CustomerID Genre Age Annual Income (k$) Spending Score (1-100) 1 Male 19 15 39

2 Male 21 15 81

3 Female 20 16 6

4 Female 23 16 77

5 Female 31 17 40

6 Female 22 17 76

7 Female 35 18 6

8 Female 23 18 94

9 Male 64 19 3

10 Female 30 19 72

11 Male 67 19 14

12 Female 35 19 99

13 Female 58 20 15

14 Female 24 20 77

15 Male 37 20 13

16 Male 22 20 79

17 Female 35 21 35

18 Male 20 21 66

19 Male 52 23 29

20 Female 35 23 98

21 Male 35 24 35

22 Male 25 24 73

23 Female 46 25 5

24 Male 31 25 73

25 Female 54 28 14

26 Male 29 28 82

27 Female 45 28 32

28 Male 35 28 61

29 Female 40 29 31

30 Female 23 29 87

31 Male 60 30 4

32 Female 21 30 73

33 Male 53 33 4

34 Male 18 33 92

35 Female 49 33 14

36 Female 21 33 81

37 Female 42 34 17

38 Female 30 34 73

39 Female 36 37 26

40 Female 20 37 75

41 Female 65 38 35

42 Male 24 38 92

43 Male 48 39 36

44 Female 31 39 61

45 Female 49 39 28

46 Female 24 39 65

47 Female 50 40 55

48 Female 27 40 47

49 Female 29 40 42

50 Female 31 40 42

Phase 1- perform data cleansing step if required

phase 2-

In the second phase of the project:

Select specific technique (e.g. Clustering, Association Rule, etc.) that you will apply on the dataset

User appropriate techniques and Analyze results of evaluation.

Phase 3 (4 marks)

In the third phase of the project:

Submit your results in the form of a report (word format only).

Description: Please explain each phase too