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This project tackles the issue of clean water access in Tanzania, a developing country with over 57 million people. It aims to build a classifier that predicts water well conditions using data like pump type and installation date, helping NGOs and the government identify wells needing repairs, improve future construction, and reduce failure.

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Enhancing Lives through Well Waters: Classification Analysis for Sustainable Water Management in Tanzania

Author: Jelimo Marion

Overview

Enhancing Lives through Well Waters

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Several countries struggle with providing easily accessible, inexpensive, and clean water to its citizens. Tanzania, a developing country, with a population of 57, 000, 000 is one of these countries. As a solution, the government has established wells in numerous places in the country to cater to her citizens

However, the government currently faces a predicament in maintaining the wells as some break down and others fail completely. Therefore, there is need for a means to ensure that the wells that have broken down are repaired and that future wells that are built have reduced if not eliminated break-down possibility. This project oversees the attempt to fill the gap at hand.

Business Understanding

Business Problem

The Government of Tanzania wants to repair the wells that have broken down in their country and build new wells with minimized if not eliminated break-down rate. However, they need to identify and locate these wells as well as identify reasons of breakdown for future building.

Problem Statement

The task at hand is to create a classifier algorithm that predicts the condition of the water well, using information gathered from existing wells.

Objectives

  • To create a classifier algorithm that predicts the condition of a water well
  • To predict how likely a well is to break down
  • To identify which wells are in need of repair
  • To reduce the break-down rate of future wells

Data Understanding

Data Sources

Training set values: The independent variables that need predictions

Features

  • amount_tsh - Total static head (amount water available to waterpoint)
  • date_recorded - The date the row was entered
  • funder - Who funded the well
  • gps_height - Altitude of the well
  • installer - Organization that installed the well
  • longitude - GPS coordinate
  • latitude - GPS coordinate
  • wpt_name - Name of the waterpoint if there is one
  • num_private -
  • basin - Geographic water basin
  • subvillage - Geographic location
  • region - Geographic location
  • region_code - Geographic location (coded)
  • district_code - Geographic location (coded)
  • lga - Geographic location
  • ward - Geographic location
  • population - Population around the well
  • public_meeting - True/False
  • recorded_by - Group entering this row of data
  • scheme_management - Who operates the waterpoint
  • scheme_name - Who operates the waterpoint
  • permit - If the waterpoint is permitted
  • construction_year - Year the waterpoint was constructed
  • extraction_type - The kind of extraction the waterpoint uses
  • extraction_type_group - The kind of extraction the waterpoint uses
  • extraction_type_class - The kind of extraction the waterpoint uses
  • management - How the waterpoint is managed
  • management_group - How the waterpoint is managed
  • payment - What the water costs
  • payment_type - What the water costs
  • water_quality - The quality of the water
  • quality_group - The quality of the water
  • quantity - The quantity of water
  • quantity_group - The quantity of water
  • source - The source of the water
  • source_type - The source of the water
  • source_class - The source of the water
  • waterpoint_type - The kind of waterpoint
  • waterpoint_type_group - The kind of waterpoint

About

This project tackles the issue of clean water access in Tanzania, a developing country with over 57 million people. It aims to build a classifier that predicts water well conditions using data like pump type and installation date, helping NGOs and the government identify wells needing repairs, improve future construction, and reduce failure.

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