---
language: en
tags:
- lstm
- roulette
- betting
- prediction
license: apache-2.0
datasets:
- roulette_betting_sessions.csv
metrics:
- mean_squared_error
model-index:
- name: LSTM Roulette Betting Prediction Model
  results:
  - task:
      type: prediction
      name: Prediction
    dataset:
      name: roulette_betting_sessions
      type: csv
    metrics:
      - name: Mean Squared Error
        type: mean_squared_error
        value: 0.0245
  base_model: lstm
---

# LSTM Roulette Betting Prediction Model

## Model Description
This LSTM model is designed to predict the total outcome of a betting session in a roulette game. The model takes in features such as bet amount, payout odds, total wagered, and session duration to make predictions.

## Training
The model is trained using the following steps:
1. **Preprocessing**: The input data is preprocessed to ensure numeric values and scaled using a `MinMaxScaler`.
2. **Data Splitting**: The data is split into training and testing sets.
3. **Model Building**: An LSTM model is built with an input shape of `(1, number_of_features)`.
4. **Training**: The model is trained for 20 epochs with a batch size of 32, using the training data and validating on the testing data.

## Usage
To use this model, you need to preprocess your session data and scale it using the same `MinMaxScaler` used during training. The model can then make predictions for each bet in the session, and the total winnings can be calculated by summing the predictions.

### Example Code
```python
import joblib
import pandas as pd
import tensorflow as tf

# Load the model and scaler
model = tf.keras.models.load_model('model.pkl')
scaler = joblib.load('scaler.pkl')

# Example session data
session_data = pd.DataFrame({
    'bet_amount': [30, 50],
    'payout_odds': [35, 17],
    'total_wagered': [30, 80],
    'session_duration': [10, 20]
})

# Ensure correct data types and scale the session data
numeric_columns = ['bet_amount', 'payout_odds', 'total_wagered', 'session_duration']
session_data[numeric_columns] = session_data[numeric_columns].apply(pd.to_numeric, errors='coerce')
session_data = session_data.dropna(subset=numeric_columns)

features = ['bet_amount', 'payout_odds', 'total_wagered', 'session_duration']
X_scaled = scaler.transform(session_data[features])

# Reshape for LSTM input (each row corresponds to a bet)
X_scaled = X_scaled.reshape((X_scaled.shape[0], 1, X_scaled.shape[1]))

# Make predictions for each bet
predictions_scaled = model.predict(X_scaled)

# Inverse transform the predictions back to the original scale
predictions = scaler.inverse_transform([[0, 0, 0, pred] for pred in predictions_scaled.flatten()])[:, -1]

# Sum the predictions to get the total winnings for the entire session
total_winnings = predictions.sum()
print(f"Total Winnings: {total_winnings}")


## Model Details

**Input Features:** 
- `bet_amount`
- `payout_odds`
- `total_wagered`
- `session_duration`

**Output:** 
- Predicted total winnings for the session

**Architecture:** 
- LSTM with 64 units
- Dense layer with 32 units and ReLU activation
- Dense output layer with a single unit and linear activation

## Limitations

- The accuracy of model depends on the quality and quantity of the training data.
- The model may not generalize well to unseen data if the training data is not representative of real-world scenarios.

## Example Bets

**First Bet:**
- **Bet Amount:** 30 USD
- **Payout Odds:** 2.0 (e.g., bet on a Dozen)
- **Outcome:**  
    Since the bet is won, the outcome is calculated as:
    ```
    Outcome = 30 × 2.0 = 60 USD
    ```
Total Wagered: 30 USD
Total Winnings: The total winnings after the first bet is 60 USD.

**Second Bet:**
- **Bet Amount:** 50 USD
- **Payout Odds:** 1.0 (e.g., bet on Red/Black)
- **Outcome:**  
    The bet is lost, so the outcome is:
    ```
    Outcome = 0 USD
    ```
Total Wagered: The total wagered now becomes 80 USD (30 from the first bet + 50 from the second bet).
Total Winnings: The total winnings after both bets is updated to 60 USD.

## About AI

### Understanding LSTM
LSTM (Long Short-Term Memory) is a type of Recurrent Neural Network (RNN) that is particularly good at learning from sequential data. It is designed to remember information for long periods, which is beneficial for time-series data such as betting sessions.

**Key Features:**
- **Cell State:** LSTMs maintain a cell state that carries relevant information across time steps, allowing them to remember past inputs that may be critical for predicting future outcomes.
- **Forget Gate:** It determines which information should be discarded from the cell state.
- **Input Gate:** It decides which new information will be added to the cell state.
- **Output Gate:** It determines what the next hidden state (output) should be, based on the cell state.

## Preprocessing Data
The `preprocess_data` function is responsible for preparing the data for training the LSTM model. It performs the following steps:
- Ensures that the input data contains numeric values for the relevant columns.
- Drops rows with missing values in the numeric columns.
- Aggregates the data by `player_id` and `session_id` to compute the sum, mean, or max of the numeric features.
- Scales the numeric features using a `MinMaxScaler`.
- Returns the scaled features, target variable (`total_winnings`), and the scaler.

## Building the LSTM Model
The `build_lstm_model` function constructs an LSTM model with the following architecture:
- An LSTM layer with 64 units.
- A Dense layer with 32 units and ReLU activation.
- A Dense output layer with a single unit and linear activation for regression.
The model is compiled with the Adam optimizer and mean squared error loss function.

## Training the LSTM Model
The `train_lstm_model` function trains the LSTM model using the preprocessed data. It performs the following steps:
- Calls `preprocess_data` to get the scaled features, target variable, and scaler.
- Splits the data into training and testing sets.
- Reshapes the data for LSTM input.
- Builds the LSTM model using `build_lstm_model`.
- Trains the model for 20 epochs with a batch size of 32, using the training data and validating on the testing data.
- Returns the trained model and the scaler.

## Predicting Session Outcomes
The `predict_lstm_session` function predicts the total outcome of a betting session using the trained LSTM model. It performs the following steps:
- Ensures that the session data contains numeric values for the relevant columns and drops rows with missing values.
- Scales the session data using the provided scaler.
- Reshapes the scaled data for LSTM input.
- Makes predictions for each bet in the session.
- Inverse transforms the predictions to the original scale.
- Sums the predictions to get the total winnings for the session.

## Citation
If you use this model in your research, please cite it as follows:

```
@misc{your-username_lstm_roulette_prediction,
    author = {MichaelB-AI},
    title = {LSTM Roulette Betting Prediction Model},
    year = {2024},
    publisher = {HuggingFace},
    url = {https://huggingface.co/MichaelB-AI/lstm-roulette-prediction}
}
```

## Contact
For questions or issues, please contact [aienthusiastpro@gmail.com].