American Express Data Science Intern Interview Questions and Answers

Q1. Detailed project questions
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Q2. Explain completed gen ai project
Ans. 
Developed a generative AI model to create realistic images of fictional characters.
• Used GANs (Generative Adversarial Networks) to generate new images based on existing data.

• Trained the model on a dataset of character images from various sources.

• Implemented techniques like style transfer to enhance the diversity and creativity of generated images.

• Evaluated the model's performance based on image quality metrics and user
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Q1. Given a variable, how to do Linear Regression?
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Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Collect data on the variables of interest

• Plot the data to visualize the relationship between the variables

• Choose a suitable linear regression model (simple or multiple)

• Fit the model to the data using a regression algorithm (e.g. least squares)

• Evaluate the model's performance using ...read more
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Q2. How Linear Regression handles noise?
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Linear Regression minimizes noise by fitting a line that best represents the relationship between variables.
• Linear Regression minimizes the sum of squared errors between the actual data points and the predicted values on the line.

• It assumes that the noise in the data is normally distributed with a mean of zero.

• Outliers in the data can significantly impact the regression line and its accuracy.

• Regularization techniques l...read more
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Q3. Solve two equations to find coefficients?
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Use linear algebra to solve for coefficients in two equations.
• Set up the two equations with unknown coefficients

• Solve the equations simultaneously using methods like substitution or elimination

• Example: 2x + 3y = 10 and 4x - y = 5, solve for x and y
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Q4. Probability question on picking a red ball from Red, blue, black ball bag with replacement.
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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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Q1. How much experience do you have?
Ans. 
I have 8 years of experience in data science, with a focus on machine learning and predictive modeling.
• 8 years of experience in data science

• Specialize in machine learning and predictive modeling

• Worked on various projects involving big data analysis

• Experience with programming languages such as Python and R
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Q2. What is the tech stake you ahve worked on?
Ans. 
I have worked on developing machine learning models for predictive maintenance in the manufacturing industry.
• Developed machine learning algorithms to predict equipment failures in advance

• Utilized sensor data and historical maintenance records to train models

• Implemented predictive maintenance solutions to reduce downtime and maintenance costs
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Q1. Asked about ml algos
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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

Python coding question and ML question

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