Difference between revisions of "ANLY482 AY2016-17 T1 Group2: Project Overview"

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[[ANLY482_AY2016-17_T1_Group2 | <font color="#bbdefb">Home</font>]]
  
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[[ANLY482_AY2016-17_T1_Group2: Team | <font color="#bbdefb">Team</font>]]
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[[ANLY482_AY2016-17_T1_Group2: Project Overview | <font color="#fff">Project Overview</font>]]
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[[ANLY482_AY2016-17_T1_Group2: Project Findings | <font color="#bbdefb">Project Findings</font>]]
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[[ANLY482_AY2016-17_T1_Group2: Project_Management | <font color="#bbdefb">Project Management</font>]]
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[[ANLY482_AY2016-17_T1_Group2: Documentation | <font color="#bbdefb">Documentation</font>]]
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<div style="background:#0096da; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#bbdefb solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>Business Problem & Motivation</strong></font></div></div>
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As TixCo is the only authorised ticketing service provider for certain events, it is crucial for them to cater to the demand of the mass public. Currently, TixCo is unable to anticipate the demand for the event organised and this post challenge for TixCo to fully capture the demand efficiently. As such, any uncaptured demand is an opportunity lost for TixCo. Hence, it is important for them to understanding the demand for an event.
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For this, TixCo will need to:
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* Understand the trend and pattern of the number of tickets sold per event
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* Identifying the possible bottleneck for demand
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<div style="background:#0096da; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#bbdefb solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>Project Objective</strong></font></div></div>
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The objective of this project is to:
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* Examine the underlying factors that affect the number of tickets sold per event
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* Understanding the distribution of the Total Bet Count
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* Understanding the relationship between the attribute and the Total Bet Count
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The project team also aims to develop an appropriate model to predict the number of tickets sold per event, based on the historical data provided by TixCo.
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</div>
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<div style="background:#0096da; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#bbdefb solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>Datasets</strong></font></div></div>
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The datasets provided by TixCo are:
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# Profit & Loss (P&L) records
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# Timetables of the events serviced
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# Types of events
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The data are presented in the form of Microsoft Excel worksheets and contains records over the time span of more than 6 years (Jan 2010 to May 2016).
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Revision as of 06:43, 16 October 2016

Home

Team

Project Overview

Project Findings

Project Management

Documentation


Business Problem & Motivation

As TixCo is the only authorised ticketing service provider for certain events, it is crucial for them to cater to the demand of the mass public. Currently, TixCo is unable to anticipate the demand for the event organised and this post challenge for TixCo to fully capture the demand efficiently. As such, any uncaptured demand is an opportunity lost for TixCo. Hence, it is important for them to understanding the demand for an event.

For this, TixCo will need to:

  • Understand the trend and pattern of the number of tickets sold per event
  • Identifying the possible bottleneck for demand


Project Objective

The objective of this project is to:

  • Examine the underlying factors that affect the number of tickets sold per event
  • Understanding the distribution of the Total Bet Count
  • Understanding the relationship between the attribute and the Total Bet Count

The project team also aims to develop an appropriate model to predict the number of tickets sold per event, based on the historical data provided by TixCo.


Datasets

The datasets provided by TixCo are:

  1. Profit & Loss (P&L) records
  2. Timetables of the events serviced
  3. Types of events

The data are presented in the form of Microsoft Excel worksheets and contains records over the time span of more than 6 years (Jan 2010 to May 2016).

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