Search for a command to run...
The goal of this project was to create a Machine Learning model to predict the occurrence of diabetes.
I help companies to leverage Machine Learning to create innovative products through an end-to-end machine learning development process that designs, builds, and manages reproducible, testable, scalable, and evolvable ML-powered software with minimal cost.
No comments yet. Be the first to comment.
Here I show a summary description of the projects I've been working on in the last few years. Most of the projects have the source code publicly available.
This project was built using Scrapy (Scraping and Web Crawling Framework). It contains a set of Spiders to gather product data from Etsy Website. Problem The client needs data from thousands of products of etsy.com to perform data analysis. Task Crea...
How AI-Driven Programming is Transforming the Tech Industry and Reshaping Developer Roles
How Automated Tests Can Transform Your Nights From Stressful to Restful
Streamlining Business Communication for Enhanced Collaboration, Customer Support, Marketing, and Decision-Making.
Machine Learning Operations is a process of automating and productionalizing machine learning applications and workflows.
In this project, I've built a Computer Vision model to find basket icons in e-commerces.
This project is the result of a Kaggle competition promoted by Data Science Academy in January of 2019.
The goal of the competition was to create a Machine Learning model to predict the occurrence of diabetes.
Data source: National Institute of Diabetes and Digestive and Kidney Diseases
Competition page: https://www.kaggle.com/c/competicao-dsa-machine-learning-jan-2019/
The goal is to predict, based on diagnostic measures, whether a patient has diabetes.
Create a Machine Learning model to estimate the probability of the occurrence of diabetes.
I've used Python to perform an Exploratory Data Analysis (EDA) using visual and quantitative methods to understand and summarize a dataset without making any assumptions about its contents. Then I've performed Data Cleaning and built several Machine Learning models to compute the probability of occurrence of diabetes. The Logistic Regression model presented the best results.
In this competition, I've reached the accuracy of 76.27% and I got position 41 on the leaderboard.
I've written a blog post with details about the solution.
The code is also available at GitHub.
