train_model.py

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import joblib
import math

def calculate_distance(x, y):
    return np.sqrt((100 - x)**2 + (50 - y)**2)

def calculate_angle(x, y):
    angle = math.atan2(abs(50 - y), (100 - x))
    return angle

def train_and_save():
    # Use dummy data for demonstration; replace with actual CSV if available
    X_dummy = np.random.rand(100, 3)
    y_dummy = np.random.randint(0, 2, 100)
    
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X_dummy, y_dummy)
    
    joblib.dump(model, 'shot_quality_model.joblib')
    print("Model saved to shot_quality_model.joblib")

if __name__ == "__main__":
    train_and_save()