Hey there,

AWS Lambda is currently one of the top used resources on the so called Serverless Applications. The versatility abstracted from this tool can't be miss-used by the excuse like - "does not contain libs X or Y on it's environment."

On this post my goal is to share a handy tip that comes along with Lambda Functions: own built layers.

A Layer allows you to deploy .zip files containing any sort of dependency need for the function, such as code libraries or custom runtimes. On the following I will demonstrate how to..

  1. Install Python libraries on a specific…

Hello there!

If you ever read some of my posts so far, you probably know I'm developing a special series called Practical Implementation.

In this series, it's quite obvious the amount of details given on the pre-processing stage for each algorithm I put in practice. Not because the theory behind these models are not important, they are. A lot.

But at the end of the day, knowing when to use the algorithm X or Y is enough to put your model in production. …


Please allow me to start this post by pretending I have more then a hand-full of recurring readers (Hey mom!) and apologize for this long period without releasing new posts. Things been very rushed at work but I'm planning to return a solid posting frequency now.

As a data-driven decision maker I couldn't ignore the fact that the topic of Ibovespa Stock Price's Scraping had the highest return on my page so far. So today’s topic will be relating the same purpose of combining programing language's and the financial market.

If you haven't catch the stock price's one…

Hello there!

Today I’ll make a quick complement from K-means Algorithm Practical Implementation with Python. If you didn’t have the chance of reading it yet, you can find it below:

If you record it, we and up clustering our data and at some point we had our DataFrame modeled like this:

Four clusters were found!

On the last post, I didn't talked much about plotting. Although, this might be the coolest part on cluster creation.

On this post I just wanted to bring out a quick tip on that. I'll use plotly.graph_objects library to create this 3d plot.

Warning: we…

Hello there!

Today I'll make a quick complement from Tree Based Decision Model for Classification — Practical Implementation. If you didn't have the chance of reading it yet, you can find it below:

Last time we used Decision Tree Classifiers to predict an animal Class Type based on multiple numeric features.

We know some logic was used to determinate the classes each animal belongs to, but which was it? Where did the algorithm performed each split?

Today I want to show you a quick tip on how to visualize these Tree Nodes, and consequently, the path of decisions.

The libraries

Hello there!

It's a known fact that Machine Learning Algorithms will reach higher accuracy levels according to the amount of information you feed to them of a certain class/target.

A balanced target not always comes that easily, specially when it comes to rare events. That's when balancing techniques like SMOTE comes in handy.

This function allows you to generate artificial data that will mirror the statistical relations between the targets while balancing the dataset.

To show how the practical application works, I'll use a dataset that represents one of the most famous case of rare events. …

Hello there!

Welcome to another post from my Practical Implementation Series.

Before anything, I'll leave the link for the post of the Algorithms I showed the implementation so far. If you enjoy this one please don't forget to check them all after!

Decision Tree Models

Decision Tree Based Models are supervised algorithms that are capable to predict continuous values or classify data based on a given label.

The classification usage can be either from a boolean target or multiple labels.

One of my favorite things about this Machine Learning technique is that the practical use of it could not be more close from…

The worlds most valuable resource is no longer oil, but data.

That was the headline from a post from The Economist back in 2017. It's obviously a figurative way to say it.. or isn't?

Among all possible ways to take raw data and turn it into something valuable you'll find the reason why I'm writing this post - Market Basket Analysis. That a common technique used specially by large retailers to find hidden patterns on customer behaviors.

The easiest way I could define it would be like,

If the customer has Hamburgers and Bread on his supermarket basket, the chances…

Hello there!

This is the second post on my Machine Learning Practical Implementation series. The objective with this series is to show that you can still benefit from these popular algorithms on your daily analysis even if you don't master the whole mathematical knowledge behind them.

If you haven't check the first post from this series yet, I invite you to do so in the link below.

On this post I'll keep the same pipeline from the last one. …

Hello there,

Pre-processing your data is a crucial stage of Model Training. Most algorithms expect numerical data as input. Here's two quick tips to transform your categorical features into numerical.

Let's pretend I want to train a Model to predict my future revenue using historical data I have from last year sells's.

Among all other features, these are the categorical one's that need to be treated. Each register represents a unique day of sells and the corresponding revenue of that day.

Sklearn LabelEncoder()

Of course you could map all existing values for each categorical feature and use the good old IF THEN…

Rodrigo Dutcosky

Fraud Analytics Coordinator at EBANX

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