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ETA ASCON Group Interview Questions and Answers
Q1. 4. What is the difference between Linear Regression and Logistic Regression?
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 Regression assumes a linear relationship between the variables...read more
Q2. 2. Why did you choose Data Science Field?
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 in finance, personalized medicine in healthcare
Q3. 6. Can we use confusion matrix in Linear Regression?
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.
Q4. 1. Why Machine Learning?
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 detection and fraud prevention
Q5. 8. Explain Random Forest and Decision Tree?
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.
Random Forest is more accurate than a single decision tree an...read more
Q6. 7. Explain KNN Algorithm?
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 computationally expensive for large datasets.
Q7. 5. Explain Confusion Matrix?
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 problem, the confusion matrix can be used to evaluate how m...read more
Q8. How to connect adls gen2 with databricks
To connect ADLS Gen2 with Databricks, you can use Azure Blob Storage and set up a linked service in Databricks.
Create an Azure Blob Storage account in the Azure portal
Set up a linked service in Databricks to connect to the Azure Blob Storage account
Use the Azure Blob Storage account as the storage account for Databricks to access ADLS Gen2 data
Q9. Summarize CV
Experienced Business Analyst with expertise in data analysis, process improvement, and project management.
5+ years of experience in business analysis
Proficient in SQL and data visualization tools
Led successful projects resulting in cost savings and increased efficiency
Collaborated with cross-functional teams to identify and implement process improvements
Strong communication and presentation skills
Interview Process at ETA ASCON Group
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