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https://github.com/markjacksonfishing/pipedreams
A play on pipelines, with a focus on making data accessible and insightful.
https://github.com/markjacksonfishing/pipedreams
backend data-engineering data-processing data-visualization deployment etl frontend machine-learning python streamlit
Last synced: 6 days ago
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A play on pipelines, with a focus on making data accessible and insightful.
- Host: GitHub
- URL: https://github.com/markjacksonfishing/pipedreams
- Owner: markjacksonfishing
- License: mit
- Created: 2024-10-31T16:50:46.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-18T15:23:04.000Z (about 1 month ago)
- Last Synced: 2024-12-06T16:48:17.124Z (20 days ago)
- Topics: backend, data-engineering, data-processing, data-visualization, deployment, etl, frontend, machine-learning, python, streamlit
- Language: Python
- Homepage: https://www.anuclei.com
- Size: 7.92 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PipeDreams - CSV Data Explorer
PipeDreams is a data exploration and visualization tool designed to be simple, flexible, and powerful. Upload any CSV file, perform basic ETL transformations, visualize your data, and gain insights with built-in machine learning—all from a user-friendly Streamlit interface. If no file is uploaded, a default dataset (`customers-100000.csv`) is used for demonstration.
## Features
- **CSV Upload**: Upload any CSV file for immediate analysis and visualization.
- **Default Dataset**: If no file is uploaded, the app loads a sample dataset (`customers-100000.csv`) located in the `data/` directory.
- **ETL Transformations**: Clean and transform data, remove missing values, and auto-convert data types.
- **Data Visualization**: Interactive charts (scatter, bar, line, histogram, and box plots) to gain insights from your data.
- **Clustering Analysis**: Use KMeans clustering to identify natural groupings within the data, helping to segment and classify.
- **Predictive Analysis**: A synthetic column (`Annual Purchase Amount`) is included for testing linear regression, allowing users to explore predictive analysis features.
## Getting Started with Docker
You can run PipeDreams using Docker to avoid setting up dependencies locally. The pre-built Docker image is available on [DockerHub](https://hub.docker.com/repository/docker/anuclei/pipedreams).
### Pulling the Docker Image
Pull the latest Docker image from DockerHub:
```bash
docker pull anuclei/pipedreams:latest
```
### Running the Docker Container
Run the application with Docker, exposing it on port 8501:
```bash
docker run -p 8501:8501 anuclei/pipedreams:latest
```
Once the container is running, open your browser and go to `http://localhost:8501` to access the application.
## Kubernetes Deployment
PipeDreams can also be deployed on a Kubernetes cluster. This deployment scenario uses Minikube for local Kubernetes clusters and includes configurations for high availability and autoscaling.
For detailed instructions and YAML configurations, refer to the [Kubernetes Deployment Guide](k8s/k8s.md) in the `k8s` directory.
## Manual Installation
If you prefer not to use Docker, you can set up the app manually.
### Prerequisites
- **Python** (version 3.6 or higher)
### Installation
1. **Clone the repository**:
```bash
git clone https://github.com/markjacksonfishing/pipedreams.git
cd pipedreams
```
2. **Set up a virtual environment**:
- **MacOS/Linux**:
```bash
python3 -m venv venv
source venv/bin/activate
```
- **Windows**:
```bash
python -m venv venv
.\venv\Scripts\activate
```
3. **Install dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run the application**:
- **MacOS/Linux**:
```bash
source venv/bin/activate
streamlit run app.py
```
- **Windows**:
```bash
.\venv\Scripts\activate
streamlit run app.py
```
The application will open in your default web browser at `http://localhost:8501` and will look like this:
5. **Deactivate the virtual environment** (when finished):
```bash
deactivate
```
## How to Use
1. Start the application by following the setup steps above (or run it via Docker).
2. **Upload a CSV file** using the file uploader in the app, or view the **default dataset** if no file is uploaded.
3. Explore the data with built-in ETL transformations and interactive visualizations.
4. Perform **clustering analysis** and **predictive analysis** on available data.
### Advanced Insights: Clustering and Predictive Analysis
- **Clustering Analysis**: Select features for clustering, and the app will automatically group data into clusters using KMeans. This can reveal natural groupings in the data, such as customer segments.
- **Predictive Analysis**: Select features and a target variable (e.g., the synthetic `Annual Purchase Amount`) for linear regression. The app will generate a prediction model, display a mean squared error metric, and show an interactive scatter plot comparing actual vs. predicted values.
### Default Dataset: `customers-100000.csv`
The default dataset, `customers-100000.csv`, is located in the `data/` directory. If no CSV file is uploaded, this dataset will automatically load, allowing users to test the ETL transformations, visualizations, clustering, and predictive analysis features without needing their own data file.