Stock-Price-Prediction | Stock Price Predictor it predicts the stock prices | Business library

by Sumirmat97 Python Version: Current License: No License

X-Ray Key Features Code Snippets(1)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Stock-Price-Prediction Summary

Stock-Price-Prediction is a Python library typically used in Web Site, Business applications. Stock-Price-Prediction has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

It is a stock price prediction application made in Python. The users can either train the models with their data and then use the for predictions or predict the stock prices of "Bank of Baroda" using the existing models.

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Security

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Support

Stock-Price-Prediction has a low active ecosystem.

It has 6 star(s) with 6 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Stock-Price-Prediction has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Stock-Price-Prediction is current.

Quality

Stock-Price-Prediction has no bugs reported.

Security

Stock-Price-Prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Stock-Price-Prediction 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.

Reuse

Stock-Price-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.

Top functions reviewed by kandi - BETA

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

Predict the day of a given time series
Cleans the dataframe
Interpolate columns in a dataframe
Embeds the given newsAttribute
Remove punctuation
Predict from a TimeSeries
Generates a decision tree based on the decision tree
Calculates the Bayesian Ridge Method
Creates a graph plot
Predict from news
Combine two prediction time series
Takes a string and embeddings
Parse command line arguments
Clean a dataframe
Interpolate cols to interpolate

Get all kandi verified functions for this library.

Stock-Price-Prediction Key Features

No Key Features are available at this moment for Stock-Price-Prediction.

Stock-Price-Prediction Examples and Code Snippets

Build the symbol .

python

Lines of Code : 33

License : No License

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def build_sym(seq_len, use_cudnn=False):
    """Build the symbol for stock-price prediction

    Parameters
    ----------
    seq_len : int
    use_cudnn : bool, optional
        Whether to use the LSTM implemented in cudnn, will be faster than the

Community Discussions

Trending Discussions on Stock-Price-Prediction

Getting error (numpy.ndarray' object is not callable) while running following code in Jupyter

Confusion regarding data generator in python for use in Keras fit_generator

QUESTION

Getting error (numpy.ndarray' object is not callable) while running following code in Jupyter

Asked 2020-Aug-26 at 08:15

Checking Robustness of the model In this section we will check robustness of our LSTM model. I have used new unseen datasets for this from July 1, 2017 to July 20,2017. I have downloaded the data sets from google finance website to check for robustness of the model.

...

ANSWER

Answered 2020-Aug-26 at 08:15

The .values property shouldn't have ():

Source https://stackoverflow.com/questions/63593284

QUESTION

Confusion regarding data generator in python for use in Keras fit_generator

Asked 2019-Jul-27 at 19:38

In some question and tutorials like below:

suggest that a data generator for keras should be a class having __iter__ and __next__ methods in it.

While some other tutorials like:

use the normal python function with a yield statement providing data. While I successfully used yield in an LSTM network following the second tutorial above, I am not able to use the normal yield function in a convolutional network and getting the below error in fit_generator:

'method' object is not an iterator

I haven't tried using the __next__ method, but whoever got the above error was advised to use the __next__ method (EDIT: working after a fix suggested by Daniel Möller). Can someone please help me clarify which technique to use when and what is the difference between a function that "yields" the next sample vs a class with __iter__ & __next__?

My working code using yield: https://github.com/KashyapCKotak/Multidimensional-Stock-Price-Prediction/blob/master/StockTF1_4Sequential.ipynb

My current data generator function using yield (EDIT: working after a fix suggested by Daniel Möller):

...

ANSWER

Answered 2019-Jul-27 at 19:38

Looking carefully at the word 'method', I see you are not "calling" your generator (you are not creating it).

You are passing just the function/method.

Suppose you have:

Source https://stackoverflow.com/questions/57223191

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

Vulnerabilities

No vulnerabilities reported

Install Stock-Price-Prediction

You can download it from GitHub.
You can use Stock-Price-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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