IML Work 2

Second work of UB course "Introduction to Machine Learning" implementing classification with Lazy Learning and SVM

Names of contributers

Eva Veli, Andras Kasa and Niklas Long Schiefelbein

Table of Contents

• Project Setup Instructions
  • Manual Virtual Environment Setup
  • Installing Dependencies
  • Close Virtual Environment
• Project Execution instructions
  • Make sure to be in the root directory work2
  • Activate the Virtual Environment
  • Run app.py
• Project Structure

Prerequisites

• PyCharm IDE (Professional or Community Edition)
• Python 3.9 installed on your system

Project Setup Instructions

1. Manual Virtual Environment Setup

1. Open the project work2 in PyCharm

2. Open the terminal in PyCharm (View > Tool Windows > Terminal)

3. Optional: Verify current location being work2 by pwd

4. Optional: Navigate to work2 with cd

5. Create a virtual environment:

   # Windows
   py -3.9 -m venv venv
   
   # macOS/Linux
   python3.9 -m venv venv

6. Activate the virtual environment:

   # Windows
   venv\Scripts\activate
   
   # macOS/Linux
   source venv/bin/activate

In front of the input line in the terminal it should now say (venv)

2. Installing Dependencies

With the virtual environment activated:

pip install -r requirements.txt

From here you can directly jump to Run app.py

3. Close Virtual Environment

With the virtual environment activated:

deactivate

The (venv) in front of the terminal should be gone

Project Execution instructions

1. Make sure to be in the root directory work2

For this, just follow the optional steps 3 and 4 from the Manual Virtual Environment Setup

2. Activate the Virtual Environment

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

In front of the input line in the terminal it should now say (venv)

3. Run app.py

python app.py

The first execution takes more time than usual due to the initial compilation of the whole project. Once compiled, it prompts the user to provide an input. The user must decide whether to use the hepatitis or the pen-based dataset for the analysis. By simply pressing enter, the hepatitis dataset will be selected by default.

Now the entire project pipeline will execute, including data preprocessing, KNN and SVM analyses, various reduction techniques, and final report generation. Progress is displayed in the console, but due to frequent calculations and multithreading, following along in real-time may be difficult. It is recommended to refer to the final reports for evaluation. The program completes once the nemenyi test report is generated.

For deeper insights please consider reading the report of the project.

Project Structure

work2/
├── classifiers/               # SVM and KNN classifiers
├── csv-results/               # Performance metrics and results
├── datasetsCBR/               # Dataset files
├── metrics/                   # Performance metric calculations
├── preprocessing/             # Data preprocessing scripts
├── reduction_techniques/      # Instance reduction algorithms
├── reporting/                 # Reporting and analysis scripts
├── reports/                   # Generated reports
├── venv/                      # Virtual environment
├── app.py                     # Main application script
├── README.md                  # This file
├── requirements.txt           # Dependencies
└── utils.py                   # Utility functions