| import numpy as np
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| import pandas as pd
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| import matplotlib.pyplot as plt
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|
|
| from sklearn.model_selection import train_test_split
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| from sklearn.linear_model import LogisticRegression
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| from sklearn.metrics import accuracy_score, confusion_matrix
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| import joblib
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|
|
|
|
| np.random.seed(42)
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|
|
| n_samples = 700
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|
|
| data = {
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| "age": np.random.randint(18, 65, n_samples),
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| "monthly_income": np.random.randint(200, 2000, n_samples),
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| "debt_amount": np.random.randint(0, 5000, n_samples),
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| "job_years": np.random.randint(0, 20, n_samples)
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| }
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|
|
| df = pd.DataFrame(data)
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|
|
|
|
| df["risk"] = (
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| (df["monthly_income"] < 500).astype(int) +
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| (df["debt_amount"] > 3000).astype(int) +
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| (df["job_years"] < 2).astype(int)
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| )
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|
|
| df["risk"] = (df["risk"] >= 2).astype(int)
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|
|
| print(df.head())
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|
|
| feature_cols = ["age", "monthly_income", "debt_amount", "job_years"]
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|
|
| X = df[feature_cols].values
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| y = df["risk"].values
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|
|
|
|
|
|
| X_train, X_test, y_train, y_test = train_test_split(
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| X, y, test_size=200, random_state=42, stratify=y
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| )
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|
|
| print("Train:", len(X_train))
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| print("Test:", len(X_test))
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|
|
|
|
|
|
| model = LogisticRegression(max_iter=1000)
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| model.fit(X_train, y_train)
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|
|
| y_pred = model.predict(X_test)
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|
|
|
|
| acc = accuracy_score(y_test, y_pred)
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| print("Accuracy:", acc)
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|
|
| cm = confusion_matrix(y_test, y_pred)
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| print("Confusion matrix:\n", cm)
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|
|
|
|
| plt.hist(model.predict_proba(X_test)[:,1], bins=20)
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| plt.xlabel("Risk ehtimoli")
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| plt.ylabel("Odamlar soni")
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| plt.title("Risk ehtimollari taqsimoti")
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| plt.show()
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|
|
|
|
|
|
| def predict_person(age, income, debt, job_years):
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| data = np.array([[age, income, debt, job_years]])
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| prob = model.predict_proba(data)[0][1]
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| pred = model.predict(data)[0]
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| return pred, prob
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|
|
| result, probability = predict_person(30, 400, 3500, 1)
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|
|
| print("Natija:", result)
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| print("Ehtimol:", probability)
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|
|
|
|
| joblib.dump(model, "risk_model.pkl")
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| print("Model saqlandi")
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|
|
