04_01 - Train Baseline Model¶

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Train a baseline Logistic Regression model for credit card fraud detection using sklearn Pipeline with business metrics.

Students build the training script incrementally across 7 versions - each version adds exactly ONE concept.

Pre-requisite: Python Environment Setup¶

# Create conda environment
conda create -n mlops-env1 python=3.14 -c conda-forge -y

# Activate environment
conda activate mlops-env1

# Install dependencies (locked versions)
cd ccfd-project
pip install -r requirements.txt

Note: All sections in this course use the same mlops-env1 environment. You only need to create it once. After that, just activate it with conda activate mlops-env1 before running any scripts.

Incremental Build: v1 → v7¶

TRAINING PIPELINE - BUILD IT STEP BY STEP
==========================================

  v1          v2          v3           v4            v5            v6           v7
  Skeleton    Load        Split        Pipeline      Evaluate      Save         Save
  (stubs)     Data        Train/Test   & Train       & Metrics     Pipeline     Metrics
                                                                                JSON
  ┌─────┐   ┌─────┐    ┌─────┐     ┌─────┐       ┌─────┐      ┌─────┐      ┌─────┐
  │     │   │STEP1│    │STEP1│     │STEP1│       │STEP1│      │STEP1│      │STEP1│
  │     │   │Load │───>│Load │───> │Load │───>   │Load │───>  │Load │───>  │Load │
  │     │   │     │    │STEP2│     │STEP2│       │STEP2│      │STEP2│      │STEP2│
  │ ALL │   │     │    │Split│───> │Split│───>   │Split│───>  │Split│───>  │Split│
  │STUBS│   │     │    │     │     │STEP3│       │STEP3│      │STEP3│      │STEP3│
  │     │   │     │    │     │     │Pipe │───>   │Pipe │───>  │Pipe │───>  │Pipe │
  │     │   │     │    │     │     │STEP4│       │STEP4│      │STEP4│      │STEP4│
  │     │   │     │    │     │     │Train│───>   │Train│───>  │Train│───>  │Train│
  │     │   │     │    │     │     │     │       │STEP5│      │STEP5│      │STEP5│
  │     │   │     │    │     │     │     │       │Eval │───>  │Eval │───>  │Eval │
  │     │   │     │    │     │     │     │       │     │      │STEP6│      │STEP6│
  │     │   │     │    │     │     │     │       │     │      │Save │───>  │Save │
  │     │   │     │    │     │     │     │       │     │      │Model│      │Model│
  │     │   │     │    │     │     │     │       │     │      │     │      │STEP7│
  │     │   │     │    │     │     │     │       │     │      │     │      │Save │
  │     │   │     │    │     │     │     │       │     │      │     │      │JSON │
  └─────┘   └─────┘    └─────┘     └─────┘       └─────┘      └─────┘      └─────┘

Version Summary¶

Version	Folder	What's New	New Functions
v1	`v1_base_ccfd-project/`	Project skeleton - all files are stubs	-
v2	`v2_load_data_ccfd-project/`	STEP 1: Load CSV data	`load_data()`
v3	`v3_split_train_test_ccfd-project/`	STEP 2: Stratified train/test split	`split_data()`, `get_feature_columns()`
v4	`v4_create_and_train_pipeline_ccfd-project/`	STEP 3-4: Create & train sklearn Pipeline	`create_preprocessor()`, `create_pipeline()`
v5	`v5_evaluate_and_metrics_ccfd-project/`	STEP 5: Evaluate with technical + business metrics	`print_metrics()`, `calculate_business_metrics()`
v6	`v6_save_pipeline_ccfd-project/`	STEP 6: Save trained pipeline to disk	`save_pipeline()`
v7	`v7_save_metrics_ccfd-project/`	STEP 7: Save metrics JSON for tracking	`save_metrics_json()`
Final	`ccfd-project/`	Complete reference with full docstrings	All functions

Project Structure (Final - ccfd-project)¶

ccfd-project/
├── p1_01_generate_initial_dataset.py    # Generate synthetic dataset
├── p3_01_train_model_baseline.py        # Training script (7 steps)
├── requirements.txt                     # Python dependencies
└── utils/
    ├── __init__.py                      # Package exports
    ├── data_utils.py                    # load_data, split_data, create_preprocessor
    ├── model_utils.py                   # create_pipeline, save_pipeline
    └── metrics_utils.py                 # print_metrics, save_metrics_json

How to Run (Any Version)¶

# 1. Navigate to any version folder
cd v3_split_train_test_ccfd-project/       # or any v1-v7, or ccfd-project/

# 2. Generate data (required first - creates data/ folder)
python p1_01_generate_initial_dataset.py

# 3. Run the training script
python p3_01_train_model_baseline.py

Note: v1 is stubs only - nothing runs. v2+ will execute the steps implemented so far.

What the Pipeline Does¶

Input (8 raw features)
    │
    ▼
ColumnTransformer (Preprocessor)
├── OneHotEncoder  → 3 categorical (merchant_category, card_present, international)
└── StandardScaler → 5 numerical   (transaction_amount, transaction_hour, etc.)
    │
    ▼
LogisticRegression (C=1.0, class_weight='balanced')
    │
    ▼
Output: FRAUD / LEGITIMATE (with probability)

Utils Module - Function Inventory¶

data_utils.py¶

Function	Added In	Purpose
`load_data()`	v2	Load CSV → DataFrame
`get_feature_columns()`	v3	Return categorical + numerical column lists
`split_data()`	v3	Stratified train/test split with feature info
`create_preprocessor()`	v4	ColumnTransformer (OneHotEncoder + StandardScaler)

model_utils.py¶

Function	Added In	Purpose
`create_pipeline()`	v4	Build sklearn Pipeline (preprocessor + model)
`save_pipeline()`	v6	Save pipeline pickle + metadata JSON

metrics_utils.py¶

Function	Added In	Purpose
`print_metrics()`	v5	Print technical + business metrics
`calculate_business_metrics()`	v5	Compute net benefit, fraud caught/missed
`save_metrics_json()`	v7	Save metrics to results/ as JSON

Expected Output (v7 / ccfd-project)¶

STEP 1: Loading Data
Loaded 10,000 transactions from data/credit_card_transactions_latest.csv

STEP 2: Splitting Data
Split: 8,000 train | 2,000 test

STEP 3: Creating Pipeline
Pipeline created:
   1. Preprocessor (OneHotEncoder + StandardScaler)
   2. Model (LogisticRegression)

STEP 4: Training Pipeline
Training complete!

STEP 5: Evaluating Performance
   F1 Score:     0.2329
   Precision:    0.1373
   Recall:       0.7667
   ROC AUC:      0.8837
   Net Benefit:  $39,625

STEP 6: Saving Pipeline
   Model saved to: models/p3_01_baseline/

STEP 7: Saving Metrics
   Metrics saved to: results/

======================================================================
BASELINE TRAINING COMPLETE!
======================================================================

Files Created (v7):

File	Description
`data/credit_card_transactions_latest.csv`	Generated by p1_01
`models/p3_01_baseline/model_latest.pkl`	Trained sklearn Pipeline
`models/p3_01_baseline/metadata_latest.json`	Model metadata + metrics
`results/logistic_regression_*.json`	Detailed metrics JSON

Understanding the Metrics¶

Why Recall Matters Most in Fraud Detection¶

Cost of missing real fraud:   $1,500  (average fraud amount)
Cost of a false alarm:        $25     (investigation cost)

Missing fraud is 60x more expensive than a false alarm!

That's why we use class_weight='balanced' - prioritize catching fraud even at the cost of more false alarms.

Business Value (Net Benefit)¶

Net Benefit = (Fraud Caught × $1,475) - (False Alarms × $25) - (Fraud Missed × $1,500)

Even with low precision (13.7%), the model generates positive net benefit because catching fraud far outweighs false alarm costs.

Next Steps¶

Next	Topic	What You'll Do
04_02_Inference_API	Serve Predictions	Build FastAPI server, test via browser + scripts

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