I am working on a scatter chart to display Speed (X-axis) vs Consumption (Y-axis) of different vehicle designs. The goal of the report is to examine that for the same design, is the particular vehicle more or less efficient than that of others in the market.
I would like to know if it is possible for the user to input the specifications for the particular vehicle's X-axis and Y-axis within the report itself so that the user can compare it visually.
As seen in the image below, say the user has input the specifications for the specific vehicle when it is laden (in red) and when it is ballast (in green).
There are a few options:
When in Direct Query or Mixed Mode:
Embed PowerApps in your report to capture data and write it to your data store and then refresh the visual.
Build a companion App (in the tech of your choice) to update the DB with your parameters.
When in Import Mode: Use what if parameters to provide the input.
Related
Anyone have experience with Drilldown Choropleth recently? I have taken a step back to try ArcGIS, but want to have a multi-layer map built in Power BI with shading using this add-in. I am having issues with loading the json- one for States (USA), one for Metro Area (MSA, USA). Also, not seeing the fields to add data points. This info I researched on the app my info has a json file link that is going to a 404.
If anyone wants to provide tips to transfer over to a contained ArcGIS, I would accept that.
More on the app: https://appsource.microsoft.com/en-us/product/power-bi-visuals/wa104381044?tab=overview
I basically need one layer shading on drill down for geo with points, then add one layer for demographic stats, one layer for population stats. Help?
for topojsons that work:
https://github.com/deldersveld/topojson
I used the US Counties one, so that's all I can comment on working.
So I am a newbie to Google BigQuery ML and was wondering if the auto.arima automatically makes my time series data stationary ?
Suppose, I have a data that is not stationary and if I give the data as is to the auto arima model using Google BigQuery ML, will it first makes my data stationary before taking it as input ?
but that's part of the modeling procedure
From the documentation that explains What's inside a BigQuery ML time series model, it does appear that auto.ARIMA will make the data stationary.
However, I would not expect it to alter the source data table; it won't make that stationary, but in the course of building candidate models it may alter the input data prior to actual model fitting (transforms; e.g. box-cox, make stationary, etc.)
I need your help.
I work for a survey company and I am responsible for creating its architecture and modeling a data warehouse that analyzes the results of an international survey (50 countries).
For the architecture, we decided to create a tabular model in PowerBI to analyze our data and to create our reports.
Here below is the model as I thought:
However, I have a design problem.
Since the survey is international, the wording of my dimensions differs from country to country.
My 1st question:
-Would it make more sense to create only one PowerBI embedded model for all countries or 50 PowerBI reports?
My 2nd question:
My model must be multilingual
With my 50 countries, I have several languages (5 languages) and for the same language, I have several variants.
The British English labels differ from the US English labels.
For example, for the Response dimension for France the IdReponse = 1 has the wording 'Vrai' while for the USA the wording is 'True' and for the Britain is 'OK'.
Do you know how to model multi language in a data warehouse?
About question #1 - It's always better, if there is only one model. It will be much easier to maintain. It isn't clear from your question will these 50 reports show the same data (excluding the internationalization of texts like Vrai/True/OK), or each report/country should show it's own subset of the data. In case all reports will show the same data, then definitely it will be better to make one common model and all report use it. You can do this with Power BI by making one "master" report and publishing it, and then the rest of your "per country" reports use it as a data source. And you will need separate reports per country, because you will need to translate the texts (column names, static texts, etc.).
About question #2 - You can create lookup tables in your model (maybe even in the database, it's up to you). The key value (1) will be linked to the key of the table, and there will be columns per language. Depending on the language of the current report, you will select the appropriate column (e.g. French, British, etc.) and even you can fallback to let's say US English, in case there is no translation entered for the current language (e.g. by making a computed column). It is also an option to make separate lookup table per language, but I think it will be more cumbersome to maintain this way.
About question #1: Yes you need only one data model.
About question #2: You Load a question in the language it is asked and the response you get as is in the response DIM. You should create a new column in your response DIM such as Clean_response where you transformed original response to a uniformed value. for example "Vrai", "OK", "True" has same meaning so you may chose to put "Yes" in the Clean_response column. You can also convert different variation of "No", "Nada", "noops", "nah" to a clean value of "No", but keep the original value too.
Labeling a column in the report should be handle in the report code. For example writing a report in French should use your dim column name "Question" and show it as "interroger" as a heading on the report.
I am making a nice dashboard. I have Geo-map with projects. When a user clicks on a particular project it will take the user to a detailed page about the project. I want to give this page (sheet) a dynamic title.
For example: The user clicks on the GEO map on the project in Aalst, he will be redirected to the detail page with a tittle "Costs and revenues for Project in Aalst".
Pictures of my dashboard:
enter image description here
enter image description here
I was working with VA 7.1 and there wasn't any way to parameterized text fields in VA reports. I've heard SAS wants to add this function in future release and at this moment you have to find a different way to put dynamic label on the report.
For example you can put a flat table with combination of every posible title you need. Then change style parameters for this table. You can turn off borders and column headers, change table background color, text font size and color, so you can make it looks like as a title field. Then you define filter connection between GEO map and this table, so when you click on the GEO map it will filter single record with correct title.
I have a database which consists of 27 attributes and 597 instances .
I want to classify it with as best results as possible using Weka.
Which classifier is not important .The class attribute is nominal and the rest are numeric .
The Best results until now was LWL (83.2215) and oneR(83.389). I used attribute selection filter but the results are not improved and no other classifier can give better results even NN or SMO or meta classes.
Any idea about how to improve this database knowing that there are no missing values and the database is about 597 patients gathered in three years.
Have you tried boosting or bagging? These generally can help improve results.
http://machinelearningmastery.com/improve-machine-learning-results-with-boosting-bagging-and-blending-ensemble-methods-in-weka/
Boosting
Boosting is an ensemble method that starts out with a base classifier
that is prepared on the training data. A second classifier is then
created behind it to focus on the instances in the training data that
the first classifier got wrong. The process continues to add
classifiers until a limit is reached in the number of models or
accuracy.
Boosting is provided in Weka in the AdaBoostM1 (adaptive boosting)
algorithm.
Click “Add new…” in the “Algorithms” section. Click the “Choose”
button. Click “AdaBoostM1” under the “meta” selection. Click the
“Choose” button for the “classifier” and select “J48” under the “tree”
section and click the “choose” button. Click the “OK” button on the
“AdaBoostM1” configuration.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble method that creates
separate samples of the training dataset and creates a classifier for
each sample. The results of these multiple classifiers are then
combined (such as averaged or majority voting). The trick is that each
sample of the training dataset is different, giving each classifier
that is trained, a subtly different focus and perspective on the
problem.
Click “Add new…” in the “Algorithms” section. Click the “Choose”
button. Click “Bagging” under the “meta” selection. Click the “Choose”
button for the “classifier” and select “J48” under the “tree” section
and click the “choose” button. Click the “OK” button on the “Bagging”
configuration.
I tried Boosting and Bagging as #applecrusher has mentioned. It showed a little improvement in the accuracy; but for the same data with SKLearn, I was getting a lot better accuracy. When I compared the code and output at each step, I found that train-test split function in SKLearn was, by default, shuffling the data. When I shuffled the data for WEKA using Collections.shuffle(), I saw improved results. Give it a try.