Innovation And Business Growth Powered By Big Data Analytics
In today’s fast paced business environment with vast amount of data, and growing customer demands, your company and brands are in constant desperation to make right decisions at the right time. This uncompromising agility is indispensible to design new products or serevices and gain competitive advantage. Big Data is the key to obtain actionable insights that help you take informed decisions in a time-bound manner. Predictive data modelling is essential to design a road map for future business growth.

Outlier Detection Applied to Customer Support
Improve Customer Support: We help you find outlier support tickets (and agents) that are anomalous in some way.
• Prioritize Tickets: An automatic, ranked list of the most anomalous tickets, so you can handle bad tickets before they get worse, and learn what things make a ticket stellar.
• Improve QA: Improve the QA Process and sample tickets more efficiently. Find out who your best agents are, and identify agents who have room to improve.
• Happier Customers: Improve customer satisfaction by finding and dealing with bad tickets before you lose the customer, and applying lessons from amazing tickets to improve the customer support process.

Forecasting Bike Sharing Demand
In today’s post, we document our efforts at applying a gradient boosted trees model to forecast bike sharing demand — a problem posed in a recent Kaggle competition. For those not familiar, Kaggle is a site where one can compete with other data scientists on various data challenges. Top scorers often win prize money, but the site more generally serves as a great place to grab interesting datasets to explore and play with. With the simple optimization steps discussed below, we managed to quickly move from the bottom 10% of the competition — our first-pass attempt’s score — to the top 10%: no sweat! Our work here was inspired by a post by the people at Dato.com, who used the bike sharing competition as an opportunity to demonstrate their software. Here, we go through a similar, but more detailed discussion using the python package SKlearn.

Review of “Hands-On Programming with R”
Comparatively few books, however, are focused on teaching R programming itself. So it was a pleasant surprise when a copy of Garrett Grolemund’s ‘Hands-On Programming with R: Write Your Own Functions and Simulations’ (O’Reilly 2015) came my way. This is a superb book: well conceived, unusual in the choice of material and sufficiently streamlined (185 pages not including the appendices) to make it a non-stop beginning-to-end read.

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