Adidas Data Scientist Interview Questions and Answers

Q1. What is decision tree
Ans. 
A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label.
• Decision trees are a popular machine learning algorithm used for classification and regression tasks.

• They are easy to interpret and visualize, making them useful for understanding the decision-making process.

• Each internal...read more
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Q2. What are model evaluation metrics?
Ans. 
Model evaluation metrics are used to assess the performance of machine learning models.
• Model evaluation metrics help in determining how well a model is performing in terms of accuracy, precision, recall, F1 score, etc.

• Common evaluation metrics include accuracy, precision, recall, F1 score, ROC-AUC, confusion matrix, and mean squared error.

• These metrics help in comparing different models and selecting the best one for a...read more
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Q1. Intermediate level SQL questions using joins and case when
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Q2. String manipulation advanced level question in python
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Q1. Questions on NLP frameworks, feature engineering, decision tree, dimensionality reduction techniques.
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Q1. A time when you delivered more than expected
Ans. 
I delivered more than expected by implementing a new machine learning algorithm that significantly improved model accuracy.
• Identified the need for a more advanced algorithm based on data analysis

• Researched and implemented a cutting-edge machine learning algorithm

• Tested the new algorithm on a sample dataset and compared results with existing models

• Achieved a significant increase in model accuracy, exceeding initial expe
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Q1. Questions related to Python , ML, Deep Learning and resume based questions
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Q1. Project related
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Q1. Usual HR questions
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Q1. Assessment was given and I was asked to explain about the steps and procedures carried out and few basic questions on my assessment was asked
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Test based on Python Programming nomenclatures and statistical concepts.

One topic to discuss based on latest in technology. For example Impact of AI on human jobs

Q1. Technical and HR round is combined for understanding behaviour of the candidate.
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Q2. What is Naive Bayes Algorithm?
Ans. 
Naive Bayes Algorithm is a simple probabilistic classifier based on Bayes' theorem with strong independence assumptions.
• It is based on the assumption that the presence of a particular feature in a class is unrelated to the presence of any other feature.

• It calculates the probability of each class given a set of input features and selects the class with the highest probability.

• Commonly used in text classification, spam f...read more
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Q3. What is Logistic Regression and what are the assumptions of linear regression?
Ans. 
Logistic Regression is a statistical method used to model the probability of a binary outcome.
• Logistic Regression is used when the dependent variable is binary (e.g., 0 or 1, Yes or No).

• It estimates the probability that a given input belongs to a certain category.

• Assumptions of linear regression include linearity, independence of errors, homoscedasticity, and normality of errors.
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Q1. Describe a decision tree
Ans. 
A decision tree is a flowchart-like model that shows the possible outcomes of a decision based on certain conditions.
• It is a tree-like structure with nodes representing decisions and branches representing outcomes

• Each node has a condition that determines which branch to follow

• It is commonly used in machine learning for classification and regression tasks

• Example: A decision tree for predicting whether a customer will bu...read more
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Q2. What is random forest
Ans. 
Random forest is an ensemble learning method for classification, regression and other tasks.
• Random forest builds multiple decision trees and combines their outputs to improve accuracy.

• It is a popular machine learning algorithm due to its high accuracy and ability to handle large datasets.

• Random forest can be used for both classification and regression tasks.

• It is resistant to overfitting and can handle missing data.

• Exa...read more
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Q3. What is neural network
Ans. 
Neural network is a type of machine learning algorithm inspired by the structure and function of the human brain.
• Consists of layers of interconnected nodes that process information

• Used for tasks such as image recognition, natural language processing, and prediction

• Can be trained using supervised, unsupervised, or reinforcement learning

• Examples include convolutional neural networks, recurrent neural networks, and deep n
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Leetcode style coding