Flipkart Data Scientist Interview Questions and Answers

Q1. Coding and data structure
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Q1. ML Breadth and depth
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Q1. What do you know about Uber?
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Q2. Went over my current role
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Q1. What was the most interesting project you worked on
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Q2. Design and experiment to validate the new recommendation engine
Ans. 
Designing an experiment to validate a recommendation engine involves A/B testing, metrics, and user feedback for effectiveness.
• A/B Testing: Split users into two groups, one using the new engine and the other using the old one, to compare performance metrics.

• Key Metrics: Measure click-through rates, conversion rates, and user engagement to assess the effectiveness of the new engine.

• User Feedback: Collect qualitative fee...read more
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It been for 45 mins. question asked from python,ML,Deep learning and maths.

Q1. 1) explain correlation and convaraince 2) how logistic differ from linear regression
Ans. 
Correlation measures the strength and direction of a linear relationship between two variables, while covariance measures the extent to which two variables change together.
• Correlation ranges from -1 to 1, where 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship.

• Covariance can be positive, negative, or zero. A positive covariance indicates that as o...read more
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Q1. What is Linearregression
Ans. 
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables.
• Linear regression is used to predict the value of a dependent variable based on the value of one or more independent variables.

• It assumes a linear relationship between the independent and dependent variables.

• The goal of linear regression is to find the best-fitting line that minimi...read more
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Q2. What is random forest
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Random forest is an ensemble learning method used for classification and regression tasks.
• Random forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.

• Random forest is robust to overfitting and noisy data, and it can handle large datasets...read more
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Q3. WHat is xgboost
Ans. 
XGBoost is an optimized distributed gradient boosting library designed for efficient and accurate large-scale machine learning.
• XGBoost stands for eXtreme Gradient Boosting.

• It is a popular machine learning algorithm known for its speed and performance.

• XGBoost is used for regression, classification, ranking, and user-defined prediction problems.

• It is based on the gradient boosting framework and uses decision trees as bas...read more
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Q1. Describe LSTM and GRU
Ans. 
LSTM and GRU are types of recurrent neural networks used for processing sequential data.
• LSTM (Long Short-Term Memory) networks are capable of learning long-term dependencies in data.

• GRU (Gated Recurrent Unit) networks are simpler than LSTM and have fewer parameters.

• LSTM has three gates (input, output, forget) while GRU has two gates (update, reset).

• LSTM is better at capturing long-term dependencies but is more complex,...read more
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Q2. Define 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.

• It aims to determine if there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

• Common methods of hypothesis testing include t-tests, chi-square tests, and ANOVA.

• The p-value is used to dete...read more
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