import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error

Step 1: Collect historical data of Aviator game outcomes

This can be done by scraping the casino's website or by accessing their API

For example, let's assume we have a CSV file called 'aviator_data.csv' with the following columns:

'PlayerID', 'BetAmount', 'WinAmount', 'Result'

data = pd.read_csv('aviator_data.csv')

Step 2: Preprocess the data

Convert categorical variables to numerical

data['Result'] = data['Result'].map({'Win': 1, 'Loss': 0})

Split the data into features (X) and target (y)

X = data[['PlayerID', 'BetAmount']] y = data['Result']

Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 3: Train a linear regression model

model = LinearRegression() model.fit(X_train, y_train)

Step 4: Evaluate the model

y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print(f"Mean Squared Error: {mse}")

Step 5: Make predictions for new data

Assuming we have a new player with ID 1234 and bet amount of 100

new_player = pd.DataFrame({'PlayerID': [1234], 'BetAmount': [100]}) prediction = model.predict(new_player) print(f"Predicted probability of winning: {prediction[0]:.2f}")