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karthik-2905/
AnomalyDetection

Name: AnomalyDetection
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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.

karthik-2905/AnomalyDetection