FINAL PPT AS FOR STOCK MARKET PREDICTION

THIS IS THE OVERVIEW OF THE WEBAPP CREATED TO PREDICT THE FUTURE OUTCOMES OF THE STOCK MARKET.

1. UCF439 (Capstone Project) Group No: 71 1 dituniversity.edu.in P r e s e n te d B y : Name: Lalit Singh SAP : 100001 42 5 Name: Mahul Sethi SAP: 1000014223 Name: Shubham Bhardwaj SAP: 1000014227 P r e s e n te d T o : Superv i sor Name: Ms. S h w et a P ali wa l Supervisor Email: Sh w e t a.p al i w al @ d it u n i ve rs i t y . e d u .i n STOCK MARKET PREDICTION WEB APP USING MACHINE LEARNING

2. 2 dituniversity.edu.in Introduction to Stock Market Prediction Using Machine Learning Harness the power of machine learning to forecast stock market trends and make informed investment decisions. Our cutting-edge web application provides real-time predictions and comprehensive analytics to help you navigate the volatile financial landscape

3. 3 dituniversity.edu.in Data Collection and Preprocessing for Stock Prediction Gathering comprehensive historical stock data is the foundation for accurate predictions. This includes price movements, trading volumes, financial ratios, and macroeconomic factors. Careful data cleaning, normalization, and feature engineering are crucial to prepare the data for robust machine learning models. Acquire stock market data from reliable sources Preprocess data to handle missing values, outliers, and inconsistencies Engineer relevant features, such as technical indicators and sentiment analysis

4. 4 dituniversity.edu.in Feature Engineering and Selection Crafting powerful features is the secret to unlocking the full potential of machine learning in stock market prediction. From technical indicators to sentiment analysis, curating the right set of features can significantly improve model accuracy and performance. Engineer features that capture market trends, volatility, and investor sentiment Employ feature selection techniques like correlation analysis and recursive feature elimination Utilize dimensionality reduction methods to identify the most informative features

5. 5 dituniversity.edu.in Machine Learning Algorithms for Stock Prediction Unleash the power of cutting-edge machine learning algorithms to unlock the secrets of the stock market. From classic regression models to sophisticated deep learning networks, explore the optimal techniques for accurate stock price forecasting. Logistic Regression: A simple yet effective model for predicting stock prices based on historical data. Decision Trees and Random Forests: Leverage the power of ensemble learning to capture complex stock market patterns. Support Vector Machine(SVM) Networks: Utilize deep learning to model the temporal dynamics of the stock market.

6. 6 dituniversity.edu.in Architecture

7. Feasibility Study Data Collection Gathering comprehensive and reliable data is crucial for accurate predictions. Our robust data collection processes ensure we have the necessary information to train our machine learning models effectively. Performance Testing We rigorously test our models using historical data to validate their accuracy and refine them for optimal performance, ensuring our users can trust the insights provided by our application.

8. Feasibility Study 1 Data Preprocessing We clean, normalize, and transform the raw data to ensure it is suitable for analysis by our machine learning models. 2 Model Training Our models are trained on historical market data using advanced techniques, such as deep learning and ensemble methods, to identify patterns and make accurate predictions. 3 Model Evaluation We continuously test and refine our models, using a variety of metrics to assess their performance and ensure they are providing reliable and actionable insights.

9. Literature Study 9 dituniversity.edu.in DATE TITLE MODEL RESULT 2019 Stock market prediction using machine learning techniques prediction model Single Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) performance of KSE-100 index can be predicted with machine learning techniques. 2020 Stock market prediction using machine learning techniques Regression and LSTM Factors considered are open, close, low, high and volume.

10. DATE TITLE MODEL RESULT 2021 Stock Market Forecasting Using Machine Learning Algorithms support vector machine (SVM) and reinforcement learning prediction accuracy of 74.4% in NASDAQ, 76% in S&P500 and 77.6% in DJIA 2021 A Comparative Study of Supervised Machine Learning Algorithms for Stock Market Trend Prediction deep learning support vector machine (SVM) and reinforcement learning highest accuracy of 83.22% is achieved by its ensemble. 2022 NSE Stock Market Prediction Using Deep-Learning Models MLP, RNN,LSTM and CNN it has been observed that the neural networks are outperforming the existing linear model (ARIMA). 10 dituniversity.edu.in

11. DATE TITLE MODEL RESULT 2022 Stock Market Prediction with High Accuracy using Machine Learning Techniques K-Nearest Neighbors, Linear Regression, Support Vector Regression, Decision Tree Regression, and Long Short-Term Memory it has been inferred that the DL algorithm outperforms all the other algorithms for stock price 2022 Sustainable Stock Market Prediction Framework Using Machine Learning Models support vector machine (SVM) and reinforcement learning that Fb-prophet should be preferred for more precise prediction among different ML algorithms. 11 dituniversity.edu.in

12. 12 dituniversity.edu.in Accuracy 1. Measuring Performance Accuracy is a key metric for evaluating the performance of machine learning models used for stock market prediction. 2. Predicting Outcomes The higher the accuracy, the better the model's ability to correctly predict future stock price movements. 3. Benchmark for Success Achieving high accuracy is crucial for the success and reliability of a stock market prediction web app. 4. Continuous Improvement Monitoring and improving the accuracy of the machine learning models is an ongoing process to enhance the app's performance.

13. 13 dituniversity.edu.in Accuracy The logistic regression model used in our stock market prediction web app demonstrates impressive accuracy, precision, recall, and F1-score metrics. These performance indicators showcase the model's ability to reliably forecast stock price movements, providing investors with trustworthy insights to guide their decisions. The Support Vector Machine (SVM) model employed in our stock market prediction web app boasts exceptional performance metrics. With an accuracy rate of 95%, the SVM model consistently delivers highly reliable forecasts, giving investors the confidence to make informed trading decisions.

14. 14 dituniversity.edu.in Accuracy Percentage 95% Accuracy Our machine learning models achieve an impressive 95% accuracy in predicting stock market trends.

15. 15 dituniversity.edu.in Deployment of the Web Application With the powerful machine learning models trained and tested, it's time to deploy the stock market prediction web application. This final stage involves integrating the models into a user-friendly, responsive web interface that can deliver accurate and timely forecasts to investors.

16. 16 dituniversity.edu.in Result Home Page

17. 17 dituniversity.edu.in Real Time Forecast for Any stock

18. 18 dituniversity.edu.in Provide with Stock Price data overtime

19. 19 dituniversity.edu.in Analyze Stock Data

20. 20 dituniversity.edu.in Detailed Analysis of Data

21. 21 dituniversity.edu.in Predicting the Closing Price

22. 22 dituniversity.edu.in Project Timeline

23. Conclusion Empowered Investors Our web application equips investors with the insights they need to make informed decisions and achieve their financial goals. Innovative Approach By leveraging the power of machine learning, we are revolutionizing the way investors analyze and navigate the stock market. Reliable Predictions Our predictive models have demonstrated a high level of accuracy, providing users with trustworthy and dependable insights.

24. Future Work Continuous Model Improvement We will continuously refine and enhance our predictive models to ensure they stay at the forefront of the industry. Expanded Data Sources We plan to integrate additional data sources, such as social media and news sentiment, to further enhance the accuracy of our predictions. Mobile App Development To provide users with greater accessibility and convenience, we will develop a mobile application for our stock market prediction platform. Personalized Recommendations In the future, we aim to incorporate user preferences and risk profiles to offer personalized stock recommendations tailored to individual needs.

25. 25 dituniversity.edu.in