The AI-powered employee Appraisal system based on a credit system is a software application that aims to provide an efficient and fair way of calculating employee incentives in an organization. The system will use artificial intelligence (AI) algorithms (classification) to analyze employee performance data and assign credits to each employee based on their performance. The system will work by first defining a set of key performance indicators (KPIs) that are relevant to the organization's goals and objectives. These KPIs could include metrics such as sales revenue, customer satisfaction scores, or project completion rates. Each employee's performance data will then be measured against these KPIs, and the system will assign credits to each employee based on their performance. The credits assigned to each employee will be used to determine their incentive payout, with higher-performing employees receiving a higher payout. The system will also have the capability to adjust the weight age of different KPIs based on the organization's priorities and objectives. The classification algorithm used in the system will continuously learn and improve over time, as they are fed more data and feedback from the organization. This will ensure that the system remains relevant and accurate as the organization's goals and objectives evolve.

1. .
AI-POWERED EMPLOYEE APPRAISAL
SYSTEM BASED ON CREDIT SYSTEM
DONE BY
CHANDRASEKARAN.K
II - MCA

2. INTRODUCTION
 The AI-powered employee Appraisal system based on a credit system is a
software application that aims to provide an efficient and fair way of calculating
employee incentives in an organization.
 The system will use artificial intelligence (AI) algorithms to analyze employee
performance data and assign credits to each employee based on their performance.
 The system will work by first defining a set of key performance indicators (KPIs)
that are relevant to the organization's goals and objectives.
 These KPIs could include metrics such as sales revenue, customer satisfaction
scores, or project completion rates. Each employee's performance data will then be
measured against these KPIs, and the system will assign credits to each employee
based on their performance.

3. EXISTING SYSYTEM
 Biases: Appraisals can be subject to various biases, such as the halo effect (where
an employee is rated highly on one trait, and that rating influences the overall
appraisal), or the leniency bias (where appraisers tend to rate all employees as above
average).
 Inconsistency: Different managers may use different standards or criteria to
evaluate employee performance, leading to inconsistent appraisals across the
organization.
 Lack of employee involvement: Employees may feel disconnected from the
appraisal process if they are not given an opportunity to provide input or feedback on
their performance.

4. DISADVANTAGE OF EXISTING SYSTEM
 Lack of human touch
 Data quality and bias
 Need for ongoing maintenance and updates
 Limited context awareness

5. PROPOSED SYSTEM
 The AI-powered employee incentive calculation system based on a credit
system is a software application that aims to provide an efficient and fair
way of calculating employee incentives in an organization. The system will
use artificial intelligence (AI) algorithms to analyze employee performance
data and assign credits to each employee based on their performance.
 The system will work by first defining a set of key performance indicators
(KPIs) that are relevant to the organization's goals and objectives. These
KPIs could include metrics such as sales revenue, customer satisfaction
scores, or project completion rates. Each employee's performance data will
then be measured against these KPIs, and the system will assign credits to
each employee based on their performance.

6. The benefits of implementing this system include:
 Fairness: The system ensures that incentives are distributed fairly, based on
individual performance, rather than on subjective criteria.
 Efficiency: The use of AI algorithms streamlines the incentive calculation
process, saving time and resources for the organization.
 Motivation: The system provides employees with clear, quantifiable goals to
work towards, and incentivizes high performance, leading to increased
motivation and productivity.

7. HARDWARE REQUIREMENT
 PROCESSOR : Linux server
 RAM : 8 GB
 HARD DISK : 255 SSD
 SYSTEM TYPE : 64-bit operating system, x64-based processor

8. SOFTWARE REQUIREMENT 
 OPERATING SYSTEM : WINDOWS 10
 AI -ALGROITHM : CLASSIFICATION MACHINE LEARNINGALGORITHM
 TECHNOLOGY : MERN STACK
 FRONT-END : REACT.JS
 BACK-END : NODE.JS
 IDE : VS CODE, JUPYTER NOTEBOOK

9. REACT.JS
 React.js, commonly referred to as React, is an open-source JavaScript library for building user
interfaces (UIs).
 The main objective of ReactJS is to develop User Interfaces (UI) that improves the speed of the
apps.
 It uses virtual DOM (JavaScript object), which improves the performance of the app. The
JavaScript virtual DOM is faster than the regular DOM.
 We can use ReactJS on the client and server-side as well as with other frameworks. It uses
component and data patterns that improve readability and helps to maintain larger apps.
NODE.JS
 Node.js is an open-source, server-side JavaScript runtime environment built on
Chrome's V8 JavaScript engine.
 It allows developers to run JavaScript code outside of a web browser, making it well-
suited for creating scalable and efficient web applications and network servers.
 Node.js is cross-platform, meaning it can run on various operating systems, including
Windows, macOS, and Linux.

10. EXPRESS.JS
 Develops Node.js web applications quickly and easily.
 It’s simple to set up and personalise.
 Allows you to define application routes using HTTP methods and URLs.
 Simple to interface with a variety of template engines, including Jade, Vash, and EJS.
 Allows you to specify a middleware for handling errors
MONGO DB
 MongoDB is a popular open-source, NoSQL document-oriented database that provides
high scalability, flexibility, and performance for modern application development.
 MongoDB is widely used in various application domains, including web applications,
mobile apps, real-time analytics, content management systems, and more.
 Its scalability, flexibility, and rich querying capabilities make it a popular choice for
handling diverse and rapidly evolving data requirements.

11. KPI
 KPI stands for Key Performance Indicator. It is a measurable value that helps
organizations or individuals evaluate their success in achieving specific objectives
or goals.
 KPIs are essential in various fields, including business, project management,
healthcare, education, and more
 It is also reffered to as ‘KEY SUCCESS INDICATORS (KSI)’ .
 Metrics measure the success of everyday business activities that support your KPIs.

12. USE SMART CRITERIA
Use the SMART Criteria: Apply the SMART criteria to each KPI:
 Specific: Clearly define what the employee needs to achieve.
 Measurable: Set metrics or targets to gauge performance objectively.
 Achievable: Ensure the goals are realistic and attainable.
 Relevant: Ensure that the KPIs align with the employee's role and overall
company objectives.
 Time-bound: Set a timeframe or deadline for achieving the KPI.

13. PROCESS FLOW DIAGRAM

14. MODULES
 MANAGER
1. LOGIN
2. MEEETING LIST
3. EMPLOYEE LOG
 EMPLOYEE
1. CREDIT MODULE
2. ENCASHMENT – REQUEST
 HR
1. ENCASHMENT- APPROVAL

15. MANAGER
Meeting List
 Manager logs in into the portal and he selects the meeting list module then
selects the employee and to Set KPIs for employee quarterly basis.
 Review KPI’s data shared by employee and Provide feedback and Credits to
employee .

16. Employee Log
 Manager logs in into the portal and he selects the employee log module then he
selects the employee along with from date and to date.
 After that classification algorithm will be generated plot chart based on the
employee performance and then manager will give credits to the employee
based on that plot chart.

17. EMPLOYEE
Credit module
 Works on the quarterly provided KPIs. Share the Inputs for every KPIs.
 Submit it for Managerial review.
 Post review completes from manager Acknowledge the Review and Credits .
 Submit Encashment of Available credit

18. Encashment - request
 To encash the credit that is given by the manager to the employee, the
employee sends a request to the HR for the approval of the cash.
 If the employee encash for the first time, they wait for the three months to
encash the credit .
 If the employee’s credit balance more than 3 months, they sent encash request
to the hr.

19. HR
Encashment - approval
 Approves or Deny the encash request.
 Once approved Process the credit encashment’s equivalent cast to employee
via payroll

20. SCIKIT LEARN AI TOOL
 Scikit-learn is a Python package designed to facilitate use of machine learning and AI
algorithms.
 Scikit-learn (short for "Scientific Kit-Learn") is a popular open-source machine
learning library for the Python programming language. It provides simple and efficient
tools for data mining and data analysis, as well as for building and evaluating machine
learning models.
CLASSIFICATION ALGORITHM
 The Classification algorithm is a Supervised Learning technique that is used to identify
the category of new observations on the basis of training data.
 In Classification, a program learns from the given dataset or observations and then
classifies new observation into a number of classes or groups.

21. Matplotlib
Matplotlib is a powerful and very popular data visualization library in Python.

22. Importing Libraries:
 import matplotlib.pyplot as plt: This imports the matplotlib.pyplot module
and gives it the alias plt. matplotlib.pyplot is a plotting library in Python
used for creating various types of plots and visualizations.
 from sklearn.datasets import make_blobs, make_classification,
make_gaussian_quantiles: This imports three functions from the
sklearn.datasets module. Scikit-learn (or sklearn) is a popular machine
learning library in Python that provides various tools for data manipulation,
preprocessing, and model building. These functions are used to generate
synthetic datasets with specific properties for testing and experimentation.

23. Generating Synthetic Datasets:
 make_blobs: This function generates clusters of data points in a n-
dimensional space. It is often used for creating datasets with well-separated
clusters.
 make_classification: This function generates a random classification
dataset. It allows you to control various parameters like the number of
samples, features, informative features, and class distribution.
 make_gaussian_quantiles: This function generates isotropic Gaussian
blobs for classification. It creates clusters of data points with a specified
number of classes and a certain variance within each class.
These functions are useful when you want to quickly create synthetic datasets
for testing machine learning algorithms or visualizing their behavior. After
generating the synthetic data, you can use the matplotlib library to create
visualizations and plots to better understand the data distribution and
characteristics.

24. DOWNLOADED EXCEL DATA FROM SERVER

25. DOWNLOADED EXCEL DATA FROM SERVER

26. MANAGER MODULE
LOGIN

27. METTING LIST

28. SETTING KPI

29. EMPLOYEE MODULE

30. VIEW KPI

31. SUBMIT KPI

32. GENERATE PLOT CHART

33. HR MODULE

34. CONCLUSION
 The employee credit management system plays a crucial role in assessing
the creditworthiness of employees within an organization.
 Through this system, an organization can make informed decisions
regarding employee loans, credit limits, and financial benefits
 Key benefits of an employee credit management system include improved
risk management, efficient loan processing, accurate credit scoring, and
enhanced financial planning for the organization and its employees.
.

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THANK YOU