Code for Group 66 python implementation of Cyber Data Analytics assigment 1 CS4035. ๐
Team members:
The structure of the project is as follows:
main.py
- The main entry point in the project. This uses all the necessary components to run the pre-procesing, feature extraction, classificantion and optimization.card_tracker.py
- Contains the class used to store the context associated with each card and calculates the additional features.Exploratory_Analysis.ipynb
- The notebook used for analisys. A saved version of the output can be found underExploratory_Analysis.html
.config.txt
- Used for configuration variables. For the moment only the path to the data set is used.cross_valid.py
- Used for the cross-validation algorithm.data_preproc.py
- Used for preprocessing of data(droping + conversions).feats_utils.py
- Utilities. Mainly used for functions to run apply in pandas.hyperparam_tunning.py
- The algorithm used perform random grid search.logger.py
- Logger class.plot_utils.py
- Utilities used to plot the results.data/
- Folder containing the datasetdata_for_student_case.csv
.logs/
- Folder where the logs are stored.models/
- Folder where the parameters for the classifiers are stored.output/
- Folder where the images output.
For a more detailed look check the code comments.
The scripts can be run in Anaconda Windows/Linux environment.
You need to create an Anaconda ๐ python 3.6
environment named cyber1
.
Inside that environment some addition packages needs to be installed. Run the following commands inside Anaconda Prompt โจ:
(base) conda create -n cyber1 python=3.6 anaconda
(base) conda activate cyber1
(cyber1) conda install -c conda-forge imbalanced-learn
(cyber1) conda install -c anaconda py-xgboost