Adn503enjavhdtoday01022024020010 Min Best May 2026

def preprocess_string(input_string): # Tokenize tokens = re.findall(r'\w+|\d+', input_string) # Assume date is in the format DDMMYYYY date_token = None for token in tokens: try: date = datetime.strptime(token, '%d%m%Y') date_token = date.strftime('%Y-%m-%d') # Standardized date format tokens.remove(token) break except ValueError: pass # Simple manipulation: assume 'min' and 'best' are of interest min_best = [token for token in tokens if token in ['min', 'best']] other_tokens = [token for token in tokens if token not in ['min', 'best']] # Example of one-hot encoding for other tokens # This part highly depends on the actual tokens you get and their meanings one_hot_encoded = token: 1 for token in other_tokens features = 'date': date_token, 'min_best': min_best, 'one_hot': one_hot_encoded return features