New York City Taxi Fare

📖 About

This project implements a complete pipeline for taxi fare prediction in New York City, using an event-based data stream and a data lake for data storage and analysis.

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🧪 Technology

The project was developed with:

→ Python
→ Apache Kafka
→ Apache Airflow
→ Apache Spark
→ Fast API
→ Docker

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🔖 Proposed solution to the challenge

🏗️ Proposed architecture

📁 Project structure

taxi-fare/
│
├── dags/
│   └── taxi_raides_dag.py
├── data/
│   └── train.csv
├── docker/
│   ├── airflow.dockerfile
│   └── api.dockerfile
├── jars/
│   ├── aws-java-sdk-bundle-1.12.262.jar                
│   └── hadoop-aws-3.3.4.jar
├── src/
│   ├── api.py                
│   ├── consolidate.py                
│   ├── consumer.py                
│   ├── producer.py                
│   ├── utils.py                
├── docker-compose.yml             
├── requirements.txt               
└── README.md                      

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🔌 Getting started

Clone the project:

$ git clone https://github.com/GesielLopes/taxi-fare.git

Access the project folder:

$ cd taxi-fare

Download the train.csv file in https://www.kaggle.com/competitions/new-york-city-taxi-fare-prediction/data and save it in the data folder

Download the aws-java-sdk-bundle-1.12.262.jar in https://mvnrepository.com/artifact/com.amazonaws/aws-java-sdk-bundle/1.12.262 and save it in the jars folder

Download the hadoop-aws-3.3.4.jar in https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-aws/3.3.4 and save it in the jars folder

Execute docker compose to run the data project:

# Execute docker compose
$ docker compose up -d

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🚀 Using the project

1. Access MinIO web client:
   • http://localhost:9000
   • username and password 'minioadmin'
   • Create buckets RAW and REFINED for manipulete files like a AWS S3 🍷.
2. Access airflow web client:
   • http://localhost:8081
   • username and password 'airflow'
   • Execute dag taxi_raides_dag
3. Access the API

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📕 Using the api

Accessing via terminal, with curl for example:

$ curl -X 'GET' 'http://localhost:8000/api/' -H 'accept: application/json'

$ curl -X 'GET' 'http://localhost:8000/api/?pickup_date=2011-12-13' -H 'accept: application/json'

$ curl -X 'GET' 'http://localhost:8000/api/?pickup_longitude=-73.9755630493164&pickup_latitude=40.752681732177734' -H 'accept: application/json'

$ curl -X 'GET' 'http://localhost:8000/api/?pickup_date=2011-12-13&pickup_longitude=-73.9755630493164&pickup_latitude=40.752681732177734' -H 'accept: application/json'

Accessing via browser. Just access via url:

http://localhost:8000/api

http://localhost:8000/api/?pickup_date=2011-12-13

http://localhost:8000/api/?pickup_longitude=-73.9755630493164&pickup_latitude=40.752681732177734

http://localhost:8000/api/?pickup_date=2011-12-13&pickup_longitude=-73.9755630493164&pickup_latitude=40.752681732177734

Accessing API Swagger via browser. Just access via url:

http://localhost:8000/docs

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📋 TODO List

• Add an ENV file for sensitive data
• Create the project's unit tests
• Automate bucket creation
• Automate data flow in the API when refined data does not exist
• Add data science environment
• Feel free to open issues or submit pull requests for improvements or fixes

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📝 License

This project is licensed under the MIT License.