Kotak Mahindra Bank Data Scientist Interview Questions and Answers

Q1. Python basic concepts
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Q2. ML algorithm rule, scoring models, library details, statistics questions
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Hard level aptitude questions... Time taking sums

Q1. Puzzles and probability questions
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Q2. Asking completely about data science projects in your resume
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Q1. Just asked some puzzle questions
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Q2. Asked if iam willing to relocate and about my family
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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
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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
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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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Q1. Metrics and related questions
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Aptitude test for about an hour.

Q1. Questions related to data science were asked based on your resume. Puzzles were asked too. Mostly they were from gfg.
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Q2. What are parameters used in a random forest.
Ans. 
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
• Number of trees: The number of decision trees to be used in the random forest.

• Maximum depth of trees: The maximum depth allowed for each decision tree.

• Minimum samples split: The minimum number of samples required to split a node.

• Maximum features: The maximum number of features to consider when
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Q1. What do you do if your model has higher false positives
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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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