IsaacMwendwa/productive-employment-prediction

Domain:

socioeconomic

Record type:

project

Creator:

Isa

Host:

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HTML) # Predictive Analysis of Productive Employment in Kenya ## Current App Link: wage-employment-prediction.… (Outdated, new link to be posted soon) ## Introduction This project is aimed at providing actionable insights to support SDG Number 8, by allowing users/stakeholders to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 * Installation * Contributions * Bug / Feature Request * Authors ## Build_Tools * Python 3.6.9 - The programming language used. * SciKit Learn - The machine learning library used. ## 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 3. Locate requirements.txt and pip install all the packages in the document 4. Navigate to the deployment folder (containing code for deployment) 5. Copy the path/address of the deployment folder 6. In the terminal/prompt, cd into that directory using the command ```cd path```. Replace path with the deployment folder's path 10. Run the following command in the terminal: ```Flask run``` 12. The command will fire up the Flask server 13. Wait to be provided with a link on the terminal, which you can then paste in your browser to access the application 14. Locate the test file Wage_Employment_and_GDP_2018.csv in the resulting home page, select the test file upload it to get predictions 15. The predictions of the next year will then be displayed shortly thereafter ## Contributions Contributions are welcome using pull reques …

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