Research Article 1
- Volume 2, Issue 1 2026
By Kashif Iqbal1,SikandarAli,MustafaHaiderAli,Muhammad Hammad, Faraz Ali, Raheema Agha
10.21621/ijec.20260201.04
Keywords: Spam classification, TF-IDF, Logistic Regression, Na ??ve Bayes, Calibrated Linear SVC, Calibrated Classifier CV, machine translation, MarianMT, multilingual NLP, Streamlit, Deep Learning, BERT

This paper presents a multilingual spam email classification framework capable of processing both German and English texts using classical machine learning models combined with a translation-enhanced pipeline. The system integrates TF-IDF vectorization, Logistic Regression, Multinomial Naive Bayes, and a probability-calibrated Linear Support Vector Classifier (SVC). A key contribution is the incorporation of an automatic German-to-English translation layer using MarianMT, allowing cross-lingual evaluation and robustness analysis. Multiple models are trained on a manually curated dataset of 3,790 emails, achieving up to 98.55% accuracy and 0.9860 AUC-ROC on the evaluated dataset. A Streamlit-based application is implemented for real-time inference, supporting auto-detection of input language, dual-model evaluation for German emails, and calibrated probability scores. Experimental results demonstrate that Logistic Regression provides the most consistent overall performance, while Calibrated Linear SVC delivers the highest AUC-ROC and stable decision boundaries. The system represents a practical, expandable multilingual spam detection pipeline suitable for lightweight deployment scenarios.

Submission Date: 13 Apr, 2026 Reviews Completed: 11 May, 2026
Acceptance Date: 18 May, 2026 Publication Date: 23 Jun, 2026