From c14b4aa6d4e2aac8976f90d7c2e3aa8e144ec338 Mon Sep 17 00:00:00 2001 From: iLern <416138794@qq.com> Date: Tue, 15 Sep 2020 22:47:34 +0800 Subject: [PATCH 1/3] FirstCommit --- .../README.md" | 1 + .../Figure_1.png" | Bin 0 -> 44438 bytes .../Figure_2.png" | Bin 0 -> 115375 bytes .../Normalization.py" | 23 +++ .../README.md" | 36 +++++ .../__pycache__/Normalization.cpython-38.pyc" | Bin 0 -> 948 bytes .../__pycache__/display_lr.cpython-38.pyc" | Bin 0 -> 770 bytes .../__pycache__/predict.cpython-38.pyc" | Bin 0 -> 1610 bytes .../display_lr.py" | 21 +++ .../eth2.csv" | 150 ++++++++++++++++++ .../main.py" | 9 ++ .../predict.ipynb" | 139 ++++++++++++++++ .../predict.py" | 53 +++++++ 13 files changed, 432 insertions(+) create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/Figure_1.png" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/Figure_2.png" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/Normalization.py" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/__pycache__/Normalization.cpython-38.pyc" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/__pycache__/display_lr.cpython-38.pyc" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/__pycache__/predict.cpython-38.pyc" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/display_lr.py" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/eth2.csv" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/main.py" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.ipynb" create mode 100644 "5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.py" diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/README.md" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/README.md" index 00a9c521..c376d08c 100644 --- "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/README.md" +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/README.md" @@ -3,3 +3,4 @@ 近年来，随着人工智能、大数据、云计算等技术的成熟，环保领域正向智能化转变，因为 AI 的加入环境保护充满了无限的想象，而其中环境污染预测就是重要一环——利用环境传感器、智能摄像头、环保设备 IoT 化，我们可以实现对空气、水源、噪声等污染情况进行实时监测及预警，做到“未病先知，未病先治”，进一步改善环境污染。 请以“环境污染的预测”为主题，利用人工智能技术完成一款产品（软件或硬件）的开发。 + diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/Figure_1.png" "b/5 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\347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/Normalization.py" @@ -0,0 +1,23 @@ +import pandas as pd +import matplotlib as mpl +import seaborn as sns +import csv + +from sklearn import preprocessing +from matplotlib import pyplot as plt + +def Normalization(): + data_file = "..\PRSA2017_Data_20130301-20170228\PRSA_Data_20130301-20170228\simpleDataSet.csv" + pd_data = pd.read_csv(data_file) + sam = [] + a = ["PM10", "SO2","NO2","CO","PRES","DEWP"] + for i in a: + y = pd_data.loc[:, i] + ys = list(preprocessing.scale(y)) + sam.append(ys) + + print(len(sam)) + with open('eth2.csv', 'w') as file: + writer = csv.writer(file) + for i in range(len(sam[0])): + writer.writerow([sam[0][i],sam[1][i],sam[2][i],sam[3][i],sam[4][i],sam[5][i]]) diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" new file mode 100644 index 00000000..a1e695b6 --- /dev/null +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" @@ -0,0 +1,36 @@ +# 基于线性回归的PM2.5的预测模型 + +## 作品介绍 + +### 背景 + +随着经济社会发展，空气污染情况成为我们不得不关注的问题，其中Pm2.5为重要的空气质量评价指标，本项目使用`sklearn`进行线性回归建模，并进行预测。 + +### 截图 + +![](E:\Code\PM2.5 predict\aws-hackathon-2020\5 环境污染的预测\pm2.5的线性回归预测模型-今天睡醒了吗\Figure_2.png) + +经过绘图发现，PM2.5和空气中$SO_2$、$NO_2$、$CO$, 以及气压、露点温度近似成线性关系，使用sklearn中的线性回归模型进行训练拟合并预测 + +![](E:\Code\PM2.5 predict\aws-hackathon-2020\5 环境污染的预测\pm2.5的线性回归预测模型-今天睡醒了吗\Figure_1.png) + +取数据中的$20\%$作为测试数据验证模型的可靠性，如上图所示。 + +**数据来源**： + +[UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.php?format=mat&task=&att=&area=phys&numAtt=10to100&numIns=&type=ts&sort=attUp&view=list) + +## 团队：今天睡醒了吗 + +**成员**：iLern + +**联系方式**： + +> email: Taisitong@outlook.com +> +> qq: 416138794 + +## 使用到的AWS技术 + +`Amazon SageMaker` + diff --git "a/5 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+import seaborn as sns + +from matplotlib import pyplot as plt + +# data_file = "..\PRSA2017_Data_20130301-20170228\PRSA_Data_20130301-20170228\PRSA_Data_Aotizhongxin_20130301-20170228.csv" +data_file = "..\PRSA2017_Data_20130301-20170228\PRSA_Data_20130301-20170228\simpleDataSet.csv" + +def display_data(): + pd_data = pd.read_csv(data_file) + print(f'pd_data.head(10) = \n{pd_data.head(10)}') + mpl.rcParams['axes.unicode_minus'] = False + sns.pairplot(pd_data, + x_vars = ["PM10", "SO2","NO2","CO","PRES","DEWP"], + y_vars = ["PM2.5"], + # dropna = True, + kind = "reg", + height = 5, + aspect = 0.7) + plt.show() diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/eth2.csv" "b/5 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+-0.2844305524822677,0.3705992324802287,3.2307549090082177,-0.8686584210843888,-0.6162765069432249,-1.3001322906599873,1.8782834965606159 diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/main.py" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/main.py" new file mode 100644 index 00000000..a6b73f59 --- /dev/null +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/main.py" @@ -0,0 +1,9 @@ +import display_lr +import Normalization +import predict + +if __name__ == "__main__": + # display_lr.display_data() + # Normalization.Normalization() + + predict.build_lr() \ No newline at end of file diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.ipynb" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.ipynb" new file mode 100644 index 00000000..99b1e7ab --- /dev/null +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.ipynb" @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-13T13:56:40.324016Z", + "start_time": "2020-09-13T13:56:36.982915Z" + } + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib as mpl\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.linear_model import LinearRegression\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"..\\PRSA2017_Data_20130301-20170228\\PRSA_Data_20130301-20170228\\simpleDataSet.csv\"\n", + "def build_lr():\n", + " pd_data = pd.read_csv(data_file)\n", + "\n", + " X = pd_data.loc[:, (\"PM10\", \"SO2\",\"NO2\",\"CO\",\"PRES\",\"DEWP\")]\n", + " y = pd_data.loc[:, \"PM2.5\"]\n", + " \n", + " #选择20%为测试集\n", + " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=532) \n", + "\n", + " print('训练集测试及参数:')\n", + " print(f'X_train.shape={X_train.shape}\\ny_train.shape ={y_train.shape}\\nX_test.shape={X_test.shape}\\ny_test.shape={y_test.shape}')\n", + "\n", + " linear_reg = LinearRegression()\n", + " model = linear_reg.fit(X_train, y_train)\n", + "\n", + " print('模型参数')\n", + " print(model)\n", + "\n", + " print('模型截距')\n", + " print(linear_reg.intercept_)\n", + "\n", + " print('参数权重')\n", + " print(linear_reg.coef_)\n", + "\n", + " y_pred = linear_reg.predict(X_test)\n", + " sum_mean = 0\n", + "\n", + " for i in range(len(y_pred)):\n", + " sum_mean += (y_pred[i] - y_test.values[i]) ** 2\n", + "\n", + " sum_erro = np.sqrt(sum_mean / len(y_pred))\n", + "\n", + " print(sum_erro)\n", + "\n", + " plt.figure()\n", + " plt.figure()\n", + " plt.plot(range(len(y_pred)), y_pred, 'b', label=\"predict\")\n", + " plt.plot(range(len(y_pred)), y_test, 'r', label=\"test\")\n", + " plt.legend(loc=\"upper right\")\n", + " plt.xlabel(\"the number of sales\")\n", + " plt.ylabel('value of sales')\n", + " plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.py" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.py" new file mode 100644 index 00000000..4c0f0c7b --- /dev/null +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/predict.py" @@ -0,0 +1,53 @@ +import pandas as pd +import matplotlib as mpl +import seaborn as sns +import numpy as np + +from sklearn.model_selection import train_test_split +from sklearn.linear_model import LinearRegression +from matplotlib import pyplot as plt + + +data_file = "..\PRSA2017_Data_20130301-20170228\PRSA_Data_20130301-20170228\simpleDataSet.csv" +def build_lr(): + pd_data = pd.read_csv(data_file) + + X = pd_data.loc[:, ("PM10", "SO2","NO2","CO","PRES","DEWP")] + y = pd_data.loc[:, "PM2.5"] + + #选择20%为测试集 + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=532) + + print('训练集测试及参数:') + print(f'X_train.shape={X_train.shape}\ny_train.shape ={y_train.shape}\nX_test.shape={X_test.shape}\ny_test.shape={y_test.shape}') + + linear_reg = LinearRegression() + model = linear_reg.fit(X_train, y_train) + + print('模型参数') + print(model) + + print('模型截距') + print(linear_reg.intercept_) + + print('参数权重') + print(linear_reg.coef_) + + y_pred = linear_reg.predict(X_test) + sum_mean = 0 + + for i in range(len(y_pred)): + sum_mean += (y_pred[i] - y_test.values[i]) ** 2 + + sum_erro = np.sqrt(sum_mean / len(y_pred)) + + print(sum_erro) + + plt.figure() + plt.figure() + plt.plot(range(len(y_pred)), y_pred, 'b', label="predict") + plt.plot(range(len(y_pred)), y_test, 'r', label="test") + plt.legend(loc="upper right") + plt.xlabel("the number of sales") + plt.ylabel('value of sales') + plt.show() \ No newline at end of file From 48bf0a166801f076f9c9c66979ea74f1fd336d2e Mon Sep 17 00:00:00 2001 From: iLern <416138794@qq.com> Date: Tue, 15 Sep 2020 22:49:31 +0800 Subject: [PATCH 2/3] fix picture path --- .../README.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" index a1e695b6..02990e3d 100644 --- "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" @@ -8,11 +8,11 @@ ### 截图 -![](E:\Code\PM2.5 predict\aws-hackathon-2020\5 环境污染的预测\pm2.5的线性回归预测模型-今天睡醒了吗\Figure_2.png) +![](.\Figure_2.png) 经过绘图发现，PM2.5和空气中$SO_2$、$NO_2$、$CO$, 以及气压、露点温度近似成线性关系，使用sklearn中的线性回归模型进行训练拟合并预测 -![](E:\Code\PM2.5 predict\aws-hackathon-2020\5 环境污染的预测\pm2.5的线性回归预测模型-今天睡醒了吗\Figure_1.png) +![](.\Figure_1.png) 取数据中的$20\%$作为测试数据验证模型的可靠性，如上图所示。 From 1f0c9e7f66b9c98fb5ffbc52e7aba6c59644e418 Mon Sep 17 00:00:00 2001 From: iLern <416138794@qq.com> Date: Tue, 15 Sep 2020 22:51:11 +0800 Subject: [PATCH 3/3] fix picture path --- .../README.md" | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" index 02990e3d..a7795ced 100644 --- "a/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" +++ "b/5 \347\216\257\345\242\203\346\261\241\346\237\223\347\232\204\351\242\204\346\265\213/pm2.5\347\232\204\347\272\277\346\200\247\345\233\236\345\275\222\351\242\204\346\265\213\346\250\241\345\236\213-\344\273\212\345\244\251\347\235\241\351\206\222\344\272\206\345\220\227/README.md" @@ -8,11 +8,11 @@ ### 截图 -![](.\Figure_2.png) +![](./Figure_2.png) 经过绘图发现，PM2.5和空气中$SO_2$、$NO_2$、$CO$, 以及气压、露点温度近似成线性关系，使用sklearn中的线性回归模型进行训练拟合并预测 -![](.\Figure_1.png) +![](./Figure_1.png) 取数据中的$20\%$作为测试数据验证模型的可靠性，如上图所示。