notebooks/05t_01_classification.ipynb

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   "source": [
    "# บทที่ 5t (1/2) - Classification\n",
    "\n",
    "อ.ดร.ธนชาตย์ ฤทธิ์บำรุง (อ.เชค)\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ai-builders/curriculum/blob/main/notebooks/05t_01_classification.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SyACHrJVpRxC"
   },
   "source": [
    "*   Classification เป็นเทคนิคที่เราพยายามสร้างโมเดลเพื่อทำนายผลลัพธ์ที่ตัวแปร y เป็น categorical data\n",
    "*   โมเดลจะทำนายความน่าจะเป็นออกมาเป็นความน่าจะเป็น"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PEXx8FbHELpk"
   },
   "source": [
    "**เป้าหมายการเรียนรู้**\n",
    "1. เข้าใจเทคนิคการวิเคราะห์ Classification\n",
    "2. เข้าใจถึงเทคนิคที่หลากหลายในกลุ่มของ Classification\n",
    "3. สามารถวิเคราะห์และพัฒนา Classification Model ได้"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J94aeyjPNIku"
   },
   "source": [
    "# Case Study - Marketing Campaign"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qyJZTkVFQhpT"
   },
   "source": [
    "A response model can provide a significant boost to the efficiency of a marketing campaign by increasing responses or reducing expenses. The objective is to predict who will respond to an offer for a product or service."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e0SF6z9yOguF"
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   "source": [
    "Columns\n",
    "*   ID - Customer's id\n",
    "*   Year_Birth - Customer's year of birth\n",
    "*   Education - Customer’s level of education\n",
    "*   Marital_Status - Customer’s marital status\n",
    "*   Income - Customer’s yearly household income\n",
    "*   Kidhome - Number of small children in customer’s household\n",
    "*   Teenhome - Number of teenagers in customer’s household\n",
    "*   Dt_Customer - Date of customer’s enrolment with the company\n",
    "*   Recency - Number of days since the last purchase\n",
    "*   MntWines - Amount spent on wine products in the last 2 years\n",
    "*   MntFruits - Amount spent on fruits products in the last 2 years\n",
    "*   MntMeatProducts - Amount spent on meat products in the last 2 years\n",
    "*   MntFishProducts - Amount spent on fish products in the last 2 years\n",
    "*   MntSweetProducts - Amount spent on sweet products in the last 2 years\n",
    "*   MntGoldProds - Amount spent on gold products in the last 2 years\n",
    "*   NumDealsPurchases - Number of purchases made with discount\n",
    "*   NumWebPurchases - Number of purchases made through company’s web site\n",
    "*   NumCatalogPurchases - Number of purchases made using catalogue\n",
    "*   NumStorePurchases - Number of purchases made directly in stores\n",
    "*   NumWebVisitsMonth - Number of purchases made through company’s web site\n",
    "*   AcceptedCmp3 - 1 if customer accepted the offer in the 3rd campaign, 0 otherwise\n",
    "*   AcceptedCmp4 - 1 if customer accepted the offer in the 4th campaign, 0 otherwise\n",
    "*   AcceptedCmp5 - 1 if customer accepted the offer in the 5th campaign, 0 otherwise\n",
    "*   AcceptedCmp1 - 1 if customer accepted the offer in the 1st campaign, 0 otherwise\n",
    "*   AcceptedCmp2 - 1 if customer accepted the offer in the 2nd campaign, 0 otherwise\n",
    "*   Complain - 1 if customer complained in the last 2 years\n",
    "*   Z_CostContact - Cost to contact a customer\n",
    "*   Z_Revenue - Revenue after client accepting campaign\n",
    "*   Response - 1 if customer accepted the offer in the last campaign, 0 otherwise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K48MSqLxfqmW"
   },
   "source": [
    "ในเคสนี้ \n",
    "\n",
    "*   ถ้าลูกค้าตอบรับ Campaign ล่าสุด ซึ่งเป็น Positive Case จะมีค่าเท่ากับ 1\n",
    "*   ถ้าลูกค้าไม่ตอบรับ Campaign ล่าสุด ซึ่งเป็น Nagative Case จะมีค่าเท่ากับ 0\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "he42aiekK6M8"
   },
   "source": [
    "# Data Prepration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
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   "source": [
    "# Load module ที่เราต้องการใช้ทั้งหมด\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
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   "outputs": [],
   "source": [
    "# อ่านไฟล์ข้อมูลที่ต้องการวิเคราะห์\n",
    "\n",
    "url = 'https://github.com/ai-builders/curriculum/raw/main/data/marketing_campaign.csv'\n",
    "df = pd.read_csv(url, parse_dates=['Dt_Customer'])"
   ]
  },
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       "        ID  Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "663   7118        1957  Graduation        Married  73803.0        0         1   \n",
       "664   2632        1954  Graduation        Married  50501.0        1         1   \n",
       "665  11133        1973         PhD           YOLO  48432.0        0         1   \n",
       "666   7366        1982      Master         Single  75777.0        0         0   \n",
       "667   9405        1954         PhD        Married  52869.0        1         1   \n",
       "\n",
       "    Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  AcceptedCmp3  \\\n",
       "663  2012-08-01       61       833  ...                  6             1   \n",
       "664  2013-06-18       10       297  ...                  6             1   \n",
       "665  2012-10-18        3       322  ...                  8             0   \n",
       "666  2013-07-04       12       712  ...                  1             0   \n",
       "667  2012-10-15       40        84  ...                  7             0   \n",
       "\n",
       "     AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  \\\n",
       "663             0             0             0             0         0   \n",
       "664             0             0             0             0         0   \n",
       "665             0             0             0             0         0   \n",
       "666             1             1             0             0         0   \n",
       "667             0             0             0             0         0   \n",
       "\n",
       "     Z_CostContact  Z_Revenue  Response  \n",
       "663              3         11         1  \n",
       "664              3         11         1  \n",
       "665              3         11         1  \n",
       "666              3         11         1  \n",
       "667              3         11         1  \n",
       "\n",
       "[5 rows x 29 columns]"
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   "source": [
    "# แสดงผลตัวอย่างข้อมูล\n",
    "\n",
    "df.tail(5)"
   ]
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>665.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5358.886228</td>\n",
       "      <td>1969.065868</td>\n",
       "      <td>55496.433083</td>\n",
       "      <td>0.359281</td>\n",
       "      <td>0.432635</td>\n",
       "      <td>43.187126</td>\n",
       "      <td>393.600299</td>\n",
       "      <td>31.534431</td>\n",
       "      <td>222.016467</td>\n",
       "      <td>45.779940</td>\n",
       "      <td>...</td>\n",
       "      <td>5.224551</td>\n",
       "      <td>0.133234</td>\n",
       "      <td>0.119760</td>\n",
       "      <td>0.149701</td>\n",
       "      <td>0.133234</td>\n",
       "      <td>0.031437</td>\n",
       "      <td>0.013473</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3239.622817</td>\n",
       "      <td>12.060760</td>\n",
       "      <td>21947.625057</td>\n",
       "      <td>0.495516</td>\n",
       "      <td>0.542039</td>\n",
       "      <td>28.880917</td>\n",
       "      <td>385.010485</td>\n",
       "      <td>42.634182</td>\n",
       "      <td>258.899559</td>\n",
       "      <td>60.097904</td>\n",
       "      <td>...</td>\n",
       "      <td>2.450811</td>\n",
       "      <td>0.340082</td>\n",
       "      <td>0.324925</td>\n",
       "      <td>0.357045</td>\n",
       "      <td>0.340082</td>\n",
       "      <td>0.174627</td>\n",
       "      <td>0.115375</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1943.000000</td>\n",
       "      <td>2447.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2631.750000</td>\n",
       "      <td>1959.000000</td>\n",
       "      <td>37760.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5092.500000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>56937.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8145.000000</td>\n",
       "      <td>1978.000000</td>\n",
       "      <td>73356.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>363.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11178.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>105471.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1492.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1293ef49-5e6e-46ef-97b2-4b0780215ff3')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-1293ef49-5e6e-46ef-97b2-4b0780215ff3 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-1293ef49-5e6e-46ef-97b2-4b0780215ff3');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                 ID   Year_Birth         Income     Kidhome    Teenhome  \\\n",
       "count    668.000000   668.000000     665.000000  668.000000  668.000000   \n",
       "mean    5358.886228  1969.065868   55496.433083    0.359281    0.432635   \n",
       "std     3239.622817    12.060760   21947.625057    0.495516    0.542039   \n",
       "min        1.000000  1943.000000    2447.000000    0.000000    0.000000   \n",
       "25%     2631.750000  1959.000000   37760.000000    0.000000    0.000000   \n",
       "50%     5092.500000  1970.000000   56937.000000    0.000000    0.000000   \n",
       "75%     8145.000000  1978.000000   73356.000000    1.000000    1.000000   \n",
       "max    11178.000000  1996.000000  105471.000000    2.000000    2.000000   \n",
       "\n",
       "          Recency     MntWines   MntFruits  MntMeatProducts  MntFishProducts  \\\n",
       "count  668.000000   668.000000  668.000000       668.000000       668.000000   \n",
       "mean    43.187126   393.600299   31.534431       222.016467        45.779940   \n",
       "std     28.880917   385.010485   42.634182       258.899559        60.097904   \n",
       "min      0.000000     0.000000    0.000000         1.000000         0.000000   \n",
       "25%     17.000000    40.000000    3.000000        25.000000         3.000000   \n",
       "50%     40.000000   267.000000   14.500000       111.000000        19.000000   \n",
       "75%     68.000000   677.000000   40.500000       363.000000        65.000000   \n",
       "max     99.000000  1492.000000  197.000000      1725.000000       258.000000   \n",
       "\n",
       "       ...  NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  \\\n",
       "count  ...         668.000000    668.000000    668.000000    668.000000   \n",
       "mean   ...           5.224551      0.133234      0.119760      0.149701   \n",
       "std    ...           2.450811      0.340082      0.324925      0.357045   \n",
       "min    ...           1.000000      0.000000      0.000000      0.000000   \n",
       "25%    ...           3.000000      0.000000      0.000000      0.000000   \n",
       "50%    ...           6.000000      0.000000      0.000000      0.000000   \n",
       "75%    ...           7.000000      0.000000      0.000000      0.000000   \n",
       "max    ...          17.000000      1.000000      1.000000      1.000000   \n",
       "\n",
       "       AcceptedCmp1  AcceptedCmp2    Complain  Z_CostContact  Z_Revenue  \\\n",
       "count    668.000000    668.000000  668.000000          668.0      668.0   \n",
       "mean       0.133234      0.031437    0.013473            3.0       11.0   \n",
       "std        0.340082      0.174627    0.115375            0.0        0.0   \n",
       "min        0.000000      0.000000    0.000000            3.0       11.0   \n",
       "25%        0.000000      0.000000    0.000000            3.0       11.0   \n",
       "50%        0.000000      0.000000    0.000000            3.0       11.0   \n",
       "75%        0.000000      0.000000    0.000000            3.0       11.0   \n",
       "max        1.000000      1.000000    1.000000            3.0       11.0   \n",
       "\n",
       "         Response  \n",
       "count  668.000000  \n",
       "mean     0.500000  \n",
       "std      0.500375  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.500000  \n",
       "75%      1.000000  \n",
       "max      1.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1652777068040,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "vK4Dd9w6H2KA",
    "outputId": "67cc44ea-37e8-4f7a-ca91-449c4da32c7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
       "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
       "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
       "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
       "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
       "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
       "       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1652777068041,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "e06P374LNBHr",
    "outputId": "cb78f758-8957-4751-fa0f-d461e6bf8e0b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(668, 29)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "crf7Gmc8ND75"
   },
   "source": [
    "## จัดการ Missing Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1652777068041,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "cfQxaI-WMWkk",
    "outputId": "2186564c-d386-4a0a-d374-9922c17921d2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     0\n",
       "Year_Birth             0\n",
       "Education              0\n",
       "Marital_Status         0\n",
       "Income                 3\n",
       "Kidhome                0\n",
       "Teenhome               0\n",
       "Dt_Customer            0\n",
       "Recency                0\n",
       "MntWines               0\n",
       "MntFruits              0\n",
       "MntMeatProducts        0\n",
       "MntFishProducts        0\n",
       "MntSweetProducts       0\n",
       "MntGoldProds           0\n",
       "NumDealsPurchases      0\n",
       "NumWebPurchases        0\n",
       "NumCatalogPurchases    0\n",
       "NumStorePurchases      0\n",
       "NumWebVisitsMonth      0\n",
       "AcceptedCmp3           0\n",
       "AcceptedCmp4           0\n",
       "AcceptedCmp5           0\n",
       "AcceptedCmp1           0\n",
       "AcceptedCmp2           0\n",
       "Complain               0\n",
       "Z_CostContact          0\n",
       "Z_Revenue              0\n",
       "Response               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing data\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1652777068041,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "25C-xIfxMuSn"
   },
   "outputs": [],
   "source": [
    "# ลบ row ที่มี missing value\n",
    "\n",
    "df = df.dropna(how='any',axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1652777068041,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "tQ6bXRC9MvAN",
    "outputId": "7b202ecf-53ff-42b5-df3a-5d9e60eb659a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     0\n",
       "Year_Birth             0\n",
       "Education              0\n",
       "Marital_Status         0\n",
       "Income                 0\n",
       "Kidhome                0\n",
       "Teenhome               0\n",
       "Dt_Customer            0\n",
       "Recency                0\n",
       "MntWines               0\n",
       "MntFruits              0\n",
       "MntMeatProducts        0\n",
       "MntFishProducts        0\n",
       "MntSweetProducts       0\n",
       "MntGoldProds           0\n",
       "NumDealsPurchases      0\n",
       "NumWebPurchases        0\n",
       "NumCatalogPurchases    0\n",
       "NumStorePurchases      0\n",
       "NumWebVisitsMonth      0\n",
       "AcceptedCmp3           0\n",
       "AcceptedCmp4           0\n",
       "AcceptedCmp5           0\n",
       "AcceptedCmp1           0\n",
       "AcceptedCmp2           0\n",
       "Complain               0\n",
       "Z_CostContact          0\n",
       "Z_Revenue              0\n",
       "Response               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing data\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1652777068041,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "nfcKfBqgM7v1",
    "outputId": "5901beb4-f099-447e-e42b-741fd2b0627b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(665, 29)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hxr4NjZoSiXs"
   },
   "source": [
    "## จัดการ Time-related column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vEJjZ4dcSqGq"
   },
   "source": [
    "หา cut-off date ของ dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 587,
     "status": "ok",
     "timestamp": 1652777068619,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "cJMiYpGNRPN5",
    "outputId": "f3b414ce-3e79-48d7-e064-dd25a0c8a4f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Date : 2012-07-30 00:00:00\n",
      "Max Date : 2014-06-26 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print('Min Date : ' + str(df['Dt_Customer'].min()))\n",
    "print('Max Date : ' + str(df['Dt_Customer'].max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9g8CqJP-Sx1c"
   },
   "source": [
    "วันที่สุดท้ายที่มีลูกค้าสมัครเป็นสมาชิก คือ 2014-06-26"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1652777068619,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "m98dv2fLRmNq",
    "outputId": "bf9a041a-6783-4ae3-fb12-cf0d9984cf2b"
   },
   "outputs": [
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       "       ID  Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "206  1225        1963  Graduation        Married  80124.0        0         0   \n",
       "\n",
       "    Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  AcceptedCmp3  \\\n",
       "206  2014-06-26       47       483  ...                  1             1   \n",
       "\n",
       "     AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  Complain  \\\n",
       "206             0             0             0             0         0   \n",
       "\n",
       "     Z_CostContact  Z_Revenue  Response  \n",
       "206              3         11         0  \n",
       "\n",
       "[1 rows x 29 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Dt_Customer'] == '2014-06-26 00:00:00']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n3E3LSFPS5re"
   },
   "source": [
    "แต่เมื่อดูข้อมูลแล้ว เรามีข้อมูลการซื้อของลูกค้าในช่วง 2 ปีที่ผ่านมา\n",
    "\n",
    "เราจึงจะสมมติว่าวันที่ที่ไฟล์นี้ถูกสร้างขึ้นมา คือ 2016-06-26 (2 ปีถัดจากลูกค้าคนสุดท้ายที่มาสมัครสมาชิก)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1652777068620,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "EaCtbfK2Tbih"
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "cut_off_date = datetime.datetime(2016,6,26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068620,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "6XRU4Nq8cUhz"
   },
   "outputs": [],
   "source": [
    "cut_off_year = 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068620,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "ODwNMTHHTki2"
   },
   "outputs": [],
   "source": [
    "df['Age'] = cut_off_year - df['Year_Birth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068620,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "GnrC9EbXTvSS"
   },
   "outputs": [],
   "source": [
    "df['Member_Age'] = cut_off_year - df['Dt_Customer'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
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    "colab": {
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     "height": 270
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    "executionInfo": {
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      "displayName": "Thanachart Ritbumroong",
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    "id": "KO0x18WHUBez",
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   },
   "outputs": [
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-a05c9a3a-c3e9-4861-9148-30f90d2af6ff button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-a05c9a3a-c3e9-4861-9148-30f90d2af6ff');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Complain  Z_CostContact  Z_Revenue  Response  Age  Member_Age  \n",
       "0         0              3         11         0   53           3  \n",
       "1         0              3         11         0   59           2  \n",
       "2         0              3         11         0   66           2  \n",
       "3         0              3         11         0   61           2  \n",
       "4         0              3         11         0   71           4  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,20:].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Fgo_FoVVR_e"
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1652777068621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "E51bRQlwVRDX"
   },
   "outputs": [],
   "source": [
    "df['Total_Spend'] = df[['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "YjfqA-gpVpj0"
   },
   "outputs": [],
   "source": [
    "df['Total_Purchases'] = df[['NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "165wXle0V_Mu"
   },
   "outputs": [],
   "source": [
    "for i in ['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']:\n",
    "  col_name = 'PCT_' + i\n",
    "  df[col_name] = df[i]/df['Total_Spend']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "vUNiSHd0Wo-f"
   },
   "outputs": [],
   "source": [
    "for i in ['NumWebPurchases', 'NumCatalogPurchases', 'NumStorePurchases']:\n",
    "  col_name = 'PCT_' + i\n",
    "  df[col_name] = df[i]/df['Total_Purchases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "wlrMKpkEWwiH"
   },
   "outputs": [],
   "source": [
    "df['PCT_DealsPurchases'] = df['NumDealsPurchases']/df['Total_Purchases']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "vPm1XqWlXWP9"
   },
   "outputs": [],
   "source": [
    "df['PCT_Campaigns'] = df[['AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1', 'AcceptedCmp2']].sum(axis=1) / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1652777068622,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "HUnPUFm5Wh_q",
    "outputId": "7be7d61f-62c4-460b-df98-4c7869dc047b"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>AcceptedCmp3</th>\n",
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c25e008a-b24b-4dfd-93d5-2a2fce5bdd0f')\"\n",
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       "      \n",
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       "    .colab-df-container {\n",
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       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-c25e008a-b24b-4dfd-93d5-2a2fce5bdd0f button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-c25e008a-b24b-4dfd-93d5-2a2fce5bdd0f');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
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       "  "
      ],
      "text/plain": [
       "   AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Complain  Z_CostContact  Z_Revenue  Response  Age  ...  PCT_MntFruits  \\\n",
       "0         0              3         11         0   53  ...       0.024784   \n",
       "1         0              3         11         0   59  ...       0.057314   \n",
       "2         0              3         11         0   66  ...       0.000000   \n",
       "3         0              3         11         0   61  ...       0.063063   \n",
       "4         0              3         11         0   71  ...       0.009379   \n",
       "\n",
       "   PCT_MntMeatProducts  PCT_MntFishProducts  PCT_MntSweetProducts  \\\n",
       "0             0.132543             0.010776              0.024784   \n",
       "1             0.427716             0.170231              0.000000   \n",
       "2             0.230769             0.000000              0.000000   \n",
       "3             0.288288             0.009009              0.040541   \n",
       "4             0.145955             0.025205              0.009379   \n",
       "\n",
       "   PCT_MntGoldProds  PCT_NumWebPurchases  PCT_NumCatalogPurchases  \\\n",
       "0          0.165948             0.380952                 0.428571   \n",
       "1          0.139435             0.214286                 0.357143   \n",
       "2          0.076923             0.000000                 0.000000   \n",
       "3          0.171171             0.444444                 0.222222   \n",
       "4          0.019343             0.400000                 0.240000   \n",
       "\n",
       "   PCT_NumStorePurchases  PCT_DealsPurchases  PCT_Campaigns  \n",
       "0               0.190476            0.095238            0.0  \n",
       "1               0.428571            0.214286            0.0  \n",
       "2               1.000000            0.333333            0.0  \n",
       "3               0.333333            0.111111            0.0  \n",
       "4               0.360000            0.080000            0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,20:].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PvC8XcuxaLx-"
   },
   "source": [
    "## One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068622,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "N-NBJ0HJaOFR",
    "outputId": "c479bafc-87a7-49e9-fa71-9439e5bc0171"
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "     Education_2n Cycle  Education_Basic  Education_Graduation  \\\n",
       "0                     0                0                     1   \n",
       "1                     0                0                     0   \n",
       "2                     0                0                     0   \n",
       "3                     0                0                     0   \n",
       "4                     0                0                     0   \n",
       "..                  ...              ...                   ...   \n",
       "663                   0                0                     1   \n",
       "664                   0                0                     1   \n",
       "665                   0                0                     0   \n",
       "666                   0                0                     0   \n",
       "667                   0                0                     0   \n",
       "\n",
       "     Education_Master  Education_PhD  Marital_Status_Absurd  \\\n",
       "0                   0              0                      0   \n",
       "1                   1              0                      0   \n",
       "2                   0              1                      0   \n",
       "3                   1              0                      0   \n",
       "4                   0              1                      0   \n",
       "..                ...            ...                    ...   \n",
       "663                 0              0                      0   \n",
       "664                 0              0                      0   \n",
       "665                 0              1                      0   \n",
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       "667                 0              1                      0   \n",
       "\n",
       "     Marital_Status_Alone  Marital_Status_Divorced  Marital_Status_Married  \\\n",
       "0                       0                        1                       0   \n",
       "1                       0                        0                       0   \n",
       "2                       0                        0                       1   \n",
       "3                       0                        1                       0   \n",
       "4                       0                        0                       1   \n",
       "..                    ...                      ...                     ...   \n",
       "663                     0                        0                       1   \n",
       "664                     0                        0                       1   \n",
       "665                     0                        0                       0   \n",
       "666                     0                        0                       0   \n",
       "667                     0                        0                       1   \n",
       "\n",
       "     Marital_Status_Single  Marital_Status_Together  Marital_Status_Widow  \\\n",
       "0                        0                        0                     0   \n",
       "1                        0                        1                     0   \n",
       "2                        0                        0                     0   \n",
       "3                        0                        0                     0   \n",
       "4                        0                        0                     0   \n",
       "..                     ...                      ...                   ...   \n",
       "663                      0                        0                     0   \n",
       "664                      0                        0                     0   \n",
       "665                      0                        0                     0   \n",
       "666                      1                        0                     0   \n",
       "667                      0                        0                     0   \n",
       "\n",
       "     Marital_Status_YOLO  \n",
       "0                      0  \n",
       "1                      0  \n",
       "2                      0  \n",
       "3                      0  \n",
       "4                      0  \n",
       "..                   ...  \n",
       "663                    0  \n",
       "664                    0  \n",
       "665                    1  \n",
       "666                    0  \n",
       "667                    0  \n",
       "\n",
       "[665 rows x 13 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# สร้างตัวแปร one-hot encoding เฉพาะตัวแปรที่เป็น categorical\n",
    "\n",
    "df_dummmies = pd.get_dummies(df[['Education','Marital_Status']])\n",
    "df_dummmies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1652777068622,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "tzu8QLiCahEK"
   },
   "outputs": [],
   "source": [
    "df = df.merge(df_dummmies, left_index=True, right_index=True, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
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     "height": 300
    },
    "executionInfo": {
     "elapsed": 24,
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     "timestamp": 1652777068622,
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      "displayName": "Thanachart Ritbumroong",
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    "id": "iWvcEUztakH-",
    "outputId": "94796b05-58f6-4934-e1cf-7fc0fb5d9c7d"
   },
   "outputs": [
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       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-670458c7-cb27-4167-8b89-999701dfd025')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-670458c7-cb27-4167-8b89-999701dfd025');\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
      ],
      "text/plain": [
       "      ID  Year_Birth   Education Marital_Status   Income  Kidhome  Teenhome  \\\n",
       "0   5011        1963  Graduation       Divorced  68118.0        0         1   \n",
       "1  10652        1957      Master       Together  65487.0        0         0   \n",
       "2   7196        1950         PhD        Married  41145.0        1         1   \n",
       "3  10420        1955      Master       Divorced  46390.0        0         1   \n",
       "4   9260        1945         PhD        Married  70356.0        0         0   \n",
       "\n",
       "  Dt_Customer  Recency  MntWines  ...  Education_Master  Education_PhD  \\\n",
       "0  2013-10-18       51       595  ...                 0              0   \n",
       "1  2014-01-10       48       240  ...                 1              0   \n",
       "2  2014-02-08       20         9  ...                 0              1   \n",
       "3  2014-05-05       56        95  ...                 1              0   \n",
       "4  2012-11-05       20      1349  ...                 0              1   \n",
       "\n",
       "   Marital_Status_Absurd  Marital_Status_Alone  Marital_Status_Divorced  \\\n",
       "0                      0                     0                        1   \n",
       "1                      0                     0                        0   \n",
       "2                      0                     0                        0   \n",
       "3                      0                     0                        1   \n",
       "4                      0                     0                        0   \n",
       "\n",
       "   Marital_Status_Married  Marital_Status_Single  Marital_Status_Together  \\\n",
       "0                       0                      0                        0   \n",
       "1                       0                      0                        1   \n",
       "2                       1                      0                        0   \n",
       "3                       0                      0                        0   \n",
       "4                       1                      0                        0   \n",
       "\n",
       "   Marital_Status_Widow  Marital_Status_YOLO  \n",
       "0                     0                    0  \n",
       "1                     0                    0  \n",
       "2                     0                    0  \n",
       "3                     0                    0  \n",
       "4                     0                    0  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xVk0hPNganVD"
   },
   "source": [
    "## สร้าง Train-Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1652777068622,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "ewy6ZqCE9kch"
   },
   "outputs": [],
   "source": [
    "X = df.drop(['ID', 'Response', 'Year_Birth', 'Dt_Customer', 'Education','Marital_Status', 'Z_CostContact', 'Z_Revenue'], axis=1)\n",
    "y = df['Response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1652777068623,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "oJX4tFXZ9q9f",
    "outputId": "2d06784c-3b14-47d1-ab38-0e89bc07308b"
   },
   "outputs": [
    {
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      "text/plain": [
       "    Income  Kidhome  Teenhome  Recency  MntWines  MntFruits  MntMeatProducts  \\\n",
       "0  68118.0        0         1       51       595         23              123   \n",
       "1  65487.0        0         0       48       240         67              500   \n",
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       "\n",
       "   MntFishProducts  MntSweetProducts  MntGoldProds  ...  Education_Master  \\\n",
       "0               10                23           154  ...                 0   \n",
       "1              199                 0           163  ...                 1   \n",
       "2                0                 0             1  ...                 0   \n",
       "3                2                 9            38  ...                 1   \n",
       "4               43                16            33  ...                 0   \n",
       "\n",
       "   Education_PhD  Marital_Status_Absurd  Marital_Status_Alone  \\\n",
       "0              0                      0                     0   \n",
       "1              0                      0                     0   \n",
       "2              1                      0                     0   \n",
       "3              0                      0                     0   \n",
       "4              1                      0                     0   \n",
       "\n",
       "   Marital_Status_Divorced  Marital_Status_Married  Marital_Status_Single  \\\n",
       "0                        1                       0                      0   \n",
       "1                        0                       0                      0   \n",
       "2                        0                       1                      0   \n",
       "3                        1                       0                      0   \n",
       "4                        0                       1                      0   \n",
       "\n",
       "   Marital_Status_Together  Marital_Status_Widow  Marital_Status_YOLO  \n",
       "0                        0                     0                    0  \n",
       "1                        1                     0                    0  \n",
       "2                        0                     0                    0  \n",
       "3                        0                     0                    0  \n",
       "4                        0                     0                    0  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1652777068623,
     "user": {
      "displayName": "Thanachart Ritbumroong",
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     "user_tz": -420
    },
    "id": "evEKhiA99snq",
    "outputId": "364bcbff-316f-459b-cb01-3bdcf2836aa8"
   },
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 61,
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   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1652777068623,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "zuKMpt0WY0MC"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ewZEbtOtLBuO"
   },
   "source": [
    "# สร้าง Classification Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UgdbWqxcs2al"
   },
   "source": [
    "## 1) Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zuVpFpDk-ZoK"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/26.png width=\"750px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PrVi9b-q-iZm"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/27.png width=\"750px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2XTBFjy-rxy4"
   },
   "source": [
    "Sigmoid Function\n",
    "\n",
    "$$ log \\bigg( \\frac{p(x)}{1-p(x)} \\bigg) =  b_0 + b_1x_1 + b_2x_2 + ...  $$ \n",
    "\n",
    "\n",
    "$$ p (x) =  \\frac{\\mathrm{1} }{\\mathrm{1} + e^- f (x) }  $$ \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1652777068624,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "-X1vyX9BYTTs",
    "outputId": "1bdb9218-d0bf-4410-c3f1-c171ecef5ef8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.84      0.83       268\n",
      "           1       0.83      0.82      0.83       264\n",
      "\n",
      "    accuracy                           0.83       532\n",
      "   macro avg       0.83      0.83      0.83       532\n",
      "weighted avg       0.83      0.83      0.83       532\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.80      0.80        64\n",
      "           1       0.81      0.81      0.81        69\n",
      "\n",
      "    accuracy                           0.80       133\n",
      "   macro avg       0.80      0.80      0.80       133\n",
      "weighted avg       0.80      0.80      0.80       133\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "logreg = LogisticRegression(solver='liblinear', class_weight='balanced')\n",
    "ls_y_hat_train = []\n",
    "ls_y_train = []\n",
    "\n",
    "logreg_model = logreg.fit(X_train, y_train)\n",
    "y_hat_train = logreg_model.predict(X_train)\n",
    "ls_y_hat_train.extend(y_hat_train)\n",
    "ls_y_train.extend(y_train)\n",
    "report_train = classification_report(ls_y_train, ls_y_hat_train)\n",
    "print('training set')\n",
    "print(report_train) \n",
    "\n",
    "ls_y_hat_test = []\n",
    "ls_y_test = []\n",
    "y_hat_test = logreg_model.predict(X_test)\n",
    "ls_y_hat_test.extend(y_hat_test)\n",
    "ls_y_test.extend(y_test)\n",
    "report_test = classification_report(ls_y_test, ls_y_hat_test)\n",
    "print('test set')\n",
    "print(report_test) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_eMvLtb5iG0T"
   },
   "source": [
    "accuracy = (จำนวนที่ทายถูก / จำนวนทั้งหมด)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1652777068624,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "ZlQk0k0veZa1",
    "outputId": "c56b48d2-26d4-4e6f-a36a-9e70567bc93a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.28741922]\n"
     ]
    }
   ],
   "source": [
    "# ค่า b0\n",
    "\n",
    "print(logreg_model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1652777068624,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "p064MLpKecN_",
    "outputId": "e6483bae-ad46-43d9-894f-e37693fd68d1"
   },
   "outputs": [
    {
     "data": {
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       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Income</td>\n",
       "      <td>0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kidhome</td>\n",
       "      <td>0.090512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Teenhome</td>\n",
       "      <td>-0.607408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Recency</td>\n",
       "      <td>-0.028697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MntWines</td>\n",
       "      <td>-0.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MntFruits</td>\n",
       "      <td>-0.001325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MntMeatProducts</td>\n",
       "      <td>0.001307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MntFishProducts</td>\n",
       "      <td>-0.006888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MntSweetProducts</td>\n",
       "      <td>0.007256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MntGoldProds</td>\n",
       "      <td>0.000483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NumDealsPurchases</td>\n",
       "      <td>0.078299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NumWebPurchases</td>\n",
       "      <td>0.166283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NumCatalogPurchases</td>\n",
       "      <td>0.034221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NumStorePurchases</td>\n",
       "      <td>-0.220638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NumWebVisitsMonth</td>\n",
       "      <td>0.047235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AcceptedCmp3</td>\n",
       "      <td>0.753994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AcceptedCmp4</td>\n",
       "      <td>0.574824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AcceptedCmp5</td>\n",
       "      <td>0.726976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AcceptedCmp1</td>\n",
       "      <td>0.584593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AcceptedCmp2</td>\n",
       "      <td>0.250869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Complain</td>\n",
       "      <td>-0.016132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.015958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Member_Age</td>\n",
       "      <td>0.559836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Total_Spend</td>\n",
       "      <td>0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Total_Purchases</td>\n",
       "      <td>-0.020133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PCT_MntWines</td>\n",
       "      <td>-0.284073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PCT_MntFruits</td>\n",
       "      <td>-0.019566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PCT_MntMeatProducts</td>\n",
       "      <td>0.026830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>PCT_MntFishProducts</td>\n",
       "      <td>-0.062990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>PCT_MntSweetProducts</td>\n",
       "      <td>0.001810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>PCT_MntGoldProds</td>\n",
       "      <td>0.050568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>PCT_NumWebPurchases</td>\n",
       "      <td>-0.025773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PCT_NumCatalogPurchases</td>\n",
       "      <td>0.088310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PCT_NumStorePurchases</td>\n",
       "      <td>-0.349956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PCT_DealsPurchases</td>\n",
       "      <td>0.071556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>PCT_Campaigns</td>\n",
       "      <td>0.578251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Education_2n Cycle</td>\n",
       "      <td>-0.162857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Education_Basic</td>\n",
       "      <td>-0.250799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Education_Graduation</td>\n",
       "      <td>-0.160211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Education_Master</td>\n",
       "      <td>-0.200236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Education_PhD</td>\n",
       "      <td>0.486683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Marital_Status_Absurd</td>\n",
       "      <td>0.032497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Marital_Status_Alone</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Marital_Status_Divorced</td>\n",
       "      <td>0.344125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Marital_Status_Married</td>\n",
       "      <td>-0.527696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Marital_Status_Single</td>\n",
       "      <td>0.385331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Marital_Status_Together</td>\n",
       "      <td>-0.601950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Marital_Status_Widow</td>\n",
       "      <td>0.080274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Marital_Status_YOLO</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                    Feature  Coefficients\n",
       "0                    Income      0.000008\n",
       "1                   Kidhome      0.090512\n",
       "2                  Teenhome     -0.607408\n",
       "3                   Recency     -0.028697\n",
       "4                  MntWines     -0.000028\n",
       "5                 MntFruits     -0.001325\n",
       "6           MntMeatProducts      0.001307\n",
       "7           MntFishProducts     -0.006888\n",
       "8          MntSweetProducts      0.007256\n",
       "9              MntGoldProds      0.000483\n",
       "10        NumDealsPurchases      0.078299\n",
       "11          NumWebPurchases      0.166283\n",
       "12      NumCatalogPurchases      0.034221\n",
       "13        NumStorePurchases     -0.220638\n",
       "14        NumWebVisitsMonth      0.047235\n",
       "15             AcceptedCmp3      0.753994\n",
       "16             AcceptedCmp4      0.574824\n",
       "17             AcceptedCmp5      0.726976\n",
       "18             AcceptedCmp1      0.584593\n",
       "19             AcceptedCmp2      0.250869\n",
       "20                 Complain     -0.016132\n",
       "21                      Age     -0.015958\n",
       "22               Member_Age      0.559836\n",
       "23              Total_Spend      0.000805\n",
       "24          Total_Purchases     -0.020133\n",
       "25             PCT_MntWines     -0.284073\n",
       "26            PCT_MntFruits     -0.019566\n",
       "27      PCT_MntMeatProducts      0.026830\n",
       "28      PCT_MntFishProducts     -0.062990\n",
       "29     PCT_MntSweetProducts      0.001810\n",
       "30         PCT_MntGoldProds      0.050568\n",
       "31      PCT_NumWebPurchases     -0.025773\n",
       "32  PCT_NumCatalogPurchases      0.088310\n",
       "33    PCT_NumStorePurchases     -0.349956\n",
       "34       PCT_DealsPurchases      0.071556\n",
       "35            PCT_Campaigns      0.578251\n",
       "36       Education_2n Cycle     -0.162857\n",
       "37          Education_Basic     -0.250799\n",
       "38     Education_Graduation     -0.160211\n",
       "39         Education_Master     -0.200236\n",
       "40            Education_PhD      0.486683\n",
       "41    Marital_Status_Absurd      0.032497\n",
       "42     Marital_Status_Alone      0.000000\n",
       "43  Marital_Status_Divorced      0.344125\n",
       "44   Marital_Status_Married     -0.527696\n",
       "45    Marital_Status_Single      0.385331\n",
       "46  Marital_Status_Together     -0.601950\n",
       "47     Marital_Status_Widow      0.080274\n",
       "48      Marital_Status_YOLO      0.000000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ค่า b ของแต่ละตัวแปร\n",
    "\n",
    "pd.DataFrame({\"Feature\":X_train.columns.tolist(),\"Coefficients\":logreg_model.coef_[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1652777068625,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "sm4b4kb5guk9"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "x_scaled = scaler.fit_transform(X_train)\n",
    "X_train_scaled = pd.DataFrame(x_scaled)\n",
    "X_train_scaled.columns = X_train.columns\n",
    "\n",
    "x_scaled = scaler.fit_transform(X_test)\n",
    "X_test_scaled = pd.DataFrame(x_scaled)\n",
    "X_test_scaled.columns = X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "executionInfo": {
     "elapsed": 19,
     "status": "ok",
     "timestamp": 1652777068625,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "LwhX09esiT2x"
   },
   "outputs": [],
   "source": [
    "def classification_model(clf, X_train, y_train, X_test, y_test, show_feature_importance=True):\n",
    "  ls_y_hat_train = []\n",
    "  ls_y_train = []\n",
    "\n",
    "  clf_model = clf.fit(X_train, y_train)\n",
    "  y_hat_train = clf_model.predict(X_train)\n",
    "  ls_y_hat_train.extend(y_hat_train)\n",
    "  ls_y_train.extend(y_train)\n",
    "  report_train = classification_report(ls_y_train, ls_y_hat_train)\n",
    "  print('training set')\n",
    "  print(report_train) \n",
    "  print('\\n')\n",
    "  ls_y_hat_test = []\n",
    "  ls_y_test = []\n",
    "  y_hat_test = clf_model.predict(X_test)\n",
    "  ls_y_hat_test.extend(y_hat_test)\n",
    "  ls_y_test.extend(y_test)\n",
    "  report_test = classification_report(ls_y_test, ls_y_hat_test)\n",
    "  print('test set')\n",
    "  print(report_test) \n",
    "  print('\\n')\n",
    "\n",
    "  if show_feature_importance:\n",
    "    try:\n",
    "      print('Standardized Coeff.')\n",
    "      print(pd.DataFrame({\"Feature\":X_train.columns.tolist(),\"Coefficients\":clf_model.coef_[0]}).to_string())\n",
    "    except:\n",
    "      try:\n",
    "        print(pd.DataFrame({\"Feature\":X_train.columns.tolist(),\"Coefficients\":clf_model.feature_importances_}).to_string())\n",
    "      except:\n",
    "        print(\"No Standardized Coeff.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 525,
     "status": "ok",
     "timestamp": 1652777069132,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "m8Hwn3neiwVJ",
    "outputId": "15d96725-f904-481f-9050-fb8f6fb5c781"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.86      0.87       268\n",
      "           1       0.86      0.88      0.87       264\n",
      "\n",
      "    accuracy                           0.87       532\n",
      "   macro avg       0.87      0.87      0.87       532\n",
      "weighted avg       0.87      0.87      0.87       532\n",
      "\n",
      "\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.83      0.83        64\n",
      "           1       0.84      0.84      0.84        69\n",
      "\n",
      "    accuracy                           0.83       133\n",
      "   macro avg       0.83      0.83      0.83       133\n",
      "weighted avg       0.83      0.83      0.83       133\n",
      "\n",
      "\n",
      "\n",
      "Standardized Coeff.\n",
      "                    Feature  Coefficients\n",
      "0                    Income      1.339724\n",
      "1                   Kidhome     -0.367692\n",
      "2                  Teenhome     -0.579997\n",
      "3                   Recency     -1.044705\n",
      "4                  MntWines     -0.023545\n",
      "5                 MntFruits     -0.189275\n",
      "6           MntMeatProducts      0.123624\n",
      "7           MntFishProducts     -0.292013\n",
      "8          MntSweetProducts      0.192031\n",
      "9              MntGoldProds     -0.149833\n",
      "10        NumDealsPurchases     -0.479604\n",
      "11          NumWebPurchases      0.002587\n",
      "12      NumCatalogPurchases     -0.538740\n",
      "13        NumStorePurchases      0.443188\n",
      "14        NumWebVisitsMonth      0.882674\n",
      "15             AcceptedCmp3      0.519297\n",
      "16             AcceptedCmp4      0.269888\n",
      "17             AcceptedCmp5      0.725759\n",
      "18             AcceptedCmp1      0.324757\n",
      "19             AcceptedCmp2      0.362308\n",
      "20                 Complain     -0.189002\n",
      "21                      Age      0.078893\n",
      "22               Member_Age      0.987469\n",
      "23              Total_Spend     -0.004074\n",
      "24          Total_Purchases     -0.043306\n",
      "25             PCT_MntWines     -0.455953\n",
      "26            PCT_MntFruits      0.303240\n",
      "27      PCT_MntMeatProducts      0.565225\n",
      "28      PCT_MntFishProducts     -0.303166\n",
      "29     PCT_MntSweetProducts      0.239018\n",
      "30         PCT_MntGoldProds      0.161711\n",
      "31      PCT_NumWebPurchases      0.339199\n",
      "32  PCT_NumCatalogPurchases      0.713055\n",
      "33    PCT_NumStorePurchases     -1.021482\n",
      "34       PCT_DealsPurchases      1.606801\n",
      "35            PCT_Campaigns      0.729912\n",
      "36       Education_2n Cycle     -0.209978\n",
      "37          Education_Basic     -0.527944\n",
      "38     Education_Graduation     -0.048287\n",
      "39         Education_Master     -0.131103\n",
      "40            Education_PhD      0.495169\n",
      "41    Marital_Status_Absurd      0.139857\n",
      "42     Marital_Status_Alone      0.000000\n",
      "43  Marital_Status_Divorced      0.295293\n",
      "44   Marital_Status_Married     -0.221167\n",
      "45    Marital_Status_Single      0.347743\n",
      "46  Marital_Status_Together     -0.359820\n",
      "47     Marital_Status_Widow      0.026124\n",
      "48      Marital_Status_YOLO      0.000000\n"
     ]
    }
   ],
   "source": [
    "classification_model(logreg, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yDjLjwzBtjp1"
   },
   "source": [
    "## 2) Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lm22zw-Z-l6H"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/28.png width=\"750px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hclF6lclXfYV"
   },
   "source": [
    "**Entopy**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_GIwZxojWPZ7"
   },
   "source": [
    "$$ entropy = -{(p\\log_2(p) + (1 - p)\\log_2(1 - p))}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Ta7vNhqW29a"
   },
   "source": [
    "50:50\n",
    "\n",
    "$$ entropy = -{(0.50\\log_2(0.50) + (1 - 0.50)\\log_2(1 - 0.50))} = 1$$\n",
    "\n",
    "0:100\n",
    "\n",
    "$$ entropy = -{(0.00\\log_2(0.00) + (1 - 0.00)\\log_2(1 - 0.00))} = 0$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PWCXWq_V-oE3"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/29.png width=\"750px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1652777069134,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "oWwYxxPRdesP",
    "outputId": "b792e501-6df3-4f11-9697-ff8f15e16f38"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       268\n",
      "           1       1.00      1.00      1.00       264\n",
      "\n",
      "    accuracy                           1.00       532\n",
      "   macro avg       1.00      1.00      1.00       532\n",
      "weighted avg       1.00      1.00      1.00       532\n",
      "\n",
      "\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.75      0.72        64\n",
      "           1       0.75      0.68      0.71        69\n",
      "\n",
      "    accuracy                           0.71       133\n",
      "   macro avg       0.72      0.72      0.71       133\n",
      "weighted avg       0.72      0.71      0.71       133\n",
      "\n",
      "\n",
      "\n",
      "Standardized Coeff.\n",
      "                    Feature  Coefficients\n",
      "0                    Income      0.067744\n",
      "1                   Kidhome      0.000000\n",
      "2                  Teenhome      0.000000\n",
      "3                   Recency      0.082376\n",
      "4                  MntWines      0.013772\n",
      "5                 MntFruits      0.011626\n",
      "6           MntMeatProducts      0.000000\n",
      "7           MntFishProducts      0.038556\n",
      "8          MntSweetProducts      0.005032\n",
      "9              MntGoldProds      0.019380\n",
      "10        NumDealsPurchases      0.000000\n",
      "11          NumWebPurchases      0.006888\n",
      "12      NumCatalogPurchases      0.024873\n",
      "13        NumStorePurchases      0.046137\n",
      "14        NumWebVisitsMonth      0.002287\n",
      "15             AcceptedCmp3      0.000000\n",
      "16             AcceptedCmp4      0.000000\n",
      "17             AcceptedCmp5      0.000000\n",
      "18             AcceptedCmp1      0.000000\n",
      "19             AcceptedCmp2      0.000000\n",
      "20                 Complain      0.000000\n",
      "21                      Age      0.021966\n",
      "22               Member_Age      0.084710\n",
      "23              Total_Spend      0.010063\n",
      "24          Total_Purchases      0.070385\n",
      "25             PCT_MntWines      0.015656\n",
      "26            PCT_MntFruits      0.000000\n",
      "27      PCT_MntMeatProducts      0.064861\n",
      "28      PCT_MntFishProducts      0.009172\n",
      "29     PCT_MntSweetProducts      0.014176\n",
      "30         PCT_MntGoldProds      0.000000\n",
      "31      PCT_NumWebPurchases      0.006604\n",
      "32  PCT_NumCatalogPurchases      0.008424\n",
      "33    PCT_NumStorePurchases      0.071911\n",
      "34       PCT_DealsPurchases      0.021775\n",
      "35            PCT_Campaigns      0.230264\n",
      "36       Education_2n Cycle      0.008011\n",
      "37          Education_Basic      0.000000\n",
      "38     Education_Graduation      0.000000\n",
      "39         Education_Master      0.006789\n",
      "40            Education_PhD      0.010063\n",
      "41    Marital_Status_Absurd      0.000000\n",
      "42     Marital_Status_Alone      0.000000\n",
      "43  Marital_Status_Divorced      0.000000\n",
      "44   Marital_Status_Married      0.000000\n",
      "45    Marital_Status_Single      0.026501\n",
      "46  Marital_Status_Together      0.000000\n",
      "47     Marital_Status_Widow      0.000000\n",
      "48      Marital_Status_YOLO      0.000000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_clf = DecisionTreeClassifier()\n",
    "\n",
    "classification_model(dt_clf, X_train_scaled, y_train, X_test_scaled, y_test, show_feature_importance=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RSnxbadcttFP"
   },
   "source": [
    "## 3) Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aSujvnGC-tNf"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/30.png width=\"750px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1652777069135,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "3CS5KuFGkFVX",
    "outputId": "314c81a6-cfa9-4a0e-9bc0-8cc90d7376ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88       268\n",
      "           1       0.86      0.90      0.88       264\n",
      "\n",
      "    accuracy                           0.88       532\n",
      "   macro avg       0.88      0.88      0.88       532\n",
      "weighted avg       0.88      0.88      0.88       532\n",
      "\n",
      "\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.78      0.79        64\n",
      "           1       0.80      0.81      0.81        69\n",
      "\n",
      "    accuracy                           0.80       133\n",
      "   macro avg       0.80      0.80      0.80       133\n",
      "weighted avg       0.80      0.80      0.80       133\n",
      "\n",
      "\n",
      "\n",
      "Standardized Coeff.\n",
      "                    Feature  Coefficients\n",
      "0                    Income      0.859445\n",
      "1                   Kidhome     -0.284489\n",
      "2                  Teenhome     -0.429931\n",
      "3                   Recency     -0.843800\n",
      "4                  MntWines     -0.167052\n",
      "5                 MntFruits     -0.170879\n",
      "6           MntMeatProducts      0.137631\n",
      "7           MntFishProducts     -0.199733\n",
      "8          MntSweetProducts      0.326957\n",
      "9              MntGoldProds     -0.092543\n",
      "10        NumDealsPurchases     -0.477990\n",
      "11          NumWebPurchases     -0.168494\n",
      "12      NumCatalogPurchases     -0.689339\n",
      "13        NumStorePurchases      0.785956\n",
      "14        NumWebVisitsMonth      0.685078\n",
      "15             AcceptedCmp3      0.435575\n",
      "16             AcceptedCmp4      0.204887\n",
      "17             AcceptedCmp5      0.422701\n",
      "18             AcceptedCmp1      0.290855\n",
      "19             AcceptedCmp2      0.252739\n",
      "20                 Complain     -0.006165\n",
      "21                      Age      0.016618\n",
      "22               Member_Age      0.759095\n",
      "23              Total_Spend     -0.058143\n",
      "24          Total_Purchases     -0.018504\n",
      "25             PCT_MntWines     -0.251374\n",
      "26            PCT_MntFruits      0.321318\n",
      "27      PCT_MntMeatProducts      0.431226\n",
      "28      PCT_MntFishProducts     -0.413325\n",
      "29     PCT_MntSweetProducts      0.175065\n",
      "30         PCT_MntGoldProds      0.010478\n",
      "31      PCT_NumWebPurchases      0.329621\n",
      "32  PCT_NumCatalogPurchases      0.756157\n",
      "33    PCT_NumStorePurchases     -1.058418\n",
      "34       PCT_DealsPurchases      1.476805\n",
      "35            PCT_Campaigns      0.532934\n",
      "36       Education_2n Cycle     -0.173122\n",
      "37          Education_Basic     -0.401422\n",
      "38     Education_Graduation      0.008135\n",
      "39         Education_Master     -0.105150\n",
      "40            Education_PhD      0.338480\n",
      "41    Marital_Status_Absurd      0.003610\n",
      "42     Marital_Status_Alone      0.000000\n",
      "43  Marital_Status_Divorced      0.209324\n",
      "44   Marital_Status_Married     -0.201845\n",
      "45    Marital_Status_Single      0.324384\n",
      "46  Marital_Status_Together     -0.304610\n",
      "47     Marital_Status_Widow      0.075510\n",
      "48      Marital_Status_YOLO      0.000000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc_clf = SVC(kernel='linear', gamma='auto')\n",
    "\n",
    "classification_model(svc_clf, X_train_scaled, y_train, X_test_scaled, y_test, show_feature_importance=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c4bqbwn7uDBm"
   },
   "source": [
    "## 4) K Nearest Neighbor (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hx7W7DUF-wq1"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/31.png width=\"750px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 346,
     "status": "ok",
     "timestamp": 1652777069473,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "_tpy-tvvk7Vf",
    "outputId": "63d42fe0-2ca5-469b-f35d-bcb7d3469b3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.89      0.86       268\n",
      "           1       0.88      0.82      0.85       264\n",
      "\n",
      "    accuracy                           0.86       532\n",
      "   macro avg       0.86      0.85      0.85       532\n",
      "weighted avg       0.86      0.86      0.86       532\n",
      "\n",
      "\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.84      0.77        64\n",
      "           1       0.82      0.67      0.74        69\n",
      "\n",
      "    accuracy                           0.75       133\n",
      "   macro avg       0.76      0.76      0.75       133\n",
      "weighted avg       0.76      0.75      0.75       133\n",
      "\n",
      "\n",
      "\n",
      "Standardized Coeff.\n",
      "No Standardized Coeff.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_clf = KNeighborsClassifier(5)\n",
    "\n",
    "classification_model(knn_clf, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eFaEOedk2xWw"
   },
   "source": [
    "# Ensemble Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zd7q2LmoHOZB"
   },
   "source": [
    "เทคนิคที่นำหลาย Model มาทำร่วมกันทำนายพยากรณ์เพื่อให้ได้ผลลัพธ์สูงที่สุด โดยมี 2 แนวทางได้แก่\n",
    "\n",
    "1.   Bagging เป็นการสุ่มข้อมูล หรือ features ส่วนหนึ่งขึ้นมาสร้างโมเดล\n",
    "2.   Boosting เป็นการผลของการทำนายครั้งก่อน มาปรับปรุงให้ดีขึ้น\n",
    "\n",
    "https://tupleblog.github.io/bagging-boosting/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HLD-8CfkBbfb"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/32.png width=\"750px\">\n",
    "\n",
    "https://mateusmaiads.github.io/ensemble_qualify/#7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "my6_HpIetmay"
   },
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ATqA3ITg_mCd"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/33.png width=\"750px\">\n",
    "\n",
    "https://towardsdatascience.com/understanding-random-forest-58381e0602d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1652777069473,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "ngDdyHiIl5VH",
    "outputId": "c53be247-eb46-4a9f-ff50-c27e61d731a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.88      0.88       268\n",
      "           1       0.88      0.87      0.87       264\n",
      "\n",
      "    accuracy                           0.87       532\n",
      "   macro avg       0.87      0.87      0.87       532\n",
      "weighted avg       0.87      0.87      0.87       532\n",
      "\n",
      "\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.73      0.72        64\n",
      "           1       0.75      0.72      0.74        69\n",
      "\n",
      "    accuracy                           0.73       133\n",
      "   macro avg       0.73      0.73      0.73       133\n",
      "weighted avg       0.73      0.73      0.73       133\n",
      "\n",
      "\n",
      "\n",
      "Standardized Coeff.\n",
      "                    Feature  Coefficients\n",
      "0                    Income      0.054224\n",
      "1                   Kidhome      0.000169\n",
      "2                  Teenhome      0.010467\n",
      "3                   Recency      0.058759\n",
      "4                  MntWines      0.026034\n",
      "5                 MntFruits      0.007910\n",
      "6           MntMeatProducts      0.044237\n",
      "7           MntFishProducts      0.016869\n",
      "8          MntSweetProducts      0.007678\n",
      "9              MntGoldProds      0.021487\n",
      "10        NumDealsPurchases      0.005874\n",
      "11          NumWebPurchases      0.006291\n",
      "12      NumCatalogPurchases      0.025312\n",
      "13        NumStorePurchases      0.015944\n",
      "14        NumWebVisitsMonth      0.035825\n",
      "15             AcceptedCmp3      0.024599\n",
      "16             AcceptedCmp4      0.007275\n",
      "17             AcceptedCmp5      0.042597\n",
      "18             AcceptedCmp1      0.033323\n",
      "19             AcceptedCmp2      0.000825\n",
      "20                 Complain      0.000425\n",
      "21                      Age      0.011550\n",
      "22               Member_Age      0.049624\n",
      "23              Total_Spend      0.069825\n",
      "24          Total_Purchases      0.007620\n",
      "25             PCT_MntWines      0.008475\n",
      "26            PCT_MntFruits      0.008924\n",
      "27      PCT_MntMeatProducts      0.018583\n",
      "28      PCT_MntFishProducts      0.009091\n",
      "29     PCT_MntSweetProducts      0.011447\n",
      "30         PCT_MntGoldProds      0.010268\n",
      "31      PCT_NumWebPurchases      0.025244\n",
      "32  PCT_NumCatalogPurchases      0.027915\n",
      "33    PCT_NumStorePurchases      0.098900\n",
      "34       PCT_DealsPurchases      0.032653\n",
      "35            PCT_Campaigns      0.145527\n",
      "36       Education_2n Cycle      0.000392\n",
      "37          Education_Basic      0.000678\n",
      "38     Education_Graduation      0.000610\n",
      "39         Education_Master      0.000504\n",
      "40            Education_PhD      0.001605\n",
      "41    Marital_Status_Absurd      0.000000\n",
      "42     Marital_Status_Alone      0.000000\n",
      "43  Marital_Status_Divorced      0.000894\n",
      "44   Marital_Status_Married      0.003455\n",
      "45    Marital_Status_Single      0.006498\n",
      "46  Marital_Status_Together      0.002371\n",
      "47     Marital_Status_Widow      0.001224\n",
      "48      Marital_Status_YOLO      0.000000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_clf = RandomForestClassifier(max_depth=4, random_state=0)\n",
    "\n",
    "classification_model(rf_clf, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9EyHUZVcuE4s"
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q92obCQ8AqFR"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/34.png width=\"750px\">\n",
    "\n",
    "https://blog.quantinsti.com/xgboost-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 366,
     "status": "ok",
     "timestamp": 1652777069837,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "StXXhA-8mtHw",
    "outputId": "ea7efc48-8c8a-4848-abed-5480c3a5a33b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.84      0.85       268\n",
      "           1       0.84      0.86      0.85       264\n",
      "\n",
      "    accuracy                           0.85       532\n",
      "   macro avg       0.85      0.85      0.85       532\n",
      "weighted avg       0.85      0.85      0.85       532\n",
      "\n",
      "\n",
      "\n",
      "test set\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.78      0.77        64\n",
      "           1       0.79      0.77      0.78        69\n",
      "\n",
      "    accuracy                           0.77       133\n",
      "   macro avg       0.77      0.77      0.77       133\n",
      "weighted avg       0.77      0.77      0.77       133\n",
      "\n",
      "\n",
      "\n",
      "Standardized Coeff.\n",
      "                    Feature  Coefficients\n",
      "0                    Income      0.056440\n",
      "1                   Kidhome      0.001433\n",
      "2                  Teenhome      0.043435\n",
      "3                   Recency      0.042151\n",
      "4                  MntWines      0.016524\n",
      "5                 MntFruits      0.000000\n",
      "6           MntMeatProducts      0.046738\n",
      "7           MntFishProducts      0.016042\n",
      "8          MntSweetProducts      0.014972\n",
      "9              MntGoldProds      0.007237\n",
      "10        NumDealsPurchases      0.006471\n",
      "11          NumWebPurchases      0.004708\n",
      "12      NumCatalogPurchases      0.051113\n",
      "13        NumStorePurchases      0.015255\n",
      "14        NumWebVisitsMonth      0.042463\n",
      "15             AcceptedCmp3      0.040946\n",
      "16             AcceptedCmp4      0.000000\n",
      "17             AcceptedCmp5      0.078545\n",
      "18             AcceptedCmp1      0.000000\n",
      "19             AcceptedCmp2      0.000000\n",
      "20                 Complain      0.000000\n",
      "21                      Age      0.003405\n",
      "22               Member_Age      0.072142\n",
      "23              Total_Spend      0.022920\n",
      "24          Total_Purchases      0.013980\n",
      "25             PCT_MntWines      0.013111\n",
      "26            PCT_MntFruits      0.017531\n",
      "27      PCT_MntMeatProducts      0.039231\n",
      "28      PCT_MntFishProducts      0.015757\n",
      "29     PCT_MntSweetProducts      0.007955\n",
      "30         PCT_MntGoldProds      0.012838\n",
      "31      PCT_NumWebPurchases      0.013804\n",
      "32  PCT_NumCatalogPurchases      0.025934\n",
      "33    PCT_NumStorePurchases      0.073730\n",
      "34       PCT_DealsPurchases      0.018901\n",
      "35            PCT_Campaigns      0.110262\n",
      "36       Education_2n Cycle      0.004673\n",
      "37          Education_Basic      0.000000\n",
      "38     Education_Graduation      0.000000\n",
      "39         Education_Master      0.004952\n",
      "40            Education_PhD      0.000000\n",
      "41    Marital_Status_Absurd      0.000000\n",
      "42     Marital_Status_Alone      0.000000\n",
      "43  Marital_Status_Divorced      0.000000\n",
      "44   Marital_Status_Married      0.017691\n",
      "45    Marital_Status_Single      0.023152\n",
      "46  Marital_Status_Together      0.003560\n",
      "47     Marital_Status_Widow      0.000000\n",
      "48      Marital_Status_YOLO      0.000000\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "xgb_clf = xgb.XGBClassifier(objective='binary:logistic', eval_metric='auc',\n",
    "                              learning_rate =0.01,\n",
    "                              n_estimators=100,\n",
    "                              max_depth=3,\n",
    "                              gamma=0.0,\n",
    "                              colsample_bytree=0.6)\n",
    "\n",
    "classification_model(xgb_clf, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NyIIaMU8uJeH"
   },
   "source": [
    "# Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zLZ5XQ28C7FD"
   },
   "source": [
    "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/35.png width=\"750px\">\n",
    "\n",
    "https://medium.com/@nishant.rao173/modeling-churn-via-deep-learning-with-pytorch-848136e0ca06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "executionInfo": {
     "elapsed": 3416,
     "status": "ok",
     "timestamp": 1652777073252,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "PhEaDBnInoZ9"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class LogisticRegression(nn.Module):\n",
    "    \n",
    "    def __init__(self, num_input_features):\n",
    "        super(LogisticRegression, self).__init__()\n",
    "        self.linear1 = nn.Linear(num_input_features, 100)\n",
    "        self.linear2 = nn.Linear(100, 80)\n",
    "        self.linear3 = nn.Linear(80, 1)\n",
    "    \n",
    "    def forward(self, xb):\n",
    "        prediction = torch.sigmoid(input=self.linear1(xb))\n",
    "        prediction = torch.sigmoid(input=self.linear2(prediction))\n",
    "        prediction = torch.sigmoid(input=self.linear3(prediction))\n",
    "        return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1652777073252,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "SjzspYpforbY"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "X_train_dl = torch.from_numpy(X_train_scaled.to_numpy().astype(np.float32))\n",
    "X_test_dl = torch.from_numpy(X_test_scaled.to_numpy().astype(np.float32))\n",
    "y_train_dl = torch.from_numpy(y_train.to_numpy().astype(np.float32))\n",
    "y_test_dl = torch.from_numpy(y_test.to_numpy().astype(np.float32))\n",
    "\n",
    "y_train_dl = y_train_dl.view(len(y_train_dl), 1)\n",
    "y_test_dl = y_test_dl.view(len(y_test_dl), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7371,
     "status": "ok",
     "timestamp": 1652777080621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "-eaS_lHvn7mM",
    "outputId": "74f3f218-98c2-43ef-e496-2c385bff64f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Architecture:\n",
      "LogisticRegression(\n",
      "  (linear1): Linear(in_features=49, out_features=100, bias=True)\n",
      "  (linear2): Linear(in_features=100, out_features=80, bias=True)\n",
      "  (linear3): Linear(in_features=80, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Training the model...\n",
      "\n",
      "Epoch 200 \t Loss: 0.0033\n",
      "Accuracy (on test-set): 0.8722\n",
      "\n",
      "Epoch 400 \t Loss: 0.0028\n",
      "Accuracy (on test-set): 0.8722\n",
      "\n",
      "Epoch 600 \t Loss: 0.0027\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Epoch 800 \t Loss: 0.0027\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Epoch 1000 \t Loss: 0.0026\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Epoch 1200 \t Loss: 0.0026\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Epoch 1400 \t Loss: 0.0026\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Epoch 1600 \t Loss: 0.0026\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Epoch 1800 \t Loss: 0.0026\n",
      "Accuracy (on test-set): 0.8872\n",
      "\n",
      "Final Accuracy (on test-set): 0.8872\n",
      "CPU times: user 6.84 s, sys: 34.5 ms, total: 6.88 s\n",
      "Wall time: 6.97 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "lr = 0.01\n",
    "model = LogisticRegression(num_input_features=X_train_dl.shape[1])\n",
    "print(f\"Model Architecture:\\n{model}\")\n",
    "criterion = nn.BCELoss()\n",
    "optimizer = torch.optim.Adam(params=model.parameters(), lr=lr)\n",
    "\n",
    "nb_epochs = 1800\n",
    "print_offset = 200\n",
    "\n",
    "df_tracker = pd.DataFrame()\n",
    "print(\"\\nTraining the model...\")\n",
    "for epoch in range(1, nb_epochs+1):\n",
    "    y_pred = model(X_train_dl)\n",
    "    loss = criterion(input=y_pred, target=y_train_dl)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    if epoch % print_offset == 0:\n",
    "        print(f\"\\nEpoch {epoch} \\t Loss: {round(loss.item(), 4)}\")\n",
    "    \n",
    "    # Print test-accuracy after certain number of epochs\n",
    "    with torch.no_grad():\n",
    "        y_pred = model(X_test_dl)\n",
    "        y_pred_class = y_pred.round()\n",
    "        accuracy = y_pred_class.eq(y_test_dl).sum() / float(len(y_test_dl))\n",
    "        if epoch % print_offset == 0:\n",
    "            print(f\"Accuracy (on test-set): {round(accuracy.item(), 4)}\")\n",
    "    \n",
    "    df_temp = pd.DataFrame(data={\n",
    "        'Epoch': epoch,\n",
    "        'Loss': round(loss.item(), 4),\n",
    "        'Accuracy': round(accuracy.item(), 4)\n",
    "    }, index=[0])\n",
    "    df_tracker = pd.concat(objs=[df_tracker, df_temp], ignore_index=True, sort=False)\n",
    "\n",
    "print(f\"\\nFinal Accuracy (on test-set): {round(accuracy.item(), 4)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1652777080621,
     "user": {
      "displayName": "Thanachart Ritbumroong",
      "userId": "18414212507793256437"
     },
     "user_tz": -420
    },
    "id": "Pc6rVKAfG0Gt",
    "outputId": "747ec99d-048c-48cc-8099-9cdf1fc015f4"
   },
   "outputs": [
    {
     "data": {
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Epoch</th>\n",
       "      <th>Loss</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.7186</td>\n",
       "      <td>0.4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.7184</td>\n",
       "      <td>0.4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6996</td>\n",
       "      <td>0.6617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.6705</td>\n",
       "      <td>0.6391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.6657</td>\n",
       "      <td>0.5338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795</th>\n",
       "      <td>1796</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1796</th>\n",
       "      <td>1797</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>1798</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>1799</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.8872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>1800</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.8872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1800 rows × 3 columns</p>\n",
       "</div>\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-475e2cce-88ed-416d-89c0-16a023a7d807');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "            + ' to learn more about interactive tables.';\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      Epoch    Loss  Accuracy\n",
       "0         1  0.7186    0.4812\n",
       "1         2  0.7184    0.4812\n",
       "2         3  0.6996    0.6617\n",
       "3         4  0.6705    0.6391\n",
       "4         5  0.6657    0.5338\n",
       "...     ...     ...       ...\n",
       "1795   1796  0.0026    0.8872\n",
       "1796   1797  0.0026    0.8872\n",
       "1797   1798  0.0026    0.8872\n",
       "1798   1799  0.0026    0.8872\n",
       "1799   1800  0.0026    0.8872\n",
       "\n",
       "[1800 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tracker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SaDHad8CEjqB"
   },
   "source": [
    "# Checkpoint ท้ายบท"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WRiI-PWwEkSz"
   },
   "source": [
    "*   ☑️ เข้าใจเทคนิคการวิเคราะห์ Classification\n",
    "*   ☑️ เข้าใจถึงเทคนิคที่หลากหลายในกลุ่มของ Classification\n",
    "*   ☑️ สามารถวิเคราะห์และพัฒนา Classification Model ได้"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPHo46I5CnjIlPeAqv40ibt",
   "collapsed_sections": [],
   "name": "04t_04_classification.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "default:Python",
   "language": "python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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