International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 07 | July 2023
p-ISSN: 2395-0072
www.irjet.net
StocKuku - AI-Enabled Mindfulness for Profitable Stock Trading Dhiren Gangishetty1 1School of Computer Science and Engineering
VIT-AP University Amaravati-522237, Andhra Pradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In today's dynamic and volatile stock market,
portfolios. However, with the advent of advanced technologies and the rise of artificial intelligence, new avenues for predictive analytics have emerged [1]. The proliferation of online financial platforms and the availability of vast amounts of real-time financial data have made web scraping a valuable tool for gathering up-to-date information on stock prices, market trends, and relevant financial indicators. By leveraging web scraping techniques, we can extract comprehensive datasets from popular financial platforms such as Yahoo Finance, which serve as the foundation for our predictive models. Parallel to this, the introduction of intelligent Chatbots [2] has completely changed how users connect with and engage with various platforms. Intelligent Chatbots have evolved into potent conversational agents that can comprehend user requests and offer tailored responses by fusing natural language processing techniques with smart dialogue management [3].
accurate prediction of stock prices has become an essential aspect of investment decision-making. This research paper presents an innovative approach that combined the power of Natural Language Processing, intelligent Chabot technology and advanced machine learning algorithms like Linear Regression, Random Forest Regressor and XGBoost Algorithm to enhance stock price prediction and provide valuable insights to investors. The Chabot provides a userfriendly interface for inverstors to inquire about stock information, receive personalized recommendations, and obtain real-time predictions on whether to buy or sell a particular stock. Time series data is extracted using web scraping techniques, forming the foundation for developing distinct models. These models capture different aspects of stock price behaviour, enabling comprehensive prediction capabilities. The comparative analysis highlights the benefits and drawbacks of each method, enabling investors to decide which strategy best suits their tolerance for risk and investment preferences. Participants with a range of experiences offer feedback on its applicability, dependability, and overall user experience. Investors can effortlessly navigate the complexity of the stock market due to the integration of real-time data, machine learning algorithms, and interactive discourse. Also, this study contributes to the field of financial technology by outlining a cutting-edge strategy that combines many methodologies to improve stock price prediction. It also provides opportunities for further research into revolutionary technologies that could fundamentally alter how investors interact with financial data and make investment decisions, such as natural language processing and intelligent Chatbots.
1.1 Web Scraping Web scraping is the process of extracting information from a website by “scraping” it. Theoretically, it is feasible to scrape extra data sources, such as document papers. Nonetheless, the vast majority of scraping is often performed on web pages [4]. In this work, web scraping plays a crucial role in data acquisition from the Yahoo Finance website. Web scraping is a technique used to extract data from websites by programmatically accessing and parsing the underlying HTML code of web pages. It allows us to gather real-time stock data, such as historical prices, trading volumes, and financial indicators, which serve as the foundation for our stock price prediction models.
Key Words: Stock Price Prediction, Intelligent Chabot,
1.2 Linear Regression
XGBoost Algorithm, Natural Language Processing, Financial Technology, Random Forest Regression, Personalized Recommendations
To predict the value of one variable based on the value of another variable, linear regression analysis is utilized. Based on confidence levels, when compared with polynomial and RBF regression techniques, Linear Regression provides a better prediction [5].
1. INTRODUCTION Stock price forecasting is essential in today's quickly changing financial environment while making investing decisions. Investors and traders are always looking for precise and dependable ways to forecast stock market movements and make educated decisions about their
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1.3 Random Forest Regressor A random forest is a meta-estimator that employs averaging to increase predicted accuracy and reduce
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