Delete app.py

app.py

@@ -1,41 +0,0 @@
-import pandas as pd
-import numpy as np
-import joblib
-from xgboost import XGBRegressor
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
-from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
-
-# 1. Veriyi Yükle
-df = pd.read_csv("manufacturing.csv")
-
-# 2. Özellik ve hedef değişkeni seç
-X = df[["Pressure (kPa)", "Temperature x Pressure", "Material Fusion Metric"]]
-y = df["Quality Rating"]
-
-# 3. Veriyi eğitim ve test olarak ayır
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# 4. Veriyi ölçekle
-scaler = StandardScaler()
-X_train_scaled = scaler.fit_transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-
-# 5. XGBoost modelini eğit
-xgb_model = XGBRegressor()
-xgb_model.fit(X_train_scaled, y_train)
-
-# 6. Test verisi ile tahmin yap
-y_pred = xgb_model.predict(X_test_scaled)
-
-# 7. Performans metriklerini yazdır
-print("🔹 Metrikler:")
-print(f" MSE: {mean_squared_error(y_test, y_pred):.2f}")
-print(f" MAE: {mean_absolute_error(y_test, y_pred):.2f}")
-print(f" R² : {r2_score(y_test, y_pred):.2f}")
-
-# 8. Model ve scaler'ı kaydet
-joblib.dump(xgb_model, "xgb_model.pkl")
-joblib.dump(scaler, "scaler.pkl")
-
-print("\n✅ Model ve scaler başarıyla kaydedildi.")