Data Analysis Proof of Concept Project

Welcome to the Data Analysis Proof of Concept Project! This project serves as a demonstration of data analysis, statistical analysis, and visualization using Flask, Docker, matplotlib and Pandas.

This is what you get at your localhost:5000

Tech Stack

Flask

• A web framework for building and serving the data analysis application.
• Flask allows you to create web applications in Python.

Docker

• A containerization platform to ensure a consistent and isolated environment for your application.
• Simplifies deployment by encapsulating the application and its dependencies in a container.

Pandas

• A powerful data manipulation and analysis library for Python.
• Used for loading and analyzing datasets in this project.

Matplotlib

• A popular plotting library for Python.
• Utilized for creating visualizations, such as scatter plots.

Project Structure

da-flask-docker
│   .dockerignore
│   .gitignore
│   app.py ------------ this is main app file
│   compose.yaml
│   Dockerfile
|   requirements.txt
│   environment.yml --- conda env file
│   README.Docker.md
│   README.md
│
├───data
│       kc_house_data.csv
│
├───scripts
│       analyze_data.py
│   
│
├───static
│   │   scatter_plot.png
│   │
│   └───css
│           style.css
│
├───templates  
        index.html

How to Use

Installation

1. Install the required Python packages. It's recommended to use a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'
   pip install -r requirements.txt

   note - python 3.12 is recommended

• or, Install the Conda environment:

  conda env create -f environment.yml
  conda activate python-flask

Running

If you prefer to run the application without Docker, follow these steps:

1. Make sure you have activated your virtual environment:

   source venv/bin/activate

   On Windows, use

   venv\Scripts\activate
   

   Or if using Conda:

   conda activate python-flask

2. Run the Flask application:

   python app.py

3. Open your web browser and go to http://localhost:5000 to access the application.

Analyzing Data

1. The data analysis script is located in scripts/analyze_data.py.

2. Customize the analysis as needed in the analyze_data function.

3. Run the script:

   python scripts/analyze_data.py

   This will generate summary statistics, correlation matrices, and a scatter plot.

How to Use with docker

Installation

1. Install Docker: Docker Installation Guide

2. Clone the repository:

   git clone https://github.com/your-username/da-flask-docker.git

3. Change to the project directory:

   cd da-flask-docker

4. Build and run the Docker container:

   docker-compose up --build

   Your application will be available at http://localhost:5000.

Deploying to the Cloud

1. Build your Docker image:

   docker build -t myapp .

2. If your cloud uses a different CPU architecture, specify the platform:

   docker build --platform=linux/amd64 -t myapp .

3. Push the image to your registry:

   docker push myregistry.com/myapp

   Replace myregistry.com with your actual registry.

Consult Docker's getting started docs for more details on building and pushing.

References

• Docker's Python Guide