Hexaware Technologies Data Scientist Interview Questions and Answers

Q1. Model evaluation and performance metrices
Add your answer
Q2. Explaination of bagging and boosting techniques
Ans. 
Bagging and boosting are ensemble techniques used to improve the accuracy of machine learning models.
• Bagging involves training multiple models on different subsets of the training data and then combining their predictions through voting or averaging.

• Boosting involves iteratively training models on the same data, with each subsequent model focusing on the samples that the previous models misclassified.

• Bagging reduces va...read more
Answered by AI
Add your answer

Q1. Different ML Algorithms
Ans. 
There are various ML algorithms such as linear regression, decision trees, random forests, SVM, KNN, neural networks, etc.
• Linear regression is used for predicting continuous values

• Decision trees and random forests are used for classification and regression

• SVM is used for classification and regression

• KNN is used for classification and regression

• Neural networks are used for complex problems such as image recognition and
Answered by AI
Add your answer
Q2. Python Libraries
Add your answer

Coding test with mcq - aptitude , coding , stats , ml

Q1. Project related like deployement
Add your answer

Q1. Machine learning related questions and the theory of its operation
Add your answer

They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
Add your answer
Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
Answered by AI
Add your answer

Q1. Tell me about yourself
Add your answer
Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
Answered by AI
Add your answer

SQL, select statment and DDL commands

Q1. Python lists and conditional statements and loos
Add your answer