Seller Drive Data Analyst Interview Questions and Answers

Q1. What is difference between random forest and xgboost
Ans. 
Random forest is an ensemble learning method using decision trees, while XGBoost is a gradient boosting algorithm.
• Random forest builds multiple decision trees and combines their predictions, while XGBoost builds trees sequentially to correct errors.

• Random forest is less prone to overfitting compared to XGBoost.

• XGBoost is computationally efficient and often outperforms random forest in terms of predictive accuracy.

• Rando...read more
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Q2. What is your project problem statement
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Q3. How you convince for clients the model is useful, what are the matrix
Ans. 
I convince clients of model usefulness by showcasing its accuracy, precision, recall, and F1 score.
• Explain the model's accuracy in predicting outcomes compared to actual results

• Discuss precision - the proportion of true positive predictions out of all positive predictions

• Highlight recall - the proportion of true positive predictions out of all actual positives

• Mention F1 score - the balance between precision and recall,
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Q1. How you take data from client
Ans. 
I collect data from clients through various methods such as surveys, interviews, API integrations, and data sharing agreements.
• Conduct surveys to gather specific data points from clients

• Interview clients to understand their data needs and preferences

• Integrate with client's systems through APIs to access real-time data

• Establish data sharing agreements to receive data from clients securely
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Q2. What are hyper parameters you turned for your model
Ans. 
Tuned hyperparameters include learning rate, batch size, number of layers, and activation functions.
• Adjusted learning rate to improve model convergence

• Optimized batch size for better training efficiency

• Experimented with different numbers of layers to find optimal model complexity

• Tried various activation functions to enhance model performance
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Q3. What difference model you use in your project
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Q4. How you work with llm
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Q5. What are the features in your data set
Ans. 
The features in the data set include age, gender, income, education level, location, and purchase history.
• Age

• Gender

• Income

• Education level

• Location

• Purchase history
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Q6. What feature engineering you did in project
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Python, Pyspark, Scala, Kafka

Q1. CTE Expectation
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