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// Personal project
AutoMLify
I built a Streamlit web app that automates the entire machine learning workflow, from uploading a raw dataset to exploratory analysis, cleaning, model training, and comparison, without requiring any code.
Tech Stack
PythonStreamlitMachine LearningAutomated EDA and Model Comparison
How it works
The point was to remove every manual step between a raw CSV and a usable model.
- Built automatic exploratory-analysis reporting for every column in the uploaded dataset.
- Built one-click data cleaning and preprocessing.
- Built automated training and comparison across multiple models using accuracy, F1 score, and recall.
- Automatically saved the best-performing model for download or direct use in predictions.
Why I built it
Getting from a raw dataset to a trained, comparable set of models usually takes a lot of manual, repetitive work: exploration, cleaning, training loops, model comparison, rewritten for nearly every project.
How I approached it
I automated the whole pipeline end to end: exploratory analysis per column, one-click cleaning, automated multi-model training and comparison, and automatic saving of whichever model actually performed best.
