📅 2024-10-06 — Session: Email Classification and Preprocessing Enhancement

🕒 01:00–02:00
🏷️ Labels: Email_Classification, Machine_Learning, TF-IDF, Preprocessing, Naive_Bayes
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance email classification techniques using machine learning, focusing on improving preprocessing steps and classifier performance.

Key Activities

  • Explored effective approaches for email classification, including feature extraction techniques and algorithm selection.
  • Addressed NLTK package download issues by adjusting preprocessing steps.
  • Discussed challenges with small datasets and suggested improvements through dataset expansion and hyperparameter tuning.
  • Outlined strategies for improving model performance, including TF-IDF vectorization and feature importance analysis.
  • Fixed input errors in Naive Bayes classifier by ensuring proper text vectorization.
  • Implemented TF-IDF feature extraction to identify influential words in emails.
  • Corrected stopword handling in TfidfVectorizer for Spanish text using scikit-learn and NLTK.

Achievements

  • Developed a comprehensive plan for email classification using machine learning.
  • Successfully adjusted preprocessing steps to handle package download issues.
  • Improved understanding of model performance enhancement techniques.

Pending Tasks

  • Further testing and validation of the enhanced email classification model.
  • Explore additional feature extraction methods and dataset balancing techniques.