Supervised Learning Text Classification

Example of an end-to-end Text Classification (i.e., Supervised Learning) employing Topic Modelling and covering the steps of PreProcess (e.g., clean up and validations), Training and Model Exploration, Final Validation, and Explainability.

Setup Virtual Python Environment

Prepare Python Virtual Environment:

python3.10 -m venv venv

Install required Python Libraries:

pip install -r requirements.txt

Setup Dataset

Download df_file.csv from Kaggle Datasets

Train and Evaluate Classification

This Jupyter Notebook provides an end-to-end example of preparing a predictor for an unstructured text dataset.

For Examples of Explainability, please refer to the following Folder.