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I applied via Recruitment Consultant and was interviewed in Dec 2018. There were 3 interview rounds.
Machine learning enables computers to learn from data and make predictions or decisions without being explicitly programmed.
Machine learning can automate and optimize complex processes
It can help identify patterns and insights in large datasets
It can improve accuracy and efficiency in decision-making
Examples include image recognition, natural language processing, and predictive analytics
It can also be used for anomaly
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 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.
C5i interview questions for designations
Top trending discussions
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 applied via Naukri.com and was interviewed in Dec 2024. There was 1 interview round.
I applied via campus placement at Sastra University and was interviewed in Sep 2024. There were 2 interview rounds.
Along with coding round..there's a communication test at the end
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 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 Company Website and was interviewed in Sep 2024. There was 1 interview round.
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