Tell me about a research project you previously worked on. What alternative techniques would you have used to conduct the project?

Question Analysis

This question is aimed at understanding your experience with research projects and your ability to critically evaluate and reflect on your work. It not only seeks to determine your technical skills and knowledge of research methodologies but also assesses your problem-solving skills and flexibility in considering alternative approaches. The question involves two parts: describing a past research project and then discussing what alternative techniques you might have used. It is important to provide a clear and concise narrative of your project while also demonstrating your ability to think critically about the methods used.

Answer

Situation:
In my final year of university, I undertook a research project that involved developing a predictive model for customer churn in the telecommunications industry. The goal was to analyze historical customer data to identify the key factors contributing to churn and to predict future churn behavior.

Task:
My responsibility was to lead the data analysis process, perform feature engineering, and build the predictive model using machine learning algorithms. The main technique I used was a combination of logistic regression and decision tree algorithms due to their effectiveness in classification tasks.

Action:
I collected data from various sources, cleaned and preprocessed it, and performed exploratory data analysis to understand the underlying patterns. I then selected relevant features and trained the model using logistic regression to determine the probability of churn. For better interpretability and to capture non-linear relationships, I also employed decision trees.

Result:
The model achieved an accuracy of 85% on the test dataset, allowing the company to focus its retention efforts on high-risk customers, which led to a 10% reduction in churn over the following quarter.

Alternative Techniques:
In hindsight, I would consider using ensemble methods such as Random Forest or Gradient Boosting, as they often provide better predictive performance by combining multiple models. Additionally, employing neural networks could capture more complex patterns in the data, especially if we had a larger dataset. Lastly, incorporating techniques like cross-validation would enhance the robustness of the model's performance evaluation.