Enhanced Email Classification with TF-IDF and Naive Bayes

• Day: 2024-10-06
• Time: 01:00 to 02:00
• Project: Dev
• Workspace: WP 2: Operational
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Email_Classification, TF-IDF, Naive Bayes, Machine_Learning, NLP

Description

Session Goal: The session aimed to enhance email classification techniques using machine learning, focusing on TF-IDF feature extraction and Naive Bayes classifier optimization.

Key Activities:

1. Explored effective approaches for email classification, including feature extraction, algorithm selection, and data preprocessing.
2. Adjusted preprocessing steps due to package download issues, specifically with NLTK’s stopwords and wordnet, and planned for code adjustments.
3. Addressed challenges of classifier performance with small datasets, discussing model tuning and dataset balancing.
4. Made key decisions to improve model performance through preprocessing, TF-IDF vectorization, and feature importance analysis.
5. Fixed input errors in the Naive Bayes classifier by ensuring numerical input through text cleaning and vectorization.
6. Implemented TF-IDF feature extraction to identify influential words, improving model understanding and performance.
7. Corrected and combined Spanish and English stopwords in TF-IDF vectorization using scikit-learn and NLTK.

Achievements:

• Developed a comprehensive strategy for email classification improvement.
• Implemented TF-IDF feature extraction and addressed preprocessing challenges.
• Corrected input errors in Naive Bayes classifier.

Pending Tasks:

• Re-run preprocessing steps once NLTK package issues are resolved.
• Further explore hyperparameter tuning for improved classifier performance.

Evidence

• source_file=2024-10-06.sessions.jsonl, line_number=1, event_count=0, session_id=10cbeb496d0b04099f25ac93f9ed55c49500388d13279e01ec3ca5bc8d752165
• event_ids: []