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I applied via Recruitment Consulltant and was interviewed in Mar 2022. There were 2 interview rounds.
Top trending discussions
I applied via Job Fair and was interviewed in Sep 2022. There were 3 interview rounds.
Supervised machine learning is a type of ML where the algorithm learns from labeled data. Random forest is used over normal decision tree for better accuracy and to avoid overfitting.
Supervised ML learns from labeled data to make predictions on new data
Random forest is an ensemble learning method that uses multiple decision trees to improve accuracy
Random forest is preferred over normal decision tree to avoid overfitti...
Case study on a successful marketing campaign for a new product launch
Identified target audience and their needs
Developed a unique selling proposition
Created a multi-channel marketing plan
Implemented the plan and tracked results
Adjusted strategy based on data analysis
Achieved high sales and positive customer feedback
Retail case study , company trends salw.. Total sale. Profit charts and reports
I applied via Walk-in and was interviewed before Jan 2023. There were 3 interview rounds.
I don’t remember now
I applied via Naukri.com and was interviewed before Nov 2023. There were 2 interview rounds.
Create a business requirement document
Strengths include strong analytical skills and attention to detail. Weaknesses may include difficulty with public speaking and time management.
Strengths: strong analytical skills
Strengths: attention to detail
Weaknesses: difficulty with public speaking
Weaknesses: time management
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 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 appeared for an interview before Jan 2024.
It was related to a Food & Beverage Industry Data
I applied via Recruitment Consulltant and was interviewed before Mar 2023. There was 1 interview round.
I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.
Basic code questions
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