import pandas as pd from sklearn.preprocessing import StandardScaler
# Scaling scaler = StandardScaler() df[['Year', 'Runtime']] = scaler.fit_transform(df[['Year', 'Runtime']])
# One-hot encoding for genres genre_dummies = pd.get_dummies(df['Genre']) df = pd.concat([df, genre_dummies], axis=1)
# Dropping original genre column df.drop('Genre', axis=1, inplace=True)
import pandas as pd from sklearn.preprocessing import StandardScaler
# Scaling scaler = StandardScaler() df[['Year', 'Runtime']] = scaler.fit_transform(df[['Year', 'Runtime']])
# One-hot encoding for genres genre_dummies = pd.get_dummies(df['Genre']) df = pd.concat([df, genre_dummies], axis=1)
# Dropping original genre column df.drop('Genre', axis=1, inplace=True)