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Author: rogers-cyber

Credit-risk-prediction-system/generate_sample_data.py

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+import pandas as pd
+import numpy as np
+
+np.random.seed(42)
+n_train = 500
+
+train_data = pd.DataFrame({
+    "age": np.random.randint(21, 65, size=n_train),
+    "income": np.random.randint(20000, 120000, size=n_train),
+    "loan_amount": np.random.randint(1000, 50000, size=n_train),
+    "credit_score": np.random.randint(300, 850, size=n_train),
+    "default": np.random.choice([0, 1], size=n_train, p=[0.8, 0.2])
+})
+
+train_data.to_csv("train_data.csv", index=False)
+print("Sample training file 'train_data.csv' created!")
+
+# Optional: sample prediction data
+n_pred = 10
+predict_data = pd.DataFrame({
+    "age": np.random.randint(21, 65, size=n_pred),
+    "income": np.random.randint(20000, 120000, size=n_pred),
+    "loan_amount": np.random.randint(1000, 50000, size=n_pred),
+    "credit_score": np.random.randint(300, 850, size=n_pred)
+})
+
+predict_data.to_csv("new_applicants.csv", index=False)
+print("Sample prediction file 'new_applicants.csv' created!")