The government of Tanzania to adopt the model developed in order to improve on the general maintenance operations on the water points to ultimately meet the water needs of its citizens.
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date_recorded - The date the row was entered
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funder - Who funded the well
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gps_height - Altitude of the well
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installer - Organization that installed the well
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longitude - GPS coordinate
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latitude - GPS coordinate
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wpt_name - Name of the waterpoint if there is one
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num_private - Number of private waterpoints
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basin - Geographic water basin
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subvillage - Geographic location
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region - Geographic location
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region_code - Geographic location
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district_code - Geographic loc
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lga - Geographic location
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ward - Geographic location
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population - Population around the well
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public_meeting - True/False
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recorded_by - Group entering this row of data
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scheme_management - Who operates the waterpoint
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scheme_name - Who operates the waterpoint
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permit - If the waterpoint is permitted
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construction_year - Year the waterpoint was constructed
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extraction_type - The kind of extraction the waterpoint uses
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extraction_type_group - The kind of extraction the waterpoint uses
25 . status_group - Condition of the well
The Random Forest algorithm, having the highest precision score of all performed better than the other models and shall be used as the final model The precision score of the model was 66% which means that it was able to precisely determine the status of the waterpoint 66% of the time
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The government should prioritize drawing water from springs when building the waterpoints and should not draw water sourced from shallow wells or boreholes as they spoil the pumps quicker
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Water points with enough water should be closely monitored, as the high use could lead to their failure.