Basics of ML & AL Week 2 Challenge

1.

2.

If proability of occurance of event ka equal then we can compute with simple mean. but if occurance of event are not given or it mat be more than one times the in this situation considered as the expected mean overcome the simple mean.

3.

In the Real life distributions are commonly skewed. If there are too much skewness in the data, then many statistical model don’t work.

skewed data, the tail region may act as an outlier for the statistical model and as know that outliers adversely affect the model’s performance specifically regression-based models. There are statistical model that are robust to outlier like a Tree-based models but it will limit the possibility to try other models. So there is a necessity to transform the skewed data to close enough to a Gaussian distribution or Normal distribution. This will allow us to try more number of statistical model.

If We have a skewed data then it may harm our results. So, in order to use a skewed data we have to apply a log transformation over the whole set of values to discover patterns in the data and make it usable for the statistical model.

4.

A)P(X<711)

Z Score = (X-Mean)/ Standard Deviation

Z Score = (711-680)/31 = 1

Probability Value = 0.84

B)P(X>740)

Z Score = (X-Mean)/ Standard Deviation

Z Score = (740-680)/31 = 1.93

Probability Value = 1- 0.9732 =0.02

C)P(600<X<720)

X=720 then Z=1.29 and Probability value = 0.90

X=600 then Z=-2.58 and Probability value = 0.004

Final Probability Value = 0.90 -0.004 =0.89

D)P(X=720)

P(Z=1.29) = P(Z<1.29)-P(Z<1.29) = 0

5.