What experience do you have with data preprocessing, and can you provide an example of how you've approached feature selection?

Well, I've had some hands-on experience with data preprocessing during my college projects where we had to sift through datasets for our assignments. For feature selection specifically, there was a project where we used scikit-learn's SelectKBest method to choose the top features that had the most impact on our model's predictive performance. We were working on predicting housing prices, and after running the feature selection, we found that aspects like the size of the house and the year it was built were really important for our predictions.

In my final year at university, I did a capstone project which involved a lot of data preprocessing, particularly feature selection. We were tasked with improving the accuracy of a real estate valuation model. We utilized Python and the RandomForestClassifier from the scikit-learn library to determine feature importance. By integrating domain knowledge, like understanding the significance of location and property condition, we manually selected features before feeding them to the algorithm. After iterative testing, we realized that removing features with low importance, such as the number of photographs in a listing, actually improved our model's accuracy by 5%. This process helped me learn the balance between domain expertise and algorithmic selection in feature determination.

My most noteworthy experience with data preprocessing took place in my final year university project. Our team was developing a predictive analytics model for real estate prices based on several variables. We embarked on extensive data cleansing to handle missing values and outliers using Python's Pandas library. My role particularly focused on feature selection. I spearheaded a collaborative effort to identify the most predictive attributes, combining both statistical techniques and domain-specific insights. Employing Python's scikit-learn library, I guided the team through using the ExtraTreesClassifier to compute feature importances. Our iterative strategy involved rigorous A/B testing to refine our feature set, ultimately settling on 'location proximity to city center' and 'recent renovations' as pivotal. This meticulous approach, founded on teamwork and effective communication, culminated in a model with a 10% higher predictive accuracy over the baseline. Through this process, I became adept at balancing machine learning theories with practical applications, and the experience has significantly honed my skills in problem-solving and collaboration.