predictive_maintenance_project.ipynb

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   "source": [
    "# Predictive Maintenance for Manufacturing Equipment"
   ]
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    "*The dataset being analyzed contains sensor readings and operational parameters from an industrial machine, where the goal is to predict failure types. This classification task is crucial for preventive maintenance and ensuring smooth operation in industrial settings. Each row represents a snapshot of the machine's operational state at a given time, while each column contains a specific type of sensor reading or machine attribute.*"
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pymongo import MongoClient\n",
    "from scipy.stats import zscore\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import os\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "from sklearn.ensemble import RandomForestClassifier, IsolationForest\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, confusion_matrix, classification_report,fbeta_score, make_scorer, silhouette_score\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from IPython.display import display"
   ]
  },
  {
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       "      <th>UDI</th>\n",
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       "      <td>1433</td>\n",
       "      <td>39.5</td>\n",
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       "      <td>No Failure</td>\n",
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      "text/plain": [
       "   UDI Product ID Type  Air temperature [K]  Process temperature [K]  \\\n",
       "0    1     M14860    M                298.1                    308.6   \n",
       "1    2     L47181    L                298.2                    308.7   \n",
       "2    3     L47182    L                298.1                    308.5   \n",
       "3    4     L47183    L                298.2                    308.6   \n",
       "4    5     L47184    L                298.2                    308.7   \n",
       "\n",
       "   Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  Target Failure Type  \n",
       "0                    1551         42.8                0       0   No Failure  \n",
       "1                    1408         46.3                3       0   No Failure  \n",
       "2                    1498         49.4                5       0   No Failure  \n",
       "3                    1433         39.5                7       0   No Failure  \n",
       "4                    1408         40.0                9       0   No Failure  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = MongoClient('mongodb://localhost:27017')\n",
    "db = client['FinalProject']\n",
    "collection = db['PredictiveMaintainence']\n",
    "result = list(collection.find())\n",
    "data = pd.DataFrame(result)"
   ]
  },
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    },
    "id": "rs50TVLexeG1",
    "outputId": "e8298710-6086-48df-f615-0d1bd28bb98a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
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   "id": "RqFCLTOdxio1",
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    },
    "id": "RqFCLTOdxio1",
    "outputId": "8b140ebb-663e-4f0e-c3e8-d9c6ff562902"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UDI', 'Product ID', 'Type', 'Air temperature [K]',\n",
       "       'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]',\n",
       "       'Tool wear [min]', 'Target', 'Failure Type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "VUxL22E7xk9v",
   "metadata": {
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    },
    "id": "VUxL22E7xk9v",
    "outputId": "648a137e-7a8f-4bc1-9aa9-24a7f5d90e7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   UDI                      10000 non-null  int64  \n",
      " 1   Product ID               10000 non-null  object \n",
      " 2   Type                     10000 non-null  object \n",
      " 3   Air temperature [K]      10000 non-null  float64\n",
      " 4   Process temperature [K]  10000 non-null  float64\n",
      " 5   Rotational speed [rpm]   10000 non-null  int64  \n",
      " 6   Torque [Nm]              10000 non-null  float64\n",
      " 7   Tool wear [min]          10000 non-null  int64  \n",
      " 8   Target                   10000 non-null  int64  \n",
      " 9   Failure Type             10000 non-null  object \n",
      "dtypes: float64(3), int64(4), object(3)\n",
      "memory usage: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1SBfwaIBe5lP",
   "metadata": {
    "id": "1SBfwaIBe5lP"
   },
   "outputs": [],
   "source": [
    "# data = data.drop('_id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "w02iKyqpxnvM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w02iKyqpxnvM",
    "outputId": "300f060c-f0f9-4567-bb15-610c10be7145"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Duplicates in Product ID\n"
     ]
    }
   ],
   "source": [
    "#checking whether product ID is categorical feature or unique identifier\n",
    "if(data['Product ID'].nunique() == data.shape[0]):\n",
    "    print(\"No Duplicates in Product ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "morrHzrDySeU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 209
    },
    "id": "morrHzrDySeU",
    "outputId": "27f731ed-2bbf-462e-bf85-c6eabd1b9d5e"
   },
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>2997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "Product ID\n",
       "L    6000\n",
       "M    2997\n",
       "H    1003\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No Duplicates in Product ID shows that it is unique identifier\n",
    "# but it also contains the type of the product\n",
    "Product_ID_type = data['Product ID'].apply(lambda x: x[0])\n",
    "Product_ID_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eZoVbhx3yZ0M",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 209
    },
    "id": "eZoVbhx3yZ0M",
    "outputId": "4b587669-4766-4483-9822-fd4a77850237"
   },
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>2997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "Type\n",
       "L    6000\n",
       "M    2997\n",
       "H    1003\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "Vd0XNwTrfQdf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 268
    },
    "id": "Vd0XNwTrfQdf",
    "outputId": "2508d475-6d11-4502-dac4-71bc4da8344f"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "type_percent = 100 * data['Type'].value_counts() / data.shape[0]\n",
    "plt.pie(x=type_percent.array, labels=type_percent.index.array,\n",
    "        colors=sns.color_palette('tab10')[0:4], autopct='%.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ICX8gaGVycr8",
   "metadata": {
    "id": "ICX8gaGVycr8"
   },
   "outputs": [],
   "source": [
    "# The type in product ID is equivalent to Type feature so we can consider Product ID as unique values\n",
    "# removing unique values\n",
    "data.drop(['UDI', 'Product ID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "z51SXuWJfVqh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "z51SXuWJfVqh",
    "outputId": "f95e7315-4a52-407d-ccca-74f89d1cca71"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Air temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3446.112587891836,\n        \"min\": 2.0002586829157574,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          300.00493,\n          300.1,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Process temperature [K]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3443.232448462189,\n        \"min\": 1.4837342191657419,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          310.00556,\n          310.1,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rotational speed [rpm]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3103.301433905864,\n        \"min\": 179.28409591342628,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1538.7761,\n          1503.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Torque [Nm]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3522.9553380464768,\n        \"min\": 3.8,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          39.986909999999995,\n          40.1,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tool wear [min]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3498.6028684989537,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          107.951,\n          108.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3535.4725618125285,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0339,\n          1.0,\n          0.18098084265064265\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-75c2242f-cbb5-4b94-ac24-06266e3c5e00\" class=\"colab-df-container\">\n",
       "    <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>Air temperature [K]</th>\n",
       "      <th>Process temperature [K]</th>\n",
       "      <th>Rotational speed [rpm]</th>\n",
       "      <th>Torque [Nm]</th>\n",
       "      <th>Tool wear [min]</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>300.004930</td>\n",
       "      <td>310.005560</td>\n",
       "      <td>1538.776100</td>\n",
       "      <td>39.986910</td>\n",
       "      <td>107.951000</td>\n",
       "      <td>0.033900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.000259</td>\n",
       "      <td>1.483734</td>\n",
       "      <td>179.284096</td>\n",
       "      <td>9.968934</td>\n",
       "      <td>63.654147</td>\n",
       "      <td>0.180981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>295.300000</td>\n",
       "      <td>305.700000</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>298.300000</td>\n",
       "      <td>308.800000</td>\n",
       "      <td>1423.000000</td>\n",
       "      <td>33.200000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>300.100000</td>\n",
       "      <td>310.100000</td>\n",
       "      <td>1503.000000</td>\n",
       "      <td>40.100000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>301.500000</td>\n",
       "      <td>311.100000</td>\n",
       "      <td>1612.000000</td>\n",
       "      <td>46.800000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>304.500000</td>\n",
       "      <td>313.800000</td>\n",
       "      <td>2886.000000</td>\n",
       "      <td>76.600000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75c2242f-cbb5-4b94-ac24-06266e3c5e00')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-75c2242f-cbb5-4b94-ac24-06266e3c5e00 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-75c2242f-cbb5-4b94-ac24-06266e3c5e00');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-19fff283-21c4-40e4-bb46-80e563548a90\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19fff283-21c4-40e4-bb46-80e563548a90')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-19fff283-21c4-40e4-bb46-80e563548a90 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "       Air temperature [K]  Process temperature [K]  Rotational speed [rpm]  \\\n",
       "count         10000.000000             10000.000000            10000.000000   \n",
       "mean            300.004930               310.005560             1538.776100   \n",
       "std               2.000259                 1.483734              179.284096   \n",
       "min             295.300000               305.700000             1168.000000   \n",
       "25%             298.300000               308.800000             1423.000000   \n",
       "50%             300.100000               310.100000             1503.000000   \n",
       "75%             301.500000               311.100000             1612.000000   \n",
       "max             304.500000               313.800000             2886.000000   \n",
       "\n",
       "        Torque [Nm]  Tool wear [min]        Target  \n",
       "count  10000.000000     10000.000000  10000.000000  \n",
       "mean      39.986910       107.951000      0.033900  \n",
       "std        9.968934        63.654147      0.180981  \n",
       "min        3.800000         0.000000      0.000000  \n",
       "25%       33.200000        53.000000      0.000000  \n",
       "50%       40.100000       108.000000      0.000000  \n",
       "75%       46.800000       162.000000      0.000000  \n",
       "max       76.600000       253.000000      1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "Zsaredinx8Dg",
   "metadata": {
    "id": "Zsaredinx8Dg"
   },
   "outputs": [],
   "source": [
    "data['Tool wear [min]'] = data['Tool wear [min]'].astype('float64')\n",
    "data['Rotational speed [rpm]'] = data['Rotational speed [rpm]'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "qk7IaLlAyAdF",
   "metadata": {
    "id": "qk7IaLlAyAdF"
   },
   "outputs": [],
   "source": [
    "data = data.rename(columns = {'Air temperature [K]': 'Air temperature', 'Process temperature [K]': 'Process temperature',\n",
    "       'Rotational speed [rpm]': 'Rotational speed', 'Torque [Nm]': 'Torque', 'Tool wear [min]': 'Tool wear'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "HRlkU961feBs",
   "metadata": {
    "id": "HRlkU961feBs"
   },
   "outputs": [],
   "source": [
    "sensor_readings = ['Air temperature', 'Process temperature', 'Rotational speed', 'Torque', 'Tool wear']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bwMTipy3fiIL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 930
    },
    "id": "bwMTipy3fiIL",
    "outputId": "c1724d78-767d-4a4c-91f2-9ec9e5cb2000"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[sensor_readings].hist(figsize=(12,10), bins=20, color='skyblue', edgecolor = 'black')\n",
    "plt.suptitle('Distribution of Sensor Readings', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "rdFGrrJYflE1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 757
    },
    "id": "rdFGrrJYflE1",
    "outputId": "7280680e-8592-4cd5-abe4-882b99bd0fc9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15,10))\n",
    "for i, col in enumerate(data[sensor_readings]):\n",
    "    row, column = i//3, i%3\n",
    "    sns.boxplot(data = data[col], ax = axes[row,column])\n",
    "    axes[row, column].set_title(col)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "rCz8L7GOyqBF",
   "metadata": {
    "id": "rCz8L7GOyqBF"
   },
   "outputs": [],
   "source": [
    "df = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9PXFRZU7fwlx",
   "metadata": {
    "id": "9PXFRZU7fwlx"
   },
   "source": [
    "Target and Failure Type Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "19Nt96vEzunZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "19Nt96vEzunZ",
    "outputId": "f620542b-3dcd-4624-fae8-4d28b597bba7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "sns.countplot(x='Failure Type', data = df, hue = 'Target', width=0.8, palette = 'Set2', legend = True)\n",
    "for p in plt.gca().patches:\n",
    "    plt.annotate(f'{int(p.get_height())}', (p.get_x() + p.get_width() / 2, p.get_height()), ha = 'center', va = 'bottom')\n",
    "plt.yscale('log')\n",
    "plt.title('Target vs Failure Type')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "KW2z1oxEz9Yn",
   "metadata": {
    "id": "KW2z1oxEz9Yn"
   },
   "source": [
    "*The* above plot shows **9 No Failure type** having Target as 1 and **Random Failures Type** having all Target values as 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5rMZ-F_rz8BD",
   "metadata": {
    "id": "5rMZ-F_rz8BD"
   },
   "outputs": [],
   "source": [
    "df = df[~((df['Failure Type']=='No Failure') & (df['Target']==1))]\n",
    "df = df[~(((df['Failure Type']=='Random Failures') & (df['Target']==0)))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43Vh9JgJggCR",
   "metadata": {
    "id": "43Vh9JgJggCR"
   },
   "source": [
    "Handling imbalanced data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "nULnygrigi-u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 412
    },
    "id": "nULnygrigi-u",
    "outputId": "0dc15c4c-fe40-47dd-f6af-eb09cc6640d9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "sns.countplot(x='Target', data=df, hue='Target', palette='Set2', legend=False)\n",
    "plt.title('Distribution of Target feature', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3C-SK6Engn3b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 524
    },
    "id": "3C-SK6Engn3b",
    "outputId": "e34eff4a-9901-47ef-cdd3-e0303d6ff15e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "sns.countplot(x='Failure Type', data=df, hue='Failure Type', palette='Set2', legend=False)\n",
    "plt.yscale('log')\n",
    "plt.title('Distribution of Failure Type feature', fontsize=16)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "L5dzzzk2g4CK",
   "metadata": {
    "id": "L5dzzzk2g4CK"
   },
   "outputs": [],
   "source": [
    "no_failure = int((df['Failure Type'] == 'No Failure').sum())\n",
    "minority_class = df['Failure Type'].nunique() - 1\n",
    "minority_percent = 0.20\n",
    "final_count = no_failure / (1 - minority_percent)\n",
    "sample_per_class = round((final_count - no_failure) / minority_class)\n",
    "balance_cause = {'No Failure':no_failure,\n",
    "                 'Overstrain Failure':sample_per_class,\n",
    "                 'Heat Dissipation Failure':sample_per_class,\n",
    "                 'Power Failure':sample_per_class,\n",
    "                 'Tool Wear Failure':sample_per_class}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cnEJm5UxkOXM",
   "metadata": {
    "id": "cnEJm5UxkOXM"
   },
   "outputs": [],
   "source": [
    "sm = SMOTENC(categorical_features=[0,7], sampling_strategy=balance_cause, random_state=0)\n",
    "df_res, y_res = sm.fit_resample(df, df['Failure Type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GpqmrwP8o5A1",
   "metadata": {
    "id": "GpqmrwP8o5A1"
   },
   "source": [
    "Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bf93zWRKpAz-",
   "metadata": {
    "id": "bf93zWRKpAz-"
   },
   "outputs": [],
   "source": [
    "df2 = df_res.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "wggjQ9oQo9X4",
   "metadata": {
    "id": "wggjQ9oQo9X4"
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "df2[sensor_readings] = sc.fit_transform(df2[sensor_readings])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "XWROebe5pQOe",
   "metadata": {
    "id": "XWROebe5pQOe"
   },
   "outputs": [],
   "source": [
    "df2['Type'] = df2['Type'].map({'L': 0, 'M': 1, 'H': 2})\n",
    "df2['Failure Type'] = df2['Failure Type'].map({'No Failure': 0, 'Power Failure': 1, 'Tool Wear Failure': 2,\n",
    "                                                 'Overstrain Failure': 3, 'Heat Dissipation Failure': 4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d60ab9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_to_be_inserted = df2.to_dict('records')\n",
    "res = db['CleanedData'].insert_many(data_to_be_inserted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22e9f19e",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data = list(db['CleanedData'].find())\n",
    "final_df = pd.DataFrame(result_data)\n",
    "final_df = final_df.drop('_id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ab84d13",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T11:27:44.837040Z",
     "iopub.status.busy": "2023-11-04T11:27:44.836620Z",
     "iopub.status.idle": "2023-11-04T11:27:44.975007Z",
     "shell.execute_reply": "2023-11-04T11:27:44.973428Z"
    },
    "id": "3ab84d13",
    "papermill": {
     "duration": 0.167449,
     "end_time": "2023-11-04T11:27:44.977832",
     "exception": false,
     "start_time": "2023-11-04T11:27:44.810383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = final_df.drop(['Target', 'Failure Type'], axis=1)\n",
    "y = final_df[['Target','Failure Type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "wERCw4OFpu5l",
   "metadata": {
    "id": "wERCw4OFpu5l"
   },
   "outputs": [],
   "source": [
    "X_trainval, X_test, y_trainval, y_test = train_test_split(X, y, test_size=0.1, stratify=df2['Failure Type'], random_state=0)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_trainval, y_trainval, test_size=0.11, stratify=y_trainval['Failure Type'], random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e46HU8eAqBjb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e46HU8eAqBjb",
    "outputId": "73bfa132-d127-4a1b-88f8-143b2e05ba34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set percentage:  80.09%\n",
      "Validation set percentage:  9.90%\n",
      "Test set percentage:  10.00%\n"
     ]
    }
   ],
   "source": [
    "print(f'Train set percentage: {X_train.shape[0] * 100 /df_res.shape[0] : .2f}%')\n",
    "print(f'Validation set percentage: {X_val.shape[0] * 100 /df_res.shape[0] : .2f}%')\n",
    "print(f'Test set percentage: {X_test.shape[0] * 100 /df_res.shape[0] : .2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QtNLCZ9bqQcx",
   "metadata": {
    "id": "QtNLCZ9bqQcx"
   },
   "source": [
    "### Supervised Learning - Binary Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "RUIeDvIbqhDF",
   "metadata": {
    "id": "RUIeDvIbqhDF"
   },
   "outputs": [],
   "source": [
    "models = {\n",
    "    'Random Forest': {\n",
    "        'model': RandomForestClassifier(random_state=42),\n",
    "        'param_grid': {\n",
    "            'n_estimators':[100,300,500,700],\n",
    "            'max_depth':[5,7,10],\n",
    "            'random_state':[0]\n",
    "        }\n",
    "    },\n",
    "    'XGBoost': {\n",
    "        'model': XGBClassifier(random_state=42),\n",
    "        'param_grid': {\n",
    "            'n_estimators':[300,500,700],\n",
    "            'max_depth':[5,7],\n",
    "            'learning_rate':[0.01,0.1],\n",
    "            'objective':['binary:logistic']\n",
    "        }\n",
    "    },\n",
    "    'Neural Network': {\n",
    "        'model': MLPClassifier(max_iter=1000, random_state=42),\n",
    "        'param_grid': {\n",
    "            'hidden_layer_sizes': [(10,), (20,)],\n",
    "            'activation': ['relu'],\n",
    "            'solver': ['adam'],\n",
    "            'alpha': [0.0001],\n",
    "            'learning_rate': ['constant']\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "qOoCpz3Tqq4A",
   "metadata": {
    "id": "qOoCpz3Tqq4A"
   },
   "outputs": [],
   "source": [
    "def evaluate_binary_models(model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    cm = confusion_matrix(y_test['Target'], y_pred)\n",
    "    y_pred_prob = model.predict_proba(X_test)[:,1]\n",
    "    acc = round(accuracy_score(y_test['Target'], y_pred), 3)\n",
    "    auc = round(roc_auc_score(y_test['Target'], y_pred_prob), 3)\n",
    "    f1 = round(f1_score(y_test['Target'], y_pred, pos_label=1), 3)\n",
    "\n",
    "    metrics = {\n",
    "        'model': model.__class__.__name__,\n",
    "        'accuracy': acc,\n",
    "        'roc score': auc,\n",
    "        'f1 score': f1,\n",
    "    }\n",
    "    return metrics, cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "F0_4WIxhqswp",
   "metadata": {
    "id": "F0_4WIxhqswp"
   },
   "outputs": [],
   "source": [
    "def conf_matrix(cm_list, title, class_labels):\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=len(cm_list), figsize=(18, 5))\n",
    "\n",
    "    fig.suptitle(f'Confusion Matrices for {title}', fontsize=16)\n",
    "\n",
    "    for i, (name, cm) in enumerate(cm_list.items()):\n",
    "        sns.heatmap(cm, annot=True, fmt='g', cmap='Blues',\n",
    "                    xticklabels=class_labels, yticklabels=class_labels, ax=axes[i])\n",
    "        axes[i].set_title(name)\n",
    "        axes[i].set_xlabel('Predicted Label')\n",
    "        axes[i].set_ylabel('True Label')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1MYAl_RWqvdd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1MYAl_RWqvdd",
    "outputId": "25e8efb4-19a0-4cdd-a05c-36e3a03f294f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting and Tuning Random Forest...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/numpy/ma/core.py:2820: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 10, 'n_estimators': 700, 'random_state': 0}\n",
      "Fitting and Tuning XGBoost...\n",
      "{'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 700, 'objective': 'binary:logistic'}\n",
      "Fitting and Tuning Neural Network...\n",
      "{'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (20,), 'learning_rate': 'constant', 'solver': 'adam'}\n"
     ]
    }
   ],
   "source": [
    "fitted_models = {}\n",
    "for name, details in models.items():\n",
    "    model = details['model']\n",
    "    params = details['param_grid']\n",
    "\n",
    "    print(f'Fitting and Tuning {name}...')\n",
    "\n",
    "    f2_scorer = make_scorer(fbeta_score, pos_label=1, beta=2)\n",
    "    grid_search = GridSearchCV(model, param_grid=params, cv=5, scoring=f2_scorer)\n",
    "    grid_search.fit(X_train, y_train['Target'])\n",
    "    best_params = grid_search.best_params_\n",
    "\n",
    "    fitted_models[name] = {\n",
    "        'model': grid_search.best_estimator_,\n",
    "        'best_params': best_params\n",
    "    }\n",
    "    print(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "MhyoPg2IqzqN",
   "metadata": {
    "id": "MhyoPg2IqzqN"
   },
   "outputs": [],
   "source": [
    "val_metrics_list = []\n",
    "val_cm_list = {}\n",
    "test_metrics_list = []\n",
    "test_cm_list = {}\n",
    "\n",
    "for name, details in fitted_models.items():\n",
    "    val_metrics, val_cm = evaluate_binary_models(details['model'], X_val, y_val)\n",
    "\n",
    "    val_cm_list[name] = val_cm\n",
    "\n",
    "    val_metrics_list.append({\n",
    "        'Model' : name,\n",
    "        'Accuracy' : val_metrics['accuracy'],\n",
    "        'ROC Score' : val_metrics['roc score'],\n",
    "        'F1 score': val_metrics['f1 score']\n",
    "       })\n",
    "\n",
    "    val_metrics_df = pd.DataFrame(val_metrics_list)\n",
    "\n",
    "    test_metrics, test_cm = evaluate_binary_models(details['model'], X_test, y_test)\n",
    "\n",
    "    test_cm_list[name] = test_cm\n",
    "\n",
    "    test_metrics_list.append({\n",
    "        'Model' : name,\n",
    "        'Accuracy' : test_metrics['accuracy'],\n",
    "        'ROC Score' : test_metrics['roc score'],\n",
    "        'F1 score': test_metrics['f1 score']\n",
    "       })\n",
    "    test_metrics_df = pd.DataFrame(test_metrics_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "UQITiQ85q2aE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 304
    },
    "id": "UQITiQ85q2aE",
    "outputId": "695e8697-7631-4a8f-ab80-e52b91faa9ce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics of Validation Dataset\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"val_metrics_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Random Forest\",\n          \"XGBoost\",\n          \"Neural Network\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.020404247923737205,\n        \"min\": 0.947,\n        \"max\": 0.987,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.974,\n          0.987,\n          0.947\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00585946527708232,\n        \"min\": 0.988,\n        \"max\": 0.999,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.997,\n          0.999,\n          0.988\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05076416058598822,\n        \"min\": 0.868,\n        \"max\": 0.967,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.937,\n          0.967,\n          0.868\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
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       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
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       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
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       "            'suggestCharts', [key], {});\n",
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       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('val_metrics_df');\n",
       "      }\n",
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       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "            Model  Accuracy  ROC Score  F1 score\n",
       "0   Random Forest     0.974      0.997     0.937\n",
       "1         XGBoost     0.987      0.999     0.967\n",
       "2  Neural Network     0.947      0.988     0.868"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation Metrics of Test Dataset\n"
     ]
    },
    {
     "data": {
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       "summary": "{\n  \"name\": \"test_metrics_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Random Forest\",\n          \"XGBoost\",\n          \"Neural Network\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02381176179958134,\n        \"min\": 0.939,\n        \"max\": 0.984,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.975,\n          0.984,\n          0.939\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ROC Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006658328118479399,\n        \"min\": 0.987,\n        \"max\": 0.999,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.998,\n          0.999,\n          0.987\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0593408234972631,\n        \"min\": 0.849,\n        \"max\": 0.961,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.939,\n          0.961,\n          0.849\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "      <td>0.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neural Network</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.987</td>\n",
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       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
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       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "\n",
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       "        cursor: pointer;\n",
       "        display: none;\n",
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       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
       "        background-color: #E2EBFA;\n",
       "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
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       "\n",
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       "            title=\"Generate code using this dataframe.\"\n",
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       "    </button>\n",
       "    <script>\n",
       "      (() => {\n",
       "      const buttonEl =\n",
       "        document.querySelector('#id_b1d36c8c-f232-4cca-a321-06a747459b16 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('test_metrics_df');\n",
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       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "            Model  Accuracy  ROC Score  F1 score\n",
       "0   Random Forest     0.975      0.998     0.939\n",
       "1         XGBoost     0.984      0.999     0.961\n",
       "2  Neural Network     0.939      0.987     0.849"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Evaluation Metrics of Validation Dataset')\n",
    "display(val_metrics_df)\n",
    "print('Evaluation Metrics of Test Dataset')\n",
    "display(test_metrics_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "lwl-tm0iq4h1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "id": "lwl-tm0iq4h1",
    "outputId": "0ef3c152-a071-4735-f0e6-65a66a58feeb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fq9b2/x6fHTBI7y19V9/u2W3rjKtQoYIqhYa6ojyg1Hhl3Jt2y7FDRunJh9rquyMHVb9RU2Vl/q51n32sAS+NVcMm/3OpzeARGvjEQzp8YK/q1G+ozN/Pa+n8mXpxzFQ1bPo/tmNVr1W7RK8Frmf2DOUWnXEfffTRVesKCgrUv39/3XrrrS6oCM6Wn39R+fn5slqtduutVqu++WaXi6oCSqcLWZdeZuznH3jN7TnZF/TlmpUKjYhUSKXrjzq98EfmdY+BssnkGahMIEOZ12uvjlaLFi3VPOZuOuOAIrJlKL+Aa26/lKGSFRp+/Qy1Ze1qWa0VdOc93Lw3EzJU6UeGMpf8/Hx98flnunDhD0VHN7GtX71qpVYlf6KQSqFq2aq1+j39jLwZHQfc0B+2e1CX8tN3Rw4q/+JFNWp6l63NLdVqqlJYhA4f+FZ16jfUtynbZRQYOvfLzxr4RDdd+OMP1a3fSD2fHqRKYREuuQ64htkzlFt0xl2Lh4eH4uPj1apVKw0ZMsTV5aCY+fr6KbpxE701e6Zq1qqlkJBK+nR1sr7ds1tVq1VzdXlAqVFQUKBF/56s2vWjVaWG/f80/if5fS2bN1052RdUuUp1DRn7psqVL3/N4xw58K12bFqj+FFTSqJsAE5Ehir7Pl29SgcPHtCSZe+7uhSg1CooKNDit6aodv1GV2Wotcnva9n8N20ZavDY6dfNUJu++ETNW7a3Gy0HoHQiQ5U9Rw6n6rF/PaLc3Bz5+PhoyrQZujUqSpLU8e+dVDkyUmFhYTp8OFVTJ0/UiRPHNeWNN29yVMC8CgoKlDRzoureHq1qNS99l9LP/apy5cvL18/frm1gxRCl//arJOnM6R9VYBTow3fn6fFnXpCPr7+Wzp+pMUOf0cS3lqn8dXIWUNa4bWecJB07dkwXL168YZucnBzl5OTYrTM8rVeNuIL7GZs4XiOGvaS/tW4hT09P3Vavvjr8/QEdPLDf1aUBpcbCmeP14/ff6ZWJb1217e7WHdSgyf8o/dwv+vTDxZqR+JJemfi2vLzsfx9/OHFMU0e/oC7/elINmzYvqdLhBjzMPj9AGUaGKrvSTp/W+HFj9e+35/HvCvgLFs6aoB+//04vT/j3VdtiWnfQ7U3+R+m//apPP7h+hjp6cK9+OnVC/Z4fWUJVw12QocouMlTZUqNGTb33wQplZv6uNV98rmEvDdXcpEW6NSpKD3V/2Naudp26qlQpVP369Napkyd5SBy4jjnTxunUiWMaM3WuQ/sZhqH8ixf1ROxgRd9x6d27A15+TX27t9P+3V+p8Z13O6NcuCGzZyi36IyLj4+3WzYMQ6dPn9aqVavUq1evG+6bmJioUaNG2a17edgIvTJ8ZHGXiWJWtVo1zVuwSH/88YeysjIVGhqmwc8PVJUqVV1dGlAqLJw5Qbt3btbL4/+t4GtMneTj6ycfXz9F3FJNUbc11NPd71fK1g2KadXe1ubHk99p3EuxatWxizr/s09Jlg83YO4IVDaQocznwIH9Ovfrr3rkf7va1uXn5yvl66+09N3F+uqbvfL09HRhhYD7Wzhrgvbs3KyXXi9EhqrbQP0fbntVhpKkjZ9/rGq16qhm7XolVTrcBBmq9CNDmUN5Ly9Vq15dklT/9gbav2+vFi9aqOEjR1/VtmGjaEnSyZPf0xkHXMOc6a9r147NGjX5bYWE/jc/BQWH6GJenrIyf7cbHZfx268KqhgiSaoYXEmSVKV6Ldv2wKCKCggI0i8/p5XQFcAdmD1DuUVn3DfffGO37OHhodDQUE2aNElPPPHEDfdNSEi4OkR58jRSaeLj4yMfHx+dz8jQti2bNTB+sKtLAtyaYRh6Z9ZEpWzboIRxsxQaccvN95EhydDFvDzbuh++P6ZxCbG69/6/6397PePEigE4CxnKfO5q3lzvr1hpt27EywmqUauWHu/Tl4444AYMw9A7sycqZdtGJSTOVGhE5M33uUaGkqTsC39o5+a1eogMBZRKZChzKigoUF5u7jW3pR46KEkKDQ0tyZIAt2cYhua+OV47N6/XqElvKbyy/T2oWrXrybNcOe3dtVPNW9wvSfrx1An98nOa6tRvJEmq2+BSZ/dPp763deT9fj5D58+nKzS8cgleDeBabtEZt379+iLva7VePRVA9o1nFICb2LL5S8kwVL1mTZ06eVJTJo5XjZq11PnBrjffGTCxBTPHa/uGzzVw+ERV8PZR+rlfJF16itvLWkE/n/5ROzatUYOmd8k/sKJ+++VnJS9foPJeVkX/39D/H04cU2LCM2rYtLk6PPgv2zE8PD0VEFjRZdeGEmb2R5LKADKU+fj6+ql27Tp267x9fBQUGHTVegD2Fs6coO0bP9eAYRNUwdtX6ecuvcfEx9f3vxnqyzVq0OQuBQRW1Llfflby8oV2GeqyHZv+o/z8fN3duoMrLgWuRoYq9chQZd8bUybp3vtaKKJyZf2RlaXVq5L19Vc7NeutuTp18qRWr1qp+1q0VGBQkI6kpmrC+EQ1u+NO1al7m6tLB9zKnGnjtHndZxoyerIq+Pjotz/dg7JaK8jXz19tOnTWgtmT5RcQIG8fP817c7zq1G+kOvUbSpIiq1TXnXe31PyZE/XUoJfl7eOrJXPf1C1Va+j2xne48vJQ0kyeodyiMw7mlJn5u6ZNnawzaWkKDAzS/X9rp2cHDOKlncBNrFv1gSTptaFP263vO2i47vtbJ5X38lLq/t36/OOlyso8r8CgYNVt0ETDJ81VQFCwJGnn5rX6PeM3bV3/qbau/9R2jEphlTU56eOSuxi4lMXsKQgAYCrrVl/KUIkv9rdb/+TAYbYMdXj/bn3x8VJlZf5uy1DDJs6xZajLNn3xie64u5XddEwwDzIU4P7OnftVryQM1dmzP8vP31916tTVrLfmKubue5R2+rR2bN+mxe8s1IULfygiorLatm2nvk8z2hm40hcr35ckjXy+n936ZwaPUOv2/0+S1PuZ5+Xh4aGJo4boYl6uou+I0ZPPvWjXPm7oaCXNmqzElwfIYvFQ/eimejlxusqV4z6wmZg9Q1kMwzBcceImTZrIUsgX9u3atcuhY/NEEuBce77PcHUJQJl1162BJXauHceK57tckjWDDAWUZrtPpLu6BKDMah4VVGLnIkOVTmQooHQ6fDrT1SUAZVqjqn4ldi6zZyiXjYzr0qWLq04NAAAkFfJeBNwMGQoAANciQ5VOZCgAAFzL7BnKZZ1xI0aMcNWpAQCATD9Vd6lFhgIAwLXIUKUTGQoAANcye4bycHUBAAAAAAAAAAAAQFnlspFxwcHBOnz4sCpVqqSKFSvecN7uc+fOlWBlAACYhNkfSSqlyFAAALgYGapUIkMBAOBiJs9QLuuMmzJlivz9/SVJU6dOdVUZAACYlsXsKaiUIkMBAOBaZKjSiQwFAIBrmT1DWQzDMFxdRHHLvujqCoCybc/3Ga4uASiz7ro1sMTOlXLifLEcp1mNgGI5DlyPDAU41+4T6a4uASizmkcFldi5yFC4EhkKcJ7DpzNdXQJQpjWq6ldi5zJ7hnLZyLjryc7OVm5urt26gIDS+ccFAAAoKWQoAAAAx5GhAABASfBwdQGSlJWVpbi4OIWFhcnX11cVK1a0+wAAgOJnKaYPXIcMBQBAyXNFhkpMTNSdd94pf39/hYWFqUuXLkpNTbVrk52drdjYWIWEhMjPz0/dunXTmTNn7NqcPHlSDzzwgHx8fBQWFqbBgwfr4kXzDesiQwEAUPLMfh/KLTrjhgwZonXr1mnWrFmyWq2aM2eORo0apcjISC1cuNDV5QEAUDa5IAVxI6l4kaEAAHABF2SojRs3KjY2Vtu3b9eaNWuUl5endu3aKSsry9Zm0KBBWrlypZYvX66NGzfqp59+UteuXW3b8/Pz9cADDyg3N1dbt27VggULlJSUpOHDhxfxD1F6kaEAAHABk/fGucU746pVq6aFCxeqVatWCggI0K5duxQVFaV33nlH7777rlavXu3Q8ZirG3Au3hkHOE9JvjNu1/fFM1d30+qFn8anQ4cOeuSRR3TnnXfq4sWLeumll7Rv3z4dOHBAvr6+kqT+/ftr1apVSkpKUmBgoOLi4uTh4aEtW7ZIunQjqXHjxoqIiNCECRN0+vRp9ezZU3379tVrr71WLNdUWpChgNKFd8YBzlOS74xzRYa60tmzZxUWFqaNGzeqRYsWysjIUGhoqJYsWaKHHnpIknTo0CHVq1dP27ZtU/PmzfXpp5+qU6dO+umnnxQeHi5Jmj17toYOHaqzZ8/Ky8urWK6rNCBDAaUH74wDnKsk3xnnDhnKldxiZNy5c+dUq1YtSZfm5T537pwk6d5779WmTZtcWRoAAGWWpZj+ccRnn32m3r176/bbb1d0dLSSkpJ08uRJpaSkSJIyMjI0d+5cTZ48WW3atFGzZs00f/58bd26Vdu3b5ckffHFFzpw4IAWLVqkxo0bq2PHjhozZoxmzJhx1fs+yjoyFAAAJa+4MlROTo7Onz9v98nJySlUDRkZlx6QDA4OliSlpKQoLy9Pbdu2tbW57bbbVK1aNW3btk2StG3bNjVs2NDWESdJ7du31/nz57V///7i+vOUCmQoAABKnivuQ7kTt+iMq1Wrlo4fPy7pUlh87733JEkrV65UUFCQCysDAKDssliK58ONJNchQwEAUPKKK0MlJiYqMDDQ7pOYmHjT8xcUFGjgwIG655571KBBA0lSWlqavLy8rvrvf3h4uNLS0mxt/pyfLm+/vM1MyFAAAJS84spQpZVLO+O+++47FRQU6PHHH9eePXskSS+++KJmzJihChUqaNCgQRo8eLArSwQAADfBjaSSR4YCAKD0S0hIUEZGht0nISHhpvvFxsZq3759Wrp0aQlUWbaQoQAAgKuUc+XJa9eurdOnT2vQoEGSpIcffljTpk3ToUOHlJKSoqioKDVq1MiVJQIAUGYV18NECQkJio+Pt1tntVpvut/lG0mbN28upkrMgww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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_labels = ['No Failure', 'Failure']\n",
    "conf_matrix(val_cm_list, 'Validation Dataset', class_labels)\n",
    "conf_matrix(test_cm_list, 'Test Dataset', class_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3xTWXLMrq-Ew",
   "metadata": {
    "id": "3xTWXLMrq-Ew"
   },
   "source": [
    "### Supervised Learning - Multi Class Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "xyreTvhUrBHo",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xyreTvhUrBHo",
    "outputId": "a4f9a1ee-9b67-4388-c244-0c9f81312b9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting and Tuning Random Forest...\n",
      "{'max_depth': 10, 'n_estimators': 500, 'random_state': 0}\n",
      "Fitting and Tuning XGBoost...\n",
      "{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 500, 'objective': 'binary:logistic'}\n",
      "Fitting and Tuning Neural Network...\n",
      "{'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (20,), 'learning_rate': 'constant', 'solver': 'adam'}\n"
     ]
    }
   ],
   "source": [
    "fitted_models_multi = {}\n",
    "for name, details in models.items():\n",
    "    model = details['model']\n",
    "    params = details['param_grid']\n",
    "\n",
    "    print(f'Fitting and Tuning {name}...')\n",
    "\n",
    "    f2_scorer = make_scorer(fbeta_score, beta=2, average='weighted')\n",
    "    grid_search = GridSearchCV(model, param_grid=params, cv=5, scoring=f2_scorer)\n",
    "    grid_search.fit(X_train, y_train['Failure Type'])\n",
    "    best_params = grid_search.best_params_\n",
    "\n",
    "    fitted_models_multi[name] = {\n",
    "        'model': grid_search.best_estimator_,\n",
    "        'best_params': best_params\n",
    "    }\n",
    "    print(best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "zrL80BpOrDl6",
   "metadata": {
    "id": "zrL80BpOrDl6"
   },
   "outputs": [],
   "source": [
    "def evaluate_multiclass_models(model, X_test, y_test):\n",
    "    y_pred = model.predict(X_test)\n",
    "    cm = confusion_matrix(y_test['Failure Type'], y_pred)\n",
    "    y_pred_prob = model.predict_proba(X_test)\n",
    "    acc = round(accuracy_score(y_test['Failure Type'], y_pred), 3)\n",
    "    auc = round(roc_auc_score(y_test['Failure Type'], y_pred_prob,multi_class='ovr', average='weighted'), 3)\n",
    "    f1 = round(f1_score(y_test['Failure Type'], y_pred, average='weighted'), 3)\n",
    "\n",
    "    metrics = {\n",
    "        'model': model.__class__.__name__,\n",
    "        'accuracy': acc,\n",
    "        'roc score': auc,\n",
    "        'f1 score': f1,\n",
    "    }\n",
    "    return metrics, cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ZnUIqIIKyEoW",
   "metadata": {
    "id": "ZnUIqIIKyEoW"
   },
   "outputs": [],
   "source": [
    "val_metrics_list_multi = []\n",
    "val_cm_list_multi = {}\n",
    "test_metrics_list_multi = []\n",
    "test_cm_list_multi = {}\n",
    "\n",
    "for name, details in fitted_models_multi.items():\n",
    "    val_metrics_multi, val_cm_multi = evaluate_multiclass_models(details['model'], X_val, y_val)\n",
    "\n",
    "    val_cm_list_multi[name] = val_cm_multi\n",
    "\n",
    "    val_metrics_list_multi.append({\n",
    "        'Model' : name,\n",
    "        'Accuracy' : val_metrics_multi['accuracy'],\n",
    "        'ROC Score' : val_metrics_multi['roc score'],\n",
    "        'F1 score': val_metrics_multi['f1 score']\n",
    "       })\n",
    "\n",
    "    val_metrics_df_multi = pd.DataFrame(val_metrics_list_multi)\n",
    "\n",
    "    test_metrics_multi, test_cm_multi = evaluate_multiclass_models(details['model'], X_test, y_test)\n",
    "\n",
    "    test_cm_list_multi[name] = test_cm_multi\n",
    "\n",
    "    test_metrics_list_multi.append({\n",
    "        'Model' : name,\n",
    "        'Accuracy' : test_metrics_multi['accuracy'],\n",
    "        'ROC Score' : test_metrics_multi['roc score'],\n",
    "        'F1 score': test_metrics_multi['f1 score']\n",
    "       })\n",
    "    test_metrics_df_multi = pd.DataFrame(test_metrics_list_multi)"
   ]
  },
  {
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   "execution_count": 41,
   "id": "cQX3Jwu6yGTK",
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    "id": "cQX3Jwu6yGTK",
    "outputId": "61825ae0-de4a-43a8-951f-e8a83d432188"
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     "output_type": "stream",
     "text": [
      "Evaluation Metrics of Validation Dataset\n"
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      "text/plain": [
       "            Model  Accuracy  ROC Score  F1 score\n",
       "0   Random Forest     0.977      0.998     0.977\n",
       "1         XGBoost     0.984      0.999     0.984\n",
       "2  Neural Network     0.956      0.994     0.954"
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     "text": [
      "Evaluation Metrics of Test Dataset\n"
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       "      <td>0.974</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.984</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neural Network</td>\n",
       "      <td>0.959</td>\n",
       "      <td>0.994</td>\n",
       "      <td>0.959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "        document.querySelector('#df-51add354-f70e-4412-abc9-33dce35c0f3f button.colab-df-convert');\n",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "\n",
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       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
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       "    60% {\n",
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       "    80% {\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
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       "        document.querySelector('#df-b6fd4d06-c110-404b-b581-a2e5b67dd499 button');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "</div>\n",
       "\n",
       "  <div id=\"id_a4869c43-cffb-44a8-825c-d25bcbb7c118\">\n",
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       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
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       "\n",
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       "\n",
       "      [theme=dark] .colab-df-generate:hover {\n",
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       "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "      }\n",
       "    </style>\n",
       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('test_metrics_df_multi')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "    </button>\n",
       "    <script>\n",
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       "        document.querySelector('#id_a4869c43-cffb-44a8-825c-d25bcbb7c118 button.colab-df-generate');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      buttonEl.onclick = () => {\n",
       "        google.colab.notebook.generateWithVariable('test_metrics_df_multi');\n",
       "      }\n",
       "      })();\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "            Model  Accuracy  ROC Score  F1 score\n",
       "0   Random Forest     0.974      0.998     0.975\n",
       "1         XGBoost     0.984      0.999     0.984\n",
       "2  Neural Network     0.959      0.994     0.959"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Evaluation Metrics of Validation Dataset')\n",
    "display(val_metrics_df_multi)\n",
    "print('Evaluation Metrics of Test Dataset')\n",
    "display(test_metrics_df_multi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fiLPMHqdyHaN",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 842
    },
    "id": "fiLPMHqdyHaN",
    "outputId": "0fae3fff-502d-4fb4-e507-577350dd886f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_labels = ['No Failure', 'Power Failure', 'Tool Wear Failure', 'Overstrain Failure', 'Heat Dissipation Failure']\n",
    "conf_matrix(val_cm_list_multi, 'Validation Dataset', class_labels)\n",
    "conf_matrix(test_cm_list_multi, 'Test Dataset', class_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3GDwaOnGzHWf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "3GDwaOnGzHWf",
    "outputId": "45e87509-0125-4456-cf02-c256a87fd1e1"
   },
   "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>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=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=700, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></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=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=700, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "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=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=7, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=700, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_model = fitted_models['XGBoost']['model']\n",
    "binary_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "OwkJQp_xzJra",
   "metadata": {
    "id": "OwkJQp_xzJra"
   },
   "outputs": [],
   "source": [
    "with open(os.path.join('pickle', 'binary_xgb.pkl'),'wb') as f:\n",
    "    pickle.dump(binary_model, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "kvUqovdxzL9q",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "kvUqovdxzL9q",
    "outputId": "eda460ec-acc9-4672-ff57-a58774363251"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\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-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><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=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></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=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "              num_parallel_tree=None, objective=&#x27;multi:softprob&#x27;, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "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=0.1, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=5, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "              num_parallel_tree=None, objective='multi:softprob', ...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_model = fitted_models_multi['XGBoost']['model']\n",
    "multi_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "SVfg_jG-zOy3",
   "metadata": {
    "id": "SVfg_jG-zOy3"
   },
   "outputs": [],
   "source": [
    "with open(os.path.join('pickle', 'multi_xgb.pkl'),'wb') as f:\n",
    "    pickle.dump(multi_model, f)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
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  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "papermill": {
   "default_parameters": {},
   "duration": 1258.31434,
   "end_time": "2023-11-04T11:48:20.025547",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-11-04T11:27:21.711207",
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