data_analysis/决策树分类器预测工资收入/决策树分类器预测工资收入.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'income']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names = (\"age, workclass, fnlwgt, education, education-num, \"\n",
    "         \"marital-status, occupation, relationship, race, sex, \"\n",
    "         \"capital-gain, capital-loss, hours-per-week, \"\n",
    "         \"native-country, income\").split(', ')    \n",
    "data = pd.read_csv('adult.data',names = names)\n",
    "col_names = data.columns.tolist()\n",
    "print(col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt   education  education-num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        marital-status          occupation    relationship    race      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   capital-gain  capital-loss  hours-per-week  native-country  income  \n",
       "0          2174             0              40   United-States   <=50K  \n",
       "1             0             0              13   United-States   <=50K  \n",
       "2             0             0              40   United-States   <=50K  \n",
       "3             0             0              40   United-States   <=50K  \n",
       "4             0             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()#查看数据形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               32561\n",
       "workclass         32561\n",
       "fnlwgt            32561\n",
       "education         32561\n",
       "education-num     32561\n",
       "marital-status    32561\n",
       "occupation        32561\n",
       "relationship      32561\n",
       "race              32561\n",
       "sex               32561\n",
       "capital-gain      32561\n",
       "capital-loss      32561\n",
       "hours-per-week    32561\n",
       "native-country    32561\n",
       "income            32561\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()#查看各变量样本数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32561.000000</td>\n",
       "      <td>3.256100e+04</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "      <td>32561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.581647</td>\n",
       "      <td>1.897784e+05</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>40.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.640433</td>\n",
       "      <td>1.055500e+05</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>12.347429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.228500e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.178270e+05</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.783560e+05</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.370510e+05</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.484705e+06</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        fnlwgt  education-num  capital-gain  capital-loss  \\\n",
       "count  32561.000000  3.256100e+04   32561.000000  32561.000000  32561.000000   \n",
       "mean      38.581647  1.897784e+05      10.080679   1077.648844     87.303830   \n",
       "std       13.640433  1.055500e+05       2.572720   7385.292085    402.960219   \n",
       "min       17.000000  1.228500e+04       1.000000      0.000000      0.000000   \n",
       "25%       28.000000  1.178270e+05       9.000000      0.000000      0.000000   \n",
       "50%       37.000000  1.783560e+05      10.000000      0.000000      0.000000   \n",
       "75%       48.000000  2.370510e+05      12.000000      0.000000      0.000000   \n",
       "max       90.000000  1.484705e+06      16.000000  99999.000000   4356.000000   \n",
       "\n",
       "       hours-per-week  \n",
       "count    32561.000000  \n",
       "mean        40.437456  \n",
       "std         12.347429  \n",
       "min          1.000000  \n",
       "25%         40.000000  \n",
       "50%         40.000000  \n",
       "75%         45.000000  \n",
       "max         99.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()#部分变量描述性统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation\n",
       " Prof-specialty       4140\n",
       " Craft-repair         4099\n",
       " Exec-managerial      4066\n",
       " Adm-clerical         3770\n",
       " Sales                3650\n",
       " Other-service        3295\n",
       " Machine-op-inspct    2002\n",
       " ?                    1843\n",
       " Transport-moving     1597\n",
       " Handlers-cleaners    1370\n",
       " Farming-fishing       994\n",
       " Tech-support          928\n",
       " Protective-serv       649\n",
       " Priv-house-serv       149\n",
       " Armed-Forces            9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按职业分组、计数并排序\n",
    "data.groupby('occupation').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制教育年限直方图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "data.hist(column='education-num', bins=15);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f95fa4252b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以年龄为横坐标、周平均工作时长为纵坐标绘制散点图\n",
    "data.plot(x='age', y='hours-per-week', kind='scatter',\n",
    "          alpha=0.02, s=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income\n",
       " <=50K    24720\n",
       " >50K      7841\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将样本数据按照收入水平分组，并计算各组样本数\n",
    "data.groupby('income')['income'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data['income'].unique()\n",
    "data = data.dropna() # pandas.DataFrame.dropna 可以删除数据框中的缺失值\n",
    "data['income'].unique() # 如果变量income中含有缺失值，可在此步验证是否已经将缺失值删除\n",
    "low_income = data[data['income'] == ' <=50K']\n",
    "high_income = data[data['income'] == ' >50K']\n",
    "bins = np.linspace(10, 90, 20)\n",
    "plt.hist(low_income['age'].values, bins=bins, alpha=0.5, label='<=50K')\n",
    "plt.hist(high_income['age'].values, bins=bins, alpha=0.5, label='>50K')\n",
    "plt.legend(loc='best'); # 添加变量标注说明，参数为位置设置\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']\n",
      "   age  fnlwgt  education-num  capital-gain  capital-loss  hours-per-week\n",
      "0   39   77516             13          2174             0              40\n",
      "1   50   83311             13             0             0              13\n",
      "2   38  215646              9             0             0              40\n",
      "3   53  234721              7             0             0              40\n",
      "4   28  338409             13             0             0              40\n",
      "ROC AUC Decision Tree: 0.8969 +/-0.0030\n"
     ]
    }
   ],
   "source": [
    "target = data['income']\n",
    "features_data = data.drop('income', axis=1)\n",
    "numeric_features = [c for c in features_data if features_data[c].dtype.kind in ('i', 'f')] # 提取数值类型为整数或浮点数的变量\n",
    "print(numeric_features)\n",
    "numeric_data = features_data[numeric_features]\n",
    "print(numeric_data.head(5))\n",
    "categorical_data = features_data.drop(numeric_features, 1)\n",
    "categorical_data.head(5)\n",
    "categorical_data_encoded = categorical_data.apply(lambda x: pd.factorize(x)[0]) # pd.factorize即可将分类变量转换为数值表示\n",
    "                                                                                # apply运算将转换函数应用到每一个变量维度\n",
    "categorical_data_encoded.head(5)\n",
    "features = pd.concat([numeric_data, categorical_data_encoded], axis=1)\n",
    "features.head()\n",
    "X = features.values.astype(np.float32) # 转换数据类型\n",
    "y = (target.values == ' >50K').astype(np.int32) # 收入水平 \">50K\" 记为1，“<=50K” 记为0\n",
    "\n",
    "from sklearn.cross_validation import train_test_split # sklearn库中train_test_split函数可实现该划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=0) # 参数test_size设置训练集占比\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "# 建立一个clf的决策树分类器\n",
    "clf = DecisionTreeClassifier(max_depth=8) # 参数max_depth设置树最大深度\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# 交叉验证，评价分类器性能，此处选择的评分标准是ROC曲线下的AUC值，对应AUC更大的分类器效果更好\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=5, scoring='roc_auc') \n",
    "print(\"ROC AUC Decision Tree: {:.4f} +/-{:.4f}\".format(\n",
    "    np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测你的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "输入一个人所需要的信息参数，我们输入模型里面，通过模型来预测一些它的收入到底是多少呢"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'age':年龄,'workclass':工作类型,'fnlwgt':序号,'education':教育水平,'education-num':受教育时间, \n",
    "'marital_status':婚姻状况,'occupation':职业,'relationship':关系,'race':种族,'sex':性别,'capital-gain':资本收益, \n",
    "'capital-loss':资本损失, 'hours-per-week':每周工作小时数,'native_country':原籍"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_info = pd.DataFrame([{\n",
    "    'age':50,\n",
    "    'workclass':2,\n",
    "    'fnlwgt':284582,\n",
    "    'education':12,\n",
    "    'education-num':13, \n",
    "    'marital_status':2,\n",
    "    'occupation':11,\n",
    "    'relationship':0,\n",
    "    'race':4,\n",
    "    'sex':1,\n",
    "    'capital-gain':0, \n",
    "    'capital-loss':0, \n",
    "    'hours-per-week':40,\n",
    "    'native_country':38\n",
    "}])\n",
    "main_info.shape\n",
    "clf.predict(main_info)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "这里的array[0]表示最后的预测收入是小于50k的，而实际上这组数据的收入也确实如此！学机器学习还是很有用的，有兴趣的小伙伴可以动手试试！"
   ]
  }
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