twitter-stock-recommendation | Simple Stock Investment Recommendation System | Predictive Analytics library

 by   alvarobartt Python Version: Current License: MIT

kandi X-RAY | twitter-stock-recommendation Summary

kandi X-RAY | twitter-stock-recommendation Summary

twitter-stock-recommendation is a Python library typically used in Analytics, Predictive Analytics applications. twitter-stock-recommendation has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However twitter-stock-recommendation build file is not available. You can download it from GitHub.

Simple Stock Investment Recommendation System based on Machine-Learning algorithms for prediction and Twitter Sentiment Analysis.
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              twitter-stock-recommendation has a low active ecosystem.
              It has 77 star(s) with 30 fork(s). There are 7 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 3 have been closed. On average issues are closed in 2 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of twitter-stock-recommendation is current.

            kandi-Quality Quality

              twitter-stock-recommendation has no bugs reported.

            kandi-Security Security

              twitter-stock-recommendation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              twitter-stock-recommendation is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              twitter-stock-recommendation releases are not available. You will need to build from source code and install.
              twitter-stock-recommendation has no build file. You will be need to create the build yourself to build the component from source.

            Top functions reviewed by kandi - BETA

            kandi has reviewed twitter-stock-recommendation and discovered the below as its top functions. This is intended to give you an instant insight into twitter-stock-recommendation implemented functionality, and help decide if they suit your requirements.
            • Decorator to collect the phase of a function .
            • Calculate least squares .
            • Generate a table from a text file .
            • Minimize the function .
            • Analyze group .
            • Computes the least squares solution of a linear operator .
            • r Lars path .
            • Calculate the LOBP objective function .
            • Linear linear operator .
            • Linprog simplification .
            Get all kandi verified functions for this library.

            twitter-stock-recommendation Key Features

            No Key Features are available at this moment for twitter-stock-recommendation.

            twitter-stock-recommendation Examples and Code Snippets

            No Code Snippets are available at this moment for twitter-stock-recommendation.

            Community Discussions

            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 twitter-stock-recommendation

            You can download it from GitHub.
            You can use twitter-stock-recommendation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            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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