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sites = pd.DataFrame( 'site': ['banflix.com', 'similar_site1.com', 'similar_site2.com'], 'category': [['Streaming Services', 'Entertainment'], ['Streaming Services'], ['Online Shopping']], 'keywords': [['streaming', 'movies', 'TV shows'], ['streaming', 'movies'], ['shopping']], 'content': ['This is a streaming site', 'This site offers streaming services', 'This is an online shopping site'], 'alexa_rank': [1000, 1200, 2000], 'moztrust_flow': [20, 18, 15] ) feature, allowing users to host virtual screenings with

sites['domain_similarity'] = sites['site'].apply(lambda site: domain_similarity(banflix_site['site'], site)) sites['categorical_relevance'] = sites['category'].apply(lambda category: categorical_relevance(banflix_site['category'], category)) sites['keyword_co_occurrence'] = sites['keywords'].apply(lambda keywords: keyword_co_occurrence(banflix_site['keywords'], keywords)) sites['link_graph_proximity'] = 0.5 # placeholder sites['content_overlap'] = sites['content'].apply(lambda content: content_overlap(banflix_site['content'], content)) sites['alexa_rank_proximity'] = sites['alexa_rank'].apply(lambda rank: alexa_rank_proximity(banflix_site['alexa_rank'], rank)) sites['moztrust_flow_similarity'] = sites['moztrust_flow'].apply(lambda score: moztrust_flow(banflix_site['moztrust_flow'], score)) The servers are optimized for low-latency streaming, meaning

The Draw: Speed. The servers are optimized for low-latency streaming, meaning less buffering during peak hours.