ASP. NET C# (Lec-16) POS Inventory Website : Brand Page Design & Validation code| Final Year Project
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Library Project asp. net Video Part-19 Source code👆

1. What is the difference between supervised learning and unsupervised learning? Give concrete examples.

Supervised learning involves learning a function that maps an input to an output.
For example, if I had a dataset with two variables, age (input) and height (output), I could implement a supervised learning model to predict the height of a person based on their age.

Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. A common use of unsupervised learning is grouping customers by purchasing behavior to find target markets.


2.How do you assess the statistical significance of an insight?

Ans: You would perform hypothesis testing to determine statistical significance. First, you would state the null hypothesis and alternative hypothesis.
Second, you would calculate the p-value, the probability of obtaining the observed results of a test assuming that the null hypothesis is true. Last, you would set the level of the significance (alpha) and if the p-value is less than the alpha, you would reject the null — in other words, the result is statistically significant.


3. What is the Law of Large Numbers?

Ans: The Law of Large Numbers is a theory that states that as the number of trials increases, the average of the result will become closer to the expected value.
Eg. flipping heads from fair coin 100,000 times should be closer to 0.5 than 100 times.

4.If a Company says that they want to double the number of ads in Newsfeed, how would you figure out if this is a good idea or not?

Ans: You can perform an A/B test by splitting the users into two groups: a control group with the normal number of ads and a test group with double the number of ads. Then you would choose the metric to define what a “good idea” is. For example, we can say that the null hypothesis is that doubling the number of ads will reduce the time spent on Facebook and the alternative hypothesis is that doubling the number of ads won’t have any impact on the time spent on Facebook. However, you can choose a different metric like the number of active users or the churn rate. Then you would conduct the test and determine the statistical significance of the test to reject or not reject the null.
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Library Project in asp .net Part 20 complete source code👆👆