{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 机器学习100天——第二天：简单线性回归\n", "## 第一步：数据预处理" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这里导入我们需要的库，值得注意的是，这里比第一天多了一个matplotlib.pyplot,matplotlib是python上的一个2D绘图库,\n", "matplotlib下的模块pyplot是一个有命令样式的函数集合，\n", "matplotlib.pyplot是为我们对结果进行图像化作准备的。" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "导入相关数据" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Hours Scores\n0 2.5 21\n1 5.1 47\n2 3.2 27\n3 8.5 75\n4 3.5 30\n5 1.5 20\n6 9.2 88\n7 5.5 60\n8 8.3 81\n9 2.7 25\n10 7.7 85\n11 5.9 62\n12 4.5 41\n13 3.3 42\n14 1.1 17\n15 8.9 95\n16 2.5 30\n17 1.9 24\n18 6.1 67\n19 7.4 69\n20 2.7 30\n21 4.8 54\n22 3.8 35\n23 6.9 76\n24 7.8 86\n25 2.1 93\n26 2.2 93\n27 2.5 93\n Hours Scores\n15 8.9 95\n27 2.5 93\n26 2.2 93\n25 2.1 93\n6 9.2 88\n24 7.8 86\n10 7.7 85\n8 8.3 81\n23 6.9 76\n3 8.5 75\n19 7.4 69\n18 6.1 67\n11 5.9 62\n7 5.5 60\n21 4.8 54\n1 5.1 47\n13 3.3 42\n12 4.5 41\n22 3.8 35\n20 2.7 30\n4 3.5 30\n16 2.5 30\n2 3.2 27\n9 2.7 25\n17 1.9 24\n0 2.5 21\n5 1.5 20\n14 1.1 17\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Hours Scores\n", "0 2.5 21\n", "1 5.1 47\n", "2 3.2 27\n", "3 8.5 75\n", "4 3.5 30\n", "5 1.5 20\n", "6 9.2 88\n", "7 5.5 60\n", "8 8.3 81\n", "9 2.7 25\n", "10 7.7 85\n", "11 5.9 62\n", "12 4.5 41\n", "13 3.3 42\n", "14 1.1 17\n", "15 8.9 95\n", "16 2.5 30\n", "17 1.9 24\n", "18 6.1 67\n", "19 7.4 69\n", "20 2.7 30\n", "21 4.8 54\n", "22 3.8 35\n", "23 6.9 76\n", "24 7.8 86\n", "25 2.1 93\n", "26 2.2 93\n", "27 2.5 93" ], "text/html": "
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HoursScores
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23.227
38.575
43.530
51.520
69.288
75.560
88.381
92.725
107.785
115.962
124.541
133.342
141.117
158.995
162.530
171.924
186.167
197.469
202.730
214.854
223.835
236.976
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252.193
262.293
272.593
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" }, "metadata": {}, "execution_count": 90 } ], "source": [ "dataset = pd.read_csv('../datasets/studentscores.csv')\n", "print(dataset)\n", "df = dataset.sort_values(\"Scores\",ascending=False)\n", "print(df)\n", "dataset.head(30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这里我们需要使用pandas的iloc(区分于loc根据index来索引，iloc利用行号来索引)方法来对数据进行处理，第一个参数为行号，:表示全部行，第二个参数 ：1表示截到第1列(也就是取第0列)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "X: [[2.5]\n [5.1]\n [3.2]\n [8.5]\n [3.5]\n [1.5]\n [9.2]\n [5.5]\n [8.3]\n [2.7]\n [7.7]\n [5.9]\n [4.5]\n [3.3]\n [1.1]\n [8.9]\n [2.5]\n [1.9]\n [6.1]\n [7.4]\n [2.7]\n [4.8]\n [3.8]\n [6.9]\n [7.8]]\nY: [[21]\n [47]\n [27]\n [75]\n [30]\n [20]\n [88]\n [60]\n [81]\n [25]\n [85]\n [62]\n [41]\n [42]\n [17]\n [95]\n [30]\n [24]\n [67]\n [69]\n [30]\n [54]\n [35]\n [76]\n [86]]\n" ] } ], "source": [ "X = dataset.iloc[ 0: 25, : 1 ].values\n", "Y = dataset.iloc[ 0: 25, -1: ].values\n", "print(\"X:\",X)\n", "print(\"Y:\",Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "导入sklearn库的cross_validation类来对数据进行训练集、测试集划分" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[7.8]\n [6.9]\n [1.1]\n [5.1]\n [7.7]\n [3.3]\n [8.3]\n [9.2]\n [6.1]\n [3.5]\n [2.7]\n [5.5]\n [2.7]\n [8.5]\n [2.5]\n [4.8]\n [8.9]\n [4.5]] [[1.5]\n [3.2]\n [7.4]\n [2.5]\n [5.9]\n [3.8]\n [1.9]]\n[[86]\n [76]\n [17]\n [47]\n [85]\n [42]\n [81]\n [88]\n [67]\n [30]\n [25]\n [60]\n [30]\n [75]\n [21]\n [54]\n [95]\n [41]] [[20]\n [27]\n [69]\n [30]\n [62]\n [35]\n [24]]\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "#拆分数据，0.25作为测试集\n", "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) \n", "print(X_train,X_test)\n", "print(Y_train,Y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 训练线性回归" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "#使用训练集对模型进行训练\n", "regressor = LinearRegression()\n", "regressor = regressor.fit(X_train, Y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 预测结果" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[16.84472176]\n [33.74557494]\n [75.50062397]\n [26.7864001 ]\n [60.58810646]\n [39.71058194]\n [20.8213931 ]]\n[[20]\n [27]\n [69]\n [30]\n [62]\n [35]\n [24]]\n" ] } ], "source": [ "Y_pred = regressor.predict(X_test)\n", "print(Y_pred)\n", "print(Y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 可视化" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 训练集结果可视化" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "#散点图\n", "plt.scatter(X_train , Y_train, color = 'red')\n", "#线图\n", "plt.plot(X_train , regressor.predict(X_train), 'bo-')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 测试集结果可视化" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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hZncDu81srLs3mdlYoLmQhUp5GD68+867zfjxxatFJOt67cDd/WZ3H+fu1cC1wCPu/ilgDTA3+ra5wOqCVSmZt359mBrpGN6d7zgZNgwWLixuXSJZls994IuAWWa2FZgVjUW6MIMZM9rHL70U7jBZsqR9znvChDCeMye9OkWyxryIe3DW1NR4Q0ND0Z5P0vXzn8Pll7ePJ06EbdvSq0ckq8xsg7vXdL6upfSSuO42n3rjDTjttHTqESlXWkovibrrrnh4X3ppCHSFt0jy1IFLIo4ehUGD4tf27YOTT06nHpFKoA5c8jZ/fjy8580LXbfCW6Sw1IFLvx04ACedFL92+HDXTlxECkMduPTL1VfHw/v220PXrfAWKR514NInzc0wZkz8mjafEkmHOnDJ2ZQp8fBeuTJ03QpvkXQowKVXL78cQvqFF9qvucMnP5nnL+64IXh1dRiLSM40hSLH1bm7fvJJOO+8BH5x5w3BGxvDGLSeXiRH6sClW0891TW83RMKb+h+Q/CDB8N1EcmJOnDponNwb9kCkycn/CQ9bfytDcFFcqYOvMJ1nIauqoqH91lnha478fCGnjf+1obgIjlTgFewtmnoxsYQ1G+91f61N98M274WzMKF2hBcJE8K8Ap2vHMpO9/rnbg5c7QhuEietB94hTpyBAYP7v5rZmFxjoiUhp72A1cHXoH+8R97Dm/QNLRIVugulAqyfz+MGBG/NmxY13MqNQ0tkg3qwCvEX/5lPLzvvFPnUopknTrwMtfU1PU0nI6bT82Zo8AWySp14GXsgx+Mh/eaNdp8SqScqAMvQ7/9LZx9dvxaEW82EpEiUQdeZszi4f3MMwpvkXKlAC8Tjz0WnxoZOjQE9/Tp6dUkIoXV6xSKmQ0FfgUMib5/pbt/3cxGAfcC1cAO4Bp3f7twpUpPOs9pv/IKnHFGOrWISPHk0oG/B/yFu08FzgEuNrM/A+YD9e4+CaiPxtKbBA8xWLEiHt41NaHrVniLVIZeO3APa+3fjYaDog8HrgAujK4vB9YBNyVeYTlJ6BAD95D/HbW0wOjRCdUpIpmQ0xy4mQ00s01AM7DW3Z8Bxrh7E0D0eGrhyiwTCRxi8G//Fg/vOXNCoCu8RSpPTrcRunsrcI6ZjQRWmdkf5/oEZlYL1AKMr/RNNvI4xODwYRgyJH7t4EE48cQE6hKRTOrTXSju/g5hquRiYLeZjQWIHpt7+Jkl7l7j7jVVVVV5lptx/TzE4Prr4+H9ta+FrlvhLVLZcrkLpQo44u7vmNmJwMeAfwXWAHOBRdHj6kIWWhYWLozPgcNxd4/auxdGjoxfO3oUBg4sYI0ikhm5dOBjgUfN7HngWcIc+AOE4J5lZluBWdFYjqcPhxh87GPx8F66NHTdCm8RaaMDHUrM66/D6afHr3XcfEpEKo8OdMiA006Lh/dDD2nzKRHpmTazKgHPPw9Tp8avaf8SEemNOvAi67wQ0ywe3hs3Fjm8E1wZKiLFpQ68iLpbiNlm1CjYs6cECurHylARSYc68CLqbiEmwPvfn0J4QyIrQ0UkPQrwIuppweWuXcWt4/fyWBkqIulTgBeBO1x0Uc9z26ntMNDPlaEiUhoU4AX22GPh/cF168K4834mx1mIWXgLF4YCOkq1IBHpCwV4gRw9CpMnw4UXhvGZZ8KRI7BsWU4LMYujDytDRaT0aCVmAfzXf8FVV7WPf/Ur+OhH06tHRLKtp5WYuo0wQYcOQVUVHDgQxjNnwtq1WkkpIoWhKZSELFsWpo/bwvu55+B//kfhLSKFU5kBnuDqw7ffDiF93XVh/OlPh7tNpkxJpFIRkR5VXoC3rT5sbAxJ27b6sB8hvmhRWEHZZts2+NGPEqxVROQ4Ki/AE1h9uGtX6LpvvjmMb7op/F0wcWKCdYqI9KLy3sTMc/XhDTfAd7/bPn7zTRgzJoG6RET6qPI68H6uPty6NXTdbeH9H/8Rum6Ft4ikpfICvI+rD93hmmvgj/6o/drevXDjjQWsUUQkB5UX4H1YfbhxY7hR5b77wvhHPwqBPmJEkWsWEelG5c2BQwjr4ywXP3YMLrgAnnwyjKuqwhT50KFFqk9EJAeV14H34tFHw8nvbeH9wAPQ3KzwFpHSU5kdeDeOHAmbT736ahhPnQobNoQwFxEpRerAgZUrYfDg9vB+8knYtEnhLSKlrSIDvONKejO4+upw/ZJLwvz3eeelWp6ISE4qLsA7r6Rvs2gRPPigNp8SkezoNcDN7HQze9TMNpvZS2Y2L7o+yszWmtnW6PGUglSY4MZTEJa/d3ew8OLFef1aEZGiy6UDPwp82d3PBP4M+DszOwuYD9S7+ySgPhonK8GNpyAcb/baa91/Tef4ikjW9Brg7t7k7hujz/cDm4H3A1cAy6NvWw5cmXh1CWw8BbBvH1x/fTje7IQe7rvROb4ikjV9mgM3s2rgXOAZYIy7N0EIeeDUHn6m1swazKyhpaWlb9XlufEUwC9+AWefHRZb3ngjfP/7OsdXRMpDzgFuZicBPwNucPd9uf6cuy9x9xp3r6mqqupbdf3ceArgrbfgU5+Cyy4LS9+feipsQPXZz+ocXxEpDzkFuJkNIoR3nbvfH13ebWZjo6+PBZoTr66PG09BmCpfsSKcAn/vvfD1r4c9TWbMaP+eOXNgx45wy+COHQpvEcmmXO5CMWAZsNndb+vwpTXA3OjzucDqxKvrw8ZTAG+8AVdeCbNnwwc+EIL7lltgyJDEKxMRSZ15x5uhu/sGs48AjwMvAMeiy/9CmAf/KTAe2Alc7e7/d7zfVVNT4w0NDfnW3IU7LF0KX/lKWBL/rW+Fgxe0klJEyoGZbXD3ms7Xe90Lxd2fAHpa3jIz38LytW0bfP7zYROqCy+E//xP+OAH065KRKTwMrsSs7UVbrsN/uRPwqZT3/8+1NcrvEWkcmRyN8IXX4TPfQ7Wrw93mSxeDOPGpV2ViEhxlXwH3nEl/YQJ8MlPwrRpsH07/OQnsGaNwltEKlNJd+BtK+nbFmPu3Bk+PvxhWL06nJQjIlKpSroD724lPcCuXQpvEZGSDvAEVtKLiJStkg7wPFbSi4iUvZIO8H6spBcRqRglHeB9XEkvIlJRSvouFAhhrcAWEemqpDtwERHpmQJcRCSjFOAiIhmlABcRySgFuIhIRvV6oEOiT2bWAjQW7Qn7ZzTwVtpFJKicXk85vRYor9dTTq8FSu/1THD3LhuIFDXAs8DMGro7+SKryun1lNNrgfJ6PeX0WiA7r0dTKCIiGaUAFxHJKAV4V0vSLiBh5fR6yum1QHm9nnJ6LZCR16M5cBGRjFIHLiKSUQpwEZGMUoBHzOx0M3vUzDab2UtmNi/tmvrLzIaa2Xozey56Ld9Iu6Z8mdlAM/uNmT2Qdi35MrMdZvaCmW0ys4a068mXmY00s5VmtiX6/8+H066pv8xscvTfpe1jn5ndkHZdPdEceMTMxgJj3X2jmZ0MbACudPffplxan5mZAcPd/V0zGwQ8Acxz96dTLq3fzOxGoAYY4e6XpV1PPsxsB1Dj7qW0UKTfzGw58Li7LzWzwcAwd38n7bryZWYDgTeAGe5ekgsQ1YFH3L3J3TdGn+8HNgPvT7eq/vHg3Wg4KPrI7N/UZjYOuBRYmnYtEmdmI4ALgGUA7n64HMI7MhPYVqrhDQrwbplZNXAu8Ey6lfRfNOWwCWgG1rp7Zl8LcDvwz8CxtAtJiAO/NLMNZlabdjF5mgi0AD+MpriWmtnwtItKyLXAPWkXcTwK8E7M7CTgZ8AN7r4v7Xr6y91b3f0cYBww3cz+OO2a+sPMLgOa3X1D2rUk6Hx3nwZcAvydmV2QdkF5OAGYBix293OBA8D8dEvKXzQVdDlwX9q1HI8CvINovvhnQJ273592PUmI/jm7Drg45VL663zg8mjeeAXwF2Z2d7ol5cfdd0WPzcAqYHq6FeXldeD1Dv/CW0kI9Ky7BNjo7rvTLuR4FOCR6I2/ZcBmd78t7XryYWZVZjYy+vxE4GPAlnSr6h93v9ndx7l7NeGftI+4+6dSLqvfzGx49CY50VTDx4EX062q/9z9TeA1M5scXZoJZO6N/27MpsSnTyADhxoX0fnAp4EXorljgH9x9wdTrKm/xgLLo3fRBwA/dffM335XJsYAq0K/wAnAT9z94XRLytvfA3XRtMN24G9SricvZjYMmAV8Ie1aeqPbCEVEMkpTKCIiGaUAFxHJKAW4iEhGKcBFRDJKAS4iklEKcBGRjFKAi4hk1P8DE9nwnKBxd3cAAAAASUVORK5CYII=\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "#散点图\n", "plt.scatter(X_test , Y_test, color = 'red')\n", "#线图\n", "plt.plot(X_test ,Y_pred, 'bo-')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[3.2]\n [3.8]\n [1.1]\n [1.9]\n [1.5]\n [5.9]\n [7.8]] [[27]\n [35]\n [17]\n [24]\n [20]\n [62]\n [86]]\n" ] } ], "source": [ "print(X_test,Y_test)" ] } ], "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": "3.8.3-final" } }, "nbformat": 4, "nbformat_minor": 2 }