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 and pandas
    import pandas as pd

    # Load your dataset
    df = pd.read_csv('your_dataset.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'] = df['column_name'].fillna(df['column_name'].mean())

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

    # Option 4: Use sklearn's SimpleImputer for more advanced imputation
    from sklearn.impute import SimpleImputer
    imputer = SimpleImputer(strategy='mean')
    df_imputed = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
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