What techniques would you use to handle imbalanced datasets in a classification problem?

In my final year project, I worked on predicting student performance based on various factors like attendance, assignments, and exam scores. I noticed that the dataset was imbalanced, with many students performing well and only a few struggling. To address this, I applied the SMOTE (Synthetic Minority Over-sampling Technique) method, which helped generate synthetic examples of the underrepresented class. This balanced the dataset and improved our model's accuracy significantly. By evaluating the model's performance using metrics like F1-score and precision, I could demonstrate that handling the imbalance made a real difference in the model's effectiveness.

While volunteering for a community health initiative, I analyzed health survey data where only a small percentage of respondents reported severe health issues. To handle this imbalance, I utilized undersampling techniques on the majority class to ensure that our model focused more on the minority class. Additionally, I implemented stratified sampling to maintain the distribution of the target variable in each training batch. This experience taught me the importance of adjusting our approach to different datasets and how such techniques could lead to more insightful analyses that could affect community health strategies.

In my first job as a data analyst, I was tasked with building a sales prediction model for a product that had low sales in certain regions. I encountered an imbalanced dataset where successful sales were predominant. I opted for cost-sensitive learning by assigning higher costs to misclassifying the minority class, which encouraged the model to take those cases more seriously. This approach, along with using ensemble methods like Random Forest, improved our model's ability to predict low-sales scenarios, and ultimately, it helped the marketing team refine their strategies based on the predictions.