TensorFlow is mainly developed by Google and released under open source license. We can easily access Tensorflow in Python to create Deep Learning models. I had to use Keras library for Recurrent Neural Networks and found that I need to install Tensorflow to use Keras. I really found the process very tough. For people like me who has very less knowledge about Python or pip or conda, it was a nightmare. Finally, I could solve the issue and installed the package successfully but had to read hundreds of articles and followed more than 10-15 methods, installed Anaconda more than 7-10 times to get the right version. As I have faced all these issues and installed it successfully, I thought of writing about it. Also, one of my dearest friends keeps me pushing to write a blog at least once a week so hopefully this will help me. Though there are hundreds of articles and there is very less chance that my blog would appear in Google search like other articles, but it is worth sharing and writing.
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Few 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 
In Data Science visualization is a powerful tool. You can’t sell a concept but a product. Data analytics is science but presenting them through different graphs and charts is an art. We have many ways to present the numbers and a lot of software which make our life easy. Few of them which I have tried are Tableau, R Shiny, D3, QlikView and VBA Dashboard. Every tool has its own advantages and properties. Currently, I am working on R Shiny dashboard which is so beautiful and easy to learn. I would really encourage you to learn R Shiny if you do coding in R. It has a lot of in-built packages which can easily be deployed. Today let’s work on cascading filters one of the most important kinds of filters. Every other filter is connected to the predecessor filter. 
In my previous post I mentioned some techniques one should know if they want to pursue a career in Data Analytics. In those lines, I would like to discuss a few topics in detail. This blog post is on Linear Regression, that would serve as an easy starter and then I would slowly move to other techniques. Linear regression is the most used technique in data science. Generally, when we say linear regression we refer to additive model which means dependent variable will be a function of all independent variables and there would linear and additive relationships. 
Recently 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.