!pip install numpy scipy scikit-learn pandas
!pip install deap update_checker tqdm stopit
C:\Users\KOGENTIX>git clone –recursive https://github.com/dmlc/xgboost
Download DLL from http://www.picnet.com.au/blogs/guido/post/2016/09/22/xgboost-windows-x64-binaries-for-download/
and put it in xgboost/python-package folder
Change Environment Variables so it finds xgboost dll
PyTorch – Deep Learning http://pytorch.org/about/
TPot -Automated Machine Learning https://rhiever.github.io/tpot/
projx -Network Analysis http://davebshow.github.io/projx/getting-started/
also see https://neo4j.com/developer/python/
E-Commerce or electronic commerce has grown rapidly in the past decade, leveraging the internet to deliver a wide variety of goods and services. These include players like Amazon, Flipkart and Alibaba that sell a wide variety of products, or players like Pepper Fry that sells furniture.Eventually electronic commerce is supposed to eclipse the traditional brick and mortar enterprises.
E-commerce is efficient in multiple ways. It can save on inventory by using warehouses for dispatch and logistics instead of storing in showrooms. They can use the data captured by online analytics software to better forecast demand of certain stock keeping units (sku’s). Lastly they can offer room for faster experimentation in interfaces including things like recommendation engines (- i.e those who bought this book also bought these other books). The online data captured from customer clickstream can be used to refine pricing and discounts which are critical in a very competitive market.
Ecommerce and Big Data
A large number of customers come to electronic commerce site every day, every hour. They click on certain links, follow certain pages, post reviews, view (but don’t purchase), and finally purchase items. This continuous stream of data, called click-stream adds up to really big numbers of volume and velocity of data, with the different behaviors creating huge variety as well ( i.e some customers view a page and buy, some view twenty pages to buy). This data is like crude oil, it needs to be refined for business to take action on the insights.
Cosnider for example association analysis/ or recommendation engines. Past data will be a huge sparse matrix ( a matrix where most data is 0) where column headers will be huge variety of goods (lets say book titles). By looking at the various book titles that sell well together, the final book page will have a section ( people who viewed this also viewed that or people who bought this also bought that). This in turn will trigger impulse purchases by future customers.
The math behind this is simple, but it cannot be done on static data, it has to be done on rapidly changing data. Thus it will be needing both big data, machine learning and automated interface design. In addition we can do A/B testing on the interface to make it in sync with customer flow.
Behind the scenes data will be in distributed manner on Hbase, and MySQL, using map/reduce and spark to process, and using MLlib and R and scikit-learn for Machine Learning. Big data also helps identify problem pages, where either search rank is low, or where there is high bounce rate (customers leave soon after reaching page).
E-commerce and Analytics and Machine Learning
Just like the association analysis example above, e-commerce uses analytics in a wide variety of way. The following are the ways e-commerce uses analytics
E-commerce and AI
Both Analytics and Machine Learning are subsets of Artificial Intelligence. Using AI we can build better discounts (by using xgboost regression rather than OLS regression), better web pages (faster A/B testing or using eyeball studies), better routes to travel for trucks, delivery boys (and even drones), better prediction of customer needs (and showing it in prompts). The key thrust will be of course on deep learning and tensor flow sitting on top of Hadoop big data for better eCommerce insights. Indeed the company with the best AI system will dwarf the competition since everything else in the eCommerce world can be copied easily apart from AI based analytics.m at
(Created as part of a blogathon contest by Lymbyc, a company that creates Epoch, AI based platform, and a virtual data scientist at https://www.lymbyc.com/ )