Explain the significance and techniques of handling missing data in a real$-$world data

analysis scenario. Discuss the challenges posed by missing data, the potential implications

of different imputation methods, and the importance of assessing and documenting the

handling of missing data in a data analysis project. Provide practical examples to illustrate

the impact of various missing data strategies on the quality and reliability of results.