Fancylit is a python module that contains pre-packaged Streamlit code to render fancy visualizations, run modeling tasks, and data exploration
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Updated
Oct 19, 2021 - Python
Fancylit is a python module that contains pre-packaged Streamlit code to render fancy visualizations, run modeling tasks, and data exploration
Here I will share some of my data visualizations using a variety of datasets, technologies and tools.
Evaluation of Machine Learning Models with Yellowbrick
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.
Capstone Project for the Data Scientist Nanodegree by Udacity.
Perform Feature Analysis with Yellowbrick!
A Deep Dive of Craigslist US Used Car Sales Data Using ML and Visualizations Presented Within a Webpage
Performing a clustering model for Bank Customer Dataset using K-Means clustering
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.
Training neural networks to classify network traffic by L7 protocol.
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.
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…
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.
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.
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
Análisis de datos utilizando datasets obtenidos de Kaggle, una plataforma de competencias y recursos de ciencia de datos
Yellowbrick is an useful machine learning visualization library for visualizing model performance. This Jupyter notebook gives an example for using yellowbrick to visualize model performance of a ternary classification task.
Customer-Segmentation---Purchasing-Behavior
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