Shefali's Journey of Becoming a Data Analyst | Learnbay Review — Transcript

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1. 0:00hi my name is shifanism and I belong to
2. 0:04raipur chitaskar but presently living in
3. 0:06Hyderabad I did my graduation in 2018
4. 0:10from Kalinga Institute from nature the
5. 0:13computer science engineer the CDP of 8.4
6. 0:17in working with a senior python
7. 0:19developer for past four years in Telecom
8. 0:21domain
9. 0:22I got to know about learn way through
10. 0:24quora references so from the admission
11. 0:26consultation till now I didn't find any
12. 0:29flaws till so I have completed this
13. 0:32python machine learning and deep
14. 0:33learning areas in this institute so as a
15. 0:38part of this experience here the world
16. 0:40Winters are very well working
17. 0:42professional and they have very much
18. 0:44practical exposure so the best thing
19. 0:46about this institute is a one-to-one
20. 0:48mentoring where we can the mentors
21. 0:50questions related to anything which is
22. 0:52related to job specific or anything
23. 0:54related to projects
24. 0:56so yeah the learning experience has been
25. 0:59very thorough and the celebration on
26. 1:02this very systematic so you can get from
27. 1:05restart till end with a very lenient
28. 1:07approach so as part of the project I
29. 1:12have done around four to five projects
30. 1:14and one project for my domain area
31. 1:16domain specific that is a telecom domain
32. 1:18so recently have been working in this
33. 1:21customer churn prediction use cases so
34. 1:26as we know that customer retention is
35. 1:28one of the biggest pillar of the
36. 1:30products which are following the SAS
37. 1:32market so here we need to satisfy the
38. 1:37customers continuously to bring our
39. 1:39product grow to a to a certain high
40. 1:42level so I have used this customer churn
41. 1:45prediction data set to build a machine
42. 1:47learning classifier which can calculate
43. 1:49the propensity of being a customer being
44. 1:52retented into our sector so I have used
45. 1:56this random Forest classifier to do the
46. 1:59prediction of my customer Journey so I
47. 2:03have used this model because
48. 2:05it's was giving the accuracy around 90
49. 2:08to 95 percent and also from this model I
50. 2:12was able to do the calculation of the
51. 2:15propensity score as well because when we
52. 2:18are doing anything any predictions so we
53. 2:20need to know how much percentage that
54. 2:23protection is in so that we can focus on
55. 2:25those areas where we can see that
56. 2:28customer is going to be retented or we
57. 2:30are going to need that product so thank
58. 2:32you