How would you approach feature selection when building a machine learning model?

In my final year data science project, I worked on predicting house prices using a dataset from Kaggle. Initially, I included all available features, such as square footage, number of bedrooms, and age of the house. However, I quickly realized that some features, like the color of the house, had no real impact on pricing. To improve my model, I conducted exploratory data analysis, which helped me identify the most significant features. I used correlation matrices and visualizations to understand which variables were most closely related to the target variable. Ultimately, by selecting just the most relevant features, I was able to reduce the model's complexity and improve its accuracy by 15%. This taught me the importance of thoughtful feature selection in machine learning.

While volunteering for a local non-profit that organized community events, I was tasked with gathering data to assess the impact of our activities. I initially collected a wide range of data, including attendance numbers, participant demographics, and feedback ratings. However, it became clear that not all of this data was necessary for our analysis. To focus our efforts, I collaborated with my team to prioritize features that correlated with successful events, such as participant engagement and satisfaction scores. By selecting only the most impactful features, we improved our ability to plan future events effectively and enhanced overall community engagement. This experience highlighted how feature selection can streamline efforts and drive better outcomes, even outside a technical setting.

In my first job as a junior data analyst at a retail company, I was involved in a project aimed at improving our sales forecasting model. I worked under a senior analyst who guided me in applying feature selection techniques. We started with a dataset containing dozens of variables, including seasonality, promotions, and customer demographics. By using backward elimination, we systematically removed less significant features based on their p-values. This process not only simplified the model but also enhanced its predictive power by focusing on the most critical factors affecting sales. The final model achieved a 20% increase in forecasting accuracy, which was celebrated by our team. This experience reinforced the value of feature selection in delivering actionable insights in a business context.