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DSA and ML, AI, Coding question
I applied via Campus Placement and was interviewed in Oct 2023. There were 3 interview rounds.
2hrs - sections include aptitude, machine learning, deep learning and two easy python coding questions
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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 was interviewed in Jan 2025.
I have 5 years of experience in analyzing large datasets to extract valuable insights and make data-driven decisions.
Analyzed customer behavior data to optimize marketing strategies
Built predictive models to forecast sales trends
Utilized machine learning algorithms to improve product recommendations
Presented findings to stakeholders in a clear and actionable manner
Questions related to work experience in data science field.
Asked about previous projects worked on
Inquired about specific data analysis techniques used
Discussed challenges faced and how they were overcome
I was interviewed in May 2024.
Questions based on ML,PYTHON, DATA VISUALIZATION
TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.
TF-IDF stands for Term Frequency-Inverse Document Frequency
It is used in Natural Language Processing (NLP) to determine the importance of a word in a document
TF-IDF is calculated by multiplying the term frequency (TF) by the inverse document frequency (IDF)
It helps in identifying the most important
ML,DL,Python,NLP,Data VIsualization
TF-IDF is a numerical statistic that reflects the importance of a word in a document relative to a collection of documents.
TF-IDF stands for Term Frequency-Inverse Document Frequency.
It is used in Natural Language Processing (NLP) to determine the importance of a word in a document.
TF-IDF is calculated by multiplying the term frequency (TF) of a word by the inverse document frequency (IDF) of the word.
It helps in ident...
I applied via Naukri.com and was interviewed before Dec 2023. There were 3 interview rounds.
Test of Basic data structures in Python include lists, tuples, and dictionaries, as well as loops and conditional statements.
Framework and requirements for chatbot implementation.
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...
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