Machine Learning Project Data Labeling
OtherTextClassification
Developed supervised models for sentiment analysis, spam detection, and predictive modeling by manually labeling text data for training. Labeled and preprocessed input as positive, negative, or neutral for sentiment, and as spam or non-spam for email detection. Used word frequency, TF-IDF scoring and feature engineering to enhance label quality.
• Conducted detailed text cleansing and tokenization prior to labeling.
• Iteratively reviewed and corrected label assignments for improved model accuracy.
• Collaborated using GitHub for workflow transparency and reproducibility.
• Evaluated labeling quality with accuracy, precision, recall, RMSE, and F1-score metrics.
Developed supervised models for sentiment analysis, spam detection, and predictive modeling by manually labeling text data for training. Labeled and preprocessed input as positive, negative, or neutral for sentiment, and as spam or non-spam for email detection. Used word frequency, TF-IDF scoring and feature engineering to enhance label quality. • Conducted detailed text cleansing and tokenization prior to labeling. • Iteratively reviewed and corrected label assignments for improved model accuracy. • Collaborated using GitHub for workflow transparency and reproducibility. • Evaluated labeling quality with accuracy, precision, recall, RMSE, and F1-score metrics.
Not specified