📅 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.