| import numpy as np
|
| import pandas as pd
|
| from sklearn.linear_model import LinearRegression
|
| from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
|
| from sklearn.model_selection import train_test_split
|
|
|
|
|
|
|
| np.random.seed(42)
|
|
|
| n_samples = 500
|
|
|
| year = np.random.randint(2005, 2024, n_samples)
|
| mileage = np.random.randint(0, 250000, n_samples)
|
|
|
| color = np.random.choice(['white', 'black', 'red', 'blue'], n_samples)
|
| transmission = np.random.choice(['manual', 'auto'], n_samples)
|
| fuel = np.random.choice(['petrol', 'gas', 'hybrid'], n_samples)
|
|
|
|
|
| price = (
|
| 200 + (year - 2005) * 15
|
| - mileage * 0.0005
|
| + (transmission == 'auto') * 50
|
| + (fuel == 'hybrid') * 80
|
| + np.random.normal(0, 20, n_samples)
|
| )
|
|
|
| data = pd.DataFrame({
|
| "year": year,
|
| "mileage": mileage,
|
| "color": color,
|
| "transmission": transmission,
|
| "fuel": fuel,
|
| "price": price
|
| })
|
|
|
|
|
| data = pd.get_dummies(data, drop_first=True)
|
|
|
|
|
| X = data.drop("price", axis=1)
|
| y = data["price"]
|
|
|
| X_train, X_test, y_train, y_test = train_test_split(
|
| X, y, train_size=400, random_state=42
|
| )
|
|
|
|
|
|
|
| model = LinearRegression()
|
| model.fit(X_train, y_train)
|
|
|
|
|
| y_pred = model.predict(X_test)
|
|
|
| def predict_car_price(model, columns):
|
| print("Mashina ma'lumotlarini kiriting:")
|
|
|
| year = int(input("Yili: "))
|
| mileage = int(input("Yurgan km: "))
|
| color = input("Rangi (white/black/red/blue): ")
|
| transmission = input("Uzatma (manual/auto): ")
|
| fuel = input("Yoqilg'i (petrol/gas/hybrid): ")
|
|
|
| data = {
|
| "year": year,
|
| "mileage": mileage,
|
| "color": color,
|
| "transmission": transmission,
|
| "fuel": fuel
|
| }
|
|
|
| df = pd.DataFrame([data])
|
| df = pd.get_dummies(df)
|
|
|
| df = df.reindex(columns=columns, fill_value=0)
|
|
|
| price = model.predict(df)[0]
|
| print(f"\nTaxminiy narx: {price:.1f}")
|
|
|
| predict_car_price(model, X.columns)
|
|
|
|
|
|
|
| mae = mean_absolute_error(y_test, y_pred)
|
| mse = mean_squared_error(y_test, y_pred)
|
| rmse = np.sqrt(mse)
|
| r2 = r2_score(y_test, y_pred)
|
|
|
| print("=" * 50)
|
| print("MODEL EVALUATION")
|
| print("=" * 50)
|
| print(f"MAE: {mae:.2f}")
|
| print(f"RMSE: {rmse:.2f}")
|
| print(f"R²: {r2:.3f}")
|
|
|
|
|
| import matplotlib.pyplot as plt
|
|
|
| plt.figure(figsize=(6,6))
|
| plt.scatter(y_test, y_pred)
|
| plt.xlabel("Haqiqiy narx")
|
| plt.ylabel("Model aytgan narx")
|
| plt.title("Haqiqiy vs Bashorat")
|
|
|
| plt.plot([y_test.min(), y_test.max()],
|
| [y_test.min(), y_test.max()])
|
|
|
| plt.show()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
