saas enterprise miner

Using data from the Tanzanian government, you have to predict
which water pumps are functional and which are not. You will have to use algorithms learned in this class to predict
these two classes based on the different features of the dataset. A model that can predict these classes accurately will
be helpful to improve maintenance operations and facilitate access to such an important resource.
Goal: In this competition you must strive to run a prediction model that attains the highest possible accuracy in
a validation dataset (after a 70/30 split). Details on how grades will be distributed according to your models’
performance can be reviewed in the last section of this document.
The dataset has the following 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
2

• 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
As for the labels, there are three possible values:
• functional - the waterpoint is operational and there are no repairs needed
• non functional - the waterpoint is not operational
The goal of this project is to run at least 3 different algorithms learned in class and discuss their results. The
models have to be built in SAS Enterprise Miner. For each algorithm, you may run several versions or models. In
addition, you are expected to perform a descriptive analysis of the data and data cleaning as needed to run your
algorithms as effectively as possible.