Infosys Data Scientist Interview Questions, Process, and Tips

Q1. Explain XGBoost algoritm
Ans. 
XGBoost is a powerful machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosting machines.

• It is widely used in machine learning competitions and is known for its speed and performance.

• XGBoost uses a technique called boosting, where multiple weak learners are combined to create a strong learner.

• It...read more
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Q2. XgBoost algorithm has 10-20 features. How are the splits decided, on which feature are they going to be divided?
Ans. 
XgBoost algorithm uses a greedy approach to determine splits based on feature importance.
• XgBoost algorithm calculates the information gain for each feature to determine the best split.

• The feature with the highest information gain is chosen for the split.

• This process is repeated recursively for each node in the tree.

• Features can be split based on numerical values or categories.

• Example: If a feature like 'age' has the hi...read more
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Q3. Do you have any experience on cloud platform?
Ans. 
Yes, I have experience working on cloud platforms such as AWS and Google Cloud.
• Experience with AWS services like S3, EC2, and Redshift

• Familiarity with Google Cloud services like BigQuery and Compute Engine

• Utilized cloud platforms for data storage, processing, and analysis
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Q4. What is entropy, information gain?
Ans. 
Entropy is a measure of randomness or uncertainty in a dataset, while information gain is the reduction in entropy after splitting a dataset based on a feature.
• Entropy is used in decision tree algorithms to determine the best feature to split on.

• Information gain measures the effectiveness of a feature in classifying the data.

• Higher information gain indicates that a feature is more useful for splitting the data.

• Entropy ...read more
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Q5. What is hypothesis testing?
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Hypothesis testing is a statistical method used to make inferences about a population based on sample data.
• Hypothesis testing involves formulating a null hypothesis and an alternative hypothesis.

• The null hypothesis is assumed to be true until there is enough evidence to reject it.

• Statistical tests are used to determine the likelihood of observing the data if the null hypothesis is true.

• The p-value is used to determine ...read more
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Q6. Explain precision and recall, when are they used in which scenario?
Ans. 
Precision and recall are metrics used in evaluating the performance of classification models.
• Precision measures the accuracy of positive predictions, while recall measures the ability of the model to find all positive instances.

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• For example, in a spam email de...read more
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Q7. What is data imbalance?
Ans. 
Data imbalance refers to unequal distribution of classes in a dataset, where one class has significantly more samples than others.
• Data imbalance can lead to biased models that favor the majority class.

• It can result in poor performance for minority classes, as the model may struggle to accurately predict them.

• Techniques like oversampling, undersampling, and using different evaluation metrics can help address data imbala...read more
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Q8. What is SMOTE? Do you have any experience working on Time Series? Code analysis of global variable?
Ans. 
SMOTE stands for Synthetic Minority Over-sampling Technique, used to balance imbalanced datasets by generating synthetic samples.
• SMOTE is commonly used in machine learning to address class imbalance by creating synthetic samples of the minority class.

• It works by generating new instances of the minority class by interpolating between existing instances.

• SMOTE is particularly useful in scenarios where the minority class i...read more
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Q9. Find 5th highest salary in every department. What are window functions Difference between union and union all Difference between delete and truncate.
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Basic operations on dataframe using Pandas and SQL basics.

Q1. Data preprocessing related questions like steps took. Experience about working projects.
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Q2. Random forest and decision tress related questions
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Q1. Knn and logistic regression
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Q2. Correlation vs covariance
Ans. 
Covariance measures the relationship between two variables, while correlation measures the strength and direction of the relationship.
• Covariance can be positive, negative, or zero, indicating the direction of the relationship between variables.

• Correlation is always between -1 and 1, with 1 indicating a perfect positive relationship, -1 indicating a perfect negative relationship, and 0 indicating no relationship.

• Covaria...read more
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Q1. Basic Statistics
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Q2. Basic ML, DL question
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