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I applied via Walk-in and was interviewed in Mar 2024. There were 2 interview rounds.
Data Science , machine learning, python
Coding on python, mysql, r
I applied via Approached by Company and was interviewed before Jun 2022. There were 2 interview rounds.
I have completed assignments. I done all methods of algorithms and data models with good accuracy
I applied via Company Website and was interviewed before Oct 2021. There were 2 interview rounds.
Coding questions related to basic python, and Loops, oops concept.
I am a data science intern with strong analytical skills and a passion for problem-solving.
Strong analytical skills
Passion for problem-solving
Experience in data analysis and visualization
Proficient in programming languages like Python and R
Ability to work with large datasets
Excellent communication and teamwork skills
My weakness is that I tend to be overly critical of my own work.
I often spend too much time reviewing and revising my work to ensure it is perfect.
I have a tendency to doubt my abilities and seek validation from others.
I sometimes struggle with prioritizing tasks and can become overwhelmed with multiple projects.
I am working on improving my self-confidence and learning to trust my own judgment.
I applied via Campus Placement and was interviewed in Nov 2024. There were 3 interview rounds.
There were verbal, non verbal, reasoning , English and maths questions
I worked on a project analyzing customer behavior using machine learning algorithms.
Used Python for data preprocessing and analysis
Implemented machine learning models such as decision trees and logistic regression
Performed feature engineering to improve model performance
Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
Proficient in Python for data analysis and machine learning tasks
Experience with R for statistical analysis and visualization
Knowledge of SQL for querying databases and extracting data
Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
I currently stay in an apartment in downtown area.
I stay in an apartment in downtown area
My current residence is in a city
I live close to my workplace
I am a data science enthusiast with a strong background in statistics and machine learning.
Background in statistics and machine learning
Passionate about data science
Experience with data analysis tools like Python and R
I applied via Campus Placement
Basic DSA questions will be asked Leetcode Easy to medium
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.
BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.
For exa...
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
It is commonly used in text classification tasks, such as spam detection or sentiment analysis.
It is suitable for features that represent counts or frequencies, like word counts in text data.
It calculates the probability of each class given the input features and selects the class with the
I applied via campus placement at Chennai Mathematical Institute, Chennai and was interviewed in Dec 2023. There was 1 interview round.
Large Language Models are advanced AI models that can generate human-like text based on input data.
Large Language Models use deep learning techniques to understand and generate text.
Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).
They are trained on vast amounts of text data to improve their language generation capabilities.
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
RAGs is commonly used in project management to quickly communicate the status of tasks or projects.
Red typically indicates tasks or projects that are behind schedule or at risk.
Amber signifies tasks or projects that are on track but may require attention.
Green represents tasks or projects that ar...
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.
K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.
DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...
I applied via Campus Placement and was interviewed in Jun 2024. There were 2 interview rounds.
Some basic aptitude questions were asked , but had to be solved in 20 minutes
Medium level 2 leet code questions were asked and i cleared both
I was interviewed in Dec 2023.
English, number system, grammar
Python , data science, machine learning
Python machine learning, natural language precossing
Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.
Python basics cover syntax, variables, data types, and control structures.
NumPy is a library for numerical computing, providing powerful array operations.
Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.
Matplotlib is a libr...
Indian environment, village, college days
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