AI Analytics Hub Data Science Intern Interview Questions and Answers

Q1. Explain any ML model.
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Q2. Create Dataframe from two lists.
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Basic mathematical and resoning questions.

Q1. Tell me about your self
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Q2. Explain the recent project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data including usage patterns and demographics

• Used machine learning algorithms such as logistic regression and random forest

• Evaluated model performance using metrics like accuracy and AUC-ROC curve
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Q3. What is the difference between random forest and decision tree
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Random forest is an ensemble learning method that uses multiple decision trees to make predictions, while a decision tree is a single tree-like structure that makes decisions based on features.
• Random forest is a collection of decision trees that work together to make predictions.

• Decision tree is a single tree-like structure that makes decisions based on features.

• Random forest reduces overfitting by averaging the predic...read more
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Q4. What is cost function
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A cost function is a mathematical formula used to measure the cost of a particular decision or set of decisions.
• Cost function helps in evaluating the performance of a model by measuring how well it is able to predict the outcomes.

• It is used in optimization problems to find the best solution that minimizes the cost.

• Examples include mean squared error in linear regression and cross-entropy loss in logistic regression.
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Q1. Explain the RAG pipeline?
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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...read more
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Q2. Explain Confusion metrics
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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...read more
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Q1. Gen AI projects
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Q2. Architecture of projects I worked
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DSA and ML, AI, Coding question

Q1. Case study which was easy
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Q1. In depth questions on ML
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Q1. Bio activities example
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Q2. Adverse drug reaction
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SQL, Python coding …

Q1. Explain different KPIs of Classification Model
Ans. 
KPIs of Classification Model
• Accuracy: measures the proportion of correct predictions

• Precision: measures the proportion of true positives among predicted positives

• Recall: measures the proportion of true positives among actual positives

• F1 Score: harmonic mean of precision and recall

• ROC Curve: plots true positive rate against false positive rate

• Confusion Matrix: summarizes the performance of a classification model
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Q2. What are the differences between Left and Right Join
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Left join returns all records from left table and matching records from right table. Right join returns all records from right table and matching records from left table.
• Left join keeps all records from the left table and only matching records from the right table

• Right join keeps all records from the right table and only matching records from the left table

• Left join is denoted by LEFT JOIN keyword in SQL

• Right join is d...read more
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