Predicting Market Performance Using Machine and Deep Learning Techniques

Aim:

           The aim of this study is to evaluate the effectiveness of various machine learning and deep learning algorithms, including LSTM networks, ARIMA models, and traditional machine learning techniques, for forecasting market prices. We analyze the performance of these models on stock historical datasets and compare their predictive accuracy to determine the most suitable approach for real-time market analysis. This research seeks to provide insights into the predictability of markets and support informed decision-making for investors

Abstract:

         Stock price prediction is a challenging task due to the volatile and complex nature of markets. This paper investigates the application of machine learning and deep learning techniques to forecast price movements. We employ several models, including Long Short-Term Memory (LSTM) networks, Autoregressive Integrated Moving Average (ARIMA) models, and machine learning algorithms such as Linear Regression, Stochastic Gradient Descent Regressor, Adaptive Boosting, Bagging, and Random Forest. These models are trained and evaluated on datasets.  We compare the predictive accuracy of these approaches and analyze their performance in capturing the dynamics of price fluctuations. Our results demonstrate the potential of machine learning and deep learning for price forecasting, with Bagging or LSTM. This research contributes to the growing body of knowledge on market analysis and offers valuable insights for investors seeking to understand and predict price trends.

Proposed System:

           This project develops an intelligent stock price prediction system, integrating data acquisition, advanced modeling, and interactive visualization. The system utilizes the yfinance library to collect real-time and historical stock market data, which is then preprocessed for model training. Advanced deep learning models, including sophisticated LSTM networks and ARIMA models, are employed to capture complex temporal dependencies and forecast future stock prices. These models are rigorously trained and evaluated using appropriate metrics. A user-friendly web application, built with Flask, HTML, CSS, and JavaScript, allows users to input stock tickers and visualize predicted prices, future trends, and model performance comparisons through interactive charts. The system aims to provide a practical tool for stock market analysis and forecasting. By combining advanced algorithms with a user-friendly interface, this project seeks to enhance the accuracy and accessibility of stock price predictions. The interactive visualizations empower users to make informed investment decisions. This integrated approach represents a significant advancement in stock market forecasting The outcomes of this research contribute to improved drought preparedness and management strategies, enabling stakeholders to make informed decisions and mitigate the adverse effects of droughts in vulnerable regions.

Advantages:

• Robust Evaluation: Rigorous model training and evaluation using techniques like cross-validation and back testing ensure the model’s robustness and generalization ability on unseen data, reducing the risk of over fitting.
• User-Friendly Web Application: The Flask-based web application provides an intuitive interface for users to interact with the prediction system. Users can easily input stock tickers, generate forecasts, and visualize results.
• Interactive Visualizations: Interactive charts and graphs enhance the understanding of predicted prices, future trends, and model performance, enabling users to make more informed decisions.