pyClassify - Automated ML-based Tool for Classification Problems

This project aims to develop a comprehensive Python script that automates the process of running (binary or multi-class) classification problems on any given input data matrix in the form of a standard Feature x Instance matrix (.csv) file.

Project Structure

The project consists of multiple Python scripts, each responsible for a specific step in the machine learning pipeline. The main script orchestrates the execution of these scripts, ensuring a seamless and automated workflow.

Project Components

1. Preprocessing:

   • This script handles the missing value and string conversion of the given main input file.
   • User input: classification data in .csv format.
   • Output: Preprocessed data matrix.

2. Normalization/Standardization:

   • This script handles the normalization or standardization of the input data.
   • User input: Type of normalization or standardization.
   • Output: Normalized or standardized data matrix.

3. Feature Selection:

   • This script performs feature selection on the preprocessed data.
   • User input: Feature selection method and parameters.
   • Output: Data matrix with selected features.

4. Cross-Validation Script:

   • Implements cross-validation on the data.
   • User input: Number of folds for cross-validation.
   • Output: Cross-validated performance metrics.

5. Machine Learning Modeling Script:

   • Executes the machine learning modeling for classification.
   • User input: Classification algorithm and hyperparameters.
   • Output: Trained machine learning model.

6. Prediction Script:

   • Evaluates the predictive capability of the model on a blind dataset.
   • Output: Accuracy and other performance metrics on the blind dataset.

7. Main Script:

   • Orchestrates the execution of the above scripts.
   • User input: File path of the input data matrix (.csv).
   • Output: Generates plots, heatmaps, and prints performance metrics in a PDF file.

Usage

1. Clone the repository:

   git clone https://github.com/yourusername/automated-ml-classification.git
   cd automated-ml-classification

2. Install dependencies:

   pip install fpdf

3. Run the main script:

   python main.py

4. Follow the prompts to provide input options for each step of the pipeline.

5. Check the output PDF file for performance metrics and plots.

Notes

• Ensure that the input data matrix is in the required format (Feature x Instance matrix in .csv format).

Feel free to contribute, report issues, or suggest improvements. Happy classifying!