Model-V3 - Pipeline.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
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   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, precision_recall_fscore_support, accuracy_score, confusion_matrix, roc_curve, roc_auc_score\n",
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder, MinMaxScaler\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from matplotlib.colors import ListedColormap\n",
    "import pickle, pyarrow\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn import set_config\n",
    "set_config(display=\"diagram\")\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66545447-345d-49b6-9130-7952bb312207",
   "metadata": {},
   "source": [
    "###\n",
    "---\n",
    "###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac99ec37-f40a-4ae4-bb86-bbed3e96b158",
   "metadata": {},
   "source": [
    "## Reading the Dataset"
   ]
  },
  {
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   "id": "1d3c98b2-5e82-4192-a5a1-72fb92dab162",
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   "outputs": [],
   "source": [
    "parquet_df = pd.read_parquet(\"depression_data.parquet\")"
   ]
  },
  {
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   "id": "3ff3e7c6-449a-45a8-b78a-306668ceaa93",
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   "source": [
    "df = parquet_df"
   ]
  },
  {
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   "execution_count": 47,
   "id": "da1dcb46-44cf-447c-b033-807ed3901926",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "df.rename(columns={\"Chronic Medical Conditions\":\"Depression\"}, inplace=True)"
   ]
  },
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   "execution_count": 48,
   "id": "3322c8de-9dcb-4daf-ac19-78602412ef19",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 413768 entries, 0 to 413767\n",
      "Data columns (total 16 columns):\n",
      " #   Column                        Non-Null Count   Dtype  \n",
      "---  ------                        --------------   -----  \n",
      " 0   Name                          413768 non-null  object \n",
      " 1   Age                           413768 non-null  int64  \n",
      " 2   Marital Status                413768 non-null  object \n",
      " 3   Education Level               413768 non-null  object \n",
      " 4   Number of Children            413768 non-null  int64  \n",
      " 5   Smoking Status                413768 non-null  object \n",
      " 6   Physical Activity Level       413768 non-null  object \n",
      " 7   Employment Status             413768 non-null  object \n",
      " 8   Income                        413768 non-null  float64\n",
      " 9   Alcohol Consumption           413768 non-null  object \n",
      " 10  Dietary Habits                413768 non-null  object \n",
      " 11  Sleep Patterns                413768 non-null  object \n",
      " 12  History of Mental Illness     413768 non-null  object \n",
      " 13  History of Substance Abuse    413768 non-null  object \n",
      " 14  Family History of Depression  413768 non-null  object \n",
      " 15  Depression                    413768 non-null  object \n",
      "dtypes: float64(1), int64(2), object(13)\n",
      "memory usage: 50.5+ MB\n"
     ]
    }
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   "source": [
    "df.info()"
   ]
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   "source": [
    "###\n",
    "---\n",
    "###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5fbabaff-a44c-40af-b86f-5b21201f57f5",
   "metadata": {
    "execution": {
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   "source": [
    "df = parquet_df.copy()\n",
    "df.drop(columns=[\"Education Level\", \"Family History of Depression\", \"Name\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "35d7b125-d641-4b60-8446-0c7ed6a67a14",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-09-28T11:43:07.372160Z"
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   },
   "outputs": [],
   "source": [
    "df[\"Depression\"].replace({\"No\":0, \"Yes\":1}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9f864d65-a4c2-479c-b5c0-59ec57f1f827",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Depression\n",
       "0    277561\n",
       "1    136207\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Depression\"].value_counts()"
   ]
  },
  {
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   "execution_count": 52,
   "id": "b1c7660d-9d65-49c4-abf2-63d0d7d80aaf",
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   },
   "outputs": [],
   "source": [
    "target = df[\"Depression\"]\n",
    "frame = df.drop(columns=\"Depression\")"
   ]
  },
  {
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       "      <th>0</th>\n",
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       "      <td>Moderate</td>\n",
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       "   Age Marital Status  Number of Children Smoking Status  \\\n",
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       "1   55        Married                   1     Non-smoker   \n",
       "2   78        Widowed                   1     Non-smoker   \n",
       "3   58       Divorced                   3     Non-smoker   \n",
       "4   18         Single                   0     Non-smoker   \n",
       "\n",
       "  Physical Activity Level Employment Status     Income Alcohol Consumption  \\\n",
       "0                  Active        Unemployed   26265.67            Moderate   \n",
       "1               Sedentary          Employed   42710.36                High   \n",
       "2               Sedentary          Employed  125332.79                 Low   \n",
       "3                Moderate        Unemployed    9992.78            Moderate   \n",
       "4               Sedentary        Unemployed    8595.08                 Low   \n",
       "\n",
       "  Dietary Habits Sleep Patterns History of Mental Illness  \\\n",
       "0       Moderate           Fair                       Yes   \n",
       "1      Unhealthy           Fair                       Yes   \n",
       "2      Unhealthy           Good                        No   \n",
       "3       Moderate           Poor                        No   \n",
       "4       Moderate           Fair                       Yes   \n",
       "\n",
       "  History of Substance Abuse  \n",
       "0                         No  \n",
       "1                         No  \n",
       "2                         No  \n",
       "3                         No  \n",
       "4                         No  "
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    "frame.head()"
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   "source": [
    "###\n",
    "---\n",
    "###"
   ]
  },
  {
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   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(frame, target, test_size=0.05, random_state=5)"
   ]
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       "      <td>Unemployed</td>\n",
       "      <td>9891.63</td>\n",
       "      <td>High</td>\n",
       "      <td>Unhealthy</td>\n",
       "      <td>Poor</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age Marital Status  Number of Children Smoking Status  \\\n",
       "13513    49        Married                   1        Current   \n",
       "324041   59        Married                   1     Non-smoker   \n",
       "144337   78        Married                   3     Non-smoker   \n",
       "250003   43        Married                   1         Former   \n",
       "47584    75        Widowed                   2         Former   \n",
       "\n",
       "       Physical Activity Level Employment Status     Income  \\\n",
       "13513                 Moderate          Employed  104852.57   \n",
       "324041               Sedentary          Employed   24365.22   \n",
       "144337                Moderate          Employed   29327.34   \n",
       "250003               Sedentary        Unemployed   27478.55   \n",
       "47584                 Moderate        Unemployed    9891.63   \n",
       "\n",
       "       Alcohol Consumption Dietary Habits Sleep Patterns  \\\n",
       "13513                 High      Unhealthy           Poor   \n",
       "324041            Moderate       Moderate           Fair   \n",
       "144337            Moderate       Moderate           Fair   \n",
       "250003                High       Moderate           Poor   \n",
       "47584                 High      Unhealthy           Poor   \n",
       "\n",
       "       History of Mental Illness History of Substance Abuse  \n",
       "13513                         No                         No  \n",
       "324041                       Yes                        Yes  \n",
       "144337                        No                        Yes  \n",
       "250003                       Yes                         No  \n",
       "47584                        Yes                         No  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d13d4fa-e4e6-43fa-aec6-ba3b100b051e",
   "metadata": {},
   "source": [
    "###\n",
    "---\n",
    "###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f3bf4c-b281-48f9-8438-14fcf873c8ac",
   "metadata": {},
   "source": [
    "## PipeLine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "28e4e4b6-a9e6-4c4e-afe9-a42539a66324",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:02.658827Z",
     "iopub.status.busy": "2024-09-28T11:45:02.657810Z",
     "iopub.status.idle": "2024-09-28T11:45:02.664987Z",
     "shell.execute_reply": "2024-09-28T11:45:02.664987Z",
     "shell.execute_reply.started": "2024-09-28T11:45:02.658827Z"
    }
   },
   "outputs": [],
   "source": [
    "ordinal_encodings = ColumnTransformer([\n",
    "    (\"enc_somking\", OrdinalEncoder(categories=[[\"Current\", \"Former\", \"Non-smoker\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [3]),\n",
    "    (\"enc_physical\", OrdinalEncoder(categories=[[\"Sedentary\", \"Moderate\", \"Active\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [4]),\n",
    "    (\"enc_employment\", OrdinalEncoder(categories=[['Unemployed', 'Employed']], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [5]),\n",
    "    (\"enc_alcohol\", OrdinalEncoder(categories=[[\"Low\", \"Moderate\", \"High\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [7]),\n",
    "    (\"enc_diet\", OrdinalEncoder(categories=[[\"Unhealthy\", \"Moderate\", \"Healthy\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [8]),\n",
    "    (\"enc_sleep\", OrdinalEncoder(categories=[[\"Poor\", \"Fair\", \"Good\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [9]),\n",
    "    (\"enc_mental\", OrdinalEncoder(categories=[[\"No\", \"Yes\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [10]),\n",
    "    (\"enc_absue\", OrdinalEncoder(categories=[[\"No\", \"Yes\"]], dtype=int, handle_unknown=\"use_encoded_value\", unknown_value=-1), [11])\n",
    "], remainder=\"passthrough\")\n",
    "\n",
    "onehot_encodings = ColumnTransformer([\n",
    "    (\"enc_marry\", OneHotEncoder(sparse_output=False, dtype=int, drop=\"first\", handle_unknown=\"ignore\"), [9])\n",
    "], remainder=\"passthrough\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "minmax_scalers = ColumnTransformer([\n",
    "    (\"scl_age\", MinMaxScaler(), [11]),\n",
    "    (\"scl_income\", MinMaxScaler(), [13])\n",
    "], remainder=\"passthrough\")\n",
    "\n",
    "\n",
    "# model = LogisticRegression(class_weight={0:1 , 1:2})\n",
    "model =  XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ce0c643d-867c-4f49-8a15-4b03b60cf828",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:03.417881Z",
     "iopub.status.busy": "2024-09-28T11:45:03.417881Z",
     "iopub.status.idle": "2024-09-28T11:45:03.424262Z",
     "shell.execute_reply": "2024-09-28T11:45:03.423857Z",
     "shell.execute_reply.started": "2024-09-28T11:45:03.417881Z"
    }
   },
   "outputs": [],
   "source": [
    "pipe = Pipeline([\n",
    "    (\"ordinal_encodings\", ordinal_encodings),\n",
    "    (\"onehot_encodings\", onehot_encodings),\n",
    "    (\"minmax_scalers\", minmax_scalers),\n",
    "    (\"model\", model)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "601d442f-0172-433b-8e86-0204556acbca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:04.012848Z",
     "iopub.status.busy": "2024-09-28T11:45:04.012848Z",
     "iopub.status.idle": "2024-09-28T11:45:05.449513Z",
     "shell.execute_reply": "2024-09-28T11:45:05.449513Z",
     "shell.execute_reply.started": "2024-09-28T11:45:04.012848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ordinal_encodings&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;enc_somking&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;Current&#x27;,\n",
       "                                                                              &#x27;Former&#x27;,\n",
       "                                                                              &#x27;Non-smoker&#x27;]],\n",
       "                                                                 dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [3]),\n",
       "                                                 (&#x27;enc_physical&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;Sedentary&#x27;,\n",
       "                                                                              &#x27;Moderate&#x27;,\n",
       "                                                                              &#x27;Active&#x27;]],\n",
       "                                                                 dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                 handle...\n",
       "                               feature_types=None, gamma=None, grow_policy=None,\n",
       "                               importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=None, n_jobs=None,\n",
       "                               num_parallel_tree=None, random_state=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;ordinal_encodings&#x27;,\n",
       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                                   transformers=[(&#x27;enc_somking&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;Current&#x27;,\n",
       "                                                                              &#x27;Former&#x27;,\n",
       "                                                                              &#x27;Non-smoker&#x27;]],\n",
       "                                                                 dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                 handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [3]),\n",
       "                                                 (&#x27;enc_physical&#x27;,\n",
       "                                                  OrdinalEncoder(categories=[[&#x27;Sedentary&#x27;,\n",
       "                                                                              &#x27;Moderate&#x27;,\n",
       "                                                                              &#x27;Active&#x27;]],\n",
       "                                                                 dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                                 handle...\n",
       "                               feature_types=None, gamma=None, grow_policy=None,\n",
       "                               importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=None, n_jobs=None,\n",
       "                               num_parallel_tree=None, random_state=None, ...))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;ordinal_encodings: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ordinal_encodings: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;enc_somking&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;Current&#x27;,\n",
       "                                                             &#x27;Former&#x27;,\n",
       "                                                             &#x27;Non-smoker&#x27;]],\n",
       "                                                dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=-1),\n",
       "                                 [3]),\n",
       "                                (&#x27;enc_physical&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;Sedentary&#x27;,\n",
       "                                                             &#x27;Moderate&#x27;,\n",
       "                                                             &#x27;Active&#x27;]],\n",
       "                                                dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_...\n",
       "                                                             &#x27;Good&#x27;]],\n",
       "                                                dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=-1),\n",
       "                                 [9]),\n",
       "                                (&#x27;enc_mental&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;No&#x27;, &#x27;Yes&#x27;]],\n",
       "                                                dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=-1),\n",
       "                                 [10]),\n",
       "                                (&#x27;enc_absue&#x27;,\n",
       "                                 OrdinalEncoder(categories=[[&#x27;No&#x27;, &#x27;Yes&#x27;]],\n",
       "                                                dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                                handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                unknown_value=-1),\n",
       "                                 [11])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_somking</label><div class=\"sk-toggleable__content fitted\"><pre>[3]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Current&#x27;, &#x27;Former&#x27;, &#x27;Non-smoker&#x27;]],\n",
       "               dtype=&lt;class &#x27;int&#x27;&gt;, handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "               unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_physical</label><div class=\"sk-toggleable__content fitted\"><pre>[4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Sedentary&#x27;, &#x27;Moderate&#x27;, &#x27;Active&#x27;]],\n",
       "               dtype=&lt;class &#x27;int&#x27;&gt;, handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "               unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_employment</label><div class=\"sk-toggleable__content fitted\"><pre>[5]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Unemployed&#x27;, &#x27;Employed&#x27;]], dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-75\" type=\"checkbox\" ><label for=\"sk-estimator-id-75\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_alcohol</label><div class=\"sk-toggleable__content fitted\"><pre>[7]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-76\" type=\"checkbox\" ><label for=\"sk-estimator-id-76\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Low&#x27;, &#x27;Moderate&#x27;, &#x27;High&#x27;]], dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-77\" type=\"checkbox\" ><label for=\"sk-estimator-id-77\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_diet</label><div class=\"sk-toggleable__content fitted\"><pre>[8]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-78\" type=\"checkbox\" ><label for=\"sk-estimator-id-78\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Unhealthy&#x27;, &#x27;Moderate&#x27;, &#x27;Healthy&#x27;]],\n",
       "               dtype=&lt;class &#x27;int&#x27;&gt;, handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "               unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-79\" type=\"checkbox\" ><label for=\"sk-estimator-id-79\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_sleep</label><div class=\"sk-toggleable__content fitted\"><pre>[9]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-80\" type=\"checkbox\" ><label for=\"sk-estimator-id-80\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;Poor&#x27;, &#x27;Fair&#x27;, &#x27;Good&#x27;]], dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-81\" type=\"checkbox\" ><label for=\"sk-estimator-id-81\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_mental</label><div class=\"sk-toggleable__content fitted\"><pre>[10]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-82\" type=\"checkbox\" ><label for=\"sk-estimator-id-82\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;No&#x27;, &#x27;Yes&#x27;]], dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-83\" type=\"checkbox\" ><label for=\"sk-estimator-id-83\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_absue</label><div class=\"sk-toggleable__content fitted\"><pre>[11]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-84\" type=\"checkbox\" ><label for=\"sk-estimator-id-84\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(categories=[[&#x27;No&#x27;, &#x27;Yes&#x27;]], dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "               handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-85\" type=\"checkbox\" ><label for=\"sk-estimator-id-85\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 1, 2, 6]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-86\" type=\"checkbox\" ><label for=\"sk-estimator-id-86\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-87\" type=\"checkbox\" ><label for=\"sk-estimator-id-87\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;onehot_encodings: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for onehot_encodings: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;enc_marry&#x27;,\n",
       "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                               dtype=&lt;class &#x27;int&#x27;&gt;,\n",
       "                                               handle_unknown=&#x27;ignore&#x27;,\n",
       "                                               sparse_output=False),\n",
       "                                 [9])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-88\" type=\"checkbox\" ><label for=\"sk-estimator-id-88\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">enc_marry</label><div class=\"sk-toggleable__content fitted\"><pre>[9]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-89\" type=\"checkbox\" ><label for=\"sk-estimator-id-89\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, dtype=&lt;class &#x27;int&#x27;&gt;, handle_unknown=&#x27;ignore&#x27;,\n",
       "              sparse_output=False)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-90\" type=\"checkbox\" ><label for=\"sk-estimator-id-90\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-91\" type=\"checkbox\" ><label for=\"sk-estimator-id-91\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-92\" type=\"checkbox\" ><label for=\"sk-estimator-id-92\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;minmax_scalers: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for minmax_scalers: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
       "                  transformers=[(&#x27;scl_age&#x27;, MinMaxScaler(), [11]),\n",
       "                                (&#x27;scl_income&#x27;, MinMaxScaler(), [13])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-93\" type=\"checkbox\" ><label for=\"sk-estimator-id-93\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">scl_age</label><div class=\"sk-toggleable__content fitted\"><pre>[11]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-94\" type=\"checkbox\" ><label for=\"sk-estimator-id-94\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-95\" type=\"checkbox\" ><label for=\"sk-estimator-id-95\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">scl_income</label><div class=\"sk-toggleable__content fitted\"><pre>[13]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-96\" type=\"checkbox\" ><label for=\"sk-estimator-id-96\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-97\" type=\"checkbox\" ><label for=\"sk-estimator-id-97\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-98\" type=\"checkbox\" ><label for=\"sk-estimator-id-98\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-99\" type=\"checkbox\" ><label for=\"sk-estimator-id-99\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ordinal_encodings',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('enc_somking',\n",
       "                                                  OrdinalEncoder(categories=[['Current',\n",
       "                                                                              'Former',\n",
       "                                                                              'Non-smoker']],\n",
       "                                                                 dtype=<class 'int'>,\n",
       "                                                                 handle_unknown='use_encoded_value',\n",
       "                                                                 unknown_value=-1),\n",
       "                                                  [3]),\n",
       "                                                 ('enc_physical',\n",
       "                                                  OrdinalEncoder(categories=[['Sedentary',\n",
       "                                                                              'Moderate',\n",
       "                                                                              'Active']],\n",
       "                                                                 dtype=<class 'int'>,\n",
       "                                                                 handle...\n",
       "                               feature_types=None, gamma=None, grow_policy=None,\n",
       "                               importance_type=None,\n",
       "                               interaction_constraints=None, learning_rate=None,\n",
       "                               max_bin=None, max_cat_threshold=None,\n",
       "                               max_cat_to_onehot=None, max_delta_step=None,\n",
       "                               max_depth=None, max_leaves=None,\n",
       "                               min_child_weight=None, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=None, n_jobs=None,\n",
       "                               num_parallel_tree=None, random_state=None, ...))])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(x_train.values, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b3064c02-9177-4d4c-8c94-4fc22e1c6090",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:12.877642Z",
     "iopub.status.busy": "2024-09-28T11:45:12.877642Z",
     "iopub.status.idle": "2024-09-28T11:45:12.883573Z",
     "shell.execute_reply": "2024-09-28T11:45:12.883208Z",
     "shell.execute_reply.started": "2024-09-28T11:45:12.877642Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['scl_age__remainder__remainder__x0',\n",
       "       'scl_income__remainder__remainder__x6',\n",
       "       'remainder__enc_marry__remainder__x1_Married',\n",
       "       'remainder__enc_marry__remainder__x1_Single',\n",
       "       'remainder__enc_marry__remainder__x1_Widowed',\n",
       "       'remainder__remainder__enc_somking__x3',\n",
       "       'remainder__remainder__enc_physical__x4',\n",
       "       'remainder__remainder__enc_employment__x5',\n",
       "       'remainder__remainder__enc_alcohol__x7',\n",
       "       'remainder__remainder__enc_diet__x8',\n",
       "       'remainder__remainder__enc_sleep__x9',\n",
       "       'remainder__remainder__enc_mental__x10',\n",
       "       'remainder__remainder__enc_absue__x11',\n",
       "       'remainder__remainder__remainder__x2'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe[:-1].get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "380f4070-7c81-45af-8a41-2f9bc7a5c57f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a618d361-fc05-41bb-842e-106b864d6405",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:14.814690Z",
     "iopub.status.busy": "2024-09-28T11:45:14.813691Z",
     "iopub.status.idle": "2024-09-28T11:45:14.818407Z",
     "shell.execute_reply": "2024-09-28T11:45:14.818407Z",
     "shell.execute_reply.started": "2024-09-28T11:45:14.813691Z"
    }
   },
   "outputs": [],
   "source": [
    "input_sequence = [\n",
    "    \"Age\",\n",
    "    \"Marital Status\",\n",
    "    \"Number of Children\",\n",
    "    \"Smoking Status\",\n",
    "    \"Physical Activity Level\"\n",
    "    \"Employment Status\",\n",
    "    \"Income\",\n",
    "    \"Alcohol Consumption\",\n",
    "    \"Dietary Habits\",\n",
    "    \"Sleep Patterns\",\n",
    "    \"History of Mental Illness\",\n",
    "    \"History of Substance Abuse\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c9f6eec6-76eb-42b7-827c-b63b6f633e70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:16.337069Z",
     "iopub.status.busy": "2024-09-28T11:45:16.337069Z",
     "iopub.status.idle": "2024-09-28T11:45:16.344221Z",
     "shell.execute_reply": "2024-09-28T11:45:16.343570Z",
     "shell.execute_reply.started": "2024-09-28T11:45:16.337069Z"
    }
   },
   "outputs": [],
   "source": [
    "input1 = [\n",
    "    21, \n",
    "    \"Single\", \n",
    "    0, \n",
    "    \"Non-smoker\", \n",
    "    \"Sedentary\",\n",
    "    \"Unemployed\",\n",
    "    10000,\n",
    "    \"Low\",\n",
    "    \"Moderate\",\n",
    "    \"Fair\",\n",
    "    \"No\",\n",
    "    \"No\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3f7e5f16-5e84-485f-90c9-11f096875584",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:18.468206Z",
     "iopub.status.busy": "2024-09-28T11:45:18.468206Z",
     "iopub.status.idle": "2024-09-28T11:45:18.473522Z",
     "shell.execute_reply": "2024-09-28T11:45:18.472318Z",
     "shell.execute_reply.started": "2024-09-28T11:45:18.468206Z"
    }
   },
   "outputs": [],
   "source": [
    "input2 = [\n",
    "    24, \n",
    "    \"Single\",\n",
    "    0,\n",
    "    \"Non-smoker\",\n",
    "    \"Sedentary\",\n",
    "    \"Unemployed\",\n",
    "    0,\n",
    "    \"Low\",\n",
    "    \"Unhealthy\",\n",
    "    \"Poor\",\n",
    "    \"No\",\n",
    "    \"No\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c9f0ac74-c709-4c89-882e-2906a4425a2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:19.307400Z",
     "iopub.status.busy": "2024-09-28T11:45:19.307400Z",
     "iopub.status.idle": "2024-09-28T11:45:19.326193Z",
     "shell.execute_reply": "2024-09-28T11:45:19.326193Z",
     "shell.execute_reply.started": "2024-09-28T11:45:19.307400Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict([input2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d2716498-19d1-45d0-a345-7061e4ff2e37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:21.372186Z",
     "iopub.status.busy": "2024-09-28T11:45:21.372186Z",
     "iopub.status.idle": "2024-09-28T11:45:21.380716Z",
     "shell.execute_reply": "2024-09-28T11:45:21.380716Z",
     "shell.execute_reply.started": "2024-09-28T11:45:21.372186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.predict([\n",
    "  [\n",
    "    451,\n",
    "    \"Married\",\n",
    "    2,\n",
    "    \"Non-smoker\",\n",
    "    \"Moderate\",\n",
    "    \"Unemployed\",\n",
    "    45123,\n",
    "    \"Moderate\",\n",
    "    \"Healthy\",\n",
    "    \"Poor\",\n",
    "    \"Yes\",\n",
    "    \"Yes\"\n",
    "  ]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "527cb204-513c-4d05-8a90-e0c705ada35e",
   "metadata": {},
   "source": [
    "## Depression\n",
    "### 0 -> No\n",
    "### 1 -> Yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81e5a22d-102c-41b2-9755-7a72a64e5c50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "85abf955-41c7-461b-b658-d461b3b23878",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:45:23.525654Z",
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     "iopub.status.idle": "2024-09-28T11:45:23.534131Z",
     "shell.execute_reply": "2024-09-28T11:45:23.533750Z",
     "shell.execute_reply.started": "2024-09-28T11:45:23.525654Z"
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def get_metrics(model, x_test, y_test):\n",
    "    '''\n",
    "    Parameters:\n",
    "    ------------------------\n",
    "    model : ML Model Object i.e., LiniearRegression()\n",
    "    \n",
    "    x_test : Featuers Testing DataFrame from train_test_split()\n",
    "    \n",
    "    y_test : Target Testing DataFrame from train_test_split()\n",
    "    \n",
    "    ________________________\n",
    "    Returns:\n",
    "    ------------------------\n",
    "    subplots(3) : Clasification Report, Confusion Matrix, ROC-AUC Curve\n",
    "\n",
    "\n",
    "    _____________________________________________________________\n",
    "    \n",
    "    '''\n",
    "    y_pred = model.predict(x_test)\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 8))\n",
    "\n",
    "    model_name = model if len(str(model)) <= 50 else str(model)[:50]+\"...)\"\n",
    "    fig.suptitle(f\"\\nClassification Metrics for {model_name}\\n\", fontsize=30)\n",
    "    \n",
    "    ##########################################################################################################\n",
    "    cr = pd.DataFrame(classification_report(y_test, y_pred, output_dict=True)).T.iloc[:, :3]\n",
    "    accuracy = round((float(cr.loc[\"accuracy\"][0]))*100, 2)\n",
    "    t = cr.iloc[[0,1,3,4], :]\n",
    "    t.loc[[\"macro avg\", \"weighted avg\"]] = t.loc[[\"macro avg\", \"weighted avg\"]]*100\n",
    "    t = round(t, 2)\n",
    "    \n",
    "    axes[0].axis('tight')\n",
    "    axes[0].axis('off')\n",
    "    t = axes[0].table(cellText=t.values, colLabels=t.columns, rowLabels=t.index, loc=\"center\", cellLoc=\"center\", rowLoc='right', fontsize=5, colColours=['#05C7F2']*3, rowColours=[\"#BCB7B1\"]*4, colWidths=[0.25]*3)\n",
    "    t.scale(1, 3)\n",
    "    axes[0].set_title(f\"Classification Report\\n\", fontsize=15)\n",
    "    axes[0].text(x=-0.018, y=0.035, s=f\"Accuracy : {accuracy}%\", fontsize=15)\n",
    "    ##########################################################################################################\n",
    "    ##########################################################################################################\n",
    "    ##########################################################################################################\n",
    "    ##########################################################################################################\n",
    "    l = np.array([\"True Negative\", \"False Positive\", \"False Negative\", \"True Positive\"])\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    c = cm.flatten()\n",
    "    sns.heatmap(confusion_matrix(y_test, y_pred), annot=np.array([f\"{i}\\n{j:,}\" for i, j in zip(l, c)]).reshape(2,2), fmt=\"\", cbar=False, ax=axes[1], cmap = ListedColormap(['#05C7F2']), linewidths = 1, annot_kws = {'size':15})\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            if i != j:  # Check if not diagonal\n",
    "                # Set cell background to white\n",
    "                rect = plt.Rectangle((j, i), 1, 1, facecolor='#BCB7B1')\n",
    "                axes[1].add_patch(rect)\n",
    "    axes[1].set_xlabel(\"Predicted Values\")\n",
    "    axes[1].set_ylabel(\"Actual Values\")\n",
    "    axes[1].axis('tight')\n",
    "    axes[1].set_title(f\"Confusion Matrix\\n\", fontsize=15)\n",
    "    ##########################################################################################################\n",
    "    ##########################################################################################################\n",
    "    ##########################################################################################################    \n",
    "    ##########################################################################################################\n",
    "    fpr, tpr, _ = roc_curve(y_test, model.predict_proba(x_test)[:,1])\n",
    "    axes[2].plot(fpr, tpr, color='#05C7F2', label=f\"ROC-AUC Score\\n              {roc_auc_score(y_test, model.predict_proba(x_test)[:,1]):.4f}\", fillstyle=\"full\")\n",
    "    axes[2].plot([0, 1], [0, 1],color='#A19C95', linestyle='dashed')\n",
    "    axes[2].fill_between(fpr, tpr, alpha=0.5, color='#BCB7B1')\n",
    "    axes[2].set_xlabel(\"False Postive Rate\")\n",
    "    axes[2].set_ylabel(\"True Postive Rate\")\n",
    "    axes[2].legend(loc=\"upper left\")\n",
    "    axes[2].set_title(f\"ROC-AUC Curve\\n\", fontsize=15)\n",
    "    ##########################################################################################################\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "450bd473-ca63-443c-82de-0c21bd6238b2",
   "metadata": {
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     "shell.execute_reply.started": "2024-09-28T11:45:24.055412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_metrics(pipe, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646393da-2b19-40fa-b5d0-d678dc14c6c5",
   "metadata": {},
   "source": [
    "###\n",
    "---\n",
    "###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fa534097-bc3f-4f30-a1bd-bdc23153888b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-28T11:43:08.868298Z",
     "iopub.status.busy": "2024-09-28T11:43:08.868298Z",
     "iopub.status.idle": "2024-09-28T11:43:08.873827Z",
     "shell.execute_reply": "2024-09-28T11:43:08.873199Z",
     "shell.execute_reply.started": "2024-09-28T11:43:08.868298Z"
    }
   },
   "outputs": [],
   "source": [
    "# pickle.dump(pipe, open(r\"Desktop/GL PGPDSE/Capston Project/Depression/DepApp Webapp/pipeline_model.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfa8b873-029e-4dc8-aa25-8e24f91a86b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe29cb61-b467-41f4-b9fa-50c43b6f35e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}