stock-prediction | Machine learning pipeline for training TensorFlow | Machine Learning library

by UWFlex Python Version: Current License: GPL-3.0

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

kandi X-RAY | stock-prediction Summary

stock-prediction is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. stock-prediction has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

A complete machine learning data pipeline for training TensorFlow models to forecast stock prices. Written in Python. Goal: given stock data (opening, closing and indicators), predict next day's adjusted closing price. Mean Relative error: 11.53%.

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Quality

Security

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Support

stock-prediction has a low active ecosystem.

It has 48 star(s) with 11 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 17 have been closed. On average issues are closed in 96 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of stock-prediction is current.

Quality

stock-prediction has 0 bugs and 0 code smells.

Security

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

stock-prediction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

stock-prediction is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

stock-prediction saves you 157 person hours of effort in developing the same functionality from scratch.

It has 391 lines of code, 22 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Fetch data from symbols
Splits data into training and test sets
Build url
Format path from root
Make directory if not already exists
Get json from url
Preprocess a single CSV file
Fill missing values
Split data into train and test data
Scale train and test data
Constructs a label from a dataframe
Return a list of filenames ending with suffix
Train a neural network
Train the model
Saves the model
Evaluate a single file
Evaluate the model
Plot the closing edge of the data

Get all kandi verified functions for this library.

stock-prediction Key Features

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

stock-prediction Examples and Code Snippets

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

Community Discussions

Trending Discussions on stock-prediction

How to upload files to Amazon EMR?

Wordpress widget as div

Numpy Array creation causing "ValueError: invalid literal for int() with base 10: 'n'"

QUESTION

How to upload files to Amazon EMR?

Asked 2021-Apr-13 at 08:00

My code is as follows:

...

ANSWER

Answered 2021-Apr-13 at 08:00

Your understanding is correct.

--files argument is uploading files to executors only.

See this in the spark documentation

file: - Absolute paths and file:/ URIs are served by the driver’s HTTP file server, and every executor pulls the file from the driver HTTP server.

You can read more about this at advanced-dependency-management

Now coming back to your second question

How can I upload to master?

There is a concept of bootstrap-action in EMR. From the official documentation it means the following:

You can use a bootstrap action to install additional software or customize the configuration of cluster instances. Bootstrap actions are scripts that run on cluster after Amazon EMR launches the instance using the Amazon Linux Amazon Machine Image (AMI). Bootstrap actions run before Amazon EMR installs the applications that you specify when you create the cluster and before cluster nodes begin processing data.

How do I use it in my case?

While spawning the cluster you can specify your script in BootstrapActions JSON Something like the following along with other custom configurations:

Source https://stackoverflow.com/questions/67042056

QUESTION

Wordpress widget as div

Asked 2018-Sep-05 at 18:02

I created simple widget with "Hello World!" as content:

...

ANSWER

Answered 2018-Sep-05 at 17:36

You can use it like this

Source https://stackoverflow.com/questions/52190416

QUESTION

Numpy Array creation causing "ValueError: invalid literal for int() with base 10: 'n'"

Asked 2018-Feb-25 at 23:33

I'm trying to run a predictive RNN from this repo https://github.com/jgpavez/LSTM---Stock-prediction. "python lstm_forex.py"
It seems to be having trouble creating an empty Numpy array

The function giving me problems, starting with the line 'days', fourth from the bottom.

...

ANSWER

Answered 2018-Feb-25 at 23:33

You're trying to int() the string 'n' in your assertion. To get the same error:

Source https://stackoverflow.com/questions/48979223

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

Vulnerabilities

No vulnerabilities reported

Install stock-prediction

You can download it from GitHub.
You can use stock-prediction 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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