OracleAutoMLx_Classification (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1010946a",
   "metadata": {},
   "source": [
    "***\n",
    "# <font color=red>**Building and Training a Classifier using AutoMLx versus Standard Python**</font>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42c5c46",
   "metadata": {},
   "source": [
    "# **1. Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2580807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import gzip\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_auc_score, confusion_matrix, make_scorer, f1_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.compose import make_column_selector as selector\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import automl\n",
    "from automl import init"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8192f2d3",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d75263d",
   "metadata": {},
   "source": [
    "<a id='load-data'></a>\n",
    "# **2. Load the \"Did not Attend\" dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5245c7c4",
   "metadata": {},
   "source": [
    "> ## **Predicting the likelihood a person will not attend the made appointment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e8396140",
   "metadata": {},
   "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>empi_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender_female</th>\n",
       "      <th>ethnicity_asian</th>\n",
       "      <th>ethnicity_black</th>\n",
       "      <th>ethnicity_unknown</th>\n",
       "      <th>imd_deprivation_more_deprived</th>\n",
       "      <th>imd_deprivation_unknown</th>\n",
       "      <th>anxiety</th>\n",
       "      <th>severe_mental_illness</th>\n",
       "      <th>sleep_disorders</th>\n",
       "      <th>antidepressants</th>\n",
       "      <th>alcohol_abuse</th>\n",
       "      <th>bmi_value</th>\n",
       "      <th>travel_time</th>\n",
       "      <th>phone_number_in_record</th>\n",
       "      <th>history_of_no_shows</th>\n",
       "      <th>did_not_attend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Synth100</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.95</td>\n",
       "      <td>51.863174</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Synth1000</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.50</td>\n",
       "      <td>84.345688</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Synth100000</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.66</td>\n",
       "      <td>2.083614</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Synth100002</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.87</td>\n",
       "      <td>120.735368</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Synth100004</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.33</td>\n",
       "      <td>59.734349</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       empi_id  age  gender_female  ethnicity_asian  ethnicity_black  \\\n",
       "0     Synth100   57              1                0                0   \n",
       "1    Synth1000   71              1                0                0   \n",
       "2  Synth100000   21              0                0                0   \n",
       "3  Synth100002   33              0                0                0   \n",
       "4  Synth100004   18              1                0                0   \n",
       "\n",
       "   ethnicity_unknown  imd_deprivation_more_deprived  imd_deprivation_unknown  \\\n",
       "0                  0                              0                        0   \n",
       "1                  0                              1                        0   \n",
       "2                  0                              0                        0   \n",
       "3                  0                              0                        0   \n",
       "4                  0                              0                        0   \n",
       "\n",
       "   anxiety  severe_mental_illness  sleep_disorders  antidepressants  \\\n",
       "0        0                      0                0                0   \n",
       "1        0                      0                0                0   \n",
       "2        0                      0                0                0   \n",
       "3        0                      0                0                0   \n",
       "4        0                      0                0                0   \n",
       "\n",
       "   alcohol_abuse  bmi_value  travel_time  phone_number_in_record  \\\n",
       "0              0      19.95    51.863174                       1   \n",
       "1              0      23.50    84.345688                       1   \n",
       "2              0      15.66     2.083614                       1   \n",
       "3              0      25.87   120.735368                       1   \n",
       "4              0      21.33    59.734349                       1   \n",
       "\n",
       "   history_of_no_shows  did_not_attend  \n",
       "0                    0               0  \n",
       "1                    0               0  \n",
       "2                    0               0  \n",
       "3                    0               0  \n",
       "4                    1               1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./DEMO_ML_DNA_RISK.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c65e512e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='did_not_attend', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show target\n",
    "sns.countplot(df, x=\"did_not_attend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ce140605",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% not attending appointment is 27.194741532976828\n"
     ]
    }
   ],
   "source": [
    "xx = df['did_not_attend'].value_counts()\n",
    "print(\"% not attending appointment is \" + str((xx[1]/(xx[0]+xx[1]))*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15388d1e",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbf5bdd9",
   "metadata": {},
   "source": [
    "# **3. Built ML model using Open Source**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "64ffc147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.87      0.82     23541\n",
      "           1       0.46      0.31      0.37      8773\n",
      "\n",
      "    accuracy                           0.72     32314\n",
      "   macro avg       0.62      0.59      0.59     32314\n",
      "weighted avg       0.69      0.72      0.70     32314\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(['did_not_attend', 'empi_id'], axis=1)\n",
    "y = pd.Series(df['did_not_attend'])\n",
    "\n",
    "#split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.7,random_state=0)\n",
    "\n",
    "\n",
    "# Standardize features by removing mean and scaling to unit variance:\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test) \n",
    "\n",
    "# Use the KNN classifier to fit data:\n",
    "classifier = KNeighborsClassifier(n_neighbors=5)\n",
    "classifier.fit(X_train, y_train) \n",
    "\n",
    "# Predict y data with classifier: \n",
    "y_predict = classifier.predict(X_test)\n",
    "\n",
    "# Print results: \n",
    "#print(confusion_matrix(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d90764",
   "metadata": {},
   "source": [
    "> ## **Accuracy without changes is 0.72**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f489e0dc",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "834bcadd",
   "metadata": {},
   "source": [
    "# **4. Built ML model using AutoML**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a636fbf6",
   "metadata": {},
   "source": [
    "<a id='provider'></a>\n",
    "AutoML consists of five main modules: \n",
    "- **Preprocessing** : Clean, impute, engineer, and normalize features.\n",
    "- **Algorithm Selection** : Identify the right classification algorithm -in this notebook- for a given dataset, choosing from amongst:\n",
    "    - AdaBoostClassifier\n",
    "    - DecisionTreeClassifier\n",
    "    - ExtraTreesClassifier\n",
    "    - TorchMLPClassifier\n",
    "    - KNeighborsClassifier\n",
    "    - LGBMClassifier\n",
    "    - LinearSVC\n",
    "    - LogisticRegression\n",
    "    - RandomForestClassifier\n",
    "    - SVC\n",
    "    - XGBClassifier\n",
    "    - GaussianNB\n",
    "- **Adaptive Sampling** : Select a subset of the data samples for the model to be trained on.\n",
    "- **Feature Selection** : Select a subset of the data features, based on the previously selected model.\n",
    "- **Hyperparameter Tuning** : Find the right model parameters that maximize score for the given dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "479d0148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2023-11-21 13:03:01,445] [automl.interface] Execution engine (local) has already been initialized. Reinitializing!\n",
      "[2023-11-21 13:03:01,446] [automl.xengine] Local ProcessPool execution (n_jobs=6)\n",
      "[2023-11-21 13:03:01,514] [automl.pipeline] Random state (7) is used for model builds\n",
      "[2023-11-21 13:03:01,554] [automl.pipeline] Dataset shape: (75398, 17)\n",
      "[2023-11-21 13:03:01,564] [automl.pipeline] Running Auto-Preprocessing\n",
      "[2023-11-21 13:03:01,697] [automl.pipeline] Preprocessing completed. Updated Dataset shape: (75398, 9), cv: 5\n",
      "[2023-11-21 13:03:01,721] [automl.pipeline] SVC, KNeighborsClassifier are disabled for datasets with > 10K samples or > 1K features\n",
      "[2023-11-21 13:03:01,722] [automl.pipeline] Running Model Selection\n",
      "[2023-11-21 13:03:12,775] [automl.pipeline] Model Selection completed. Selected model: ['XGBClassifier']\n",
      "[2023-11-21 13:03:12,777] [automl.pipeline] Running Adaptive Sampling. Dataset Shape: (75398, 9), Valid Shape: None, CV: 5, Class counts: [54879 20519]\n",
      "[2023-11-21 13:03:14,635] [automl.pipeline] Adaptive Sampling Completed. Updated Dataset Shape: (75398, 9), Valid Shape: None, CV: [(9078, 15080), (9078, 15080), (9078, 15080), (9078, 15079), (9078, 15079)], Class counts: [54879 20519]\n",
      "[2023-11-21 13:03:14,636] [automl.pipeline] Starting Feature Selection 0. Dataset Shape: (75398, 9)\n",
      "[2023-11-21 13:03:20,874] [automl.pipeline] Feature Selection 0 completed. Updated Dataset shape: (75398, 1)\n",
      "[2023-11-21 13:03:20,881] [automl.pipeline] Tuning XGBClassifier\n",
      "[2023-11-21 13:03:27,014] [automl.pipeline] Tuning completed. Best params: {'booster': 'dart', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}\n",
      "[2023-11-21 13:03:27,037] [automl.pipeline] (Re)fitting Pipeline\n",
      "[2023-11-21 13:03:29,753] [automl.xengine] Local ProcessPool execution (n_jobs=6)\n",
      "[2023-11-21 13:03:29,778] [automl.pipeline] AutoML completed. Time taken - 28.135 sec\n"
     ]
    },
    {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(model_list=[&#x27;DecisionTreeClassifier&#x27;, &#x27;ExtraTreesClassifier&#x27;,\n",
       "                     &#x27;GaussianNB&#x27;, &#x27;LGBMClassifier&#x27;, &#x27;LogisticRegression&#x27;,\n",
       "                     &#x27;RandomForestClassifier&#x27;, &#x27;SVC&#x27;, &#x27;XGBClassifier&#x27;,\n",
       "                     &#x27;TorchMLPClassifier&#x27;, &#x27;CatBoostClassifier&#x27;])</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(model_list=[&#x27;DecisionTreeClassifier&#x27;, &#x27;ExtraTreesClassifier&#x27;,\n",
       "                     &#x27;GaussianNB&#x27;, &#x27;LGBMClassifier&#x27;, &#x27;LogisticRegression&#x27;,\n",
       "                     &#x27;RandomForestClassifier&#x27;, &#x27;SVC&#x27;, &#x27;XGBClassifier&#x27;,\n",
       "                     &#x27;TorchMLPClassifier&#x27;, &#x27;CatBoostClassifier&#x27;])</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(model_list=['DecisionTreeClassifier', 'ExtraTreesClassifier',\n",
       "                     'GaussianNB', 'LGBMClassifier', 'LogisticRegression',\n",
       "                     'RandomForestClassifier', 'SVC', 'XGBClassifier',\n",
       "                     'TorchMLPClassifier', 'CatBoostClassifier'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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    }
   ],
   "source": [
    "#create automl instance\n",
    "init(engine='local', check_deprecation_warnings=False)\n",
    "\n",
    "#create X and Y\n",
    "X = df.drop(['did_not_attend'], axis=1)\n",
    "y = pd.Series(df['did_not_attend'])\n",
    "\n",
    "#split in train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.7,random_state=0)\n",
    "X_train.shape, X_test.shape\n",
    "\n",
    "#run the AutoML pipeline\n",
    "est1 = automl.Pipeline(task='classification', score_metric = 'accuracy')\n",
    "\n",
    "est1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cbbd9e1e",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Training Dataset size</td>\n",
       "      <td>(75398, 17)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Validation Dataset size</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CV</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Optimization Metric</td>\n",
       "      <td>accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Features</td>\n",
       "      <td>Index(['history_of_no_shows'], dtype='object')</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Algorithm</td>\n",
       "      <td>XGBClassifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Time taken</td>\n",
       "      <td>25.4691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Selected Hyperparameters</td>\n",
       "      <td>{'objective': 'binary:logistic', 'use_label_encoder': False, 'base_score': 0.5, 'booster': 'dart', 'callbacks': None, 'colsample_bylevel': 1, 'colsample_bynode': 1, 'colsample_bytree': 1, 'early_stopping_rounds': None, 'enable_categorical': False, 'eval_metric': 'logloss', 'gamma': 0, 'gpu_id': -1, 'grow_policy': 'depthwise', 'importance_type': None, 'interaction_constraints': '', 'learning_rate': 0.1, 'max_bin': 256, 'max_cat_to_onehot': 4, 'max_delta_step': 0, 'max_depth': 3, 'max_leaves': 0, 'min_child_weight': 1, 'missing': nan, 'monotone_constraints': '()', 'n_estimators': 100, 'n_jobs': 2, 'num_parallel_tree': 1, 'predictor': 'auto', 'random_state': 7, 'reg_alpha': 0, 'reg_lambda': 1, 'sampling_method': 'uniform', 'scale_pos_weight': 1, 'subsample': 1, 'tree_method': 'exact', 'validate_parameters': 1, 'verbosity': None}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AutoML version</td>\n",
       "      <td>23.2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Python version</td>\n",
       "      <td>3.8.16 (default, Jun 12 2023, 18:09:05) \\n[GCC 11.2.0]</td>\n",
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       "  <thead>\n",
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       "      <th>Algorithm</th>\n",
       "      <th>#Samples</th>\n",
       "      <th>#Features</th>\n",
       "      <th>Mean Validation Score</th>\n",
       "      <th>Hyperparameters</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Memory Usage (GB)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_HT</td>\n",
       "      <td>75398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7490</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 50, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>0.2380</td>\n",
       "      <td>(0.0, None)</td>\n",
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       "      <td>XGBClassifier_HT</td>\n",
       "      <td>75398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7490</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 5.6244132519034915}</td>\n",
       "      <td>0.4192</td>\n",
       "      <td>(0.0, None)</td>\n",
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       "      <td>XGBClassifier_HT</td>\n",
       "      <td>75398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7490</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.0001, 'max_depth': 3, 'min_child_weight': 0, 'n_estimators': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0}</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>(0.0, None)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_HT</td>\n",
       "      <td>75398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7490</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.0001, 'max_depth': 2, 'min_child_weight': 0, 'n_estimators': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0}</td>\n",
       "      <td>0.2331</td>\n",
       "      <td>(0.0, None)</td>\n",
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       "    <tr>\n",
       "      <td>XGBClassifier_HT</td>\n",
       "      <td>75398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7490</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.10242113515453982, 'max_depth': 2, 'min_child_weight': 0, 'n_estimators': 50, 'reg_alpha': 0.0, 'reg_lambda': 0.0}</td>\n",
       "      <td>0.2220</td>\n",
       "      <td>(0.0, None)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>XGBClassifier_AVGRanking_FS</td>\n",
       "      <td>75398</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5138</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>1.0997</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_ANOVAF_FS</td>\n",
       "      <td>75398</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5011</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>1.0017</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>75398</td>\n",
       "      <td>3</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>1.2237</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>75398</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4672</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>1.0896</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>XGBClassifier_AdaBoostClassifier_FS</td>\n",
       "      <td>75398</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4644</td>\n",
       "      <td>{'booster': 'gbtree', 'learning_rate': 0.1, 'max_depth': 3, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1}</td>\n",
       "      <td>1.0242</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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    "> ## **Accuracy with automated changes is 0.74**"
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",
        "encoding": "base64",
        "path": [
         "_data",
         0,
         "y",
         "buffer"
        ]
       },
       {
        "data": 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