Download this code from
Creating a sports betting bot in Python involves several steps, including gathering data, making predictions, and placing bets. Keep in mind that using such bots may be subject to the terms and conditions of betting platforms, and it’s crucial to comply with their rules. Additionally, gambling always carries risks, and it’s essential to bet responsibly.
In this tutorial, we’ll create a simple sports betting bot that utilizes historical data for predictions. We’ll use Python and a popular machine learning library, scikit-learn, for creating a basic predictive model.
Make sure you have Python installed on your machine. You can install the required libraries using the following:
To make predictions, we need historical data. You can use sports APIs or web scraping to collect data. For simplicity, let’s assume you have a CSV file named historical_data.csv with columns like ‘Team1’, ‘Team2’, ‘Date’, ‘Score1’, ‘Score2’, etc.
Load the data and preprocess it. For simplicity, we’ll focus on the ‘Team1’ and ‘Team2’ columns and the target variable ‘Result’ (1 if Team1 won, 0 if Team2 won).
Let’s use a simple logistic regression model for demonstration purposes.
Based on the model’s prediction, you can implement a simple betting logic. For example, you may decide to place a bet if the model predicts a Team1 win.
This is a basic example to get you started. In a real-world scenario, you would likely need more sophisticated models, feature engineering, and additional data sources. Moreover, remember that the success of sports betting is uncertain, and using such bots should be approached with caution and responsibility.
ChatGPT

Link do Vídeo