Train-and-Download Regression Network Builder for CSV Data

Added Jun 2025 3 design docs

The distance between understanding neural networks and having trained one on your own data is shorter than most beginners think. This project removes the setup friction entirely: bring a CSV file, train a real network in the browser, and leave with a working model file. The intern builds a Streamlit web application where users upload a CSV dataset, pick the target column, and train a regression neural network built with Keras on TensorFlow. Training progress is visualized live through loss curves, and once training completes the app plots predictions against actual values so users can judge the fit at a glance. A download button exports the trained model, completing the cycle from raw data to portable artifact. The intern handles the practical engineering underneath: parsing uploads, basic preprocessing, choosing sensible default architectures, and keeping training responsive inside a web application. The project gives the intern end-to-end experience with the deep learning workflow — data ingestion, network construction, training visualization, and model export — while producing an approachable tool that makes a first neural network achievable for the next learner who uses it.

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