C5i Interview Questions, Process, and Tips

Quant,Logical Reasoning and Data interpretation

Q1. Tell me something about your academic background?
Ans. 
I have a Bachelor's degree in Business Administration with a focus on marketing.
• Bachelor's degree in Business Administration

• Focus on marketing

• Completed coursework in market research and consumer behavior
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Q2. There were two to three Guestimates
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Q1. Explain previous work experience
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Q2. How statistical methods were used in my previous work experience
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Q1. How to evaluate ML models ? Difference between Bagging and Boosting ? What is F1 score and how change weights of Precision or recall while calculating F1 score ? What does it mean to have F1 score value 1 ...read more
Ans. 
ML models can be evaluated using metrics like accuracy, precision, recall, F1 score. Bagging combines multiple models, while boosting focuses on correcting errors. F1 score balances precision and recall.
• ML models can be evaluated using metrics like accuracy, precision, recall, and F1 score.

• Bagging is an ensemble technique where multiple models are trained independently and then combined by averaging or voting.

• Boosting ...read more
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Q1. What is proba method ? How is class probability calculated in decision trees ? How's a machine learning model evaluated ?
Ans. 
Proba method is used to calculate class probability in decision trees. Machine learning models are evaluated using metrics like accuracy, precision, recall, and F1 score.
• Proba method calculates the probability of a class label in decision trees by counting the occurrences of each class in a leaf node and dividing by the total number of samples in that node.

• Class probability in decision trees is calculated based on the ...read more
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Q1. How you delegate work to your peers
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Q2. Anything about the project you did
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Questions based on ML,PYTHON, DATA VISUALIZATION

Q1. What is TF-IDF IN NLP
Ans. 
TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.
• TF-IDF stands for Term Frequency-Inverse Document Frequency

• It is used in Natural Language Processing (NLP) to determine the importance of a word in a document

• TF-IDF is calculated by multiplying the term frequency (TF) by the inverse document frequency (IDF)

• It helps in identifying the most important
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Q2. Python coding questions based on list
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Q1. Data modeling process
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Q2. Query performance optimisation
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Q1. What are SQL Joins
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