<jupyter_start><jupyter_text><jupyter_code>!git clone https://github.com/ShubhankSinghal/Movie-Recommendation-System.git
cd "Movie-Recommendation-System/"
!pip install surprise
!python3 main.py<jupyter_output>Loading movie ratings...

Computing movie popularity ranks so we can measure novelty later...
Estimating biases using als...
Computing the cosine similarity matrix...
Done computing similarity matrix.
Evaluating  SVD ...
Evaluating accuracy...
Evaluating top-N with leave-one-out...
Computing hit-rate and rank metrics...
Computing recommendations with full data set...
Analyzing coverage, diversity, and novelty...
Computing the cosine similarity matrix...
Done computing similarity matrix.
Analysis complete.
Evaluating  ContentKNN ...
Evaluating accuracy...
Computing content-based similarity matrix...
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{'fraction_non_alphanumeric': 0.06836902800658978, 'fraction_numerical': 0.1696869851729819, 'mean_word_length': 3.278169014084507, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '285028', 'license': 'MIT', 'n_tokens_mistral': 550, 'n_tokens_neox': 413, 'n_words': 160, 'path': 'run_on_Google_Colab.ipynb', 'repo_name': 'ShubhankSinghal/Movie-Recommendation-System', 'size': '1214'}