Tesco Data Science Intern Interview Questions and Answers

Array-based question

Q1. Explain Xgboots
Ans. 
XGBoost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting.

• It is an implementation of gradient boosted decision trees designed for speed and performance.

• XGBoost is widely used in machine learning competitions and real-world applications.

• It can handle missing data, regularization, and parallel processing efficiently.

• XGBoost ...read more
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Q2. Explain random forest
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Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree in the forest makes a prediction, and the final prediction is the average or majority vote of all trees.

• Random forest is used for classification and regression tasks.

• It helps reduce overfitting and ...read more
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Q1. Explain about your project
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Q2. Explain the sequence to sequence models and transformers
Ans. 
Sequence to sequence models are used in natural language processing to convert input sequences into output sequences.
• Sequence to sequence models are commonly used in machine translation tasks, where the input is a sentence in one language and the output is the translated sentence in another language.

• Transformers are a type of sequence to sequence model that use self-attention mechanisms to weigh the importance of diffe...read more
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Q1. Bias and variance with respect to model
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Bias and variance are two types of errors that can occur in a model.
• Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.

• Variance refers to the error introduced by modeling the noise in the training data, leading to overfitting.

• Balancing bias and variance is crucial for creating a model that generalizes well to unseen data.
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Q2. Hypotheses test
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Q1. Intermediate level SQL questions using joins and case when
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Q2. String manipulation advanced level question in python
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A/B Testing, data structures

Standard DSA questions

Q1. How to do anomaly detection in un-structured data?
Ans. 
Anomaly detection in unstructured data involves using techniques like clustering, outlier detection, and natural language processing.
• Use clustering algorithms like k-means or DBSCAN to group similar data points together.

• Apply outlier detection methods such as isolation forests or one-class SVM to identify anomalies.

• Utilize natural language processing techniques like word embeddings or topic modeling for text data.

• Consi...read more
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Q1. Past experience
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Q1. Be prepared for the ML algorithms. Advanced topics preferred.
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Q2. Types of recommender system
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Recommender systems include collaborative filtering, content-based filtering, and hybrid systems.
• Collaborative filtering: Recommends items based on user behavior and preferences.

• Content-based filtering: Recommends items based on the features of the items and a profile of the user's preferences.

• Hybrid systems: Combine collaborative and content-based filtering to provide more accurate recommendations.

• Examples: Netflix us...read more
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Case Study was related to customer propensity to buy.

Q1. Linear Regression Assumptions
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Q2. ML Algorithms and Evaluation Metrics.
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Q1. What is VIF(variance inflation factor)
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Q1. Why do you want to join?
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