Difference between revisions of "ANLY482 AY2017-18T2 Group19 Data"

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[[Image:G19_logo.png|center|748x116px]]<br>
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[[Image:G19_Logo.png|center|800x150px|link=ANLY482 AY2017-18T2 Group19]]<br>
  
 
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[[ANLY482 AY2017-18T2 Group19 Background| <font face='Century Gothic' color="#FFFFFF"><b>BACKGROUND</b></font>]]
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[[ANLY482 AY2017-18T2 Group19 Project Overview| <font face='Century Gothic' color="#FFFFFF"><b>BACKGROUND</b></font>]]
  
 
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<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>DATA DESCRIPTION</u></font></div>
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We have obtained the following datasets from the client
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# Year 2016 - 2-hour transaction data 
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# Year 2016 - 3-day transaction data
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# Year 2017 - 3-hour transaction data 
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# Year 2017 - 3-day transaction data
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# Course reserve books master data
  
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The data required for us comes in 3 parts.
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Each row represents one borrowing instance. It was given to us in the following format:
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#The demand side where the patients live which is derived from the percentage distribution of elderly in the respective subzones. This data will be created at random to simulate the daily nodes for each patient.
 
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#The supply side where the foundation/organisation dispatches nurses to their scheduled patients
 
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#The details of services with details such as the mean and standard deviation to provide a more realistic constraint for the algorithm to take into account when routing the nurses to their patients.
 
  
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The dataset for the demand side will need to be generated by ourselves which includes:
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<center><i>Loan Transaction Data</i></center>
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*Address: Address of patient
 
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*Latitude: Latitude of patient’s address
 
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*Longitude: Longitude of patient’s address
 
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*Type:
 
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*Appointment_Times: Prefered appointment time
 
  
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The dataset for the supply side will need to be generated by ourselves which includes:
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[[Image:G19_MetaData1.png|500px|center]] &nbsp;
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*Id: Identification for nurse
 
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*Address: Address of origin
 
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*Latitude: Latitude of address
 
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*Longitude: Longitude of address
 
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*Type:  
 
  
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The dataset for the details of services includes:
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The course reserve books master data details the collection of course reserves available for loan. This dataset would include the following fields of information:  
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*Discipline:
 
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*Type (i.e. revisits, etc):
 
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*Request count:
 
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*Average: average time required for the treatment type
 
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*SD: standard deviation of time required for the treatment type
 
  
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<center><i>Course Reserve Books Master Data</i></center>
  
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<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>DATA COLLECTION</u></font></div>
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[[Image:G19_MetaData2.png|500px|center]] &nbsp;
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We need to conduct research to better understand the demand side and supply side to generate sufficient amount of data to work with.  
 
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<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>DATA CLEANING AND TRANSFORMATION</u></font></div>
 
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The data will need to be transformed into the required formats in order for us to perform the necessary processing later.
 
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<div style="background: #FFFFFF; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 15px;letter-spacing:-0.03em;font-size:16px;id:UT1"><font face='Century Gothic' color=#000000 ><u>EXPLORATION OF DATA</u></font></div>
 
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At this phase, the data will be explored in order for us to choose a suitable model to be built.
 

Latest revision as of 19:36, 15 April 2018

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We have obtained the following datasets from the client

  1. Year 2016 - 2-hour transaction data
  2. Year 2016 - 3-day transaction data
  3. Year 2017 - 3-hour transaction data
  4. Year 2017 - 3-day transaction data
  5. Course reserve books master data

Each row represents one borrowing instance. It was given to us in the following format:

Loan Transaction Data
G19 MetaData1.png

 

The course reserve books master data details the collection of course reserves available for loan. This dataset would include the following fields of information:

Course Reserve Books Master Data
G19 MetaData2.png

 

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