From b6047646ec550ea5d0668a350e6d0be941ad79ad Mon Sep 17 00:00:00 2001
From: HaibaraLin <2411711961@qq.com>
Date: Fri, 5 Jul 2024 12:09:40 +0000
Subject: [PATCH] upload

Signed-off-by: HaibaraLin <2411711961@qq.com>
---
 work2.py | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 work2.py

diff --git a/work2.py b/work2.py
new file mode 100644
index 0000000..9d88b8e
--- /dev/null
+++ b/work2.py
@@ -0,0 +1,36 @@
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+# 房价数据集
+datas = [
+    {"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": 140, "rooms": 3, "school": 1, "style": 1, "price": 8600},
+    {"city": "吕梁", "area": 135, "rooms": 3, "school": 1, "style": 1, "price": 8300},
+    {"city": "吕梁", "area": 140, "rooms": 3, "school": 0, "style": 0, "price": 6500},
+    {"city": "吕梁", "area": 135, "rooms": 3, "school": 0, "style": 0, "price": 6300},
+    {"city": "太原", "area": 100, "rooms": 2, "school": 1, "style": 1, "price": 10000},
+    {"city": "太原", "area": 100, "rooms": 2, "school": 1, "style": 1, "price": 9800},
+    {"city": "太原", "area": 140, "rooms": 3, "school": 1, "style": 1, "price": 10600},
+    {"city": "太原", "area": 135, "rooms": 3, "school": 1, "style": 1, "price": 10300},
+    {"city": "太原", "area": 140, "rooms": 3, "school": 0, "style": 0, "price": 8500},
+    {"city": "太原", "area": 135, "rooms": 3, "school": 0, "style": 0, "price": 8300},
+]
+
+# 将城市名转换为数值表示
+datas = [{**d, "city": 1 if d["city"] == "吕梁" else 2} for d in datas]
+
+# 转换为适合训练的格式
+X = np.array([[d["city"], d["area"], d["rooms"], d["school"], d["style"]] for d in datas])
+y = np.array([d["price"] for d in datas])
+
+# 创建并拟合线性回归模型
+model = LinearRegression()
+model.fit(X, y)
+
+# 假设要预测的房屋数据
+new_data = np.array([[2, 100, 2, 1, 1]])  # 假设城市为太原，面积100，2个房间，附近有学校，风格为1
+
+# 进行预测
+predicted_price = model.predict(new_data)
+print("预测的房屋价格为:", predicted_price)
-- 
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