Chapter 10 – 100 KNN and K Mean interview questions
Topic – KNN and K Mean Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
KNN and K Mean interview questions
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will definitely depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned in order to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
2200 Analytics Interview Questions
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Intervie
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with Solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution
Chapter 5 – Case Study and Guesstimate – 100 Case Study and Guesstimate with a complete solution
Chapter 6 -Linear Regression – 50 Most Asked Linear Regression Interview Questions with solution
Chapter 7 – Logistic Regression – 50 Most Asked Logistic Regression Interview Questions with solution
Chapter 8 – Natural Language Processing – 100 Most Asked NLP Questions with Solution
Chapter 9 – Decision Tree and Random Forest – 100 Most Asked RF and DT interview questions with solution
Chapter 10 – KNN and K Mean – 100 Most asked KNN and K Mean interview questions with solution
Kmean
1041.What is K-means?
1042. What is clustering?
1043. How does k-means clustering works?
1044. What does k represents in k-means clustering?
1045. What is a cluster?
1046. What is a centroid?
1047. What does k-means term denotes?
1048. How to find the optimal value of k in k-means?
1049. What is Elbow method?
1050. What is the Within-Cluster Sum of Squared Error?
1051. Implement WCSS Error in Python
1052. What is the drawback of K-means clustering algorithm?
1053. What are the different ways to solve the problem of initialization sensitivity in k-means algorithm?
1054. What is K-means++ algorithm?
1055. What do you mean by intracluster distance?
1056. What do you mean by intercluster distance?
1057. Difference between K-means and K-means++ algorithm?
1058. Difference between Classification and Clustering?
1059. What are the advantages of k-means clustering algorithm?
1060. What are the Disadvantages of k-means clustering algorithm?
1061. Difference between KNN and K-means algorithm?
1062. What are the different types of distance metrics used in K-means algorithm?
1063. What are the different types of clustering?
1064. What are the applications of K-means Clustering Algorithm?
1065. Steps for performing K-means Clustering Algorithm in Python.
1066. What are the libraries to apply K-Mean cluster?
1067. How to import dataset as Data Frame?
1068. How to check the missing values in the dataset?
1069. Plot using scatter plot in Python
1070. How to save the result in the excel file?
1071. What is the KNN Algorithm?
1072. Why is KNN a non-parametric Algorithm?
1073. What is “K” in the KNN Algorithm?
1074. How does the KNN algorithm make the predictions on the unseen dataset?
1075. Is Feature Scaling required for the KNN Algorithm? Explain with proper justification.
1076. What is space and time complexity of the KNN Algorithm?
1077. Can the KNN algorithm be used for regression problem statements?
1078. Why is the KNN Algorithm known as Lazy Learner?
1079. Why is it recommended not to use the KNN Algorithm for large datasets?
1080. How to handle categorical variables in the KNN Algorithm?
1081. How to choose the optimal value of K in the KNN Algorithm?
1082. How can you relate KNN Algorithm to the Bias-Variance tradeoff?
1083. Which algorithm can be used for value imputation in both categorical and continuous categories of data?
1084. Explain the statement- “The KNN algorithm does more computation on test time rather than train time”.
1085. What are the things which should be kept in our mind while choosing the value of k in the KNN Algorithm?
1086. What are the advantages of the KNN Algorithm?
1087. What are the disadvantages of the KNN Algorithm?
1088. Is it possible to use the KNN algorithm for Image processing?
1089. What are the real-life applications of KNN Algorithms?
1090. Explain the Difference between K-Means and KNN
1091. Show an implementation of KNN Algorithm
The Data Monk Product and Services
- Youtube Channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
Link – The Data Monk Youtube Channel - Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
Link – The Data Monk website - E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions
Link – The Data E-shop Page - Mock Interviews
Book a slot on Top Mate - Career Guidance/Mentorship
Book a slot on Top Mate - Resume-making and review
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The Data Monk e-book Bundle
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1200+ Interview Questions on all the important Machine Learning algorithms (including complete Python code) Ada Boost, CNN, ANN, Forecasting (ARIMA, SARIMA, ARIMAX), Clustering, LSTM, SVM, Linear Regression, Logistic Regression, Sentiment Analysis, NLP, K-M
