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

Training neural networks to classify network traffic by L7 protocol.

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…

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

Projeto de clusterização de dados de e-commerce utilizando K-Means e DBSCAN para segmentar clientes e produtos.

2023년 7월 논문게재(한국벤처창업연구) : COVID-19에 따른 글로벌 창업 트렌드 분석: Cruchbase를 중심으로(Analysis of Global Entrepreneurship Trends Due to COVID-19: Focusing on Crunchbase, 1저자

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.

Evaluation of Machine Learning Models with Yellowbrick

FastRide: NYC Taxi Trip Duration Prediction employs machine learning techniques to accurately predict the duration of taxi trips in New York City, helping transportation businesses optimize operations and enhance customer satisfaction.

K-means++ clustering on fragrance accords 🤖

Capstone Project for the Data Scientist Nanodegree by Udacity.

Performing a clustering model for Bank Customer Dataset using K-Means clustering

Perform Feature Analysis with Yellowbrick!

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

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.

Customer-Segmentation---Purchasing-Behavior

This repository contains some data science projects I have done for practical purposes.