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I applied via Naukri.com and was interviewed in Nov 2020. There were 5 interview rounds.
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I applied via Referral and was interviewed in Mar 2024. There were 2 interview rounds.
2 questions based on coding
Reverse a given string
Use built-in functions like reverse() or slice() in Python
Iterate through the string in reverse order and append each character to a new string
Convert the string to an array of characters, reverse the array, then join the characters back together
A linked list is a data structure in Java where each element points to the next element in the list.
Linked list nodes contain data and a reference to the next node
Insertion and deletion operations are efficient in linked lists
Traversal requires iterating through each node starting from the head
Example: Node class with data and next pointer, LinkedList class with methods to insert, delete, and traverse
I applied via Campus Placement and was interviewed in Jul 2023. There were 2 interview rounds.
It was basic aptitude but it was online
Python code for determining if a number is prime or not and applying bizzbuzz logic.
Write a function to check if a number is prime or not
Iterate through a range of numbers and apply bizzbuzz logic
Return 'prime' if the number is prime, 'bizz' if divisible by 3, 'buzz' if divisible by 5, and 'bizzbuzz' if divisible by both 3 and 5
I applied via Recruitment Consulltant and was interviewed in Sep 2022. There were 4 interview rounds.
Random forest uses decision trees to split data into subsets based on feature importance.
Random forest builds multiple decision trees and selects the best split based on feature importance.
Each decision tree splits data into subsets based on a randomly selected subset of features.
The best split is determined by minimizing impurity or maximizing information gain.
Random forest can handle missing values and outliers.
Rando...
I applied via Naukri.com
I applied via Naukri.com and was interviewed before Oct 2020. There were 3 interview rounds.
Data Scientist interview questions on model building, random forest, ROC curve, gradient boosting, and real estate valuation
For model building, I followed the CRISP-DM process and used various algorithms like logistic regression, decision trees, and random forest
Random forest hyperparameters include number of trees, maximum depth, minimum samples split, and minimum samples leaf
ROC curve is a graphical representation of...
Random forest is an ensemble learning method used for classification and regression tasks.
Random forest is a collection of decision trees that are trained on random subsets of the data.
Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.
Random forest is robust to overfitting and noisy data, and it can handle large datasets...
Fundamentals of classical machine learning
Classical machine learning involves algorithms that learn from data and make predictions or decisions.
Common algorithms include linear regression, decision trees, support vector machines, and k-nearest neighbors.
Key concepts include training data, testing data, model evaluation, and hyperparameter tuning.
Classical ML is often used for tasks like classification, regression, clus
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