📝 Identify the Author - Kaggle Competition

This document outlines a compact version of the full strategy for the Kaggle "Identify the Author" competition. The task is to predict the author of short literary excerpts using machine learning and NLP techniques.

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📌 Goal

Classify excerpts by one of three authors using a multi-class model evaluated by log-loss.

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🚦 Overall Plan

1. Data Loading: Use pandas to load train.csv and test.csv. Confirm structure and balance across authors.

2. Text Preprocessing: Lowercase the text, tokenize using spaCy or NLTK, lemmatize, and retain stopwords as they may carry stylistic signals. Punctuation is removed for most features but counted separately.

3. Feature Engineering: Use TF-IDF on word and character n-grams as the core feature set. Add sentence-level features like length, punctuation counts, and readability scores. Include dense vector features via GloVe and BERT sentence embeddings.

4. Model Training: Train Logistic Regression on TF-IDF (strong baseline), Naive Bayes as quick benchmark, XGBoost on dense features, Bi-LSTM with GloVe embeddings, and fine-tuned BERT for deep context.

5. Evaluation: Use Stratified K-Fold cross-validation. Focus on log-loss. Apply probability calibration if needed and analyze misclassifications with confusion matrices.

6. Ensembling: Blend Logistic Regression, BERT, and optionally LSTM or XGBoost using weighted averaging or stacking. Choose weights based on cross-val log-loss.

7. Submission: Output class probabilities for each test example. Ensure predictions are well-calibrated and formatted per sample_submission.csv.

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📁 Structure

project-root/
├── data/         # Raw CSV files
├── notebooks/    # EDA and modeling
├── src/          # Scripts
├── outputs/      # Submissions
├── README.md
└── requirements.txt

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🏁 Summary

Start with strong baselines like TF-IDF + Logistic Regression. Enhance with stylometric features and contextual embeddings. Fine-tune BERT for state-of-the-art performance. Blend multiple models for improved log-loss. Stick to rigorous validation and calibrate probabilities.

This structured strategy balances classical NLP, modern transformers, and ensemble modeling to compete effectively in the challenge.