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

During my final year college project, I worked on a research study analyzing survey data. We encountered missing responses from participants, which could skew our results. To address this, we decided to use mean imputation for numerical data and mode imputation for categorical responses. By filling in the gaps with these values, we maintained the integrity of our dataset and ensured our analysis was as accurate as possible. After applying these techniques, we found that our model's performance improved, as it could utilize a more complete dataset, leading to better insights and conclusions.

While volunteering for a non-profit organization, I was tasked with cleaning their donor data. We noticed numerous entries had missing email addresses, which were crucial for our outreach efforts. Instead of deleting these entries, I suggested using a predictive model that estimated missing emails based on other available features like name and donation history. This approach not only preserved valuable data but also increased our outreach effectiveness. The result was a significant boost in our engagement metrics, demonstrating how thoughtful data handling can enhance project outcomes.

In my first job as a data analyst, I frequently dealt with customer data that often had missing values in the product feedback section. Initially, I would remove these entries, but I soon learned that this could lead to a loss of valuable insights. Instead, I began implementing multiple imputation techniques, which allowed us to estimate missing values based on other responses. This not only improved our model's accuracy but also provided us with a more comprehensive understanding of customer sentiments, leading to more informed business decisions.