Commit c72c4d3b authored by Francesco Saltarelli's avatar Francesco Saltarelli

Update README.md

parent 6fc7e865
......@@ -17,11 +17,10 @@ All the best,
Your Team Green:
_Nico Messikommer_,
_Francesco Saltarelli_,
_Stephan Artmann_,
_Viktoria de La Rochefoucauld_
_Nico Messikommer_,
_Francesco Saltarelli_,
......@@ -34,16 +33,15 @@ _Viktoria de La Rochefoucauld_
## Terminology Clarification
In the following, we will mention PiN scores versus PiN index. These two terms are not the same thing.
With the PiN score (scale of 0 - 10 per sector), we mean the absolute calculated PiN value according to the MSNA report provided by IMPACT.
In the following, we will mention Person in Need (PiN) scores versus PiN index. These two terms are not the same thing.
With the PiN score (scale of 0 - 10 per sector), we mean the absolute calculated PiN value according to the MSNA report provided by IMPACT (Annex 6).
In order to execute our predictions, however, we had to set a threshold for when a household is 'in need' versus when it is not 'in need'.
This threshold lies at a PiN score of 4. Above, a household is considered to be 'in need'
This threshold lies at a PiN score of 4, according to the MSNA report. Above, a household is considered to be 'in need'
Therefore, the PiN index will tell us solely about whether or not a household is in need or not.
For example:
A household with e.g. a PiN score of 6 in the Wash sector would have a PiN index of 1 for this sector.
A household with a PiN score of 6 in the Wash sector would have a PiN index of 1 for this sector, i.e., it will be classified as 'in need'.
## Data Cleaning
......@@ -61,6 +59,7 @@ clean.one_hot #one-hot encoded table
**Note:** This code assumes that the xls-Tables have not changed and loads pre-computed cleaned tables. If you work with new xls-Tables, or if they have changed, change the second line to
```clean = clp.clean_table(reload_tables=True)```
## Predictions
tbd
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