AU Small Finance Bank Data Science Engineer Interview Questions and Answers

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

Q1. Logistic regression loss function
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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.
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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
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Q1. Machine learning questions
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Project Discussion on Eye flue detection

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