Day_015_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# [作業目標]\n",
    "- 請同學試著使用 pandas.corr() 這個函數來顯示相關係數並加以觀察結果 \n",
    "- 思考1 : 使用 pandas 有沒有什麼寫法, 可以顯示欄位中最大的幾筆, 以及最小幾筆呢? (Hint: 排序後列出前幾筆/後幾筆)\n",
    "- 思考2 : 試著使用散佈圖, 顯示相關度最大/最小的特徵與目標值的關係, 如果圖形不明顯, 是否有調整的方法?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# [作業重點]\n",
    "- 綜合前幾單元的作法, 試試看是否能夠用繪圖顯示出特徵與目標的相關性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 設定 data_path\n",
    "dir_data = 'C:/Users/zero0/OneDrive/cupoy/Machine Learning/教材/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(307511, 122)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 讀取資料檔\n",
    "f_app_train = os.path.join(dir_data, 'application_train.csv')\n",
    "app_train = pd.read_csv(f_app_train)\n",
    "app_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 122)\n"
     ]
    },
    {
     "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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET  NAME_CONTRACT_TYPE CODE_GENDER  FLAG_OWN_CAR  \\\n",
       "0      100002       1                   0           M             0   \n",
       "1      100003       0                   0           F             0   \n",
       "2      100004       0                   1           M             1   \n",
       "3      100006       0                   0           F             0   \n",
       "4      100007       0                   0           M             0   \n",
       "\n",
       "   FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0                1             0          202500.0    406597.5      24700.5   \n",
       "1                0             0          270000.0   1293502.5      35698.5   \n",
       "2                1             0           67500.0    135000.0       6750.0   \n",
       "3                1             0          135000.0    312682.5      29686.5   \n",
       "4                1             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將只有兩種值的類別型欄位, 做 Label Encoder, 計算相關係數時讓這些欄位可以被包含在內\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# 檢查每一個 column\n",
    "for col in app_train:\n",
    "    if app_train[col].dtype == 'object':\n",
    "        # 如果只有兩種值的類別型欄位\n",
    "        if len(list(app_train[col].unique())) <= 2:\n",
    "            # 就做 Label Encoder, 以加入相關係數檢查\n",
    "            app_train[col] = le.fit_transform(app_train[col])            \n",
    "print(app_train.shape)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 受雇日數為異常值的資料, 另外設一個欄位記錄, 並將異常的日數轉成空值 (np.nan)\n",
    "app_train['DAYS_EMPLOYED_ANOM'] = app_train[\"DAYS_EMPLOYED\"] == 365243\n",
    "app_train['DAYS_EMPLOYED'].replace({365243: np.nan}, inplace = True)\n",
    "\n",
    "# 出生日數 (DAYS_BIRTH) 取絕對值 \n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相關係數\n",
    "一樣，pandas 很貼心地讓我們可以非常容易計算相關係數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                   -0.002108\n",
       "TARGET                        1.000000\n",
       "NAME_CONTRACT_TYPE           -0.030896\n",
       "FLAG_OWN_CAR                 -0.021851\n",
       "FLAG_OWN_REALTY              -0.006148\n",
       "                                ...   \n",
       "AMT_REQ_CREDIT_BUREAU_WEEK    0.000788\n",
       "AMT_REQ_CREDIT_BUREAU_MON    -0.012462\n",
       "AMT_REQ_CREDIT_BUREAU_QRT    -0.002022\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR    0.019930\n",
       "DAYS_EMPLOYED_ANOM           -0.045987\n",
       "Name: TARGET, Length: 110, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 觀察相關係數\n",
    "app_train.corr()['TARGET']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間\n",
    "列出目標 (TARGET) 與所有欄位之間相關係數，數值最大以及最小各 15 個\n",
    "\n",
    "通過相關係數的結果觀察有興趣的欄位與 TARGET 或其他欄位的相關係數，並嘗試找出有趣的訊息\n",
    "- 最好的方式當然是畫圖，舉例來說，我們知道  EXT_SOURCE_3 這個欄位和 TARGET 之間的相關係數是 -0.178919 (在已經這個資料集已經是最負的了！)，那我們可以 EXT_SOURCE_3  為 x 軸， TARGET 為 y 軸，把資料給畫出來"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TARGET                         1.000000\n",
       "DAYS_EMPLOYED                  0.074958\n",
       "REGION_RATING_CLIENT_W_CITY    0.060893\n",
       "REGION_RATING_CLIENT           0.058899\n",
       "DAYS_LAST_PHONE_CHANGE         0.055218\n",
       "DAYS_ID_PUBLISH                0.051457\n",
       "REG_CITY_NOT_WORK_CITY         0.050994\n",
       "FLAG_EMP_PHONE                 0.045982\n",
       "REG_CITY_NOT_LIVE_CITY         0.044395\n",
       "FLAG_DOCUMENT_3                0.044346\n",
       "DAYS_REGISTRATION              0.041975\n",
       "OWN_CAR_AGE                    0.037612\n",
       "LIVE_CITY_NOT_WORK_CITY        0.032518\n",
       "DEF_30_CNT_SOCIAL_CIRCLE       0.032248\n",
       "DEF_60_CNT_SOCIAL_CIRCLE       0.031276\n",
       "FLAG_WORK_PHONE                0.028524\n",
       "Name: TARGET, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.corr()['TARGET'].sort_values(ascending=False).head(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EXT_SOURCE_3                 -0.178919\n",
       "EXT_SOURCE_2                 -0.160472\n",
       "EXT_SOURCE_1                 -0.155317\n",
       "DAYS_BIRTH                   -0.078239\n",
       "DAYS_EMPLOYED_ANOM           -0.045987\n",
       "FLOORSMAX_AVG                -0.044003\n",
       "FLOORSMAX_MEDI               -0.043768\n",
       "FLOORSMAX_MODE               -0.043226\n",
       "AMT_GOODS_PRICE              -0.039645\n",
       "REGION_POPULATION_RELATIVE   -0.037227\n",
       "ELEVATORS_AVG                -0.034199\n",
       "ELEVATORS_MEDI               -0.033863\n",
       "FLOORSMIN_AVG                -0.033614\n",
       "FLOORSMIN_MEDI               -0.033394\n",
       "LIVINGAREA_AVG               -0.032997\n",
       "Name: TARGET, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_train.corr()['TARGET'].sort_values(ascending=True).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25fa6b96b08>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "app_train.boxplot(column=['EXT_SOURCE_3'], by =['TARGET'] , showfliers = False, figsize=(12,12))"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}