This document summarizes a student project using Python to predict stock market prices. It will create graphs of financial data for companies and forecast future prices using machine learning techniques. The methodology will take input data, analyze it using ML models, preprocess the data, and train a LSTM deep learning model to make accurate predictions. It discusses the project's strengths, weaknesses, opportunities and threats in a SWOT analysis and includes design diagrams and requirements for the system.

1. School of Computing Science and Engineering (ETE B Tech Sem-VI, Fall2022-23)
Program Name: B.Tech Project Guide : Mr. Mohd. Anas
Topic: STOCK MARKET
PREDICTION USING PYTHON
Ankit Kumar - 20SCSE1010391
Khushi - 20SCSE1010840
Shiv Gupta - 20SCSE1010782

2. Introduction
Program Name: B.Tech (CSE)
• The stock market offers one of the biggest returns on the market, but
it is exceedingly difficult to predict stock prices because there are no
set standards for doing so. Despite their volatility, one can still
visualize share prices and other statistical data to make an informed
decision on which company to invest their money in.
• This project will allow us to create dynamic financial data graphs for a
single company using tabular data from the finance Python package.
Additionally, we can forecast future stock prices using machine
learning techniques.
• Investment firms and individual investors have long used money
models to gain a deeper understanding of the market and create
successful investments.

3. Problem Statement
Program Name: B.Tech (CSE)
• Stock market Trading is the most sought after and so its popularity is increasing, and researchers ought to find new
techniques for prediction. Predicting this stock value offers enormous profit opportunities which are a huge
motivation for research in this area.
• Even a fraction of a second's knowledge of a stock's worth can result in large earnings. However, the accuracy of the
existing stock market prediction models is relatively low and even though multiple algorithms exist, there is no real-
life implementation of these ideas for the beneficial of people.
• An efficient algorithm should be made available with easy accessibility and interface.

4. Methodology
Program Name: B.Tech (CSE)
• We saw that the new user was afraid to invest in the share market because he did not have the knowledge and did not
have any tool with the help of which he could do this work.
• So, we decided to design a web application of stock prediction and forecasting using dash with the help of machine
learning and deep learning, which can tell in a very precise way where the market will move by analyzing things.
• Step 1: We will do this work by taking the input data.
• Step 2: We imported this data through library and analyzed with the help of machine learning model. Step 3: We will
focus on data pre-processing after pre-processing the data, System reads stock price history and gives input to the Back
propagation algorithm.
• Step.4: In this we will used the model of Deep Learning Long Short-Term Memory (LSTM), we have trained the data in
its own way, we have learned to use it because it is very advanced and performs this task very accurately and the result
is more accurate.

5. SWOT Analysis
Program Name: B.Tech (CSE)
• Strength:
• It can monitor both cloud and on-premises servers and has virtually no limitations.
• Easy to use and configure.
• User-friendly Interface
• Threat and Server/System failure prevention.
• Weakness:
• Learning curve – New patterns and tooling can take a while to learn and adopt.
• Dealing with increased complexity- As the software expands its depth and breadth of services it provides,
which while positive in terms of capabilities also introduces a challenge in terms of how all these services are
presented to customers.

6. SWOT Analysis
Program Name: B.Tech (CSE)
• Opportunities: These are external factors that present an opportunity to expand profit, sales, and/or get
market shares.
• Marketplace enhancement in terms of functionality, innovation, and price.
• Standardized process.
• Quick solutions for the problem.
• Highly adaptive and flexible to future needs.
• System Failure Prevention.
• Threats: These are external factors that could present a hazard to the existing or future business.
• Security – Bigger surface area for loopholes and higher complexity for security audits.
• Latency due to increased complexity in the software.

7. Design Diagram
Program Name: B.Tech (CSE)

8. Requirements
Program Name: B.Tech (CSE)
SYSTEM:
User Interface
• 64-bit processor architecture
• The minimum ram requirement for proper execution is 4GB.
• Storage Capacity of 128GB is enough.
Software Interface
• Operating system: Windows
• Programming language: Python
• Database API: finance

9. Requirements
Program Name: B.Tech (CSE)
NON-FUNCTIONAL:
Performance requirements
• Troubleshooting poor performance, availability of resources, overload, and congestion
• Performed in reaction to some alert (e.g., user complaint, trouble ticket)
• Assuring acceptable performance, availability, and capacity before problems arise
• Conducted to detect and prevent deteriorating conditions
• Managing unexpected consequences of new deployments
Security requirements
• Internal software detection for individual applications, and databases.
• Configuration Management – It maintains the configuration of its infrastructure devices.
• Physical and Environmental controls
• Tier IV data centers with strong physical security.