Lecture1-4_Baslc_Algorithm_KK.ipynb

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    "# **학습 방식의 진화**\n",
    "\n",
    "[![Open in Colab](http://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/thekimk/Tutorial-Probability-Statistics-Algorithm-DataScience/blob/main/Lecture1-4_Baslc_Algorithm_KK.ipynb)\n",
    "\n",
    "**1) 배경:** \n",
    "\n",
    "> **\"`데이터분석에 이 굴레들이 어떻게 쓰이는지 정리`해 보면..\"**\n",
    "\n",
    "| **데이터 분석** \t| **목적** \t| **대응** \t|\n",
    "|:---:|:---|:---|\n",
    "| **데이터 시각화** \t| 데이터가 어떻게 생겼는지 알고 싶다 \t| (1) 전체 데이터를 한눈에 확인<br>     - 점그림, 선그림, 영역그림<br>     - 막대그림, 등고선그림, 분포그림(히스토그램)<br>     $\\rightarrow$ `통계 사용`<br>     (2) 데이터를 뿌리고 통계를 계산하기 위해 데이터 값 하나하나를 표현 <br>$\\rightarrow$ `확률/컴퓨터 사용` \t|\n",
    "| **기술적 분석** \t| 데이터가 어떻게 생겼는지 알고 싶다 \t| (1) 전체 데이터 특성을 몇 개의 숫자들로 확인   <br>$\\rightarrow$ `통계 사용`<br>     (2) 통계를 계산하기 위해 데이터 값 하나하나를 표현 <br>$\\rightarrow$ `확률/컴퓨터 사용` \t|\n",
    "| **상관관계/인과관계** \t| 여러종류 데이터끼리의 관계를 알고 싶다 \t| (1) 각 데이터를 몇 개의 숫자들로 표현   <br>$\\rightarrow$ `통계 사용`<br>     (2) 표현된 숫자들을 비교 <br>$\\rightarrow$ `확률/통계 사용` \t|\n",
    "| **통계추론** \t| 일부 데이터로 전체를 알고 싶다 \t| (1) 일부 데이터의 특성을 확인 <br>$\\rightarrow$   `통계 사용`<br>     (2) 반복적으로 실험 진행 및 통계치 재확인 <br>$\\rightarrow$ `컴퓨터 사용`<br>     (3) 전체 특성을 추론 <br>$\\rightarrow$ `통계 사용` \t|\n",
    "| **알고리즘학습** \t| 전체 데이터로 미래를 알고 싶다 \t| (1) 데이터의 관계를 수학적으로 표현   <br>$\\rightarrow$ `확률/통계/함수/컴퓨터 사용`<br>     (2) 미래를 예측한 후 정확성 확인 <br>$\\rightarrow$ `확률/통계/컴퓨터 사용` \t|\n",
    "| **가설검정(A/B Test)** \t| 뭔가 진실과 가까운 의사결정을 하고 싶다 \t| (1) 기존 데이터와 새로운 데이터 비교를 위해 숫자들로   표현 <br>$\\rightarrow$ `통계 사용`<br>     (2) 표현된 숫자들을 비교 <br>$\\rightarrow$ `확률/통계/컴퓨터 사용` \t|\n",
    "\n",
    "---\n",
    "\n",
    "**2) 통계추론 vs 알고리즘학습:** \n",
    "\n",
    "- **통계추론 기반 데이터분석 목적:** `일부집단(스몰데이터)` $\\rightarrow$ `전체집단(빅데이터)` 추론\n",
    "\n",
    "<center><img src='Image/Basic/ProbabilityStatistics_StatisticalInference_update.png' width='600'></center>\n",
    "\n",
    "- **알고리즘학습 기반 데이터분석 목적:** 과거의 특성으로 `전체집단(빅데이터)` 가정하고 `일부 미래특성(스몰데이터)` 추론\n",
    "\n",
    "<center><img src='Image/Basic/ProbabilityStatistics_AlgorithmLearning_update.png' width='600'></center>\n",
    "\n",
    "---\n",
    "\n",
    "**3) 진화:**\n",
    "\n",
    "> **\"수집한 데이터가 많을수록 `더욱 정확하게 고객과 시장의 트렌드를 추적`할 수 있지만, 모든 데이터를 하나하나 다 들여다볼 수는 없는 노릇\"**\n",
    ">\n",
    "> **`경험 및 직감`** $\\rightarrow$ **`데이터집계(기술적분석)`** $\\rightarrow$ **`상관분석(기술적분석)`** $\\rightarrow$ **`통계추론`** $\\rightarrow$ **`기계학습`** $\\rightarrow$ **`딥러닝`**"
   ]
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    "## 데이터집계 및 통계량(Cross Table and Statistics)\n",
    "\n",
    "> **\"`데이터를 분석할 때`, 다양한 변수/속성/특징에 따라 `데이터를 분류`하여 `표형태`로 정리하거나 `요약(통계량)`\"**\n",
    "\n",
    "- **데이터집계(Cross Table):** 여러개의 `변수들의 관계`에 따라 `표형식으로 정리`한 것\n",
    "- **통계량(Summary Table, Statistic):** 표형식의 데이터 관계성을 `해석`하기 위해 `몇개의 숫자로 요약`하여 표현하는 것\n",
    "\n",
    "<center><img src='Image/Basic/CrossTableExample1.png' width='700'>(https://www.qualtrics.com/au/experience-management/research/cross-tabulation/)</center>\n",
    "<center><img src='Image/Basic/CrossTableExample3.png' width='600'>(https://www.qualtrics.com/design-xm/cross-tabulation/)</center>\n",
    "<center><img src='Image/Basic/CrossTableExample2.png' width='600'>(Measurement Of Customer Satisfaction On Demographic Variables Of Banking Sector In National Capital Region - An Empirical Analysis)</center>\n",
    "\n",
    "> - 정량적인 `하위 집단의 특성`을 파악하기 위해 `2차원` 형식으로 데이터를 요약하는 것\n",
    "> - 데이터집계는 다차원의 특성을 `2차원으로 축약`하여 해석하기 때문에 해석이 왜곡될 수 있고 `자의적인 해석`이 가능하도록 조작도 가능하며, `과거 특정시점에서의 특성 스냅샷(Snapshot)`이기 때문에 사실 `현재와 미래에는 맞지 않는 해석`을 가능성 높음\n",
    "> - `최근`에는 통계추론/기계학습/딥러닝을 사용하기 위한 `데이터 전처리 용도 또는 보고서 작성`용도로만 사용하는 편"
   ]
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       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th>MaritalStatus</th>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>OrderCount</th>\n",
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       "      <th>0</th>\n",
       "      <td>50001</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50002</td>\n",
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       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50004</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50005</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5625</th>\n",
       "      <td>55626</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>5626</th>\n",
       "      <td>55627</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>5627</th>\n",
       "      <td>55628</td>\n",
       "      <td>Male</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>5628</th>\n",
       "      <td>55629</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>5629</th>\n",
       "      <td>55630</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5630 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "      CustomerID  Gender MaritalStatus  CouponUsed  OrderCount\n",
       "0          50001  Female        Single         1.0         1.0\n",
       "1          50002    Male        Single         0.0         1.0\n",
       "2          50003    Male        Single         0.0         1.0\n",
       "3          50004    Male        Single         0.0         1.0\n",
       "4          50005    Male        Single         1.0         1.0\n",
       "...          ...     ...           ...         ...         ...\n",
       "5625       55626    Male       Married         1.0         2.0\n",
       "5626       55627    Male       Married         1.0         2.0\n",
       "5627       55628    Male       Married         1.0         2.0\n",
       "5628       55629    Male       Married         2.0         2.0\n",
       "5629       55630    Male       Married         2.0         2.0\n",
       "\n",
       "[5630 rows x 5 columns]"
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   "source": [
    "# 다양한 변수/속성/특징을 갖고 있는 데이터 준비\n",
    "import pandas as pd\n",
    "df_rel = pd.read_excel(r'.\\Data\\Ecommerce\\E_Commerce_Dataset.xlsx', sheet_name='Raw')\n",
    "target_col = ['CustomerID', 'Gender', 'MaritalStatus', 'CouponUsed', 'OrderCount']\n",
    "df = df_rel[target_col].copy()\n",
    "df"
   ]
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       "      <th>16.0</th>\n",
       "      <th>All</th>\n",
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       "    <tr>\n",
       "      <th>CouponUsed</th>\n",
       "      <th></th>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <th>3.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "      <td>25</td>\n",
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       "      <th>4.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>31</td>\n",
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       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>5.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
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       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1686</td>\n",
       "      <td>1963</td>\n",
       "      <td>359</td>\n",
       "      <td>178</td>\n",
       "      <td>171</td>\n",
       "      <td>127</td>\n",
       "      <td>195</td>\n",
       "      <td>164</td>\n",
       "      <td>56</td>\n",
       "      <td>34</td>\n",
       "      <td>43</td>\n",
       "      <td>46</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>5116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "OrderCount   1.0   2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0  10.0  11.0  12.0  \\\n",
       "CouponUsed                                                                    \n",
       "0.0          832    79   23   16   13    5   13    3    0     1     5     1   \n",
       "1.0          854   922  104   37   33   16   19   17    6     2     5     6   \n",
       "2.0            0   962  104   42   27   21   24   16    7     5     4     6   \n",
       "3.0            0     0  128   38   37   20   27   25    2     4     5     7   \n",
       "4.0            0     0    0   45   31   20   30   18   10     4     5     4   \n",
       "5.0            0     0    0    0   30   26   19   19    5     2     6     4   \n",
       "6.0            0     0    0    0    0   19   33   22    5     3     5     3   \n",
       "7.0            0     0    0    0    0    0   30   21   11     4     3     5   \n",
       "8.0            0     0    0    0    0    0    0   23    7     3     0     1   \n",
       "9.0            0     0    0    0    0    0    0    0    3     4     1     1   \n",
       "10.0           0     0    0    0    0    0    0    0    0     2     2     3   \n",
       "11.0           0     0    0    0    0    0    0    0    0     0     2     0   \n",
       "12.0           0     0    0    0    0    0    0    0    0     0     0     5   \n",
       "13.0           0     0    0    0    0    0    0    0    0     0     0     0   \n",
       "14.0           0     0    0    0    0    0    0    0    0     0     0     0   \n",
       "15.0           0     0    0    0    0    0    0    0    0     0     0     0   \n",
       "16.0           0     0    0    0    0    0    0    0    0     0     0     0   \n",
       "All         1686  1963  359  178  171  127  195  164   56    34    43    46   \n",
       "\n",
       "OrderCount  13.0  14.0  15.0  16.0   All  \n",
       "CouponUsed                                \n",
       "0.0            1     0     0     0   992  \n",
       "1.0            0     3     2     0  2026  \n",
       "2.0            2     2     2     2  1226  \n",
       "3.0            1     1     3     1   299  \n",
       "4.0            5     2     1     2   177  \n",
       "5.0            3     3     0     1   118  \n",
       "6.0            4     6     2     0   102  \n",
       "7.0            2     2     2     1    81  \n",
       "8.0            1     1     2     2    40  \n",
       "9.0            1     0     1     2    13  \n",
       "10.0           1     2     2     1    13  \n",
       "11.0           1     0     2     3     8  \n",
       "12.0           1     0     0     2     8  \n",
       "13.0           4     1     0     1     6  \n",
       "14.0           0     1     2     1     4  \n",
       "15.0           0     0     1     0     1  \n",
       "16.0           0     0     0     2     2  \n",
       "All           27    24    22    21  5116  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 마케팅으로 쿠폰을 발행하였는데 실제 매출 증가 효과가 있을지 알고 싶음\n",
    "# 변수들의 관계에 따라 표형식으로 정리한 교차집계 결과\n",
    "pd.crosstab(index=df.CouponUsed, columns=df.OrderCount, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:10.739969Z",
     "start_time": "2022-07-10T08:45:10.681939Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>OrderCount</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>11.0</th>\n",
       "      <th>12.0</th>\n",
       "      <th>13.0</th>\n",
       "      <th>14.0</th>\n",
       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CouponUsed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.83871</td>\n",
       "      <td>0.079637</td>\n",
       "      <td>0.023185</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.013105</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.013105</td>\n",
       "      <td>0.003024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.42152</td>\n",
       "      <td>0.455084</td>\n",
       "      <td>0.051333</td>\n",
       "      <td>0.018263</td>\n",
       "      <td>0.016288</td>\n",
       "      <td>0.007897</td>\n",
       "      <td>0.009378</td>\n",
       "      <td>0.008391</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.002468</td>\n",
       "      <td>0.002962</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001481</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "OrderCount      1.0       2.0       3.0       4.0       5.0       6.0  \\\n",
       "CouponUsed                                                              \n",
       "0.0         0.83871  0.079637  0.023185  0.016129  0.013105  0.005040   \n",
       "1.0         0.42152  0.455084  0.051333  0.018263  0.016288  0.007897   \n",
       "\n",
       "OrderCount       7.0       8.0       9.0      10.0      11.0      12.0  \\\n",
       "CouponUsed                                                               \n",
       "0.0         0.013105  0.003024  0.000000  0.001008  0.005040  0.001008   \n",
       "1.0         0.009378  0.008391  0.002962  0.000987  0.002468  0.002962   \n",
       "\n",
       "OrderCount      13.0      14.0      15.0  16.0  All  \n",
       "CouponUsed                                           \n",
       "0.0         0.001008  0.000000  0.000000   0.0  1.0  \n",
       "1.0         0.000000  0.001481  0.000987   0.0  1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 통계량(비율)을 반영하여 실제 매출 증가 효과에 대한 해석하기\n",
    "# 쿠폰을 사용하지 않는 구매자들 약17%만 2번 이상 구매\n",
    "# 쿠폰을 한번이라도 사용한 구매자들은 약 58%가 2번 이상 구매\n",
    "# 쿠폰 발행은 매출 증가로 이어지는 것 같다고 추론 가능\n",
    "(pd.crosstab(index=df.CouponUsed, columns=df.OrderCount, margins=True).T \\\n",
    " / pd.crosstab(index=df.CouponUsed, columns=df.OrderCount).sum(axis=1)).T.iloc[:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:10.830967Z",
     "start_time": "2022-07-10T08:45:10.740938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OrderCount</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0.0</th>\n",
       "      <th>Female</th>\n",
       "      <td>0.832447</td>\n",
       "      <td>0.069149</td>\n",
       "      <td>0.031915</td>\n",
       "      <td>0.015957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.842532</td>\n",
       "      <td>0.086039</td>\n",
       "      <td>0.017857</td>\n",
       "      <td>0.016234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1.0</th>\n",
       "      <th>Female</th>\n",
       "      <td>0.429677</td>\n",
       "      <td>0.438710</td>\n",
       "      <td>0.045161</td>\n",
       "      <td>0.021935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.416467</td>\n",
       "      <td>0.465228</td>\n",
       "      <td>0.055156</td>\n",
       "      <td>0.015987</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "OrderCount              1.0       2.0       3.0       4.0\n",
       "CouponUsed Gender                                        \n",
       "0.0        Female  0.832447  0.069149  0.031915  0.015957\n",
       "           Male    0.842532  0.086039  0.017857  0.016234\n",
       "1.0        Female  0.429677  0.438710  0.045161  0.021935\n",
       "           Male    0.416467  0.465228  0.055156  0.015987"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 쿠폰 발행의 매출 증가 효과는 남녀간에 차이가 있을까?를 알고 싶음\n",
    "# 세로축에 항목을 추가하여 더 상세하게 나누어서 분석 필요\n",
    "(pd.crosstab(index=[df.CouponUsed, df.Gender], columns=df.OrderCount, margins=True).T \\\n",
    "/ pd.crosstab(index=[df.CouponUsed, df.Gender], columns=df.OrderCount, margins=True).iloc[:,-1]).T.iloc[:4,:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:10.890964Z",
     "start_time": "2022-07-10T08:45:10.831971Z"
    }
   },
   "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></th>\n",
       "      <th>OrderCount</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>Gender</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
       "      <th>Divorced</th>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>162</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>104</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
       "      <th>Divorced</th>\n",
       "      <td>59</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>276</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>184</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "OrderCount                       1.0  2.0  3.0  4.0\n",
       "CouponUsed Gender MaritalStatus                    \n",
       "0.0        Female Divorced        47    2    2    1\n",
       "                  Married        162   12    9    1\n",
       "                  Single         104   12    1    4\n",
       "           Male   Divorced        59    6    4    1\n",
       "                  Married        276   31    5    7\n",
       "                  Single         184   16    2    2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지나치게 상세하면 각 항목별 데이터 갯수가 줄어들어 신뢰도 떨어질 수 있기 때문에 분석가 역량 중요\n",
    "pd.crosstab(index=[df.CouponUsed, df.Gender, df.MaritalStatus], columns=df.OrderCount, margins=True).iloc[:6,:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 상관분석(Correlation Analysis)\n",
    "\n",
    "> **\"`교차집계`로 다양한 변수/속성은 확인이 가능하나 `관계성/특징`을 확인하기 어려울 경우 주로 사용하는 `두 연속형 변수` 사이의 `상관관계`를 파악하는 통계량\"**\n",
    "\n",
    "<center><img src='Image/Basic/Algorithm_Correlation.png' width='700'></center>\n",
    "\n",
    "---\n",
    "\n",
    "- **상관계수(Correlation Coefficient):** \n",
    "\n",
    "> - 통계학 관점의 `선형적 상관도`를 확인하여 관련성 정도를 파악하는 지표\n",
    "> - 간단한 분석이지만 기계학습과 딥러닝의 발전을 이해하기 위해 반드시 의미 파악 중요\n",
    "> - 숫자로 표현 가능한 `연속형` 데이터에 대해서만 분석 가능\n",
    "> - `-1 ~ 1` 사이의 크기를 가지며, `증가 방향성`에 대한 것이지 인과관계 아님!\n",
    "\n",
    "---\n",
    "\n",
    "- **분석단계:**\n",
    "\n",
    "> (1) `Scatter Plot`으로 두 변수의 상관성 파악\n",
    ">\n",
    "> (2) 상관계수 확인\n",
    ">\n",
    "> (3) [의사결정](https://www.scribbr.com/statistics/correlation-coefficient/)\n",
    "> \n",
    "> <br>\n",
    "> <center><img src='Image/Basic/Correlation_Plot.png' width='700'></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:12.154396Z",
     "start_time": "2022-07-10T08:45:10.891938Z"
    }
   },
   "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>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>OrderCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50001</td>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50002</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50004</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5625</th>\n",
       "      <td>55626</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5626</th>\n",
       "      <td>55627</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5627</th>\n",
       "      <td>55628</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5628</th>\n",
       "      <td>55629</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5629</th>\n",
       "      <td>55630</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5630 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Gender MaritalStatus  CouponUsed  OrderCount\n",
       "0          50001  Female        Single         1.0         1.0\n",
       "1          50002    Male        Single         0.0         1.0\n",
       "2          50003    Male        Single         0.0         1.0\n",
       "3          50004    Male        Single         0.0         1.0\n",
       "4          50005    Male        Single         1.0         1.0\n",
       "...          ...     ...           ...         ...         ...\n",
       "5625       55626    Male       Married         1.0         2.0\n",
       "5626       55627    Male       Married         1.0         2.0\n",
       "5627       55628    Male       Married         1.0         2.0\n",
       "5628       55629    Male       Married         2.0         2.0\n",
       "5629       55630    Male       Married         2.0         2.0\n",
       "\n",
       "[5630 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다양한 변수/속성/특징을 갖고 있는 데이터 준비\n",
    "import pandas as pd\n",
    "df_rel = pd.read_excel(r'.\\Data\\Ecommerce\\E_Commerce_Dataset.xlsx', sheet_name='Raw')\n",
    "target_col = ['CustomerID', 'Gender', 'MaritalStatus', 'CouponUsed', 'OrderCount']\n",
    "df = df_rel[target_col].copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:13.742163Z",
     "start_time": "2022-07-10T08:45:12.155367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='CouponUsed', ylabel='OrderCount'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "# 두 변수의 상관성 정도를 그림으로 표현\n",
    "import seaborn as sns\n",
    "sns.scatterplot(data=df, x='CouponUsed', y='OrderCount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:13.772356Z",
     "start_time": "2022-07-10T08:45:13.743346Z"
    }
   },
   "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>CustomerID</th>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>OrderCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.234302</td>\n",
       "      <td>0.139008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CouponUsed</th>\n",
       "      <td>0.234302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.745245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrderCount</th>\n",
       "      <td>0.139008</td>\n",
       "      <td>0.745245</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CustomerID  CouponUsed  OrderCount\n",
       "CustomerID    1.000000    0.234302    0.139008\n",
       "CouponUsed    0.234302    1.000000    0.745245\n",
       "OrderCount    0.139008    0.745245    1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 두 변수의 상관성 정도를 통계량(상관계수)로 표현\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 통계추론(Statistical Inference)\n",
    "\n",
    "> **\"소수의 데이터를 통해 `다수의 정보`를 추정\"**\n",
    "\n",
    "---\n",
    "\n",
    "<center><img src='Image/Basic/StatisticalInference.jpg' width='700'>(https://www.playnexacro.com/#show:techtip:268)</center>\n",
    "\n",
    "- `적은 비용과 시간`으로 `전체 시장의 변화`를 파악하는데 도움 (ex. 출구조사)\n",
    "- `실제 비즈니스 제도, 전략 또는 의사결정`을 생성하거나 변경할 때는 통계추론으로 `거시적 변화`도 필요하지만 `미시적 고객 변화`가 더욱 중요\n",
    "> - 어떤 고객의 보험 가입을 승인?\n",
    "> - 어떤 프로모션이나 이벤트가 고객들의 반응을 높일지?\n",
    "> - 특정 상품이 어떤 조건에서 잘 팔리는지?\n",
    "> - 비용을 최소화 하기 위해 재고량을 얼마나 확보?\n",
    "> - 제조 공정상 데이터로 불량품 검출?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T10:38:24.975347Z",
     "start_time": "2022-07-10T10:38:24.561313Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KK\\anaconda3\\lib\\site-packages\\scipy\\stats\\_continuous_distns.py:639: RuntimeWarning: invalid value encountered in sqrt\n",
      "  sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)\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>Distribution</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gamma</td>\n",
       "      <td>0.416882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chi2</td>\n",
       "      <td>0.416811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>beta</td>\n",
       "      <td>0.409182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>t</td>\n",
       "      <td>0.404120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>norm</td>\n",
       "      <td>0.005746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>expon</td>\n",
       "      <td>0.002145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Distribution   p-value\n",
       "4        gamma  0.416882\n",
       "3         chi2  0.416811\n",
       "5         beta  0.409182\n",
       "1            t  0.404120\n",
       "0         norm  0.005746\n",
       "2        expon  0.002145"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "제일 비슷한 분포:  gamma\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 임의 데이터 생성\n",
    "shape, scale = 2, 2    # mean=4, std=2*sqrt(2)\n",
    "sample = np.random.gamma(shape, scale, size=100)\n",
    "\n",
    "# 임의 데이터 시각화\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.histplot(data=sample, kde=True)\n",
    "plt.show()\n",
    "\n",
    "# 분포 모델 후보\n",
    "dist_list = ['norm', 't', 'expon', 'chi2', 'gamma', 'beta']\n",
    "\n",
    "# 모델과 데이터 비교 검증\n",
    "result = []\n",
    "for dist in dist_list:\n",
    "    model = getattr(scipy.stats, dist)    # 분포의 특징 불러오기\n",
    "    params = model.fit(sample)    # 데이터를 적합 및 모수 저장\n",
    "    ks_stat, p_value = scipy.stats.kstest(sample, dist, params)    # 확률적 검증\n",
    "    result.append([dist, p_value])    # 결과 누적 정리\n",
    "result = pd.DataFrame(result, columns=['Distribution', 'p-value'])\n",
    "result = result.sort_values(by='p-value', ascending=False)\n",
    "\n",
    "# 최종 결과\n",
    "display(result)\n",
    "print('제일 비슷한 분포: ', result.iloc[0,0])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 기계학습(Machine Learning)\n",
    "\n",
    "<center><img src='Image/Basic/StatisticsMachinelearning.png' width='800'>(루이, 다빈치랩스)</center>\n",
    "\n",
    "---\n",
    "\n",
    "> **\"`데이터집계나 상관분석`보다 `관계성`을 좀더 정량적으로 추정하기 위한 `모델링`\"**\n",
    "\n",
    "<center><img src='Image/Basic/Algorithm_Regression.png' width='700'></center>\n",
    "\n",
    "---\n",
    "\n",
    "- **회귀분석:** `변수` 사이의 `인과관계` 알고자 시도\n",
    "\n",
    "> - 기계학습의 `기본 알고리즘`으로, `지도학습 예측 및 분류` 문제에 사용 가능\n",
    "> - 예측 문제란, 기존 데이터로 생성한 모델로 새로운 `미래 숫자값`을 예측\n",
    "> - 분류 문제란, 기존 데이터로 생성한 모델로 새로운 `미래 카테고리값`을 예측\n",
    "> - **선형회귀분석:** 종속변수(Y)가 `연속형(숫자)`일 떄\n",
    "> - **로지스틱회귀분석:** 종속변수(Y)가 `범주형(카테고리)`일 떄\n",
    ">\n",
    ">\n",
    "> <center><img src='Image/Basic/Regression-Algorithms-Tree1.png' width='900'></center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T10:59:04.944588Z",
     "start_time": "2022-07-10T10:59:03.715110Z"
    }
   },
   "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>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>OrderCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50001</td>\n",
       "      <td>Female</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50002</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50004</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Single</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5625</th>\n",
       "      <td>55626</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5626</th>\n",
       "      <td>55627</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5627</th>\n",
       "      <td>55628</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5628</th>\n",
       "      <td>55629</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5629</th>\n",
       "      <td>55630</td>\n",
       "      <td>Male</td>\n",
       "      <td>Married</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5630 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerID  Gender MaritalStatus  CouponUsed  OrderCount\n",
       "0          50001  Female        Single         1.0         1.0\n",
       "1          50002    Male        Single         0.0         1.0\n",
       "2          50003    Male        Single         0.0         1.0\n",
       "3          50004    Male        Single         0.0         1.0\n",
       "4          50005    Male        Single         1.0         1.0\n",
       "...          ...     ...           ...         ...         ...\n",
       "5625       55626    Male       Married         1.0         2.0\n",
       "5626       55627    Male       Married         1.0         2.0\n",
       "5627       55628    Male       Married         1.0         2.0\n",
       "5628       55629    Male       Married         2.0         2.0\n",
       "5629       55630    Male       Married         2.0         2.0\n",
       "\n",
       "[5630 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다양한 변수/속성/특징을 갖고 있는 데이터 준비\n",
    "import pandas as pd\n",
    "df_rel = pd.read_excel(r'.\\Data\\Ecommerce\\E_Commerce_Dataset.xlsx', sheet_name='Raw')\n",
    "target_col = ['CustomerID', 'Gender', 'MaritalStatus', 'CouponUsed', 'OrderCount']\n",
    "df = df_rel[target_col].copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:16.000862Z",
     "start_time": "2022-07-10T08:45:15.987828Z"
    }
   },
   "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>CustomerID</th>\n",
       "      <th>CouponUsed</th>\n",
       "      <th>OrderCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.234302</td>\n",
       "      <td>0.139008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CouponUsed</th>\n",
       "      <td>0.234302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.745245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OrderCount</th>\n",
       "      <td>0.139008</td>\n",
       "      <td>0.745245</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CustomerID  CouponUsed  OrderCount\n",
       "CustomerID    1.000000    0.234302    0.139008\n",
       "CouponUsed    0.234302    1.000000    0.745245\n",
       "OrderCount    0.139008    0.745245    1.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 두 변수의 상관성 정도를 통계량(상관계수)로 표현\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:16.030862Z",
     "start_time": "2022-07-10T08:45:16.003831Z"
    }
   },
   "outputs": [],
   "source": [
    "# 전처리\n",
    "df = df.apply(pd.to_numeric, errors = 'coerce')\n",
    "df = df.fillna(method='ffill', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:16.555530Z",
     "start_time": "2022-07-10T08:45:16.031829Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.019</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.019</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   111.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 10 Jul 2022</td> <th>  Prob (F-statistic):</th> <td>7.13e-26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:45:16</td>     <th>  Log-Likelihood:    </th> <td> -13974.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  5630</td>      <th>  AIC:               </th> <td>2.795e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  5628</td>      <th>  BIC:               </th> <td>2.796e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    2.8923</td> <td>    0.040</td> <td>   73.218</td> <td> 0.000</td> <td>    2.815</td> <td>    2.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td> 3.734e-05</td> <td> 3.53e-06</td> <td>   10.570</td> <td> 0.000</td> <td> 3.04e-05</td> <td> 4.43e-05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2359.614</td> <th>  Durbin-Watson:     </th> <td>   1.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>9065.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.123</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 7.541</td>  <th>  Cond. No.          </th> <td>1.14e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.14e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.019\n",
       "Model:                            OLS   Adj. R-squared:                  0.019\n",
       "Method:                 Least Squares   F-statistic:                     111.7\n",
       "Date:                Sun, 10 Jul 2022   Prob (F-statistic):           7.13e-26\n",
       "Time:                        17:45:16   Log-Likelihood:                -13974.\n",
       "No. Observations:                5630   AIC:                         2.795e+04\n",
       "Df Residuals:                    5628   BIC:                         2.796e+04\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          2.8923      0.040     73.218      0.000       2.815       2.970\n",
       "x1          3.734e-05   3.53e-06     10.570      0.000    3.04e-05    4.43e-05\n",
       "==============================================================================\n",
       "Omnibus:                     2359.614   Durbin-Watson:                   1.933\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9065.198\n",
       "Skew:                           2.123   Prob(JB):                         0.00\n",
       "Kurtosis:                       7.541   Cond. No.                     1.14e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.14e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 회귀분석 모델링\n",
    "import statsmodels.api as sm\n",
    "Y = df['OrderCount'].values\n",
    "X = df['CouponUsed'].values\n",
    "X = sm.add_constant(X)\n",
    "model_lr = sm.OLS(Y, X).fit()\n",
    "model_lr.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-10T08:45:16.570829Z",
     "start_time": "2022-07-10T08:45:16.557840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.8924745])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 예측하기\n",
    "model_lr.predict(np.array([[1., 5]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 딥러닝(Deep Learning)\n",
    "\n",
    "> **\"`변수들의 관계성`을 `현실적`으로 추정하기 위한 `고도화 모델링`\"**\n",
    "\n",
    "---\n",
    "\n",
    "**\"어떤 문제가 나와도 당황하지 않고 시험을 잘 보기 위해서는..\"**\n",
    "\n",
    "**1. 작년도 기출문제(`스몰데이터`)가 도움이 될 수 있지만 문제의 특성과 범위가 매년 달라질 수 있으니 과거 20년치처럼 많은 기출문제(`빅데이터`)를 `학습`하는 것이 좋을 수 있습니다.**\n",
    "\n",
    "**2. 많은 기출문제를 풀어서 단순히 정답을 통째로 학습(`데이터집계 or 통계추론`)하는 것도 좋을 수 있지만, 적은 기출문제라도 풀이 방법과 과정을 학습(`기계학습`)하면 새로운 문제도 잘 풀어 시험을 잘 풀수 있습니다.**\n",
    "\n",
    "<center><img src='Image/Basic/HumanLearningMachineLearning.png' width='800'>(루이, 다빈치랩스)</center>\n",
    "\n",
    "> **\"데이터집계나 통계추론으로 분석하기 까다로운 `방대하고 복잡한 데이터`를 효과적으로 분석하여, 빅데이터 속에 숨겨진 인간이 발견하기 힘든 `다양한 고객과 시장의 변화 패턴 포착`\"**\n",
    ">\n",
    "> - 인간의 암기력(`빅데이터`)은 물론이고 학습방식(`통계추론/기계학습/딥러닝`)을 모방한 수리통계와 컴퓨터의 융합으로 개발된 `알고리즘 기술`을 사용하여 시험문제 해결\n",
    ">\n",
    "> | **인간/엑셀** | **알고리즘 기술** |\n",
    "|:---:|:---:|\n",
    "| 스몰데이터 | 빅데이터 |\n",
    "| 느린정리 | 빠른정리 |\n",
    "| 비정확한정답 | 정확한정답 |\n",
    ">\n",
    ">\n",
    "> - 인간이나 데이터정리 도구인 엑셀과 달리 `알고리즘 기술`은 고도화되어 `통계추론/머신러닝` $\\rightarrow$ `딥러닝` $\\rightarrow$ `인공지능`으로 발전중\n",
    ">\n",
    "> <center><img src='Image/Basic/DL_AutoFE.PNG' width='600'></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **데이터분석에서 학습? 알고리즘이란?**\n",
    "\n",
    "- **학습(Learning)?:** 데이터분석 과정을 `컴퓨터/기계` 스스로가 과거와 현재를 학습하게 하고 `미래를 예측`하게 하는 과정\n",
    "\n",
    "> - **과거: 사람의 `패턴을 모방`하여 `컴퓨터를 사람처럼` 만들려고 노력**\n",
    ">> - 사람의 패턴이나 `정답을` 수학적으로 모델링(알고리즘화)\n",
    ">> - 데이터 보다는 `사람의 패턴`을 수학적으로 표현하여 문제를 해결\n",
    "> - **현재: 사람의 `학습방식을 모방`하여 `컴퓨터가 사람처럼 학습`하도록 노력**\n",
    ">> - 사람의 패턴이나 `정답을 찾지 않고` 뇌의 작동방식을 모델링(알고리즘화)\n",
    ">> - 사람의 패턴은 알 필요 없이 `데이터`를 컴퓨터에게 지속적으로 전달(학습)하면 스스로 문제를 해결\n",
    "\n",
    "---\n",
    "\n",
    "<center><img src='Image/Basic/Basic_InputOutput.PNG' width='700'></center>\n",
    "<center><img src='Image/Basic/DataSplit_Concept1.png' width='700'></center>\n",
    "<center><img src='Image/Basic/DataSplit_Concept2.png' width='700'></center>"
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    "## **학습종류:** 지도학습/비지도학습/강화학습\n",
    "\n",
    "---\n",
    "\n",
    "**0) 어떤걸 풀수 있는가?**\n",
    "\n",
    "- 전체적으로 `지도학습/비지도학습/강화학습`이라는 3가지 종류의 알고리즘으로 해결중\n",
    "\n",
    "<center><img src='Image/Basic/ML_Type_CategoryKorean.png' width='800'></center>\n",
    "\n",
    "- 각 알고리즘은 인간이 해결하고 싶어하는 `특정 세부문제 맞춤해결` 방식으로 진화중\n",
    "\n",
    "| **알고리즘 종류** \t| **해결문제 종류** \t| **해결 예시** \t|\n",
    "|:---:|:---:|:---|\n",
    "| **지도학습 알고리즘** \t| **예측문제** \t| 주관식 문제의 숫자형태의 정답을 해결 \t|\n",
    "|  \t| **분류문제** \t| 객관식 문제의 보기들 중 정답을 해결 \t|\n",
    "| **비지도학습 알고리즘** \t| **군집문제** \t| 시험문제에 어떤 유형들이 있는지 해결 \t|\n",
    "|  \t| **차원변환문제** \t| 시험문제의 풀이법을 다양한 관점으로 변환 \t|\n",
    "| **강화학습 알고리즘** \t| **-** \t| 공교육/사교육 없이 스스로 지도학습/비지도학습 문제를 해결하고<br>오답노트도 스스로 만들고 학습하여 성적을 계속 끌어올림 \t|\n",
    "\n",
    "---\n",
    "\n",
    "**1) 예시 사례:**\n",
    "\n",
    "<center><img src='Image/Basic/ML_Type_Application_Upgrade.png' width='700'></center>\n",
    "<center><img src='Image/Basic/ML_Type_Category.png' width='700'></center>\n",
    "\n",
    "---\n",
    "\n",
    "**2) 기계학습의 4가지 분류:**\n",
    "<center><img src='Image/Basic/ML_Type_Application.png' width='600'></center>  \n",
    "\n",
    "| - | Supervised Learning | Unsupervised Learning | Semi-supervised Learning |\n",
    "|---------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| - | <img src='Image/Basic/Supervised-Learning-Algorithms.png' width='150'> | <img src='Image/Basic/Unsupervised-Learning-Algorithms.png' width='150'> | <img src='Image/Basic/Semi-supervised-Learning-Algorithms.png' width='150'> |\n",
    "| Input Data | labeled | unlabeled | labeled + unlabeled |\n",
    "| Output Result | labeled | unlabeled | labeled + unlabeled |\n",
    "\n",
    "---\n",
    "\n",
    "**3) 정리:**\n",
    "\n",
    "<center><img src='Image/Basic/ML_Type_Application_Upgrade.png' width='700'></center>\n",
    "\n",
    "> **알고리즘들은 크게 `3~4가지 문제들을 해결`하기 위해 만들어짐**  \n",
    "> **내가 풀어야 할 문제가 무엇인지 알면 분석설정과 해결은 수동적인 작업(단순)**  \n",
    ">\n",
    "> (1) 문제가 어디에 속하는지 $\\rightarrow$ `문제정의 및 분석기획(가설설정)` 가능  \n",
    "> (2) 알고리즘의 입력은 무엇인지 $\\rightarrow$ `분석기획(데이터준비 + 전처리)` 가능     \n",
    "> (3) 알고리즘의 출력은 무엇인지 $\\rightarrow$ `분석기획(가설검정) 및 성능검증` 가능   "
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    "## 지도학습(Supervised Learning) 알고리즘\n",
    "\n",
    "| Regression Algorithms | Instance-based Algorithms | Regularization Algorithms | Decision Tree Algorithms | Bayesian Algorithms | Artificial Neural Network Algorithms |\n",
    "|------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| <img src='Image/Basic/Regression-Algorithms.png' width='150'> | <img src='Image/Basic/Instance-based-Algorithms.png' width='150'> | <img src='Image/Basic/Regularization-Algorithms.png' width='150'> | <img src='Image/Basic/Decision-Tree-Algorithms.png' width='150'> | <img src='Image/Basic/Bayesian-Algorithms.png' width='150'> | <img src='Image/Basic/Artificial-Neural-Network-Algorithms.png' width='150'> |\n",
    "| Ordinary Least Squares Regression (OLSR) | k-Nearest Neighbor (kNN) | Ridge Regression | Classification and Regression Tree (CART) | Naive Bayes | Perceptron |\n",
    "| Linear Regression | Learning Vector Quantization (LVQ) | Least Absolute Shrinkage and Selection Operator (LASSO) | Iterative Dichotomiser 3 (ID3) | Gaussian Naive Bayes | Back-Propagation |\n",
    "| Logistic Regression | Self-Organizing Map (SOM) | Elastic Net | C4.5 and C5.0 (different versions of a powerful approach) | Multinomial Naive Bayes | Hopfield Network |\n",
    "| Stepwise Regression | Locally Weighted Learning (LWL) | Least-Angle Regression (LARS) | Chi-squared Automatic Interaction Detection (CHAID) | Averaged One-Dependence Estimators (AODE) | Radial Basis Function Network (RBFN) |\n",
    "| Multivariate Adaptive Regression Splines (MARS) | - | - | Decision Stump | Bayesian Belief Network (BBN) | - |\n",
    "| Locally Estimated Scatterplot Smoothing (LOESS) | - | - | M5 | Bayesian Network (BN) | - |\n",
    "| - | - | - | Conditional Decision Trees | - | - |\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 비지도학습(Unsupervised Learning) 알고리즘\n",
    "\n",
    "| Clustering Algorithms | Association Rule Learning Algorithms | Dimensionality Reduction Algorithms | Ensemble Algorithms | Deep Learning Algorithms |\n",
    "|------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| <img src='Image/Basic/Clustering-Algorithms.png' width='150'> | <img src='Image/Basic/Assoication-Rule-Learning-Algorithms.png' width='150'> | <img src='Image/Basic/Dimensional-Reduction-Algorithms.png' width='150'> | <img src='Image/Basic/Ensemble-Algorithms.png' width='150'> | <img src='Image/Basic/Deep-Learning-Algorithms.png' width='150'> |\n",
    "| k-Means | Apriori algorithm | Principal Component Analysis (PCA) | Boosting | Deep Boltzmann Machine (DBM) |\n",
    "| k-Medians | Eclat algorithm | Principal Component Regression (PCR) | Bootstrapped Aggregation (Bagging) | Deep Belief Networks (DBN) |\n",
    "| Expectation Maximisation (EM) | - | Partial Least Squares Regression (PLSR) | AdaBoost | Convolutional Neural Network (CNN) |\n",
    "| Hierarchical Clustering | - | Sammon Mapping | Stacked Generalization (blending) | Stacked Auto-Encoders |\n",
    "| - | - | Multidimensional Scaling (MDS) | Gradient Boosting Machines (GBM) | - |\n",
    "| - | - | Projection Pursuit | Gradient Boosted Regression Trees (GBRT) | - |\n",
    "| - | - | Linear Discriminant Analysis (LDA) | Random Forest | - |\n",
    "| - | - | Mixture Discriminant Analysis (MDA) | - | - |\n",
    "| - | - | Quadratic Discriminant Analysis (QDA) | - | - |\n",
    "| - | - | Flexible Discriminant Analysis (FDA) | - | - |\n"
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