Mahindra & Mahindra Data Science Intern Interview Questions and Answers

45 mins 15 min mcq data science and 30 mins 1 dsa problem

Q1. Python - All subsets of a list.
Ans. 
Generate all possible subsets of a given list in Python.
• Use itertools.combinations to generate all possible combinations of the list elements.

• Convert the combinations to lists and store them in a new list to get all subsets.
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Q2. Sql query - Customers who have ordered all products from all categories.
Ans. 
Use a SQL query to find customers who have ordered all products from all categories.
• Join the Customers, Orders, and Products tables

• Group by customer and count the distinct products ordered

• Filter for customers who have ordered the total number of products available in each category
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Q3. Importance of feature engineering.
Ans. 
Feature engineering is crucial in data science as it involves selecting, transforming, and creating new features to improve model performance.
• Feature engineering helps in improving model accuracy by providing relevant and meaningful input variables.

• It involves techniques like one-hot encoding, scaling, normalization, and creating interaction terms.

• Feature engineering can help in reducing overfitting and improving model...read more
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Q4. Questions on project.
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Q5. What is GAN.Have you worked with it.
Ans. 
GAN stands for Generative Adversarial Network, a type of neural network used for generating new data.
• Consists of two neural networks - generator and discriminator

• Generator creates new data samples while discriminator tries to distinguish between real and generated data

• Used in image generation, text generation, and other creative applications
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Q1. Discussion on project.Demo of project.
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Q2. Analysis sql query - whether class 11 marks or class 12 marks is greater., table given which contains student name,class,11th marks,12th marks.
Ans. 
Analyze student marks to determine if Class 11 or Class 12 marks are higher.
• Compare the '11th marks' and '12th marks' columns for each student.

• Use SQL query: SELECT student_name, CASE WHEN class_11_marks > class_12_marks THEN 'Class 11' ELSE 'Class 12' END AS higher_class FROM students;

• Example: If 'John' has 85 in Class 11 and 90 in Class 12, the result will show 'Class 12' as higher.

• Aggregate results to see how man...read more
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Q3. Similar table. Find students who scored more than avg marks of both 11th and 12th.
Ans. 
Find students who scored more than avg marks in both 11th and 12th grades.
• Calculate the average marks for each student in 11th and 12th grades.

• Compare each student's marks with the respective average marks to find those who scored higher in both grades.
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Q4. What is Cost function.
Ans. 
Cost function is a mathematical function that measures the error between predicted values and actual values in a machine learning model.
• Cost function helps in optimizing the parameters of a model to minimize the error.

• Common cost functions include Mean Squared Error (MSE) and Cross Entropy Loss.

• It is used in training machine learning models through techniques like gradient descent.

• The goal is to find the parameters tha...read more
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Q5. What is entropy.
Ans. 
Entropy is a measure of disorder or randomness in a system.
• Entropy is used in information theory to quantify the amount of uncertainty involved in predicting the value of a random variable.

• It is often used in machine learning to measure the impurity or disorder in a dataset.

• In thermodynamics, entropy is a measure of the amount of energy in a physical system that is not available to do work.
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Q6. What is ginni coefficient.
Ans. 
Gini coefficient is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents.
• Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.

• A Gini coefficient of 0.4 is considered moderate inequality, while 0.6 or higher is considered high inequality.

• It is commonly used in economics to measure income inequality with...read more
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Q7. What will happen if linear regression is used for classification
Ans. 
Using linear regression for classification can lead to inaccurate predictions and unreliable results.
• Linear regression assumes a continuous output, making it unsuitable for discrete classification tasks.

• It may not handle outliers well, leading to incorrect classification boundaries.

• The predicted values may fall outside the 0-1 range for binary classification.

• Logistic regression is a more appropriate choice for classifi...read more
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Q1. Pair sum problem in python.Discussion on space time complexity.
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Q2. Discussion on project.What algorithms used and why.
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