https://github.com/KimaruThagna/ml-pipelines-airflow

Demonstrating and Building ML pipelines in Airflow
https://github.com/KimaruThagna/ml-pipelines-airflow

Last synced: 15 days ago
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Demonstrating and Building ML pipelines in Airflow

• Host: GitHub
• URL: https://github.com/KimaruThagna/ml-pipelines-airflow
• Owner: KimaruThagna
• Created: 2021-05-29T16:54:41.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-06-12T19:04:21.000Z (about 3 years ago)
• Last Synced: 2024-02-29T12:42:02.324Z (4 months ago)
• Language: Python
• Size: 72.3 KB
• Stars: 11
• Watchers: 3
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Lists

• awesome-apache-airflow - ETL with Apache Airflow for Data Analysis on Transaction Data - thagana-4920b5181/) covers a practical case of doing an ETL process using Apache Airflow using a dummy ecommerce store's transactional, user and product data. The data is served via a flask API. (Introductions and tutorials)
• awesome-apache-airflow-br - ETL com Apache Airflow para analise de dados em dados transacionais - thagana-4920b5181/) cobre um caso pratico de fazer o processo de ETL usando Apache Airflow usando um comercio dummy para transações, usuário e dados de produtos. Os dados são entregues via uma API flask. (Introduções e tutoriais)

README

        
# ETL-pipelines-airflow

Demonstrating and Building ETL pipelines in Airflow

This repo demonstrates a use case for n Ecommerce business that has a platform that generates transaction data each time a purchase is made. With this transaction data, the functions in the pipeline seek to answer 3 business questions

1. Who is our platinum customer? Anyone with purchase value equal to or more than 5000

2. What is the purchase history like for each user? This builds a dataset that can be used for a *recommendation engine* downstream

3. What items are commonly purchased together? This builds a dataset that can be used for *Basket Analysis* downstream

## Analysis Implementation

The code can be found in `etl_utils.py` file.

Question 1 is implemented using `pd.merge()` to get the combined dataset and `df.groupby().sum()` to get total purchases. 

To get the platinum customer, we apply a filter 

`final_df = df.loc[df['total_purchase_value']>=10000]`

Both question 2 and 3 are achieved using Pandas **Pivot Tables** `pd.pivtot_table()`

# Generated Data

The sample CSVs generated by the above functions can be found in the `samples/` folder.

# Airflow Connections

I have used the PostgresOperator which requires a postgres connection and a SimpleHttpOperator which requires a http connection. This is set in the Admin UI in the connections tab.