ValueLabs Data Scientist Interview Questions and Answers

Q1. Coding question of finding index of 2 nos. having total equal to target in a list, without using nested for loop? l= [2,15,5,7] t= 9 output》》[0,3]
Ans. 
Finding index of 2 numbers having total equal to target in a list without nested for loop.
• Use dictionary to store the difference between target and each element of list.

• Iterate through list and check if element is in dictionary.

• Return the indices of the two elements that add up to target.
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Q2. What is random forest, knn?
Ans. 
Random forest and KNN are machine learning algorithms used for classification and regression tasks.
• Random forest is an ensemble learning method that constructs multiple decision trees and combines their outputs to make a final prediction.

• KNN (k-nearest neighbors) is a non-parametric algorithm that classifies new data points based on the majority class of their k-nearest neighbors in the training set.

• Random forest is us...read more
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Q1. Ll coding on python dictionary
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Python dictionaries are versatile data structures for storing key-value pairs, enabling efficient data retrieval and manipulation.
• Dictionaries are created using curly braces: example = {'key': 'value'}

• Access values using keys: example['key'] returns 'value'.

• Dictionaries are mutable; you can add or remove items: example['new_key'] = 'new_value'.

• Keys must be unique and immutable (e.g., strings, numbers, tuples).

• Use metho...read more
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Q2. Find unique keys in 2 dictionaries
Ans. 
To find unique keys in 2 dictionaries.
• Create a set of keys for each dictionary

• Use set operations to find the unique keys

• Return the unique keys
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Q3. Aws ec2 model deployment procedure
Ans. 
AWS EC2 model deployment involves creating an instance, installing necessary software, and deploying the model.
• Create an EC2 instance with the desired specifications

• Install necessary software and dependencies on the instance

• Upload the model and any required data to the instance

• Deploy the model using a web server or API

• Monitor the instance and model performance for optimization
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Q4. Overloading concept of oop
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Overloading is the ability to define multiple methods with the same name but different parameters.
• Overloading allows for more flexibility in method naming and improves code readability.

• Examples include defining multiple constructors for a class with different parameter lists or defining a method that can accept different data types as input.

• Overloading is resolved at compile-time based on the number and types of argume...read more
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Q1. Conceptual Questions on SQL and Python
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Q1. More ML and DL based questions. What is random forest. What is neural network. Early Stopping, Weights and Bias, Bagging and Boosting.
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Q2. Linear Regression and Logistics Regression and difference between both.
Ans. 
Linear Regression predicts continuous values while Logistic Regression predicts binary outcomes.
• Linear Regression is used for predicting continuous values while Logistic Regression is used for predicting binary outcomes.

• Linear Regression uses a linear approach to model the relationship between dependent and independent variables while Logistic Regression uses a logistic function to model the probability of a binary out...read more
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Q1. About the project
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Q2. Evaluation metrics in linear regression, Assumption of linear regression,
Ans. 
Evaluation metrics and assumptions in linear regression
• Evaluation metrics in linear regression include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, and Adjusted R-squared.

• Assumptions of linear regression include linearity, independence, homoscedasticity, and normality of residuals.

• Example: MSE = sum((actual - predicted)^2) / n
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Q1. Questions on Python datatypes, class, object, inheritance,
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Q2. Machine learning algorithms
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Machine learning algorithms are used to train models on data to make predictions or decisions.
• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve learning through trial and error.

• Examples of machine learning applications include image recognition, natural l...read more
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Q3. Sql queries and python coding questions
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Q1. Related to project
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Questions related to Pandas, List, String

Q1. 1) How decision tree works 2) what are the parameters used in OpenCV?
Ans. 
Decision tree is a tree-like model used for classification and regression. OpenCV parameters include image processing and feature detection.
• Decision tree is a supervised learning algorithm that recursively splits the data into subsets based on the most significant attribute.

• It is used for both classification and regression tasks.

• OpenCV parameters include image processing techniques like smoothing, thresholding, and mor...read more
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