high-income-pred.ipynb

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     "start_time": "2021-06-26T10:59:23.315964",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Import Libraries 📖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "vertical-phoenix",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/adult-census-income/adult.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split as tts\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "confident-vegetarian",
   "metadata": {
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     "start_time": "2021-06-26T10:59:29.704347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load dataset 💾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "revolutionary-tampa",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('../input/adult-census-income/adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "certain-pakistan",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>32561 rows × 15 columns</p>\n",
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      "text/plain": [
       "       age workclass  fnlwgt     education  education.num      marital.status  \\\n",
       "0       90         ?   77053       HS-grad              9             Widowed   \n",
       "1       82   Private  132870       HS-grad              9             Widowed   \n",
       "2       66         ?  186061  Some-college             10             Widowed   \n",
       "3       54   Private  140359       7th-8th              4            Divorced   \n",
       "4       41   Private  264663  Some-college             10           Separated   \n",
       "...    ...       ...     ...           ...            ...                 ...   \n",
       "32556   22   Private  310152  Some-college             10       Never-married   \n",
       "32557   27   Private  257302    Assoc-acdm             12  Married-civ-spouse   \n",
       "32558   40   Private  154374       HS-grad              9  Married-civ-spouse   \n",
       "32559   58   Private  151910       HS-grad              9             Widowed   \n",
       "32560   22   Private  201490       HS-grad              9       Never-married   \n",
       "\n",
       "              occupation   relationship   race     sex  capital.gain  \\\n",
       "0                      ?  Not-in-family  White  Female             0   \n",
       "1        Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                      ?      Unmarried  Black  Female             0   \n",
       "3      Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4         Prof-specialty      Own-child  White  Female             0   \n",
       "...                  ...            ...    ...     ...           ...   \n",
       "32556    Protective-serv  Not-in-family  White    Male             0   \n",
       "32557       Tech-support           Wife  White  Female             0   \n",
       "32558  Machine-op-inspct        Husband  White    Male             0   \n",
       "32559       Adm-clerical      Unmarried  White  Female             0   \n",
       "32560       Adm-clerical      Own-child  White    Male             0   \n",
       "\n",
       "       capital.loss  hours.per.week native.country income  \n",
       "0              4356              40  United-States  <=50K  \n",
       "1              4356              18  United-States  <=50K  \n",
       "2              4356              40  United-States  <=50K  \n",
       "3              3900              40  United-States  <=50K  \n",
       "4              3900              40  United-States  <=50K  \n",
       "...             ...             ...            ...    ...  \n",
       "32556             0              40  United-States  <=50K  \n",
       "32557             0              38  United-States  <=50K  \n",
       "32558             0              40  United-States   >50K  \n",
       "32559             0              40  United-States  <=50K  \n",
       "32560             0              20  United-States  <=50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "data"
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  },
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    "tags": []
   },
   "source": [
    "# Data Cleaning 🧹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "universal-public",
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   },
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    {
     "data": {
      "text/plain": [
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     "execution_count": 4,
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   "source": [
    "data.isna().sum()"
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   "id": "delayed-dietary",
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    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1836\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education.num        0\n",
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     "execution_count": 5,
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   ],
   "source": [
    "data.isin(['?']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "chinese-conversion",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>?</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>310152</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass  fnlwgt     education  education.num      marital.status  \\\n",
       "0       90         ?   77053       HS-grad              9             Widowed   \n",
       "1       82   Private  132870       HS-grad              9             Widowed   \n",
       "2       66         ?  186061  Some-college             10             Widowed   \n",
       "3       54   Private  140359       7th-8th              4            Divorced   \n",
       "4       41   Private  264663  Some-college             10           Separated   \n",
       "...    ...       ...     ...           ...            ...                 ...   \n",
       "32556   22   Private  310152  Some-college             10       Never-married   \n",
       "32557   27   Private  257302    Assoc-acdm             12  Married-civ-spouse   \n",
       "32558   40   Private  154374       HS-grad              9  Married-civ-spouse   \n",
       "32559   58   Private  151910       HS-grad              9             Widowed   \n",
       "32560   22   Private  201490       HS-grad              9       Never-married   \n",
       "\n",
       "              occupation   relationship   race     sex  capital.gain  \\\n",
       "0                      ?  Not-in-family  White  Female             0   \n",
       "1        Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                      ?      Unmarried  Black  Female             0   \n",
       "3      Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4         Prof-specialty      Own-child  White  Female             0   \n",
       "...                  ...            ...    ...     ...           ...   \n",
       "32556    Protective-serv  Not-in-family  White    Male             0   \n",
       "32557       Tech-support           Wife  White  Female             0   \n",
       "32558  Machine-op-inspct        Husband  White    Male             0   \n",
       "32559       Adm-clerical      Unmarried  White  Female             0   \n",
       "32560       Adm-clerical      Own-child  White    Male             0   \n",
       "\n",
       "       capital.loss  hours.per.week native.country income  \n",
       "0              4356              40  United-States  <=50K  \n",
       "1              4356              18  United-States  <=50K  \n",
       "2              4356              40  United-States  <=50K  \n",
       "3              3900              40  United-States  <=50K  \n",
       "4              3900              40  United-States  <=50K  \n",
       "...             ...             ...            ...    ...  \n",
       "32556             0              40  United-States  <=50K  \n",
       "32557             0              38  United-States  <=50K  \n",
       "32558             0              40  United-States   >50K  \n",
       "32559             0              40  United-States  <=50K  \n",
       "32560             0              20  United-States  <=50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "collective-guatemala",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:30.512729Z",
     "iopub.status.busy": "2021-06-26T10:59:30.511790Z",
     "iopub.status.idle": "2021-06-26T10:59:30.517255Z",
     "shell.execute_reply": "2021-06-26T10:59:30.516619Z",
     "shell.execute_reply.started": "2021-06-26T10:13:12.981803Z"
    },
    "papermill": {
     "duration": 0.047388,
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     "exception": false,
     "start_time": "2021-06-26T10:59:30.470007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['?', 'Private', 'State-gov', 'Federal-gov', 'Self-emp-not-inc',\n",
       "       'Self-emp-inc', 'Local-gov', 'Without-pay', 'Never-worked'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['workclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "academic-submission",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:30.592735Z",
     "iopub.status.busy": "2021-06-26T10:59:30.591970Z",
     "iopub.status.idle": "2021-06-26T10:59:30.597611Z",
     "shell.execute_reply": "2021-06-26T10:59:30.596972Z",
     "shell.execute_reply.started": "2021-06-26T10:13:12.991755Z"
    },
    "papermill": {
     "duration": 0.046071,
     "end_time": "2021-06-26T10:59:30.597751",
     "exception": false,
     "start_time": "2021-06-26T10:59:30.551680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['?', 'Exec-managerial', 'Machine-op-inspct', 'Prof-specialty',\n",
       "       'Other-service', 'Adm-clerical', 'Craft-repair',\n",
       "       'Transport-moving', 'Handlers-cleaners', 'Sales',\n",
       "       'Farming-fishing', 'Tech-support', 'Protective-serv',\n",
       "       'Armed-Forces', 'Priv-house-serv'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['occupation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "iraqi-allowance",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:30.673711Z",
     "iopub.status.busy": "2021-06-26T10:59:30.673007Z",
     "iopub.status.idle": "2021-06-26T10:59:30.678862Z",
     "shell.execute_reply": "2021-06-26T10:59:30.678354Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.005229Z"
    },
    "papermill": {
     "duration": 0.046513,
     "end_time": "2021-06-26T10:59:30.679004",
     "exception": false,
     "start_time": "2021-06-26T10:59:30.632491",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United-States', '?', 'Mexico', 'Greece', 'Vietnam', 'China',\n",
       "       'Taiwan', 'India', 'Philippines', 'Trinadad&Tobago', 'Canada',\n",
       "       'South', 'Holand-Netherlands', 'Puerto-Rico', 'Poland', 'Iran',\n",
       "       'England', 'Germany', 'Italy', 'Japan', 'Hong', 'Honduras', 'Cuba',\n",
       "       'Ireland', 'Cambodia', 'Peru', 'Nicaragua', 'Dominican-Republic',\n",
       "       'Haiti', 'El-Salvador', 'Hungary', 'Columbia', 'Guatemala',\n",
       "       'Jamaica', 'Ecuador', 'France', 'Yugoslavia', 'Scotland',\n",
       "       'Portugal', 'Laos', 'Thailand', 'Outlying-US(Guam-USVI-etc)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['native.country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "taken-membership",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:30.766720Z",
     "iopub.status.busy": "2021-06-26T10:59:30.766010Z",
     "iopub.status.idle": "2021-06-26T10:59:30.772330Z",
     "shell.execute_reply": "2021-06-26T10:59:30.771739Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.020946Z"
    },
    "papermill": {
     "duration": 0.058073,
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     "exception": false,
     "start_time": "2021-06-26T10:59:30.714397",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = data.replace('?', np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "covered-soundtrack",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:30.878264Z",
     "iopub.status.busy": "2021-06-26T10:59:30.877544Z",
     "iopub.status.idle": "2021-06-26T10:59:30.885481Z",
     "shell.execute_reply": "2021-06-26T10:59:30.885940Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.042105Z"
    },
    "papermill": {
     "duration": 0.078454,
     "end_time": "2021-06-26T10:59:30.886136",
     "exception": false,
     "start_time": "2021-06-26T10:59:30.807682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  0\n",
       "workclass         1836\n",
       "fnlwgt               0\n",
       "education            0\n",
       "education.num        0\n",
       "marital.status       0\n",
       "occupation        1843\n",
       "relationship         0\n",
       "race                 0\n",
       "sex                  0\n",
       "capital.gain         0\n",
       "capital.loss         0\n",
       "hours.per.week       0\n",
       "native.country     583\n",
       "income               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "skilled-choir",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:30.974466Z",
     "iopub.status.busy": "2021-06-26T10:59:30.973792Z",
     "iopub.status.idle": "2021-06-26T10:59:30.978144Z",
     "shell.execute_reply": "2021-06-26T10:59:30.977516Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.079331Z"
    },
    "papermill": {
     "duration": 0.056616,
     "end_time": "2021-06-26T10:59:30.978302",
     "exception": false,
     "start_time": "2021-06-26T10:59:30.921686",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   90       NaN   77053       HS-grad              9        Widowed   \n",
       "1   82   Private  132870       HS-grad              9        Widowed   \n",
       "2   66       NaN  186061  Some-college             10        Widowed   \n",
       "3   54   Private  140359       7th-8th              4       Divorced   \n",
       "4   41   Private  264663  Some-college             10      Separated   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0                NaN  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                NaN      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              40  United-States  <=50K  \n",
       "1          4356              18  United-States  <=50K  \n",
       "2          4356              40  United-States  <=50K  \n",
       "3          3900              40  United-States  <=50K  \n",
       "4          3900              40  United-States  <=50K  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "prompt-happening",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.086468Z",
     "iopub.status.busy": "2021-06-26T10:59:31.056274Z",
     "iopub.status.idle": "2021-06-26T10:59:31.104095Z",
     "shell.execute_reply": "2021-06-26T10:59:31.104607Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.099809Z"
    },
    "papermill": {
     "duration": 0.090175,
     "end_time": "2021-06-26T10:59:31.104790",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.014615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   age             32561 non-null  int64 \n",
      " 1   workclass       30725 non-null  object\n",
      " 2   fnlwgt          32561 non-null  int64 \n",
      " 3   education       32561 non-null  object\n",
      " 4   education.num   32561 non-null  int64 \n",
      " 5   marital.status  32561 non-null  object\n",
      " 6   occupation      30718 non-null  object\n",
      " 7   relationship    32561 non-null  object\n",
      " 8   race            32561 non-null  object\n",
      " 9   sex             32561 non-null  object\n",
      " 10  capital.gain    32561 non-null  int64 \n",
      " 11  capital.loss    32561 non-null  int64 \n",
      " 12  hours.per.week  32561 non-null  int64 \n",
      " 13  native.country  31978 non-null  object\n",
      " 14  income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "stuffed-interference",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.184461Z",
     "iopub.status.busy": "2021-06-26T10:59:31.183624Z",
     "iopub.status.idle": "2021-06-26T10:59:31.187981Z",
     "shell.execute_reply": "2021-06-26T10:59:31.187492Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.153395Z"
    },
    "papermill": {
     "duration": 0.046261,
     "end_time": "2021-06-26T10:59:31.188152",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.141891",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'fnlwgt', 'education', 'education.num',\n",
       "       'marital.status', 'occupation', 'relationship', 'race', 'sex',\n",
       "       'capital.gain', 'capital.loss', 'hours.per.week', 'native.country',\n",
       "       'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "infrared-chess",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.267457Z",
     "iopub.status.busy": "2021-06-26T10:59:31.266817Z",
     "iopub.status.idle": "2021-06-26T10:59:31.269114Z",
     "shell.execute_reply": "2021-06-26T10:59:31.269610Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.162424Z"
    },
    "papermill": {
     "duration": 0.044878,
     "end_time": "2021-06-26T10:59:31.269813",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.224935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categorical_features = []\n",
    "\n",
    "for column in data.columns:\n",
    "    if data[column].dtypes == 'object':\n",
    "        categorical_features.append(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "lesbian-endorsement",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.348282Z",
     "iopub.status.busy": "2021-06-26T10:59:31.347609Z",
     "iopub.status.idle": "2021-06-26T10:59:31.352680Z",
     "shell.execute_reply": "2021-06-26T10:59:31.353185Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.172027Z"
    },
    "papermill": {
     "duration": 0.046821,
     "end_time": "2021-06-26T10:59:31.353471",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.306650",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['workclass',\n",
       " 'education',\n",
       " 'marital.status',\n",
       " 'occupation',\n",
       " 'relationship',\n",
       " 'race',\n",
       " 'sex',\n",
       " 'native.country',\n",
       " 'income']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cubic-catch",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.431933Z",
     "iopub.status.busy": "2021-06-26T10:59:31.431284Z",
     "iopub.status.idle": "2021-06-26T10:59:31.446391Z",
     "shell.execute_reply": "2021-06-26T10:59:31.446923Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.184513Z"
    },
    "papermill": {
     "duration": 0.056127,
     "end_time": "2021-06-26T10:59:31.447134",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.391007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data.drop('education', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "curious-version",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.526949Z",
     "iopub.status.busy": "2021-06-26T10:59:31.526327Z",
     "iopub.status.idle": "2021-06-26T10:59:31.530285Z",
     "shell.execute_reply": "2021-06-26T10:59:31.530728Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.204210Z"
    },
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     "duration": 0.044173,
     "end_time": "2021-06-26T10:59:31.530887",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.486714",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categorical_features.remove('income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "efficient-director",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.609846Z",
     "iopub.status.busy": "2021-06-26T10:59:31.609035Z",
     "iopub.status.idle": "2021-06-26T10:59:31.611450Z",
     "shell.execute_reply": "2021-06-26T10:59:31.611874Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.210668Z"
    },
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     "duration": 0.044044,
     "end_time": "2021-06-26T10:59:31.612033",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.567989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categorical_features.remove('education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "individual-delivery",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.691567Z",
     "iopub.status.busy": "2021-06-26T10:59:31.690557Z",
     "iopub.status.idle": "2021-06-26T10:59:31.694772Z",
     "shell.execute_reply": "2021-06-26T10:59:31.695260Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.220648Z"
    },
    "papermill": {
     "duration": 0.046468,
     "end_time": "2021-06-26T10:59:31.695445",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.648977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['workclass',\n",
       " 'marital.status',\n",
       " 'occupation',\n",
       " 'relationship',\n",
       " 'race',\n",
       " 'sex',\n",
       " 'native.country']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "handy-tumor",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.777547Z",
     "iopub.status.busy": "2021-06-26T10:59:31.776821Z",
     "iopub.status.idle": "2021-06-26T10:59:31.780084Z",
     "shell.execute_reply": "2021-06-26T10:59:31.779457Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.233000Z"
    },
    "papermill": {
     "duration": 0.04676,
     "end_time": "2021-06-26T10:59:31.780229",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.733469",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_uniques(df, columns):\n",
    "    uniques = dict()\n",
    "    \n",
    "    for column in columns:\n",
    "        uniques[column] = list(df[column].unique())\n",
    "        \n",
    "    return uniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "great-tension",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.865724Z",
     "iopub.status.busy": "2021-06-26T10:59:31.865049Z",
     "iopub.status.idle": "2021-06-26T10:59:31.886023Z",
     "shell.execute_reply": "2021-06-26T10:59:31.885492Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.243654Z"
    },
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     "duration": 0.067528,
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     "exception": false,
     "start_time": "2021-06-26T10:59:31.818664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'workclass': [nan,\n",
       "  'Private',\n",
       "  'State-gov',\n",
       "  'Federal-gov',\n",
       "  'Self-emp-not-inc',\n",
       "  'Self-emp-inc',\n",
       "  'Local-gov',\n",
       "  'Without-pay',\n",
       "  'Never-worked'],\n",
       " 'marital.status': ['Widowed',\n",
       "  'Divorced',\n",
       "  'Separated',\n",
       "  'Never-married',\n",
       "  'Married-civ-spouse',\n",
       "  'Married-spouse-absent',\n",
       "  'Married-AF-spouse'],\n",
       " 'occupation': [nan,\n",
       "  'Exec-managerial',\n",
       "  'Machine-op-inspct',\n",
       "  'Prof-specialty',\n",
       "  'Other-service',\n",
       "  'Adm-clerical',\n",
       "  'Craft-repair',\n",
       "  'Transport-moving',\n",
       "  'Handlers-cleaners',\n",
       "  'Sales',\n",
       "  'Farming-fishing',\n",
       "  'Tech-support',\n",
       "  'Protective-serv',\n",
       "  'Armed-Forces',\n",
       "  'Priv-house-serv'],\n",
       " 'relationship': ['Not-in-family',\n",
       "  'Unmarried',\n",
       "  'Own-child',\n",
       "  'Other-relative',\n",
       "  'Husband',\n",
       "  'Wife'],\n",
       " 'race': ['White',\n",
       "  'Black',\n",
       "  'Asian-Pac-Islander',\n",
       "  'Other',\n",
       "  'Amer-Indian-Eskimo'],\n",
       " 'sex': ['Female', 'Male'],\n",
       " 'native.country': ['United-States',\n",
       "  nan,\n",
       "  'Mexico',\n",
       "  'Greece',\n",
       "  'Vietnam',\n",
       "  'China',\n",
       "  'Taiwan',\n",
       "  'India',\n",
       "  'Philippines',\n",
       "  'Trinadad&Tobago',\n",
       "  'Canada',\n",
       "  'South',\n",
       "  'Holand-Netherlands',\n",
       "  'Puerto-Rico',\n",
       "  'Poland',\n",
       "  'Iran',\n",
       "  'England',\n",
       "  'Germany',\n",
       "  'Italy',\n",
       "  'Japan',\n",
       "  'Hong',\n",
       "  'Honduras',\n",
       "  'Cuba',\n",
       "  'Ireland',\n",
       "  'Cambodia',\n",
       "  'Peru',\n",
       "  'Nicaragua',\n",
       "  'Dominican-Republic',\n",
       "  'Haiti',\n",
       "  'El-Salvador',\n",
       "  'Hungary',\n",
       "  'Columbia',\n",
       "  'Guatemala',\n",
       "  'Jamaica',\n",
       "  'Ecuador',\n",
       "  'France',\n",
       "  'Yugoslavia',\n",
       "  'Scotland',\n",
       "  'Portugal',\n",
       "  'Laos',\n",
       "  'Thailand',\n",
       "  'Outlying-US(Guam-USVI-etc)']}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_uniques(data, categorical_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "inclusive-danger",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:31.968293Z",
     "iopub.status.busy": "2021-06-26T10:59:31.967635Z",
     "iopub.status.idle": "2021-06-26T10:59:31.970985Z",
     "shell.execute_reply": "2021-06-26T10:59:31.970364Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.279613Z"
    },
    "papermill": {
     "duration": 0.046134,
     "end_time": "2021-06-26T10:59:31.971141",
     "exception": false,
     "start_time": "2021-06-26T10:59:31.925007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "binary_features = ['sex']\n",
    "nominal_features = ['workclass', 'marital.status', 'occupation', 'relationship', 'race', 'native.country']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-player",
   "metadata": {
    "papermill": {
     "duration": 0.038621,
     "end_time": "2021-06-26T10:59:32.047501",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.008880",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data encoding 👨‍💻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "roman-laptop",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.130806Z",
     "iopub.status.busy": "2021-06-26T10:59:32.129957Z",
     "iopub.status.idle": "2021-06-26T10:59:32.132844Z",
     "shell.execute_reply": "2021-06-26T10:59:32.132349Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.286028Z"
    },
    "papermill": {
     "duration": 0.047742,
     "end_time": "2021-06-26T10:59:32.132982",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.085240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def binary_encode(df, columns):\n",
    "    label_encoder = LabelEncoder()\n",
    "    for column in columns:\n",
    "        df[column] = label_encoder.fit_transform(df[column])\n",
    "    return df\n",
    "\n",
    "def onehot_encode(df, columns):\n",
    "    for column in columns:\n",
    "        dummies = pd.get_dummies(df[column])\n",
    "        df = pd.concat([df, dummies], axis = 1)\n",
    "        df.drop(column, axis = 1, inplace = True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "durable-benchmark",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.225142Z",
     "iopub.status.busy": "2021-06-26T10:59:32.224410Z",
     "iopub.status.idle": "2021-06-26T10:59:32.317611Z",
     "shell.execute_reply": "2021-06-26T10:59:32.317047Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.300140Z"
    },
    "papermill": {
     "duration": 0.1469,
     "end_time": "2021-06-26T10:59:32.317777",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.170877",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = binary_encode(data, binary_features)\n",
    "data = onehot_encode(data, nominal_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aware-gender",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.401459Z",
     "iopub.status.busy": "2021-06-26T10:59:32.400592Z",
     "iopub.status.idle": "2021-06-26T10:59:32.404955Z",
     "shell.execute_reply": "2021-06-26T10:59:32.404344Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.405349Z"
    },
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     "duration": 0.048563,
     "end_time": "2021-06-26T10:59:32.405128",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.356565",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.dtypes == 'object').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "utility-rebound",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.489417Z",
     "iopub.status.busy": "2021-06-26T10:59:32.488563Z",
     "iopub.status.idle": "2021-06-26T10:59:32.492153Z",
     "shell.execute_reply": "2021-06-26T10:59:32.492635Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.414977Z"
    },
    "papermill": {
     "duration": 0.049291,
     "end_time": "2021-06-26T10:59:32.492812",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.443521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = data['income']\n",
    "X = data.drop('income', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "gothic-mattress",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.586790Z",
     "iopub.status.busy": "2021-06-26T10:59:32.586083Z",
     "iopub.status.idle": "2021-06-26T10:59:32.589705Z",
     "shell.execute_reply": "2021-06-26T10:59:32.589203Z",
     "shell.execute_reply.started": "2021-06-26T10:13:13.430635Z"
    },
    "papermill": {
     "duration": 0.058913,
     "end_time": "2021-06-26T10:59:32.589852",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.530939",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: '<=50K', 1: '>50K'}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "y = label_encoder.fit_transform(y)\n",
    "y_mappings = {index : label for index, label in enumerate(label_encoder.classes_)}\n",
    "y_mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "twelve-miami",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.670587Z",
     "iopub.status.busy": "2021-06-26T10:59:32.669959Z",
     "iopub.status.idle": "2021-06-26T10:59:32.674104Z",
     "shell.execute_reply": "2021-06-26T10:59:32.674574Z",
     "shell.execute_reply.started": "2021-06-26T10:13:24.344774Z"
    },
    "papermill": {
     "duration": 0.045971,
     "end_time": "2021-06-26T10:59:32.674758",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.628787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "initial-andrew",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.764432Z",
     "iopub.status.busy": "2021-06-26T10:59:32.763566Z",
     "iopub.status.idle": "2021-06-26T10:59:32.827272Z",
     "shell.execute_reply": "2021-06-26T10:59:32.827763Z",
     "shell.execute_reply.started": "2021-06-26T10:14:08.169911Z"
    },
    "papermill": {
     "duration": 0.114389,
     "end_time": "2021-06-26T10:59:32.827965",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.713576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X = pd.DataFrame(scaler.fit_transform(X), columns = X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hollywood-printing",
   "metadata": {
    "papermill": {
     "duration": 0.039289,
     "end_time": "2021-06-26T10:59:32.906908",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.867619",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model training 🛠️"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "molecular-rotation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:32.988440Z",
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     "iopub.status.idle": "2021-06-26T10:59:33.031904Z",
     "shell.execute_reply": "2021-06-26T10:59:33.032383Z",
     "shell.execute_reply.started": "2021-06-26T10:14:10.668956Z"
    },
    "papermill": {
     "duration": 0.086311,
     "end_time": "2021-06-26T10:59:33.032565",
     "exception": false,
     "start_time": "2021-06-26T10:59:32.946254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education.num</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>Federal-gov</th>\n",
       "      <th>Local-gov</th>\n",
       "      <th>Never-worked</th>\n",
       "      <th>...</th>\n",
       "      <th>Portugal</th>\n",
       "      <th>Puerto-Rico</th>\n",
       "      <th>Scotland</th>\n",
       "      <th>South</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Trinadad&amp;Tobago</th>\n",
       "      <th>United-States</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Yugoslavia</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.506849</td>\n",
       "      <td>0.086982</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>0.561644</td>\n",
       "      <td>0.094827</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>32560</th>\n",
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       "      <td>1.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 88 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age    fnlwgt  education.num  sex  capital.gain  capital.loss  \\\n",
       "0      1.000000  0.043987       0.533333  0.0           0.0      1.000000   \n",
       "1      0.890411  0.081896       0.533333  0.0           0.0      1.000000   \n",
       "2      0.671233  0.118021       0.600000  0.0           0.0      1.000000   \n",
       "3      0.506849  0.086982       0.200000  0.0           0.0      0.895317   \n",
       "4      0.328767  0.171404       0.600000  0.0           0.0      0.895317   \n",
       "...         ...       ...            ...  ...           ...           ...   \n",
       "32556  0.068493  0.202298       0.600000  1.0           0.0      0.000000   \n",
       "32557  0.136986  0.166404       0.733333  0.0           0.0      0.000000   \n",
       "32558  0.315068  0.096500       0.533333  1.0           0.0      0.000000   \n",
       "32559  0.561644  0.094827       0.533333  0.0           0.0      0.000000   \n",
       "32560  0.068493  0.128499       0.533333  1.0           0.0      0.000000   \n",
       "\n",
       "       hours.per.week  Federal-gov  Local-gov  Never-worked  ...  Portugal  \\\n",
       "0            0.397959          0.0        0.0           0.0  ...       0.0   \n",
       "1            0.173469          0.0        0.0           0.0  ...       0.0   \n",
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       "3            0.397959          0.0        0.0           0.0  ...       0.0   \n",
       "4            0.397959          0.0        0.0           0.0  ...       0.0   \n",
       "...               ...          ...        ...           ...  ...       ...   \n",
       "32556        0.397959          0.0        0.0           0.0  ...       0.0   \n",
       "32557        0.377551          0.0        0.0           0.0  ...       0.0   \n",
       "32558        0.397959          0.0        0.0           0.0  ...       0.0   \n",
       "32559        0.397959          0.0        0.0           0.0  ...       0.0   \n",
       "32560        0.193878          0.0        0.0           0.0  ...       0.0   \n",
       "\n",
       "       Puerto-Rico  Scotland  South  Taiwan  Thailand  Trinadad&Tobago  \\\n",
       "0              0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "1              0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "2              0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "3              0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "4              0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "...            ...       ...    ...     ...       ...              ...   \n",
       "32556          0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "32557          0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "32558          0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "32559          0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "32560          0.0       0.0    0.0     0.0       0.0              0.0   \n",
       "\n",
       "       United-States  Vietnam  Yugoslavia  \n",
       "0                1.0      0.0         0.0  \n",
       "1                1.0      0.0         0.0  \n",
       "2                1.0      0.0         0.0  \n",
       "3                1.0      0.0         0.0  \n",
       "4                1.0      0.0         0.0  \n",
       "...              ...      ...         ...  \n",
       "32556            1.0      0.0         0.0  \n",
       "32557            1.0      0.0         0.0  \n",
       "32558            1.0      0.0         0.0  \n",
       "32559            1.0      0.0         0.0  \n",
       "32560            1.0      0.0         0.0  \n",
       "\n",
       "[32561 rows x 88 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "pretty-gazette",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:33.116554Z",
     "iopub.status.busy": "2021-06-26T10:59:33.115629Z",
     "iopub.status.idle": "2021-06-26T10:59:33.138231Z",
     "shell.execute_reply": "2021-06-26T10:59:33.138692Z",
     "shell.execute_reply.started": "2021-06-26T10:14:56.169030Z"
    },
    "papermill": {
     "duration": 0.065811,
     "end_time": "2021-06-26T10:59:33.138900",
     "exception": false,
     "start_time": "2021-06-26T10:59:33.073089",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = tts(X, y, train_size = 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "capital-qualification",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T10:59:33.221685Z",
     "iopub.status.busy": "2021-06-26T10:59:33.220913Z",
     "iopub.status.idle": "2021-06-26T11:00:23.366773Z",
     "shell.execute_reply": "2021-06-26T11:00:23.366194Z",
     "shell.execute_reply.started": "2021-06-26T10:50:03.798229Z"
    },
    "papermill": {
     "duration": 50.188316,
     "end_time": "2021-06-26T11:00:23.366936",
     "exception": false,
     "start_time": "2021-06-26T10:59:33.178620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "652/652 [==============================] - 4s 4ms/step - loss: 0.4681 - acc: 0.7662 - auc: 0.7695 - val_loss: 0.3670 - val_acc: 0.8278 - val_auc: 0.8795\n",
      "Epoch 2/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3513 - acc: 0.8370 - auc: 0.8849 - val_loss: 0.3456 - val_acc: 0.8399 - val_auc: 0.8920\n",
      "Epoch 3/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3370 - acc: 0.8422 - auc: 0.8963 - val_loss: 0.3335 - val_acc: 0.8459 - val_auc: 0.8979\n",
      "Epoch 4/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3333 - acc: 0.8414 - auc: 0.8974 - val_loss: 0.3294 - val_acc: 0.8482 - val_auc: 0.9005\n",
      "Epoch 5/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3211 - acc: 0.8502 - auc: 0.9045 - val_loss: 0.3276 - val_acc: 0.8491 - val_auc: 0.9029\n",
      "Epoch 6/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3177 - acc: 0.8517 - auc: 0.9077 - val_loss: 0.3298 - val_acc: 0.8491 - val_auc: 0.9019\n",
      "Epoch 7/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3128 - acc: 0.8521 - auc: 0.9100 - val_loss: 0.3233 - val_acc: 0.8512 - val_auc: 0.9041\n",
      "Epoch 8/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3093 - acc: 0.8554 - auc: 0.9138 - val_loss: 0.3242 - val_acc: 0.8455 - val_auc: 0.9044\n",
      "Epoch 9/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3073 - acc: 0.8559 - auc: 0.9138 - val_loss: 0.3294 - val_acc: 0.8491 - val_auc: 0.9048\n",
      "Epoch 10/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3019 - acc: 0.8582 - auc: 0.9170 - val_loss: 0.3228 - val_acc: 0.8509 - val_auc: 0.9042\n",
      "Epoch 11/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3027 - acc: 0.8606 - auc: 0.9171 - val_loss: 0.3242 - val_acc: 0.8468 - val_auc: 0.9047\n",
      "Epoch 12/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3007 - acc: 0.8589 - auc: 0.9169 - val_loss: 0.3257 - val_acc: 0.8495 - val_auc: 0.9039\n",
      "Epoch 13/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.3005 - acc: 0.8601 - auc: 0.9175 - val_loss: 0.3245 - val_acc: 0.8501 - val_auc: 0.9049\n",
      "Epoch 14/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2986 - acc: 0.8595 - auc: 0.9181 - val_loss: 0.3259 - val_acc: 0.8526 - val_auc: 0.9049\n",
      "Epoch 15/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2967 - acc: 0.8628 - auc: 0.9193 - val_loss: 0.3247 - val_acc: 0.8520 - val_auc: 0.9054\n",
      "Epoch 16/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2930 - acc: 0.8650 - auc: 0.9221 - val_loss: 0.3245 - val_acc: 0.8541 - val_auc: 0.9052\n",
      "Epoch 17/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2981 - acc: 0.8600 - auc: 0.9201 - val_loss: 0.3266 - val_acc: 0.8512 - val_auc: 0.9038\n",
      "Epoch 18/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2952 - acc: 0.8598 - auc: 0.9208 - val_loss: 0.3270 - val_acc: 0.8526 - val_auc: 0.9041\n",
      "Epoch 19/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2918 - acc: 0.8630 - auc: 0.9235 - val_loss: 0.3283 - val_acc: 0.8528 - val_auc: 0.9039\n",
      "Epoch 20/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2906 - acc: 0.8640 - auc: 0.9237 - val_loss: 0.3332 - val_acc: 0.8478 - val_auc: 0.9039\n",
      "Epoch 21/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2910 - acc: 0.8645 - auc: 0.9238 - val_loss: 0.3341 - val_acc: 0.8528 - val_auc: 0.9028\n",
      "Epoch 22/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2886 - acc: 0.8680 - auc: 0.9261 - val_loss: 0.3279 - val_acc: 0.8520 - val_auc: 0.9029\n",
      "Epoch 23/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2852 - acc: 0.8664 - auc: 0.9263 - val_loss: 0.3281 - val_acc: 0.8511 - val_auc: 0.9033\n",
      "Epoch 24/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2841 - acc: 0.8693 - auc: 0.9281 - val_loss: 0.3314 - val_acc: 0.8536 - val_auc: 0.9010\n",
      "Epoch 25/25\n",
      "652/652 [==============================] - 2s 3ms/step - loss: 0.2824 - acc: 0.8706 - auc: 0.9291 - val_loss: 0.3318 - val_acc: 0.8480 - val_auc: 0.9019\n"
     ]
    }
   ],
   "source": [
    "inputs = tf.keras.Input(shape = (88, ))\n",
    "x = tf.keras.layers.Dense(512, activation = 'relu')(inputs)\n",
    "x = tf.keras.layers.Dense(256, activation = 'relu')(x)\n",
    "outputs = tf.keras.layers.Dense(1, activation = 'sigmoid')(x)\n",
    "\n",
    "model = tf.keras.Model(inputs, outputs)\n",
    "\n",
    "optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0001)\n",
    "\n",
    "metrics = [\n",
    "    tf.keras.metrics.BinaryAccuracy(name = 'acc'),\n",
    "    tf.keras.metrics.AUC(name = 'auc')\n",
    "]\n",
    "\n",
    "model.compile(\n",
    "    optimizer = optimizer,\n",
    "    loss = 'binary_crossentropy',\n",
    "    metrics = metrics\n",
    ")\n",
    "\n",
    "batch_size = 32\n",
    "epochs = 25\n",
    "\n",
    "history = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    validation_split = 0.2,\n",
    "    batch_size = batch_size,\n",
    "    epochs = epochs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "human-mission",
   "metadata": {
    "papermill": {
     "duration": 0.29126,
     "end_time": "2021-06-26T11:00:23.950928",
     "exception": false,
     "start_time": "2021-06-26T11:00:23.659668",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Scores 📈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "toxic-privacy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T11:00:24.554653Z",
     "iopub.status.busy": "2021-06-26T11:00:24.553972Z",
     "iopub.status.idle": "2021-06-26T11:00:24.757806Z",
     "shell.execute_reply": "2021-06-26T11:00:24.758336Z",
     "shell.execute_reply.started": "2021-06-26T10:50:58.791915Z"
    },
    "papermill": {
     "duration": 0.516577,
     "end_time": "2021-06-26T11:00:24.758506",
     "exception": false,
     "start_time": "2021-06-26T11:00:24.241929",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14, 10))\n",
    "epochs_range = range(1, epochs + 1)\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.plot(epochs_range, train_loss, label = 'Training loss')\n",
    "plt.plot(epochs_range, val_loss, label = 'Validation loss')\n",
    "\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "stuffed-newcastle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T11:00:25.351929Z",
     "iopub.status.busy": "2021-06-26T11:00:25.351272Z",
     "iopub.status.idle": "2021-06-26T11:00:25.728949Z",
     "shell.execute_reply": "2021-06-26T11:00:25.729477Z",
     "shell.execute_reply.started": "2021-06-26T10:51:40.107557Z"
    },
    "papermill": {
     "duration": 0.679525,
     "end_time": "2021-06-26T11:00:25.729653",
     "exception": false,
     "start_time": "2021-06-26T11:00:25.050128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204/204 [==============================] - 0s 2ms/step - loss: 0.3174 - acc: 0.8554 - auc: 0.9101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.31743094325065613, 0.8553661704063416, 0.9101150035858154]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "occupied-aircraft",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T11:00:26.328490Z",
     "iopub.status.busy": "2021-06-26T11:00:26.327857Z",
     "iopub.status.idle": "2021-06-26T11:00:26.332243Z",
     "shell.execute_reply": "2021-06-26T11:00:26.331631Z",
     "shell.execute_reply.started": "2021-06-26T10:52:19.980391Z"
    },
    "papermill": {
     "duration": 0.30492,
     "end_time": "2021-06-26T11:00:26.332445",
     "exception": false,
     "start_time": "2021-06-26T11:00:26.027525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2408095574460244"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum() / len(y)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 70.116006,
   "end_time": "2021-06-26T11:00:27.740333",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-06-26T10:59:17.624327",
   "version": "2.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}