---
license: mit
---

# Anomaly Detection Suite

This repository hosts a comprehensive project on anomaly detection, evaluating and comparing multiple algorithms on a synthetic dataset. It includes the implementation notebook, trained models, results, and visualizations.

## Project Overview

This project provides a hands-on guide to identifying outliers using the following methods:

-   **Statistical Methods (Z-score)**
-   **Isolation Forest**
-   **One-Class SVM**
-   **Local Outlier Factor (LOF)**
-   **Autoencoder (Deep Learning)**

The goal is to provide a clear comparison of how these different techniques perform on the same dataset.

## Repository Contents

-   `implementation.ipynb`: The main Jupyter notebook with all the code and explanations.
-   `anomaly_detection_results/`: A directory containing all the generated files:
    -   Trained models for each algorithm.
    -   Anomaly scores and predictions.
    -   Performance metrics and results in JSON format.
    -   Visualizations comparing the different methods.

## How to Use the Models

The trained models are saved in the `anomaly_detection_results/` directory. You can load them to make predictions on new data. For example, to load the Isolation Forest model:

```python
import pickle

with open('anomaly_detection_results/isolation_forest_model.pkl', 'rb') as f:
    model = pickle.load(f)

# Now you can use the model to predict on new data
# predictions = model.predict(new_data)
```

## Dataset

The dataset is synthetically generated within the `implementation.ipynb` notebook. It consists of two-dimensional data with a clear cluster of normal points and a few scattered outliers.