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I applied via Naukri.com and was interviewed in Dec 2024. There was 1 interview round.
Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.
Random Forest is a popular machine learning model used for classification and regression tasks.
It works by creating multiple decision trees during training and outputs the mode of the classes for classification or the average prediction for regression.
Random Forest is ro...
Creating a DataFrame from two lists in Python.
Import the pandas library
Create two lists of data
Use pd.DataFrame() to create a DataFrame from the two lists
I applied via Referral and was interviewed in May 2024. There were 3 interview rounds.
I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.
Answering questions related to data science concepts and techniques.
Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.
To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...
I applied via Naukri.com and was interviewed in Oct 2023. There were 4 interview rounds.
Sql, python, Statistics mcq, Aptitude test. These were medium level questions.
Remove duplicates from list b, keep elements not in list a, and sort in ascending order.
Create a set from list b to remove duplicates
Use list comprehension to keep elements not in list a
Sort the final list in ascending order
Use the DISTINCT keyword in SQL to remove duplicates from a table.
Use the SELECT DISTINCT statement to retrieve unique rows from the table.
Identify the columns that should be used to determine uniqueness.
Example: SELECT DISTINCT column1, column2 FROM tab1;
Given 2 case studies on data science and asked different possibilities to improve the models.
How to work with imbalance dataset.
How to remove null values, what is features engineering.
What is PCA
What is the working of XGBOOST
I applied via Referral and was interviewed in Mar 2024. There were 4 interview rounds.
Test consisted 7 sections which lasted for more than a hour. There were questions related to coding , sql , analytical questions etc.
1 python coding questions and 2 Sql question
2 case study question
Nielsen interview questions for designations
My friends think of me as reliable, supportive, and always up for a good time.
Reliable - always there when they need help or support
Supportive - willing to listen and offer advice
Fun-loving - enjoys socializing and trying new things
I applied via Recruitment Consultant and was interviewed in Jul 2021. There were 3 interview rounds.
Predicting insurance claims using machine learning algorithms.
Fraud detection in insurance claims
Risk assessment for insurance policies
Pricing optimization for insurance products
Customer segmentation for targeted marketing
Predictive maintenance for insurance assets
DSA and ML, AI, Coding question
posted on 11 Sep 2024
I applied via Company Website and was interviewed in Aug 2024. There was 1 interview round.
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria
Red category typically represents data that needs immediate attention or action
Amber category represents data that requires monitoring or further investigation
Green category represents data that...
Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
Confusion matrix is a table that describes the performance of a classification model.
It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.
These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...
I applied via Campus Placement and was interviewed in Aug 2024. There were 2 interview rounds.
Aptitude test consists of 40 questions.
I am a data scientist with experience in developing predictive models and analyzing large datasets.
Developed a predictive model for customer churn prediction using machine learning algorithms
Analyzed sales data to identify key trends and patterns for business optimization
Implemented natural language processing techniques for sentiment analysis of customer reviews
Develop a predictive model to identify potential customers for a new product launch.
Define the target variable and features to be used in the model
Collect and preprocess relevant data for training the model
Select an appropriate machine learning algorithm and train the model
Evaluate the model's performance using metrics like accuracy, precision, and recall
Use the model to predict potential customers for the new product
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