Click on their name to see the source codes.
- iddb.csv : The table where event names and their corresponding ids are stored.
- Event/Event.py : The object-oriented source code for analyzing event records, extracting features, visualizing nearly everything about any event on their own. It's a custom package!
- Event/test.py : Code required for testing the functionality of Event.py
- features.py : Handles event files, acts as a bridge between Event.py and event record files, writes every event's features to a file named export_data.csv. A total of 221 pulse features is extracted from 101 event records.
- clusters.csv : The data file including KMeans and DBSCAN clusterings of all the events.
- model/kmeans.py : A code that applies various analysis techniques and machine learning algorithms to the extracted event data.
- Analysis Techniques: Principal Component Variance Analysis, Feature importance sorting, Silhouette Scores of clusterings
- Machine Learning Algorithms: K-means clustering, DBSCAN clustering, Hierarchial Clustering
- model/kmeans1.py : A statistical analysis and 3D visualization code for understanding the behaviours of features and visualizing 3D view of clusters.
- model/simulation.py : A simulation for understanding how varying specific features change explained variance ratio, also checks for correlations between Principal Components and features, and visualizes a BiPlot.
- model/test3ax.py : A test for visualizing 5D/4D/3D clusters in 3D space.
requirements.txt file includes all the necessary packages to develop and run this project properly. The main packages used in this project are:
matplotlib
pandas
sklearn
scipy
numpy
seaborn
kneed
First clone the repository:
git clone https://github.com/Abtaha/PROJ201.gitThen move into the repository and install the required packages:
pip install -r requirements.txtThen run the following python files from the root directory of the repository to see the visualizations and analysis:
model/kmeans.py
model/simulation.py