Mjunction Services Manager Interview Questions and Answers

Good interview amazing- learn pythagoras theorem

Find area of square using circle

Q1. 1.)Why are we doing this shit?
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Q2. How you doin today huh
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Q1. What is Supervised machine learning? Which algorithm you think you can explain brilliantly? Why we use random forest then normal decision tree
Ans. 
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...read more
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Q2. Example of case study
Ans. 
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
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Retail case study , company trends salw.. Total sale. Profit charts and reports

I don’t remember now

Q1. Speak for 3 minutes about any topic you like. How will you phrase a sentence for a given topic?
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Q1. Are you willing to relocate? Are you comfortable table with rotational shifts?
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It was related to a Food & Beverage Industry Data

Q1. Explain random forest
Ans. 
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...read more
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Q2. What is boosting
Ans. 
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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