Anomaly Detection for Hypertirodism

The report is available at Report - Anomaly Detection.pdf

Install the required libraries by running the following command:

pip install -r requirements.txt

To run the code, execute the following command:

python main.py

The structure of the project is as follows:

.
├── Data
│   ├── dataset.csv
│   ├── datasetWithOutliers.csv
│   └── outliers.csv
├── Media
│   ├── <output images>
│   └──[...]
├── Methods
│   ├── DBSCAN.py
│   ├── KNN.py
│   ├── PCA.py
│   └── forest.py
├── Utils
│   ├── descriptive.py
│   └── functions.py
├── main.py
├── README.md
└── requirements.txt

Where dataset.csv is the original dataset, datasetWithOutliers.csv is the original dataset with the outlier probability added to the last column, and outliers.csv is the dataset where for each observation we have the label assigned by the algorithm plus the probability of being an outlier and the label after a sharp threshold (see report).

Note: datasetWithOutliers.csv was saved with , as separator and . as decimal separator in order to be read correctly by pandas.