem statement I chose is closely tied to recent global events, which further sparked my interest. I was enthusiastic about developing a predictive model for influenza outbreaks, aiming to create a potential tool that can help control epidemics.
What it does
The developed model predicts the percentage of positive influenza cases for the upcoming week. By establishing a specific threshold, we can identify and anticipate outbreaks accurately.
How we built it
To begin, I performed data cleaning using Python. Missing values were addressed through linear interpolation. Then, Azure AutoML was employed to determine the optimal model for predicting the percentage of positive influenza cases.
Challenges we ran into
The data cleansing process presented several challenges. I made numerous adjustments to the dataset, ensuring its logical consistency and suitability for AutoML.
Accomplishments that we're proud of
I'm proud in initiating and completing this project, especially considering my beginner level and the limited time available.
What we learned
Throughout this project, I gained valuable experience working with Azure services and handling extensive datasets.
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