Swiggy Data Scientist Interview Questions and Answers

Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Q1. Explain all of decision tree and random forest?
Ans. 
Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
• Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.

• Random forest is a ...read more
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Q2. Some basic probability questions
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Q1. Data Engineering
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Q2. Data Cleaning, Models
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Q3. MLOps Pipeline, Data Preprocessing
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Q1. HR Questions mostly
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Q2. Company and Culture fit
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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. Explain past projects
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Q1. Explain resume points
Ans. 
Resume points are concise descriptions of your work experience, skills, and achievements listed on your resume.
• Resume points should be clear, specific, and quantifiable.

• Use action verbs to start each point, such as 'developed', 'implemented', 'analyzed'.

• Include relevant metrics or results to demonstrate impact, such as 'increased sales by 20%' or 'reduced processing time by 30%'.
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SQL - shared question on the screen and asked to code in SQL

Q1. Nth highes salary
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Test 45 mins 30 ques

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
Ans. 
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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