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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 机器学习100天——第6天：逻辑回归（Linear Regression）"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第1步：数据预处理\n",
+    "### 导入库"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as numpy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 导入数据集\n",
+    "<a href = \"https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Social_Network_Ads.csv\">这里</a>获取数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('../datasets/Social_Network_Ads.csv')\n",
+    "X = dataset.iloc[:, [2, 3]].values\n",
+    "Y = dataset.iloc[:,4].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 将数据集分成训练集和测试集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cross_validation import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 特征缩放"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ymao/usr/miniconda/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
+      "  warnings.warn(msg, DataConversionWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "sc = StandardScaler()\n",
+    "X_train = sc.fit_transform(X_train)\n",
+    "X_test = sc.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第二步：逻辑回归模型\n",
+    "该项工作的库将会是一个线性模型库，之所以被称为线性是因为逻辑回归是一个线性分类器，这意味着我们在二维空间中，我们两类用户（购买和不购买）将被一条直线分割。然后导入逻辑回归类。下一步我们将创建该类的对象，它将作为我们训练集的分类器。\n",
+    "### 将逻辑回归应用于训练集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
+       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
+       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
+       "          verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "classifier = LogisticRegression()\n",
+    "classifier.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第3步：预测\n",
+    "### 预测测试集结果"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = classifier.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第4步：评估预测\n",
+    "我们预测了测试集。 现在我们将评估逻辑回归模型是否正确的学习和理解。因此这个混淆矩阵将包含我们模型的正确和错误的预测。\n",
+    "### 生成混淆矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 可视化\n",
+    "![](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/LR_training.png?raw=true)\n",
+    "![](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Other%20Docs/LR_test.png?raw=true) "
+   ]
+  }
+ ],
+ "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": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}