Anomaly-Detection

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Anomaly-Detection

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Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.

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# Anomaly Detection (Transactions & Sales)
Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.

Detect anomalies in synthetic transaction data using Isolation Forest, Local Outlier Factor (LOF), and a Z-score baseline. The project generates data, injects anomalies, runs detectors, and exports flagged rows and charts for audit.

---

## Features
- Synthetic transaction generator with anomalies (bursts, extreme purchases, negative/zero entries)
- Multi-model detection: Isolation Forest, LOF, Z-score
- Unified anomaly report with model votes and severity score
- Clean visualizations: time-series spikes and amount distribution
- Reproducible scripts with deterministic seeding

---

## Project Structure
```
anomaly-detection/
├─ README.md
├─ LICENSE
├─ requirements.txt
├─ data/
│  └─ generate_transactions.py
├─ src/
│  ├─ detect_anomalies.py
│  └─ utils.py
└─ outputs/
   └─ figures & reports
```

---

## Setup
```bash
python -m venv .venv
# Windows:
.venv\Scripts\activate
# macOS/Linux:
source .venv/bin/activate
pip install -r requirements.txt
```

---

## Generate Synthetic Data
```bash
python data/generate_transactions.py --start 2023-01-01 --end 2024-12-31 --seed 42 --n-customers 500 --out data/transactions.csv
```

---

## Run Anomaly Detection
```bash
python src/detect_anomalies.py --input data/transactions.csv --outdir outputs --contamination 0.02
```

**Outputs**
- `outputs/anomalies.csv` – flagged rows with anomaly scores & model votes  
- `outputs/fig_amount_time.png` – transaction amounts over time with spikes  
- `outputs/fig_amount_hist.png` – amount distribution histogram

---

## Sample Results

### Transaction Amount Distribution
Shows most transactions are small (0–300 units). A few very large amounts (thousands) appear as outliers.

---

### Transaction Amounts Over Time
Transactions are generally stable, but occasional spikes (extreme purchases or errors) appear and are flagged as anomalies.

---

## Data Schema
| column      | description                      |
|-------------|----------------------------------|
| tx_id       | unique transaction ID            |
| date        | timestamp (daily resolution)     |
| customer_id | customer identifier              |
| category    | product category                 |
| amount      | transaction amount (float)       |

Anomaly-Detection

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