luaymatalka/tanzania-waterpoint---classification

# Module 3 Final Project ## Outline ----------------------------------------- ├── LICENSE ├── README.md <- The project layout (this file) ├── zippedData <- This is where the project data is located │ ├── student.ipynb <- This is the main notebook (contains the python code) │ ├── reports <- Reports and presentations │ └── presentation.pdf <- Non-technical presentation │ ├── images <- Where the graphs are saved ├── requirements.txt <- The requirements file for reproducing the analysis environment ## Conclusion ### Does the source of water affect the likelihood for needing repairs? According to the above Likelihood of Needing Repairs vs. Water Source bar graph, the water sources that increase the likelihood for needing repairs above the average are, in ascending order: Shallow wells, Machine dbh, Unknown, Dam, and Lakes. Based on the Likelihood of Needing Repairs vs. Basin bar graph, the bodies of water that increase the likelihood for needing repairs above the average are, in ascending order: Wami/Ruvu, Lake Victoria, Lake Tanganyika, Lake Rukwa, and Ruvuvuma/Southern Coast. ### Does the region affect the likelihood for needing repairs? Based on the Likelihood of Needing Repairs vs. Region bar graph, the regions that increase the likelihood of needing repairs above the average, in ascending order, are: Morogoro, Kagera, Mbeya, Mwanza, Kigoma, Singida, Dodoma, Mara, Tabora, Rukwa, Mtwara, and Lindi. Based on the Functionality vs. GPS Height boxplot, as the GPS height increases, the wells are less likely to need repairs. ### Do public meetings regarding the state of a pump affect its outcome in regards to needing repairs? According to the Functionality Based on Public Meetings bar graph, most wells do have public meetings. Furthermore, according to the Likelihood of Needing Repairs vs. Public Meeting bar graph, public meetings lower the lik …