fix(ctr): add a logistic regression ctr ml model.

ctr/lr.py

@@ -0,0 +1,37 @@
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, roc_auc_score
+
+# 创建虚拟数据
+data = pd.DataFrame({
+    'feature1': np.random.rand(1000),
+    'feature2': np.random.rand(1000),
+    'feature3': np.random.rand(1000),
+    'clicked': np.random.randint(0, 2, 1000)
+})
+
+# 定义特征和目标变量
+X = data[['feature1', 'feature2', 'feature3']]
+y = data['clicked']
+
+# 将数据分为训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 创建逻辑回归模型
+log_reg = LogisticRegression()
+
+# 训练模型
+log_reg.fit(X_train, y_train)
+
+# 预测测试集
+y_pred = log_reg.predict(X_test)
+y_pred_proba = log_reg.predict_proba(X_test)[:, 1]
+
+# 计算准确率和AUC
+accuracy = accuracy_score(y_test, y_pred)
+auc = roc_auc_score(y_test, y_pred_proba)
+
+print("Accuracy: ", accuracy)
+print("AUC: ", auc)