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This repository contains the full project code for a Predictive Analysis of Productive Employment in Kenya. The repository contains the code for the data science project lifecycle from Business Understanding to Model Building and Evaluation (Colab Notebook) and Model Deployment (Flask, HTML)

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IsaacMwendwa/productive-employment-prediction

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Predictive Analysis of Productive Employment in Kenya

Current App Link: https://wage-employment-prediction.herokuapp.com/ (Outdated, new link to be posted soon)

Introduction

This project is aimed at providing actionable insights to support SDG Number 8, by allowing users/stakeholders to do a Predictive Analysis of Productive Employment in Kenya based on Economic Growth. The project uses machine learning algorithms for the regression problem: Given the economic growth metrics (Contribution to GDP, Growth by GDP) according to Industry, predict the number of people in non-productive employment (working poor) and the total number in employment; per Industry.

Table of Contents

Build_Tools

Pre-requisites

  1. Anaconda from Anaconda Organization Installed on Local System

Installation

  1. Fire up an Anaconda Prompt or terminal
  2. Create a Python virtual environment using conda. Specify the Python version == 3.6.9
  3. Activate conda environment
  4. Locate requirements.txt and pip install all the packages in the document
  5. Navigate to the deployment folder (containing code for deployment)
  6. Copy the path/address of the deployment folder
  7. In the terminal/prompt, cd into that directory using the command cd path. Replace path with the deployment folder's path
  8. Run the following command in the terminal: Flask run
  9. The command will fire up the Flask server
  10. Wait to be provided with a link on the terminal, which you can then paste in your browser to access the application
  11. Locate the test file Wage_Employment_and_GDP_2018.csv in the resulting home page, select the test file upload it to get predictions
  12. The predictions of the next year will then be displayed shortly thereafter

Contributions

Contributions are welcome using pull requests. To contribute, follow these steps:

  1. Fork this repository.
  2. Create a branch: git checkout -b <branch_name>
  3. Make your changes to relevant file(s)
  4. Check status of your commits: git status
  5. Add and commit file(s) to the repo using: git add <file(s)> git commit -m "<message>"
  6. Push repo to Github: git push origin <branch_name
  7. Create the pull request. See the GitHub documentation on creating a pull request.

Bug / Feature Request

If you find a bug (the website couldn't handle the query and/or gave undesired results), kindly open an issue here by including your search query and the expected result.

If you'd like to request a new function, feel free to do so by opening an issue here. Please include sample queries and their corresponding results.

Authors

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See also the list of Contributors who participated in this project.

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This repository contains the full project code for a Predictive Analysis of Productive Employment in Kenya. The repository contains the code for the data science project lifecycle from Business Understanding to Model Building and Evaluation (Colab Notebook) and Model Deployment (Flask, HTML)

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