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# System prepended metadata

title: 'Rewards Systems Research - Homework #3: Raw Data & Simple Metrics'

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# Rewards Systems Research - Homework #3: Raw Data & Simple Metrics


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## Team: 3.14


## Case 4: TEC Praise System
#### Data:
(data was moved to the program repo) https://github.com/TokenEngineeringCommunity/rewardsresearch/tree/main/TEC/data
#### Homework questions:
Q1. Which Praise received the most Impact Hours in Batch 1? 
48.829077	for inventing bonding curves

Q2. Which user issued the most Praises in Batch 2? 
cranders71 208

Q3. Which user received the most Praises in Batch 3? 
GriffGreen 210

Q4. What were average Impact Hours per Praise in Batch 1? 
1.73

Q5. Which day in 2020 had the most Praises? 
2020-12-04 108

#### Reflection questions:
* Descripe the tools & process you used:
Python

* Any challenges / difficulties (i.e. dataset issues, definitions):
Not clean data, need to check and make manual cleaning for NaNs for example

* Is there any raw data missing that might be interesting to analyze?
Likes to praise from other community members

* Which datapoints do you think are relevant to measuring system health?
Every data paint. But it is possible to create some metrics like average IH for praise receiver and praise sender

