diff --git a/datas.py b/datas.py
new file mode 100644
index 0000000000000000000000000000000000000000..95ac7d5b31bd17489c9ff1f511be3be45c82ab11
--- /dev/null
+++ b/datas.py
@@ -0,0 +1,158 @@
+# 经验是来源于数据
+# 大数据   爬虫
+
+# 模拟太原 和吕梁的房价数据
+# 怎么模拟  城市  面积  户型  是不是学区房  装修的风格
+datas = [
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7900
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7700
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8100
+
+    },
+    # 模拟
+    {
+        "city": "吕梁",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8500
+    },
+
+    {
+        "city": "吕梁",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 4900
+    },
+    {
+        "city": "吕梁",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 5200
+    },
+    {
+        "city": "吕梁",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 6200
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7900
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7700
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8100
+
+    },
+    # 模拟
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8500
+    },
+
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8400
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "太原",
+        "area": 140,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 8500
+    },
+
+]
diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..a807d4fda1ab08cdad2e2b171b545535f7ca7421
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,20 @@
+import numpy as np
+from datas import datas
+
+# 城市到数字的映射
+cityMark = {"吕梁": 1, "太原": 2}
+
+# 构建特征矩阵 X 和目标向量 Y
+X = np.array([[cityMark[item["city"]], item["area"], item["rooms"], item["school"], item["style"]] for item in datas])
+Y = np.array([item["price"] for item in datas])
+
+# 使用正规方程求解参数 theta
+try:
+    theta = np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(Y)
+    print("模型参数 theta:", theta)
+    # 预测一个新数据点的价格
+    new_data_point = np.array([2, 100, 2, 1, 1])
+    predicted_price = theta.dot(new_data_point)
+    print("预测价格:", predicted_price)
+except np.linalg.LinAlgError:
+    print("警告：X^T X 接近奇异，无法计算逆矩阵。")
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