Create README.md

README.md

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+---
+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.