Group 6 Prediction Model

Setup Virtual Environment

python -m venv venv
source venv/bin/activate        # Linux/Mac
venv\Scripts\activate           # Windows

Install Requirements

pip install pm4py pandas numpy torch scikit-learn seaborn matplotlib tqdm

Data

Source: BPI_Challenge_2017.xes from Canvas

Included CSV files:

• train_data.csv - Training set (cases ending before 2016-11-01)
• test_data.csv - Test set (cases starting after 2016-11-01)

Temporal Split:

• Cutoff date: 2016-11-01
• Train: cases that ended before cutoff
• Test: cases that started after cutoff
• Excluded: cases spanning the cutoff (to prevent data leakage)

Features in CSV:

Column	Description
CaseID	Application ID
CreditScore	Last known credit score before decision point
OfferCount	Number of offers created
ProcessingTime	Days from start to A_Complete
Amount	Requested loan amount
Label	0 = Accepted, 1 = Cancelled/Refused
+ Activity columns	Bag-of-words count per activity

Run

Open prediction_model.ipynb and run all cells in order.