CitiBikeData-ETL

Project Overview

This project aims to perform and ETL Process, analyze and visualize Citibike data using Python, MongoDB, and Streamlit. It provides insights into bike-sharing usage patterns, peak demand times, and station activity, offering valuable information for user behavior analysis, and system optimization.

Getting Started

Prerequisites

• Python 3.9 and above
• MongoDB
• OpenAI OR Gemini AI API KEY

Installation

Clone this repository and install the required Python packages:

git clone https://github.com/IanDanielM/CitiBikeData-MongoDB.git
cd CitiBikeData-MongoDB
pip install -r requirements.txt

Running the Application

• ETL Process

  Load the data into MongoDB using the etl.py script:

  --db: The name of the MongoDB database.

  --collection: The name of the MongoDB collection.

  --uri: The URI for connecting to the MongoDB server.

  --base_url: The base URL for data ingestion.

  --year: The year for filtering the data.

  --file_path: The file path for the data file.

  Example usage:

  python etl.py --db mydatabase --collection mycollection --uri mongodb://localhost:27017 --base_url http://example.com/data --year 2021 --file_path /path/to/data/file

  This will ingest data from http://example.com/data, filter it for the year 2021, and load it into the mycollection collection of the mydatabase database on the MongoDB server running on localhost:27017.

• Visualize the data using the Streamlit dashboard:

  Change the parameter in the streamlitapp.py file to match your MongoDB URI, database, and collection.

  streamlit run streamlitapp.py

  This will start the Streamlit server and open the dashboard in a new browser window.