NRL Simulator/Score Predictor/Try Predictor

# Load historical NRL match data
data = pd.read_csv('nrl_data.csv')

# Feature engineering
# For example, create features such as player-specific performance in different weather conditions, team performance against specific opponents, etc.

# Data imputation
# Implement data imputation techniques for handling missing values in additional data sources

# Define features and targets
X = data[features]
y_score = data['score']
y_try = data['try']

# Split data into training and testing sets
X_train, X_test, y_train_score, y_test_score, y_train_try, y_test_try = train_test_split(X, y_score, y_try, test_size=0.2, random_state=42)

# Model training and tuning
# Score prediction model
score_model = RandomForestRegressor()
score_param_grid = {'n_estimators': [100, 200, 300], 'max_depth': [None, 10, 20, 30]}
score_grid_search = GridSearchCV(score_model, score_param_grid, cv=5)
score_grid_search.fit(X_train, y_train_score)
best_score_model = score_grid_search.best_estimator_

# Try prediction model
try_model = RandomForestClassifier()
try_param_grid = {'n_estimators': [100, 200, 300], 'max_depth': [None, 10, 20, 30]}
try_grid_search = GridSearchCV(try_model, try_param_grid, cv=5)
try_grid_search.fit(X_train, y_train_try)
best_try_model = try_grid_search.best_estimator_

# Model evaluation
score_predictions = best_score_model.predict(X_test)
score_mse = mean_squared_error(y_test_score, score_predictions)

try_predictions = best_try_model.predict(X_test)
try_accuracy = accuracy_score(y_test_try, try_predictions)

# Predict scores and tries for upcoming matches
upcoming_matches = pd.read_csv('upcoming_matches.csv')
upcoming_features = preprocess_features(upcoming_matches)
upcoming_features = pd.merge(upcoming_features, player_stats, on='player_id', how='left')
upcoming_features = pd.merge(upcoming_features, team_stats, on='team_id', how='left')
upcoming_features = pd.merge(upcoming_features, weather_data, on='match_id', how='left')
upcoming_features = pd.merge(upcoming_features, betting_odds, on='match_id', how='left')

upcoming_score_predictions = best_score_model.predict(upcoming_features)
upcoming_try_predictions = best_try_model.predict(upcoming_features)

# Display predictions for upcoming matches
for i in range(len(upcoming_matches)):
    print(f"Match {i+1}: Predicted Score: {upcoming_score_predictions[i]}, Predicted Tries: {upcoming_try_predictions[i]}")