2 research outputs found

    GC-511 Predicting Stock Prices Using Different Machine Learning and Deep Learning Models

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    Our project focuses on the challenge of predicting the daily closing prices and stock movements of Amazon, one of the world\u27s largest and most dynamic corporations. Amazon\u27s stock prices are known for their unpredictability and are influenced by a multitude of intricate factors. Our project aims to provide accurate and reliable forecasts for Amazon\u27s stock prices, going beyond mere predictions. The analysis employs a comprehensive approach, comparing the performance of three distinct machine learning and deep learning models: Linear Regression, Support Vector Machine (SVM), and Multi-Layered Perceptron (MLP) for financial time series data. The dataset we used spans from January 2, 2005, to August 21, 2019, covering a substantial period of Amazon\u27s stock history. Our project not only delivers precise predictions but also outlines the methodologies and techniques used for stock price forecasting

    Developing a Machine Learning Model to Categorize Mental Health Forums Using Scraping and Crawling in Python

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    Mental health forums serve as invaluable online communities where individuals struggling with mental health problems find solace, support, and valuable resources. These platforms offer a unique space where young people can openly discuss their struggles, seek guidance from moderators and fellow users, and receive vital assistance. Within these forums, it is not uncommon to encounter posts that contain severe content, indicating that the user is in acute distress and may be at risk of self-harm. Research conducted through inductive thematic analysis highlights that while forums cannot replace the role of a trained counselor or therapist, they fulfill a critical role in providing young people with essential, lower-level support requirements. Participants in these forums have consistently reported them to be supportive environments where they feel comfortable sharing their experiences, offering advice, and asking questions. This sense of community makes individuals feel less isolated and more connected to others who understand their struggles. Our current project uses the power of machine learning to enhance the functionality of these mental health forums. We aim to develop a sophisticated model capable of automatically categorizing posts and discussions enabling more efficient navigation and targeted assistance. To accomplish this, we used web scraping and crawling techniques to gather data from diverse mental health forums. This collected data will serve as the foundation for training our machine-learning model to categorize forum posts into relevant mental health topics. This project promises to provide a valuable tool for both forum users seeking specific information and mental health professionals looking to offer precise and targeted support. Ultimately, our project strives to bolster the effectiveness of these forums as vital resources in the journey toward better mental well-being
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