Analytics And Intelligent Systems

NUS ISS AIS Practice Group

CUSTOMER SEGMENTATION AND STRATEGY DEVELOPMENT BASED ON CUSTOMER LIFETIME VALUE: CASE STUDY ON A WIRELESS COMPANY — June 6, 2017

CUSTOMER SEGMENTATION AND STRATEGY DEVELOPMENT BASED ON CUSTOMER LIFETIME VALUE: CASE STUDY ON A WIRELESS COMPANY

Capture

Source: Click here for the paper

Submitted by:

Team Incognito

Pankaj Mitra (A0163319E), Deepthi Suresh (A0163328E), Anand Rajan (A0163200B) and Neeraja Lalitha Muralidharan (A0163327H)

Advertisements
STUDY AREA DETERMINATION — May 6, 2017

STUDY AREA DETERMINATION

Objective:

To find most accessible study areas for students in NUS

Problem Space:

Capture

Analysis Strategy:

  • What? – Availability of study areas for students
  • Where? – Inside NUS campus
  • Why? – The various reasons for the preferred locations
  • How? – Finding the clusters around various study locations

Data Exploration and Feature Addition:

  • Data source is OpenPaths
  • The class data was cleansed and the records pertaining to geographical coordinates of Singapore/NUS was chosen
  • The initial analysis of the sampled data was performed using various tools such as R , carto, ArcGIS to study the geographic spread
  •  A new dataset was formulated for representing the various study centres in NUS
  • Transformation was performed to achieve the variables in the necessary format for the geo-visualization
  • Reverse geocoding was performed on the dataset using the ‘ggmap’ package in R and the corresponding locations were obtained

Preliminary Analysis:

Picture1

  • Carto was used to analyse the spread of the data points during the class hours and after the class hours
  • The results of the analysis portrayed that after the class hours the population spread is more at University town owing to availability of more study areas and facilities

Modelling:

Capture

Base Map Creation and Polygon Generation:

Capture

Addition of Layers:

Picture3

Step 3: Loaded class data (master class namely) and converted coordinates for visibility

Density Analysis:

Capture

Picture4

Step 6: The high-density area was in and around National University of Singapore. We can conclude that the data points are either working in NUS or students of NUS.

Assumption and Addition of new Layer:

Capture

Hot Spot Analysis:

Step 8: Hot Spot Analysis was performed, Arc Tool Box->Spatial Statistics Tools->Mapping Clusters->Optimized Hotspot Analysis

Picture5

Model Diagram – Proximity Analysis:

Picture6

Step 9: Proximity Analysis was performed to find most accessible study areas for students in NUS, Arc Tool Box->Analysis Tools->Proximity->Near

Proximity Analysis Results:

Capture

Inferences/Solution Outline:

  • Comparing the inference obtained from CARTO and Model built in ARCGIS, We can find that the students only focus on University Town
  • From the model it is evident that there were other study areas that could be preferred as the data points were close to these study areas
  • The students can explore other areas like FOE,SOC etc for holding discussion sessions
  • We can propose a new study area at an optimal location based on the geographic distribution of student data in case the number of students enrolled increases over a period of time

Limitations:

  • It’s a student’s location data
  • Sample size is limited to ISS students only
  • Non availability of accurate shuttle bus timings data
  • Non availability of students enrolled in each and every faculty

Team Name: Incognito

Team Members: Pankaj Mitra, Deepthi Suresh, Anand Rajan, Neeraja Lalitha Muralidharan, Nandini Paila Reddy

Delay Estimation in Pedestrian crossing — April 29, 2017

Delay Estimation in Pedestrian crossing

Team Name: Incognito

Team Members: Deepthi Suresh, Neeraja Lalitha Muralidharan, Pankaj Mitra, Sindhu Rumesh Kumar

Journal:

This study uses multiple linear regression:

1.To provide theoretical support for traffic management and control

2.To increase efficiency at intersections and improve security

One Page Journal Summary “Estimates of pedestrian crossing delay based on multiple linear regression and application” authored by Li Dan and Xiaofa Shi.

Journal_Pedestrian Crossing

Click here for Journal.

Analysis on Workplace Injuries — March 17, 2017

Analysis on Workplace Injuries

Untitled

The objective of this dashboard is to demonstrate different levels and types of injuries caused in the workplaces of Singapore.

 

Analysis on Workplace Injuries – Analytics And Intelligent Systems

Questions:

  1. In which industry majority of injuries occur?

Majority of injuries occur in the Construction and Manufacturing industry consistently over the years but in the year 2016, the injuries pertaining to “Accommodation and Food Services” has also risen.

  1. With the current scenario in workplaces, which type of injury occurs more frequently?

The number of minor injuries (94.9%) greatly exceeds the number of major (4.5%) and fatal (0.5%).

  1. Is there a trend seen in the number of injuries caused in the industries through the years 2014-2016?

The fatal and major injuries are consistent through the years, while the number of minor injuries dropped in 2015 and increased drastically in 2016.

  1. What are the common types of minor injuries across different industries?

Injuries caused by slips, trips and falls are the maximum across industries.  Injuries caused by cuts or stabs by objects is more at the Accommodation and Food services industries, while workmen at Construction and Manufacturing industries are more affected by moving objects.

  1. Why has minor injury increased drastically from 2015 to 2016 while fatal and major injuries are consistent?

A good measure has been taken to avoid the fatal injuries yet minor injuries have not decreased. This could be because of the conversion of fatal injuries to minor injuries.

 

Final Analysis

We can clearly see that fatal and major injuries are very low and steps have also been taken to reduce this further, there is still a rise in the minor injuries. This may be because the fatal injuries have been converted to minor injuries.

Struck by moving/falling objects is one of the common cause of minor injury in Construction as well as Manufacturing industry, so to suggest a common solution for this, we could make use of Wireless sensors (heat and motion) on the objects which make an alert sound when it reaches within 50 m of any human being. This way the people working at the site will be aware of any moving objects in their vicinity and move out of harm.

The injuries may be minor, but if the injuries occur simultaneously to multiple people, it may affect the overall productivity of the company.

 

Tableau Public link:

Workplace Injuries in Singapore(2011-2016)

Submitted by:

Pankaj Mitra (A0163319E)

Deepthi Suresh (A0163328E)

Neeraja Lalitha Muralidharan (A0163327H)

Kriti Srivastasa (A0163206N)

Sindhu Rumesh Kumar (A0163342M)