Data Science Interview

Question: How do you handle missing values in a dataset during preprocessing?

Answer: Missing values can be handled using various techniques depending on the context:

Remove rows or columns with excessive missing data.
Impute missing values using statistical methods such as mean, median, or mode.
Use predictive modeling to estimate missing values.
For time series data, use forward-fill or backward-fill methods.

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        import pandas as pd
      
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        df = pd.read_csv('data.csv')
      
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        # Impute missing values with mean
      
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        df.fillna(df.mean(), inplace=True)