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The Future of Healthcare: How Data Science and AI are Revolutionizing Medicine

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The healthcare industry is constantly evolving, and advancements in technology are playing a crucial role in transforming the way medicine is practiced. One of the most promising areas of technological innovation is the application of data science and artificial intelligence (AI) in healthcare. Data science and AI have the potential to revolutionize medicine by helping healthcare professionals make better, faster, and more accurate diagnoses, improve treatment outcomes, and ultimately save lives. One of the most significant ways that data science and AI are being used in healthcare is through medical imaging. Medical imaging is a critical component of diagnosing and treating many diseases and conditions, including cancer, heart disease, and neurological disorders. AI algorithms can analyze medical images and identify abnormalities with greater accuracy and speed than human experts. For example, a study published in the journal Nature in 2018 found that an AI system was able to detect b
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k means clustering

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In today's world, data plays an integral role in driving business decisions. One of the most common and effective ways of analyzing data is through machine learning algorithms. K-Means clustering is an unsupervised machine learning algorithm that has gained popularity for its efficiency in data clustering. What is K-Means Clustering? K-Means clustering is an algorithm used to create clusters based on the data fed to the machine. The algorithm works by randomly assigning a color to a few data points, known as the centroid kids, and grouping the surrounding data points based on the mean distance from each centroid. Applying K-Means Clustering to Customer Segmentation For instance, retailers can use K-Means clustering to decide which customer gets promotional offers. They can create three clusters of customers, namely the loyal, somewhat loyal, and lowest priced shoppers, based on their shopping patterns. Then, they can create strategies to convert somewhat loyal customers into loyal
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Text Analytics Platforms Part 1

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Text analytics is still largely an immature science, and embraces several different approaches. Natural language processing (NLP) includes dozens of techniques for accomplishing tasks such as language translation, document categorization and tagging, extraction of meaningful terms and so on. Text mining on the other hand is primarily concerned with the extraction of meaningful metrics from unstructured text data so they can be fed into data mining algorithms for pattern discovery. Some suppliers have applied text analytics to very specific business problems, usually centering on customer data and sentiment analysis. This is an evolving field and the next few years should see significant progress. Other suppliers provide NLP based technologies so that documents can be categorized and meaning extracted from them. Text mining platforms are a more recent phenomenon and provide a mechanism to discover patterns which might be used in operational activities. Text is used to generate extra
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Techniques for Data Dimensionality Reduction

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These are some of common Data Dimensionality Reduction before any variable reduction technique Missing Values Low Variance Filter. ... High Correlation Filter. ... Random Forests / Ensemble Trees. ... Principal Component Analysis (PCA). ... Backward Feature Elimination. ... Forward Feature Construction. Please look out for details of each of the method
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Tidyverse package in R

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Tidyverse package in R - It will open many data wrangling and visualization opportunities.
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Web Scrapping code in R

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Hottest Skill in 2017: Analytics

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 In a comprehensive research carried out by Analytics India Magazine and AnalytixLabs, a key finding denotes that there has been a 22% increase in the average salaries of analytics professionals in India since the last year. The Analytics India Salary Study 2017, suggests that for the year 2017, the average salary has been INR 11.7 Lacs across all experience levels and skill sets in comparison to INR 9.5 Lacs in 2016. The study gives an insight on various aspects of salary structure for professionals across factors such as experience level, cities, tools, company types and more. The study suggests an increased demand for senior professionals, thus pushing the average salaries higher than the last year. The percentage of analytics professionals with salaries in higher bracket of INR 50+ Lacs has significantly increased to 3.7% from just 1.1% a year ago. While the percentage of analytics professionals commanding salaries of less than INR 10 Lacs has gone lower; the percentage
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