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Explore-Transform-Load: Worked collaboratively to explore Happiness Index data from csv files and web scraping. Transformed and cleaned data in a jupyter notebook. Created schema to load data into a SQL database. Created multiple quires to illustrate functionality of database.
Perform the Extract, Transform and Load (ETL) process to create a data pipeline on Kickstarter datasets using Python, Pandas, Jupyter Notebook and PostgreSQL.
Crowd-Quest: ETL Journey for Crowdfunding Data is a repository showcasing the ETL (Extract, Transform, Load) process. It involves extracting data from Excel files, transforming it into CSV format, designing an ERD and database schema, and loading the data into PostgreSQL. Tools used: Jupyter Notebook, VSCode, PostgreSQL, Quick DBD, Excel.