By Alejandro Correa and Maria Fernanda Cortes This post is part of a series in which I’m discussing several parts of my AI_at_Rappi presentation. In a previous post I discussed a particular algorithm for recommending restaurants called rest2vec, In a follow-up, I discussed how to include financial costs when analyzing a churn model. This time... Continue Reading →

# Maximizing a churn campaign’s profitability with cost-sensitive Machine Learning, part 3

This post is part of a series in which I'm discussing several parts of my AI_at_Rappi presentation. In the last two posts, we first discussed how to evaluate a churn marketing campaign using a financial evaluation measure and then how to estimate the customer lifetime value and also how it is possible to design experiments... Continue Reading →

# Maximizing a churn campaign’s profitability with cost-sensitive Machine Learning, part 2

This post is part of a series in which I'm discussing several parts of my AI_at_Rappi presentation. In the latest post, we discussed how to evaluate a churn marketing campaign using a financial evaluation measure. In this one, we're going to deep down in a couple of important concepts needed to fully being able to... Continue Reading →

# Maximizing a churn campaign’s profitability with cost-sensitive Machine Learning, part 1.

This post is part of a series in which I'm discussing several parts of my AI_at_Rappi presentation. In a previous post I discussed a particular algorithm for recommending restaurants called rest2vec. This time I wanted to talk about how to model customer churn using cost-sensitive machine learning. Churn modeling The two main objectives of subscription-based... Continue Reading →

# Rest2Vec: Recommending similar restaurants

It has been almost a year since I joined the Rappi Artificial Intelligence team. It has been a blast, lots of highly complex challenges with big rewards. As part of my role, I have been giving talks on countless occasions, and even if I have to adjust my speech according to each audience background, I... Continue Reading →

# Machine Learning Algorithms Explained – Support Vector Machines

SVMs are some of the most important and widely used algorithms available today. They’re used for supervised classification problems. As a reminder, supervised learning refers to algorithms that learn from training data comprised of both input vectors and expected output data. Classification is used to describe a dataset that the algorithm must separate into predicted... Continue Reading →

# How to download Kaggle data into Google Colab

Colab is this awesome initiative from google research that allows anyone to play with Nvidia Telsa K80 for free. I was always struggling on how to show the potential of deep learning to my students without using GPU's. Then everything changed when I discovered colab. To get started in colab (I will write a more... Continue Reading →

# Machine Learning Algorithms Explained – Logistic Regression

Logistic regression is a supervised statistical model that predicts outcomes using categorical, dependent variables. Categorical variables take on values that are names or labels, such as: win/lose, healthy/sick or pass/fail. The model can also be used on dependent variables with more than two categories, in which case it is called multinomial logistic regression. Logistic regression... Continue Reading →

# Building AI Applications Using Deep Learning

Recently, we have seen a huge boom around the field of deep learning; it is currently being implemented in a wide variety of fields, from driverless cars to product recommendation. In their most primitive form, deep learning algorithms originated in the 1960s. If the concept has been around for decades, why is it that widespread... Continue Reading →