Create train_tsh_model.py

train_tsh_model.py

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+# train_tsh_model.py
+
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score
+import joblib
+
+# Load your dataset (update path if necessary)
+df = pd.read_csv("thyroid_dataset.csv")
+
+# Choose features and drop rows with missing values
+features = ["T3", "TT4", "T4U", "FTI", "age"]
+df = df.dropna(subset=features + ["TSH"])
+
+# Prepare input (X) and output (y)
+X = df[features]
+y = df["TSH"]
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train the model
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate the model
+y_pred = model.predict(X_test)
+print("TSH Model R² Score:", r2_score(y_test, y_pred))
+
+# Save the model
+joblib.dump(model, "tsh_model.pkl")
+print("✅ Model saved as tsh_model.pkl")