I have created a new experiment in Azure Machine Learning and added two datasets by manually uploading csv's.
One is from a customer of which I'd like to predict which products he will order next.
The second dataset has the same type of data, only then from all other customers as reference for learning.
I have productid, amount, and orderdate and orderid for grouping and putting it on a timeframe.
The customer (dataset one) is always several months behind with ordering the latest products. therefor I added the dataset two with all other customers as reference.
Also because the reference can tell which products are more popular (ordered more and by several customers) so perhaps I should add a customerid column to the dataset.
I know how to start and get the data in, and I do know that it is common to split the data for training, feed it to the train model with a Ilearnerdotnet type and give the output to the score model and evaluate the model.
I do not know how to choose a classification type and how this can give an output for the next three months of order. I have read some tutorials, but I just need someone who can give me some pointers.
edit I have added the customerid to the dataset so that I have just one set now which I should split to focus on a specific customer.
edit2 found these templates. will look into it https://stackoverflow.com/a/36552849/169714
Go over this http://download.microsoft.com/download/0/5/A/05AE6B94-E688-403E-90A5-6035DBE9EEC5/machine-learning-basics-infographic-with-algorithm-examples.pdf
If above infographic doesn't help, then you can try all of the learners by going over this experiment and use the one with best results - https://gallery.cortanaintelligence.com/Experiment/Algo-Evaluater-Compare-Performance-of-Multiple-Algos-against-Your-Data-1
Related
I am currently analysing an online customer review set that contains multiple columns like:
customer_id, star_rating, text_body, helpful_votes etc.
Now I want to compare the rating behaviour of more "experienced" reviewers with less "experienced" reviewers. Therefore, I used data.value_counts()to see the frequency distribution of customer_ids. Now, I want to divide the dataset into different groups. For example: expert reviewers (>200 reviews), experienced (>100), novice (>10) and unexperienced (1-9). Finally, I want to export all reviews into new dataframes and perform NLP with Vader.
So I am currently stuck at dividing the dataset into the different groups. Could you help me out? Sorry for the rookie question by the way.
Kind regards
Wondering if anyone can help me, I am currently learning, SRSS Reports and have been tasked with a Pay & Display vehicle registration report, for staff and student carparks, to show if they are registered for P&D. I have the report laid out as a 3 table column; Name, Vehicle Number and Vehicle Registration. I have from the below code set out in my datasets the Vehicle Number and Vehicle Registrations pulling through, with a parameter to filter out if is staff or student pay and display. However I am having difficulty with pulling the Staff and Student names into one name column as they are through two different datasets. Can anyone be of help at all please? TIA
I don't see the code, but in general, to join between two different datasets you use LOOKUP.
LINK
https://learn.microsoft.com/en-us/sql/reporting-services/report-design/report-builder-functions-lookup-function?view=sql-server-2017
The basic format:
=LOOKUP
(
Fields!key column from the dataset assigned to the table.Value,
Fields!key column from the dataset you are matching to.Value,
Fields!column you want to bring back.Value,
"the name of the other dataset"
)
Warning: Performance can be pretty bad if they're big datasets. You might be better off creating a new dataset that already does this work in the database.
I'm going to try and make this as least confusing as possible, I apologize in advance if my attempt is a failure.
I work in education and am trying to create a predictive analysis document that will tell us how many class sections we will need to offer in a given semester.
I pulled data from the past five years and consolidated it into a pivot chart. I set the pivot chart to combine all courses with a common Subject, Course Title and Catalog Number (See image below for more detail), and output 3 different columns of values based on what we need.
The problem I am facing now is with curriculum changes throughout the years. There are some courses within the list that are no longer being offered and a new course with a new Course Title, Subject and Catalog Number that can now be substituted for the previously needed course. Since the data has been pulled into one pivot chart, both the old curriculum courses and the new curriculum courses are in one list.
I would like to somehow create a relationship between the old curriculum courses and the new curriculum courses. If possible I would like the names of the courses to remain separate, but the values of the old and new to be averaged out together in their respective rows.
In a new page, I plan on putting an easy to use form where the user can select a course subject and name, enter in some other necessary data and the document will output the amount of course sections needed.
Does anyone know of a way to make a relationship between two cells and have other cells effected by this relationship?
Thanks so much!
Mike
enter image description here
I have a data set, and I would like to produce a prediction model based on that data-set, usimg Microsoft Azure
This data-set contains some group of events that together make a bigger event, for example - few lines in the data-set that are close in time (there is a time column) create together one event in time.
does anybody know the method for how can I do it? is there anyway to create a prediction model that learns not from a certain column, but from a different data-set (of results, for that matter)
thanks
Yes, this does exist using Azure Machine Learning, have a look in the Gallery for various examples where this has been shown.
I think for your specific question it will be good to have a look at the Bike regression example. In that example they show how they aggregate information from multiple rows to score on a single feature.
I am trying to create a formula but with no luck for now.
My formula should work for the following situation:
I have a table that reflects data about trainings taken in one department.
As columns we have:
Region
Managers Names
Trainees Names
Training Details
Training Status
Status Comments
I need a formula that would allow me to say how many people I have trained (with no matter of what exactly training) while I filter any of the columns in the table (by Region, by Manager, by Training Status and etc. )
Important moment here is that when I filter I might have repetitive Trainees Names, but I want the final # of trained people to have included only unique values. I can’t get rid of duplicated Trainee Names in the table itself because it’s ok to have them there (for instance one person have one completed training and another that is currently ongoing and with that I have his/her name appearing twice in the “Trainees Names” column). All people in the table passes at least one training.
This is exactly what a PivotTable can do (and more) in Excel 2013. Put everything into FILTERS and Trainees Names into VALUES, as Distinct Count.