Streamlit for Data Science
Moguloju Sai
Looking for an opportunity | Data Science | Machine Learning | Deep Learning | NLP | Gen AI
Definition:
a free and open-source framework to rapidly build and share beautiful machine learning and data science web apps.
Here’s why Streamlit is useful in data science:
Advantages of Streamlit:
No need for front-end skills: Streamlit allows you to create web apps without needing HTML, CSS, or JavaScript knowledge. You can focus on your data science tasks, and Streamlit takes care of the UI.
You can build and deploy data applications quickly, making it ideal for prototyping models, dashboards, and data visualizations in a short amount of time.
Streamlit integrates well with popular Python libraries like Pandas, Matplotlib, Plotly, Altair, Scikit-learn, TensorFlow, and PyTorch, which makes it easy to turn analysis and models into interactive applications.
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Streamlit applications automatically update when you modify the underlying code. This makes it perfect for real-time data visualization and monitoring.
Streamlit provides built-in widgets (e.g., sliders, text inputs, checkboxes, buttons) for adding interactivity to applications. This allows users to interact with the data and models directly.
It's great for showcasing machine learning models, especially for presenting their results or enabling users to interact with the models (e.g., making predictions or visualizing data).
Streamlit has a strong, active community and extensive documentation, which makes it easy to get help and find resources.
Disadvantages of Streamlit: