Axis Bank Data Scientist Interview Questions and Answers

If else conditions, data merging, datetime conversions, EDA on a sample data set, duplicates removal and missing value imputation

Q1. If you have to sell a kids account, how will you Target the customer. What features will you build?
Ans. 
To target customers for a kids account, focus on features like parental controls, educational content, and interactive games.
• Implement parental controls to assure parents of child safety online.

• Include educational content to attract parents looking for learning opportunities.

• Incorporate interactive games to engage children and make the account more appealing.

• Offer rewards or incentives for completing educational activi...read more
Answered by AI
Add your answer

Coding related objective questions

Q1. Basic questions based on cv. Questions based on python,SQL,ML algorithms
Add your answer

Q1. What do you do if your model has higher false positives
Ans. 
Investigate the model performance metrics and adjust the threshold for classification.
• Analyze the confusion matrix to understand the distribution of false positives.

• Adjust the threshold for classification to reduce false positives.

• Consider using different evaluation metrics like precision, recall, and F1 score.

• Explore feature importance to identify variables contributing to false positives.
Answered by AI
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

Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test

Q1. Questions on ML techniques and practices, how to handle large data in python, lots of logical questions and handling overfitting, underfitting, etc in model building.
Add your answer

Q1. Explain Sigmoid Function
Ans. 
Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.
• Sigmoid function is commonly used in machine learning for binary classification problems.

• It is defined as f(x) = 1 / (1 + e^(-x)), where e is the base of the natural logarithm.

• The output of the sigmoid function is always in the range (0, 1).

• It is used to convert a continuous input into a probability value.

• Example: f(0) = 0.5
Answered by AI
Add your answer
Q2. What is a T-test in logistic regression
Ans. 
A T-test in logistic regression is used to determine the significance of individual predictor variables.
• T-test in logistic regression is used to test the significance of individual coefficients of predictor variables.

• It helps in determining whether a particular predictor variable has a significant impact on the outcome variable.

• The null hypothesis in a T-test for logistic regression is that the coefficient of the predi...read more
Answered by AI
Add your answer
Q3. How to fit model to an unexplored market
Ans. 
To fit a model to an unexplored market, conduct thorough market research, gather relevant data, identify key variables, test different models, and continuously iterate and refine the model.
• Conduct thorough market research to understand the dynamics of the unexplored market

• Gather relevant data on customer behavior, market trends, competition, etc.

• Identify key variables that may impact the market and model outcomes

• Test d...read more
Answered by AI
Add your answer

Q1. Focus was on SQL, projects done so far, Machine Learning and Stats
Add your answer

Q1. Logistic regression loss function
Add your answer
Q2. Step function in ML context
Ans. 
Step function is a function that returns a constant value for a certain range of inputs.
• In machine learning, step functions are used as activation functions in neural networks.

• They are typically used in binary classification problems where the output is either 0 or 1.

• Examples include Heaviside step function and sigmoid step function.
Answered by AI
Add your answer