The following are the various steps involved in an analytics project:
Understand the business problem
Explore the data and become familiar with it.
Prepare the data for modelling by detecting outliers, treating missing values, transforming variables, etc.
After data preparation, start running the model, analyse the result and tweak the approach. This is an iterative step till the best possible outcome is achieved.
Validate the model using a new data set.
Start implementing the model and track the result to analyse the performance of the model over the period of time.
What are the differences between overfitting and underfitting?
In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.
In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.
Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance.
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| Gartner Most Frequently Asked Data Science Interview Questions Answers |
Python or R – Which one would you prefer for text analytics?
We will prefer Python because of the following reasons:
Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.
R is more suitable for machine learning than just text analysis.
Python performs faster for all types of text analytics.
How does data cleaning plays a vital role in analysis?
Data cleaning can help in analysis because:
Cleaning data from multiple sources helps to transform it into a format that data analysts or data scientists can work with.
Data Cleaning helps to increase the accuracy of the model in machine learning.
It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources.
It might take up to 80% of the time for just cleaning data making it a critical part of analysis task.
Suppose a life insurance company sells a $240,000 one year term life insurance policy to a 25-year old female for $210. The probability that the female survives the year is .999592. Find the expected value of this policy for the insurance company.
A) $131
B) $140
C) $112
D) $125
Ans: (C)
P(company loses the money ) = 0.99592
P(company does not lose the money ) = 0.000408
The amount of money company loses if it loses = 240,000 – 210 = 239790
While the money it gains is $210
Expected money the company will have to give = 239790*0.000408 = 97.8
Expect money company gets = 210.
Therefore the value = 210 – 98 = $112
When an event A independent of itself?
A) Always
B) If and only if P(A)=0
C) If and only if P(A)=1
D) If and only if P(A)=0 or 1
Ans: (D)
The event can only be independent of itself when either there is no chance of it happening or when it is certain to happen. Event A and B is independent when P(AꓵB) = P(A)*P(B). Now if B=A, P(AꓵA) = P(A) when P(A) = 0 or 1.
Suppose you’re in the final round of “Let’s make a deal” game show and you are supposed to choose from three doors – 1, 2 & 3. One of the three doors has a car behind it and other two doors have goats. Let’s say you choose Door 1 and the host opens Door 3 which has a goat behind it. To assure the probability of your win, which of the following options would you choose.
A) Switch your choice
B) Retain your choice
C) It doesn’t matter probability of winning or losing is the same with or without revealing one door
Ans: (A)
I would recommend reading this article for a detailed discussion of the Monty Hall’s Problem.
Cross-fertilizing a red and a white flower produces red flowers 25% of the time. Now we cross-fertilize five pairs of red and white flowers and produce five offspring. What is the probability that there are no red flower plants in the five offspring?
A) 23.7%
B) 37.2%
C) 22.5%
D) 27.3%
Ans: (A)
The probability of offspring being Red is 0.25, thus the probability of the offspring not being red is 0.75. Since all the pairs are independent of each other, the probability that all the offsprings are not red would be (0.75)5 = 0.237. You can think of this as a binomial with all failures.
A roulette wheel has 38 slots – 18 red, 18 black, and 2 green. You play five games and always bet on red slots. How many games can you expect to win?
A) 1.1165
B) 2.3684C) 2.6316
C) 2.6316
D) 4.7368
Ans: (B)
The probability that it would be Red in any spin is 18/38. Now, you are playing the game 5 times and all the games are independent of each other. Thus, the number of games that you can win would be 5*(18/38) = 2.3684
Univariate analyses are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.
Bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and a spending can be considered as an example of bivariate analysis.
Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.
What is Cluster Sampling?
Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection or cluster of elements.
What is Systematic Sampling?
Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list, it is progressed from the top again. The best example of systematic sampling is equal probability method.
What are Eigenvectors and Eigenvalues?
Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching.
Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.
Can you cite some examples where a false positive is important than a false negative?
Let us first understand what false positives and false negatives are. False positives are the cases where you wrongly classified a non-event as an event a.k.a Type I error. False negatives are the cases where you wrongly classify events as non-events, a.k.a Type II error.
Example 1: In the medical field, assume you have to give chemotherapy to patients. Assume a patient comes to that hospital and he is tested positive for cancer, based on the lab prediction but he actually doesn’t have cancer. This is a case of false positive. Here it is of utmost danger to start chemotherapy on this patient when he actually does not have cancer. In the absence of cancerous cell, chemotherapy will do certain damage to his normal healthy cells and might lead to severe diseases, even cancer.
Example 2: Let’s say an e-commerce company decided to give $1000 Gift voucher to the customers whom they assume to purchase at least $10,000 worth of items. They send free voucher mail directly to 100 customers without any minimum purchase condition because they assume to make at least 20% profit on sold items above $10,000. Now the issue is if we send the $1000 gift vouchers to customers who have not actually purchased anything but are marked as having made $10,000 worth of purchase.
The extent of the missing values is identified after identifying the variables with missing values. If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights.
If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored. Assigning a default value which can be mean, minimum or maximum value. Getting into the data is important.
If it is a categorical variable, the default value is assigned. The missing value is assigned a default value. If you have a distribution of data coming, for normal distribution give the mean value.
If 80% of the values for a variable are missing then you can answer that you would be dropping the variable instead of treating the missing values.
In any 15-minute interval, there is a 20% probability that you will see at least one shooting star. What is the probability that you see at least one shooting star in the period of an hour?
Probability of not seeing any shooting star in 15 minutes is
= 1 – P( Seeing one shooting star )
= 1 – 0.2 = 0.8
Probability of not seeing any shooting star in the period of one hour
= (0.8) ^ 4 = 0.4096
Probability of seeing at least one shooting star in the one hour
= 1 – P( Not seeing any star )
= 1 – 0.4096 = 0.5904.
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