Epsilon Lead Data Analyst Interview Questions and Answers

Q1. Explain any ML model.
Add your answer
Q2. Create Dataframe from two lists.
Add your answer

Q1. Deep questions about Machine learning, Deep learning, Generative AI, the working of transformers etc.
Add your answer

Q1. Deep questions about Machine learning and deep learning with projects done. This was a client round.
Add your answer

Q1. Salary discussion, project discussion, why change? Why Wipro
Add your answer

AWS and Python, basic Machine learning questions

Python, project description, AWS, in build package

Basic Machine learning data science

I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.

Q1. Explain the Project undertaken during the research and follow-up questions
Add your answer

Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...read more
Ans. 
Answering questions related to data science concepts and techniques.
• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...read more
Answered by AI
Add your answer

Q1. Pandas basics and SQL joins nlp
Add your answer

Q1. Questions on MLP, ML
Add your answer

It was MCQ in simple aptitude, python and SQL questions

Q1. Python coding and explaination
Add your answer
Q2. SQL coding and explaination
Add your answer

Q1. Case study based on data understanding
Add your answer

Q1. Behavioural and questions from my resume
Add your answer

Q1. Regularization ,when to select l1 and l2
Ans. 
L1 regularization is used for feature selection and L2 regularization is used for preventing overfitting.
• Use L1 regularization when you want to perform feature selection as it tends to produce sparse feature vectors.

• Use L2 regularization when you want to prevent overfitting by penalizing large weights.

• A combination of L1 and L2 regularization (Elastic Net) can be used for a balance between feature selection and prevent
Answered by AI
Add your answer