Can you provide me with an overview of a research project you were a part of? What modifications would you have made to the method used?
Question Analysis
This question is asking you to discuss a research project you've been involved with, focusing on two main aspects:
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Overview of the Project: You need to provide a concise summary of the research project, including its objectives, methodologies, and outcomes. This should give the interviewer insight into your technical experience and understanding of research processes.
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Methodological Modifications: You are also required to critically evaluate the research methods used in the project. This involves identifying any limitations or challenges you encountered and suggesting improvements or alternative approaches. This part showcases your analytical skills and ability to learn from past experiences.
Answer
Overview of the Project:
I participated in a research project aimed at developing a machine learning model to predict customer churn in a telecommunications company. The objective was to identify patterns indicating potential churners, allowing the company to proactively engage with these customers. We used historical customer data, including demographics, usage patterns, and customer service interactions, to train and test various predictive models.
Project Methodology:
- Data Collection and Preprocessing: We collected data from multiple sources, ensuring it was clean and properly formatted for analysis.
- Model Selection: We experimented with several algorithms, including decision trees, random forests, and logistic regression, to find the most accurate model.
- Evaluation: The models were evaluated using metrics such as accuracy, precision, recall, and AUC-ROC curves.
Outcomes:
The random forest model achieved the best performance with an accuracy of 85% and helped reduce churn by 15% after targeted customer engagement strategies were implemented.
Modifications to the Method:
- Data Enrichment: I would incorporate additional data sources such as social media interactions and customer feedback surveys to enhance the model's predictive power.
- Feature Engineering: More advanced feature engineering techniques could be employed to extract deeper insights from the existing data, potentially improving model accuracy.
- Model Interpretability: Implementing techniques such as SHAP values would help in understanding model predictions better, aiding in more transparent decision-making processes.
These modifications could potentially improve the model's performance and provide more actionable insights for the business.