Simple Linear Regression Trading system. - page 6

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Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash

A stock market crash is a sharp and quick drop in the total value of the market with prices typically declining more than 10% within a few days, Famous examples of major stock market crashes are the black Monday in 1987 and the real estate bubble in 2008. A crash is usually attributable to the burst of a price bubble and is due to a massive sell-off that occurs when a majority of the stock market participants try to sell off their assets at the same time.

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Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash
Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash
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In this article I am going to attempt to use our logistic model to predict the stock market crash based upon the fundamentals of the US economy, the NETFLIX and APPLE are the stocks we are going to focus on, Using the previous market crashes of 2019 and 2020 let's see how our model will perform in the current dooms and glooms.
 

Data Science and Machine Learning (Part 05): Decision Trees

A decision tree is a type of supervised Machine Learning technique used to categorize or make predictions based on how a previous set of questions were answered. A model is a form of supervised learning, meaning that the model is trained and tested on a set of data that contains the desired categorization.

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Data Science and Machine Learning (Part 05): Decision Trees
Data Science and Machine Learning (Part 05): Decision Trees
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Decision trees imitate the way humans think to classify data, let's see how to build a tree and use them to classify and predict some data. The main goal of the decision trees algorithm is to separate the data with impurity and into pure or close to nodes.
 

Data Science and Machine Learning (Part 06): Gradient Descent

The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local maximum of that function; the procedure is then known as gradient ascent.

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Data Science and Machine Learning (Part 06): Gradient Descent
Data Science and Machine Learning (Part 06): Gradient Descent
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The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
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