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Data MiningUsing the dataset from Q5a, use your K-NN classifier to predict whether the following student (who overslept and missed the original exam) will pass the re-exam : | ID | Cell No. | Language | Passed all Assignments | GPA | Passed Exam | | :---: | :---: | :---: | :---: | :---: | :---: | | 9 | 93333-11109 | C++ | Yes | 3.0 | ?? |20214mDatabase Management SystemGiven the Students' relation as shown below: | StudentID | StudentName | StudentE-mail | StudentAge | CPI | |---|---|---|---|---| | 2345 | Shankar | shankar@math | X | 9.4 | | 1287 | Swati | swati@ee | 19 | 9.5 | | 7853 | Shankar | shankar@cse | 19 | 9.4 | | 9876 | Swati | swati@mech | 18 | 9.3 | | 8765 | Ganesh | ganesh@civil | 19 | 8.7 | For (StudentName, StudentAge) to be the key for this instance, the value X should not be equal to: (i) 18 (ii) 19 (iii) 15 (iv) 2020192mMACHINE LEARNINGFollowing is a data set that contains two attributes, X and Y, and two class labels, ‘+’ and ‘–’. Each attribute can take three different values: 0, 1, or 2. The concept for the ‘+’ class is Y = 1 and the concept for the ‘–’ class is X = 0 ∨ X = 2.  (a) Build a decision tree on the data set. Does the tree capture the ‘+’ and ‘–’ concepts? (b) What are the accuracy, precision, recall and F1-measure of the decision tree? (Note that precision, recall, and F1-measure are defined with respect to the ‘+’ class.)202214mMACHINE LEARNINGFor the Sun Burn data set given below, construct a decision tree. | Name | Hair | Height | Weight | Location | Class | |--------|--------|----------|---------|----------|-------| | Sunita | Blonde | Average | Light | No | Yes | | Anita | Blonde | Tall | Average | Yes | No | | Kavita | Brown | Short | Average | Yes | No | | Sushma | Blonde | Short | Average | No | Yes | | Xavier | Red | Average | Heavy | No | Yes | | Balaji | Brown | Tall | Heavy | No | No | | Ramesh | Brown | Average | Heavy | No | No | | Sweta | Blonde | Short | Light | Yes | No |202314m
PreviousWrite an SQL query that runs on the schema designed in Q4a and returns the average SGPA of boys from the State of Karnataka for each Autumn (odd) semester during the years 2005–2009.NextUsing the dataset from Q5a, use your K-NN classifier to predict whether the following student (who overslept and missed the original exam) will pass the re-exam : | ID | Cell No. | Language | Passed all Assignments | GPA | Passed Exam | | :---: | :---: | :---: | :---: | :---: | :---: | | 9 | 93333-11109 | C++ | Yes | 3.0 | ?? |