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This repository utilizes Machine Learning to solve the water crisis problem in Tanzania. This is one of the best use cases for machine learning to be used in the social causes helping people.

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avirichie/Classification-of-Water-Pumps-in-Tanzania--based-on-their-working-condition-

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Pump-it-up

Using data from Taarifa and the Tanzanian Ministry of Water, can you predict which pumps are functional, which need some repairs, and which don't work at all? This is an intermediate-level practice competition. Predict one of these three classes based on a number of variables about what kind of pump is operating, when it was installed, and how it is managed. A smart understanding of which waterpoints will fail can improve maintenance operations and ensure that clean, potable water is available to communities across Tanzania. One can also view the dataset in DrivenData

URL - https://www.drivendata.org/competitions/7/pump-it-up-data-mining-the-water-table/

The goal is to predict the operating condition of a waterpoint for each record in the dataset

Below are the features available for waterpoints:

  • 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

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This repository utilizes Machine Learning to solve the water crisis problem in Tanzania. This is one of the best use cases for machine learning to be used in the social causes helping people.

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