Introduction to R Package Opera – A very powerful ensemble package

Topbullets.comFew years back, I was working on a Demand forecasting project where I was dealing with thousands of combinations of Brand-SKU-Store combination. The challenge was to improve existing accuracy to provide better forecasts of each SKU at a store level. Even if you are dealing with 100 SKUs and 200 stores there will be 20,000 combinations and you can’t selectively pick a model for each combination. We tried various individual techniques such as ARIMA, ETS, UCM, NN, and many more but couldn’t meet the accuracy benchmark. Then one of my seniors introduced me to the OPERA package and asked me to research and optimize it for our needs. I successfully implemented it and today I will talk about the same package with detailed R code. Continue reading

How to launch your career in data analytics after college graduation?

Topbullets.comRecently I started answering on Quora. It caught my attention that people pursue a lot of interest in the Analytics domain. Being a blogger, it is my utmost responsibility to cater to the educational needs of the masses, hence this blog will be related to basics of analytics and where to learn, some misconceptions around analytics and career scope. I have 3 years of experience in the Analytics industry, and I would leave this discretion to the readers to decide if they find my blog useful.
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How to download data from Weather Underground –

Topbullets.comWeather is an important factor for fast moving consumer goods product. It is a very interesting case study to read upon. Milk which is the major component for ice cream is heavily produced during winter as cows produce more in this season but consumed more in summer. So dairy products manufacturers have to adjust their storage of raw material and production of final products in such a way which can minimize the operational cost. Forecasting of FMCG products is also very important for manufacturer and temperature play again an important role to understand the demand. To understand the relation between temperature and demand we need historical data. So today I am going to write an R code and some manual trick to download the temperature data for different cities. Continue reading