ML-Based Personal Email Spam Filter
Budget: ₹750 – ₹1,250 INR
I’m building a Python solution that automatically flags spam in my personal inbox. The pipeline should read raw message text, run it through classic NLP cleaning—then let a Naive Bayes model trained on TF-IDF features decide what is junk and what I actually want to see. I will be working exclusively with publicly available email corpora, so nothing proprietary needs to be handled.
Key pre-processing I need implemented:
• Tokenization
• Stopwords removal
• Stemming or lemmatization
The stack is already chosen: Scikit-learn, Pandas and NumPy under Python. Once training is complete, I’d like a concise script or notebook that can:
1. Load a fresh batch of .eml or plaintext messages.
2. Output a CSV (or similar) labeling each as spam or ham.
3. Report precision, recall and F1 on a held-out test split so I can quickly judge performance.
Deliverables are the cleaned, well-commented code, a brief README explaining how to run everything end-to-end, and the final trained model file. I’ll consider the project complete when the classifier reaches solid accuracy on the public dataset and can be applied to my own personal emails with the same commands.
Key pre-processing I need implemented:
• Tokenization
• Stopwords removal
• Stemming or lemmatization
The stack is already chosen: Scikit-learn, Pandas and NumPy under Python. Once training is complete, I’d like a concise script or notebook that can:
1. Load a fresh batch of .eml or plaintext messages.
2. Output a CSV (or similar) labeling each as spam or ham.
3. Report precision, recall and F1 on a held-out test split so I can quickly judge performance.
Deliverables are the cleaned, well-commented code, a brief README explaining how to run everything end-to-end, and the final trained model file. I’ll consider the project complete when the classifier reaches solid accuracy on the public dataset and can be applied to my own personal emails with the same commands.