diff --git a/7.5/__pycache__/datas.cpython-311.pyc b/7.5/__pycache__/datas.cpython-311.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..938e6ee696c79c8658ea37348a207a688f5a4022
Binary files /dev/null and b/7.5/__pycache__/datas.cpython-311.pyc differ
diff --git a/7.5/datas.py b/7.5/datas.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a775b9005e58b128a9e5324e8d598b815154155
--- /dev/null
+++ b/7.5/datas.py
@@ -0,0 +1,156 @@
+#城市：吕梁1、太原2
+
+#(city-城市) (area-面积) (rooms-房间数) (school-学区) (style-风格)
+
+datas=[
+    #吕梁第一类
+    {
+        "city":"吕梁",
+        "area":110,
+        "rooms":3,
+        "school":2,
+        "style":2,
+        "price":6000,
+    },
+    {
+        "city":"吕梁",
+        "area":120,
+        "rooms":3,
+        "school":1,
+        "style":2,
+        "price":8000,
+    },
+    {
+        "city":"吕梁",
+        "area":130,
+        "rooms":3,
+        "school":2,
+        "style":1,
+        "price":7500,
+    },
+    #吕梁第二类
+    {
+        "city":"吕梁",
+        "area":110,
+        "rooms":2,
+        "school":2,
+        "style":2,
+        "price":6500,
+    },
+    {
+        "city":"吕梁",
+        "area":120,
+        "rooms":2,
+        "school":1,
+        "style":2,
+        "price":8600,
+    },
+    {
+        "city":"吕梁",
+        "area":115,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":7000,
+    },
+    #吕梁第三类
+    {
+        "city":"吕梁",
+        "area":140,
+        "rooms":3,
+        "school":2,
+        "style":2,
+        "price":8600,
+    },
+    {
+        "city":"吕梁",
+        "area":140,
+        "rooms":3,
+        "school":2,
+        "style":1,
+        "price":9000,
+    },
+    {
+        "city":"吕梁",
+        "area":140,
+        "rooms":3,
+        "school":1,
+        "style":2,
+        "price":9500,
+    },
+    # 太原第一类
+    {
+        "city": "太原",
+        "area": 120,
+        "rooms": 3,
+        "school": 1,
+        "style": 2,
+        "price": 9000,
+    },
+    {
+        "city": "太原",
+        "area": 120,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 9500,
+    },
+    {
+        "city": "太原",
+        "area": 120,
+        "rooms": 3,
+        "school": 2,
+        "style": 2,
+        "price": 9800,
+    },
+    # 太原第二类
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 2,
+        "style": 1,
+        "price": 10000,
+    },
+    {
+        "city": "太原",
+        "area": 135,
+        "rooms": 2,
+        "school": 1,
+        "style": 2,
+        "price": 11000,
+    },
+    {
+        "city": "太原",
+        "area": 130,
+        "rooms": 3,
+        "school": 1,
+        "style": 2,
+        "price": 12000,
+    },
+    # 太原第三类
+    {
+        "city": "太原",
+        "area": 120,
+        "rooms": 3,
+        "school": 1,
+        "style": 1,
+        "price": 11000,
+    },
+    {
+        "city": "太原",
+        "area": 125,
+        "rooms": 3,
+        "school": 1,
+        "style": 2,
+        "price": 12000,
+    },
+    {
+        "city": "太原",
+        "area": 120,
+        "rooms": 3,
+        "school": 1,
+        "style": 2,
+        "price": 12500,
+    }
+]
\ No newline at end of file
diff --git a/7.5/predict.py b/7.5/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..2349cff2ccaada5f8e4dd19a657dcbf4f99a809e
--- /dev/null
+++ b/7.5/predict.py
@@ -0,0 +1,40 @@
+# a=np.array([[1,2],[4,5]])
+# b=np.array([1,2])
+# #点乘
+# print(a.dot(b))
+# #转置
+# print(a.T)
+# #求逆矩阵
+# print(np.linalg.pinv(a))
+
+import numpy as np
+from datas import datas
+
+# 初始化特征和标签列表
+X=[]
+Y=[]
+
+# 城市标记字典
+cityMark={"吕梁":1,"太原":2}
+
+# 遍历数据集，构建特征和标签
+for item in datas:
+    single=[]
+    single.append(cityMark[item["city"]])
+    single.append(item["area"])
+    single.append(item["rooms"])
+    single.append(item["school"])
+    single.append(item["style"])
+
+    X.append(single)
+    Y.append(item["price"])
+
+# 将特征和标签转换为numpy数组
+X=np.array(X)
+Y=np.array(Y)
+
+# 计算线性回归的参数theta
+theta=np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(Y)
+print(theta)
+
+