GeakMinds Interview Questions, Process, and Tips

Q1. Write SQL query to join two tables
Ans. 
SQL query to join two tables
• Use JOIN keyword to combine rows from two or more tables based on a related column between them

• Specify the columns to be selected from each table

• Use ON keyword to specify the join condition
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Q2. Perform EDA on the provided datasets and find insights
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Q3. Analyse the datasets and build a Machine Learning model
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Analyzing datasets and building a Machine Learning model for Associate Data Scientist role.
• 1. Explore and understand the datasets to identify patterns and relationships.

• 2. Preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.

• 3. Split the data into training and testing sets for model evaluation.

• 4. Choose a suitable Machine Learning algorithm based on the nature o...read more
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Q1. Explain Assumptions of Linear Regression
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Assumptions of linear regression are important for the model to be valid and reliable.
• Linear relationship between independent and dependent variables

• Independence of residuals (errors)

• Homoscedasticity (constant variance of residuals)

• Normality of residuals

• No multicollinearity among independent variables
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Q2. What is the difference between R-Squared and Adjusted R-Squared?
Ans. 
R-Squared measures the proportion of variance explained by the model, while Adjusted R-Squared adjusts for the number of predictors in the model.
• R-Squared increases as more predictors are added to the model, even if they are not relevant.

• Adjusted R-Squared penalizes for adding irrelevant predictors, making it a more reliable measure of model fit.

• R-Squared can never decrease when adding predictors, while Adjusted R-Squa...read more
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Q3. What is Central Mean Theorem?
Ans. 
Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
• The Central Limit Theorem is a fundamental concept in statistics that states that the sampling distribution of the sample mean will be approximately normally distributed, regardless of the shape of the population distribution, as the sample size increases.

• It is important because i...read more
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Q4. What is the difference between Rank and Dense Rank in SQL?
Ans. 
Rank assigns unique ranks to each row based on the order specified, while Dense Rank assigns consecutive ranks without gaps.
• Rank may have gaps in ranks if there are ties, while Dense Rank does not have gaps.

• Rank function is used to assign a unique rank to each row based on the specified order, while Dense Rank function assigns consecutive ranks.

• Example: If three rows have the same value and are ranked 1, 1, and 2 using...read more
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Q5. What is the difference between Series and Dataframe?
Ans. 
Series is a one-dimensional labeled array while Dataframe is a two-dimensional labeled data structure.
• Series can hold data of any type while Dataframe is a collection of Series.

• Dataframe is like a table with rows and columns, while Series is like a single column of that table.

• Dataframe is more versatile and powerful compared to Series.

• Example: Series - a column of employee names. Dataframe - a table with columns for em
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Q1. Explain Random Forest algorithm
Ans. 
Random Forest is an ensemble learning algorithm that creates multiple decision trees and combines their predictions.
• 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 outcome, and the final prediction is made by averaging the predictions of all trees.

• Random Forest is used for classification and regression tasks, and it...read more
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Q2. What is the difference between Stemming and Lemmatization? Which one is better and why?
Ans. 
Stemming reduces words to their root form, while lemmatization reduces words to their dictionary form.
• Stemming chops off prefixes or suffixes to get the root form (e.g. 'running' becomes 'run')

• Lemmatization uses vocabulary analysis to reduce words to their base form (e.g. 'better' becomes 'good')

• Lemmatization is more accurate but slower than stemming

• Stemming is faster but may not always result in a valid word
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Q3. Questions about my previous projects
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Logical reasoning, verbal aptitude

Q1. Stemming vs lemmatisation
Ans. 
Stemming reduces words to their root form, while lemmatisation reduces words to their dictionary form.
• Stemming is faster but less accurate than lemmatisation

• Stemming may result in non-words, while lemmatisation always results in valid words

• Example: Stemming - running, runs, ran all reduced to run. Lemmatisation - running, runs, ran all reduced to run
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Q2. Explain linear regression
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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Q1. Family background
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Q2. Compensation discussion
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Q1. List, Array, Polymorphism
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Coding Knowledge been checked

Q1. Technical skills been tested
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Case of certain domain projects

Q1. About my expectations
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Q2. About Companies expectations from me
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Q1. With CEO of Geakminds about company.
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