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I applied via Campus Placement and was interviewed before May 2023. There was 1 interview round.
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I applied via Naukri.com and was interviewed in Nov 2020. There were 4 interview rounds.
I appeared for an interview in Mar 2021.
I applied via Naukri.com and was interviewed in Jun 2019. There were 5 interview rounds.
I applied via Campus Placement and was interviewed before May 2022. There were 5 interview rounds.
It was a coding round with sql ,python questions , few aptitude questions
Basic SQL involves writing queries to manage and manipulate relational databases effectively.
SELECT statement: Used to retrieve data from a database. Example: SELECT * FROM employees;
WHERE clause: Filters records based on specified conditions. Example: SELECT * FROM employees WHERE age > 30;
JOIN operations: Combines rows from two or more tables based on a related column. Example: SELECT * FROM orders JOIN customers ...
I applied via Campus Placement
Dsa problems dp and tress problem
I applied via Referral and was interviewed in May 2024. There were 4 interview rounds.
Python and SQL questions were asked
ACID properties ensure data integrity in DBMS: Atomicity, Consistency, Isolation, Durability.
Atomicity ensures that all operations in a transaction are completed successfully or none at all.
Consistency ensures that the database remains in a consistent state before and after the transaction.
Isolation ensures that multiple transactions can be executed concurrently without affecting each other.
Durability ensures that once...
The test was in hackerrank platform, it was moderate level.
I applied via Recruitment Consultant and was interviewed in Jul 2021. There were 3 interview rounds.
Developed a machine learning model to predict customer churn for a telecom company.
Used logistic regression and decision tree algorithms for classification.
Performed feature engineering to extract relevant features from customer data.
Achieved an accuracy of 85% on the test set.
Provided actionable insights to the company to reduce customer churn.
Different performance metrics are used to measure the effectiveness of a model or system.
Accuracy
Precision
Recall
F1 Score
ROC Curve
AUC
Mean Squared Error
Root Mean Squared Error
R-squared
Bagging and boosting are ensemble learning techniques. XgBoost is a gradient boosting algorithm.
Bagging involves training multiple models on different subsets of the data and combining their predictions.
Boosting involves training models sequentially, with each model trying to correct the errors of the previous model.
XgBoost is an optimized implementation of gradient boosting that uses a combination of tree-based models...
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