docs/01_project_definition/00_quickstart.md

Project Definition - Quickstart

This quickstart explains how to prepare the environment and reproduce core experiments and inference from the repository.

1. Create a Python environment and install dependencies:

python -m venv .venv
source .venv/Scripts/activate
pip install -r requirements.txt

2. Inspect processed data and vectorizers (already available in repo):

• data/processed/ contains prepared train/valid/test CSVs and labels.
• data/vectorizers/ contains the fitted TF-IDF vectorizer and sparse matrices.

3. Run notebooks (recommended order):

• notebooks/01_data_acquisition.ipynb
• notebooks/02_eda.ipynb
• notebooks/03_data_preprocessing.ipynb
• notebooks/04_feature_engineering.ipynb
• modeling notebooks 05_*.ipynb → 14_comparsion.ipynb

4. Run the demo/app:

streamlit run app.py

Notes:

• Preprocessed artifacts and trained model joblib files are stored under data/processed, data/vectorizers, and data/models to speed up reproduction.