{"id":361,"date":"2023-12-28T23:01:30","date_gmt":"2023-12-28T15:01:30","guid":{"rendered":"https:\/\/blog.zwdblog.online\/?p=361"},"modified":"2023-12-28T23:01:30","modified_gmt":"2023-12-28T15:01:30","slug":"python%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/blog.zwdblog.online\/index.php\/2023\/12\/28\/python%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90\/","title":{"rendered":"Python\u6570\u636e\u5206\u6790"},"content":{"rendered":"\n

\u8fd8\u662f\u4e00\u6837\uff0c\u7528\u4e0a\u6b21\u7684\u90a3\u4e2a\u623f\u6e90\u6570\u636e\u8fdb\u884c\u7b80\u5355\u7684\u6570\u636e\u5206\u6790\uff0c\u57fa\u4e8ejupyter notebook<\/p>\n\n\n\n

1\u3001\u9996\u5148\uff0c\u8bfb\u53d6\u6587\u4ef6\u6570\u636e\u5e76\u521b\u5efaDataFrame\uff0c\u540c\u65f6\u67e5\u770b\u6570\u636e\u4fe1\u606f<\/p>\n\n\n\n

import pandas as pd\ndf = pd.read_csv('\u5b89\u5c45\u5ba2\u623f\u6e90.csv')  #\u8bfb\u53d6csv\u6587\u4ef6\r\ndf.head(10)<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ea\u5c55\u793a\u4e86\u5341\u6761\u6570\u636e\uff0c\u603b\u5171\u670920\u6761<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
df.info() \u00a0#\u8bfb\u53d6\u4fe1\u606f\u6570\u91cf\u4e0e\u5c5e\u6027<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u770b\u5230\u6ca1\u6709\u7f3a\u5931\u503c\uff0c\u6240\u4ee5\u4e0d\u7528\u505a\u7f3a\u5931\u503c\u5904\u7406<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
2\u3001\u6761\u5f62\u56fe\u5c55\u793a\u603b\u4ef7<\/p>\n\n\n\n
df['\u603b\u4ef7\uff08\u4e07\uff09'].plot.bar()  #\u603b\u4ef7\u7528\u6761\u5f62\u56fe\u5f62\u5f0f\u5c55\u793a<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n<\/div>\n\n\n
3\u3001\u7bb1\u7ebf\u56fe\u5c55\u793a\u603b\u4ef7<\/p>\n\n\n\n
import matplotlib.pyplot as plt\r\nplt.rcParams['font.sans-serif'] = \"Microsoft YaHei\" #\u5c55\u793a\u5b57\u4f53\u4ee5\u9632\u4e71\u7801\r\ndf['\u603b\u4ef7\uff08\u4e07\uff09'].plot.box()   #\u7bb1\u7ebf\u56fe<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u753b\u8fd9\u4e2a\u7bb1\u7ebf\u56fe\u5176\u76ee\u7684\u662f\u5728\u4e0a\u8ff0\u6761\u5f62\u56fe\u4e86\u89e3\u603b\u4ef7\u7684\u5206\u5e03\u60c5\u51b5\u4e0b\u518d\u4e00\u6b21\u770b\u603b\u4ef7\u7ec6\u81f4\u5206\u5e03\u60c5\u51b5\uff0c\u80fd\u5c55\u73b0\u6700\u5c0f\u503c\u3001\u7b2c\u4e00\u56db\u5206\u4f4d\u6570\uff08Q1\uff09\u3001\u4e2d\u4f4d\u6570\uff08Q2\uff09\u3001\u7b2c\u4e09\u56db\u5206\u4f4d\u6570\uff08Q3\uff09\u4ee5\u53ca\u6700\u5927\u503c\uff0c\u540c\u65f6\u4ece\u56fe\u4e2d\u80fd\u770b\u5230\u6709\u4e24\u4e2a\u5f02\u5e38\u503c\uff0c\u518d\u56de\u770b\u90a3\u4e2acsv\u6587\u4ef6\u6216\u8005dataframe\uff0c\u53d1\u73b0\u5206\u522b\u662f\u603b\u4ef7\u4e3a300\u548c327\u7684<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
4\u3001\u53bb\u6389\u5f02\u5e38\u503c\u7684\u884c\u5e76\u518d\u7528\u7bb1\u7ebf\u56fe\u5c55\u793a\u5f02\u5e38\u503c\u662f\u5426\u6392\u9664<\/p>\n\n\n\n
df = df[~df['\u603b\u4ef7\uff08\u4e07\uff09'].isin([300,327])]\r\ndf.head(10)<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n<\/div>\n\n\n
df['\u603b\u4ef7\uff08\u4e07\uff09'].plot.box()   #\u7bb1\u7ebf\u56fe<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u770b\u5230\u4e24\u4e2a\u5f02\u5e38\u503c\u88ab\u53bb\u6389<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
5\u3001\u76f4\u65b9\u56fe\u5c55\u793a\u603b\u4ef7\u5206\u5e03\u60c5\u51b5<\/p>\n\n\n\n
df['\u603b\u4ef7\uff08\u4e07\uff09'].plot.hist()  <\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u770b\u5230\u6bcf\u4e2a\u4ef7\u683c\u533a\u95f4\u7684\u5206\u5e03\u4e2a\u6570<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
6\u3001\u67e5\u770b\u9762\u79ef\u7bb1\u7ebf\u56fe\u4ee5\u89c2\u5bdf\u662f\u5426\u6709\u5f02\u5e38\u503c<\/p>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u7ed3\u679c\u662f\u6ca1\u6709<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
7\u3001\u4e3a\u4e86\u770b\u4e00\u770b\u5747\u4ef7\u7684\u5206\u5e03\u662f\u5426\u4e5f\u6709\u5f02\u5e38\u503c\uff0c\u6240\u4ee5\u4e5f\u753b\u4e00\u4e2a\u7bb1\u7ebf\u56fe\u5c55\u793a\u4e00\u4e0b\u5747\u4ef7\u7684\u60c5\u51b5\uff0c\u4f46\u662f\u6240\u5efa\u7684dataframe\u4e2d\u5747\u4ef7\u4e0d\u662f\u6570\u503c\u7c7b\u578b\uff0c\u90a3\u4e48\u5728\u6b64\u5c31\u5e94\u8be5\u5148\u6309\u2018\u5143\u2019\u5206\u9694\u5f00\u5e76\u8f6c\u5316\u4e3afloat\u7c7b\u578b\u518d\u753b<\/p>\n\n\n\n
df['\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09'] = df['\u5747\u4ef7'].str.split('\u5143').str[0].astype(float)\r\ndf.info() <\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u628a\u5747\u4ef7\u5206\u5272\u5e76\u7c7b\u578b\u8f6c\u5316\u4e3afloat\uff0c\u53ef\u770b\u5230\u5df2\u7ecf\u8d77\u65b0\u5217\u4e3a\u2018\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09\u2019\uff0c\u7c7b\u578b\u4e3afloat<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
df['\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09'].plot.box()<\/code><\/pre>\n\n\n\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u770b\u5230\u5747\u4ef7\u5206\u5e03\u65e0\u5f02\u5e38\u503c<\/strong><\/figcaption><\/figure>\n\n\n\n
8\u3001\u628a\u5ba4\u536b\u5385\u52a0\u4e00\u4e0b\u8d77\u65b0\u5217\u4e3a\u2018\u623f\u95f4\u2019\u4ee5\u4e0b\u9762\u8fdb\u884c\u5173\u7cfb\u5206\u6790<\/p>\n\n\n\n
df['\u623f\u95f4\uff08\u4e2a\u6570\uff09'] = df['\u5ba4']+df['\u5385']+df['\u536b']\r\ndf.head(10)<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n<\/div>\n\n\n
9\u3001\u7136\u540e\u7a81\u7136\u53d1\u73b0\u7531\u4e8ecsv\u6587\u4ef6\u5185\u5bb9\u7684\u95ee\u9898\uff0c\u5217\u2018\u9762\u79ef\u2019\u7684\u5355\u4f4d\u4e0d\u5bf9\uff0c\u4e8e\u662f\u6539\u4e86\u4e00\u4e0b<\/p>\n\n\n\n
df = df.rename(columns={'\u9762\u79ef\uff08\u5343\u7c73\uff09':\"\u9762\u79ef\uff08\u5e73\u65b9\u7c73\uff09\"})\r\ndf.head(10)<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n<\/div>\n\n\n
10\u3001\u5982\u679c\u60f3\u63a2\u7d22\u4e00\u4e0b\u603b\u4ef7\u3001\u5747\u4ef7\u3001\u9762\u79ef\u7684\u5173\u7cfb\uff0c\u4e8e\u662f\u7528\u6563\u70b9\u56fe\u5c55\u793a<\/p>\n\n\n\n
# \u7ed8\u5236\u6563\u70b9\u56fe\r\nplt.figure(figsize=(7, 4))\r\nplt.scatter(df['\u603b\u4ef7\uff08\u4e07\uff09'], df['\u9762\u79ef\uff08\u5e73\u65b9\u7c73\uff09'], c=df['\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09'], cmap='viridis', s=100, alpha=0.8)\r\nplt.colorbar(label='\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09')\r\nplt.xlabel('\u603b\u4ef7\uff08\u4e07\uff09')\r\nplt.ylabel('\u9762\u79ef\uff08\u5e73\u65b9\u7c73\uff09')\r\nplt.title('\u603b\u4ef7 vs \u9762\u79ef vs \u5747\u4ef7 \u6563\u70b9\u56fe')\r\nplt.show()<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u4ece\u8fd9\u53ef\u77e5\u9053\uff0c\u603b\u4ef7\u3001\u5747\u4ef7\u4e0e\u9762\u79ef\u7684\u5173\u7cfb\u662f\u603b\u4ef7\u4e00\u5b9a\uff0c\u5747\u4ef7\u4f4e\uff0c\u9762\u79ef\u9ad8\uff0c\u5747\u4ef7\u9ad8\uff0c\u9762\u79ef\u4f4e\uff0c\u6bd4\u5982\u623f\u4ef7180\u5de6\u53f3\u9ec4\u8272\u7684\u90a3\u4e2a\uff0c\u5747\u4ef7\u5f88\u9ad8\u4f46\u662f\u9762\u79ef\u5f88\u4f4e\uff0c\u800c\u504f\u84dd\u8272\u90a3\u4e2a\u5219\u662f\u5747\u4ef7\u6bd4\u8f83\u4f4e\u4f46\u662f\u9762\u79ef\u5927\uff0c\u56e0\u4e3a\u623f\u4ef7\u8fd8\u53d7\u5730\u57df\u5f71\u54cd\uff0c\u6709\u4e9b\u5730\u65b9\u623f\u4ef7\u9ad8\uff0c\u800c\u6709\u4e9b\u4f4e\u3002<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
11\u3001\u518d\u63a2\u7d22\u4e00\u4e0b\u5730\u533a\u3001\u5747\u4ef7\u3001\u603b\u4ef7\u7684\u5173\u7cfb\uff0c\u7531\u4e8e\u5730\u533a\u4e0d\u662f\u6570\u503c\u578b\uff0c\u6240\u4ee5\u5148\u8f6c\u7801\uff0c\u628a\u5730\u533a\u8f6c\u5316\u4e3a\u6570\u503c\u578b<\/p>\n\n\n\n
from sklearn.preprocessing import LabelEncoder\r\nlabelE = LabelEncoder()\ndf['\u533a\u57df'] = labelE.fit_transform(df['\u5730\u533a'])\r\ndf.head(10)<\/code><\/pre>\n\n\n\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n\n\n\n
\u7ed8\u5236\u603b\u4ef7\u3001\u5747\u4ef7\u3001\u533a\u57df\u4e09\u8005\u5173\u7cfb\u6563\u70b9\u56fe\uff1a<\/p>\n\n\n\n
plt.figure(figsize=(5, 3))\r\nplt.scatter(df['\u533a\u57df'], df['\u603b\u4ef7\uff08\u4e07\uff09'], c=df['\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09'], cmap='viridis', s=100, alpha=0.8)\r\nplt.colorbar(label='\u5747\u4ef7\uff08\u5143\/\u5e73\u7c73\uff09')\r\nplt.xlabel('\u533a\u57df')\r\nplt.ylabel('\u603b\u4ef7\uff08\u4e07\uff09')\r\nplt.title('\u603b\u4ef7 vs \u533a\u57df vs \u5747\u4ef7 \u6563\u70b9\u56fe')\r\nplt.show()<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u770b\u5230\u603b\u4ef7\u3001\u5747\u4ef7\u4e24\u8005\u4e5f\u53d7\u533a\u57df\u5f71\u54cd\uff0c\u4f46\u662f\u5f71\u54cd\u4e5f\u4e0d\u662f\u5f88\u5f3a\uff0c\u56e0\u4e3a\u8fd8\u53d7\u9762\u79ef\u7684\u5f71\u54cd<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
12\u3001\u518d\u770b\u4e00\u4e0b\u4e0d\u540c\u5730\u533a\u5747\u4ef7\u5bf9\u6bd4\u5206\u5e03\u60c5\u51b5\u4ee5\u770b\u770b\u8c01\u9ad8\u4e00\u70b9\u6216\u4f4e\u4e00\u70b9<\/p>\n\n\n\n
plt.figure(figsize=(5, 3))\r\nplt.boxplot([df[df['\u5730\u533a'] == region]['\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09'] for region in df['\u5730\u533a'].unique()],\r\n            labels=df['\u5730\u533a'].unique(), vert=False)\r\nplt.xlabel('\u5747\u4ef7\uff08\u5143\/\u5e73\u7c73\uff09')\r\nplt.ylabel('\u5730\u533a')\r\nplt.title('\u4e0d\u540c\u5730\u533a\u7684\u5747\u4ef7\u7bb1\u7ebf\u56fe\u5bf9\u6bd4')\r\nplt.show()<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u770b\u5230\u5728\u6211\u8fd9csv\u6587\u4ef6\u91cc\u674e\u6ca7\u7684\u5747\u4ef7\u662f\u7565\u9ad8\u4e8e\u5176\u4ed6\u5730\u533a\u7684<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
13\u3001\u4e0b\u9762\u770b\u4e00\u4e0b\u9762\u79ef\u4e0e\u623f\u95f4\u603b\u6570\uff08\u5ba4\u536b\u5385\uff09\u7684\u5173\u7cfb<\/p>\n\n\n\n
plt.figure(figsize=(7, 3))\r\nplt.scatter(df['\u9762\u79ef\uff08\u5e73\u65b9\u7c73\uff09'], df['\u5ba4'], c=df['\u536b'], \r\n               cmap='viridis', s=df['\u5385'] * 75, alpha=0.8)\r\nplt.colorbar(label='\u536b\u7684\u6570\u91cf')\r\nplt.xlabel('\u9762\u79ef\uff08\u5e73\u65b9\u7c73\uff09')\r\nplt.ylabel('\u5ba4\u7684\u6570\u91cf')\r\nplt.title('\u9762\u79ef vs \u623f\u95f4\u6570 \u6563\u70b9\u56fe')\r\nplt.show()<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u4e0a\u8ff0\u6563\u70b9\u56fe\u4e2d\u6563\u70b9\u7684\u5927\u5c0f\u4ee3\u8868\u5385\u7684\u6570\u91cf\uff0c\u53ef\u77e5\uff0c\u9762\u79ef\u8d8a\u5927\uff0c\u623f\u95f4\u6570\u91cf\u662f\u591a\u4e00\u70b9\uff0c\u800c\u4e14\u5728\u6570\u91cf\u5dee\u4e0d\u592a\u591a\u60c5\u51b5\u4e0b\uff0c\u5ba4\u591a\uff0c\u5385\u591a\uff0c\u90a3\u536b\u5c31\u5c11\uff0c\u5c31\u662f\u5176\u4e2d\u4e00\u4e2a\u591a\uff0c\u90a3\u5176\u4f59\u4fe9\u5c31\u5c11\uff0c\u5927\u81f4\u5c31\u8fd9\u6837<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
14\u3001\u4e0b\u9762\u628a\u603b\u4ef7\u5206\u5272\u4e00\u4e0b\uff0c\u7edf\u8ba1\u4e00\u4e0b\u6bcf\u4e2a\u6807\u7b7e\u5185\u7684\u6570\u91cf\u6709\u591a\u5c11\u4e2a\u5e76\u5c55\u793a<\/p>\n\n\n\n
bins = [0,50,100,150,200]\r\nbinresult = pd.cut(df['\u603b\u4ef7\uff08\u4e07\uff09'],bins)\r\nprint(binresult.value_counts().sort_index(axis = 0))  #\u8ba1\u6bcf\u4e2a\u5206\u6bb5\u7684\u6570\u91cf\n\ndf['\u7c7b\u578b'] = pd.cut(df['\u603b\u4ef7\uff08\u4e07\uff09'],bins,labels = ['\u4fbf\u5b9c','\u6b63\u5e38','\u8f83\u8d35','\u6602\u8d35'])\r\ndf.head(20)<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u6309\u4ef7\u683c\u5206\u5e76\u7ed9\u4e0e\u6807\u7b7e<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
df['\u7c7b\u578b'].value_counts().plot.pie() <\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u77e5\u9053\u4ef7\u683c\u5728150\u5230200\u7684\u6570\u91cf\u662f\u6700\u591a\u7684\uff0c\u8bc1\u660e\u6b64\u5730\u533a\u7684\u4ef7\u683c\u8fd8\u662f\u6bd4\u8f83\u504f\u8d35<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
15\u3001\u4e0b\u9762\u5c31\u5f00\u59cb\u7528\u6a21\u578b\u6765\u9884\u6d4b\u603b\u4ef7\u4e86\uff0c\u5148\u91c7\u7528\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/p>\n\n\n\n
from sklearn.model_selection import train_test_split\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \u5206\u79bb\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\r\nX = df[['\u533a\u57df','\u9762\u79ef\uff08\u5e73\u65b9\u7c73\uff09']]\r\ny = df[['\u603b\u4ef7\uff08\u4e07\uff09']]\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=43)\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nmodel = LinearRegression()\n\n# \u8bad\u7ec3\u6a21\u578b\r\nmodel.fit(X_train, y_train)\n\n#\u8fdb\u884c\u9884\u6d4b\r\ny_pred = model.predict(X_test)\n\n# \u8bc4\u4f30\u6a21\u578b\u6027\u80fd\r\nmse = mean_squared_error(y_test, y_pred)\r\nr2 = r2_score(y_test, y_pred)\n\nprint(f'\u5747\u65b9\u8bef\u5dee\uff1a{mse}')\r\nprint(f'R-squared\uff1a{r2}')<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n<\/div>\n\n\n
# \u53ef\u89c6\u5316\u9884\u6d4b\u7ed3\u679c\r\nplt.scatter(X_test['\u533a\u57df'], y_test, color='black', label='\u5b9e\u9645\u503c')\r\nplt.scatter(X_test['\u533a\u57df'], y_pred, color='blue', label='\u9884\u6d4b\u503c')\r\nplt.xlabel('\u533a\u57df')\r\nplt.ylabel('\u603b\u4ef7\uff08\u4e07\uff09')\r\nplt.legend()\r\nplt.show()<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u8fd9\u662f\u7528\u533a\u57df\u548c\u9762\u79ef\u6765\u9884\u6d4b\u603b\u4ef7\uff0c\u7531\u4e0a\u8ff0\u6a21\u578b\u53ef\u770b\u5230\u5747\u65b9\u8bef\u5dee\u4e3a467\uff0c\u53ef\u51b3\u7cfb\u6570\u4e3a0.47\uff0c\u8bc1\u660e\u6a21\u578b\u662f\u53ef\u7528\u4f46\u4e0d\u63a5\u8fd1\u5b8c\u7f8e\uff0c\u7531\u6700\u7ec8\u7684\u53ef\u89c6\u5316\u56fe\u4e5f\u770b\u5f97\u51fa\u6765\u786e\u5b9e\u6a21\u578b\u9884\u6d4b\u7684\u4e0e\u771f\u5b9e\u8fd8\u662f\u6709\u5dee\u8ddd<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
16\u3001\u91c7\u7528\u51b3\u7b56\u6811\u6a21\u578b\u9884\u6d4b<\/p>\n\n\n\n
from sklearn.tree import DecisionTreeRegressor\n\n# \u5206\u79bb\u7279\u5f81\u548c\u76ee\u6807\u53d8\u91cf\r\nX = df[['\u533a\u57df','\u5747\u4ef7\uff08\u5143\/\u5e73\u65b9\u7c73\uff09']]\r\ny = df[['\u603b\u4ef7\uff08\u4e07\uff09']]\n\n# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=35)\n\n# \u521b\u5efa\u51b3\u7b56\u6811\u6a21\u578b\r\nmodelD = DecisionTreeRegressor(random_state=35)\n\n# \u8bad\u7ec3\u6a21\u578b\r\nmodelD.fit(X_train, y_train)\n\n# \u8fdb\u884c\u9884\u6d4b\r\ny_pred = modelD.predict(X_test)\n\n# \u8bc4\u4f30\u6a21\u578b\u6027\u80fd\r\nmse = mean_squared_error(y_test, y_pred)\r\nr2 = r2_score(y_test, y_pred)\n\nprint(f'\u5747\u65b9\u8bef\u5dee\uff1a{mse}')\r\nprint(f'R-squared\uff1a{r2}')<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"<\/figure>\n<\/div>\n\n\n
# \u53ef\u89c6\u5316\u9884\u6d4b\u7ed3\u679c\r\nplt.scatter(X_test['\u533a\u57df'], y_test, color='black', label='\u5b9e\u9645\u503c')\r\nplt.scatter(X_test['\u533a\u57df'], y_pred, color='blue', label='\u9884\u6d4b\u503c')\r\nplt.xlabel('\u533a\u57df')\r\nplt.ylabel('\u603b\u4ef7\uff08\u4e07\uff09')\r\nplt.legend()\r\nplt.show()<\/code><\/pre>\n\n\n\n
\"python\u6570\u636e\u5206\u6790\"
\u8fd9\u662f\u7528\u533a\u57df\u548c\u5747\u4ef7\u6765\u9884\u6d4b\u603b\u4ef7\uff0c\u7531\u4e0a\u8ff0\u6a21\u578b\u53ef\u77e5\u9053\uff0c\u5747\u65b9\u8bef\u5dee456.5\uff0c\u53ef\u51b3\u7cfb\u6570\u4e3a0.62\uff0c\u8bc1\u660e\u6a21\u578b\u662f\u53ef\u7528\u4f46\u4e5f\u4e0d\u63a5\u8fd1\u5b8c\u7f8e\uff0c\u7531\u6700\u7ec8\u7684\u53ef\u89c6\u5316\u56fe\u4e5f\u770b\u5f97\u51fa\u6765\u786e\u5b9e\u6a21\u578b\u9884\u6d4b\u7684\u6bd4\u8d77\u56de\u5f52\u7684\u8fd8\u662f\u597d\u4e86\u70b9\u4e5f\u5927\u81f4\u9884\u6d4b\uff0c\u4f46\u8fd8\u662f\u8bef\u5dee\u7565\u5927<\/strong><\/figcaption><\/figure>\n\n\n\n
17\u3001\u9884\u6d4b\u7ed3\u679c\uff0c\u7528\u51b3\u7b56\u6811\u6a21\u578b<\/p>\n\n\n\n
import numpy as np\r\ny_out = modelD.predict(np.array([1,13050]).reshape(1,-1))\r\nprint(y_out)<\/code><\/pre>\n\n\n
\n
\"python\u6570\u636e\u5206\u6790\"
\u53ef\u77e5\u7528\u51b3\u7b56\u6811\u6a21\u578b\u53bb\u9884\u6d4b\u7ed3\u679c\uff0c\u5f53\u533a\u57df\u9009\u5b9a\u4e3a\u5e02\u5317\uff0c\u623f\u4ef7\u4e3a13050\u5143\/\u5e73\u65b9\u7c73\uff0c\u9884\u6d4b\u503c\u4e3a178.0\u4e07<\/strong><\/figcaption><\/figure>\n<\/div>\n\n\n
\u7ed3\u675f\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"
\u8fd8\u662f\u4e00\u6837\uff0c\u7528\u4e0a\u6b21\u7684\u90a3\u4e2a\u623f\u6e90\u6570\u636e\u8fdb\u884c\u7b80\u5355\u7684\u6570\u636e\u5206\u6790\uff0c\u57fa\u4e8ejup […]<\/p>\n","protected":false},"author":1,"featured_media":386,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-361","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-6"],"_links":{"self":[{"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/posts\/361","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/comments?post=361"}],"version-history":[{"count":0,"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/posts\/361\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/media\/386"}],"wp:attachment":[{"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/media?parent=361"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/categories?post=361"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.zwdblog.online\/index.php\/wp-json\/wp\/v2\/tags?post=361"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}