This project summarizes a school management system created by Rema Deosi Sundi for their class 12 computer science project. The system allows users to manage student and teacher data, attendance records, fee structures and the school library. It was developed using Python and stores data in MySQL tables. The system has functions for adding, removing, updating and displaying data for each module. While limited in online exam capabilities, the system provides a basis for automating core school administration tasks.
This project is based on Library Management. Python and MySQL are the programming platforms which are used in making of this project.
Subject-Informatics Practices
Class-11/12
This project is based on Library Management. Python and MySQL are the programming platforms which are used in making of this project.
Subject-Informatics Practices
Class-11/12
Library Management Project (computer science) class 12RithuJ
This project descibes the Library management system.This includes the code, its output and the applications. This software has main menu, admin menu that has provision to create student & book record, display student & book record, modify student & book record, delete student & book record .
Rithu
AECS Kudankulam
Informatics Practices/ Information Practices Project (IP Project Class 12)KushShah65
Project for Informatics Practices for students of Class 12.
on Car sales analysis - By Kush Shah.
Link to google drive for downloading CSV containing the data of car sales.
Python pandas and matplotlib used. Please install other required libraries and modules using pip.
No connected MySQL.
Disclaimer:
The data in the csv is dummy data and the project is made completely for spectulative and educational purpose.
https://drive.google.com/drive/folders/1W2xkH_w1yueTEGd6t7dbRCOGgHD9clxS?usp=sharing
Download link ( copy link to download )
https://drive.google.com/file/d/1TOz6arCdt4Nhfm_2emBzQCmgGSTCVQHy/view?usp=sharing
to add this to net beans just do this
1) open netbeans
2) on the top left, click file.
3) then click import project, there select from zip
4) use my file which u downloaded
5) import and thats it
Enjoy Using my Project as a reference for your own Project.
I hope that this will help you to understand what to do in your own project.
Happy Coding Nerds!!
git hub link to download it to ur system
https://github.com/Yosh1kageK1ra/12th-Class-Project-CBSE.git
class 12th computer science project Employee Management System In PythonAbhishekKumarMorla
This is the employment management system designed in python without using any interface through sql it does not have sequence structured query or sql connectivity but perhaps it has file handling concept.
How To Use It:
just replace the txt file and location on the code
also always use the id of employment as shown below:
01
because in the code it search for the index 0,1 therefore it have only two digits employee names
you can make it to 3 or 4 just by replacing the code
we have already mentioned in the code part..
Rectifier class 12th physics investigatory projectndaashishk7781
Investigatory project of physics class 12 for helping kendriya vidyalaya students
Project on rectifier whoever taking this project also requires a modal of rectifier
project report on hacking of passwords . this help to save the passwords in this software . in this project there are coding , flowcharts ,input - output , system design data design and all.......................................................................................................................
Library Management Project (computer science) class 12RithuJ
This project descibes the Library management system.This includes the code, its output and the applications. This software has main menu, admin menu that has provision to create student & book record, display student & book record, modify student & book record, delete student & book record .
Rithu
AECS Kudankulam
Informatics Practices/ Information Practices Project (IP Project Class 12)KushShah65
Project for Informatics Practices for students of Class 12.
on Car sales analysis - By Kush Shah.
Link to google drive for downloading CSV containing the data of car sales.
Python pandas and matplotlib used. Please install other required libraries and modules using pip.
No connected MySQL.
Disclaimer:
The data in the csv is dummy data and the project is made completely for spectulative and educational purpose.
https://drive.google.com/drive/folders/1W2xkH_w1yueTEGd6t7dbRCOGgHD9clxS?usp=sharing
Download link ( copy link to download )
https://drive.google.com/file/d/1TOz6arCdt4Nhfm_2emBzQCmgGSTCVQHy/view?usp=sharing
to add this to net beans just do this
1) open netbeans
2) on the top left, click file.
3) then click import project, there select from zip
4) use my file which u downloaded
5) import and thats it
Enjoy Using my Project as a reference for your own Project.
I hope that this will help you to understand what to do in your own project.
Happy Coding Nerds!!
git hub link to download it to ur system
https://github.com/Yosh1kageK1ra/12th-Class-Project-CBSE.git
class 12th computer science project Employee Management System In PythonAbhishekKumarMorla
This is the employment management system designed in python without using any interface through sql it does not have sequence structured query or sql connectivity but perhaps it has file handling concept.
How To Use It:
just replace the txt file and location on the code
also always use the id of employment as shown below:
01
because in the code it search for the index 0,1 therefore it have only two digits employee names
you can make it to 3 or 4 just by replacing the code
we have already mentioned in the code part..
Rectifier class 12th physics investigatory projectndaashishk7781
Investigatory project of physics class 12 for helping kendriya vidyalaya students
Project on rectifier whoever taking this project also requires a modal of rectifier
project report on hacking of passwords . this help to save the passwords in this software . in this project there are coding , flowcharts ,input - output , system design data design and all.......................................................................................................................
The Quest for an Open Source Data Science PlatformQAware GmbH
Cloud Native Night July 2019, Munich: Talk by Jörg Schad (@joerg_schad, Head of Engineering & ML at ArangoDB)
=== Please download slides if blurred! ===
Abstract: With the rapid and recent rise of data science, the Machine Learning Platforms being built are becoming more complex. For example, consider the various Kubeflow components: Distributed Training, Jupyter Notebooks, CI/CD, Hyperparameter Optimization, Feature store, and more. Each of these components is producing metadata: Different (versions) Datasets, different versions a of a jupyter notebooks, different training parameters, test/training accuracy, different features, model serving statistics, and many more.
For production use it is critical to have a common view across all these metadata as we have to ask questions such as: Which jupyter notebook has been used to build Model xyz currently running in production? If there is new data for a given dataset, which models (currently serving in production) have to be updated?
In this talk, we look at existing implementations, in particular MLMD as part of the TensorFlow ecosystem. Further, propose a first draft of a (MLMD compatible) universal Metadata API. We demo the first implementation of this API using ArangoDB.
Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016)
Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.
The workshop will present how to combine tools to quickly query, transform and model data using command line tools.
The goal is to show that command line tools are efficient at handling reasonable sizes of data and can accelerate the data science
process. We will show that in many instances, command line processing ends up being much faster than ‘big-data’ solutions. The content
of the workshop is derived from the book of the same name (http://datascienceatthecommandline.com/). In addition, we will cover
vowpal-wabbit (https://github.com/JohnLangford/vowpal_wabbit) as a versatile command line tool for modeling large datasets.
Transferring Software Testing Tools to PracticeTao Xie
ACM SIGSOFT Webinar co-presented by Nikolai Tillmann (Microsoft), Judith Bishop (Microsoft Research), Pratap Lakshman (Microsoft), Tao Xie (University of Illinois at Urbana-Champaign) http://www.sigsoft.org/resources/webinars.html
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
DataMind interactive learning: Dublin R User Group: September 2013DataMind-slides
Presentation explaining the motivation for building DataMind.org and the technical tools that were used. We also looked at how you can create your own interactive R tutorials with the beta version. More info on http://www.DataMind.org
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Computer Project for class 12 CBSE on school management
1. Oxford Public School
Ranchi
Session: 2020-2021
Computer Science
Project
Submitted by:-
Name: Rema Deosi Sundi
Class: XII A
Class Roll no.: 40
Board Roll no.:
2. Certificate
This is to certify that Rema Deosi Sundi of class
XII A of Oxford Public School, Ranchi has
completed her project file under the supervision
of Mrs Rolley. She has taken care and shown
sincerity in completion of this project.
I hereby certify that this project is upto my
expectations and guidance issued by CBSE.
Internal External
Signature Signature
3. Acknowledgement
I would like to express my greatest
gratitude to the people who helped and
supported me throughout my project. I
am thankful to my parents for helping me
in completion of this project. I am grateful
to Mrs. Rolley whose valuable guidance
has been the ones that helped me patch
this project and make it full proof success.
I would also like to express my gratitude to
the Principal Mr. Suraj Sharma for his
constant motivation during the course of
this investigation.
Name: Rema Deosi Sundi
Class: XII A
Board roll no.:
5. INDEX
Introduction
Software & Hardware requirements
Advantages of this project
Source Code in Python
Output Screen
MySQL tables
Limitations
Future Scopes
Bibliography
6. Introduction
This project work automates school management system.
School Management System consist of tasks such registering students, attendance
record keeping control to absentees, details of teacher, fee structure ,etc.
Data file handling has been effectively used in the program. Database is a collection of
interrelated data to serve multiple applications i.e. database programs create files of
information. So we see that files are worked with most inside the program itself.
DBMS
The software required for management of data is called DBMS. It has three models.
Relation model: It stores information in form of rows (cardinality) and columns
(degree).
Hierarchical model: In this type of model, we have multiple records inside a single
record.
Network model: In this, the data is represented by collections of record and
relationships is separated by associations.
Characteristics of DBMS
• It reduces the redundancy.
• Data sharing
• Data standardization
• Reduction of data inconsistency
Types of files based on access
• Sequential file
• Serial file
• Random file
• Test file
• Binary file
7. Software and Hardware requirement
#Software Specifications:-
• Operating system: Windows 10/8/7
• Platform : Python IDLE 3.7
• Database : MySQL
• Languages : Python
#Hardware Specifications:-
• Processor : Dual core or above
• Hard Disk : 40 GB
• RAM : 1024 MB
Advantages of Project:-
1. Saves time of teachers and administrators
2. Fee Collection
3. Improving Teaching Standards
4. Complete attendance automation
5. Effortless grades and marks management
6. Publishing of online forums and assignments
7. Easy management of class information analytical
reports
8. Ordering books for library accordingly
8. Source Code in Python
import mysql.connector as a
con=a.connect(host='localhost',user='root',database='test',passwd='rema')
def AddSt():
n=input("Student name:")
cl=input("Class:")
r=int(input("Roll no:"))
a=input("Address:")
ph=input("Phone:")
data=(n,cl,r,a,ph)
sql='insert into student values(%s,%s,%s,%s,%s)'
c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data entered successfully")
print("")
main()
def RemoveSt():
cl=input("Class:")
r=int(input("Roll no:"))
data=(cl,r)
sql='delete from student where class=%s and roll=%s'
c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data Updated")
print("")
main()
def DisplaySt():
cl=input("Class:")
data=(cl,)
sql='select * from student where class=%s'
c=con.cursor()
9. c.execute(sql,data)
d=c.fetchall()
for i in d:
print("Name:",i[0])
print("Class:",i[1])
print("Roll no:",i[2])
print("Address:",i[3])
print("Phone:",i[4])
print("")
print("")
main()
def AddT():
tcode=int(input("TCode:"))
n=input("Teacher name:")
s=int(input("Salary:"))
a=input("Address:")
ph=input("Phone:")
data=(tcode,n,s,a,ph)
sql='insert into teacher values(%s,%s,%s,%s,%s)'
c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data entered successfully")
print("")
main()
def RemoveT():
n=input("Teacher:")
tcode=int(input("Tcode:")
data=(n,tcode)
sql='delete from teacher where name=%s and tcode=%s'
c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data Updated")
print("")
10. main()
def UpdateSal():
n=input("Teacher:")
tcode=int(input("Tcode:"))
salary=int(input("Salary:"))
data=(n,tcode)
sql='update teacher set salary=%s where name=%s and tcode=%s'
c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data Update")
print("")
main()
def DisplayT():
sql='select * from teacher'
c=con.cursor()
c.execute(sql)
d=c.fetchall()
for i in d:
print("Tcode:",i[0])
print("Name:",i[1])
print("Salary:",i[2])
print("Address:",i[3])
print("Phone:",i[4])
print("")
print("")
main()
def ClAttd():
d=input("Class:")
clt=input("Class teacher:")
t=int(input("Class strenght:"))
d=input("Date:")
ab=int(input("No of absentees:"))
data=(d,clt,t,d,ab)
sql='insert into ClAttendance values(%s,%s,%s,%s,%s)'
11. c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data entered successfully")
print("")
main()
def DisplayClAttd():
sql='select * from ClAttendance'
c=con.cursor()
c.execute(sql)
d=c.fetchall()
for i in d:
print("Class:",i[0])
print("Class teacher:",i[1])
print("Total St:",i[2])
print("Date:",i[3])
print("Absentees:",i[4])
print("")
print("")
main()
def TAttd():
n=input("Name:")
d=input("Date:")
a=input("Attendance:")
data=(n,d,a)
sql='insert into tattendance values(%s,%s,%s)'
c=con.cursor()
c.execute(sql,data)
con.commit()
print("Data entered successfully")
print("")
main()
def DisplayTAttd():
sql='select * from tattendance'
c=con.cursor()
12. c.execute(sql)
d=c.fetchall()
for i in d:
print("Name:",i[0])
print("Date:",i[1])
print("Attendance:",i[2])
print("")
print("")
main()
def UpdateFees():
cl=input("Class:")
m=input("Monthly:")
b=input("BusFee:")
sc=input("ScFee:")
tc=input("TechFee:")
t=input("Total:")
data=(cl,)
sql='update FeeStructure set monthly=%s, BusFee=%s, ScFee=%s,
TechFee=%s, Total=%s'
c.execute(sql,data)
con.commit()
print("Data Updated")
print("")
main()
def DisplayFees():
sql='select * from FeeStructure'
c=con.cursor()
c.execute(sql)
d=c.fetchall()
for i in d:
print("Class:",i[0])
print("Monthly:",i[1])
print("BusFee:",i[2])
print("ScFee:",i[3])
print("TechFee:",i[4])
14. for i in d:
print("Bid:",i[0])
print("Title:",i[1])
print("Author:",i[2])
print("Publisher:",i[3])
print("Genre:",i[4])
print("")
print("")
main()
def main():
ch='y'
while ch in ['y','Y']:
print("Pitts Modern School")
print("1.Student")
print("2.Teacher")
print("3.ClAttendance")
print("4.TAttendance")
print("5.FeeStructure")
print("6.Library")
table=int(input("enter table no:"))
print("")
if table==1:
op='y'
while op in ['y','Y']:
print("1.Add student")
print("2.Remove student")
print("3.Display St detail")
task=int(input("enter task no:"))
if task==1:
AddSt()
elif task==2:
RemoveSt()
elif task==3:
DisplaySt()
else:
15. print("Enter Valid Choice!!")
op=input("Continue in this table(y/n):")
elif table==2:
op='y'
while op in ['y','Y']:
print("1.Add teacher")
print("2.Remove teacher")
print("3.Update Salary")
print("4.Display Tdetails")
task=int(input("enter task no:"))
if task==1:
AddT()
elif task==2:
RemoveT()
elif task==3:
UpdateSal()
elif task==4:
DisplayT():
else:
print("Enter Valid Choice!!")
op=input("Continue in this table(y/n):")
elif table==3:
op='y'
while op in ['y','Y']:
print("1.Class Attendance")
print("2.Display ClAttd details")
task=int(input("enter task no:"))
if task==1:
ClAttd()
elif task==2:
DisplayClAttd()
else:
print("Enter Valid Choice!!")
op=input("Continue in this table(y/n):")
elif table==4:
16. op='y'
while op in ['y','Y']:
print("1.Teacher attendance")
print("2.Display TAttd details")
task=int(input("enter task no:"))
if task==1:
TAttd()
elif task==2:
DisplayTAttd()
else:
print("Enter Valid Choice!!")
op=input("Continue in this table(y/n):")
elif table==5:
op='y'
while op in ['y','Y']:
print("1.Update Fees")
print("2.Display Fees details")
task=int(input("enter task no:"))
if task==1:
UpdateFees()
elif task==2:
DisplayFees()
else:
print("Enter Valid Choice!!")
op=input("Continue in this table(y/n):")
elif table==6:
op='y'
while op in ['y','Y']:
print("1.Add Book")
print("2.Remove Book")
print("3.Display Book")
task=int(input("enter task no:"))
if task==1:
AddBook()
elif task==2:
22. Limitations
• The limitation of the application is more
improvement in online examinations.
Limited questions had been stored and
need more updation and maintenance of
the application.
• Storage capacity is too small so that it
cannot be stored large amount of data so
that backup is necessary for the future
improvement.
23. Future Scope
• In future our system can include accounting
system, good backup, and restore facility.
• System is so much flexible so in future it can
increase easily and new modules can be added
easily.
• You can add student admission as well as pay
online fees.
• Make online exams more effective, efficient and
more dynamic so that it helps to get good
support from the student.
24. Bibliography
• Computer Science with Sumita Arora
• Computer Science with Preeti Arora
• www.wikipedia.org
• www.w3resource.com
• Under the guidance of subject teacher