train.py

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import joblib

# Load data
df = pd.read_csv("asl_hand_landmarks.csv")
X = df.drop('label', axis=1)
y = df['label']

# Scale features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Evaluate
accuracy = model.score(X_test, y_test)
print(f"Model Accuracy: {accuracy:.2f}")

# Save model and scaler
joblib.dump(model, "asl_model.pkl")
joblib.dump(scaler, "asl_scaler.pkl")