## Read, summarize and describe the approach using a data set different from the one provided in the paper.

### computer science

##### Description

 Student Name:                                                    ID:

Applying Naïve Bayes to continuous features using binning.

1.      Read, summarize and describe the approach using a data set different from the one provided in the paper.

2.      Prepare a 2 to 3 slides presentation explaining the approach.

Exercice 2:

Given the following data set:

 X1 X2 X3 X5 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 1 1 q= 1 0 0 ?

1.      Create a naive Bayes model for this data set.

2.      What prediction will the naive Bayes model return for the query q=(1,0,0)?

Exercice 3: (Use of probabilities)

Mohamed flies frequently and likes to upgrade his seat to first class.  He has determined that, if he checks in for his flight at least two hours early, the probability that he will get the upgrade is .75; otherwise, the probability that he will get the upgrade is .35. With his busy schedule, he checks in at least two hours before his flight only 40% of the time.  Suppose Ali didn’t receive an upgrade on his most recent attempt. What is the probability that he arrived late?

Exercice 4:

The task in this question is to create a naïve Bayes model to monitor a waste water treatment plant. The table below lists a dataset containing details of activities at a waste water treatment plant for 13 days. Each day is described in terms of six descriptive features that are generated from different sensors at the plant. SS-IN measures the solids coming into the plant per day; SED-IN measures the sediment coming into the plant per day; COND-IN measures the electrical conductivity of the water coming into the plant.  The features SS-OUT, SED-OUT, and CONDOUT are the corresponding measurements for the water flowing out of the plant. The target feature, STATUS, reports the current situation at the plant:

ok, everything is working correctly; settler, there is a problem with the plant settler equipment; or solids, there is a problem with the amount of solids going through the plant.

3.      Create a naive Bayes model that uses probability density functions to model the descriptive features in this dataset (assume that all the descriptive features are normally distributed).

4.      What prediction will the naive Bayes model return for the following query?

SS-IN = 222, SED-IN = 4.5, COND-IN = 1,518, SS-OUT = 74, SED-OUT = 0.25, COND-OUT = 1,642

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