Capgemini Data Scientist Interview Questions, Process, and Tips

Very easy and but selection is basis

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Q1. What is overfitting and underfitting
Ans. 
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
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Q2. What are LLM Models
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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
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Q1. Precision recall Gen AI etc.
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Q1. Project Related Questions
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Q1. Overall Experience and reasons for Job change
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Q1. 2 round technical interview, both of them about ML and last one concerning statistics and ML.
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Q2. What is linear regression?
Ans. 
Linear regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.
• It assumes a linear relationship between the variables

• It is used to predict the value of the dependent variable based on the independent variable(s)

• It can be simple linear regression (one independent variable) or multiple linear regression (more than one independent variable)

• It is commo...read more
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Q3. Question regarding confusion matrix?
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Q4. Name various ML algorithm?
Ans. 
ML algorithms are used to train models on data to make predictions or decisions. Some popular ones are SVM, KNN, and Random Forest.
• Support Vector Machines (SVM)

• K-Nearest Neighbors (KNN)

• Random Forest

• Naive Bayes

• Decision Trees

• Linear Regression

• Logistic Regression

• Neural Networks

• Gradient Boosting

• Clustering Algorithms (K-Means, Hierarchical)

• Association Rule Learning (Apriori)

• Dimensionality Reduction Algorithms (PCA, LDA)

• Reinf
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Q1. How do you handle outliers? How to handle imbalance dataset? Feature engineering techniques?
Ans. 
Outliers can be handled by removing, transforming or imputing them. Imbalanced datasets can be handled by resampling techniques. Feature engineering involves creating new features from existing ones.
• Outliers can be removed using statistical methods like z-score or IQR.

• Outliers can be transformed using techniques like log transformation or box-cox transformation.

• Outliers can be imputed using techniques like mean imputat...read more
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Q1. 1. Explain the project in detail 2. Explain me your 5 favourite models 3. Questions on probability
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Q1. It was a HR round and HR has asked me what's your salary expectations.
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