This repository documents a CRISP‑DM plan to automate legal document and clause classification, organized for clear review with runnable notebooks, minimal dependencies, and practical troubleshooting.
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├─ docs/ # Project briefs, PDFs, and any slide notes
├─ notebooks/ # EDA, feature engineering, training, evaluation (.ipynb)
├─ src/ # Reusable Python modules if split from notebooks
├─ data/ # Small sample data or schemas (no sensitive raw data)
└─ reports/ # Generated figures, tables, and exports
- Frame contract review as a measurable analytics problem and define outputs that reduce expert effort and oversight risk.
- Classify documents/clauses and surface key attributes for faster, more consistent legal review.
Use a fresh Python 3 environment, install dependencies, and run notebooks in order for reproducibility.
# create and activate a virtual environment
python -m venv .venv
# Windows:
# .venv\Scripts\activate
# macOS/Linux:
# source .venv/bin/activate
# upgrade pip and install dependencies
python -m pip install -U pip
# if you have a requirements file (preferred)
python -m pip install -r requirements.txt
# otherwise install a minimal stack
python -m pip install pandas scikit-learn nltk transformers jupyter matplotlib seaborn
- Open files in notebooks/ and execute cells top‑to‑bottom, updating any local paths to sample data you include.
- If code modules exist in src/, ensure the working directory is the repo root or add src/ to the Python path in the first cell.
Install packages into the current Jupyter kernel from inside a notebook to avoid interpreter mismatches.
import sys
!{sys.executable} -m pip install -U pip "pandas[excel]" numpy matplotlib seaborn scipy
Verify imports and optional Excel support.
import pandas as pd, openpyxl
print(pd.__version__)
# Example .xlsx read (uncomment and set your path if needed):
# df = pd.read_excel("example.xlsx")
# df.head()
Automate rapid review of contractual clauses so legal and risk teams focus on high‑value judgment instead of repetitive reading; outcomes include time savings and fewer servicing mistakes.
Inputs: commercial loan agreements, credit‑default swaps, custody agreements, and similar contracts in PDF, scans, or word‑processed formats. Challenges: clause wording variation, structure differences, OCR for scans, and large volumes.
Consolidate documents, run OCR where needed, normalize/clean text, and label clauses into categories (e.g., repayment, default, interest rate). Create train/validation/test splits.
Start with supervised text classifiers (Logistic Regression, Random Forest) and compare to transformer‑based models (e.g., BERT). Combine simple rule patterns for templated contracts with clustering (k‑means) to discover unknown clause groups.
Report accuracy, precision, recall, and F1; validate on fresh, real‑world documents. Iterate by improving labels, features, and hyperparameters when metrics underperform targets.
Integrate with document management systems, collect reviewer feedback, and monitor drift as templates and legal language evolve. Plan periodic retraining and targeted error analysis.
- docs/Course‑1‑Project.pdf — project brief with scenario, learning outcomes, and submission instructions.
- docs/Final‑Project‑Course1‑Introduction‑to‑Data‑Analytics.pdf — full CRISP‑DM write‑up across all phases.
Recommended for public portfolios: MIT License for permissive reuse with attribution and an “as is” disclaimer; change if your constraints differ.