Leave Maintenance project in python
Customized for MNC's Leave Maintenance in python
PROJECT ID: PYTHON40
PROJECT NAME: Customized for MNC's Leave Maintenance in python
PROJECT ABSTRACT:
This task is gone for building up an online leave administration framework that is of significance to either an association. The Leave Management System (LMS) is an Intranet based application that can be gotten to all through the association or a predetermined gathering/Dept. This framework can be utilized to computerize the work process of leave applications and their endorsements. The occasional crediting of leave is likewise robotized. There are highlights like email warnings, programmed endorsement of leave, report generators and so forth in this framework. Leave Management application will lessen paperwork and keeps up the record in a more proficient way.
EXISTING SYSTEM
In the existing framework, each school takes after the manual system in which workforce ought to enter in time and out time in a record book. Toward the finish of every month, Head of the office will figure leaves of each employee which is a period taking procedure and there are risks of losing records.
PROPOSED SYSTEM
In proposed framework odds of losing information isn’t conceivable in light of the fact that information is kept up as a database. Consistently participation, leaves and notification data is refreshed into database utilizing an easy to understand GUI. This will lessen work for Head of divisions. This application is an online application which makes more adaptable to get to data.
Number of Modules
After watchful examination the framework has been recognized to have the accompanying modules:
1. Admin Module
2. Employee Module
3. Hod Module
4. Principal Module
1.Admin Module
The administrator will keep up all the worker points of interest, representative leave record subtle elements, Hod leaves record subtle elements in the database.
2. EmployeeModule
In this module, staff will enlist their day by day participation data like approaching time and active time and other data like leave endorsements and new warnings. Individuals should sign in to their record and refresh data. On the off chance that there are any notices identified with the earlier day it is shown as fly up. Individuals should react to that warning by sending replay to higher experts.
3.HODModule
In this module Head of the office will have authorizations to take care of information of each employee of their area of expertise. HOD can see information as reports and get it as print out. Reports can be shown in view of the day, month. HOD can support leave this application and he can see leaves data of each person.
4.Principal Module
Among the whole modules, rule module is given more authorization. He can see data of each office personnel data and make a move in view of the reports. The guideline can see data like employee’s leaves, participation, and notices including Head of offices. He can print gives an account of day by day or month to month premise. In this module Leave, endorsement and renunciation are incorporated.
SOFTWARE REQUIREMENTS:
OS : Windows
Python IDE : Python 2.7.x and above
Language : Python Programming
Database : MYSQL
HARDWARE REQUIREMENTS:
RAM : 4GB and Higher
Processor : Intel i3 and above
Hard Disk : 500GB Minimum
Setting up Software Environment
Python is a high level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:
• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning .
• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R.
• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter. This module is used to stream live tweets directly from Twitter in real-time.
• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions.
• scikit-learn: It is a simple and efficient tool for data mining and data analysis.
• matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.
In this work matplotlib.pyplot module is used to plot the metrics.
• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents.
• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research a system of supervised learning and anomaly detection system a system of unsupervised learning. What is the difference between supervised learning and unsupervised learning? The answer is that supervised learning studies labeled datasets. They use labeled datasets to train and to render it accurate by changing the parameters of the learning rate. After that, they apply parameters of learning rate to the dataset, the techniques that implement supervised learning such as multilayerperceptron (MLP) to build the model based on the history of the database. This supervised learning has a disadvantage, since if new fraud transactions happen that do not match with the records of the database, then this transaction will be considered genuine. While, unsupervised learning acquires information from new transactions and finds anomalous patterns from new transaction. This unsupervised learning is more difficult than supervised learning, because we have to use appropriate techniques to detect anomalous behavior.
TABLE OF CONTENTS
·
Title
Page
·
Declaration
·
Certification
Page
·
Dedication
·
Acknowledgements
·
Table of
Contents
·
List of
Tables
· Abstract
CHAPTER SCHEME
CHAPTER ONE: INTRODUCTION
CHAPTER TWO: OBJECTIVES
CHAPTER THREE: PRELIMINARY
SYSTEM ANALYSIS
·
Preliminary
Investigation
·
Present System in Use
·
Flaws In Present System
·
Need Of New System
·
Feasibility Study
·
Project Category
CHAPTER FOUR: SOFTWARE
ENGINEERING AND PARADIGM APPLIED
·
Modules
·
System / Module Chart
CHAPTER FIVE: SOFTWARE AND
HARDWARE REQUIREMENT
CHAPTER SIX: DETAIL SYSTEM
ANALYSIS
·
Data Flow Diagram
·
Number of modules and
Process Logic
·
Data Structures and Tables
·
Entity- Relationship
Diagram
·
System Design
·
Form Design
·
Source Code
·
Input Screen and Output
Screen
CHAPTER SEVEN:
TESTING
AND VALIDATION CHECK
CHAPTER EIGHT:
SYSTEM SECURITY MEASURES
CHAPTER NINE:
IMPLEMENTATION, EVALUATION &
MAINTENANCE
CHAPTER TEN:
FUTURE SCOPE OF THE PROJECT
CHAPTER ELEVEN: SUGGESTION AND CONCLUSION
CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES
Other Information
|
PROJECT
SOFWARE |
ZIP |
|
PROJECT REPORT PAGE |
60
-80 Pages |
|
CAN BE USED IN |
Marketing
(MBA) |
|
PROJECT COST |
1500/-
Only |
|
PDF SYNOPSIS COST |
250/-
Only |
|
PPT PROJECT COST |
300/-
Only |
|
PROJECT WITH SPIRAL BINDING |
1750/-
Only |
|
PROJECT WITH HARD BINDING |
1850/-
Only |
|
TOTAL
COST (SYNOPSIS, SOFTCOPY, HARDBOOK, and SOFTWARE, PPT) |
2500/-
Only |
|
DELIVERY TIME |
1
OR 2 Days (In
case Urgent Call: 8830288685) |
|
SUPPORT / QUERY |
|
|
CALL |
8830288685 |
|
|
help@projectsready.in |
|
[Note:
We Provide Hard Binding and Spiral Binding only Nagpur Region] |
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