Detecting True and Deceptive Hotel Reviews using Machine Learning

In this tutorial, you´ll use a machine learning algorithm to implement a real-life problem in Python. You will learn how to read multiple text files in python, extract labels, use dataframes and a lot more!

Automated Text Feature Engineering using textfeatures in R

It could be the era of Deep Learning where it really doesn´t matter how big is your dataset or how many columns you´ve got. Still, a lot of Kaggle Competition Winners and Data Scientists emphasis on one thing that could put you on the top of the leaderboard in a Competition is ‘Feature Engineering’. Irrespective of how sophisticated your model is, good features will always help your Machine Learning Model building process better than others.

Best (and Free!!) Resources to understand Nuts and Bolts of Deep learning

The internet is filled with tutorials to get started with Deep Learning. You can choose to get started with the superb Stanford courses CS221 or CS224, Fast AI courses or Deep Learning AI courses if you are an absolute beginner. All except Deep Learning AI are free and accessible from the comfort of your home. All you need is a good computer (preferably with a Nvidia GPU) and you are good to take your first steps into Deep Learning. This blog is however not addressing the absolute beginner. Once you have a bit of intuition about how Deep Learning algorithms work, you might want to understand how things work below the hood. While most work in Deep Learning (the 10% apart from Data Munging viz 90% of total work) is adding layers like Conv2d, changing hyperparameters in different types of optimization strategies like ADAM or using batchnorm and other techniques just by writing one line commands in Python (thanks to the awesome frameworks available), a lot of the people might feel a deep desire to know what happens behind the scenes. This is the list of resources which might help you get to know what happens inside the hood when you (say) put a conv2d layer or call T.grad in Theano.

Call Centre Workforce Planning Using Erlang C in R language

Call centre performance can be expressed by the Grade of Service, which is the percentage of calls that are answered within a specific time, for example, 90% of calls are answered within 30 seconds. This Grade of Service depends on the volume of calls made to the centre, the number of available agents and the time it takes to process a contact. Although working in a call centre can be chaotic, the Erlang C formula describes the relationship between the Grade of Service and these variables quite accurately.

Machine Learning Results in R: one plot to rule them all!

To automate the process of modeling selection and evaluate the results with visualization, I have created some functions into my personal library and today I´m sharing the codes with you. I run them to evaluate and compare Machine Learning models as fast and easily as possible. Currently, they are designed to evaluate binary classification models results.

The ultimate list of Web Scraping tools and software

Here’s your guide to pick the right web scraping tool for your specific data needs.

Monte Carlo Shiny: Part Three

In previous posts, we covered how to run a Monte Carlo simulation and how to visualize the results. Today, we will wrap that work into a Shiny app wherein a user can build a custom portfolio, and then choose a number of simulations to run and a number of months to simulate into the future.