Python69-数据挖掘与机器学习作业（七）.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入相关包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>ID</th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cards</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Student</th>\n",
       "      <th>Married</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>106.025</td>\n",
       "      <td>6645</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asian</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>148.924</td>\n",
       "      <td>9504</td>\n",
       "      <td>681</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>55.882</td>\n",
       "      <td>4897</td>\n",
       "      <td>357</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>80.180</td>\n",
       "      <td>8047</td>\n",
       "      <td>569</td>\n",
       "      <td>4</td>\n",
       "      <td>77</td>\n",
       "      <td>10</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20.996</td>\n",
       "      <td>3388</td>\n",
       "      <td>259</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>12</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>African American</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>71.408</td>\n",
       "      <td>7114</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>15.125</td>\n",
       "      <td>3300</td>\n",
       "      <td>266</td>\n",
       "      <td>5</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>71.061</td>\n",
       "      <td>6819</td>\n",
       "      <td>491</td>\n",
       "      <td>3</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>African American</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID   Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
       "0   1   14.891   3606     283      2   34         11    Male      No     Yes   \n",
       "1   2  106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
       "2   3  104.593   7075     514      4   71         11    Male      No      No   \n",
       "3   4  148.924   9504     681      3   36         11  Female      No      No   \n",
       "4   5   55.882   4897     357      2   68         16    Male      No     Yes   \n",
       "5   6   80.180   8047     569      4   77         10    Male      No      No   \n",
       "6   7   20.996   3388     259      2   37         12  Female      No      No   \n",
       "7   8   71.408   7114     512      2   87          9    Male      No      No   \n",
       "8   9   15.125   3300     266      5   66         13  Female      No      No   \n",
       "9  10   71.061   6819     491      3   41         19  Female     Yes     Yes   \n",
       "\n",
       "          Ethnicity  Balance  \n",
       "0         Caucasian      333  \n",
       "1             Asian      903  \n",
       "2             Asian      580  \n",
       "3             Asian      964  \n",
       "4         Caucasian      331  \n",
       "5         Caucasian     1151  \n",
       "6  African American      203  \n",
       "7             Asian      872  \n",
       "8         Caucasian      279  \n",
       "9  African American     1350  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('files/data/python69-data/Credit.csv')\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Student, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Student']=data['Student'].apply(lambda x: 1 if x=='Yes' else 0)\n",
    "data['Student'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 问题 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- student加入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       333\n",
      "1       903\n",
      "2       580\n",
      "3       964\n",
      "4       331\n",
      "5      1151\n",
      "6       203\n",
      "7       872\n",
      "8       279\n",
      "9      1350\n",
      "10     1407\n",
      "11        0\n",
      "12      204\n",
      "13     1081\n",
      "14      148\n",
      "15        0\n",
      "16        0\n",
      "17      368\n",
      "18      891\n",
      "19     1048\n",
      "20       89\n",
      "21      968\n",
      "22        0\n",
      "23      411\n",
      "24        0\n",
      "25      671\n",
      "26      654\n",
      "27      467\n",
      "28     1809\n",
      "29      915\n",
      "       ... \n",
      "370     992\n",
      "371       0\n",
      "372     840\n",
      "373    1003\n",
      "374     588\n",
      "375    1000\n",
      "376     767\n",
      "377       0\n",
      "378     717\n",
      "379       0\n",
      "380     661\n",
      "381     849\n",
      "382    1352\n",
      "383     382\n",
      "384       0\n",
      "385     905\n",
      "386     371\n",
      "387       0\n",
      "388    1129\n",
      "389     806\n",
      "390    1393\n",
      "391     721\n",
      "392       0\n",
      "393       0\n",
      "394     734\n",
      "395     560\n",
      "396     480\n",
      "397     138\n",
      "398       0\n",
      "399     966\n",
      "Name: Balance, Length: 400, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "X=data[['Rating','Income','Student','Limit']]\n",
    "y=data['Balance']\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 划分数据集合测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 4)\n",
      "(100, 4)\n",
      "(300,)\n",
      "(100,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=1)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练线性模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bias is 7.90\n",
      "MSE is :11839.59\n",
      "Var is 209960.23\n"
     ]
    }
   ],
   "source": [
    "# 模型拟合测试集\n",
    "# 计算偏差bias\n",
    "y_pred=linreg.predict(X_test)\n",
    "mean_y_pred=np.mean(y_pred)\n",
    "bias=np.sum(y_test-mean_y_pred)/y_test.shape[0]\n",
    "print(\"Bias is %.2f\" % (bias))\n",
    "# 计算MSE\n",
    "print(\"MSE is :%.2f\" % metrics.mean_squared_error(y_test,y_pred))\n",
    "\n",
    "# 计算var\n",
    "print(\"Var is %.2f\" % np.var(y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- student 不加入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bias is 12.32\n",
      "MSE is :30307.14\n",
      "Var is 193242.39\n"
     ]
    }
   ],
   "source": [
    "X=data[['Rating','Income','Limit']]\n",
    "y=data['Balance']\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=1)\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)\n",
    "y_pred=linreg.predict(X_test)\n",
    "mean_y_pred=np.mean(y_pred)\n",
    "bias=np.sum(y_test-mean_y_pred)/y_test.shape[0]\n",
    "print(\"Bias is %.2f\" % (bias))\n",
    "# 计算MSE\n",
    "print(\"MSE is :%.2f\" % metrics.mean_squared_error(y_test,y_pred))\n",
    "\n",
    "# 计算var\n",
    "print(\"Var is %.2f\" % np.var(y_pred))"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}