Big data analytic’s is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytic’s can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.
Predictive analytic’s encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
In business, predictive models exploit patterns found in historical and transaction 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.
Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is not feasible. Example applications include spam filtering,optical character recognition (OCR),search engines and computer vision. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.
We specialize in following Big Data Analytic’s and Artificial intelligence,Deep learning,& Machine Learning Platforms:
- Map Reduce
- Apache Spark
- Tensorflow [Google platform for AI/DL]
- Torchnet[Facebook platform for AI/DL]
Some of the projects we have worked and continue to work with our customers spans:
- Analytical Customer Relationship Management (CRM)
- Clinical decision support systems
- Collection analytic’s
- Cross-sell,Customer retention
- Direct marketing
- Fraud detection, Advanced Virus/Malware detection
- Portfolio, product or economy-level prediction
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