Use Artificial Intelligence (AI) to Predict the Stock Market with Python
AI Summary
- Introduction to Python and AI for predicting S&P 500 prices using XGBoost.
- Utilizes Google Colab for Python programming.
- Instructions for setting up a new notebook on Google Colab.
- Import necessary libraries: pandas, XGBoost, and matplotlib.
- Explanation of XGBoost as a machine learning library for regression, classification, and ranking.
- Reminder to subscribe and like the channel for updates.
- Loading and displaying the S&P 500 dataset from a CSV file.
- Dataset spans from January 29, 1993, to May 4, 2023, with 7621 rows and 7 columns.
- Visual representation of the S&P 500 closing price data.
- Splitting the dataset into training (99%) and testing (1%) sets.
- Defining features (open price and volume) and target variable (close price).
- Creating and training the XGBoost model with the training dataset.
- Making predictions on the test dataset and comparing them to actual values.
- Evaluating the model’s accuracy (approximately 70%).
- Plotting predictions against actual close prices.
- Acknowledgment of Patreon supporters and disclaimer about the educational nature of the content.
- Encouragement to enjoy the programming tutorial and anticipation for the next video.