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I applied via Naukri.com and was interviewed in Dec 2020. There was 1 interview round.
I applied via Recruitment Consultant and was interviewed in Feb 2021. There were 4 interview rounds.
I applied via Referral and was interviewed in May 2021. There was 1 interview round.
FMCG stands for Fast Moving Consumer Goods, which are products that are sold quickly and at a relatively low cost.
FMCG products are typically non-durable goods that are consumed or used up quickly, such as food, beverages, toiletries, and cleaning products.
These products are sold in large quantities and at a low profit margin, but generate high sales volume due to their high demand.
FMCG companies often rely on extensiv...
Developing distribution involves identifying target markets, creating a distribution strategy, and building relationships with distributors.
Conduct market research to identify potential target markets
Create a distribution strategy that aligns with the company's goals and objectives
Build relationships with distributors by offering incentives and providing excellent customer service
Regularly evaluate and adjust the distr...
I applied via Recruitment Consultant and was interviewed in Jun 2020. There was 1 interview round.
I applied via Naukri.com and was interviewed in Apr 2022. There were 2 interview rounds.
Yes, I have system knowledge.
I have experience working with various operating systems such as Windows, macOS, and Linux.
I am familiar with different software applications and can troubleshoot common issues.
I have a good understanding of computer networks and can configure routers and switches.
I have knowledge of database management systems like MySQL and can write SQL queries.
I am proficient in using productivity tools
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.
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