From b926b806bde13c6531c36a8e77f2ed7ccf474440 Mon Sep 17 00:00:00 2001 From: jacksu <371387455@qq.com> Date: Wed, 28 Nov 2018 10:45:48 +0800 Subject: [PATCH] add 2 day jupyter notebook --- Code/Day 2_Simple_Linear_Regression.ipynb | 202 ++++++++-------------- 1 file changed, 68 insertions(+), 134 deletions(-) diff --git a/Code/Day 2_Simple_Linear_Regression.ipynb b/Code/Day 2_Simple_Linear_Regression.ipynb index a08a275..fc8f5d9 100644 --- a/Code/Day 2_Simple_Linear_Regression.ipynb +++ b/Code/Day 2_Simple_Linear_Regression.ipynb @@ -12,17 +12,15 @@ "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.pyploy:matplotlib是python上的一个2D绘图库,\n", - "matplotlib下的模块pyplott是一个有命令样式的函数集合,\n", - "matplotlib.pyploy是为我们对结果进行图像化作准备的。" + "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": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -89,14 +87,48 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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]]\n", + "Y: [21 47 27 75 30 20 88 60 81 25 85 62 41 42 17 95 30 24 67 69 30 54 35 76\n", + " 86]\n" + ] + } + ], "source": [ "X = dataset.iloc[ : , : 1 ].values\n", - "Y = dataset.iloc[ : , 1 ].values" + "Y = dataset.iloc[ : , 1 ].values\n", + "print(\"X:\",X)\n", + "print(\"Y:\",Y)" ] }, { @@ -108,10 +140,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, + "outputs": [], "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) " ] }, @@ -119,42 +153,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 第二步:训练集使用简单线性回归模型来训练" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "从sklearn的线性模型类中调用线性回归模型" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.linear_model import LinearRegression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "创建一个线性回归对象regressor,并对训练集利用fit()方法进行训练" + "## 训练线性回归" ] }, { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "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)" ] @@ -163,22 +172,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 第三步:预测结果" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "利用predict()方法对测试集进行预测" + "## 预测结果" ] }, { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "Y_pred = regressor.predict(X_test)" @@ -198,77 +198,16 @@ "### 训练集结果可视化" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "首先调用scatter方法,对训练集作散点图" - ] - }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { + "image/png": "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\n", "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.scatter(X_train , Y_train, color = 'red')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "调用plot方法对训练集的预测作曲线图" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plt.plot(X_train , regressor.predict(X_train), color ='blue')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "将结果可视化" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKNBFRFJCgS4ikhIKdBGRlPh/Rc+jTuQbrmQAAAAASUVORK5CYII=\n", - "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -276,6 +215,10 @@ } ], "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()" ] }, @@ -288,14 +231,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -303,26 +246,17 @@ } ], "source": [ + "#散点图\n", "plt.scatter(X_test , Y_test, color = 'red')\n", - "plt.plot(X_test , regressor.predict(X_test), color ='blue')\n", + "#线图\n", + "plt.plot(X_test ,Y_pred, 'bo-')\n", "plt.show()" ] }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "完整的项目请前往Github项目100-Days-Of-ML-Code查看。有任何的建议或者意见欢迎在issue中提出~" - ] - }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -343,7 +277,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.5" } }, "nbformat": 4,