pandas

🚛✈️An advanced Streamlit dashboard designed as an AI-powered assistant for World Movers Phils Inc. This application leverages Google Gemini for multimodal interactions, enabling users to get information, request quotes, marketing, analyze documents/images, use voice commands, and more, all within a custom-themed interface.

Repositorio que analiza problemáticas ambientales, sociales y administrativas en México, con un enfoque en el uso de datos, validación estructural y análisis geoespacial.

🤖🚛Supercharge logistics with GCP ADK agent & Gemini on Cloud Run. It has multimodal chat, manages the fleet, tracks inventory, and uses tools for live data. All interactions are logged to BigQuery for instant insights.

Praktisi Mengajar Teknik Informatika Universitas Nusa Putra 2024

A data science project focused on analyzing a car market dataset from Turkey in 2020. The goal is to explore the data, apply various analytical techniques, and derive insights. The specific direction of analysis will be determined through exploration, with potential for building predictive models or visualizing trends in the market.

Project for E-commerce. Product analysis, cohort analysis, RFM analysis in Python.

Data pipeline for handling invoicing data using Python, Pandas (and other packages) and Ragic DB's RESTful API.

The part of the CharityGuard-Ragic DB datapipeline for the CG management

A generic dash/plotly app builder with reusable components and simple layout options.

English: Python script to collect data from datasets and insert them into a Postgres database via ORM. It can be expanded to extract other data sources (such as PDFs, Word, CSV, etc.) and apply custom functions. It comes with an Excel spreadsheet of the State of Minas Gerais' expenses from 2005-2016 as an example of a dataset for collection.

pt-BR: Script Python para coletar dados de datasets e inserir em um banco de dados Postgres via ORM. Pode ser expandido para extrair outras fontes de dados (como pdf's, word, csv, etc) e aplicar funções customizadas. Acompanha um excel dos gastos do Estado de Minas Gerais do ano de 2005-2016 como exemplo de dataset para a coleta.

English: Python project to extract PDF data from the current directory.

One script transforms the PDF data into a pandas dataframe for eventual conversion to Excel. The other script stores this data in JSON for indexing in a non-relational Elasticsearch database.

pt-BR:Projeto em Python para extração de dados de pdf do diretório atual.

Um script transforma os dados do pdf em pandas dataframe para eventual conversão para excel. O outro script armazena esses dados em JSON para indexar em um banco de dados não relacional Elasticsearch.

A dataframe library for Crystal.

Jupyter notebook to pull data from the FPL website, specifically made for H2H Draft Leagues.

The main database of the French National Health Data System (SNDS) contains data from Health Insurance reimbursements, hospital treatment and medical causes of death. In order to characterise its use for health research and innovation, an interactive cartography has been produced to understand the framework of its use and to identify the stakeholders of the SNDS ecosystem. A bibliographic search via PubMed (available here), LiSSa, HAL was conducted to identify scientific articles published starting January 2007 on studies using SNDS data. The list of authors, their affiliations, keywords, the number of citations and much more were collected. A descriptive analysis was carried out in order to assess temporal and geographical trends in the use of SNDS main database. The graphs where generated with networkx, a python package used for the creation manipulation and study of complex networks. To generate the Author/Affiliations graphs we first create the adjacency matrix between the Authors/Affiliations and the article PMIDs. We then use the networkx.Graph class to create the needed undirected graphs, using the adjacency matrices as the data to intialize the graphs.

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

Notebooks for Pandas, Spark and Python experiments.

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

Proyecto que implementa una API de creación y venta de productos construido en Python usando el framework FastAPI, Pandas Dataframe para la manipulación de datos y archivos con formato Json para la persistencia de la información.

Common statistics and functions to work with financial time series.