Notebook Outline: Document Ranking & Information Retrieval

1. Kaggle Setup and Data Download
   Kaggle credentials are configured, and competition files (documents, queries, qrels) are downloaded via the Kaggle CLI.

2. Data Loading and Inspection
   All downloaded JSONL/JSON files are read into Python lists or dictionaries, and basic stats (document and query counts) are displayed.

3. Evaluation Metric (P-Found)
   A function pfound_score is defined to measure retrieval performance, demonstrating how to compute a score based on ranked predictions.

4. Text Preprocessing
   Titles plus a portion of content are tokenized, stemmed, and filtered for stopwords. A dictionary of document frequencies (df) is built, and very low-frequency tokens are removed.

5. TF-IDF Construction
   A sparse TF matrix is created for each document, IDF values are computed, and both are combined to form the final TF-IDF matrix.

6. Query Processing and Similarity Computation
   Queries undergo the same tokenization and stemming. Their term frequencies are assembled into a sparse matrix, and cosine-like similarity scores are calculated by multiplying document TF-IDF by query term frequencies.

7. Submission File Creation
   (Query, document) pairs and their computed scores are gathered into a dataframe and saved as a CSV file for submission.