How I Predicted the Rise in BTC Price: An Iterative and Experimental Approach with machine learning

In the uncertain world of cryptocurrencies, predicting Bitcoin’s next price move is a genuine challenge. In this article, I’ll walk you through how I built a robust machine learning model to predict the rise in BTC price. The heart of the system lies in two core functions of my code: <code>main()</code> and <code>run_experiment()</code>. <h1>The Infinite Loop: The Secret Weapon of <code>main()</code></h1> My <code>main()</code> function is a comprehensive set that runs the entire machine learning pipeline, from data collection to price prediction. The genius of this function lies in its incorporation into an infinite loop. This loop serves two main purposes: <ol> <li>Auto Recovery: At the beginning of each iteration, the code tries to load a saved model and scaler. If it fails, it kicks off the <code>run_experiment()</code> function.</li> <li>Continuous Updates: The loop allows for regular updates to the predictions, ensuring the model is always current.</li> </ol> <a href="https://medium.com/@Smarty01/how-i-predicted-the-rise-in-btc-price-an-iterative-and-experimental-approach-with-machine-learning-78aefb349207">Visit Now</a>