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I applied via Referral and was interviewed before May 2023. There were 2 interview rounds.
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 Dec 2018. There were 3 interview rounds.
I chose Data Science field because of its potential to solve complex problems and make a positive impact on society.
Fascination with data and its potential to drive insights
Desire to solve complex problems and make a positive impact on society
Opportunity to work with cutting-edge technology and tools
Ability to work in a variety of industries and domains
Examples: Predictive maintenance in manufacturing, fraud detection
Linear Regression is used for predicting continuous numerical values, while Logistic Regression is used for predicting binary categorical values.
Linear Regression predicts a continuous output, while Logistic Regression predicts a binary output.
Linear Regression uses a linear equation to model the relationship between the independent and dependent variables, while Logistic Regression uses a logistic function.
Linear Regr...
Confusion matrix is a table used to evaluate the performance of a classification model.
It is a 2x2 matrix that shows the number of true positives, false positives, true negatives, and false negatives.
It helps in calculating various metrics like accuracy, precision, recall, and F1 score.
It is useful in identifying the strengths and weaknesses of a model and improving its performance.
Example: In a binary classification p...
No, confusion matrix is not used in Linear Regression.
Confusion matrix is used to evaluate classification models.
Linear Regression is a regression model, not a classification model.
Evaluation metrics for Linear Regression include R-squared, Mean Squared Error, etc.
KNN is a non-parametric algorithm used for classification and regression tasks.
KNN stands for K-Nearest Neighbors.
It works by finding the K closest data points to a given test point.
The class or value of the test point is then determined by the majority class or average value of the K neighbors.
KNN can be used for both classification and regression tasks.
It is a simple and easy-to-understand algorithm, but can be compu
Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to improve accuracy.
Random Forest is a type of supervised learning algorithm used for classification and regression tasks.
It creates multiple decision trees and combines their outputs to make a final prediction.
Each decision tree is built using a random subset of features and data points to reduce overfitting.
Ran...
I have worked on various projects involving data analysis, machine learning, and predictive modeling.
Developed a predictive model to forecast customer churn for a telecommunications company.
Built a recommendation system using collaborative filtering for an e-commerce platform.
Performed sentiment analysis on social media data to understand customer opinions and preferences.
Implemented a fraud detection system using anom...
I applied via Approached by Company
Transformers are a type of neural network architecture that utilizes self-attention mechanisms to process sequential data.
Transformers use self-attention mechanisms to weigh the importance of different input elements, allowing for parallel processing of sequences.
Unlike RNNs and LSTMs, Transformers do not rely on sequential processing, making them more efficient for long-range dependencies.
Transformers have been shown ...
Different types of Attention include self-attention, global attention, and local attention.
Self-attention focuses on relationships within the input sequence itself.
Global attention considers the entire input sequence when making predictions.
Local attention only attends to a subset of the input sequence at a time.
Examples include Transformer's self-attention mechanism, Bahdanau attention, and Luong attention.
GPT is a generative model while BERT is a transformer model for natural language processing.
GPT is a generative model that predicts the next word in a sentence based on previous words.
BERT is a transformer model that considers the context of a word by looking at the entire sentence.
GPT is unidirectional, while BERT is bidirectional.
GPT is better for text generation tasks, while BERT is better for understanding the cont
Data scientists analyze data to gain insights, machine learning (ML) involves algorithms that improve automatically through experience, and artificial intelligence (AI) refers to machines mimicking human cognitive functions.
Data scientists analyze large amounts of data to uncover patterns and insights.
Machine learning involves developing algorithms that improve automatically through experience.
Artificial intelligence r...
I applied via Campus Placement and was interviewed in Jul 2022. There were 4 interview rounds.
Medium level of question are there in this section
Basic level of question are there in this section
Joints are connections between bones that allow movement and provide support to the body.
Joints are found throughout the body, such as the knee, elbow, and shoulder.
They are made up of bones, cartilage, ligaments, and synovial fluid.
Joints enable various types of movements, including flexion, extension, rotation, and abduction.
Different types of joints include hinge joints, ball-and-socket joints, and pivot joints.
Join...
I'm not sure how joints relate to data science, but my hobby is playing guitar.
Joints can refer to the connection between two bones in the body or the way two things are connected or joined together.
Playing guitar is a hobby that helps me relax and unwind after a long day of working with data.
While seemingly unrelated to data science, playing an instrument can actually improve cognitive function and creativity, which c
I applied via Naukri.com and was interviewed before May 2023. There were 4 interview rounds.
Simple Classification problem with some MCQ questions
I applied via Recruitment Consultant and was interviewed in Jun 2021. There were 4 interview rounds.
Developed a predictive model for customer churn using Random Forest algorithm.
Used Python and scikit-learn library for model development
Performed data cleaning, feature engineering, and exploratory data analysis
Tuned hyperparameters using GridSearchCV and evaluated model performance using cross-validation
Random Forest is an ensemble learning method that builds multiple decision trees and combines their predictions
Other...
I applied via Naukri.com and was interviewed before Aug 2021. There were 4 interview rounds.
Machine learning MCQ questions. 2 model-building questions.
I applied via Company Website and was interviewed in Feb 2022. There were 2 interview rounds.
There were 13 questions
2 was coding and other multiple choice questions
one coding question was wine quality test
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