Shapoorji Pallonji Group Data Scientist Interview Questions and Answers

Q1. What is decision tree
Ans. 
A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label.
• Decision trees are a popular machine learning algorithm used for classification and regression tasks.

• They are easy to interpret and visualize, making them useful for understanding the decision-making process.

• Each internal...read more
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Q2. What are model evaluation metrics?
Ans. 
Model evaluation metrics are used to assess the performance of machine learning models.
• Model evaluation metrics help in determining how well a model is performing in terms of accuracy, precision, recall, F1 score, etc.

• Common evaluation metrics include accuracy, precision, recall, F1 score, ROC-AUC, confusion matrix, and mean squared error.

• These metrics help in comparing different models and selecting the best one for a...read more
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Python programming for 45 for minutes

Q1. What are the hyperparameters of SVM
Ans. 
Hyperparameters of SVM include C, kernel, gamma, degree, and coef0.
• C: Regularization parameter that controls the trade-off between achieving a low error on the training data and minimizing model complexity.

• Kernel: Specifies the type of hyperplane used to separate the data.

• Gamma: Kernel coefficient for 'rbf', 'poly', and 'sigmoid' kernels.

• Degree: Degree of the polynomial kernel function.

• Coef0: Independent term in kernel...read more
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Q2. How to tune that hyperparameters
Ans. 
Hyperparameters can be tuned using techniques like grid search, random search, and Bayesian optimization.
• Grid search: Exhaustively search through a specified subset of hyperparameters.

• Random search: Randomly sample hyperparameter combinations.

• Bayesian optimization: Use probabilistic models to predict the performance of different hyperparameter configurations.
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Q1. What is decision tree
Ans. 
A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label.
• Decision trees are a popular machine learning algorithm used for classification and regression tasks.

• They are easy to interpret and visualize, making them useful for understanding the decision-making process.

• Each internal...read more
Answered by AI
Add your answer
Q2. What are model evaluation metrics?
Ans. 
Model evaluation metrics are used to assess the performance of machine learning models.
• Model evaluation metrics help in determining how well a model is performing in terms of accuracy, precision, recall, F1 score, etc.

• Common evaluation metrics include accuracy, precision, recall, F1 score, ROC-AUC, confusion matrix, and mean squared error.

• These metrics help in comparing different models and selecting the best one for a...read more
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Online home-based aptitude test for 45 minutes which included basic apti q's, py, sudo code etc.

Q1. Tell me about your self.
Ans. 
I am a data science enthusiast with a strong background in statistics and machine learning.
• Completed coursework in data analysis, statistical modeling, and predictive analytics

• Proficient in programming languages such as Python, R, and SQL

• Experience with data visualization tools like Tableau and Power BI

• Completed projects involving regression analysis, classification algorithms, and clustering techniques
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Q2. Project-based q's.
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