1603494000

** Note from Towards Data Science’s editors:**_ While we allow independent authors to publish articles in accordance with our

Prediction of stock price is quite a challenging task because prices are highly volatile and mostly stochastic movement with non-linear nature. Here, the problem we have in hand is a price prediction issue and we’re trying to predict a numerical value defined in a range (from 9000 to 12500 approx). This problem fits the Regression Analysis framework. We shall be using neural network architecture to try to solve the problem here. Motivation of using neural network is that, it is one of the intelligent data mining techniques that identify a fundamental trend from data and to generalize from it.

We’ll build a Deep Neural Network here that does some forecasting for us and use it to predict future price. Let us load the hourly frequency data.

We have a total of 2001 data points representing Bitcoin in USD . We’re interested in predicting the closing price for future dates.

#timeseries-forecasting #predictive-analytics #neural-networks #data-analysis

1603494000

** Note from Towards Data Science’s editors:**_ While we allow independent authors to publish articles in accordance with our

Prediction of stock price is quite a challenging task because prices are highly volatile and mostly stochastic movement with non-linear nature. Here, the problem we have in hand is a price prediction issue and we’re trying to predict a numerical value defined in a range (from 9000 to 12500 approx). This problem fits the Regression Analysis framework. We shall be using neural network architecture to try to solve the problem here. Motivation of using neural network is that, it is one of the intelligent data mining techniques that identify a fundamental trend from data and to generalize from it.

We’ll build a Deep Neural Network here that does some forecasting for us and use it to predict future price. Let us load the hourly frequency data.

We have a total of 2001 data points representing Bitcoin in USD . We’re interested in predicting the closing price for future dates.

#timeseries-forecasting #predictive-analytics #neural-networks #data-analysis

1619240400

How to Predict Stock Prices with LSTM

In a previous post, we explained how to predict stock prices using machine learning models. Today, we will show how we can use advanced artificial intelligence models such as the Long-Short Term Memory (LSTM). In the previous post, we have used the LSTM models for Natural Language Generation (NLG) models, like the word-based and the character-based NLG models.The LSTM ModelLong short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning having feedback connections. Not only can process single data points such as images, but also entire sequences of data such as speech or video. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, anomaly detection, time series analysis, etc.

he LSTM models are computationally expensive and require many data points. Usually, we train the LSTM models using GPU instead of CPU. Tensorflow is a great library for training LSTM models.

#lstm #tensorflow #stock-price-prediction #prediction-markets #python

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Natural Language Processing is one of the artificial intelligence tasks performed with natural languages. The word ‘natural’ refers to the languages that evolved naturally among humans for communication. A long-standing goal in artificial intelligence is to make a machine effectively communicate with humans. Language modeling and Language generation (such as neural machine translation) have been popular among researchers for over a decade. For an AI beginner, learning and practicing Natural Language Processing can be initialized with classification of texts. Sentiment Analysis is among the text classification applications in which a given text is classified into a positive class or a negative class (sometimes, a neutral class, too) based on the context. This article discusses sentiment analysis using TensorFlow Keras with the IMDB movie reviews dataset, one of the famous Sentiment Analysis datasets.

TensorFlow’s Keras API offers the complete functionality required to build and execute a deep learning model. This article assumes that the reader is familiar with the basics of deep learning and Recurrent Neural Networks (RNNs). Nevertheless, the following articles may yield a good understanding of deep learning and RNNs:

- Getting Started With Deep Learning Using TensorFlow Keras
- Implementing A Recurrent Neural Network (RNN) From Scratch

#developers corner #imdb dataset #keras #lstm #lstm recurrent neural network #natural language processing #nlp #recurrent neural network #rnn #sentiment analysis #sentiment analysis nlp #tensorflow

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📺 The video in this post was made by K Crypto

The origin of the article: https://www.youtube.com/watch?v=-uWpSe8GuP0

🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.

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#bitcoin #blockchain #polygon #price prediction 2021 #price explosion incoming #big news!!! polygon (matic) price prediction 2021 - price explosion incoming

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Predictive modeling using machine learning comes with a trick to generalize new cases and not merely memorizing past cases. In order to achieve that, the ML algorithm must look through multiple rows of data, and different features which have significant correlations with target variable. In designing predictive modeling the key is to find a way to identify price trends without the uncertainty and bias of the our mental model. A successful approach could be linear regression. Stock’s price and time period determine the system parameters for linear regression.Most of the online resources which are available, where we can find that, the prediction problem ends with validating on test set. Very few resources are available which clearly shows the actual prediction report with future dates and foretasted prices.Here, our exercise in this article will not only validate the model, but show how to use the developed model to predict future prices. We will use simple linear regression to predict daily closing price of bitcoin based on over 2000 days of historical data. The goal here to predict future 15 days prices which are unknown.

#linear-regression #predictive-modeling #technical-analysis #machine-learning