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Home Page: https://projectadabubble582.netlify.app/
Project for the Autumn 2019 Applied Data Analysis course given by Robert West at the EPFL.
Home Page: https://projectadabubble582.netlify.app/
Choose from the user's guide which datasets we want. Write a short readme describing each dataset (column names, content, etc).
Clean up the transaction dataset on different jupyter notebook. Try to find budgets and total amount spent per household.
Load datasets into panda dataframes. Add names of dataframes to df_names.md
We focused until now on understanding the relationship between the demographic data and the transactions of the consumers. Thus, we had to filter out a lot of data because demographic data was only available for approximately one third of the households. Now, we saw that there are some correlations between product quantities, and we would like to know if they are accentuated in the whole transaction data.
maybe doxygen ?
Assign numbers or letters to different categories so that we can plot them and sort them more easily.
Write a tutorial for ZenHub and add it to the doc folder.
Run again the Products notebook or fix it
Have a look and think about some simple plots we can do to have a better idea of what could be some trends or interesting things in the data.
Cleaning the People dataset, making sense of household size, number of kids, marital status and household composition.
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