regression-analysis | Analyzing correlation between stock , revenue , market cap | Business library

by puneetjaiswal R Version: Current License: No License

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kandi X-RAY | regression-analysis Summary

regression-analysis is a R library typically used in Web Site, Business applications. regression-analysis has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Analyzing correlation between stock, revenue, market cap and number of employees

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regression-analysis has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

regression-analysis has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of regression-analysis is current.

Quality

regression-analysis has no bugs reported.

Security

regression-analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

regression-analysis does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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regression-analysis releases are not available. You will need to build from source code and install.

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regression-analysis Key Features

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regression-analysis Examples and Code Snippets

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Community Discussions

Trending Discussions on regression-analysis

Using predict() across multiple models to generate confidence intervals in R

R shiny how to "box" a simple text on a shiny page

Difference between ordinal and categorical data as labels in scikit learn

Plotting and Linear Regression models with R

Django - when best to calculate statistics on large amounts of data

make ggplot panel-background like in gtable

QUESTION

Using predict() across multiple models to generate confidence intervals in R

Asked 2021-Mar-17 at 01:14

My goal is to create multiple models from a dataframe and then generate confidence intervals around the fitted values that correspond to those different models.

Pulling in libraries:

...

ANSWER

Answered 2021-Mar-17 at 01:14

We can use invoke, specify the data as 'data_1' and the models as a list (the model_dna column in the model_dna is a list)

Source https://stackoverflow.com/questions/66664273

QUESTION

R shiny how to "box" a simple text on a shiny page

Asked 2021-Jan-05 at 23:31

I'm using documentation https://shiny.rstudio.com/tutorial/written-tutorial/lesson2/ and more precisely the following code to add a simple paragraph to my shiny page:

...

ANSWER

Answered 2021-Jan-05 at 23:31

It's not about HTML it's CSS what you should look for. (;

For example you could copy & paste the CSS styling rules from the webpage you linked into you shiny app (not the recommend way but quick & dirty) to change the appearance of the code tag like so:

Source https://stackoverflow.com/questions/65587869

QUESTION

Difference between ordinal and categorical data as labels in scikit learn

Asked 2020-Feb-12 at 22:07

I know that as features ordinal data could be assigned arbitrary numbers and OneHotEncoding could be done for categorical data. But I am a bit confused how these two types of data should be handled when they are the feature to be predicted. For instance in the iris dataset in scikitlearn:

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ANSWER

Answered 2020-Jan-29 at 22:05

You don't need to encode your label. scikitlearn takes care of it. Same table used to build a classifier:

Source https://stackoverflow.com/questions/59974434

QUESTION

Plotting and Linear Regression models with R

Asked 2019-Oct-03 at 18:12

I am working a homework project for R, I have a CSV file that i need to import, so some analysis on and create a scatter plot and linear regression.

At one point my code has an error, and I am not sure why or where, I think it might be in this line:

...

ANSWER

Answered 2019-Oct-02 at 19:55

The t() function is creating a matrix with dimensions greater than 2. The error is saying that you are only giving the matrix two column names, but the matrix array needs more than two.

Is there a reason you are doing header = F? If not, then the following may work:

Source https://stackoverflow.com/questions/58207160

QUESTION

Django - when best to calculate statistics on large amounts of data

Asked 2018-Jan-19 at 08:18

I'm working on a Django application that consists of a scraper that scrapes thousands of store items (price, description, seller info) per day and a django-template frontend that allows the user to access the data and view various statistics.

For example: the user is able to click on 'Item A', and gets a detail view that lists various statistics about 'Item A' (Like linegraphs about price over time, a price distribution, etc)

The user is also able to click on reports of the individual 'scrapes' and get details about the number of items scraped, average price. Etc.

All of these statistics are currently calculated in the view itself.

This all works well when working locally, on a small development database with +/100 items. However, when in production this database will eventually consist of 1.000.000+ lines. Which leads me to wonder if calculating the statistics in the view wont lead to massive lag in the future. (Especially as I plan to extend the statistics with more complicated regression-analysis, and perhaps some nearest neighbour ML classification)

The advantage of the view based approach is that the graphs are always up to date. I could offcourse also schedule a CRONJOB to make the calculations every few hours (perhaps even on a different server). This would make accessing the information very fast, but would also mean that the information could be a few hours old.

I've never really worked with data of this scale before, and was wondering what the best practises are.

...

ANSWER

Answered 2018-Jan-19 at 08:18

As with anything performance-related, do some testing and profile your application. Don't get lured into the premature optimization trap.

That said, given the fact that these statistics don't change, you could perform them asynchronously each time you do a scrape. Like the scrape process itself, this calculation process should be done asynchronously, completely separate from your Django application. When the scrape happens it would write to the database directly and set some kind of status field to processing. Then kick off the calculation process which, when completed, will fill in the stats fields and set the status to complete. This way you can show your users how far along the processing chain they are.

People love feedback over immediate results and they'll tolerate considerable delays if they know they'll eventually get a result. Strand a user and they'll get frustrate more quickly than any computer can finish processing; Lead them on a journey and they'll wait for ages to hear how the story ends.

Source https://stackoverflow.com/questions/48080579

QUESTION

make ggplot panel-background like in gtable

Asked 2017-Sep-20 at 15:24

I'm trying to make a graphic like this :

Optimal/efficient plotting of survival/regression analysis results

hier is my example code :

...

ANSWER

Answered 2017-Sep-20 at 15:24

As @MrFlick said you have to add some kind of geom layers in the background. This is my approach:
Create two data frames, one for points and one for segments and adjust segment size to match table rows.

Source https://stackoverflow.com/questions/46324520

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install regression-analysis

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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