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Abstract
Heart is the main organ that pumps blood to the body and for proper functioning of the body. Heart disease is a fatal human disease increasing globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyze huge complex medical data, helping healthcare professionals to predict heart disease. The proposed method is to build a machine learning model capable of classifying whether the person has heart disease or not. Different algorithms are compared and the
best model is used for predicting the outcome.
INTRODUCTION
OUTLINE OF THE PROJECT
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. The term “data science” has been traced back to 1974, when Peter Naur proposed it as an alternative name for computer science. In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic. However, the definition was still in flux. The term “data science” was first coined in 2008 by D.J. Patil, and Jeff Hammerbacher, the pioneer leads of data and analytics efforts at LinkedIn and Facebook. In less than a decade, it has become one of the hottest and most trending
professions in the market. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from
data. Data science can be defined as a blend of mathematics, business acumen, tools, algorithms and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
Data Scientist:
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.
Required Skills for a Data Scientist:
● Programming: Python, SQL, Scala, Java, R, MATLAB.
● Machine Learning: Natural Language Processing, Classification, Clustering.
● Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries.
● Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera
