ml-cv-lab

A personal portfolio of data science + machine learning projects (with some CV/Deep Learning work mixed in), built to practice end-to-end workflows: data prep → modeling → evaluation → visualization/reporting.

Each project lives in its own folder with its own notebook/script, dependencies, and dataset(s).

Projects

Project	Type	What it does	Key tools
credit_fraud_detector/	Classification	Detects fraudulent credit card transactions (imbalanced ML)	pandas, scikit-learn, seaborn
disease_prediction/	Multi-class classification	Predicts disease from symptom inputs	pandas, scikit-learn
game-of-wines/	Classification + Reporting	Predicts wine quality class + exports reports (HTML)	pandas, scikit-learn, matplotlib
stock_predictor/	Time series (Deep Learning)	Predicts stock closing prices using LSTM	yfinance, TensorFlow/Keras

Repo layout

ml-cv-lab/
├─ credit_fraud_detector/
├─ disease_prediction/
├─ game-of-wines/
└─ stock_predictor/

Each project folder typically includes:

• readme.md — project overview + run instructions
• requirements.txt (or similar) — dependencies
• data/ or dataset/ — datasets (when included)
• *.ipynb / *.py — the main notebook or script

Quick start

• Choose a project folder (example: credit_fraud_detector/)
• Open that project’s readme.md
• Install its dependencies: pip install -r requirements.txt
• Run the notebook/script as instructed in that project.

Some projects include datasets in the repo for convenience. For larger datasets, a common best practice is keeping them out of Git history and documenting how to download/place them.