NACSCOP Data Collection Tools - Indicator Selection Model

Welcome to the NACSCOP Data Collection Tools - Indicator Selection Model repository! This project aims to provide an unbiased approach to select the appropriate indicator for data collection and reporting during the transition period when two sets of tools are in use.

The goal of this project is to develop a classification model that advises on the indicator to use based on historical data values, facility characteristics, changes in the indicator, and other relevant factors.

By using this model, NACSCOP will be able to make informed decisions on which indicator to use, ensuring accurate and consistent data collection and reporting. The model will help NACSCOP select the most suitable indicator for each facility based on their unique characteristics and changes in the data over time.

Table of Contents

1. Introduction
2. Getting Started
3. Running the Model
4. Contributing
5. Conclusion

Introduction

This repository contains the code and resources needed to implement the NACSCOP Data Collection Tools - Indicator Selection Model. The project uses Python and various libraries, such as NumPy, Pandas, and Scikit-learn, to create a classification model that determines the appropriate indicator for each facility.

The repository contains the following files:

• demo.py - the main file containing the code for the classification model
• model.pkl - the packaged model in a pickle file
• README.md - this file contains information about the project, including how to get started
• jupyter notebook - this file contains the notebook used to work on the data and shape the model

Getting Started

To get started with this project, you will need to:

1. Clone the repository using git clone https://github.com/Briankim254/NASCOP-Classification-tool.git
2. Navigate to the project directory using cd NASCOP-Classification-tool
3. Install the necessary dependencies, which include NumPy, Pandas, and Scikit-learn
4. Open the demo.py file and inspect the code

Running the Model

To run the model, simply open the demo.py file and run it in your Python environment. The model will take in the data from data.csv and use it to train the classifier. The output of the model will be the recommended indicator to use for each facility.

Contributing

We welcome contributions to this project! If you would like to contribute, please follow these steps:

1. Fork the repository and create a new branch for your changes
2. Make your changes or additions to the project
3. Create a pull request and wait for a review from a team member

Please ensure that your code adheres to best practices for code quality and documentation.

Conclusion

The NACSCOP Data Collection Tools - Indicator Selection Model is an important project that will help ensure accurate and consistent data collection and reporting during the transition period. We are excited to work on this project, and we hope it will be a valuable tool for NACSCOP and other organizations.