What strategies would you use to handle class imbalance in a dataset?

During my final year project, I worked on classifying patient data for a healthcare application. The dataset had a significant class imbalance, with only a small percentage of patients diagnosed with a rare condition. To handle this, I first applied oversampling techniques, specifically using SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic samples of the minority class. This helped balance the dataset and improved the model’s performance. I also implemented cross-validation to ensure that the model generalized well to unseen data. Ultimately, this approach led to a more accurate model that could better identify the rare condition, which is crucial in a healthcare setting.

While volunteering for a non-profit organization, I was involved in a fundraising campaign that had a significant class imbalance in donor responses. Many people responded positively, while a few did not engage at all. To address this, we used targeted outreach strategies to re-engage the less responsive group. We segmented our audience based on previous engagement and tailored our messaging to resonate better with them. By focusing on the minority group and understanding their concerns, we managed to boost engagement rates significantly, leading to an increase in donations. This experience taught me the importance of addressing class imbalances, not just in data but also in real-world scenarios.

In my first job as a data analyst, I was tasked with analyzing customer feedback for a product that had mixed reviews. The feedback was heavily skewed, with many positive comments and only a few negative ones. To address this imbalance, I employed undersampling techniques to reduce the number of positive feedback entries. This allowed me to focus on the negative feedback and draw deeper insights into customer pain points. By analyzing a more balanced dataset, I could provide actionable recommendations to the product team, which ultimately led to improvements in the product and higher customer satisfaction. This experience highlighted the importance of addressing class imbalance in meaningful analysis.