A REPORT OF 8TH SEMESTER INDUSTRIAL TRAINING

at

PREPINSTA TECHNOLOGIES

SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE AWARD OF DEGREE

BACHELOR OF TECHNOLOGY IN COMPUTER SCIENCE

ENGINEERING

SUBMITTED BY:

NAME : MAYUR GOGOI

UNIVERSITY ROLL NO.(s) : 1907202

DEPARTMENT OF COMPUTER SCIENCE ENGINEERING

Feb-May, 2023

1|Page
QUEST INFOSYS FOUNDATION GROUP OF INSTITUTIONS, JHANJERI

CANDIDATE'S DECLARATION

I “Mayur Gogoi ” hereby declare that I have undertaken 8 th semester Industrial

Training at “PREPINSTA TECHNOLOGIES ” during a period from Feb-May,

2023 in partial fulfillment of requirements for the award of degree of B.Tech

(Computer Science Engineering) from IK Gujral Punjab Technical University. The

work which is being presented in the training report submitted to Department of

Computer Science Engineering.

Signature of the Student

The Industrial training Viva–Voce Examination of has been held on

and accepted.

Signature of Internal Examiner Signature of Head of Department

Signature of External Examiner

2|Page
Training letter

3|Page
CERTIFICATE OF TRAINING

4|Page
 ABSTRACT

Price Prediction analysing system is a system designed to help users manage their
Price or expenses in a more efficient and organized manner..

The main goal of the system is to provide users with a centralized platform to manage
their daily Prices expenses, create new tasks, set deadlines, prioritize tasks, and mark
them as completed. The application also allows users to collaborate with Brands by
sFiltering as per their needs.

The backend of the application is built using Python, Data sets and ML Algortihm,
which are both powerful and widely used for building Algotihmns,Data
sets,Predicting different application’s business logicthe business logic of the
application, including database operations, authentication, and authorization. Python
and SQL is used as the database for storing task-related information, such as task title,
description, deadline, and status.

The frontend of the application is in Jupyter Notebook, a popular Python

Notebook/library for Coding in Python and for data sets in Data Science and
Ananlysing data. It allows for the creation of dynamic and interactive user interfaces
that are responsive to user actions. The frontend communicates with the backend
using APIs to retrieve and update task-related data.

The System includes several key features that make it a valuable tool for managing
tasks. Firstly, users can create and prioritize tasks by setting deadlines, assigning them
to specific projects or categories, and marking them as high, medium, or low priority.
Users can also set reminders for tasks and receive notifications when they are
approaching their deadline.

Secondly, users can collaborate with others by sharing tasks and assigning them to
different team members. The application also includes a commenting system, which
allows users to discuss tasks and provide feedback to each other.

5|Page
Thirdly, the application includes a dashboard that provides users with an overview of
their tasks and progress. The dashboard includes charts and graphs that display task
completion rates, overdue tasks, and task distribution by priority and project.

Finally, the application includes an authentication and authorization system that

ensures that only authorized users can access and modify task-related data. Users can
create accounts, log in, and log out of the application, and their data is stored securely
in the Python, database.

In conclusion, the Prediction System is a powerful tool for managing prices and
filtering in a more efficient and organized manner. The application is built using Data
science, ML,AI which is a popular choice for building modern Data scientist due to
its scalability, flexibility, and ease of use. This system includes several key features
that make it a valuable tool for managing tasks, including task prioritization,
collaboration, dashboard, and authentication and authorization.

6|Page
 ACKNOWLEDGEMENT
I take this opportunity to express my sincere gratitude to the Principal, Quest Group
of Institutions, Jhanjeri, for providing this opportunity to carry out the present work.

The constant guidance and encouragement received from Prof. Navdeep Kaur,
Professor and Head, Department of Computer Science Engineering, has been of great
help in carrying our present work and helped us in completing this project with
success.

I would like to express a deep sense of gratitude to the “PREPINSTA” team and my
Project Guide Atulya Kaushik, Python(AI,ML,DATA SCIENCE) department for
the guidance and support in defining the design problem and towards the completion
of my project work. Without their wise counsel and able guidance, it would have
been
impossible to complete the thesis in this manner.

I am also thankful to all the faculty and staff members of Prepinsta ORGANISATION
for their intellectual support throughout the course of this work.

MAYUR GOGOI(1907202)

7|Page
 Table of Contents

Certificate

Candidate's Declaration

Abstract

Acknowledgement

List of Figures

Chapter 1 Introduction
1.1 Introduction Profiles
1.1.1 Project Profile
1.1.2 Company Profile
1.1.3 About Course

1.2 Introduction of Project

1.4 Problem Statement
1.5 Project Objective
1.6 Scope of Study
1.7 Feasibility Study

Chapter 2 Literature Review

Chapter 3 Methodology

Chapter 4 Project Setup

1. Hardware Specifications and Requirements
2. Software Requirements
3. Setup a MERN stack Project

Chapter 5 Implementation

5.1 Project Manager interface with React components:

5.1.1 simple User interface with React components.

5.2 Building the Taskcafe:
5.2.1 Send responses back to the user in the chat interface.

8|Page
 5.3.2 Testing and Deployment:
5.3.1 Test the Taskcafe functionality:
5.3.2 Deploy the application to a hosting platform like
Heroku or AWS:

Chapter 6 Screenshots of the project

Chapter 7 Conclusions and Future Work

References

LIST OF FIGURES

Sr. No. Figure Description Page No.

1 Figure 1.2.1 Technologies Used 19

2 Figure 3.1 The WaterFall Methodology 27

Figure 3.2 Project Activities 28

Figure 5.1.1 Simple User interface with React components 37

3 Chapter – 6 Screenshots of the Project 47

9|Page
 CHAPTER 1 – INTRODUCTION

Introduction Profiles

Project Profile

Title PREDICTION ANALYSIS SYSTEM

Organization Prepinsta Organisation

Category Python(AI,ML)

Duration 6 Months

Front-End JUPYTER,AI (DATA SETS)(Library)

Back-End Python+ SQL +Artificial Intelligence

Guide Atulya Kaushik

Submitted by Mayur Gogoi

Roll no. : 1907185
Submitted to Department of CSE, Quest Group of Institutions, Jhanjeri

10 | P a g e
 1.2. COMPANY PROFILE

ABOUT COMPANY:

PRPEINSTA is a group of professionals who love to think outside the box.

Everything we do to refine and achieve absolute excellence is our motto. We value
your unbeatable talent, your innovative ideas and turn them into reality. Let us get
together and head for the successful brand!

PHILOSOPHY

 To impart hardcore practical quality training among students/developers about latest

technologies trending today.
 To share knowledge of information security and create awareness in the market.
The solution to clients' as per the International standard practices and governance.
 To support good business practices through continual employee training and
education
 To equip a local team with a strong knowledge of international best practices and
international expert support so as to provide practical advisories in the best interests
of our clients

COMPANY’S VISION & MISSION

Prepinsta technologies only vision is to provide with cutting edge practical skills so that
students can easily cope with and quickly adapt to the ever- changing technologies in the
corporate environment. Our mission at Prepinsta is to create the highest standards in
education through ithe mprovisation of quality and practical skills.

SERVICES
 Software Testing
 Mobile Application Testing
 Web Development
 Web Designing
 Mobile App Development
 Digital Marketing Services
 Embedded System Services

11 | P a g e
Why Choose Us?

Hundreds of Clients & Nearly a Decade of Experience

Goal Oriented, ROI-Driven Focus
A Streamlined / Quality-Driven Process
Talented Designers & Expert
Developers
Our Websites & E-marketing Platforms are Easy to Manage
We Are Dedicated to Our Clients’ Success

 We focus on imparting practical skills to the trainees & not just theoretical
knowledge. The courses are designed in this way at PREPINSTA correspond to
the standards of the corporate divisions and industries. Only through the
acquisition of practical skills; you can handle the everlasting technologies that
venture out in real-time situations.
 At PRepinsta we have competence to expand and adjust as per client specific
requirements.
 Skilled Workforce: At PREPINSTA, you deal with the highly professional and
proficient employees.
 Cost Efficiency: We help you to reduce the unnecessary investment and ask for the
reasonable amount of money.
 Quality Of the Product: Our software service sector has been maintaining the
highest international standards of quality.
 Infrastructure: Well organized team and tools to handle the projects with responsible
approach Hardware, Software, Networking, Voice, Conferencing, disaster recovery all
infra all you need for international projects.
 Ongoing Involvement: Prepinsta, products are “built for change” as we are well
responsive that the necessity to improve a Web solution generally arises even before
the solution is out of the door.
 Partnership: Prepinsta, considers every client a partner. From the initial stages, you
are closely involved into the procedure of technical classification, development, and
testing.

12 | P a g e
 KEY PROFESSIONALS

In addition to a panel of eminent consultants and advisors, we have a dedicated pool

of trained Developers and Trainer, investigators, working under the guidance of
professional managers. “A Ship is as good as the crew who sail her.” Our Technical
team of professionals handing, designing & delivering of projects has a strong
presence in India & the US.

13 | P a g e
 About the course
Python
Pythonisiscommonly used object-oriented,
an interpreted, for developing websites
high-levelandprogramming
software, task automation,
language with data analysis,
dynamic
and data visualization.
semantics. Since
Its high-level builtit’s
inrelatively easy tocombined
data structures, learn, Python
with has been adopted
dynamic by many
typing and
non-programmers such as accountants and scientists, for a variety of everyday
dynamic binding, make it very attractive for Rapid Application Development, as well tasks, like
as for
organizing finances.
use as a scripting or glue language to connect existing components together. Python's
simple, easy to learn syntax emphasizes readability and therefore reduces the cost of
program maintenance. Python supports modules and packages, which encourages program
modularity and code reuse. The Python interpreter and the extensive standard library are
available
What can in source
you or binary
do with python? form without charge for all major platforms, and can be freely
distributed.

Often, programmers fall in love with Python because of the increased productivity it
provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast.
Some things Python
Debugging include:programs is easy: a bug or bad input will never cause a segmentation
 Data
fault. Instead,and
analysis machine
when learning discovers an error, it raises an exception. When the
the interpreter
 Web development
program doesn't catch the exception, the interpreter prints a stack trace. A source level
 Automation or scripting
debugger allows inspection of local and global variables, evaluation of arbitrary
 Software
expressions, setting prototyping
testing and breakpoints, stepping through the code a line at a time, and so on. The
 Everyday
debuggertasks
is written in Python itself, testifying to Python's introspective power. On the other
hand, often the quickest way to debug a program is to add a few print statements to the
source: the fast edit-test-debug cycle makes this simple approach very effective.

14 | P a g e
 1.1 Introduction of Project:

Preditciton system anlayiss is a application designed to help users manage their tasks
in an efficient and organized manner. With the ever-increasing complexity of daily
tasks, it's essential to have a tool that allows users to keep track of their activities,
deadlines, and priorities. The project in python ai ml stack is a great solution to this
problem.
Python is an interpreted, object-oriented, high-level programming language with
dynamic semantics. Its high-level built in data structures, combined with dynamic
typing and dynamic binding, make it very attractive for Rapid Application
Development, as well as for use as a scripting or glue language to connect existing
components together. Python's simple, easy to learn syntax emphasizes readability and
therefore reduces the cost of program maintenance. Python supports modules and
packages, which encourages program modularity and code reuse. The Python
interpreter and the extensive standard library are available in source or binary form
without charge for all major platforms, and can be freely distributed

The project in ML,AI provides hands-on experience in building a web application from
scratch. The project covers all aspects of application development ,aterfallsigning the user
interface to implementing authentication and authorization. Learners get to practice coding
skills, work on assignments and projects, and receive feedback from instructors and peers.

The project includes several key features that make it a valuable tool for managing
tasks. Firstly, users can create new tasks and assign them to specific projects or
categories. This allows users to organize their tasks based on their projects or
categories, making it easier to manage and prioritize them.

15 | P a g e
Secondly, users can set deadlines for their tasks and receive reminders when they are
approaching their deadline. This feature ensures that users don't forget about their
tasks and can complete them on time.

Thirdly, users can prioritize their tasks based on their importance or urgency. The
application allows users to mark tasks as high, medium, or low priority, making it
easier to focus on the most critical tasks.

Fourthly, the Taskcafe project allows users to collaborate with others by sharing tasks
and tracking progress together. This feature is particularly useful for team projects,
where multiple people are working on the same task.

Fifthly, the project includes a dashboard that provides users with an overview of their
tasks and progress. The dashboard includes charts and graphs that display task
completion rates, overdue tasks, and task distribution by priority and project. This
feature allows users to monitor their progress and identify areas where they need to
focus more.

Finally, the project includes an authentication and authorization system that ensures
that only authorized users can access and modify task-related data. Users can create
accounts, log in, and log out of the application, and their data is stored securely in the
database.

In conclusion, the project in Python(AI,ML) is an exciting and rewarding project that

provides learners with valuable skills and experience in building modern web
applications. The project covers key technologies and concepts, provides hands-on
experience, and culminates in a portfolio project that learners can be proud of.
Whether you are a beginner or an experienced developer, this project is a great way to
enhance your skills and advance your career in ARTIFICIAL
INTELLIGENCE,MACHINE LEARNING..

16 | P a g e
1.2 Background of Study:
The background of the project is the increasing need for individuals and teams to
manage their tasks efficiently. With the rise of remote work and the increasing
complexity of daily tasks, there is a growing demand for Python developers and AI
MLDEVELOPERS that can provide a centralized platform for task management.

It a popular technology stack used for building modern web applications. It is known
for its scalability, flexibility, and ease of use, making it a preferred choice for web
developers. The project aims to provide learners with hands-on experience in building
application using the latest technologies.

The project is designed for learners with some prior programming experience,
particularly in Python,AI,ML. The course covers key concepts and technologies used
in building the application, including SQL, Python, AI/ML, and APIs. Learners will
have the opportunity to practice coding skills, work on assignments and projects, and
receive feedback from instructors and peers. By the end of the course, learners will
have a solid understanding of the python stack and its key components, as well as
practical experience in building application.

Overall, the project in provides a valuable opportunity for learners to enhance their w
skills and gain practical experience in building a application that meets a real-world
need.

17 | P a g e
1.3 Novel Feature of this Project

One novel feature of the project in Python is its ability to allow users to create and
manage tasks collaboratively. The application provides a centralized platform for
teams to assign tasks, set deadlines, and track progress, enabling better collaboration
and communication among team members.

Another unique feature of the project is its use of React components to build the user
interface, making the application more responsive and user-friendly. The use of React
also allows for easy customization and scalability, making it possible to add new
features and functionality as needed.

In addition, the project leverages the power of SQL, a NoSQL database, to store and
manage data in a more flexible and efficient way than traditional relational databases.
This allows for faster data access and more efficient query processing, resulting in a
better user experience for this system.

Overall, the project combines the strengths of various technologies to create a

powerful and efficient application that meets the growing need for better task
management in today's fast-paced work environment.

1.4 Problem Statement

The problem that the project aims to address is the growing complexity of task
management in today's fast-paced work environment. With the increasing amount of
information and tasks that individuals and teams need to manage, it has become more
challenging to keep track of everything and ensure that nothing falls through the
cracks.

Existing task management tools are often either too basic or too complex, making it
difficult for users to find a tool that meets their specific needs. Furthermore, many
existing tools do not allow for collaborative task management, making it difficult for
teams to work together effectively.

The application provides a centralized platform for users to manage their tasks, set
deadlines, and track progress, with the ability to assign tasks to team members and

18 | P a g e
collaborate on projects.

19 | P a g e
The project also provides learners with the opportunity to gain practical experience in
building a Python application using modern development technologies.

1.5 Project Objective: -

The main objective of the project using PYTHON AI ML stack is to provide learners
with practical experience in building application using modern development
technologies. The project aims to achieve this objective by:
1. Introducing learners to key concepts and technologies used in building PYTHON,
including Machine learning, AI, MongoDB,SQL and APIs.
2. Providing learners with hands-on experience in building a real-world .
3. Enabling learners to practice their coding skills by working on assignments and
projects, and receive feedback from instructors and peers.
4. Teaching learners how to design and implement responsive user interfaces using
React components.
5. Teaching learners how to store and manage data using MongoDB, a NoSQL
database.
6. Teaching learners how to create and use APIs to connect the frontend and
backend of a web application.
7. Enabling learners to understand the importance of collaborative task management
and how to build a collaborative Taskcafe application using python.
8. Providing learners with the knowledge and skills to customize and scale the
Taskcafe application to meet the needs of different users and organizations.
9. Overall, the objective of the Taskcafe project using python is to equip learners
with the practical skills and knowledge needed to build a modern web application
that meets a real-world need. The project aims to provide learners with a strong
foundation in python development, enabling them to pursue further study or enter
the workforce as web developers.
1.6. Scope of Study: -

The scope of projdct is broad, as it encompasses the development of a full-stack web

application from start to finish. The project covers a wide range of including concepts
and technologies used in modern development, , backend development, database
management, and API creation.

20 | P a g e
The project begins with an introduction to the Python Language and key concepts
such as Statements,Loops Fucntions ,. Learners are then introduced to the application
and the user stories that the application aims to fulfill. The project proceeds with a
step-by- step development of the application, covering each stage in detail.
The scope of the project includes:
1. Designing the application: Learners learn how to design a responsive and user-
friendly user interface using React components. They are introduced to concepts
such as state management, props, and component lifecycle methods.
2. Building the frontend: Learners learn how to build the DATA of the application
using Python (Kaggle) They learn how to create components for displaying tasks,
forms for creating and editing tasks, and modals for displaying alerts.
3. Building the backend: Learners learn how to build the backend datestes of the
application using SQL and Python,backend. They learn how to create APIs for
performing CRUD operations on the database, and how to handle authentication
and authorization.
4. Database management: Learners learn how to use MongoDB, a NoSQL database,
to store and manage data for the Taskcafe application. They learn how to create
collections and documents, how to perform queries and updates, and how to
handle errors.
5. Collaborative task management: Learners learn how to enable collaboration in the
Taskcafe application by allowing users to assign tasks to team members, set
deadlines, and track progress.
6. Customization and scaling: Learners learn how to customize the Taskcafe
application to meet the needs of different users and organizations. They learn how
to add new features and functionality, and how to optimize the application for
performance and scalability.
7. Deployment: Learners learn how to deploy the application to a production server,
and how to configure the server for security and performance.

The scope of the project is comprehensive, and provides learners with a thorough
understanding of modern web development technologies and best practices. The
project enables learners to gain practical experience in building a Analysis
application, and provides them with the skills and knowledge needed to pursue further
study or enter the workforce.

21 | P a g e
1.7. FEASIBILITY STUDY:

The project using PYTHON stack is a feasible project to undertake, given the
availability of the necessary resources and technologies. A feasibility study considers
the technical, economic, operational, and schedule feasibility of the project.

Technical Feasibility: From a technical perspective, the Taskcafe project using

PYTHON stack is feasible as it utilizes widely adopted and proven development
technologies. SQL,AI,ML MongoDB, and React are all widely used in the industry
and have extensive documentation and community support. Additionally, there are a
plethora of online resources available to assist with the development process, making
it relatively straightforward to overcome any technical challenges.

Economic Feasibility: From an economic standpoint, the Taskcafe project using

MERN stack is feasible, as the required technologies and resources are freely
available. All components of the MERN stack, including Node.js, Express,
MongoDB, and React, are open-source and free to use. Therefore, the project does not
require a significant financial investment beyond the cost of the hardware and
software required to run the project.

Operational Feasibility: From an operational standpoint, the Taskcafe project using

Python(Data science) is feasible. It can be easily managed and operated by a small
team of developers. The project's components are modular, making it easy to modify
and scale as needed. Furthermore, the Taskcafe application is a commonly used tool
that has a broad user base, making it likely that the project will find traction with
potential users.

Schedule Feasibility: From a schedule perspective, the Taskcafe project using

MERN stack is feasible. The project can be broken down into manageable
development sprints, allowing for incremental progress and feedback. Additionally,
the project can be developed within a reasonable timeframe, with the potential for
iteration and improvement over time.

Overall, the project using Python(Data science) is a feasible project to undertake. The
technical, economic, operational, and schedule feasibility considerations indicate that

22 | P a g e
the project can be developed within reasonable parameters and is likely to be
successful. With the necessary resources and expertise, the Prediction analysis project
using Python(data scidnce) stack provides a valuable opportune y to gain hands-on
experience in data scince development and build a useful application that meets a
real- world need.

23 | P a g e
 CHAPTER 2 – LITERATURE REVIEW

The Taskcafe project using MERN stack is a web application that allows users to
manage their tasks and track their progress. To better understand the underlying
concepts and technologies behind this project, a literature review was conducted. The
review focused on web application development, MERN stack, and task management
applications.

PYTHON :. Python is an interpreted, object-oriented, high-level programming

language with dynamic semantics. Its high-level built in data structures, combined with
dynamic typing and dynamic binding, make it very attractive for Rapid Application
Development, as well as for use as a scripting or glue language to connect existing
components
ML : Machinetogether. Python's
learning simple,
is a field easy to intelligence
of artificial learn syntaxthat
emphasizes readability
focuses on and
the development
therefore reduces the cost of program maintenance. Python supports modules
of algorithms and models that enable computers to learn from and make predictions or and
packages, based
decisions whichon encourages program
data. It involves themodularity and code
use of statistical reuse. The
techniques andPython interpreter
computational
and the extensive standard library are available in source or binary form without
algorithms to train a computer system to perform specific tasks without being explicitly charge
for all major platforms, and can be freely distributed.
programmed.

Often,
In programmers
machine learning, fall in love
a model with Python
is trained becausewhich
on a dataset, of theconsists
increasedofproductivity it
input data and
corresponding output labels or targets. The model learns patterns and relationships fast.
provides. Since there is no compilation step, the edit-test-debug cycle is incredibly in
Debugging
the Pythonitprograms
data, allowing is easy: a bug
to make predictions or bad input
or decisions willpresented
when never cause
witha new,
segmentation
fault. Instead,
unseen data. when the interpreter discovers an error, it raises an exception. When the
program doesn't catch the exception, the interpreter prints a stack trace. A source level
Supervised Learning:
debugger allowsIninspection
this approach, theand
of local model is trained
global using
variables, labeled data,
evaluation where the input data is accompanie
of arbitrary
Unsupervised
expressions, setting breakpoints, stepping through the code a line at a time, and sodata,
Learning: This type of learning involves training the model on unlabeled where the algorithm m
on. The
debugger is written in Python itself, testifying to Python's introspective power. On the
other hand, often the quickest way to debug a program is to add a few print statements to
the source: the fast edit-test-debug cycle makes this simple approach very effective.

24 | P a g e
guidance. Clustering and dimensionality reduction are common unsupervised learning techniques.
3. Reinforcement Learning: This learning paradigm involves an agent interacting with an environment and learni

R
Machine learning finds applications in various fields, including image and speech
recognition, natural language processing, recommendation systems, fraud detection,
autonomous vehicles, and many more. It has become a fundamental tool for
extracting insights and making predictions from large and complex datasets.
Task Management Applications: Task management applications are software
applications used to manage tasks and track progress. They are commonly used in
project management, personal organization, and team collaboration. The primary
features of task management applications include task creation, task assignment, task
completion tracking, and deadline management. There are many task management
applications available in the market, including Asana, Trello, and Todoist.

The literature review revealed several valuable insights into the development of the
Taskcafe project using Python stack. Firstly, the Python stack provides a
comprehensive set of technologies to build the application. M Python provides a
scalable and flexible database solution, Express provides a robust API framework,
React provides a user-friendly interface, and SQL MYSQL provides a server-side
runtime environment. The pYthon stack also facilitates the development process by
allowing developers to use a single language, SQL and MYSQL , for both the
frontend and backend development.

Secondly, task management applications have become an essential tool for individuals
and teams to manage their work effectively. The project using Python stack can be
designed to incorporate the essential features of task management applications, such
as task creation, task assignment, task completion tracking, and deadline management.

Lastly, the literature review revealed that web development is an ever-evolving field,
with new frameworks and technologies emerging regularly. As such, the Taskcafe
project provides an opportunity for developers to gain practical experience in Python
application development using popular and widely adopted technologies.

25 | P a g e
 In conclusion, the literature review demonstrates the potential of the Prediction
analysis project using Python ,AI,ML stack. provides a comprehensive set of
technologies to build the system application, task management applications have
become an essential tool for individuals and teams to manage their work effectively,
and python development is an ever-evolving field with opportunities for learning and
growth. The project using stack is a valuable opportunity to gain practical experience
in web application development and build a useful application that meets a real-world
need.
DIFFERENCE BETWEEN ARTIFICAL INTELLIGENCE &
MACHINE LEARNING:-

Artificial intelligence (AI) and machine learning (ML) are related fields within the
b Broader field of computer science, but they are not the same. Here's the difference
between AI and ML:

1. Scope:
Artificial Intelligence: AI is a broad field that aims to create intelligent machines that can perform tasks that typic
Machine Learning: ML is a subset of AI that focuses on developing algorithms and models that enable computers
2. Approach:
Artificial Intelligence: AI can be achieved through various techniques, including rule-based systems, symbolic re
Machine Learning: ML primarily relies on statistical techniques and algorithms to enable machines to learn from

26 | P a g e
 patterns and relationships in data, allowing them to make predictions or take actions without being explicitly pr
3. Data Dependency:
Artificial Intelligence: AI systems can utilize structured and unstructured data as inputs, but they often require s
Machine Learning: ML heavily relies on data. It requires labeled or unlabeled data to train models and make pre
4. Flexibility and Adaptability:
Artificial Intelligence: AI systems can be designed with a fixed set of rules or knowledge bases. They are often
Machine Learning: ML models are designed to be flexible and adaptable. They can learn and adapt to new data

In summary, AI is a broader field concerned with creating intelligent machines, while ML

TYPES
is a subset of AI OF MACHINE
that focuses LEARNING
on developing algorithms:- that enable machines to learn from
data and improve their performance. ML is data-driven and relies on statistical techniques,
whereas
MachineAI can use
learning various
can techniques,
be broadly including
categorized into rule-based
three main systems and on
types based expert
the learning
knowledge. ML models are flexible and
process and the availability of labeled data:adaptable, while AI systems may require more
human intervention and predefined rules.
1. Supervised Learning:
Supervised learning algorithms learn from labeled data, where the input data is accompanied by the correct outpu
The goal is to learn a mapping function that can predict the correct output for new, unseen inputs.
Common supervised learning algorithms include linear regression, logistic regression, decision trees, random fore
Supervised learning is used for tasks such as classification (assigning inputs to predefined categories) and regress
2. Unsupervised Learning:
Unsupervised learning algorithms learn from unlabeled data, where the input data does not have corresponding ou
The goal is to discover patterns, relationships, or structures within the data without explicit guidance.
Common unsupervised learning algorithms include clustering algorithms (K-means, hierarchical clustering), dime
- PCA, t-SNE), and generative models (Gaussian Mixture Models - GMM, autoencoders).
Unsupervised learning is used for tasks such as data exploration, anomaly detection, and recommendation system
3. Reinforcement Learning:

27 | P a g e
Reinforcement learning involves an agent that interacts with an environment and learns to make decisions or tak
The agent receives feedback in the form of rewards or penalties based on its actions and uses this feedback to le
Reinforcement learning algorithms employ exploration-exploitation trade-offs to learn from trial and error.
Common reinforcement learning algorithms include Q-learning, Deep Q-Networks (DQN), and Proximal Policy
Reinforcement learning is used in scenarios such as game playing, robotics, and autonomous systems.

It's worth noting that these categories are not mutually exclusive, and there are also hybrid
approaches that combine elements of different types of learning, such as semi-supervised
learning and transfer learning. Additionally, there are specialized techniques within each
type, and the choice of the learning algorithm depends on the specific problem and the
nature of the available data.

28 | P a g e
 CHAPTER 3 - METHODOLOGY

3.1 RESEARCH METHODOLOGY:

Methodology is an essential element in the software development process.

Methodology acts as a means of risk management during the different stages and
processes of software development.
It aims to improvise the management and control of the Data science life cycle
(DSLC). There are many different categories of project development methodologies
and each one has its own unique structure to develop the project based on DSLC
phase.

Fig. 3.1. The DSLC Methodology

The methodology used to complete this project is a structured design. The DSLC
model is a sequential design process, in which progress is seen to be flowing steadily
downwards from the Identifying , collection , processing , analyzing and maintenance
phase. The project is done following one stage to the other from the first to the last
stage.

29 | P a g e
First of all, during the planning stage, brain storming of ideas is carried out in order to
find out the suitable field to focus on for FYP project. Few researches are carried out
to determine the needs of innovation of systems in the specific field. After specified
the field, some critical thinking is done in finding the problem arising from the field.
Discussion is held with lecturers to determine the most suitable and executable project
title. The project is then moves to the analysis phase as we conduct a background
studies, researches and data gathering process.

All the data collected will be analyzed. The data consist of two main areas which are
paper based research (e.g., current status of image processing, application of image
processing and facial detection etc.) as well as the technical aspects (e.g., technology
to be used in developing the system). Learning of new programming languages will
start in this stage. Once the analysis part is done, the project moves to the design
phase, in which the analysis models as well as the interface design for the system will
be determined. The development process starts with the design of the framework and
interface of the system.

The major focus in design phase is to write program in order to detect the facial
expression. After developing the facial expression detection system, the system will
be integrated with the music player. Finally, when the design of the project is done,
the proposed model will then be tested to find out if there any bugs and to test its
functionality and accuracy in facial detection for various emotions.

3.2 PROJECT ACTIVITIES:

30 | P a g e
 Fig. 3.2. Project Activities
A project manager using Data science python ,stack should follow a structured
methodology to ensure successful project delivery. Here are the key steps to follow:
Requirement Gathering: The first step is to understand the client's requirements and
objectives. This involves conducting meetings with the client and stakeholders to
identify the goals of the project, functionalities, and technical requirements.In a data
science project, there are several key activities that typically take place. These activities may
vary depending on the specific project and its objectives, but here are some common activities
involved in a data science project:
Project Planning and Scoping: Define the scope, objectives, and deliverables of the project. Identify the reso

Data Acquisition: Gather the required data from various sources. This may involve accessing databases, obt

Data Cleaning and Preprocessing: Clean the data by handling missing values, outliers, and inconsistencies. P

Exploratory Data Analysis (EDA): Explore and visualize the data to gain insights and understand its charact

Feature Engineering: Create new features or transform existing features to improve model performance. Sel
Consider domain knowledge and use appropriate techniques.

31 | P a g e
6. Model Development and Training: Select suitable machine learning algorithms or
statistical models based on the problem and available data. Split the data into training,
validation, and testing sets. Train the models using appropriate techniques such as cross-
validation or grid search for hyperparameter tuning.

7. Model Evaluation: Evaluate the performance of the trained models using

appropriate metrics such as accuracy, precision, recall, F1 score, or area under the
curve (AUC).
Compare models and select the best performing one based on the evaluation results.

8. Model Deployment and Integration: Deploy the trained model into a production
environment or integrate it into existing systems. Ensure scalability, performance, and
reliability of the deployed model. Monitor and maintain the model's performance over
time.

9. Documentation and Reporting: Document the entire process, including data sources, data
preprocessing steps, feature engineering techniques, model selection, training, and
evaluation results. Prepare reports or presentations to communicate findings, insights, and
recommendations to stakeholders.
models, feature engineering techniques, or data preprocessing steps based on insights gained
during the project or feedback received.

10. Collaboration and Communication: Collaborate with teaClient’ss, stakeholders, or clients

throughout the project. Maintain regular communication, provide progress updates, and
address any concerns or changes in requirements.
11. Ethical Considerations: Consider ethical implications, privacy concerns, and legal
requirements related to the data and the project. Ensure compliance with regulations
and industry standards.

It's important to note that the sequence and intensity of these activities may vary
CHAPTER
depending on the specific project and4its
- PROJECT
requirements. SETUP

4.1 HARDWARE REQUIREMENTS

The hardware requirements for running a Taskcafe web application using the MERN
stack will depend on the expected traffic and usage patterns of the application. Here
are some general hardware recommendations:

CPU: A modern multi-core processor with at least 2 cores is recommended to handle

multiple requests and perform the necessary computations.

RAM: The amount of RAM required will depend on the size of the dataset being used
for training the GPT model and the expected number of concurrent users. At
minimum, 4GB of RAM is recommended, but for larger datasets and higher traffic,
8GB or more may be required.

32 | P a g e
Storage: The amount of storage required will depend on the size of the dataset and
any media files being stored, such as images or videos. A minimum of 10GB of
storage is recommended, but for larger datasets or media files, more storage may be
required.

Network bandwidth: The network bandwidth required will depend on the expected
traffic to the application. A high-speed internet connection with sufficient bandwidth
is recommended to ensure smooth operation of the application.

33 | P a g e
 4.2 SOFTWARE REQUIREMENTS

The software requirements for running a prediction analysis application/system using

the Python stack are:

Here are some commonly used software requirements for a data science project:
1. Programming Languages: Data science projects often involve programming in languages
such as Python or R. Python is widely used in the data science community due to its rich
ecosystem of libraries and frameworks, including NumPy, pandas, scikit-learn, TensorFlow,
and PyTorch. R is also popular, especially for statistical analysis and visualization.
2. Integrated Development Environment (IDE): An IDE provides a comprehensive environment
for coding, debugging, and managing projects. Commonly used IDEs for data science include
Jupyter Notebook, JupyterLab, Spyder, and RStudio. These IDEs offer features like code
autocompletion, inline documentation, and data visualization capabilities.
3. Data Manipulation and Analysis: Libraries like pandas in Python and dplyr in R are
essential for data manipulation, cleaning, and preprocessing tasks. These libraries provide
functionalities to handle missing values, filter and sort data, merge datasets, and perform
aggregations.
4. Visualization: Data visualization is crucial for understanding patterns and communicating
insights. Libraries such as Matplotlib, Seaborn, and Plotly in Python, and ggplot2 in R,
enable you to create various types of plots, charts, and interactive visualizations.
5. Machine Learning and Statistical Modeling: Libraries like scikit-learn in Python and caret in
R provide a wide range of machine learning algorithms and statistical modeling techniques.
These libraries allow you to train and evaluate models, perform feature selection, and tune
hyperparame and ters.
6. Deep Learning: If your project involves deep learning, frameworks like TensorFlow, Keras,
or PyTorch provide powerful tools for building and training neural networks. These
frameworks offer pre-trained models, customizable architectures, and optimization
techniques for deep learning tasks.
7. Big Data Processing: For handling large-scale datapplicationss like Apache Spark and
Hadoop can be used. They provide distributed computing capabilities and support for
processing big data efficiently.
8. Version Control: Version control systems like Git are crucial for managing codebase,
tracking changes, and collaborating with team members. Platforms like GitHub and
GitLab facilitate code sharing, collaboration, and version control in data science projects.
9. Deployment and Productionization: To deploy and serve your models in production,
frameworks like Flask or Django (Python) or Shiny (R) can be used to create APIs or web
applications. Containerization tools like Docker and orchestration tools like Kubernetes
help with managing and scaling deployments.
10. Cloud Platforms: Cloud platforms like Amazon Web Services (AWS), Google Cloud
Platform (GCP), or Microsoft Azure offer services and infrastructure for storing data,
running experiments, and deploying models. They provide scalable computing resources and
tools for managing data pipelines.

These software requirements can vary depending on the specific project, team preferences,
and industry standards. It's important to choose the tools and frameworks that best fit the
requirements of your project and align with your team's expertisS

34 | P a g e
4.3 SETTING UP THE PROJECT

This involves setting up the development environment, installing the necessary tools
and dependencies, and configuring the PYTHON stack project directory structure. It's
important to ensure that all tools and dependencies are properly installed and
configured for the project to work correctly.

To set up a application using the Python(Data science) stack, you will need to follow
these steps:

RUN Jupyter Notebook : Jupyter Notebook supports various programming languages,

including Python, R, Julia, and more. It provides an interactive environment where you
can write and execute code, view the results, and create rich documents that combine
code, text, and multimedia elements. You can download and run and install them from
ANACONDA DISTRIBUTION

Download the appropriate Anaconda distribution for your computer. Unless yo have a
specific reason, it’s a good idea to download the latest version. In my case downloaded
the Mac Python 3.8 64-bit Graphical Installer.
Once the download has been completed, double-click on the download file to go
through the setup steps, leaving everything as default. This will install Anaconda on your
computer. It may take a couple of minutes and you’ll need up to 3 GB of space available.
To check the installation, open Anaconda Prompt from the start menu. If it was successful, you’ll
see (base) appear next to your name.

35 | P a g e
 Name is the package name. Remember, a package is
a collection of code someone else has written.

Version is the version number of the package and build

is the Python version the package is made for. For now, we won’t
worry about either of these but what you should know is some
projects require specific versions and build numbers.

Channel is the Anaconda channel the package came from,

no channel means the default channel.

36 | P a g e
Today the most used Python package manager is pip, used to install and manage python
software packages, found in the Python Package Index. Pip helps us, python developers,
effortlessly “manually” control the installation and lifecycle of publicly available
Python packages from their online repositories.

Actions will be similar to the one below:

 Create a virtual environment

 Install packages using $pip install <package> command.
 Save all the packages in the file with pip freeze > requirements.txt. Keep in mind that
in this case, the requirements.txt file will list all packages that have been installed in
the environment, regardless of where they came from.
 When if you’re going to share the project with the rest of the world you will need
to install dependencies by running $pip install -r requirements.txt

That's it! You now have a basic setup for prediction system using python(Data
science) application using the Python stack. You can then proceed with implementing
the other features outlined in the project, such as designing the it chat interface and
integrating.

CHAPTER 5 – IMPLEMENTATION

When implementing a project manager for a project using Python stack with
DATASCIENCE, it is important to follow a structured approach. Here is an outline of
the key steps to take:

Requirement Gathering: The first step is to understand the client's requirements and
objectives. This involves conducting meetings with the client and stakeholders to
identify the goals of the project, functionalities, and technical requirements. It is
essential to document these requirements to ensure that they are clear, concise, and
complete.
Project Planning: Once the requirements are gathered, the project manager should
create a comprehensive project plan. This plan should include project scope,
timelines, milestones, budgets, resource allocation, and risk management strategies.
The plan should also identify the technology stack to be used, including the MERN
stack with TypeScript.

37 | P a g e
 Design and Architecture: After the planning stage, the team should move to the
design and architecture phase. In this phase, the team designs the system architecture
and defines the technology stack. The design phase should include wireframes,
mockups, and prototypes. In addition, the team should design the database schema,
API architecture, and user interface.

Develop Data Pipelines: Build data pipelines to ingest, clean, preprocess

transform the data as required. Implement the necessary data clea g and preprocessing
steps identified during the data exploration and analysis phase.
Ensure data quality, consistency, and integrity throughout the pipelines.
Implement Machine Learning Models: Integrate the trained machine learning
models into the system.
 Develop code to load the models, preprocess input data, and make predictions or
decisions based on the models.
 Handle model versioning and updates as necessary.
2. Build User Interfaces or APIs:
 Develop user interfaces or application programming interfaces (APIs) to interact with
the system.
 Design and implement user interfaces for visualizing data, presenting insights, or
allowing user input.
 Create APIs to expose the functionality of the system for other applications or users
to access.
3. Test and Validate:
 Conduct testing and validation to ensure the functionality, performance, and accuracy
of the implemented system.
 Test the data pipelines, machine learning models, and user interfaces/APIs for
correctness and robustness.
 Validate the system against predefined success criteria and use cases.
4. Deployment:
 Deploy the implemented system to a production or operational environment.
 Set up the necessary infrastructure, servers, or cloud resources to host the system.
 Ensure scalability, availability, and security of the deployed system.
5. Monitoring and Maintenance:
 Establish monitoring mechanisms to track the performance and behavior of the
system in real-time.
 Continuously monitor the data pipelines, machine learning models, and user
interfaces/APIs for any issues or anomalies.
 Maintain and update the system as required, including bug fixes, feature
enhancements, or model retraining.
6. Documentation and Knowledge Transfer:
 Document the implemented system, including technical specifications, architecture,
data flows, and system dependencies.
 Create user guides or documentation for end-users or stakeholders to understand and
use the system.
 Provide knowledge transfer and training to relevant teams or individuals involved in
operating or maintaining the system.

5.1 Project Manager interface with DATA SCIENCE components

38 | P a g e
 As a project manager for a project that requires a user interface using Python stack, it
is crucial to follow a structured approach to ensure the successful delivery of the
project. Here are the key steps to take when developing a user interface using
PYTHON stack:
1. Project Management Software: Project management tools like Jira, Trello, or Asana can help
you track and manage tasks, assign responsibilities, set deadlines, and monitor progress.
These tools allow you to create a project roadmap, organize tasks into sprints or phases, and
keep track of milestones and dependencies.
2. Communication and Collaboration Tools: Platforms like Slack, Microsoft Teams, or Zoom
provide communication channels for team members to collaborate and share updates,
progress, and challenges. These tools enable real-time messaging, file sharing, video
conferencing, and screen sharing, promoting effective communication within the team.
3. Documentation and Knowledge Sharing: Tools like Confluence, Google Docs, or Microsoft
SharePoint facilitate documentation and knowledge sharing. They allow you to create and
maintain project documentation, including project plans, meeting notes, data dictionaries,
and technical specifications. These platforms provide a centralized repository for easy access
and collaboration.
4. Version Control and Code Repository: Version control systems such as Git, along with code
hosting platforms like GitHub or GitLab, are essential for managing code versions, tracking
changes, and facilitating collaboration among data scientists and developers. These tools
help ensure code integrity, enable code reviews, and promote seamless integration of code
changes.
5. Data Visualization and Reporting: Data visualization tools like Tableau, Power BI, or
Python libraries such as Matplotlib and Plotly can help you create visual reports and
dashboards to communicate project insights and findings effectively. These tools enable you
to present
data-driven insights to stakeholders in a visually appealing and intuitive manner.
6. Data Management and Storage: Depending on the project's requirements, you may use
databases or data management tools like SQL databases (e.g., MySQL, PostgreSQL) or
NoSQL databases (e.g., MongoDB, Cassandra) to store and manage project-related data.
Cloud storage platforms such as Amazon S3, Google Cloud Storage, or Microsoft Azure
Blob Storage can also be used for scalable and secure data storage.

39 | P a g e
5.1.1 Simple DATA import from KAGGLE
Here's an example of a simple DATA import with Kaggle components
Kaggle.com

https://www.kaggle.com/competitions/house-prices-advanced-regression-
techniques/overview/evaluation

MyDATASET.kaggle

40 | P a g e
5.2 Building the Project:

The Python stack is a popular and powerful combination of technologies used to build
modern predictive applications. AI stands for artificial intelligence, In this article, we
will discuss building a Taskcafe project using the Python (AI,ML) stack.
The first step in building any project is to define its requirements. For our Predictive
system e project, we want to create a price predictive system that allows users to
create, edit, and delete prices as per their needs. Users should be able to sign up, log
in, and view their tasks on a dashboard. The project will also include authentication
and authorization features to ensure that only authorized users can access the
application.

The back-end server will handle all the business logic and data storage for our Project.
project. We will create Data endpoints for creating, updating, and deleting tasks. We

With the back-end server in place, the next step is to analyse the data using
commands ML is a branch library for building user interfaces. We will create a data
system
By cleaning the data.

41 | P a g e
Build a server-side code to create a new task

In this code, we define the task schema using Jupyter notebook

42 | P a g e
 5.2.1 Cleaning and visualizing the data:-

Cleaning Data:
1. Import Libraries: Start by importing the necessary libraries, including Pandas and NumPy.

import pandas as pd
import numpy as np

: Load yourData
Load dataset into a Pandas DataFrame using the appropriate function, such as pd.read_csv() for CSV files.
Copy code
' python
Explore the Data: Use
df = pd.read_csv( various DataFrame
your_dataset.csv' ) methods and attributes to explore the data. For example, you can check
Handle Missing Data: Identify and handle missing data in your dataset. Pandas provides functions likeandto detec
Clean and Transform Data: Perform data cleaning and transformation operations as needed. This may involve co
df.shape
df.head()

df.isnull() df.dropna() df.fillna()

Visualizing Data:

1. Import Libraries : Import libraries for data visualization, such as Matplotlib or Seaborn.
Copypython
code
import matplotlib.pyplot as plt import seaborn as sns
Choose the Right Plot: Select an appropriate plot type based on the data and the insights you want to convey. Co
Create Plots: Use the plotting functions provided by the chosen library to create

visualizations. For example, plt.plot() or plt.scatter() for Matplotlib, or sns.barplot() or

for Seaborn.
sns.histplot()
Customize
: Customize yourPlots
plots by adding labels, titles, legends, changing colors, adjusting axis scales, and other visual el
Interpret and Analyze: Analyze the visualized data and draw insights from the plots. Look for patterns, trends,
Iterate and Refine: Iterate the visualization and analysis process, refining your plots, and exploring different vis

Remember, data cleaning and visualization are iterative processes. You may need to go
back and forth between cleaning and visualizing the data to gain a deeper understanding and
uncover insights effectively.

43 | P a g e
CHAPTER 6 – SCREENSHOTS OF THE PROJECT

Analyzing the property age and sales price

44 | P a g e
 Analysing theprice over the years

CHAPTER 7 – CONCLUSIONS AND FUTURE WORK

The prediction anlaysing project using the ML AI stack is a comprehensive web

application that allows users to manage their expenses effectively.

The project's main objective is to enable users to create, update, and manage tasks
easily. The application's architecture follows the data science paradigm, which allows
for seamless communication between the client and server..

The working is simple and intuitive, making it easy for users to navigate and manage
their tasks. The use of different libraries also enables efficient rendering of data,
reducing the application's response time.

45 | P a g e
 FUTURE WORK:
There are several potential areas for future work in the project using the Python stack,
including:

Data science projects have a wide range of future scope and potential for growth. Here are
1. Machine Learning and Artificial Intelligence: The field of machine learning and AI
some areas that have significant future potential in the field of data science:
continues to evolve rapidly. Future data science projects may involve developing advanced
machine learning models, exploring deep learning algorithms, and leveraging AI
techniques for solving complex problems.
2. Big Data Analytics: As the volume, variety, and velocity of data continue to increase, the
need for effective big data analytics solutions will grow. Future data science projects may
focus on handling large-scale datasets, implementing distributed computing frameworks
like Apache Hadoop or Apache Spark, and developing algorithms for efficient data
processing.
3. Natural Language Processing (NLP): NLP involves enabling machines to understand,
interpret, and generate human language. With the increasing use of text data in various
industries, future data science projects may focus on developing NLP models for tasks
such as sentiment analysis, text classification, language translation, and chatbot
development.
4. Internet of Things (IoT) Analytics: The proliferation of IoT devices generates vast amounts
of sensor data. Future data science projects may involve analyzing and extracting insights
from IoT data, developing predictive models for anomaly detection, or building smart
systems that leverage IoT data for decision-making.
5. Recommendation Systems: Recommendation systems are widely used in e-commerce,
entertainment, and content platforms to personalize user experiences. Future data science
projects may focus on developing advanced recommendation algorithms that incorporate
deep learning techniques, context-aware recommendations, and real-time personalization.
6. Data Privacy and Security: With increasing concerns about data privacy and security,
future data science projects may revolve around developing techniques and algorithms
to protect sensitive data, ensuring compliance with privacy regulations, and
implementing secure data sharing frameworks.
7. Automated Data Science and AutoML: The automation of data science tasks is an
emerging area. Future projects may involve developing automated data cleaning, feature
engineering, and model selection techniques, as well as designing frameworks that make
data science more accessible to non-experts.
8. Ethical Data Science: As data science continues to impact society, ethical considerations
become paramount. Future projects may focus on developing frameworks for responsible
data collection, addressing bias and fairness in models, and ensuring transparency and
accountability in data-driven decision-making.

These are just a few examples of the future scope in data science projects. The field is
dynamic, and advancements in technology and new challenges will continue to shape the
landscape of data science. As a data scientist, staying updated with the latest developments
and trends will be crucial for identifying future opportunities.

46 | P a g e
REFERENCES

[1] Applications. International Journal of Innovative Technology and Exploring

Engineering (IJITEE), 9(2), 212-217. https://doi.org/10.35940/ijitee.B1536.1192S20

[2] Hegde, G., Patil, P., & Bhat, S. (2019). MERN Stack: A Comprehensive Review.
International Journal of Innovative Technology and Exploring Engineering
(IJITEE), 8(9), 186-189. https://doi.org/10.35940/ijitee.F1127.0989S219

[3] Majumder, N., & Maity, S. P. (2020). An Overview of Natural Language

Processing (NLP) and its Applications in Healthcare. Journal of Health and
Medical Informatics, 11(2). https://doi.org/10.4172/2157-7420.1000343

[4] Wang, S., Manning, C. D., & Basu, S. (2018). Reinforcement Learning for Natural
Language Processing. In Proceedings of the 56th Annual Meeting of the Association
for Computational Linguistics (Volume 1: Long Papers) (pp. 1-12). Association for
Computational Linguistics. https://doi.org/10.18653/v1/P18-1001

[5] Zhang, W., Li, Y., Li, X., Zhang, W., Li, Y., & Li, X. (2020). Machine
Learning in Natural Language Processing. In International Conference on
Advanced Information Networking and Applications (pp. 449-457). Springer.
https://doi.org/10.1007/978-3-030-42526-5_35
[6] Prepinsta Training platform
[7] Backend Documentation: https://Python.org/docs/getting-started.html
[8] python Documentation: https://pythonrouter.com/web/guides/quick-start
[9] AI Documentation: https://AI.js.org/introduction/getting-started
[11] ARTIFICIAL TUTORIAL Tutorial for Beginners:
https://www.tutorialspoint.com/AI/index.htm
[12] OpenAI API Documentation: https://beta.openai.com/docs/api-
reference/introduction
[13] Taskcafe API Documentation: https://github.com/vishalbpatil1/Task Manager-
api
[14] Deep Learning-Based Chatbot Models Using GPT-2:

47 | P a g e
48 | P a g e