Cdasilver29/well-classification-documented

📊 A well-documented version of my Tanzania Water Wells Machine Learning project — created for educational purposes and code documentation practice. Includes cleaned code, docstrings, logic breakdown, and reproducible results. # # Tanzania Water Wells – Documented ML Project This is a cleaned and documented version of my original project predicting the functionality of water wells in rural Tanzania. ## **Business Understanding** Tanzania faces a water crisis due to limited infrastructure, uneven distribution, poor water quality, climate change, and population growth. We are working on a project that utilizes machine learning to predict well success, optimize resource allocation, and ultimately break the cycle of water insecurity and poverty. ## **Project Goal** Our goal in this project is to utilize machine learning algarithms to build a model predicting water point functionality in Tanzania. This empowers Tanzanian authorities to identify wells needing maintenance, guide future well placement for better success rates, and ultimately optimize water resource management across the country. By predicting well functionality, the model can help target limited resources for repairs, inform data-driven decisions on new well construction, and promote efficient water utilization, tackling Tanzania's water crisis head-on. ## 🧠 Purpose This repository was created to fulfill a code documentation exercise as part of a generative AI learning module. It includes: - Final notebook with code explanations - Function-level documentation - Step-by-step logic breakdown - Educational structure for others to reuse or understand the codebase ## 📁 Files | File | Description | |----------------------------------|--------------------------------------------------| | `documented.ipynb` | Final documented version of the notebook | | `original-undocumented.ipynb` | Raw version before any changes | | `docs/prompt2-logic-explanation.md` | Step-by-step explanation of project workflow | | `requirements.txt` | Python packages used in the project ## Findings * Water Quality: Soft water p …