Hi Reza, have you learned of the specialized services in IBM Cloud ? I am referring to Watson Studio, whch is an IDE for Data Science.
Here is the link:
https://www.ibm.com/uk-en/cloud/watson-studioYou can freely create an IBM Cloud account with Lite plan meaning no costs.
https://cloud.ibm.com/loginModels you can build using various instruments: code oriented (Python and R in Jupyter Notebooks, where you can use the scikit learn library), but also the very new, graphical and ML automatized model building tool, developed by IBM Research: Auto AI. This, besides the better known SPSS Modeler, which is available using Watson Machine Learning service in conjunction with Watson Studio.
AutoAI uses various algorithms for the task selected, at the end of the processing, several models being transparently being compared.
The Data being used to train the model can be uploaded to a Cloud Object Storage to which the mentioned services connect.
This, aside from the possibility to feed real time data, with message hubs and streaming analytics, which you can also provision.
Let me/us if you need more information.
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Viktor Kaznovsky
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Original Message:
Sent: Fri September 27, 2019 06:48 PM
From: Reza Hashemi
Subject: Machine Learning
I have a csv dataset and i want to use two or more clustering algorithms, build an unsupervised time-series classifier to identify characteristic day-length patterns. in csv data. in csv dataset each of the columns in the csv data set includes sensor measurements of the same kind for light in a room (units in Lux). I want to use appropriate quantitative metrics to determine the number of time series clusters and to evaluate their quality. In light of the data and the differences between algorithms, i want to speculate on why a given method yielded quantitatively better clusters. I have used many different Prerequisites but I've not been successful to do so.. some where in directional i am going wrong .. any idea ?
in my dataset :↓
' Data columns (total 15 columns): 300000 non-null int64 Date 300000 non-null object d1 300000 non-null int64 d2 300000 non-null int64 d3 300000 non-null int64 d4 300000 non-null int64 d5 300000 non-null int64 d6 300000 non-null int64 d7 300000 non-null int64 d8 300000 non-null int64 d9 300000 non-null int64 d10 300000 non-null int64 d11 300000 non-null int64 d12 300000 non-null int64 d13 300000 non-null int64dtypes: int64(14), object(1)memory usage: 34.3+ MB~~~' Elapsed time : 0.07872253399997135n = 300001Elapsed time : 0.08887843000004068n = 300001Elapsed time : 0.35768378400010986n = 300001Elapsed time : 0.4113426979999349n = 300001Elapsed time : 0.08758717699995032n = 300001~~~' df.shape (300000, 15)~~~'data.shape (300000, 13)~~~
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Reza Hashemi
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