This document outlines a proposed system for evaluating employee performance. It describes collecting data on various indicators like attendance, customer feedback, and task completion. It then discusses using a decision tree algorithm to generate rules for evaluating performance based on the data. A fuzzy logic system is also proposed to map input data to linguistic variables and output an evaluation. The goal is to develop a more objective, data-driven approach to performance reviews.

1. Performance Evaluation of
employee

2. OVERVIEW
 Introduction
 Problem with existing system
 Objectives
 Methodology
 System diagram
 References

3. Introduction
• Evaluate employee on basis of the various
indicators.
• With the development of this system we will
find an interesting and better way for knowing
about the performances.
• The various indicators are attendance,
customer feedback, task completion, dealing
capacity, etc. Once all the data's are fetched in
the system, it rates the employees.

4. Problem with existing system
• Manual
• Not Potential candidate
• Focusing on Negative
• Rating in Middle
• Ruins teamwork and team spirit.

5. Objective
• To evaluate the performance of any employee based
on indicators.
• A performance evaluation is a constructive process
to acknowledge an employee’s performance.
• to improve the employee's contributions to the
organization's goals.
• This will help in increasing and improving the
efficiency and productivity of any organization

6. Swimlane

7. Fig:Rule generation

8. Figure:- System diagram

9. Methodology
Data collection
• We collected data from managers , sub
managers ,supervisor e.t.c
• We gone through all the reports
• We consult with 360 feedback receiver
• We consult with all the co wokers , staffs as
well as consumers

10. Types of data collection
• Third party feedback
–360 survey
• Competencies
–Sincerity

11.  Repot submission
 Attendance
 Creativity
 Reliability
 Total client handle
 Total profit made
 Sincerity
 360 degree survey
Goal progress generic data
Competencies third party
feedback
Data classification

12. Algorithm :-ID3
• If every element in the subset belongs to the
same class , then the node is turned into a leaf
and labeled with the class of the examples
• If the examples do not belong to the same class
• Calculate entropy and hence information gain to
select the best node to split data.
• Partition the data into subset.
• Repeat

13. Reason for Decision Tree
• Decision trees implicitly perform variable
screening or feature selection
• Decision trees require relatively little effort
from users for data preparation
• Nonlinear relationships between parameters do
not affect tree performance
• The best feature of using trees for analytics -
easy to interpret and explain to executives!

17. Rule generation
• Rule base classifier
• using decision tree for generating rule
• Extract rule from decision tree

18. Rules
FEEDBA
CK
TOTAL
PROFIT
MADE
REPORT
SUBMIS
SION
CREATIV
ITY
ATTEND
ENCE
TOTAL
CLIENT
HANDLE
SINCIERI
TY
PERFOM
ANCE
B OR G B OR VG B OR VG B G VG - -
BORG B OR VG B OR VG B G G
B OR G B OR VG B OR VG B B OR G BAD BAD -
B OR G B OR VG VG VG VG - - -
B ORG B OR VG VG G - - - -
B OR G VG G - - - - -
B G - - - - - -
G G - - - - - -
VG VG - - - - - -
EVALU
ATION
GOOD
GOOD
MED
EX
MED
MED
MED
GOOD
EX

20. Fuzzy Logic
• fuzzy logic
• Fuzzifier : used to convert crisp vale into
linguistic form
• Knowledge base : where knowledge is stored
• Inference engine : used to map the rules ,
priority base.
• Defuzzifier : used to convert linguistic variable
into crisp value

21. Fuzzy logic system diagram

22. INPUT INPUTNAME LINGUSTIC RANGE
Input1
Attendance
Bad 1-50
Good 25-75
VG 50-100
Input2 Total client handle Bad 1-50
Good 25-75
VG 50-100
Input3 Total profit made
Bad 1-501-50
Good 25-75
VG 50-100
Input4 Sincerity
Bad 1-50
Good 25-75
Very good 50-100
Input5 Feedback
Bad 1-50
Good 25-75
Very good 50-100
Input6 Report submission
Bad 1-50
Good 25-75
Very good 50-100
Input7 Reliability
Bad 1-50
Good 25-75
Very good 50-100
Input8 Creativity
Bad 1-50
Good 25-75
Very good 50-100

23. Fig. Dom vs crisp input

27. Before

28. After

29. REFERENCES
• [1] “SlideShare.net", www.slideshare.net, 2016.
[Online]. Available: http://www.slideshare.net/.
[Accessed: 1- Dec- 2016].
• [2] “Trakstar.com”, 2016. [Online]. Available:
http://www.trakstar.com/. [Accessed: 15- Dec- 2016].
• [3] “Capterra.com”, 2016. [Online]. Available:
http://www.capterra.com/. [Accessed: 11- Dec- 2016].
• [4] “Synergita–Performance Management Software",
Synergita.com, 2016. [Online]. Available:
https://www.synergita.com/. [Accessed: 16- Dec-
2016].

30. Thank you
….!!!!!!!!!