Straive Data Scientist Interview Questions and Answers

I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.

Q1. Explain the Project undertaken during the research and follow-up questions
Add your answer

Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...read more
Ans. 
Answering questions related to data science concepts and techniques.
• Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.

• To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...read more
Answered by AI
Add your answer

DSA and ML, AI, Coding question

Q1. Case study which was easy
Add your answer

Q1. In depth questions on ML
Add your answer

Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
Answered by AI
Add your answer
Q2. Explain Confusion metrics
Ans. 
Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
Answered by AI
Add your answer

Q1. Project related questions
Add your answer
Q2. Coding questions
Add your answer

Basic aptitude , tech aptitude

Q1. What is overfitting
Add your answer
Q2. How to handle missing values
Add your answer

Q1. Cross validation
Add your answer
Q2. How to handle imbalanced dataset
Ans. 
Handling imbalanced datasets involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Utilize algorithms that are robust to imbalanced datasets, such as Random Forest, XGBoost, or SVM.

• Adjust class weights in the model to give more importance to the minority class.

• Use techniques like SMO...read more
Answered by AI
Add your answer