RISE-NG-ML/riseng-ltfu-ml

Repository for RISE Nigeria LTFU ML Analysis # JHPIEGO-RISE ## Table of Contents 1. Overview 2. Technologies 3. Installation 4. Folder Structure 5. How to Run 6. FAQs 7. Other Links and Resources ## Overview *** This repository represents work completed by Palindrome Data in developing a predictive model for interruption in treatment. It includes data validation, exploratory data analysis, feature engineeering, and modelling. For more information check out the docs folder. ## Technologies *** Below is a list of the programming languages and software used: - **Programming Languages:** `Python > 3.6` for these notebooks `Python 3.8` was used. - **Logging Software:** `MLFlow` - **Environment and IDE:** `Anaconda Jupyter Notebooks` ## Installation *** If you have not already downloaded anaconda, download it here Below are steps to create an environment (if you have not yet), install the relevant packages and create a local folder: ### Repo Download 1. If you have not cloned the repo yet, open a terminal window anaconda and use: `git clone github.com` to any folder on your local repo. 2. If you already have the repo on your local computer, go to the rise git repo here and `git pull` in you command. 3. Then run the command below to unzip the contents: `unzip jhupiego_rise_phase1-feature-paper_stats_info.zip` 4. Then access the directory by using: `cd jhupiego_rise_phase1-feature-paper_stats_info/` 5. Then check the contents by running: `ls -a` for linux and `dir` for windows ### Environment Setup 1. In the terminal run `conda info -e` to check what environments are installed. 2. If you would like to use en existing environment you use the command `conda activate ` 3. To create a new environment, run `conda create -name RISE38 python=3.8`. Then run `conda activate RISE`. 3. Run `conda list` to check the installed packages. **Note:** If you are using an existing environment (i.e.not RISE38), packages will have to be installed individually. For the newly created ` …