Data Science Interview Experience for the role of Junior Data Scientist at Prodapt Solutions
1. Telecom Customer Churn Prediction. Explain the project end to end?
2. Data Pre-Processing Steps used.
3. Sales forecasting how is it done using Statistical vs DL models – Efficiency.
4. Logistic Regression – How much percent of Customer has churned and how much have not churned?
5. What are the Evaluation Metric parameters for testing Logistic Regression?
6. What packages in Python can be used for ML? Why do we prefer one over another?
7. Numpy vs Pandas basic difference.
8. Feature on which this Imputation was done, and which method did we use there?
9. Tuple vs Dictionary. Where do we use them?
10. What is NER – Named Entity Recognition?
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