# 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)
# Example DataFrame data = { 'Movie': ['Kaal', 'Movie2', 'Movie3'], 'Genre': ['Action', 'Comedy', 'Drama'], 'Year': [2005, 2010, 2012], 'Runtime': [120, 100, 110] } df = pd.DataFrame(data)
print(df) This example doesn't cover all aspects but gives you a basic understanding of data manipulation and feature generation. Depending on your specific goals, you might need to dive deeper into natural language processing for text features (e.g., movie descriptions), collaborative filtering for recommendations, or computer vision for analyzing movie posters or trailers.
# Dropping original genre column df.drop('Genre', axis=1, inplace=True)
import pandas as pd from sklearn.preprocessing import StandardScaler
AM I GOING TO HAVE TO PRINT THE PDF FILE IT CREATED?
If you file your tax return electronically, you should not have to print it. You can keep an electronic copy for your tax records.
I am seeing conflicting information about the standard deduction for a single senior tax payer. In one place it says $$16,550. and in another it says $15,000.00. Which is correct?
For a single taxpayer, the standard deduction (for 2024) is $14,600. For a taxpayer who is either legally blind or age 65 or older, the standard deduction is $16,550. For a taxpayer who is both legally blind AND age 65 or older, the standard deduction is $18,500.
For 2025, the standard deduction for single taxpayers (without adjustments for age or blindness) is $15,000.