In this project, an ETL pipeline was created. We used Python, Pandas and regular expressions while working with the data. We first cleaned the data to improve the readability and later transformed the data into four dataframes (i.e., category/subcategory, campaign, contacts, and crowdfunding). These dataframes were then converted into CSV files. A Postgres database and an entity relationship diagram was created, and the CSV files were uploaded.
- Create Repo in GitHub and Maintain READ.me (Juhi & Lora)
- Create Category DataFrame and Subcategory DataFrame (Juhi & Hima)
- Create Campaign DataFrame (Hima & Kesha)
- Create Contacts DataFrame (Kesha & Austin)
- Create Crowdfunding Database (Lora & Austin)