This project focuses on preprocessing and transforming raw email text data into a clean, structured format suitable for machine learning–based spam detection. The primary goal was to prepare high-quality, model-ready input by handling noise, inconsistencies, and formatting issues commonly found in real-world email datasets.
Note: This project focuses on data preprocessing and feature preparation. The machine learning model itself was not developed as part of this work.
- Convert raw email text into structured, machine-learning-ready data
- Improve data quality and consistency for downstream spam classification models
- Apply standard text preprocessing techniques used in real-world ML pipelines
- Cleaning raw email text (removing unnecessary characters, formatting issues, etc.)
- Text normalization (lowercasing, whitespace handling)
- Tokenization and text transformation
- Feature preparation for use in spam detection models
- Structured dataset output suitable for training and evaluation