Knowledge is power – with these lines, Angelina Jolie-Pitt concluded her article in The New York Times dated 24th March 2015. Angelina Jolie is considered as among the most attractive women in world cinema. In the last couple of years, she has undergone multiple surgeries to remove female organs in her body i.e. breasts and ovaries. Now
When they leave, they leave footprints for others to follow Customer Churn A couple of months ago, I closed down my savings account with a leading bank in India. I had this account for more than a decade. Since I have worked with the banking and financial services industry, I know I was a reasonably high-profit
Every data scientist knows that in any business analytics and data science exercise 70-80% of the time is consumed in data preparation and data preprocessing. This is usually considered a drudgery in comparison to the actual statistical modeling, machine learning, and business insights part. However, every good data scientist understands that data preparation is an art
Healthcare Analytics In 2012, Larry Smarr concludes his TEDMED talk (a TED conference for medicine, healthcare, and biology) with the phrase: ‘Because of big data and because of our ability to analyze it – we got hope’ Larry is no medical professional or biologist rather he is a physicist (astrophysicist), and computer scientist by profession. Before
Recently in a BBC interview, Stephen Hawking, the eminent theoretical physicist, warned about a possibility of a doomsday scenario because of the rise of artificial intelligence. In his words “the development of full artificial intelligence could spell the end of the human race”. This is the same fear the Terminator series of movies have showcased. It
There is a lot of exuberance about data science as a career choice among young professionals. This exuberance (for once) is not at all irrational because the field has tons of potential. I have been asked on many occasions by professionals considering a career change to data science and young graduates trying to start their careers in
This article is the epilogue to our case study example about campaign and marketing analytics for an online retail company. You could read the previous parts at Problem definition: Part 1 & Part 2 Description: Part 3 Association: Part 4 Classification: Part 5, Part 6, Part 7, Part 8 Estimation: Part 9, Part 10 In this part we
This article is a continuation of our marketing analytics case study example for campaign management solutions. In this case, we started with the following two goals to build models to identify Most responsive customers Most revenue generating customers Problem definition: Part 1 & Part 2 Description: Part 3 Association: Part 4 Classification: Part 5, Part 6, Part 7, Part 8 Estimation:
Welcome back to our retail case study example for marketing analytics. In the previous 8 parts, we have covered some of the key tasks of data science such as: Problem definition: Part 1 & Part 2 Description: Part 3 Association: Part 4 Classification: Part 5, Part 6, Part 7 & Part 8 In this part, we will learn about estimation
Welcome back to our retail case study example for campaign and marketing analytics. So far in this case study, we were working on a classification problem to identify customers with a higher likelihood to purchase products from the campaign catalogues. In the last article on model selection, we noticed that artificial neural networks performed better than logistic regression, and decision tree