This project focuses on creating a data pipeline using multiple data engineering tools (Airflow, dbt, Google Cloud Platform, etc.) to extract, transform and load data info Bigquery and finally use NBA data in Power BI as our dashboard visualization tool.
This project was made using Python, specifically Pandas, Plotly and specially Streamlit, this web app lets you explore and interact through a dashboard with soccer data from the 2011-2021 seasons with statistics in different levels (Club and Player).
This project was made using Python, specifically Pandas, Plotly and specially Streamlit, this web app lets you explore and interact through a dashboard with soccer data from the 2011-2021 seasons with statistics in different levels (Club and Player).
This project was made using Python, specifically Pandas, to analyze NBA statistics through the 2003 till 2019 season, and how the 2012 season changed the way basketball is played (focusing more in the 3 point shot).
This project was made using Python, focused on Pandas and ScikitLearn, to generate prediction models based on multiple linnear regression and Random Forest regression using individual statistics as independent variables and Value as dependent variable.