What are some common techniques for handling missing data in a dataset, and how do they affect model performance?

During my final year project, I worked on a data analysis project where I had to assess the impact of social media on student engagement. We encountered missing data in our survey responses. To handle this, we applied imputation techniques like mean and median substitution for numerical responses, as well as mode substitution for categorical data. By doing this, we were able to retain a larger dataset for analysis, leading to more robust findings about engagement patterns. This experience taught me the importance of handling missing data carefully, as it directly influenced our project's conclusions.

In my volunteer work with a local NGO, I helped analyze survey data collected from community members about their needs. We faced missing responses, particularly on questions regarding income levels. To address this, we used a technique called 'last observation carried forward,' where we filled in missing values with the most recent responses from the same individuals. This method allowed us to maintain the integrity of our analysis, ensuring that we could still draw meaningful conclusions about funding needs without losing valuable insights from the data.

In my first job as a data analyst, I was part of a team that worked on improving data quality for a client project. We faced significant missing data issues, especially in customer feedback forms. We employed multiple strategies, including data imputation and removal of missing entries when appropriate. I learned that while removing data could sometimes simplify analysis, it also risked losing important information. Balancing these techniques was crucial, as it directly affected our model's predictive accuracy. This experience underscored the importance of thoughtful data handling in achieving reliable outcomes.