Top Guesstimate Questions & Informative Methods for Data Science [2024]
Updated on Dec 30, 2024 | 7 min read | 13.5k views
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Updated on Dec 30, 2024 | 7 min read | 13.5k views
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Guesstimate is a methodological method of theory and evaluation; it helps you work efficiently with a higher degree of accuracy. It is the study of the data to consolidate the result. It is also an essential part of the Business Analyst or Data Science and Data Architects or Data Techies.
When a guesstimate question can ask for the size of a market, it’s then called a “market-sizing” question.
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Here are the basic questions about guesstimate:
The process of solving a guesstimate problem is pretty manageable:
This approach is typically used when the number to guesstimate is a ratio of some sorts. The task is to obtain the numerator and denominator then we are done!
1. Per capita approach-
This approach is used when the number to guess can be thought of as a consumption item at a person, household, or population level within geography.
2. Supply & Demand approach-
This approach needs thinking of the guesstimate number from either the supply or the demand (or both) side of the item.
Generally speaking, you can propose guesstimates in one of these two ways:
In the top-down, you start with the largest possible universe, of which your guesstimate is a portion of.
With the broadest base at the top. To this universe, you then keep applying a set of conditions or filters (however you want to put it) that reduce the number from the universe to a number that is appropriate for your guesstimate.
The key to the top-down estimation process lies in:
Tips for guesstimate questions for Data Science:
While solving the guesstimate questions for Data Science, you need to understand these points:
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Here are some guesstimate questions for Data Science-
Question:1 Create an Experiment with the k-means algorithm on the UCI Iris data set:
In this experiment, Perform k-means clustering using all the features in the dataset, and then compare the clustering results with the true class label for all samples.
Use the Multiclass Logistic Regression module to perform multiclass classification and compare its performance with that of k-means clustering.
Question:2 In a very simple format, explain Precision & Recall?
Question:3 If you have been given a data set, how do you decide on which ML algorithm to the user?
Question:4 Is it better to have too many false positives? Or too many false negatives?
Question:5 What is model accuracy and model performance? What scenario can you apply?
Question:6 How do you ensure you are not over-fitting with a model? Explain with an example.
Question:7 When you run a binary classification tree algorithm is quite easy. In the Binary algorithm, how does the tree decide on which variable to split at the root node and its succeeding child nodes?
Question:8 How are NumPy and SciPy described?
Question:9 Write a basic Machine learning program to check the accuracy of the dataset importing any dataset using any classifier?
Question:10 Create a Regression algorithm to predict the price of a car based on different variables.
Question:11 Develop a model that uses different network features to detect which network activities are part of an intrusion/attack using Binary classifications.
Question:12 How to Group (Clustering) to find similar organizations together based on their Wikipedia description.
Question:13 How would you predict who will renew their subscriptions next month?
Question:14 How would you map nicknames (Alen, Bob, Alex, Tim, etc.) to real names?
Question:15 Create a prediction on whether scheduled passenger flight is delayed or not using a Binary-classifier with R or python script.
Question:16 Predict automobile prices using Linear Regression with Prepare and Cleaned the data by removing the normalized losses column.
Since it has many missing values, create an experiment and model.
Question:17 How many ways can you split 14 people into 4 teams of 5?
Question:18 Area under the standard normal curve is?
Question:19 Create a Regression algorithm to predict the price of a car based on different variables.
Question:20 Your manager asked to build a random forest model with 10000 trees during your training, and you got a training error as 0.00. But, on testing, the validation error was 34.23. What basis will you assume what went wrong? How would you check your model if it’s not trained perfectly?
Question:21 ‘People who bought this, also bought…’ recommendations seen on Amazon are based on which algorithm?
Question:22 Which algorithms are linked in recommendations you see as ‘Today’s News and views’?
Read: Data Science Interview Questions
We hope this article helped you understand guesstimate questions for data science and how to overcome them. You will find more useful articles like this one at upGrad; we offer an extensive range of courses, MBA, Data Science, Machine Learning, etc. We provide mentorship from the industries’ best individuals!
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