Exploration and Production Deployment with Scikit-Learn

This project has been implemented using the powerful machine learning library, scikit-learn, to explore different machine learning models, automated to find the most effective one across our various datasets.

To ensure a clean and reproducible development environment, we have "partitioned" this project into 3 main sections:

1. Virtual Environment and Project Structure
2. Machine Learning Techniques with Scikit-Learn
3. Production Deployment with Flask

Roadmap Project Organization

---

├── README.md          <- The top-level README for developers using this project.
├── data               <- Data for this project
│
├── files               <- An Explained Data for this project
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks.
│
├── scripts            <- Python Scripts, same as notebooks but with another format
│
│
├── tools              <- Script tool for this project, read csv, create the analysis, for automatization
│
│
├── environment.yml    <- If you want to install the same Environment 

├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── main.py            <- Run main.py in console to run the analysis
│
└── server.py          <- Run server.py in console to run the local server

Installation and Deployment with Flask

To clone this repository to your local machine, follow these steps:

1. Open your terminal.

2. Use the git clone command followed by the repository URL to clone it to your local machine. Run the following command:

   git clone https://github.com/EdwLearn/scikit-Learn

3. Open a terminal and navigate to the directory of the cloned repository.

4. Create a virtual environment using Conda or Python's default virtual environment:

   • Conda:

     conda create --name scikit python=3.9

   • Python's Virtual Environment:

     python3 -m venv scikit

5. Activate the newly created virtual environment:

   • Conda:

     conda activate scikit

   • Python's Virtual Environment (on Linux/Mac):

     source scikit/bin/activate

     Python's Virtual Environment (on Windows):

     .\scikit\Scripts\activate

6. Install the necessary dependencies using pip and the requirements.txt file:

   pip install -r requirements.txt

   This will ensure that all required libraries are properly installed in your virtual environment.

Running the Project

Once you have set up the virtual environment and installed the dependencies, you can run the project by executing the following command:

python server.py

This command will start the project execution, and you can view the results in the console.

Additionally, if you wish to view the result on the local server, visit http://localhost:8080/predict in your web browser once the project is running.