科维德-19预测

Baggyalakshmi N., Jayasri K., Revathi R.
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引用次数: 2

摘要

COVID-19 大流行已成为全球紧急事件。如果世界各国政府真想降低这种世界性流行病的影响,就必须投入大量资源来研究这种疾病。本研究调查了该疾病的爆发情况,为印度地区训练了数据集,并测试了随后三周内的病例数量。基于从印度政府官方门户网站获取的整个关键时期的数据,我们利用包括逻辑回归模型在内的各种机器学习算法进行了预测。在测试模型的有效性时,我们使用了多个回归模型。在为期三周的测试数据中,预计实例数约为 17.5 万-20 万,与实际数字非常接近。政府和医疗专业人员都可以利用这些信息为自己的未来计划提供依据。数据科学的目标是通过应用统计和计算方法在大型数据集中发现新的模式和关系。数据清洗和准备、数据可视化、统计建模、机器学习和许多其他任务都是数据科学的组成部分。使用这些方法可以发现数据中的趋势和模式、创建预测并提供决策支持。他们可能遇到的数据类型包括结构化数据(如电子表格中的日期和数字)和非结构化数据(如文本、照片或音频)。许多不同的行业都在使用数据科学,包括银行业、医药业、零售业等。
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Covid -19 Prediction
A global emergency has emerged in the form of the COVID-19 pandemic. If governments around the world are serious about lowering the impact of this worldwide epidemic, they must devote significant resources to studying this disease. This research looks into the disease's outbreak, trains datasets for the Indian region, and tests the number of cases over the following three weeks. Based on data acquired from the official portal of the Government of India throughout the crutial time period, we have utilised various machine learning algorithms, including a logistic regression model, to make predictions. When testing the models' efficacy, several regression models were used. In the three-week period of test data, the expected instances are anticipated to be approximately 175K--200K, which closely matches the actual figures. Both the government and medical professionals can use this information to inform their future plans. The goal of data science is to discover new patterns and relationships in large datasets by applying statistical and computational methods. Cleansing and preparing data, visualising data, statistical modelling, machine learning, and many other tasks are all part of it. Discovering trends and patterns in data, creating forecasts, and providing decision-making support are all possible with the use of these methods. Data types that they might encounter include both structured (like dates and numbers in a spreadsheet) and unstructured (like text, photos, or audio). Many different sectors make use of data science, including banking, medicine, retail, and many more.
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