How can I handle missing values in a dataset before building a predictive model?

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    # Example of handling missing values using Python's pandas
    import pandas as pd

    # Load your dataset
    df = pd.read_csv('data.csv')

    # Option 1: Drop rows with missing values
    df_dropped = df.dropna()

    # Option 2: Impute missing values with mean (for numerical columns)
    df['column_name'].fillna(df['column_name'].mean(), inplace=True)

    # Option 3: Impute missing values with mode (for categorical columns)
    df['categorical_column'].fillna(df['categorical_column'].mode()[0], inplace=True)
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