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--- |
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license: mit |
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language: |
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- en |
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--- |
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# BMW Model Predictor |
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This model predicts the BMW car model using XGBoost based on price, mileage, fuel type, engine power, and car type. |
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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- xgboost |
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- tabular-classification |
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- bmw |
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- car-model-prediction |
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- automotive |
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datasets: |
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- custom |
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inference: false |
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model-index: |
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- name: BMW Model Predictor |
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results: [] |
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--- |
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# 🚗 BMW Model Predictor |
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This machine learning model predicts the **BMW car model** using **XGBoost** based on the following features: |
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- `engine_power` (horsepower) |
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- `price` (EUR) |
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- `mileage` (km) |
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- `fuel` type |
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- `car_type` (e.g., sedan, SUV) |
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## 🔧 Training Information |
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- Model: XGBoost Classifier |
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- Training set: Custom dataset from `bmw_pricing_challenge.csv` |
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- Target variable: `model_key` (encoded) |
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- Preprocessing: Label Encoding for categorical features (`fuel`, `car_type`, `model_key`) |
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- Saved with: `joblib` |
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## 📁 Files |
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-Load encoders from .npy |
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fuel_encoder = LabelEncoder(); fuel_encoder.classes_ = np.load("fuel_classes.npy", allow_pickle=True) |
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car_type_encoder = LabelEncoder(); car_type_encoder.classes_ = np.load("car_type_classes.npy", allow_pickle=True) |
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target_encoder = LabelEncoder(); target_encoder.classes_ = np.load("target_classes.npy", allow_pickle=True) |
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- |
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## 📊 BMW Model Predictor Overview |
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## 🚀 How to Use |
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import sys, subprocess, os, json |
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def pip_install(pkg): |
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try: |
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__import__(pkg) |
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except ImportError: |
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subprocess.check_call([sys.executable, "-m", "pip", "install", pkg]) |
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for p in ["huggingface_hub", "numpy", "scikit_learn", "xgboost"]: |
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pip_install(p) |
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from huggingface_hub import snapshot_download |
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import numpy as np |
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from sklearn.preprocessing import LabelEncoder |
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from xgboost import XGBClassifier |
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# 1) Download model files |
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repo_id = "MahmutCanBoran/bmw-model-predictor" |
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local_dir = "bmw-model-predictor" |
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snapshot_download(repo_id=repo_id, repo_type="model", local_dir=local_dir, allow_patterns=["*"]) |
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# 2) Path and file controls |
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path = local_dir + os.sep |
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required_files = ["bmw_model.json", "fuel_classes.npy", "car_type_classes.npy", "target_classes.npy"] |
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missing = [f for f in required_files if not os.path.exists(path + f)] |
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if missing: |
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raise FileNotFoundError(f"Şu dosyalar eksik: {missing}\nKlasör içeriği: {os.listdir(path)}") |
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# 3) Restore the encoders. |
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fuel_le = LabelEncoder(); fuel_le.classes_ = np.load(path + "fuel_classes.npy", allow_pickle=True) |
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car_type_le = LabelEncoder(); car_type_le.classes_ = np.load(path + "car_type_classes.npy", allow_pickle=True) |
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target_le = LabelEncoder(); target_le.classes_ = np.load(path + "target_classes.npy", allow_pickle=True) |
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# 4) Upload XGBoost model(JSON) |
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model = XGBClassifier() |
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model.load_model(path + "bmw_model.json") |
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# 5)Helper: encode the input and make a prediction |
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def predict_bmw_model(engine_power:int, mileage:int, price:float, fuel:str, car_type:str): |
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# kategori doğrulama (hata mesajları daha anlaşılır olsun) |
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if fuel not in set(fuel_le.classes_): |
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raise ValueError(f"fuel='{fuel}' geçersiz. Geçerli fuel sınıfları: {list(fuel_le.classes_)}") |
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if car_type not in set(car_type_le.classes_): |
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raise ValueError(f"car_type='{car_type}' geçersiz. Geçerli car_type sınıfları: {list(car_type_le.classes_)}") |
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X = np.array([[ |
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engine_power, |
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mileage, |
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price, |
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fuel_le.transform([fuel])[0], |
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car_type_le.transform([car_type])[0] |
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]], dtype=float) |
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y_pred = model.predict(X) |
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return target_le.inverse_transform(y_pred)[0] |
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# 6) Info: display the available classes |
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print("Fuel classes:", list(fuel_le.classes_)) |
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print("Car type classes:", list(car_type_le.classes_)) |
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# 7) Example prediction |
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example = {"engine_power": 200, "mileage": 50000, "price": 25000, "fuel": "diesel", "car_type": "suv"} |
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pred = predict_bmw_model(**example) |
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print("Predicted BMW model:", pred) |
