Here I will share some of my data visualizations using a variety of datasets, technologies and tools.

An analysis that predicts individual health insurance costs charged by health insurance companies based on age, sex, BMI, children, smoking, and region using predictive modeling and machine learning.

Perform Feature Analysis with Yellowbrick!

Evaluate Machine Learning Models with Yellowbrick

Evaluation of Machine Learning Models with Yellowbrick

Yellowbrick wraps the scikit-learn and matplotlib to create publication-ready figures and interactive data explorations. It is a diagnostic visualization platform for machine learning that allows us to steer the model selection process by helping to evaluate the performance, stability, and predictive value of our models and further assist in dia…

K-means++ clustering on fragrance accords 🤖

This repo consists of data visualization project done for wealth management dataset from Kaggle. I have used various Machine Learning classifiers to calculate accuracy and precision to determine which model works best for this dataset. The agenda of this project is to analyze the trend of customer churn from a wealth management company.

Training neural networks to classify network traffic by L7 protocol.

A Deep Dive of Craigslist US Used Car Sales Data Using ML and Visualizations Presented Within a Webpage

For our final project, our group chose to use a dataset (from Kaggle) that contained medical transcriptions and the respective medical specialties (4998 datapoints). We chose to implement multiple supervised classification machine learning models - after heavily working on the corpora - to see if we were able to correctly classify the medical sp…

Vous êtes consultant pour Olist, une solution de vente sur les marketplaces en ligne. Olist souhaite que vous fournissiez à ses équipes d'e-commerce une segmentation des clients qu’elles pourront utiliser au quotidien pour leurs campagnes de communication.

Streamlit component for the Yellowbrick visualization and model diagnostics library

Fancylit is a python module that contains pre-packaged Streamlit code to render fancy visualizations, run modeling tasks, and data exploration

In this analysis, I will demonstrate how PCA and K-Means clustering can be applied to credit risk data. In this data set, we do not have a target variable, which leads us to build an unsupervised machine learning model.

Desafio de clusterização de clientes feito para o IFood e Tera. Utilizando as bibliotecas Plotly, Sklearn e Yellowbrick conseguimos fazer a clusterização em 3 dimensões de forma eficiente e visual utilizando as features construídas no feature engineering a partir de bases de clientes, pedidos e sessões do iFood.

An assignment on setting up python environment for machine learning

An assignment on basics of scikit-learn