ForecastGA | Python tool to forecast Google Analytics data | Predictive Analytics library

by jroakes Python Version: 0.1.16 License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | ForecastGA Summary

ForecastGA is a Python library typically used in Analytics, Predictive Analytics, Tensorflow applications. ForecastGA has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install ForecastGA' or download it from GitHub, PyPI.

A Python tool to forecast Google Analytics data using several popular time series models.

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Quality

Security

License

Reuse

Support

ForecastGA has a low active ecosystem.

It has 36 star(s) with 9 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ForecastGA is 0.1.16

Quality

ForecastGA has 0 bugs and 0 code smells.

Security

ForecastGA has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ForecastGA code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ForecastGA is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

ForecastGA releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 3696 lines of code, 332 functions and 49 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ForecastGA and discovered the below as its top functions. This is intended to give you an instant insight into ForecastGA implemented functionality, and help decide if they suit your requirements.

Pull data from Google Analytics
Load model from checkpoint
Loads a user s profile
Load an identity
Train the model
Generate a series of data
Formats the input data
Train 100 gradients
Serialize a value
Return a list of values from obj
Create a Column from a metadata dict
Get a logger object
Performs an insamples sampling
Generate a query based on the given description
Return a list of queries that match the given profile
Calculates the prediction of the training data
Calculate out - of - sample data
Plot a colaborn plot
Calculate the forecast for the given steps_ahead
Calculate the prediction
Builds a LightGBM Ensemble from the forecast data
Revoke credentials
Serialize the table
Generate holidays file
Plot the contribution of the singular_i
Parse the dataframe

Get all kandi verified functions for this library.

ForecastGA Key Features

No Key Features are available at this moment for ForecastGA.

ForecastGA Examples and Code Snippets

How To Use

Python

Lines of Code : 21

License : Permissive (MIT)

Copy

data = { 'client_id': '',
         'client_secret': '',
         'identity': '',
         'ga_start_date': '2018-01-01',
         'ga_end_date': '2019-12-31',
         'ga_metric': 'sessions',
         'ga_segment': 'organic traffic',
         'ga_ur

Installation

Python

Lines of Code : 2

License : Permissive (MIT)

Copy

pip install --upgrade mxnet-cu101
pip install --upgrade torch 1.7.0+cu101

Community Discussions

Trending Discussions on Predictive Analytics

will TensorFlow utilize GPU for predictive Analysis?

Restructuring Pandas Dataframe for large number of columns

Display data from two json files in react native

QUESTION

will TensorFlow utilize GPU for predictive Analysis?

Asked 2020-Nov-21 at 21:35

GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

note: predictive Analysis requires no graphics processing.

...

ANSWER

Answered 2020-Nov-21 at 21:35

The short answer: yes, it will! The slightly longer answer:

The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

If you want to know more about this, I'd recommend you start here or here.

some things to consider:

how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.

Source https://stackoverflow.com/questions/64948197

QUESTION

Restructuring Pandas Dataframe for large number of columns

Asked 2020-Nov-01 at 19:39

I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

The current structure is as below.

What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.

...

ANSWER

Answered 2020-Nov-01 at 19:39

You might want try pivoting your table:

Source https://stackoverflow.com/questions/64630691

QUESTION

Display data from two json files in react native

Asked 2020-May-17 at 23:55

I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

...

ANSWER

Answered 2020-May-17 at 23:55

The new object to get params in React Navigation 5 is:

Source https://stackoverflow.com/questions/61859411

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

Vulnerabilities

No vulnerabilities reported

Install ForecastGA

Windows users may need to manually install the two items below via conda :. otherwise, pip install --upgrade forecastga. This repo support GPU training. Below are a few libraries that may have to be manually installed to support.
conda install pystan
conda install pytorch -c pytorch
!pip install --upgrade git+https://github.com/jroakes/ForecastGA.git

Support

The goal of this repo is to grow the list of available models to test. If you would like to contribute one please read on. Feel free to have fun naming your models. If you enjoyed this tool consider buying me some beer at: Paypalme.

Find more information at: