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Dataset Evaluation for Statistical Learning and Data Mining Methods

Data evaluation
The dataset (project_EU_1080) represents an extract of a greater data set which contains project data sets similar to the one presented here.


The task is to elaborate and evaluate which statistical learning and data mining methods would be appropriate to give insight into the hidden knowledge of the entire data set, provided that the other datasets are similar with respect to structure and quantity. Of special interest is if the data would allow association rule based learning. This includes the text data hence the data needs to be preprocessed with natural language tools in order to develop metrics for the text data that allow more sophisticated mining methods. 

Questions to be answered:

  • Can data mining methods provide valuable insight into the data and which methods would that be (e.g. clustering with dbscan with the following parameters...)? 
  • What can be expected with respect to the results? 
  • What would be the the costs (money and time)? 
  • If the data does not suffice the requirements of data mining what should be changed be different (structural problem, amount of data, etc.)?
  • What would be the cost for this evaluation (time, money)?

Consumer Goods and Retail
Brand Equity
Brand Perceptual Mapping

$4,000 - $7,000

Starts Mar 29, 2017

9 Proposals Status: HIRING

Company small

Client: A*************

Posted: Mar 27, 2017

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Data Visualization Code Review and UI Testing

We have two web apps that require code review and UI testing. The UX is basically a UI dataviz--let us know what this means to you.

Our Criteria for the Ideal Expert

  1. Experience testing the user interface (PHP and D3.js) for a software product where the user clicks on elements of the user experience and triggers changes to data visualization that is the UI.
  2. An emphasis on detail exploring features for selecting things like buttons and areas of the screen
  3. Must have experience testing across browsers including working iOS and Android using native apps and mobile sites
  4. Because the dataviz changes based on the data you will need to provide a test plan and execute it across different data sets that you will load as well as all browsers and native apps including mobile sites.
  5. Ability to log detail in a bug tracking system or video recording so that we can see the problem
  6. Agile

Please share your experience building test plans and testing UIs across browsers and platforms.  How good are you at PHP and D3.js to be able to do code review?  Would you use any specific tools?

You must answer all questions and provide your approach to be considered.

Hi-Tech
Data Mashups
Statistical Graphics

$75/hr - $125/hr

0 Proposals Status: HIRING

Net 7

Company small

Client: V********

Posted: Mar 26, 2017

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Machine Learning and AI for E-recruitment

We are an Applicant Tracking System (ATS) solution for HR professionals and recruiters. 

Problem:  Identify the best talent based on historical data, social media and information submitted.

Expert and Skills:

Using existing unstructured data and structured data from variety sources (document, social profile, resume, skill setup, database fields) and historical hiring information from the client to build a statistical model and algorithm tailored to specific clients hiring process and industries. And use the model to perform machine learning and further refine the model and algorithm moving forward to be able to identify the top talent for any new applicant.

Person will have knowledge of using varies technique and Platforms - Hadoop, Spark, Mapreduce, Distributed computing, and predictive analytics with big data to build a model/algorithm that can be trained and perform deep learning and update based on the data set provided.

Deliverables: The deliverable is an algorithm/model/platform that can be used to work with existing HR system as stand alone product.

Human Resources
Artificial Intelligence
Machine Learning

$20,000 - $30,000

Starts Apr 01, 2017

13 Proposals Status: HIRING

Company small

Client: P****************

Posted: Mar 23, 2017

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Healthcare Data Website with Visualization

We are an international database initiative that was founded in 2009 as a joint collaboration of several dialysis providers. The initiative forms a consortium in which a variety of academic and non-academic institutions from around the world work together on research projects to analyze primary clinical databases of dialysis patients. We are looking to create a website that will have the following pages:

Home Page – Our Story – First Page

  1. Introduction
  2. Vision statement
  3. Mission statement
  4. Video introduction (about 5 – 10 minutes)

What We Have – Our Product – Second Page

1. Data

  • General description of how our data captured, what we have.
  • Interactive map showing our product in different geolocation of the world. Enclosed is the map that we have data (in red). When the cursor points to a certain region, for example, USA – it would show some brief information - N of patients 5k, average age 65, percentage of male 54%.

2. Publications (Projects we had done)

  • Our published abstract (including poster & oral presentation), papers that published in journals, talks that given in national and international events.

Collaboration – Third Page

1. Current collaborating institutions

  • Description and link to website.
  • Member’s information.

2. Collaboration inquiry

  • Contact
  • Submit questions, inquiry.

Web Development
Web Design
Web Programming

$3,000 - $4,000

Starts Apr 01, 2017

6 Proposals Status: HIRING

Company small

Client: R************************

Posted: Mar 22, 2017

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Business Intelligence Dashboard for a Large Financial Advisory Practice

We are one of the largest group of financial advisors in Australia that seeks to develop a dashboard that would provide greater insight into our business, which consists of a network of financial advisors (who are our clients). We have a number of sources of data that need to be combined to provide a coherent view and ability to set alerts when anomalies are detected.

PROJECT OBJECTIVES

By cross analysing the data to create alerts, we are aiming to:

  • Minimise fraud.  Data feeds from XPlan draw direct from banks and fund managers.  
  • Minimise overcharging by using data from funds under management & revenues
  • Minimise churning (re-writing insurance policies to generate continual upfront fees which are higher than ongoing fees) by analysing insurance policies and ‘new’ client data
  • Identify which advisers are generating strong investment portfolio returns, how they are doing it, by looking at portfolio returns and then individual funds within portfolios
  • Accurate funds under management reporting at a dealer level

Real time view of all the above data, with the ability to drill down at an adviser level and then by each individual field.  The alert and reporting system would ideally be customisable as our requirements in terms of what we report on and how we cross analyse data would have a set of initial parameters, but would continually grow and change.  

WHAT WE WANT IN A DASHBOARD

Funds under management (source is XPlan)

- Total $ value, drill down to Adviser, drill down to fund manager

Compliance (source is Accordance Systems)

- # advisers on ‘pass’

- # advisers on ‘watch list’

- # advisers on ‘fail’

Revenue (source is Revex)

- Revenue year to date, drill down to Adviser, drill down to source

Insurance policies (source is XPlan)

- Number of policies and type, drill down to Adviser, drill down to insurer

ALERTS AND REPORTS TO BE GENERATED

Alert : Unsatisfactory Compliance reports

Alert : Fees exceeding 1% of total funds under management

Alert : Upfront Risk revenues that don’t match up with number of new clients

Alert : Declining FUM, not related to market movements

Alert : Outperformance of portfolios (in comparison to a benchmark)

Alert : Correlation of mid range Compliance reports + poor investment returns

Reports : Dealer level FUM report

Reports : Monthly ‘Adviser’ report that summarises all fields as outlined above

DATA AVAILABLE FOR ANALYSIS AND SOURCES OF THIS DATA

CRM – Data source is ‘Accordance Systems’ plus 3 additional fields  

  • Adviser name (manual input)
  • Practice name (manual input)
  • Year they joined the industry (manual input)
  • Qualifications (manual input)
  • Inbound / Outbound communication with the dealer (draws from Outlook) *additional field
  • Dealer assisted projects (manual) *additional field
  • Audit reports (auto generated from Compliance system)
  • Monthly ‘dealer’ report (auto generated from LysensE) *additional field

REVENUE – Data source for everything is ‘Revex.’  

  • Total revenue
  • Total retained revenue
  • Total investment upfront
  • Total investment ongoing
  • Total Risk upfront
  • Total Risk ongoing
  • Total revenue – other
  • Comparison of the above, month on month
  • Revenue as a % of funds under management

INVESTMENTS – Data source for everything is ‘XPlan’ 

  • Total funds under management
  • Total investment returns across asset classes
  • Total investment returns across all portfolios
  • Total investment returns ‘vs’ new funds under management
  • Comparisons month on month
  • Number of insurance policies across Life, TPD, Trauma, Income protection

COMPLIANCE – Data source for everything is ‘Accordance Systems’ 

  • Traffic light system for compliance status

CLIENTS – Data source for everything is ‘XPlan’ 

  • Total clients
  • Total Risk only clients
  • Total investment only clients
  • Total clients – other
  • Total new clients

ACCESS TO DATA SOURCES

You have have full access to all data sources via APIs.  The only system that does not have an API is Accordance Systems.  They are willing to build an API based on your specific requirements because we have a good relationship with the vendor.  The time you will need to spend to provide requirements to Accordance Systems should be factored into your bid.

TECHNOLOGY STACK

We are looking for a cloud-based solution and open to all technologies.  We would like to build this initial system using existing dashboarding tools and we are willing to pay reasonable licence fees for tools that may speed-up the developement.  We strongly prefer technologies that can scale since our eventual goal is to productize this solution and sell to others. There will be additional phases to this project to add more features and functionality.

PROPOSAL

Please provide specific milestones and payment amounts for each and an approximately timeline.  Examples of other dashboarding work you have performed would be helpful.  If you intend to license a cloud-based solution, please provide the monthly cost.

Financial Services
Anomaly Detection
Risk and Compliance

$15,000 - $25,000

15 Proposals Status: CLOSED

Company small

Client: P**********************

Posted: Mar 15, 2017

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SaaS Tool to AWS Benchmarking


We create software that can be activated from a vendor SaaS platform to start a process on AWS.  The AWS process returns results to the SaaS, which are then used to populate an interface.  


We seek a benchmark that the process a) supports over N number of rows b) the total time from clicking "start" the software populating with data and c) how long each component - the SaaS and AWS - takes to process/run.


The outcome is simple:

1) The ability to claim that our software processes N rows. Where N is number we will tell you after awarding the deal.
2) Identify the total time to process across the SaaS & AWS - from start to results populating the software
2a) The time it takes to process on the SaaS
2b) The time it takes to process only on AWS

Data Management
Amazon Web Services
Big Data and Cloud

$100/hr

7 Proposals Status: IN PROGRESS

Net 7

Company small

Client: V********

Posted: Mar 08, 2017

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Create Consistent Work Experiences from Shared Linkedin Profiles, Resumes and Structured Application Forms

Background


We are a provider of eRecruitment technology which is used by our clients to manage the workflow of recruiting new hires including the following steps: posting vacancies, providing online application forms, integration of recruitment tests, communication with candidates etc.
We host online job application processes for our clients, where applicants typically complete a structured application form comprising contact details, education scores and possibly work experience, applicants can also upload their resume/CV as part of their job application. Some candidates also provide the url to their LinkedIn profile and through the LinkedIn API allow us to access their details.  
Our clients’ HR managers and recruiters use the candidates’ education, work history and past leadership achievements to select candidates for job interviews.  
For recruitment into graduate level jobs, e.g. graduate intern, analyst and associate roles, we have developed a target list of important achievements and leadership positions (classified into 19 categories) which are deemed prestigious or important by recruiters.  Some target terms are generic, others are specific to individual universities or countries.  This target list is constructed using regex, for search purposes, and each achievement has an associated score value.  For example “President of the University Student Finance Society” or “All-American Basketball team member” might be awarded 10 points.
We have a large database, approx. 5 million, of existing resumes/CVs which are stored in pdf format, and a similar number of structured application forms, and a small number of shared Linkedin profiles.  

Goals


We receive data in multiple formats and need assistance in extracting consistent homogeneous work experience, achievement and education features for machine learning independent of whether the source is Linkedin, application forms, or resumes.
In this project we’d like to focus on our most pressing need.  We are increasingly accepting LinkedIn profiles and wish to convert these profiles into homogeneous work experience, achievement and education features to pre-populate the candidate’s application form, for checking prior to submission by the candidate.
The goals are to develop a solution which based on a LinkedIn profile (both a shared profile or a link) identifies work experience, achievement and education features and outputs them in a uniform structured format (in which equivalent items, with differing descriptions, are recognized as such) for pre-populating a structured application form.
The resulting features will then be used in machine learning algorithms to predict successful job applicants.  

Deliverables
The solution would need to be developed as a service with an API to work with our proprietary system.

Skills required

Creating APIs
Data management
Natural language processing

Milestones/deadline

We are looking for a working solution that we can implement during Q2 2017.

Note that we have posted three related projects and are willing to work with one supplier on all three, or with separate suppliers according to expertise and interest.
The projects are:
“Create consistent work experiences from shared Linkedin profiles, resumes and structured application forms “

“Create homogeneous consistent features from unstructured and structured data sets comprising vacancies, resumes, application forms, test scores and shared LinkedIn profiles”

“Use machine learning to predict successful hires from homogeneous features collected from vacancies, resumes, and application forms”

Professional Services
Job Applicant Scoring
Human Resources

$6,000 - $8,000

8 Proposals Status: HIRING

Net 30

Company small

Client: W*******

Posted: Mar 06, 2017

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Machine Learning Algorithm to Generate Job Vacancy Descriptions

Background

We are a provider of eRecruitment technology which is used by our clients to manage the workflow of recruiting new hires including the following steps: posting vacancies, providing online application forms, integration of recruitment tests, communication with candidates etc.

We host online job application processes for our clients, where clients create vacancies and applicants typically complete a structured application, upload their resume/CV and provide their LinkedIn profile as part of their job application.

During this process recruiters and line managers need to write vacancy adverts including structured and unstructured information including

             job title

             location

             company

             industry

             vacancy type

             description

             salary

             essential and desirable qualifications

             person specification

             required skills & experience

             application form and process

             shortlist & interview criteria

             online assessment

             interview questions

We have a database of 10,000’s of vacancies in similar but different formats.  On provision of the job title, company, location, industry and type we would like to pre-fill the remainder of the vacancy template with suggested text. 

 

Goals

The goal is to develop a solution that automatically recommends how to complete a vacancy templated by pre-filling it based on the vacancy title and other limited information.

 

Deliverables

The solution would need to be developed as a service with an API to work with our proprietary system.

Skills required

API creation

Data management

Predictive modelling

Milestones/deadline

We are looking for a working solution that we can implement into our systems during Q3 2017.

Professional Services
Job Applicant Scoring
Talent Aquisition Modeling

$12,500 - $14,000

8 Proposals Status: HIRING

Net 30

Company small

Client: W*******

Posted: Mar 06, 2017

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Create a Chatbot That Helps Solve Candidate and Recruiter Queries

Background

We are a provider of eRecruitment technology which is used by our clients to manage the workflow of recruiting new hires including the following steps: posting vacancies, providing online application forms, integration of recruitment tests, communication with candidates etc.

We host online job application processes for our clients, where clients create vacancies and applicants typically complete a structured application, upload their resume/CV and provide their LinkedIn profile as part of their job application.

During this process our help desk receives 1000’s of candidate and 100’s of recruiter email queries asking both for technical support and about the recruitment process.  We respond by email and categorize the candidate queries by type.

We have a database of approximately 150,000 candidate requests and answers and 50,000 recruiter requests and answers. 

 

Goals

The goal is to develop a solution that automatically responds to candidate and recruiter written requests and so resolves their problems asap.

 

Deliverables

The solution would need to be developed as a service with an API to work with our proprietary system.

Skills required

Data management

NLP

API creation

Milestones/deadline

We are looking for a working solution that we can implement into our systems during Q3 2017.

Professional Services
Human Resources

$20,000 - $21,000

16 Proposals Status: HIRING

Net 30

Company small

Client: W*******

Posted: Mar 06, 2017

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Use Machine Learning to Predict Successful Hires from Homogeneous Features Collected from Vacancies, Resumes, and Application Forms

Background

We are a provider of eRecruitment technology which is used by our clients to manage the workflow of recruiting new hires including the following steps: posting vacancies, providing online application forms, integration of recruitment tests, communication with candidates etc.

We host online job application processes for our clients, where clients create vacancies and applicants typically complete a structured application form comprising contact details, education scores and possibly work experience, applicants can also upload their resume/CV as part of their job application. Some candidates also provide the url to their LinkedIn profile and through the LinkedIn API allow us to access their details. 

Our clients’ HR managers and recruiters use the candidates’ education, work history and past leadership achievements to select candidates for job interviews. 

For recruitment into graduate level jobs, e.g. graduate intern, analyst and associate roles, we have developed a target list of important achievements and leadership positions (classified into 19 categories) which are deemed prestigious or important by recruiters.  Some target terms are generic, others are specific to individual universities or countries.  This target list is constructed using regex, for search purposes, and each achievement has an associated score value.  For example “President of the University Student Finance Society” or “All-American Basketball team member” might be awarded 10 points.

We have a large database, approx. 5 million, of existing resumes/CVs which are stored in pdf format, a similar number of structured application forms, and a smaller number of shared LinkedIn profiles.  Additionally we have 10,000’s of vacancies consisting of structured and unstructured data.

 Goals

We have posted another related project to identify candidates achievements, work experience and education and vacancy requirements from a range of inputs (e.g. structured application forms, pdf based resume, linked profile, vacancy templates) and outputs them in a uniform structured format.

 The goal of this project is to develop a machine learning algorithm that uses the homogenous features derived from the related project and the historical candidate outcomes (interviewed or hired) to predict the likelihood of success of new applicants to specific vacancies.

 

Deliverables

The solution would need to be developed as a service with an API to work with our proprietary system.

Skills required

Data management

Data cleaning

Machine learning

Predictive modelling

Creating APIs

 

Milestones/deadline

We are looking for a working solution that we can implement into our systems during Q3 2017.

 Note that we have posted three related projects and are willing to work with one supplier on all three, or with separate suppliers according to expertise and interest.

The projects are:

  • “Create consistent work experiences from shared Linkedin profiles, resumes and structured application forms “

  • “Create homogeneous consistent features from unstructured and structured data sets comprising vacancies, resumes, application forms, test scores and shared LinkedIn profiles”
  • “Use machine learning to predict successful hires from homogeneous features collected from vacancies, resumes, and application forms”

Professional Services
Job Applicant Scoring
Human Resources

$6,000 - $7,500

7 Proposals Status: HIRING

Net 30

Company small

Client: W*******

Posted: Mar 06, 2017

350

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