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DescrIptIon of LInkedIn Job LIstIngs Dataset with ArtIficial IntellIgence

This dataset contains 862,231 labeled tweets and associated stock returns, providing a comprehensive look into the impact of social media on company-level stock market performance. For each tweet, researchers have extracted data such as the date of the tweet and its associated stock symbol, along with metrics such as last price and various returns (1-day return, 2-day return, 3-day return, 7-day return). Also recorded are volatility scores for both 10 day intervals and 30 day intervals. Finally, sentiment scores from both Long Short – Term Memory (LSTM) and TextBlob models have been included to quantify the overall tone in which these messages were delivered. With this dataset you will be able to explore how tweets can affect a company’s share prices both short term and long term by leveraging all of these data points for analysis!

Asthma PredIction Dataset: AdvancIng Healthcare wIth AI

“The Asthma Disease Prediction” 😮‍💨 dataset is a carefully curated collection of anonymized health records and patient data, selected meticulously for predictive modeling and research purposes. It encompasses vital patient information, environmental factors, and medical history, allowing for the development of advanced machine learning models to predict the onset, severity, and treatment outcomes of asthma. This dataset serves as a valuable resource to enhance early diagnosis and management of asthma, ultimately improving the quality of care for affected individuals 🤩

AnalyzIng the Impact of SocIal MedIa on Stock Market Performance

Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains almost comprehensive records of over 15,000 job postings listed over a period of two days. Each post includes 27 valuable attributes, including title, job description, salary, location, application URL, and job types (remote, contract, etc.), along with separate files containing associated benefits, skills, and industries. Most of the jobs are also linked to a company, and all of these are listed in another CSV file with features such as company description, headquarters location, number of employees, and follower count.

With so many data points, the potential for exploration in this dataset is immense and includes researching the highest-paying job titles, companies, and locations; predicting salaries/benefits through NLP; and examining how industries and companies differ in terms of internship offerings and benefits. Future updates will allow for further research into time-based trends, including company growth, the prevalence of remote jobs, and the demand for individual job titles over time.