DataVigilAI is a Python-based project aimed at detecting anomalies in data using Isolation Forest and DBSCAN clustering algorithms. Anomalies, also known as outliers, are data points that deviate significantly from the normal behavior of the dataset and may indicate errors, fraud, or rare events.
- Detect anomalies in data using Isolation Forest algorithm.
- Detect anomalies in data using DBSCAN clustering algorithm.
- Visualize detected anomalies for better understanding.
- Evaluate the performance of anomaly detection algorithms using precision, recall, and F1-score.
- Clone the repository:
git clone https://github.com/lixavi/DataVigilAI.git
cd DataVigilAI- Install the required dependencies using pip:
pip install -r requirements.txt
- Run the main script:
python main.py
- Place your dataset file (data.csv) in the project directory.
- Modify the main.py file to customize data preprocessing and anomaly detection settings if needed.
- Run the main script to detect anomalies and visualize the results.