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License: GNU General Public License v3.0
RFM modeling in Python
License: GNU General Public License v3.0
Feedback by anyone using this is welcome. This was written to automate a typical RFM modeling process I would work on where there was no available package I was able to find for Python. If anyone has any feedback in regards to it or any issues feel free to reach out.
I was comparing three different scoring methods, mean median and quintile.
I noticed that somebody who has a recency of 381.0 will get a score of 1 with scoring='Mean' and a score of 5 if you use scoring='Quintile'.
My data:
print(transactions.head())
iban_hash local_date amount
0 0a00c7432fb237806ee8c073c620f56edf8c5b42936657... 2016-09-30 8.1
1 97287183cee5705d3c2ab6b2ccd207fbeae1c86bf88a0c... 2016-10-02 4.0
2 1b4645137726e017339bc5b10de23eb1138bd9ee15a76d... 2016-10-03 3.0
3 f9b50094d36c3fbcd4e912d7b0ded2f74b16243dd2ee3c... 2016-10-03 9.7
4 0a00c7432fb237806ee8c073c620f56edf8c5b42936657... 2016-10-04 10.0
Dtypes:
transactions.dtypes
iban_hash object
local_date datetime64[ns]
amount float64
Running the model:
# set up RFM instance
myrfm = rfm.RFM()
# apply scoring to dataset
myrfm.fit(data=transactions,dataset_type='transactional',scoring_method='Median', recency_end_date='02/29/2020')
Output of myrfm.get_fitted_data()
I have just upgraded to version 1.0.4. I don't remember seeing NAs in previous versions. Even if their relative values were low, e.g. Somebody with a monetary value of $1 would still get a low score rather than NAN
.
I did have to edit one date time object to get the package to work (see pull request). Could this have caused the problem? I can't revert my change because then the package doesn't work at all. :)
In line 16, the user specified end date was not converted to a datetime object. It was throwing an error. I fixed it manually by adding datetime.strptime(recency_end_date, '%m/%d/%y')
around recency_end_date
.
From the rfm_utils.py
file:
Before:
def convert_transaction_to_user(df, recency_end_date=None):
id_col = df.columns[0]
date_col = df.columns[1]
transaction_col = df.columns[2]
df[date_col] = pd.to_datetime(df[date_col])
if recency_end_date is not None:
end_date = recency_end_date
elif recency_end_date is None:
end_date = datetime.today()
After
def convert_transaction_to_user(df, recency_end_date=None):
id_col = df.columns[0]
date_col = df.columns[1]
transaction_col = df.columns[2]
df[date_col] = pd.to_datetime(df[date_col])
if recency_end_date is not None:
end_date = **datetime.strptime(recency_end_date, '%m/%d/%y')**
elif recency_end_date is None:
end_date = datetime.today()
This is useful for data from today or yesterday, but I am currently working with historical data. Also the example .csv you have is from 5 years ago.
It would be useful to have an optional argument that perhaps sets Now as the latest date in the dataframe, rather than today, to make testing easier to interpret.
Thanks!
I can see that weights are pre-set and must sum to 1
def __init__(self,weights=(0.2,0.2,0.6)):
if sum(weights) == 1 and len(weights) == 3:
self.weights = weights
else:
raise ValueError
What if I don't want to weight the scores?
I was using version 1.0.1
for quite a while, then noticed today that there was a 1.0.3
version.
I updated via pip, and now the import doesn't work.
The .py
files for this package are also nowhere to be found on my computer. The only reference to your package is in anaconda3\lib\site-packages\crm_rfm_modeling-1.0.3.dist-info\
. Can't find any actual .py files like I could with version 1.0.1.
I then uninstalled the package entirely and simply pip installed version 1.0.3 rather than updating. This didn't work either.
I reverted back to 1.0.1
and everything works fine, .py
files are present on my system.
"recency_scoring_method","frequency_scoring_method", and "monetary_scoring_method";
Set the value of these **kwargs to the following defined scoring methods listed above.
These are part of the fit()
method. What are these for and how can we use them?
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