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HTML
CSS
Python
PHP
MySQL
Linux
Javascript
Cloud
Shobha
The best way to predict the future is to invent it
What are the common pitfalls when working with real-time data in analytics?
RELATED INTERVIEW QUESTIONS
What are the key steps in building a robust machine learning pipeline for predictive analytics?
What is the role of Shannon entropy in feature selection?
How does regularization help to reduce overfitting in machine learning models?
What is feature importance, and how do you evaluate it for machine learning models?
Explain bootstrapping and how it helps with estimating uncertainty in your analysis.
What is time-series forecasting, and how do you approach it using machine learning algorithms?
What is recurrent neural network (RNN), and how is it different from traditional neural networks? In what kind of problems would you apply
How would you perform real-time data analysis using Kafka and Spark?
How do you handle imbalanced datasets in classification problems, and what techniques do you use to improve the model’s performance?
How do you ensure reproducibility in your data analysis pipelines?
Explain how you would approach feature engineering for a time-series dataset.
Explain how principal component analysis (PCA) works for dimensionality reduction.
What is the importance of data privacy in analytics, and how do you ensure compliance?
How do you handle multicollinearity in regression models?
What is data leakage, and how can you prevent it in your analytics projects?
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