Stephen 52 Yahoo Com Gmail Com Mail Com 2020 21 Txt -

# 10. Text entropy (as a measure of unpredictability) import math freq = {} for ch in text: freq[ch] = freq.get(ch, 0) + 1 entropy = -sum((count/len(text)) * math.log2(count/len(text)) for count in freq.values()) features['entropy'] = round(entropy, 3)

"stephen 52 yahoo com gmail com mail com 2020 21 txt" A deep feature in machine learning or data processing typically means extracting meaningful, higher-level attributes from raw input — going beyond simple keyword extraction into inferred patterns, relationships, or embeddings. stephen 52 yahoo com gmail com mail com 2020 21 txt

# 9. Embedded feature: "year + number" combo if len(years) == 1 and len(numbers) > 1: other_nums = [n for n in numbers if n not in years] if other_nums: features['year_num_pair'] = (years[0], other_nums[0]) Embedded feature: "year + number" combo if len(years)

# 7. File extension hint if 'txt' in tokens: features['file_extension'] = 'txt' features['looks_like_filename'] = True else: features['looks_like_filename'] = False or embeddings. # 9.

# 5. Possible email construction (name + domain) if features['has_name'] and found_domains: possible_emails = [f"{features['first_token_is_name']}@{d}.com" for d in found_domains] features['possible_emails'] = possible_emails

# 1. Basic stats features['token_count'] = len(tokens) features['char_count'] = len(text) features['digit_count'] = sum(c.isdigit() for c in text) features['alpha_count'] = sum(c.isalpha() for c in text)

# 2. Name detection (if first token looks like a name) if tokens and tokens[0].isalpha() and tokens[0][0].isupper(): features['has_name'] = True features['first_token_is_name'] = tokens[0] else: features['has_name'] = False