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portfolio

naam: Adnan Akbas studentnummer: 17005116

Project ActivPal

Team members

  • Ali
  • Colin
  • Mark
  • Matthew

Datacamp

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Course Statement of Accomplishment
Introduction to python proof
Python Data Science Toolbox (Part 1) proof
Intermediate Python proof
Python Data Science Toolbox (Part 2) proof
Pandas Foundations proof
Introduction to Data Visualization in Python proof
Manipulating Dataframes with pandas proof
Data Types for Data Science in Python proof
Cleaning data in Python proof
Preprocessing for Machine Learning in Python proof

Research project

Task Definition

In the research plan I have written the second iteration of the problem domain/context while my teammates have worden on other parts. Where they have defined the research question among others. In the second iteration I have processed the feedback we got from one of our teachers.

Statistics Netherlands (CBS) has the wish to see if their respondents are moving for at least 150 minutes per week in moderately intense physical activity. Currently, they are measuring by asking their respondent or health surveys. The issue with this is that people are not very good at estimating the time they spent on moving and sport. This of course causes that they don't have very reliable data to work with. Therefor CBS has been looking into alternatives like the ActivPal accelerometer in combination with machine learning to give better and more accurate results when measuring the intensity of certain activities. Because of this the CBS started to collect lab tests and started to measure the movements of 41 correspondents in their regular workweek by using the Activepal Accelerator. It's our job to analyse, structure and build machine learning algorithms based on the collected data to see if we can determine if people adhere to (inter)national norm for physical activities and if we could measure the intensity of movement (without the heart rate information).

src: research_plan.pdf

Evaluation

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Conclusions

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Planning

At the beginning of our project we have decided that we will use Jira as our scrum board and will implement Scrum in our way and won't follow it to the detail. Our group was based on trust that's also the reason why we didn't setup nor sign a contract for teamwork.

Our process looked as following. At the start of each sprint we decided what goal is for the sprint. With this goal in mind we created task which each team member could choose and take it on themself. The task can be modified, removed or created while the sprint were going onbut that was always first discussed within the group before any modification. Each morning at 9:30 our group was holding a stand-up. There we discussed what we did day earlier, going to do today and if we are stuck with something. At the end of each sprint our group was holding retrospective where we discussed our progress and teamwork last sprint. The role of scrum master was taken on by Ali Safdari.

My role in scrum

I didn't have specific role in the scrum process other than developer but i did actively participate in each phase of the scrum process.At the sprint planning I have actively created task of course after discussing with the team. At the same time I would assign task to myself unless other team members would want them. In some cases we would assign multiple members to a task. Unfortunately Jira doesn't support that so we would write the names in description of the task. Each morning I would join stand-up with my other team members and explain what i did yesterday, what i am gonna do today and if i need help. Unfortnatly and reasonably we didn't take notes so I don't have evidence for this. At the end of each sprint I would join retrospective and give my input. I would say what we did well, what didn't go well and what i wish next sprint would get better.

More information on our take

Domain knowledge

Introduction of the subject field

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literature Research

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Explanation of Terminology, jargon and definitions

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Data preprocessing

Data exploration

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Data cleaning

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Data preparation

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Data visualization

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Predictive Analytics

selecting a model

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training model

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evaluating a model

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Visualizing the outcome of a model

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configuring a model

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communication

presentation

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writing paper

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  • Jupyter Notebook 99.6%
  • Python 0.4%