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diff --git "a/7.5  \345\210\230\345\230\211\346\254\243/datas.py" "b/7.5  \345\210\230\345\230\211\346\254\243/datas.py"
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+# 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  装修风格
+
+datas=[
+    #模拟第一类房子
+    {
+        "city":"吕梁",
+        "area":100,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":8000
+    },
+    {
+        "city":"吕梁",
+        "area":100,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":7800
+    },
+    {
+        "city":"吕梁",
+        "area":100,
+        "rooms":2,
+        "school":1,
+        "style":1,
+        "price":8200
+    },
+    # 模拟第二类房子
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8500
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8300
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8600
+    },
+    # 模拟第三类房子
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 6300
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 5600
+    },
+    {
+        "city": "吕梁",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 5800
+    },
+    # 太原模拟第一类房子
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8000
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8500
+    },
+    # 模拟第二类房子
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8300
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8200
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8600
+    },
+    # 模拟第三类房子
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7300
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 8600
+    },
+    {
+        "city": "太原",
+        "area": 100,
+        "rooms": 2,
+        "school": 1,
+        "style": 1,
+        "price": 7800
+    },
+    # 模拟房价数据的权重计算
+    # 3000 10  1000 1300 1500
+    # 太原2 100 3    1    1
+    # 3000+1400+3000+2600+3000
+    # 城市 x1 面积 x2 房间数 x3 学校 x4 样式 x5
+    # x1=1 x2=100 x3=3 x4=1 x5=1
+    # a1*1+a2*2+a3*3+a4*4+a5*5=y
+    # [a1,a2,......] 数学模型 机器学习 机器学习软件开发
+    ]
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diff --git "a/7.5  \345\210\230\345\230\211\346\254\243/predict.py" "b/7.5  \345\210\230\345\230\211\346\254\243/predict.py"
new file mode 100644
index 0000000000000000000000000000000000000000..84708898873d23db9c5b4c78b9738bd2b4e0a28c
--- /dev/null
+++ "b/7.5  \345\210\230\345\230\211\346\254\243/predict.py"	
@@ -0,0 +1,35 @@
+#  1.安装一个科学计算的框架
+# import numpy as np
+# a=np.array([[3,2,3],[3,2,3],[3,2,3]])
+# b=np.array([1,2,3])
+# print(a.dot(b))
+#
+# print(a)
+# print(a.T)
+# 奇异矩阵
+# 求 点乘  转置  逆
+# print(np.linalg.pinv(a))
+import numpy as np
+from datas import datas
+X=[]
+Y=[]
+cityMark={"吕梁":1,"太原":2}
+for item in datas:
+    single=[]
+    # 城市
+    single.append(cityMark[item["city"]])
+    # 面积
+    single.append(item["area"])
+    # 房间数
+    single.append(item["rooms"])
+    # 学区房
+    single.append(item["school"])
+    # 装修
+    single.append(item["style"])
+    X.append(single)
+    Y.append(item["price"])
+
+X = np.array(X)
+Y = np.array(Y)
+theta = np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(Y)
+print(theta)
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