Delta Air Lines Data Scientist Interview Questions and Answers

Q1. What kind of project you have done give some brief. How you will avoid over fiting to model. How will you handle missing data points in a data frame. Why you want to join our company.
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Q1. Behavioral question was asked by manager.
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Q1. CTC discussion was done
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Q1. How regularisation works in random forest
Ans. 
Regularisation in random forest helps prevent overfitting by controlling the complexity of the model.
• Regularisation in random forest is achieved by limiting the depth of the trees in the forest.

• It helps prevent overfitting by reducing the complexity of the model and improving generalization.

• Regularisation parameters like max_depth, min_samples_split, and min_samples_leaf can be tuned to control the complexity of the mo
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Q1. Asked about Projects ?
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Q2. Asked about algorithms
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Based on different scenarios they interviewed me

Q1. How comfortable I am moving to qatar
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Q2. Salary expectstion
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Q1. Picks one of the Projects to discuss the structure and methodology.
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Q2. Reverse a String
Ans. 
Reverse a given string
• Use built-in functions like reverse() or slice() in Python

• Iterate through the string in reverse order and append characters to a new string

• Convert the string to an array of characters, reverse the array, and join it back into a string
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Q3. Basic ML Questions about Resume Projects
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Q4. What is Regularization?
Ans. 
Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function.
• Regularization helps to reduce the complexity of the model by penalizing large coefficients.

• It adds a penalty term to the loss function, such as L1 (Lasso) or L2 (Ridge) regularization.

• Regularization helps to improve the generalization of the model by discouraging overfitting.

• Examples of re...read more
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Q5. Basics about Forescasting.
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Q1. What kind of project you have done give some brief. How you will avoid over fiting to model. How will you handle missing data points in a data frame. Why you want to join our company.
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Q1. Behavioral question was asked by manager.
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Q1. CTC discussion was done
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Hackerank Assignment

Q1. Coding and ML questions
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Q1. About projects and questions related to that
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Basic coding questions

Q1. Explain in detail about current project? Reason for choosing the algorithm, evaluation metrics and analysis of data
Ans. 
Developed a predictive model for customer churn using logistic regression
• Chose logistic regression for its interpretability and ability to handle binary outcomes

• Evaluated model performance using accuracy, precision, recall, and F1 score

• Analyzed customer data including demographics, purchase history, and customer service interactions
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Q2. Python and sql coding questions - basic
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Q3. Linear regression, xgboost, precision, recall, F1 Score,Correlation
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