# Assuming 'data' is a DataFrame with a column 'title' for video titles # and a column 'genre' for video genres Atid566decensoredwidow Sad Announcement M Work Impact Of Sad
# Fit the vectorizer to the training data and transform both the training and testing data X_train = vectorizer.fit_transform(train_titles) y_train = train_genres Patnja I Ozdravljenje Serija Sa Prevodom Verified Instant
# Split data into training and testing sets train_titles, test_titles, train_genres, test_genres = train_test_split(data['title'], data['genre'], test_size=0.2, random_state=42)
import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split
# You can now use X_train and y_train to train a classifier This example is quite basic and focuses on text analysis. Depending on your specific needs, you might need to incorporate more advanced techniques or use pre-trained models like those provided by TensorFlow or PyTorch.
# Create a TF-IDF vectorizer vectorizer = TfidfVectorizer()