Datasets & Competitions
This study explores the relationship between social media sentiment and stock market movements using a dataset of tweets related to various publicly traded companies. The dataset comprises time-stamped tweets containing company-specific information, stock ticker symbols, and company names. By leveraging natural language processing (NLP) techniques, we analyze the sentiment of tweets to determine their impact on stock price fluctuations. This research aims to develop predictive models that incorporate tweet sentiment and frequency as features to forecast stock price movements.
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The database compiled for this study is a comprehensive and meticulously curated repository designed to evaluate the efficacy of anti-VEGF therapy in patients with Diabetic Macular Edema (DME). It includes clinical and imaging data from 193 diabetic patients, aged 18-70 years, who participated in a single-center, randomized, parallelgroup, double-masked clinical trial. The database encompasses detailed demographic and clinical information, such as age, gender, medical history, duration of diabetes, and baseline measurements like blood pressure and intraocular pressure.
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The dataset we have used, provides hourly traffic counts from four distinct junctions, comprising a total of 48,120 observations. Each entry includes a timestamp (DateTime), a junction identifier (Junction), the observed vehicle count (Vehicles), and a unique identifier (ID). The data highlights real-world complexities, as the sensors at these junctions operated over varying durations. While some junctions offer consistently recorded data, others have sparse or irregular observations.
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74 Views
Building meaningful connections between students and alumni is critical for enhancing students’ professional growth, career advice, and networking. Despite these benefits, traditional platforms often lack personalization and scalability, limiting their ability to meet diverse student needs. This paper presents an AI-driven approach to revolutionize student-alumni interactions wih career guidance by leveraging advanced recommendation systems.
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