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dataprofessor/streamlit_freecodecamp
Build 12 Data Apps in Python with Streamlit
A companion repo to a freeCodeCamp course teaching Streamlit through 12 progressively more involved data apps — stock prices, DNA analysis, sports EDA, ML classifiers, bioinformatics regression. It's a learning resource, not a library or tool. The target audience is Python beginners wanting to ship something that looks like a real app.
Each app is self-contained in its own directory with its own data and model files, so you can run any one without touching the others. The progression is well-paced: EDA apps come before ML apps, simple widgets before file uploads and model inference. Shipping pre-trained .pkl files means the ML examples run immediately without a training step. The bioinformatics examples (DNA nucleotide counter, solubility prediction) are a genuinely unusual choice that avoids the nth Iris classifier fatigue.
The repo is a static snapshot tied to a 2021-era video course — the Streamlit API has changed enough since then that some patterns (st.sidebar.header, caching decorators) may not work out of the box with a current install. No requirements.txt files in most app directories, so dependency versions are a guessing game. App 9 uses the Boston Housing dataset, which scikit-learn removed in 2022 due to ethical concerns — that example will break on any recent sklearn. Nothing here teaches state management, authentication, or deployment, which are the parts that actually trip people up when building real apps.