Aditya Birla Group MIS Executive Interview Questions and Answers for Experienced

Q1. Introduction your self
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Q2. What are the language you learned
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I have learned multiple programming languages including Python, R, SQL, and Java.
• Python

• R

• SQL

• Java
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Python programming for 45 for minutes

Q1. What are the hyperparameters of SVM
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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
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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 difference between lstm and rnn
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LSTM is a type of RNN that addresses the vanishing gradient problem by using memory cells.
• RNN stands for Recurrent Neural Network, a type of neural network that processes sequential data.

• LSTM stands for Long Short-Term Memory, a type of RNN that includes memory cells to retain information over long sequences.

• LSTM is designed to overcome the vanishing gradient problem, which occurs when training RNNs on long sequences.

• L...read more
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Q1. What are different evaluation matrix ?
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Evaluation matrices are used to assess the performance of models in data science.
• Confusion matrix: used to evaluate the performance of classification models.

• Precision, recall, and F1 score: measures for binary classification models.

• Mean squared error (MSE): evaluates the performance of regression models.

• R-squared: assesses the goodness of fit for regression models.

• Area under the ROC curve (AUC-ROC): evaluates the perfo...read more
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Q1. Tell me about yourself
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Q2. Questions from projects which was mentioned on resume
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Q1. Fitment based questions
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Q2. Explain a project
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Developed a machine learning model to predict customer churn for a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall
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Math, English, reasoning

Q1. Asked me about expected salary
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