Hello everyone!
I started using Streamlit in August of 2023 and I have created a handful of apps since then. Most are work-related so I can’t show them here, but I can share two other apps I worked on for fun.
Stroke Probability Predictor
This Streamlit application utilizes a logistic regression model trained on a Kaggle dataset to predict stroke probability based on user input. Additionally, it provides valuable information and resources on strokes to raise awareness and educate users.
Features
- Stroke Probability Prediction: Input your details to determine your likelihood of experiencing a stroke (high vs. low chance).
- Educational Resources: Explore a dedicated page with information and resources related to strokes.
- Personal Journey: Read about my grandpa’s experience with a stroke, fostering empathy and understanding.
Python Libraries Used
- Streamlit
- Scikit-learn
- Pandas
Link
To view the app, click here
Nintendo Switch KPI Dashboard
This Streamlit app utilizes a Kaggle dataset about Nintendo Switch games to create an interactive dashboard. It showcases key performance indicators (KPIs), a graph, and tables, providing insights into the world of Nintendo Switch gaming.
Features
- KPI Cards: Four key performance indicators provide at-a-glance metrics.
- Graph: A visual representation of data trends.
- Tables: Two tables offering detailed information.
- Filters: Use filters to customize your view based on specific criteria.
Python Libraries Used
- Streamlit
- Pandas
Link
To view the app, click here
Additional
If you have any questions about either of these apps, please feel free to ask. I can’t wait to see what’s in store for Streamlit in 2024! Happy New Year everyone!
