Indiafilings Data Scientist Interview Questions and Answers

SQL & aptitude question

1 coding question for 45 min

Q1. Detailed questing for machine learning model's.
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Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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Sql and pandas problems along mcqs on ML

Q1. Linear Regression
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Q2. Basic Neural Network Implementation
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Q1. Question about working of BERT
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Q2. Word2vec explanation
Ans. 
Word2vec is a technique used to create word embeddings by training a neural network on a large corpus of text.
• Word2vec is a shallow neural network model that learns to represent words as vectors in a continuous vector space.

• It captures semantic relationships between words by placing similar words close together in the vector space.

• There are two main architectures for Word2vec: Continuous Bag of Words (CBOW) and Skip-gr...read more
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Q1. How do you define model Gini?
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Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
• Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).

• It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.

• A Gini coefficient of 0.5 indicates a model that is no better than random guessing.

• Commonly used in credit
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Q2. How to you train XG boost model
Ans. 
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
• Specify parameters for XGBoost model such as learning rate, max depth, and number of trees

• Split data into training and validation sets using train_test_split function

• Fit the XGBoost model on training data using fit method

• Tune hyperparameters using techniques like grid search
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I was asked Python, sql, coding questions

Case study on how would you identify the total number of footfall on a airport

Q1. Asked about ml algos
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Q1. What is cross validation ?
Ans. 
Cross validation is a technique used to assess the performance of a predictive model by splitting the data into training and testing sets multiple times.
• Cross validation helps to evaluate how well a model generalizes to new data.

• It involves splitting the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset.

• Common types of cross validation include k-fold cross validation and lea...read more
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Python coding question and ML question

Q1. ML questions from resume + general
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Q1. Techno managerial round
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