Create README.md

README.md

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+---
+language: uz
+license: mit
+tags:
+- xgboost
+- regression
+- car-price-prediction
+- automotive
+- uzbekistan
+metrics:
+- r2: 0.9007
+- mae: 682.02
+---
+
+# 🚗 XGBoost Avtomobil Narxini Bashorat Qilish (Uzbekistan Market)
+
+Ushbu model O'zbekistondagi mashhur avtomobil modellari va ularning bozor narxlari asosida o'qitilgan. Model yili, masofasi va transmissiyasiga qarab avtomobil narxini ($) bashorat qiladi.
+
+## 📊 Model Natijalari
+* **Aniqlik (R2 Score):** 0.9007
+* **O'rtacha xatolik (MAE):** $682.02
+
+## 🛠 Modelni Ishlatish (Usage Guide)
+
+Modelni yuklash va bashorat qilish uchun quyidagi Python kodidan foydalaning:
+
+```python
+import xgboost as xgb
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# 1. Modelni yuklab olish
+model_path = hf_hub_download(repo_id="Mehriddin1997/xgboost_car_model", filename="xgboost_car_model.json")
+
+# 2. Modelni yuklash
+model = xgb.XGBRegressor()
+model.load_model(model_path)
+
+# 3. Mashina modelini aniqlash (Dictionary)
+car_models = {
+    1: "Captiva", 2: "Cobalt", 3: "Damas", 4: "Epica", 5: "Equinox",
+    6: "Gentra", 7: "Labo", 8: "Lacetti", 9: "Malibu", 10: "Matiz",
+    11: "Nexia", 12: "Onix", 13: "Orlando", 14: "Spark", 15: "Tracker"
+}
+
+def predict_car(year, mileage, transmission, model_id):
+    # Model o'qitilgan ustun nomlari bilan bir xil dataframe yaratamiz
+    data = pd.DataFrame([[year, mileage, transmission, model_id]], 
+                        columns=['year', 'yurgan_masofasi', 'transmission', 'model'])
+    
+    price = model.predict(data)[0]
+    return price
+
+# Test: 2022-yil, 35,000 km, Avtomat (1), Cobalt (2)
+res = predict_car(2022, 35000, 1, 2)
+print(f"Bashorat qilingan narx: ${res:,.2f}")