Machine Learning Script Breakdown
Data Preparation & Cleaning
Before training the model, the raw roulette game data is loaded and inspected. The dataset is cleaned by converting numerical values to the correct format and encoding categorical variables like bet types. This ensures the data is properly formatted for machine learning.
Feature Engineering & Data Balancing
To improve model accuracy, the data is standardized using a scaler, and synthetic samples are generated with SMOTE to balance the dataset. This prevents the model from being biased towards the more frequent outcomes and improves generalization.
Neural Network Training
A neural network is built using TensorFlow and Keras to predict the outcome of bets based on past data. The model consists of multiple dense layers with activation functions and is optimized using Adam. Early stopping is applied to prevent overfitting.
Model Performance & Evaluation
The trained model is evaluated on test data to measure accuracy. Performance metrics, such as loss and accuracy over multiple epochs, are visualized to assess the model's learning progress. The graphs illustrate improving accuracy and decreasing loss, indicating effective training. Early stopping prevents overfitting by halting training when validation loss stops improving. Below is the complete Python script for loading, cleaning, training, and evaluating the model.
Download ML Project Files
Click the button below to download the complete folder containing the ML script and related resources.