Assistente feita em Python utilizando Speech_recognition, e APIs do Google

In this Course we are explaining some basic python uses in Data Science and also we will practice top python libraries such as NumPy, pandas, matplotlib, sklearn and so more and last we learn some algorithms.

Projeto de demonstração para testar a API do HIDROWEB da Agência Nacional de Águas (ANA). https://www.snirh.gov.br/hidroweb

I have built a Customer Complaint Analyzer which Seamlessly process and categorize consumer complaints across text, voice, images, and videos. This analyzer Harness the power of AI to streamline your customer service and improve satisfaction.

StanCal :: ANA Standards Calculator

ANA乗ろう。 ✈️ https://mizukisonoko.github.io/buttobi-site/

This is a exploratory data analysis for Netflix dataset.

Preprocessing and analysis of the resume database from hh.ru

This project evaluates movie recommendation systems, comparing **Content-Based Filtering (CBF)**, **Collaborative Filtering (CF)**, and **Hybrid Systems**. The **hybrid approach** proves most effective, combining CBF and CF to deliver more personalized, accurate, and explainable recommendations, enhancing the user experience in movie discovery.

jose

Data Cleaning and Data Wrangling of Horizontal database into Vertical database

Mostro como realizar o download dos dados da ANA (00_AnaDados) e utilizar os dados do INMET (01_InmetDados) em formato de banco dados. Além disso para cada um foi avaliado a completude dos dados no estado de Pernambuco no período de 1992-2022.

நம்Chain Cryptocurrency

The objective is create a package that help to use data of Agencia Nacional de Águas (ANA) Hidroweb, Instituto Nacional de Meteorologia (INEMET), Merge, Brazilian Daily Weather Gridded Data (BR-DWGD) and Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM). All the data above is from Brazil, minus the IDEAM that is from Colombia.

creado trabajo practico 1

This project focuses on building a regression model to predict crop yield ('yield') using a dataset with various agricultural metrics. We employ extensive data analysis, feature engineering, and model tuning to minimize the model's error.