Axis Bank Analytics Manager Interview Questions and Answers

Q1. SQL and Linux based Questions
Add your answer

Q1. Explain about how recommendation engine work
Ans. 
Recommendation engines analyze user data to suggest items based on preferences and behavior.
• Recommendation engines use collaborative filtering to suggest items based on user behavior and preferences.

• They can also use content-based filtering to recommend items similar to ones the user has liked in the past.

• Some recommendation engines combine both collaborative and content-based filtering for more accurate suggestions.

• Ex...read more
Answered by AI
Add your answer

It was okay as the interview was scheduled on time.

Q1. They asked questions in the interview Including Python, Machine learning, SQL, Power BI
Add your answer

Q1. HR was just asking about myself and my current job profile.
Add your answer

Q1. Explain about your current job process?
Add your answer

Q1. No of atm machines in your locality. Basic statistics questions and CPC based questions
Add your answer

Q1. Work experience driven questions
Add your answer

Q1. Machine learning algorithms.
Ans. 
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
Answered by AI
Add your answer
Q2. Credit risk life cycle
Add your answer
Q3. Pandas related questions
Add your answer

Q1. Steps of developing a credit risk model
Ans. 
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
Answered by AI
Add your answer
Q2. Pandas related questions
Add your answer
Q3. Bagging and Boosting
Add your answer

Q1. Explain AIC and BIC
Ans. 
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
Answered by AI
Add your answer
Q2. Difference between xgboost and lightgbm
Ans. 
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
Answered by AI
Add your answer
Q3. Bagging and boosting
Add your answer

Find second max in list, cal moving avg of a df