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.