What factors do you consider when selecting a machine learning model for a given dataset?

During my final year, I worked on a project where I had to predict student grades based on various input features like attendance, homework scores, and participation. I started by analyzing the dataset to understand its characteristics, such as the number of features and whether the target variable was continuous or categorical. For this regression problem, I experimented with linear regression for its simplicity and interpretability. However, I also tried decision trees to capture non-linear relationships. Ultimately, I selected the model that had the best performance based on cross-validation metrics and explained my choice in detail during my presentation.

While volunteering for a non-profit organization, I was tasked with predicting potential donor contributions based on past donation data. I collaborated with a team that included both technical and non-technical members. We discussed the importance of model interpretability since stakeholders needed to understand the predictions. I suggested starting with logistic regression for its straightforwardness and ease of interpretation. We also considered decision trees for their ability to visualize decision paths. Our final choice was a logistic regression model that provided clear insights into donor behavior, which helped the organization strategize their fundraising efforts.

In my first job as a data analyst, I was involved in a project to forecast sales for an e-commerce platform. I considered multiple models, including ARIMA for time series forecasting and a random forest model for its accuracy. Since the dataset included seasonal patterns, I ensured to evaluate models that could handle time dependencies. I also engaged in discussions with the marketing team to understand what features might influence sales, such as promotions or holidays. After testing several models, we settled on a random forest approach due to its high accuracy and ability to handle complex interactions, which ultimately improved our sales predictions and inventory planning.