Marcelo Wecchi's Projects
Fraud risk is everywhere, but for companies that advertise online, click fraud can happen at an overwhelming volume, resulting in misleading click data and wasted money.
[PORTUGUÊS] Exemplo de hospedagem de aplicação Node.js na IBM Cloud utilizando Cloud Foundry
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
Documentos de especificação, imagens, etc dos projetos DRH
Walkthrough the data science life cycle with different tools, techniques, and algorithms. Use AIF360, pandas, and Jupyter notebooks to build and deploy a model on Watson Machine Learning.
The minimal amount of CSS to replicate the GitHub Markdown style
About my life
Projeto em Ciência de Dados do programa de mentoria entre alunos DSA
LSI-TEC: IOT104 Programação física com Arduino
LSI-TEC: IOT105 Aplicativos para dispositivos móveis
Learn Prolog Now LaTeX sources
data science projects for marketing analytics
Arquivos dos trabalhos realizados para avaliação
README.md template for your open-source project
Lots and lots of web scrapers
Skin cancer Analyzer - Streamlit Application
Skin cancers are the most common forms of human malignancies in fair skinned populations. Although malignant melanoma is the form of skin cancer" with the highest mortality, the non-melanoma skin cancers" (basal cell carcinomas and squamous cell carcinomas, etc.) are far more common. The incidence of both melanoma and non-melanoma skin cancers is increasing, with the number of cases being diagnosed doubling approximately every 15 years. In this manner, early finding of skin cancer can diminish mortality and dreariness of patients. In this paper we are investigating various techniques for early stage melanoma skin cancer detection. The objective of this project is to tell doctors and lab technologists the three highest probability diagnosis for a given skin lesion using a machine learning model that is trained using a public dataset ‘HAM10000’ provided by Harvard which consists of 10,015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts.