gurezende
Data Scientist [R] [Python] [Databricks] [SQL] I extract insights from data to help people and companies to make better and data driven decisions.
Pinned Repositories
Car_Prices_Regression_Streamlit
Regression Analysis Project and Web App created with Streamlit
CSV_to_Parquet_Program
Windows application that converts files from CSV to Parquet format or Parquet to CSV.
Finance-App-with-Streamlit
This repository brings a complete Data Analysis of a Financial tracking file and a Financial App with a Dashboard and the predictions whether the projects will or will not meet the budget
NYC-Airbnb
Jupyter Notebook with my Data Viz study on the Kaggle's 'New York City Airbnb Open Data' dataset
Predict-Turnover-Company
Project from the Data Scientist Degree by Data Science Academy Brazil
Studying
This is a repository with my tests and studies of new packages
Wine_Quality_Predictions
In this project, the idea was to simulate a business problem where a client (winery) had some data about their products and they wanted to see how the variables measured could possibly affect the wine quality, therefore, resulting in higher or lower grades for their wines. The work was divided in 3 main parts: 1. Exploring the data and creating some data visualizations, understanding the correlations between variables. The result is on the first three tabs Histograms, Boxplots and Report. 2. Divide the dataset in two clusters. I have gathered both datasets (red + white) to simulate a request from the client where they needed to know which wine was what kind. The result was 98% accurate and that can be seen on the Clustering tab. 3. The final part was to create a Prediction Interactive Tool for the White Wines from the dataset that allows the client to input some values and have a prediction whether that wine is going to be qualified as High Quality (grades from 7-10) or Low Quality (grades from 3-6). You can interact with the tool in the tab Predictions. The accuracy is 95%.
gurezende's Repositories
gurezende/Studying
This is a repository with my tests and studies of new packages
gurezende/CSV_to_Parquet_Program
Windows application that converts files from CSV to Parquet format or Parquet to CSV.
gurezende/Finance-App-with-Streamlit
This repository brings a complete Data Analysis of a Financial tracking file and a Financial App with a Dashboard and the predictions whether the projects will or will not meet the budget
gurezende/Car_Prices_Regression_Streamlit
Regression Analysis Project and Web App created with Streamlit
gurezende/Sentiment_Analysis_TidyText
Text Mining with R
gurezende/weather_app
Weather App created with Streamlit to show historic temperatures in different cities
gurezende/NYC-Airbnb
Jupyter Notebook with my Data Viz study on the Kaggle's 'New York City Airbnb Open Data' dataset
gurezende/Predict-Turnover-Company
Project from the Data Scientist Degree by Data Science Academy Brazil
gurezende/Wine_Quality_Predictions
In this project, the idea was to simulate a business problem where a client (winery) had some data about their products and they wanted to see how the variables measured could possibly affect the wine quality, therefore, resulting in higher or lower grades for their wines. The work was divided in 3 main parts: 1. Exploring the data and creating some data visualizations, understanding the correlations between variables. The result is on the first three tabs Histograms, Boxplots and Report. 2. Divide the dataset in two clusters. I have gathered both datasets (red + white) to simulate a request from the client where they needed to know which wine was what kind. The result was 98% accurate and that can be seen on the Clustering tab. 3. The final part was to create a Prediction Interactive Tool for the White Wines from the dataset that allows the client to input some values and have a prediction whether that wine is going to be qualified as High Quality (grades from 7-10) or Low Quality (grades from 3-6). You can interact with the tool in the tab Predictions. The accuracy is 95%.
gurezende/Audiobook-Analysis-Classification
Jupyter Notebook for a Classification Problem
gurezende/Bible_WordClouds
One word cloud for each book in the Bible.
gurezende/Credit-Card-Fraud-Detection
Classification Model to prevent Credit Card Fraud
gurezende/Dash-Loan-Assessment
This is a little project made with Dash and Python to gather input from the user and output a suggestion to either give or not credit to someone. The output is given by a Machine Learning Model.
gurezende/Dashboards-with-Dash
Dashboards with Dash and Plotly using Python.
gurezende/data-science-escalavel
Translation to brazilian portuguese of the book Build a Reproducible and Maintainable Data Science Project from Khuyen Tran author.
gurezende/Distribution_Tests
Publicly shared code
gurezende/Electricity-Consumption-Regression
Projeto de modelo preditivo de machine learning para prever o consumo de energia de eletrodomésticos usando dados de sensores (Internet of Things).
gurezende/GustavoSantos_Portfolio
Data Science Portfolio of Gustavo R Santos
gurezende/Linear-Regression-House-Pricing
Linear Regression Model created with R Language after running a Random Forest for feature selection.
gurezende/ML-Model-Diabetes
Created some data visualization for exploration, analized and trained a few models to choose the best performer.
gurezende/music-through-the-ages
Analyzing shifting trends in music through the ages
gurezende/News_Classifier
This small project covers some web scraping and the creation of a news headline classifier created with Multinomial Naïve Bayes from sklearn.
gurezende/Plotly-Dashboards-with-Dash
This is the repo for the Udemy Course Python Dashboards with Plotly's Dash
gurezende/PowerBI-DataScience
Repositório do Curso Microsoft Power BI Para Data Science
gurezende/PySpark_Churn
Telecom Churn Analysis
gurezende/reproducible-data-science
Tutorials on creating a reproducible and maintainable data science project
gurezende/Streamlit_demonstration
Demonstration app using the toy dataset tips
gurezende/WebScrapping_Treinando
This is only a demonstration of a Web Scrapping. The program goes to a fictional web page in a local server in my machine and extract 3 variables chosen by the user.
gurezende/World_Cup_2022
Project modeling results of the matches in the FIFA World Cup 2022.