Kaewin/Predicting-Water-Wells-In-Need-Of-Repair-For-The-Government-Of-Tanzania
Domaine:
environment and energyType de record:
dataset
Créateur:
Kae
Hôte:
# Overview:
## Links:
**Presentation**
* Google Slides
* PDF
**Lab Notebook (mostly unused)**
* Google Document
## Business & Data Understanding
### Business Problem:
Located in East Africa within the African Great Lakes region, Tanzania has a population of over 57,000,000 that faces significant challenges in accessing clean water. Many wells need repairs or have stopped working, while others have been added. To help address this issue, the Ministry of Water needs a classification model to identify broken wells.
I looked at three questions:
Is there a pattern in regards to:
* WHO?
* Government
* private business
* etc
* WHERE
* Hotspots
* Patterns in location
* PERMIT STATUS
* Does it affect probability of repair status?
### Data Source:
The Taarifa Waterpoints dashboard is an open-source platform aggregating data from the Tanzania Ministry of Water. It helps citizens stay informed about water-related issues in Tanzania, empowering them to participate in water resource management.
**Data Source**
* Pump it Up: Data Mining the Water Table
# Modeling
I began with a baseline model, which had an accuracy score of 54% on the test data.
I then developed a decision tree model, mainly for it's speed when compared to a logistic model.
# Evaluation
When working with models that identify broken wells, it is important to prioritize minimizing false negatives. Showing up to a functioning well is worse than ignoring a broken one, which is why the Precision metric was used. The model can correctly identify negative instances and trade-offs with Accuracy by minimizing false negatives. However, this may result in identifying more false positives. The final model precision was 79%, correctly identifying 79% of broken wells as positive.
# Results:
## WHO:
There was no clear pattern to identify individuals involved and instances were equally divided between breaking and not breaking.
## WHERE:
In Iringa, there is a high number of broken wells, which is highl …