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I applied via Walk-in and was interviewed before Mar 2020. There was 1 interview round.
Top trending discussions
I applied via Walk-in and was interviewed before Nov 2020. There were 3 interview rounds.
Market capitalisation is the total value of a company's outstanding shares.
Market cap is calculated by multiplying the current stock price by the total number of outstanding shares.
It is used to determine the size of a company and its overall worth.
Companies with higher market caps are generally considered more stable and less risky investments.
For example, Apple Inc. has a market cap of over $2 trillion as of 2021.
Mar...
I applied via Referral and was interviewed in Jul 2020. There were 3 interview rounds.
I applied via Naukri.com and was interviewed before Feb 2023. There were 2 interview rounds.
I applied via Campus Placement and was interviewed in Nov 2023. There were 3 interview rounds.
It was the easy round to clear they ask basic quans which include DBMS and grammar over all the round was easy
Some mcq questions and coding question. Both where easy to medium level. Prepare combination/permutation/Time complexity etc
A question related to Binary search and some other follow ups.
I appeared for an interview before Feb 2024.
Basic speaking test, english
The graphs show a steady increase in sales over the past year.
Sales have been consistently rising month over month.
There was a significant spike in sales during the holiday season.
The overall trend is positive and indicates growth in the business.
I applied via Campus Placement and was interviewed before Sep 2023. There was 1 interview round.
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
Random forest is a type of ensemble learning method.
It builds multiple decision trees during training.
Each tree is built using a subset of the training data and a random subset of features.
The final prediction is made by averaging the predictions of all the individual trees.
Random...
Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.
Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Boosting is used to improve the accuracy of models and reduce bias and variance.
I applied via Walk-in and was interviewed in Jul 2022. There were 2 interview rounds.
Current domain knowledge test
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