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C5i Data Scientist Interview Questions and Answers

Updated 20 Jun 2024

Q1. 4. What is the difference between Linear Regression and Logistic Regression?

Ans.

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

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Q2. 2. Why did you choose Data Science Field?

Ans.

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

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C5i Data Scientist Interview Questions and Answers for Freshers
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Q3. 6. Can we use confusion matrix in Linear Regression?

Ans.

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.

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Q4. 1. Why Machine Learning?

Ans.

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

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Q5. 8. Explain Random Forest and Decision Tree?

Ans.

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

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Q6. 7. Explain KNN Algorithm?

Ans.

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.

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Q7. 5. Explain Confusion Matrix?

Ans.

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

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Q8. What is TF-IDF IN NLP

Ans.

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 words in a document

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Q9. Explain TF-IDF in NLP

Ans.

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 identifying the most important words in a document by giving hi...read more

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