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I applied via campus placement at Birla Institute of Technology and Science (BITS), Pilani and was interviewed in Dec 2024. There were 2 interview rounds.
I applied via Recruitment Consulltant and was interviewed in Aug 2024. There were 2 interview rounds.
I handled data imbalance by using techniques like oversampling, undersampling, SMOTE, or using ensemble methods.
Used oversampling to increase minority class instances
Used undersampling to decrease majority class instances
Applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for minority class
Utilized ensemble methods like Random Forest or Gradient Boosting to handle imbalance
Advanced sql and basics of data science
I applied via Walk-in and was interviewed in Mar 2024. There were 2 interview rounds.
Basic coding questions
Developed a predictive model for customer churn using logistic regression
Chose logistic regression for its interpretability and ability to handle binary outcomes
Evaluated model performance using accuracy, precision, recall, and F1 score
Analyzed customer data including demographics, purchase history, and customer service interactions
I applied via Approached by Company and was interviewed in Mar 2022. There were 3 interview rounds.
I applied via Company Website and was interviewed in May 2024. There was 1 interview round.
I tackle pressurized situations by staying calm, prioritizing tasks, seeking help when needed, and maintaining a positive attitude.
Stay calm and composed under pressure
Prioritize tasks based on urgency and importance
Seek help or guidance from colleagues or supervisors
Maintain a positive attitude and focus on finding solutions
I applied via Referral and was interviewed in May 2024. There was 1 interview round.
Regularisation in random forest helps prevent overfitting by controlling the complexity of the model.
Regularisation in random forest is achieved by limiting the depth of the trees in the forest.
It helps prevent overfitting by reducing the complexity of the model and improving generalization.
Regularisation parameters like max_depth, min_samples_split, and min_samples_leaf can be tuned to control the complexity of the mo
I applied via Referral and was interviewed in Jan 2023. There were 2 interview rounds.
I was interviewed before Nov 2023.
Based on different scenarios they interviewed me
I applied via Recruitment Consulltant and was interviewed in Aug 2024. There were 2 interview rounds.
I handled data imbalance by using techniques like oversampling, undersampling, SMOTE, or using ensemble methods.
Used oversampling to increase minority class instances
Used undersampling to decrease majority class instances
Applied SMOTE (Synthetic Minority Over-sampling Technique) to generate synthetic samples for minority class
Utilized ensemble methods like Random Forest or Gradient Boosting to handle imbalance
Advanced sql and basics of data science
I applied via Naukri.com and was interviewed in Feb 2024. There was 1 interview round.
Reverse a given string
Use built-in functions like reverse() or slice() in Python
Iterate through the string in reverse order and append characters to a new string
Convert the string to an array of characters, reverse the array, and join it back into a string
Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function.
Regularization helps to reduce the complexity of the model by penalizing large coefficients.
It adds a penalty term to the loss function, such as L1 (Lasso) or L2 (Ridge) regularization.
Regularization helps to improve the generalization of the model by discouraging overfitting.
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