Raghavan Madabusi
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Short Bio:
A Principal Technical Architect with strong business acumen and technical experience in Big Data, Cloud Computing & Traditional IT Projects.
Treselle Systems
Software Services
LinkedIn Profile:
Networking, Other

Raghavan Madabusi's Blog

Importing and Analyzing Data in Datameer

Posted on June 19, 2017 at 6:30pm 0 Comments


Datameer, an end-to-end big data analytics platform, is built on Apache Hadoop to perform integration, analysis, and visualization of massive volumes of both structured and unstructured data. It can be rapidly integrated with any data sources such as new and existing data sources to deliver an easy-to-use, cost-effective, and sophisticated solution for big data analytics.

It simplifies data extraction, data transformation, data loading, and…


Sales Data Analysis using DataIku Studio

Posted on June 15, 2017 at 2:30pm 0 Comments


Dataiku Data Science Studio (DSS), a complete data science software platform, is used to explore, prototype, build, and deliver data products. It significantly reduces the time taken by data scientists, data analysts, and data engineers to perform data loading, data cleaning, data preparation, data integration, and data transformation when building powerful predictive applications.

It is easy and more user-friendly to explore the data and…


Loan Prediction – Using PCA and Naive Bayes Classification with R

Posted on May 10, 2017 at 4:30pm 1 Comment


Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers.

Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age,…


Call Detail Record Analysis – K-means Clustering with R

Posted on May 10, 2017 at 2:00pm 1 Comment


Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics. Most of the telecom companies use CDR information for fraud detection by clustering the user profiles, reducing customer churn by usage activity, and targeting the profitable customers by using RFM…


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