I would like to guide you through the process of building a machine-learning model to predict the high and low stock prices of a company for a single trading day. Keep in mind that this model is for educational purposes, and any trading decisions based on its outputs should be taken with caution.

First off, we need to set up our environment. We will be using Python because of its comprehensive ecosystem of data analysis and machine learning libraries. Ensure you have pandas, numpy, matplotlib, scikit-learn, and yfinance libraries installed in your Python environment. You can install them using pip - the Python package installer.

The next step is to fetch the data. We'll use yfinance library to download the historical data of the company we're interested in. For example, if we're predicting for Apple Inc., the ticker symbol would be 'AAPL', or 'TSLA', or 'AMZN', etc. This library provides us with data including Open, High, Low, Close, and Volume.

Now, let's move on to the data preparation phase. We'll create additional features from the existing ones that could potentially help in our predictions. We'll consider the previous day's high, low, open, close, and volume as our features.

After creating our features, we need to divide our dataset into a training set and a testing set. The training set is used to train our machine learning model while the testing set is used to evaluate the model's performance.

Next, we'll build our machine-learning model. In our case, we will use the Random Forest Regressor from the sklearn library. Random Forest is an ensemble learning method that can model complex relationships.

After defining the model, we need to train it. We'll use our training set to fit the model. This allows the model to learn the relationship between our features and the target variables.

Once our model is trained, it's time to test its performance. We will predict the high and low prices for the data in our testing set and compare them to the actual prices.

Thank you for watching, and we hope this tutorial was helpful in your journey to understanding stock price prediction with machine learning.

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