Capgemini Data Scientist Interview Questions, Process, and Tips for Experienced

Developed a machine learning model to predict customer churn for a telecommunications company.

• Used historical customer data to train the model

• Implemented various classification algorithms such as logistic regression and random forest

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

RF algorithm is chosen for its ability to handle large datasets, high accuracy, and resistance to overfitting.

• RF algorithm is an ensemble learning method that builds multiple decision trees and merges them together to improve accuracy.

• It can handle large datasets with high dimensionality and is less prone to overfitting compared to other algorithms.

• RF algorithm is versatile and can be used for both classification and r...read more

Overfitting occurs when a model learns the training data too well, leading to poor performance on new data. Underfitting occurs when a model is too simple to capture the underlying patterns in the data.

• Overfitting: Model is too complex, fits noise in the training data, performs poorly on new data

• Underfitting: Model is too simple, fails to capture underlying patterns in the data, performs poorly on both training and new...read more

LLM models, or Language Model Models, are a type of machine learning model that focuses on predicting the next word in a sequence of words.

• LLM models are commonly used in natural language processing tasks such as text generation, machine translation, and speech recognition.

• They are trained on large amounts of text data to learn the relationships between words and predict the most likely next word in a given context.

• Exa...read more