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Predictive analytics

Predictive analytics
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.[3] Predictive analytics is used in actuarial science,[4] marketing,[5] financial services,[6] insurance, telecommunications,[7] retail,[8] travel,[9] healthcare,[10] pharmaceuticals[11] and other fields. One of the most well known applications is credit scoring,[1] which is used throughout financial services. Definition[edit] Types[edit] Predictive models[edit] Descriptive models[edit] Decision models[edit] Applications[edit] Collection analytics[edit]

http://en.wikipedia.org/wiki/Predictive_analytics

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Step By Step Guide To Extract Information Text Mining is one of the most complex analysis in the industry of analytics. The reason for this is that, while doing text mining, we deal with unstructured data. We do not have clearly defined observation and variables (rows and columns). Hence, for doing any kind of analytics, you need to first convert this unstructured data into a structured dataset and then proceed with normal modelling framework. Data analysis Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data and CDA on confirming or falsifying existing hypotheses.

Index (search engine) Popular engines focus on the full-text indexing of online, natural language documents.[1] Media types such as video and audio[2] and graphics[3] are also searchable. Meta search engines reuse the indices of other services and do not store a local index, whereas cache-based search engines permanently store the index along with the corpus. Unlike full-text indices, partial-text services restrict the depth indexed to reduce index size. Steps For Effective Text Data Cleaning The days when one would get data in tabulated spreadsheets are truly behind us. A moment of silence for the data residing in the spreadsheet pockets. Today, more than 80% of the data is unstructured – it is either present in data silos or scattered around the digital archives.

Data mining Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.[1] It is an interdisciplinary subfield of computer science.[1][2][3] The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[4] Etymology[edit] In the 1960s, statisticians used terms like "Data Fishing" or "Data Dredging" to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. (4) Modeling

Optimization (mathematics) In mathematics, computer science, or management science, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.[1] Optimization problems[edit] An optimization problem can be represented in the following way: Sought: an element x0 in A such that f(x0) ≤ f(x) for all x in A ("minimization") or such that f(x0) ≥ f(x) for all x in A ("maximization"). Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming – see History below). An Introduction to Text Mining using Twitter Streaming API and Python // Adil Moujahid // Data Analytics and more Text mining is the application of natural language processing techniques and analytical methods to text data in order to derive relevant information. Text mining is getting a lot attention these last years, due to an exponential increase in digital text data from web pages, google's projects such as google books and google ngram, and social media services such as Twitter. Twitter data constitutes a rich source that can be used for capturing information about any topic imaginable.

These big data companies are ones to watch Which companies are breaking new ground with big data technology? We ask 10 industry experts. It’s hard enough staying on top of the latest developments in the technology industry. That’s doubly true in the fast-growing area known as big data, with new companies, products and services popping up practically every day. There are scores of promising big data companies, but Fortune sought to cut through the noise and reached out to a number of luminaries in the field to ask which big data companies they believe have the biggest potential. Which players are really the ones to watch? Monte Carlo method Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results; typically one runs simulations many times over in order to obtain the distribution of an unknown probabilistic entity. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to obtain a closed-form expression, or infeasible to apply a deterministic algorithm. Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration and generation of draws from a probability distribution. The modern version of the Monte Carlo method was invented in the late 1940s by Stanislaw Ulam, while he was working on nuclear weapons projects at the Los Alamos National Laboratory. Immediately after Ulam's breakthrough, John von Neumann understood its importance and programmed the ENIAC computer to carry out Monte Carlo calculations. Introduction[edit]

50+ Data Science and Machine Learning Cheat Sheets Gear up to speed and have Data Science & Data Mining concepts and commands handy with these cheatsheets covering R, Python, Django, MySQL, SQL, Hadoop, Apache Spark and Machine learning algorithms. By Bhavya Geethika Cheatsheets on Python, R and Numpy, Scipy, Pandas There are thousands of packages and hundreds of functions out there in the Data science world! An aspiring data enthusiast need not know all.

Big Data 50 – the hottest Big Data startups of 2014 - Startup 50 From “Fast Data” to visualization software to tools used to track “Social Whales,” the Big Data 50 has it covered. The 50 startups in the Big Data 50 are an impressive lot. In fact, the Big Data space in general is so hot that you might start worrying about it overheating – kind of like one of those mid-summer drives through the Mojave Desert. The signs warn you to turn off your AC for a reason. Personally, I think we’re a long way away from any sort of Big Data bubble. Our economy is so used to trusting decision makers who “trust their gut” that we have much to learn before the typical business is even ready for data Kindergarten.

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