Project Abstract

Abstract
Stock price prediction is a crucial area in the financial industry that has garnered significant attention from researchers and practitioners alike. In recent years, machine learning techniques have emerged as powerful tools for forecasting stock prices due to their ability to analyze large datasets and identify complex patterns. This research project aims to develop a machine learning model for predicting stock prices by leveraging historical stock data and various predictive features. The project will begin with a comprehensive review of existing literature on stock price prediction and machine learning models commonly used in this field. By analyzing the strengths and weaknesses of different approaches, the project will identify gaps in the current research and propose a novel methodology for predicting stock prices more accurately. The research methodology will involve collecting historical stock data from various sources, preprocessing the data to handle missing values and outliers, and selecting relevant features for model training. Several machine learning algorithms, including regression models, neural networks, and ensemble methods, will be implemented and evaluated for their performance in predicting stock prices. The findings of the study will be presented in Chapter Four, where the performance of each machine learning model will be analyzed based on metrics such as accuracy, precision, recall, and F1 score. The discussion will also delve into the interpretability of the models and their potential applications in real-world stock trading scenarios. In conclusion, this research project aims to contribute to the growing body of knowledge on stock price prediction using machine learning techniques. By developing a robust and accurate model for forecasting stock prices, this project seeks to provide valuable insights for investors, financial analysts, and policymakers in making informed decisions in the dynamic and volatile stock market environment.

Project Overview