diff --git a/TP1/Solution/ai-for-io-t-fire-alarm-detection.ipynb b/TP1/Solution/ai-for-io-t-fire-alarm-detection.ipynb
new file mode 100644
index 0000000..7b64985
--- /dev/null
+++ b/TP1/Solution/ai-for-io-t-fire-alarm-detection.ipynb
@@ -0,0 +1,918 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99ff33ec",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:08.026599Z",
+     "iopub.status.busy": "2025-11-09T21:03:08.026228Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.110628Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.109664Z"
+    },
+    "papermill": {
+     "duration": 2.095131,
+     "end_time": "2025-11-09T21:03:10.112449",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:08.017318",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5c9e8041",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.126967Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.126543Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.389752Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.388764Z"
+    },
+    "papermill": {
+     "duration": 0.272681,
+     "end_time": "2025-11-09T21:03:10.391914",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.119233",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame()\n",
+    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "    for filename in filenames:\n",
+    "        df = pd.read_csv(os.path.join(dirname, filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a88baeb8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.409843Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.409517Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.431856Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.430494Z"
+    },
+    "papermill": {
+     "duration": 0.032959,
+     "end_time": "2025-11-09T21:03:10.433844",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.400885",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(columns=[\"Unnamed: 0\", \"UTC\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ce2ae151",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.449978Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.448980Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.455304Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.454204Z"
+    },
+    "papermill": {
+     "duration": 0.01633,
+     "end_time": "2025-11-09T21:03:10.457142",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.440812",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.columns = df.columns.str.strip()\n",
+    "df.columns = df.columns.str.replace(r'[^A-Za-z0-9_]+', '_', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "983c7ecf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.471525Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.471156Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.490273Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.489083Z"
+    },
+    "papermill": {
+     "duration": 0.028323,
+     "end_time": "2025-11-09T21:03:10.492090",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.463767",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of data: (62630, 14)\n",
+      "\n",
+      "Missing values:\n",
+      " Temperature_C_    0\n",
+      "Humidity_         0\n",
+      "TVOC_ppb_         0\n",
+      "eCO2_ppm_         0\n",
+      "Raw_H2            0\n",
+      "Raw_Ethanol       0\n",
+      "Pressure_hPa_     0\n",
+      "PM1_0             0\n",
+      "PM2_5             0\n",
+      "NC0_5             0\n",
+      "NC1_0             0\n",
+      "NC2_5             0\n",
+      "CNT               0\n",
+      "Fire_Alarm        0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Shape of data:\", df.shape)\n",
+    "print(\"\\nMissing values:\\n\", df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "57ef1af7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.507291Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.506999Z",
+     "iopub.status.idle": "2025-11-09T21:03:12.597016Z",
+     "shell.execute_reply": "2025-11-09T21:03:12.596043Z"
+    },
+    "papermill": {
+     "duration": 2.100192,
+     "end_time": "2025-11-09T21:03:12.598867",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.498675",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c904c52f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:12.613730Z",
+     "iopub.status.busy": "2025-11-09T21:03:12.613174Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.260624Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.259345Z"
+    },
+    "papermill": {
+     "duration": 0.657069,
+     "end_time": "2025-11-09T21:03:13.262655",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:12.605586",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(df.corr(), cmap=\"coolwarm\", annot=False)\n",
+    "plt.title(\"Correlation Heatmap\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e0da765d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.279770Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.279466Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.291049Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.290222Z"
+    },
+    "papermill": {
+     "duration": 0.022361,
+     "end_time": "2025-11-09T21:03:13.292805",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.270444",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X = df.drop(columns=[\"Fire_Alarm\"])\n",
+    "y = df[\"Fire_Alarm\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9da9984e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.309578Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.309255Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.545083Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.543845Z"
+    },
+    "papermill": {
+     "duration": 0.246164,
+     "end_time": "2025-11-09T21:03:13.547011",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.300847",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b3286833",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.563831Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.562995Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.605038Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.604194Z"
+    },
+    "papermill": {
+     "duration": 0.052379,
+     "end_time": "2025-11-09T21:03:13.607014",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.554635",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "minmax_scaler = MinMaxScaler()\n",
+    "X_minmax = minmax_scaler.fit_transform(X)\n",
+    "X_minmax = pd.DataFrame(X_minmax, columns=X.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ebdad4d9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.623461Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.623053Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.652861Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.651861Z"
+    },
+    "papermill": {
+     "duration": 0.039933,
+     "end_time": "2025-11-09T21:03:13.654551",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.614618",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temperature_C_</th>\n",
+       "      <th>Humidity_</th>\n",
+       "      <th>TVOC_ppb_</th>\n",
+       "      <th>eCO2_ppm_</th>\n",
+       "      <th>Raw_H2</th>\n",
+       "      <th>Raw_Ethanol</th>\n",
+       "      <th>Pressure_hPa_</th>\n",
+       "      <th>PM1_0</th>\n",
+       "      <th>PM2_5</th>\n",
+       "      <th>NC0_5</th>\n",
+       "      <th>NC1_0</th>\n",
+       "      <th>NC2_5</th>\n",
+       "      <th>CNT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.512692</td>\n",
+       "      <td>0.723239</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.522488</td>\n",
+       "      <td>0.525685</td>\n",
+       "      <td>0.986014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.512875</td>\n",
+       "      <td>0.712535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.534928</td>\n",
+       "      <td>0.547185</td>\n",
+       "      <td>0.987013</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.513046</td>\n",
+       "      <td>0.701520</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.544179</td>\n",
+       "      <td>0.565731</td>\n",
+       "      <td>0.986347</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.513229</td>\n",
+       "      <td>0.690971</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.549282</td>\n",
+       "      <td>0.579682</td>\n",
+       "      <td>0.986125</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.513412</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.553429</td>\n",
+       "      <td>0.591498</td>\n",
+       "      <td>0.987013</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Temperature_C_  Humidity_  TVOC_ppb_  eCO2_ppm_    Raw_H2  Raw_Ethanol  \\\n",
+       "0        0.512692   0.723239        0.0        0.0  0.522488     0.525685   \n",
+       "1        0.512875   0.712535        0.0        0.0  0.534928     0.547185   \n",
+       "2        0.513046   0.701520        0.0        0.0  0.544179     0.565731   \n",
+       "3        0.513229   0.690971        0.0        0.0  0.549282     0.579682   \n",
+       "4        0.513412   0.681818        0.0        0.0  0.553429     0.591498   \n",
+       "\n",
+       "   Pressure_hPa_  PM1_0  PM2_5  NC0_5  NC1_0  NC2_5      CNT  \n",
+       "0       0.986014    0.0    0.0    0.0    0.0    0.0  0.00000  \n",
+       "1       0.987013    0.0    0.0    0.0    0.0    0.0  0.00004  \n",
+       "2       0.986347    0.0    0.0    0.0    0.0    0.0  0.00008  \n",
+       "3       0.986125    0.0    0.0    0.0    0.0    0.0  0.00012  \n",
+       "4       0.987013    0.0    0.0    0.0    0.0    0.0  0.00016  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_minmax.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c769db30",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.672003Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.671667Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.933724Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.932680Z"
+    },
+    "papermill": {
+     "duration": 0.273076,
+     "end_time": "2025-11-09T21:03:13.935619",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.662543",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4ee3b6e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.952615Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.952259Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.994485Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.993276Z"
+    },
+    "papermill": {
+     "duration": 0.052908,
+     "end_time": "2025-11-09T21:03:13.996539",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.943631",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_minmax, y, test_size=0.2, random_state=42, stratify=y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dc40cf89",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.013011Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.012689Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.017374Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.016481Z"
+    },
+    "papermill": {
+     "duration": 0.014651,
+     "end_time": "2025-11-09T21:03:14.018873",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.004222",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "log_reg = LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5bf88923",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.035605Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.035249Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.828082Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.826463Z"
+    },
+    "papermill": {
+     "duration": 0.806094,
+     "end_time": "2025-11-09T21:03:14.832676",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.026582",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_reg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f42f186a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.865519Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.865129Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.877009Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.873061Z"
+    },
+    "papermill": {
+     "duration": 0.028268,
+     "end_time": "2025-11-09T21:03:14.878819",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.850551",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = log_reg.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "29f5fd1e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.907578Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.906802Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.911713Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.910672Z"
+    },
+    "papermill": {
+     "duration": 0.018673,
+     "end_time": "2025-11-09T21:03:14.913286",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.894613",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b0c49095",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.930787Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.930450Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.154379Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.153087Z"
+    },
+    "papermill": {
+     "duration": 0.235271,
+     "end_time": "2025-11-09T21:03:15.156555",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.921284",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
+    "            xticklabels=[\"No Alarm\", \"Fire Alarm\"],\n",
+    "            yticklabels=[\"No Alarm\", \"Fire Alarm\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "130fc2d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.175438Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.175038Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.521626Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.520496Z"
+    },
+    "papermill": {
+     "duration": 0.358078,
+     "end_time": "2025-11-09T21:03:15.523589",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.165511",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from xgboost import XGBClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "19ded50e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.541793Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.541457Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.546690Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.545575Z"
+    },
+    "papermill": {
+     "duration": 0.016439,
+     "end_time": "2025-11-09T21:03:15.548509",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.532070",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xgb_clf = XGBClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "877ed54c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.571036Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.570712Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.905784Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.904931Z"
+    },
+    "papermill": {
+     "duration": 0.346471,
+     "end_time": "2025-11-09T21:03:15.907710",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.561239",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+      ],
+      "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=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "              num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xgb_clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6b23e21b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.928243Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.927846Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.945072Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.944333Z"
+    },
+    "papermill": {
+     "duration": 0.028884,
+     "end_time": "2025-11-09T21:03:15.947225",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.918341",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_xgb = xgb_clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4a74df94",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.971009Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.969821Z",
+     "iopub.status.idle": "2025-11-09T21:03:16.167310Z",
+     "shell.execute_reply": "2025-11-09T21:03:16.166091Z"
+    },
+    "papermill": {
+     "duration": 0.210109,
+     "end_time": "2025-11-09T21:03:16.169032",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.958923",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm_xgb = confusion_matrix(y_test, y_pred_xgb)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm_xgb, annot=True, fmt=\"d\", cmap=\"Greens\",\n",
+    "            xticklabels=[\"No Alarm\", \"Fire Alarm\"],\n",
+    "            yticklabels=[\"No Alarm\", \"Fire Alarm\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [
+    {
+     "datasetId": 2424784,
+     "sourceId": 4100314,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31089,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 17.106672,
+   "end_time": "2025-11-09T21:03:18.998540",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2025-11-09T21:03:01.891868",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/TP2/Solution/ai-for-io-t-fire-alarm-detection.ipynb b/TP2/Solution/ai-for-io-t-fire-alarm-detection.ipynb
new file mode 100644
index 0000000..d8a3ce2
--- /dev/null
+++ b/TP2/Solution/ai-for-io-t-fire-alarm-detection.ipynb
@@ -0,0 +1,1423 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99ff33ec",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:08.026599Z",
+     "iopub.status.busy": "2025-11-09T21:03:08.026228Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.110628Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.109664Z"
+    },
+    "papermill": {
+     "duration": 2.095131,
+     "end_time": "2025-11-09T21:03:10.112449",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:08.017318",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5c9e8041",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.126967Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.126543Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.389752Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.388764Z"
+    },
+    "papermill": {
+     "duration": 0.272681,
+     "end_time": "2025-11-09T21:03:10.391914",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.119233",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame()\n",
+    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "    for filename in filenames:\n",
+    "        df = pd.read_csv(os.path.join(dirname, filename))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a88baeb8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.409843Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.409517Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.431856Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.430494Z"
+    },
+    "papermill": {
+     "duration": 0.032959,
+     "end_time": "2025-11-09T21:03:10.433844",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.400885",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(columns=[\"Unnamed: 0\", \"UTC\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ce2ae151",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.449978Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.448980Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.455304Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.454204Z"
+    },
+    "papermill": {
+     "duration": 0.01633,
+     "end_time": "2025-11-09T21:03:10.457142",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.440812",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df.columns = df.columns.str.strip()\n",
+    "df.columns = df.columns.str.replace(r'[^A-Za-z0-9_]+', '_', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "983c7ecf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.471525Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.471156Z",
+     "iopub.status.idle": "2025-11-09T21:03:10.490273Z",
+     "shell.execute_reply": "2025-11-09T21:03:10.489083Z"
+    },
+    "papermill": {
+     "duration": 0.028323,
+     "end_time": "2025-11-09T21:03:10.492090",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.463767",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of data: (62630, 14)\n",
+      "\n",
+      "Missing values:\n",
+      " Temperature_C_    0\n",
+      "Humidity_         0\n",
+      "TVOC_ppb_         0\n",
+      "eCO2_ppm_         0\n",
+      "Raw_H2            0\n",
+      "Raw_Ethanol       0\n",
+      "Pressure_hPa_     0\n",
+      "PM1_0             0\n",
+      "PM2_5             0\n",
+      "NC0_5             0\n",
+      "NC1_0             0\n",
+      "NC2_5             0\n",
+      "CNT               0\n",
+      "Fire_Alarm        0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Shape of data:\", df.shape)\n",
+    "print(\"\\nMissing values:\\n\", df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "57ef1af7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:10.507291Z",
+     "iopub.status.busy": "2025-11-09T21:03:10.506999Z",
+     "iopub.status.idle": "2025-11-09T21:03:12.597016Z",
+     "shell.execute_reply": "2025-11-09T21:03:12.596043Z"
+    },
+    "papermill": {
+     "duration": 2.100192,
+     "end_time": "2025-11-09T21:03:12.598867",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:10.498675",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c904c52f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:12.613730Z",
+     "iopub.status.busy": "2025-11-09T21:03:12.613174Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.260624Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.259345Z"
+    },
+    "papermill": {
+     "duration": 0.657069,
+     "end_time": "2025-11-09T21:03:13.262655",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:12.605586",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+4AAAMLCAYAAADQbc2PAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADCHElEQVR4nOzdeVhVVfv/8c8B5DAJSA6oqago4ohp+cUep6LAehyaNLUUx6xMc2igQVBLnLVBszLFnq9jk5WaZihmSmoZ9lTOY5k4ZEJOoJzz+6Of59uJQZCN+3h6v65rXRdn7bX3vvfGzPvca69tsdvtdgEAAAAAAJfkYXYAAAAAAACgcCTuAAAAAAC4MBJ3AAAAAABcGIk7AAAAAAAujMQdAAAAAAAXRuIOAAAAAIALI3EHAAAAAMCFkbgDAAAAAODCSNwBAAAAAHBhJO4AAJRQSkqKLBaLDh48aNgxDx48KIvFopSUFMOOCQAA3AOJOwDAJezbt0+PPPKI6tSpIx8fHwUGBurWW2/VK6+8ovPnz5sdnmEWLlyoGTNmmB2Gk/j4eAUEBBS63WKxaMiQIWUaw6xZs/jSAgCAQniZHQAAACtWrNADDzwgq9Wq3r17q3HjxsrNzdVXX32lp556Sj/++KPeeusts8M0xMKFC/XDDz/oySefdOqvVauWzp8/r3LlypkTmMlmzZqlihUrKj4+3uxQAABwOSTuAABTHThwQA8++KBq1aqltWvXqmrVqo5tjz/+uPbu3asVK1aU+jx2u10XLlyQr69vvm0XLlyQt7e3PDzMm4hmsVjk4+Nj2vkBAIDrYqo8AMBUkyZN0pkzZ/TOO+84Je2XhYeHa9iwYY7Ply5d0rhx41S3bl1ZrVaFhYXpueeeU05OjtN+YWFh+ve//63Vq1erZcuW8vX11Ztvvqm0tDRZLBYtXrxYL7zwgqpXry4/Pz9lZ2dLkjZv3qy4uDgFBQXJz89P7dq108aNG694HR9//LHuvvtuVatWTVarVXXr1tW4ceOUl5fnGNO+fXutWLFChw4dksVikcViUVhYmKTCn3Ffu3at2rRpI39/fwUHB6tLly7asWOH05ikpCRZLBbt3btX8fHxCg4OVlBQkPr27atz585dMfarkZOTo8TERIWHh8tqtapGjRp6+umn8/0e5s2bp9tuu02VK1eW1WpVw4YN9cYbbziNCQsL048//qj169c77kv79u0l/d96Al999ZWGDh2qSpUqKTg4WI888ohyc3N1+vRp9e7dWxUqVFCFChX09NNPy263Ox1/ypQpat26tW644Qb5+vqqRYsWev/99/Nd0+VHAhYsWKCIiAj5+PioRYsW+vLLL429eQAAlBAVdwCAqT799FPVqVNHrVu3Ltb4AQMGaP78+br//vs1cuRIbd68WcnJydqxY4c++ugjp7G7du1Sjx499Mgjj2jgwIGKiIhwbBs3bpy8vb01atQo5eTkyNvbW2vXrlXHjh3VokULJSYmysPDw5F4btiwQbfcckuhcaWkpCggIEAjRoxQQECA1q5dq9GjRys7O1uTJ0+WJD3//PPKysrSL7/8ounTp0tSkc+Wf/HFF+rYsaPq1KmjpKQknT9/Xq+99ppuvfVWbdu2zZH0X9atWzfVrl1bycnJ2rZtm+bMmaPKlStr4sSJxbq3J0+eLNY4m82mzp0766uvvtKgQYMUGRmp//73v5o+fbp2796tZcuWOca+8cYbatSokTp37iwvLy99+umneuyxx2Sz2fT4449LkmbMmKEnnnhCAQEBev755yVJVapUcTrnE088odDQUI0ZM0Zff/213nrrLQUHB2vTpk2qWbOmxo8fr5UrV2ry5Mlq3Lixevfu7dj3lVdeUefOndWrVy/l5uZq8eLFeuCBB7R8+XLdfffdTudZv369lixZoqFDh8pqtWrWrFmKi4vTli1b1Lhx42LdHwAADGcHAMAkWVlZdkn2Ll26FGt8RkaGXZJ9wIABTv2jRo2yS7KvXbvW0VerVi27JPuqVaucxq5bt84uyV6nTh37uXPnHP02m81er149e2xsrN1mszn6z507Z69du7b9jjvucPTNmzfPLsl+4MABp3F/98gjj9j9/PzsFy5ccPTdfffd9lq1auUbe+DAAbsk+7x58xx9UVFR9sqVK9t/++03R9/27dvtHh4e9t69ezv6EhMT7ZLs/fr1czrmPffcY7/hhhvynevv+vTpY5dUZHv88ccd4//zn//YPTw87Bs2bHA6zuzZs+2S7Bs3bizyvsTGxtrr1Knj1NeoUSN7u3bt8o29fK///nuJjo62WywW++DBgx19ly5dst944435jvP3GHJzc+2NGze233bbbU79l6/1m2++cfQdOnTI7uPjY7/nnnvyxQYAwLXCVHkAgGkuT08vX758scavXLlSkjRixAin/pEjR0pSvmfha9eurdjY2AKP1adPH6fn3TMyMrRnzx717NlTv/32m06ePKmTJ0/q7Nmzuv322/Xll1/KZrMVGttfj/XHH3/o5MmTatOmjc6dO6edO3cW6/r+6ujRo8rIyFB8fLxCQkIc/U2bNtUdd9zhuBd/NXjwYKfPbdq00W+//ea4z0Xx8fHRmjVrCmx/99577ykyMlINGjRw3KeTJ0/qtttukyStW7fOMfav9yUrK0snT55Uu3bttH//fmVlZV35Rvx//fv3l8VicXxu1aqV7Ha7+vfv7+jz9PRUy5YttX//fqd9/xrD77//rqysLLVp00bbtm3Ld57o6Gi1aNHC8blmzZrq0qWLVq9e7fTYAwAA1xJT5QEApgkMDJT0Z6JbHIcOHZKHh4fCw8Od+kNDQxUcHKxDhw459deuXbvQY/192549eyT9mdAXJisrSxUqVChw248//qgXXnhBa9euzZcolyRBvezytfx1ev9lkZGRWr16tc6ePSt/f39Hf82aNZ3GXY71999/d9zrwnh6eiomJqZYse3Zs0c7duxQpUqVCtx+/Phxx88bN25UYmKi0tPT8z1vn5WVpaCgoGKd8+/Xdnm/GjVq5Ov//fffnfqWL1+ul156SRkZGU7P4P/1i4DL6tWrl6+vfv36OnfunE6cOKHQ0NBixQsAgJFI3AEApgkMDFS1atX0ww8/lGi/ghKughS0gnxh2y5X0ydPnqyoqKgC9ynsefTTp0+rXbt2CgwM1NixY1W3bl35+Pho27ZteuaZZ4qs1BvJ09OzwH773xZrKy2bzaYmTZpo2rRpBW6/nEzv27dPt99+uxo0aKBp06apRo0a8vb21sqVKzV9+vQS3ZfCrq2g/r9e74YNG9S5c2e1bdtWs2bNUtWqVVWuXDnNmzdPCxcuLPb5AQAwE4k7AMBU//73v/XWW28pPT1d0dHRRY6tVauWbDab9uzZo8jISEf/sWPHdPr0adWqVeuq46hbt66kP79MKG7l+bK0tDT99ttv+vDDD9W2bVtH/4EDB/KNLe6XDpevZdeuXfm27dy5UxUrVnSqtl9LdevW1fbt23X77bcXeT2ffvqpcnJy9MknnzhVzP86lf6y4t6Xkvrggw/k4+Oj1atXy2q1OvrnzZtX4PjLMy/+avfu3fLz8yt0hgEAAGWNZ9wBAKZ6+umn5e/vrwEDBujYsWP5tu/bt0+vvPKKJOmuu+6S9Ocq5H91ufL79xXCS6JFixaqW7eupkyZojNnzuTbfuLEiUL3vVz1/WulNzc3V7Nmzco31t/fv1hT56tWraqoqCjNnz9fp0+fdvT/8MMP+vzzzx33wgzdunXTkSNH9Pbbb+fbdv78eZ09e1ZSwfclKyurwKTZ39/f6TqN4unpKYvF4vR8+sGDB51Wvv+r9PR0p2fff/75Z3388ce68847C636AwBQ1qi4AwBMVbduXS1cuFDdu3dXZGSkevfurcaNGys3N1ebNm3Se++9p/j4eElSs2bN1KdPH7311luO6elbtmzR/Pnz1bVrV3Xo0OGq4/Dw8NCcOXPUsWNHNWrUSH379lX16tV15MgRrVu3ToGBgfr0008L3Ld169aqUKGC+vTpo6FDh8piseg///lPgVPUW7RooSVLlmjEiBG6+eabFRAQoE6dOhV43MmTJ6tjx46Kjo5W//79Ha+DCwoKUlJS0lVfa2k9/PDDWrp0qQYPHqx169bp1ltvVV5ennbu3KmlS5dq9erVatmype688055e3urU6dOeuSRR3TmzBm9/fbbqly5so4ePep0zBYtWuiNN97QSy+9pPDwcFWuXNmx2F1p3H333Zo2bZri4uLUs2dPHT9+XDNnzlR4eLi+//77fOMbN26s2NhYp9fBSdKYMWNKHQsAAFeLxB0AYLrOnTvr+++/1+TJk/Xxxx/rjTfekNVqVdOmTTV16lQNHDjQMXbOnDmqU6eOUlJS9NFHHyk0NFQJCQlKTEwsdRzt27dXenq6xo0bp9dff11nzpxRaGioWrVqpUceeaTQ/W644QYtX75cI0eO1AsvvKAKFSrooYce0u23355vVfvHHntMGRkZmjdvnqZPn65atWoVmrjHxMRo1apVSkxM1OjRo1WuXDm1a9dOEydOLHLhvbLm4eGhZcuWafr06Xr33Xf10Ucfyc/PT3Xq1NGwYcNUv359SX8urPf+++/rhRde0KhRoxQaGqpHH31UlSpVUr9+/ZyOOXr0aB06dEiTJk3SH3/8oXbt2hmSuN9222165513NGHCBD355JOqXbu2Jk6cqIMHDxaYuLdr107R0dEaM2aMDh8+rIYNGyolJUVNmzYtdSwAAFwti93oFWsAAACuQxaLRY8//rhef/11s0MBAMAJz7gDAAAAAODCSNwBAAAAAHBhJO4AAAAAALgwEncAAAD9+do6nm8HgH+WL7/8Up06dVK1atVksVgKfV3oX6Wlpemmm26S1WpVeHi4UlJSyjxOEncAAAAAwD/S2bNn1axZM82cObNY4w8cOKC7775bHTp0UEZGhp588kkNGDBAq1evLtM4WVUeAAAAAOA2cnJylJOT49RntVpltVqL3M9iseijjz5S165dCx3zzDPPaMWKFfrhhx8cfQ8++KBOnz6tVatWlSruovAedzhZUS7C7BAMt/fDnWaHYLgODU6YHYLhbLKYHYLhdp6oaHYIhqsYkGt2CIb74aC32SGUidb1s80OwXA2swMoA+449dEdf092u/v9P+rEWX+zQzDc6bOeZodQJnq3MzuCq2NmXrH1+R4aM2aMU19iYqKSkpJKfez09HTFxMQ49cXGxurJJ58s9bGLQuIOAAAAAHAbCQkJGjFihFPflartxZWZmakqVao49VWpUkXZ2dk6f/68fH19DTnP35G4AwAAAADcRnGmxV9vSNwBAAAAAIaylHO/R0wkKTQ0VMeOHXPqO3bsmAIDA8us2i6556NVAAAAAAAYLjo6WqmpqU59a9asUXR0dJmel4o7AAAAAMBQHl7XR8X9zJkz2rt3r+PzgQMHlJGRoZCQENWsWVMJCQk6cuSI3n33XUnS4MGD9frrr+vpp59Wv379tHbtWi1dulQrVqwo0zipuAMAAAAA/pG++eYbNW/eXM2bN5ckjRgxQs2bN9fo0aMlSUePHtXhw4cd42vXrq0VK1ZozZo1atasmaZOnao5c+YoNja2TOOk4g4AAAAA+Edq37697HZ7odtTUlIK3Oe7774rw6jyI3EHAAAAABjKUo7J3UbibgIAAAAA4MKouAMAAAAADHW9LE53vaDiDgAAAACAC6PiDgAAAAAwlKUcFXcjUXEHAAAAAMCFkbgDAAAAAODCmCoPAAAAADAUi9MZi4o7AAAAAAAurNiJu8ViKbIlJSWVYZjmCAsL04wZM8wOwyE7O1vPP/+8GjRoIB8fH4WGhiomJkYffvih7Ha72eEBAAAAgKQ/F6czq7mjYk+VP3r0qOPnJUuWaPTo0dq1a5ejLyAgwNjIyojdbldeXp68vK7dUwK5ubny9vYu1TFOnz6tf/3rX8rKytJLL72km2++WV5eXlq/fr2efvpp3XbbbQoODjYmYAAAAACAyyh2xT00NNTRgoKCZLFYnPoWL16syMhI+fj4qEGDBpo1a5Zj34MHD8pisWjp0qVq06aNfH19dfPNN2v37t3aunWrWrZsqYCAAHXs2FEnTpxw7BcfH6+uXbtqzJgxqlSpkgIDAzV48GDl5uY6xthsNiUnJ6t27dry9fVVs2bN9P777zu2p6WlyWKx6LPPPlOLFi1ktVr11Vdfad++ferSpYuqVKmigIAA3Xzzzfriiy8c+7Vv316HDh3S8OHDHbMKJCkpKUlRUVFO92bGjBkKCwvLF/fLL7+satWqKSIiQpL0888/q1u3bgoODlZISIi6dOmigwcPFuv+P/fcczp48KA2b96sPn36qGHDhqpfv74GDhyojIyM6+aLEwAAAABAyRhSdl6wYIFGjx6t119/Xc2bN9d3332ngQMHyt/fX3369HGMS0xM1IwZM1SzZk3169dPPXv2VPny5fXKK6/Iz89P3bp10+jRo/XGG2849klNTZWPj4/S0tJ08OBB9e3bVzfccINefvllSVJycrL+93//V7Nnz1a9evX05Zdf6qGHHlKlSpXUrl07x3GeffZZTZkyRXXq1FGFChX0888/66677tLLL78sq9Wqd999V506ddKuXbtUs2ZNffjhh2rWrJkGDRqkgQMHlviepKamKjAwUGvWrJEkXbx4UbGxsYqOjtaGDRvk5eWll156SXFxcfr++++LrMjbbDYtXrxYvXr1UrVq1fJtJ2kHAAAA4EpYnM5YhiTuiYmJmjp1qu69915JUu3atfXTTz/pzTffdErcR40apdjYWEnSsGHD1KNHD6WmpurWW2+VJPXv318pKSlOx/b29tbcuXPl5+enRo0aaezYsXrqqac0btw4Xbx4UePHj9cXX3yh6OhoSVKdOnX01Vdf6c0333RK3MeOHas77rjD8TkkJETNmjVzfB43bpw++ugjffLJJxoyZIhCQkLk6emp8uXLKzQ0tMT3xN/fX3PmzHEk5P/7v/8rm82mOXPmOKr38+bNU3BwsNLS0nTnnXcWeqyTJ0/q999/V4MGDUocR1FycnKUk5Pj1HfRblM5C2sWAgAAAICrKHXifvbsWe3bt0/9+/d3qkxfunRJQUFBTmObNm3q+LlKlSqSpCZNmjj1HT9+3GmfZs2ayc/Pz/E5OjpaZ86c0c8//6wzZ87o3LlzTgm59Ocz5c2bN3fqa9mypdPnM2fOKCkpSStWrNDRo0d16dIlnT9/XocPHy7J5ReqSZMmTlX07du3a+/evSpfvrzTuAsXLmjfvn1FHqusFp5LTk7WmDFjnPp6WELUy7NimZwPAAAAwD+DxZOKu5FKnbifOXNGkvT222+rVatWTts8PT2dPpcrV87x8+Wq89/7bDZbic+9YsUKVa9e3Wmb1Wp1+uzv7+/0edSoUVqzZo2mTJmi8PBw+fr66v7773d6fr4gHh4e+RLpixcv5hv39/OdOXNGLVq00IIFC/KNrVSpUpHnrFSpkoKDg7Vz584ix5VUQkKCRowY4dS3NqSFoecAAAAAAJROqRP3KlWqqFq1atq/f7969eplRExOtm/frvPnz8vX11eS9PXXXysgIEA1atRQSEiIrFarDh8+7DQtvjg2btyo+Ph43XPPPZL+TKz/vlCct7e38vLynPoqVaqkzMxM2e12x5cPGRkZVzzfTTfdpCVLlqhy5coKDAwsUaweHh568MEH9Z///EeJiYn5nnM/c+aMfHx8SrxSvtVqzfcFB9PkAQAAAJSWBxV3QxmSpY0ZM0bJycl69dVXtXv3bv33v//VvHnzNG3atFIfOzc3V/3799dPP/2klStXKjExUUOGDJGHh4fKly+vUaNGafjw4Zo/f7727dunbdu26bXXXtP8+fOLPG69evX04YcfKiMjQ9u3b1fPnj3zVfvDwsL05Zdf6siRIzp58qSkP1ebP3HihCZNmqR9+/Zp5syZ+uyzz654Hb169VLFihXVpUsXbdiwQQcOHFBaWpqGDh2qX3755Yr7v/zyy6pRo4ZatWqld999Vz/99JP27NmjuXPnqnnz5o7ZBwAAAAAA92JI4j5gwADNmTNH8+bNU5MmTdSuXTulpKSodu3apT727bffrnr16qlt27bq3r27OnfurKSkJMf2cePG6cUXX1RycrIiIyMVFxenFStWXPHc06ZNU4UKFdS6dWt16tRJsbGxuummm5zGjB07VgcPHlTdunUd09kjIyM1a9YszZw5U82aNdOWLVs0atSoK16Hn5+fvvzyS9WsWVP33nuvIiMj1b9/f124cKFYFfiQkBB9/fXXeuihh/TSSy+pefPmatOmjRYtWqTJkyfnW08AAAAAAOAeLPayWvnMAPHx8Tp9+rSWLVtmdij/GCvKRZgdguH2fmjs2gCuoEODE2aHYDib3G861c4T7rfQY8WAotcBuR79cLDw13Fez1rXzzY7BMMVfxWc64c7PqDmjr8nu939/h914qz/lQddZ06f9bzyoOtQ75I9EewyNjY3b+2sW7/71rRzlxV3/P8FAAAAAABuw5D3uKP0AgICCt322WefqU2bNkXuv2HDBnXs2LHQ7TwDDwAAAOBasXhSIzaSSyfuKSkpZodwzRS1Mv3fX3VXkJYtWxZrdXsAAAAAwPXFpRP3f5Lw8PBS7e/r61vqYwAAAAAAXA+JOwAAAADAULzH3Vg8eAAAAAAAgAuj4g4AAAAAMJTFg4q7kai4AwAAAADgwqi4AwAAAAAMxTPuxqLiDgAAAACACyNxBwAAAADAhTFVHgAAAABgKAtT5Q1FxR0AAAAAABdGxR0AAAAAYCiLBzViI3E3AQAAAABwYSTuAAAAAAC4MKbKw8neD3eaHYLhwu9tYHYIhpuasN7sEAwXemOQ2SEYbtS/tpodguF+KVfH7BAM12vLU2aHUCYWe841OwTDBfi530JHJ07ZzA7BcJVvcL+6kL+P+/2e2pd3v/9HBZzZb3YIZaSH2QFcFYuH+/2dbSb3+5sVAAAAAAA3QsUdAAAAAGAoD14HZygq7gAAAAAAuDASdwAAAAAAXBhT5QEAAAAAhmJxOmNRcQcAAAAAwIVRcQcAAAAAGMriQY3YSNxNAAAAAABcGBV3AAAAAICheMbdWFTcAQAAAABwYSTuAAAAAAC4MKbKAwAAAAAM5eHJVHkjUXEHAAAAAMCFUXEHAAAAABiKxemMRcUdAAAAAAAXRuJusIMHD8pisSgjI6PQMWlpabJYLDp9+rQkKSUlRcHBwdckPgAAAADA9cUtE/f4+Hh17do1X//fE+ayUKNGDR09elSNGzcu9j7du3fX7t27HZ+TkpIUFRVVBtEBAAAAQNmzeHiY1twRz7gbzNPTU6GhoSXax9fXV76+vmUUEQAAAADgeuaeX0cUQ0FV7RkzZigsLMzx+XLlfvz48apSpYqCg4M1duxYXbp0SU899ZRCQkJ04403at68eY59Cpoqv3LlStWvX1++vr7q0KGDDh486HTev06VT0lJ0ZgxY7R9+3ZZLBZZLBalpKSoX79++ve//+2038WLF1W5cmW98847RtwSAAAAADCExcNiWnNHVNyvYO3atbrxxhv15ZdfauPGjerfv782bdqktm3bavPmzVqyZIkeeeQR3XHHHbrxxhvz7f/zzz/r3nvv1eOPP65Bgwbpm2++0ciRIws9X/fu3fXDDz9o1apV+uKLLyRJQUFBql+/vtq2baujR4+qatWqkqTly5fr3Llz6t69e9lcPAAAAADAdG5bcV++fLkCAgKcWseOHUt8nJCQEL366quKiIhQv379FBERoXPnzum5555TvXr1lJCQIG9vb3311VcF7v/GG2+obt26mjp1qiIiItSrVy/Fx8cXej5fX18FBATIy8tLoaGhCg0Nla+vr1q3bq2IiAj95z//cYydN2+eHnjgAQUEBJT4uiQpJydH2dnZTu3SxZyrOhYAAAAAXEbF3Vhum7h36NBBGRkZTm3OnDklPk6jRo3k8ZcFDqpUqaImTZo4Pnt6euqGG27Q8ePHC9x/x44datWqlVNfdHR0ieOQpAEDBjim5R87dkyfffaZ+vXrd1XHkqTk5GQFBQU5tTXvJV/18QAAAAAAxnPbqfL+/v4KDw936vvll18cP3t4eMhutzttv3jxYr7jlCtXzumzxWIpsM9ms5U25Cvq3bu3nn32WaWnp2vTpk2qXbu22rRpc9XHS0hI0IgRI5z63v7Cu7RhAgAAAAAM5LaJ+5VUqlRJmZmZstvtslj+nE5R1LvXr1ZkZKQ++eQTp76vv/66yH28vb2Vl5eXr/+GG25Q165dNW/ePKWnp6tv376lis1qtcpqtTr1eZWzFzIaAAAAAIrHXaesm8Vtp8pfSfv27XXixAlNmjRJ+/bt08yZM/XZZ58Zfp7Bgwdrz549euqpp7Rr1y4tXLhQKSkpRe4TFhamAwcOKCMjQydPnlROzv89dz5gwADNnz9fO3bsUJ8+fQyPFwAAAADgWv6xiXtkZKRmzZqlmTNnqlmzZtqyZYtGjRpl+Hlq1qypDz74QMuWLVOzZs00e/ZsjR8/vsh97rvvPsXFxalDhw6qVKmSFi1a5NgWExOjqlWrKjY2VtWqVTM8XgAAAAAoLYuHh2mtpGbOnKmwsDD5+PioVatW2rJlS5HjZ8yYoYiICPn6+qpGjRoaPny4Lly4cLW3qlgs9r8/6A2XdubMGVWvXl3z5s3Tvffea/jxX/nU/f44hN/bwOwQDLc0Yb3ZIRgu9MYgs0Mw3Kh//dfsEAz3S7k6ZodguBvnP2V2CGVicfRcs0MwXICf+027PHGq7NfIudYq3+B+dSF/H/f7PbUvv9XsEAwXcGK/2SGUCd/2PcwO4ars6XWXaeeut2BlsccuWbJEvXv31uzZs9WqVSvNmDFD7733nnbt2qXKlSvnG79w4UL169dPc+fOVevWrbV7927Fx8frwQcf1LRp04y8DCfu9zerm7LZbDp+/LjGjRun4OBgde7c2eyQAAAAAOC6Nm3aNA0cOFB9+/ZVw4YNNXv2bPn5+Wnu3IK/AN+0aZNuvfVW9ezZU2FhYbrzzjvVo0ePK1bpS4vE/Tpx+PBhValSRQsXLtTcuXPl5eXltO3v76z/azt8+LCJkQMAAAD4p/HwtJjWcnJylJ2d7dT+um7YZbm5ufr2228VExPzf3F7eCgmJkbp6ekFXlfr1q317bffOhL1/fv3a+XKlbrrrrKdYfCPXVX+ehMWFpbv9XWXVatWrcgV8XkWHgAAAMA/RXJyssaMGePUl5iYqKSkJKe+kydPKi8vT1WqVHHqr1Klinbu3FngsXv27KmTJ0/qX//6l+x2uy5duqTBgwfrueeeM/Qa/o7E3Q14eXnle2c9AAAAAJjFzNfBJSQkaMSIEU59f38N9tVKS0vT+PHjNWvWLLVq1Up79+7VsGHDNG7cOL344ouGnKMgJO4AAAAAALdhtVqLlahXrFhRnp6eOnbsmFP/sWPHFBoaWuA+L774oh5++GENGDBAktSkSROdPXtWgwYN0vPPPy+Pq1jVvjh4xh0AAAAAYKjr4XVw3t7eatGihVJTUx19NptNqampio6OLnCfc+fO5UvOPT09JanQR5uNQMUdAAAAAPCPNGLECPXp00ctW7bULbfcohkzZujs2bPq27evJKl3796qXr26kpOTJUmdOnXStGnT1Lx5c8dU+RdffFGdOnVyJPBlgcQdAAAAAPCP1L17d504cUKjR49WZmamoqKitGrVKseCdYcPH3aqsL/wwguyWCx64YUXdOTIEVWqVEmdOnXSyy+/XKZxkrgDAAAAAAxl5uJ0JTVkyBANGTKkwG1paWlOn728vJSYmKjExMRrENn/4Rl3AAAAAABcGBV3AAAAAIChrqeK+/WAijsAAAAAAC6MxB0AAAAAABfGVHkAAAAAgKFK8j51XBl3EwAAAAAAF0bFHQAAAABgKBanMxaJO5x0aHDC7BAMNzVhvdkhGK5bcjuzQzBc22mdzQ7BcJ1G32t2CIbLu7jD7BAM1/PReWaHUCb67n/W7BAM51X9RrNDMNy5Hf81OwTD+UVFmR2C4S75ut+fva8uxpodguEO5rQyO4QyMdjsAOASSNwBAAAAAIbiGXdjcTcBAAAAAHBhJO4AAAAAALgwpsoDAAAAAIxlYXE6I1FxBwAAAADAhVFxBwAAAAAYitfBGYuKOwAAAAAALozEHQAAAAAAF8ZUeQAAAACAoXiPu7G4mwAAAAAAuDAq7gAAAAAAQ7E4nbGouAMAAAAA4MKouAMAAAAADMUz7sbibgIAAAAA4MJI3AEAAAAAcGFMlb+OxMfH6/Tp01q2bJnZoQAAAABAoViczljXVcXdYrEU2Tp16iSLxaKvv/66wP1vv/123XvvvY7PmzZt0l133aUKFSrIx8dHTZo00bRp05SXl5dv33Xr1umuu+7SDTfcID8/PzVs2FAjR47UkSNHyux6AQAAAAC4rhL3o0ePOtqMGTMUGBjo1Ldo0SI1a9ZMc+fOzbfvwYMHtW7dOvXv31+S9NFHH6ldu3a68cYbtW7dOu3cuVPDhg3TSy+9pAcffFB2u92x75tvvqmYmBiFhobqgw8+0E8//aTZs2crKytLU6dOvWbXDwAAAADXA4uHxbTmjq6rxD00NNTRgoKCZLFYnPoCAgLUv39/LVmyROfOnXPaNyUlRVWrVlVcXJzOnj2rgQMHqnPnznrrrbcUFRWlsLAwDRgwQPPnz9f777+vpUuXSpJ++eUXDR06VEOHDtXcuXPVvn17hYWFqW3btpozZ45Gjx59xbhTUlIUHBysZcuWqV69evLx8VFsbKx+/vlnx5ikpCRFRUXpzTffVI0aNeTn56du3bopKysr3/HGjBmjSpUqKTAwUIMHD1Zubm4p7ywAAAAAwFVdV4l7cfTq1Us5OTl6//33HX12u13z589XfHy8PD099fnnn+u3337TqFGj8u3fqVMn1a9fX4sWLZIkvffee8rNzdXTTz9d4PmCg4OLFde5c+f08ssv691339XGjRt1+vRpPfjgg05j9u7dq6VLl+rTTz/VqlWr9N133+mxxx5zGpOamqodO3YoLS1NixYt0ocffqgxY8YUKwYAAAAAwPXH7RL3kJAQ3XPPPU7T5detW6eDBw+qb9++kqTdu3dLkiIjIws8RoMGDRxj9uzZo8DAQFWtWrVUcV28eFGvv/66oqOj1aJFC82fP1+bNm3Sli1bHGMuXLigd999V1FRUWrbtq1ee+01LV68WJmZmY4x3t7emjt3rho1aqS7775bY8eO1auvviqbzVbimHJycpSdne3UcnNzSnWdAAAAACAPD/OaG3LLq+rXr5++/PJL7du3T5I0d+5ctWvXTuHh4U7j/voce2HsdrssltI/J+Hl5aWbb77Z8blBgwYKDg7Wjh07HH01a9ZU9erVHZ+jo6Nls9m0a9cuR1+zZs3k5+fnNObMmTNO0+6LKzk5WUFBQU7tndmvlvg4AAAAAICy45aJ++23366aNWsqJSVF2dnZ+vDDDx2L0klS/fr1Jckpaf6rHTt2OMbUr19fWVlZOnr0aNkHfo0lJCQoKyvLqfUfPNTssAAAAABc5670RrCybO7ILRN3Dw8P9e3bV/Pnz9fChQvl7e2t+++/37H9zjvvVEhISIErwn/yySfas2ePevToIUm6//775e3trUmTJhV4rtOnTxcrpkuXLumbb75xfN61a5dOnz7tNF3/8OHD+vXXXx2fv/76a3l4eCgiIsLRt337dp0/f95pTEBAgGrUqFGsOP7KarUqMDDQqXl7W0t8HAAAAABA2XHLxF2S+vbtqyNHjui5555Tjx495Ovr69jm7++vN998Ux9//LEGDRqk77//XgcPHtQ777yj+Ph43X///erWrZskqUaNGpo+fbpeeeUV9e/fX+vXr9ehQ4e0ceNGPfLIIxo3blyx4ilXrpyeeOIJbd68Wd9++63i4+P1P//zP7rlllscY3x8fNSnTx9t375dGzZs0NChQ9WtWzeFhoY6xuTm5qp///766aeftHLlSiUmJmrIkCHycNNnOQAAAABcfyweHqY1d+SeV6U/nxePiYnR77//rn79+uXbfv/992vdunU6fPiw2rRpo4iICE2fPl3PP/+8Fi9e7DTF4rHHHtPnn3+uI0eO6J577lGDBg00YMAABQYGFrgyfUH8/Pz0zDPPqGfPnrr11lsVEBCgJUuWOI0JDw/Xvffeq7vuukt33nmnmjZtqlmzZjmNuf3221WvXj21bdtW3bt3V+fOnZWUlFTyGwQAAAAAuC54mR3A1YqPj1d8fHyRY1avXl3k9jZt2mjVqlXFOl9MTIxiYmKKG16B7r33Xt17771Fjnn00Uf16KOPFrgtJSXF8TOvgAMAAACAf4brNnEHAAAAALgmi4d7LhJnFredKn8tdezYUQEBAQW28ePHX5MYDh8+XGgMAQEBOnz48DWJAwAAAABgLCruBpgzZ47TSu9/FRISopCQkCtO609KSirVs+rVqlVTRkZGkdsBAAAA4Jpw00XizELiboDq1aubHYK8vLwUHh5udhgAAAAAAIPxNQgAAAAAAC6MijsAAAAAwFAsTmcsKu4AAAAAALgwKu4AAAAAAENZLNSIjcTdBAAAAADAhZG4AwAAAADgwpgqDwAAAAAwFovTGYqKOwAAAAAALoyKOwAAAADAUBYPasRG4m4CAAAAAODCqLgDAAAAAAxl4Rl3Q1FxBwAAAADAhVFxhxOb3O+bsdAbg8wOwXBtp3U2OwTDfTniE7NDMNyluH+bHYLhajUONzsEwx0/edHsEMpE7qnfzQ7BcB5Wb7NDMNyl8zlmh2A42/FMs0MwnJfF/f59dK6S+6UBoSF5ZodQRjzNDgAuwP3+iwUAAAAAmMvC5G4jcTcBAAAAAHBhVNwBAAAAAIZicTpjUXEHAAAAAMCFkbgDAAAAAODCmCoPAAAAADCWBzViI3E3AQAAAABwYVTcAQAAAACGslhYnM5IVNwBAAAAAHBhVNwBAAAAAMbiGXdDcTcBAAAAAP9YM2fOVFhYmHx8fNSqVStt2bKlyPGnT5/W448/rqpVq8pqtap+/fpauXJlmcZIxR0AAAAA8I+0ZMkSjRgxQrNnz1arVq00Y8YMxcbGateuXapcuXK+8bm5ubrjjjtUuXJlvf/++6pevboOHTqk4ODgMo2TxB0AAAAAYCiLx/WxON20adM0cOBA9e3bV5I0e/ZsrVixQnPnztWzzz6bb/zcuXN16tQpbdq0SeXKlZMkhYWFlXmcTJUHAAAAALiNnJwcZWdnO7WcnJx843Jzc/Xtt98qJibG0efh4aGYmBilp6cXeOxPPvlE0dHRevzxx1WlShU1btxY48ePV15eXpldj0TiDgAAAAAwmsXDtJacnKygoCCnlpycnC/EkydPKi8vT1WqVHHqr1KlijIzMwu8rP379+v9999XXl6eVq5cqRdffFFTp07VSy+9VCa38TKmygMAAAAA3EZCQoJGjBjh1Ge1Wg05ts1mU+XKlfXWW2/J09NTLVq00JEjRzR58mQlJiYaco6CuE3F/YMPPlD79u0VFBSkgIAANW3aVGPHjtWpU6ccY86fP6/ExETVr19fVqtVFStW1AMPPKAff/zR6Vhvv/222rRpowoVKqhChQqKiYm54sqCAAAAAADzWa1WBQYGOrWCEveKFSvK09NTx44dc+o/duyYQkNDCzx21apVVb9+fXl6ejr6IiMjlZmZqdzcXGMv5C/cInF//vnn1b17d91888367LPP9MMPP2jq1Knavn27/vOf/0j68zmHmJgYzZ07Vy+99JJ2796tlStX6tKlS2rVqpW+/vprx/HS0tLUo0cPrVu3Tunp6apRo4buvPNOHTlyxKxLBAAAAIDrh4fFvFZM3t7eatGihVJTUx19NptNqampio6OLnCfW2+9VXv37pXNZnP07d69W1WrVpW3t/fV368ruC4Sd5vNpuTkZNWuXVu+vr5q1qyZ3n//fUnSli1bNH78eE2dOlWTJ09W69atFRYWpjvuuEMffPCB+vTpI0maMWOG0tPTtXz5cnXr1k21atXSLbfcog8++ECRkZHq37+/7Ha7JGnBggV67LHHFBUVpQYNGmjOnDmOX2BxhIWFady4cerRo4f8/f1VvXp1zZw502mMxWLRG2+8oY4dO8rX11d16tRxXJMkHTx4UBaLRUuXLlWbNm3k6+urm2++Wbt379bWrVvVsmVLBQQEqGPHjjpx4oQRtxkAAAAA/lFGjBiht99+W/Pnz9eOHTv06KOP6uzZs45V5nv37q2EhATH+EcffVSnTp3SsGHDtHv3bq1YsULjx4/X448/XqZxXheJe3Jyst59913Nnj1bP/74o4YPH66HHnpI69ev14IFCxQQEKDHHnuswH0vv09v4cKFuuOOO9SsWTOn7R4eHho+fLh++uknbd++vcBjnDt3ThcvXlRISEixY548ebKaNWum7777Ts8++6yGDRumNWvWOI158cUXdd9992n79u3q1auXHnzwQe3YscNpTGJiol544QVt27ZNXl5e6tmzp55++mm98sor2rBhg/bu3avRo0cXOy4AAAAAKGsWi4dprSS6d++uKVOmaPTo0YqKilJGRoZWrVrlWLDu8OHDOnr0qGN8jRo1tHr1am3dulVNmzbV0KFDNWzYsAJfHWckl1+cLicnR+PHj9cXX3zhmK5Qp04dffXVV3rzzTd1+vRp1alTx/EOvcLs3r1bHTp0KHBbZGSkY0xUVFS+7c8884yqVavm9JqAK7n11lsdv7z69etr48aNmj59uu644w7HmAceeEADBgyQJI0bN05r1qzRa6+9plmzZjnGjBo1SrGxsZKkYcOGqUePHkpNTdWtt94qSerfv79SUlKKHddf5eTk5HstQm5ujry9jVm4AQAAAABc3ZAhQzRkyJACt6WlpeXri46OdnrU+lpw+Yr73r17de7cOd1xxx0KCAhwtHfffVf79u1zTG8vjpKMvWzChAlavHixPvroI/n4+BR7v78/ExEdHZ2vml6cMU2bNnX8fPlbnyZNmjj1HT9+vNhx/VVBr0mYO/uVqzoWAAAAADhcB8+4X09cvuJ+5swZSdKKFStUvXp1p21Wq1VTpkzRV199pYsXLxZZda9fv36+pPiyy/3169d36p8yZYomTJigL774wimBvpb+ek0Wi6XAvr8ujFASBb0mYefP2Vd1LAAAAABA2XD5invDhg1ltVp1+PBhhYeHO7UaNWqoZ8+eOnPmjNP08r86ffq0JOnBBx/UF198ke85dpvNpunTp6thw4ZOz79PmjRJ48aN06pVq9SyZcsSx/33qRNff/21Y0p+ScaUpYJek8A0eQAAAABwLS5fcS9fvrxGjRql4cOHy2az6V//+peysrK0ceNGBQYGqk+fPnr66ac1cuRIHTlyRPfcc4+qVaumvXv3avbs2frXv/6lYcOGafjw4fr444/VqVMnTZ06Va1atdKxY8c0fvx47dixQ1988YWjoj1x4kSNHj1aCxcuVFhYmDIzMyXJMU2/ODZu3KhJkyapa9euWrNmjd577z2tWLHCacx7772nli1b6l//+pcWLFigLVu26J133jH2BgIAAADANWbxcPka8XXF5RN36c+F2ypVqqTk5GTt379fwcHBuummm/Tcc89J+jPRbtGihWbOnKnZs2fLZrOpbt26uv/++x2vg/Px8dHatWs1fvx4Pffcczp06JDKly+vDh066Ouvv1bjxo0d53vjjTeUm5ur+++/3ymOxMREJSUlFSvmkSNH6ptvvtGYMWMUGBioadOmORaZu2zMmDFavHixHnvsMVWtWlWLFi1Sw4YNS3GnAAAAAADu5rpI3C0Wi4YNG6Zhw4YVOqZbt27q1q1bkcfx8/PTSy+9pJdeeqnIcQcPHryaMJ0EBgZq6dKlRY6pVq2aPv/88wK3hYWF5VtMr3379vn64uPjFR8fX6pYAQAAAMBQFvdcJM4szF8AAAAAAMCFkbiX0IYNG5xeS/f3Zqai4tqwYYOpsQEAAAAArs51MVXelbRs2VIZGRlFjinOVPureaf8lRQV199fpQcAAAAAZYbF6QxF4l5Cvr6+Cg8PNzuMArlqXAAAAACAq0fiDgAAAAAwFovTGYr5CwAAAAAAuDAq7gAAAAAAQ1l4xt1Q3E0AAAAAAFwYiTsAAAAAAC6MqfIAAAAAAGNZqBEbibsJAAAAAIALo+IOAAAAADCWB6+DMxIVdwAAAAAAXBiJOwAAAAAALoyp8gAAAAAAQ1lYnM5Q3E0AAAAAAFwYFXc42XmiotkhGG7Uv7aaHYLhOo2+1+wQDHcp7t9mh2C4hFWDzA7BcG06dDQ7BMPZGnU2O4Qy8UjGc2aHYLiKPkFmh2C4Q8d/NTsEw4XdUN3sEAxX2epvdgiGu7VmrtkhGK6q7ymzQygj1cwO4OqwOJ2hqLgDAAAAAODCqLgDAAAAAIzFM+6G4m4CAAAAAODCSNwBAAAAAHBhTJUHAAAAABjLwuJ0RqLiDgAAAACAC6PiDgAAAAAwlgc1YiNxNwEAAAAAcGEk7gAAAAAAuDCmygMAAAAAjMV73A3F3QQAAAAAwIVRcQcAAAAAGMuD18EZiYo7AAAAAAAujIo7AAAAAMBYPONuKO4mAAAAAAAujMQdAAAAAAAXRuL+N/Hx8bJYLLJYLCpXrpxq166tp59+WhcuXLjmsaSlpclisej06dP5toWFhWnGjBmSpFOnTumJJ55QRESEfH19VbNmTQ0dOlRZWVnXNmAAAAAAkCSLxbzmhnjGvQBxcXGaN2+eLl68qG+//VZ9+vSRxWLRxIkTzQ6tQL/++qt+/fVXTZkyRQ0bNtShQ4c0ePBg/frrr3r//ffNDg8AAAAAUApU3AtgtVoVGhqqGjVqqGvXroqJidGaNWskSb/99pt69Oih6tWry8/PT02aNNGiRYsc+y5fvlzBwcHKy8uTJGVkZMhisejZZ591jBkwYIAeeughw+Jt3LixPvjgA3Xq1El169bVbbfdppdfflmffvqpLl26ZNh5AAAAAKBYPDzMa27IPa/KQD/88IM2bdokb29vSdKFCxfUokULrVixQj/88IMGDRqkhx9+WFu2bJEktWnTRn/88Ye+++47SdL69etVsWJFpaWlOY65fv16tW/fvkzjzsrKUmBgoLy8mFQBAAAAANczsroCLF++XAEBAbp06ZJycnLk4eGh119/XZJUvXp1jRo1yjH2iSee0OrVq7V06VLdcsstCgoKUlRUlNLS0tSyZUulpaVp+PDhGjNmjM6cOaOsrCzt3btX7dq1K3Y8N954Y76+c+fOFTr+5MmTGjdunAYNGlTkcXNycpSTk+PUdzHXW+W8rcWODQAAAABQtqi4F6BDhw7KyMjQ5s2b1adPH/Xt21f33XefJCkvL0/jxo1TkyZNFBISooCAAK1evVqHDx927N+uXTulpaXJbrdrw4YNuvfeexUZGamvvvpK69evV7Vq1VSvXr1ix7NhwwZlZGQ4tWrVqhU4Njs7W3fffbcaNmyopKSkIo+bnJysoKAgp/bxf5KLHRcAAAAAFIjF6QxFxb0A/v7+Cg8PlyTNnTtXzZo10zvvvKP+/ftr8uTJeuWVVzRjxgw1adJE/v7+evLJJ5Wbm+vYv3379po7d662b9+ucuXKqUGDBmrfvr3S0tL0+++/l6jaLkm1a9dWcHCwU19BU+D/+OMPxcXFqXz58vroo49Urly5Io+bkJCgESNGOPV9vM27RLEBAAAAAMoWFfcr8PDw0HPPPacXXnhB58+f18aNG9WlSxc99NBDatasmerUqaPdu3c77XP5Offp06c7kvTLiXtaWlqZPN+enZ2tO++8U97e3vrkk0/k4+NzxX2sVqsCAwOdGtPkAQAAAJSaxcO85obc86oM9sADD8jT01MzZ85UvXr1tGbNGm3atEk7duzQI488omPHjjmNr1Chgpo2baoFCxY4kvS2bdtq27Zt2r17d4kr7ldyOWk/e/as3nnnHWVnZyszM1OZmZmO1e0BAAAAANcnpsoXg5eXl4YMGaJJkybpu+++0/79+xUbGys/Pz8NGjRIXbt2VVZWltM+7dq1U0ZGhiNxDwkJUcOGDXXs2DFFREQYGt+2bdu0efNmSXJM8b/swIEDCgsLM/R8AAAAAIBrx2K32+1mBwHXsXiT+/1xuD14q9khGO6B0e43k+JSTu6VB11nElYV/WaH61GbiR3NDsFwtg6dzQ6hTDyyoK7ZIRiuYmiQ2SEY7tCuX80OwXBhkdXNDsFwlav4mx2C4W5tesnsEAxX1feU2SGUiYbhBS9K7eourJpj2rl94gaYdu6ywlR5AAAAAABcGIm7iTp27KiAgIAC2/jx480ODwAAAACuDq+DMxTPuJtozpw5On/+fIHbQkJCrnE0AAAAAABXROJuourV3e8ZMAAAAABw19eymYW7CQAAAACACyNxBwAAAADAhTFVHgAAAABgLDddJM4sVNwBAAAAAHBhVNwBAAAAAMbyoEZsJO4mAAAAAAAujMQdAAAAAAAXxlR5AAAAAICh7CxOZygq7gAAAACAf6yZM2cqLCxMPj4+atWqlbZs2VKs/RYvXiyLxaKuXbuWbYAicQcAAAAAGM3iYV4rgSVLlmjEiBFKTEzUtm3b1KxZM8XGxur48eNF7nfw4EGNGjVKbdq0Kc1dKjYSdwAAAADAP9K0adM0cOBA9e3bVw0bNtTs2bPl5+enuXPnFrpPXl6eevXqpTFjxqhOnTrXJE4SdwAAAACAsUysuOfk5Cg7O9up5eTk5AsxNzdX3377rWJiYhx9Hh4eiomJUXp6eqGXNnbsWFWuXFn9+/cvk1tXEBang5OKAblmh2C4X8pdm2/BrqW8izvMDsFwtRqHmx2C4dp06Gh2CIbb8MxnZodgOPvmKWaHUCZ8A34zOwTD+fiVMzsEw/kH+ZsdguF8fN3v91SunPstsvXOgpNmh2C48MjKZodQJsa63z+RylxycrLGjBnj1JeYmKikpCSnvpMnTyovL09VqlRx6q9SpYp27txZ4LG/+uorvfPOO8rIyDAy5CsicQcAAAAAuI2EhASNGDHCqc9qtZb6uH/88Ycefvhhvf3226pYsWKpj1cSJO4AAAAAAEOZ+To4q9VarES9YsWK8vT01LFjx5z6jx07ptDQ0Hzj9+3bp4MHD6pTp06OPpvNJkny8vLSrl27VLdu3VJGXzCecQcAAAAA/ON4e3urRYsWSk1NdfTZbDalpqYqOjo63/gGDRrov//9rzIyMhytc+fO6tChgzIyMlSjRo0yi5WKOwAAAADAWCV8LZtZRowYoT59+qhly5a65ZZbNGPGDJ09e1Z9+/aVJPXu3VvVq1dXcnKyfHx81LhxY6f9g4ODJSlfv9FI3AEAAAAA/0jdu3fXiRMnNHr0aGVmZioqKkqrVq1yLFh3+PBheXiY/yUEiTsAAAAA4B9ryJAhGjJkSIHb0tLSitw3JSXF+IAKQOIOAAAAADCWiYvTuSPza/4AAAAAAKBQVNwBAAAAAMZygefC3Ql3EwAAAAAAF0bFHQAAAABgKDvPuBuKijsAAAAAAC6MxB0AAAAAABfGVHkAAAAAgLEs1IiNxN0EAAAAAMCFkbgb6ODBg7JYLMrIyDA7FCUlJSkqKsrsMAAAAAD8A9ktHqY1d+QyVxUfHy+LxSKLxaJy5cqpdu3aevrpp3XhwoVrHktaWpojlr+3zMxMR7xdu3a95rEBAAAAAP5ZXOoZ97i4OM2bN08XL17Ut99+qz59+shisWjixImmxLNr1y4FBgY69VWuXNmUWAAAAAAA/0wuU3GXJKvVqtDQUNWoUUNdu3ZVTEyM1qxZI0n67bff1KNHD1WvXl1+fn5q0qSJFi1a5Nh3+fLlCg4OVl5eniQpIyNDFotFzz77rGPMgAED9NBDDxU7nsqVKys0NNSpeXh4KCkpSfPnz9fHH3/sqMSnpaU59tu/f786dOggPz8/NWvWTOnp6Y5tV7oOSWrfvr2GDh2qp59+WiEhIQoNDVVSUpLTmMOHD6tLly4KCAhQYGCgunXrpmPHjhX72gAAAACgzFgs5jU35FKJ+1/98MMP2rRpk7y9vSVJFy5cUIsWLbRixQr98MMPGjRokB5++GFt2bJFktSmTRv98ccf+u677yRJ69evV8WKFZ0S6vXr16t9+/aljm3UqFHq1q2b4uLidPToUR09elStW7d2bH/++ec1atQoZWRkqH79+urRo4cuXbpUrOu4bP78+fL399fmzZs1adIkjR071vElhs1mU5cuXXTq1CmtX79ea9as0f79+9W9e/dSXxsAAAAAwLW41FT55cuXKyAgQJcuXVJOTo48PDz0+uuvS5KqV6+uUaNGOcY+8cQTWr16tZYuXapbbrlFQUFBioqKUlpamlq2bKm0tDQNHz5cY8aM0ZkzZ5SVlaW9e/eqXbt2xY7nxhtvdPpcq1Yt/fjjjwoICJCvr69ycnIUGhqab79Ro0bp7rvvliSNGTNGjRo10t69e9WgQYMrXsdlTZs2VWJioiSpXr16ev3115Wamqo77rhDqamp+u9//6sDBw6oRo0akqR3331XjRo10tatW3XzzTcX6/pycnKUk5Pj1JebK3l7W4u1PwAAAAAUxF0XiTOLS93NDh06KCMjQ5s3b1afPn3Ut29f3XfffZKkvLw8jRs3Tk2aNFFISIgCAgK0evVqHT582LF/u3btlJaWJrvdrg0bNujee+9VZGSkvvrqK61fv17VqlVTvXr1ih3Phg0blJGR4WgrV64s1n5NmzZ1/Fy1alVJ0vHjx4t9HX8/xuXjXD7Gjh07VKNGDUfSLkkNGzZUcHCwduzYUezrS05OVlBQkFNb/M6kYu8PAAAAACh7LlVx9/f3V3h4uCRp7ty5atasmd555x31799fkydP1iuvvKIZM2aoSZMm8vf315NPPqnc3FzH/u3bt9fcuXO1fft2lStXTg0aNFD79u2Vlpam33//vUTVdkmqXbu2goODS3wd5cqVc/xs+f/PWNhsNkkq1nX8/RiXj3P5GEZJSEjQiBEjnPq+2m3oKQAAAAD8E7nps+ZmcamK+195eHjoueee0wsvvKDz589r48aN6tKlix566CE1a9ZMderU0e7dzlnm5efcp0+f7kjSLyfuaWlphjzffpm3t7djIbySKM51XElkZKR+/vln/fzzz46+n376SadPn1bDhg2LfRyr1arAwECnxjR5AAAAAHAtLpu4S9IDDzwgT09PzZw5U/Xq1dOaNWu0adMm7dixQ4888ki+VdQrVKigpk2basGCBY4kvW3bttq2bZt2795d4or78ePHlZmZ6dQuXrwoSQoLC9P333+vXbt26eTJk47+KynOdVxJTEyMmjRpol69emnbtm3asmWLevfurXbt2qlly5YlOhYAAAAAwLW5dOLu5eWlIUOGaNKkSRo5cqRuuukmxcbGqn379goNDVXXrl3z7dOuXTvl5eU5EveQkBA1bNhQoaGhioiIKNH5IyIiVLVqVaf27bffSpIGDhyoiIgItWzZUpUqVdLGjRuLdcwXXnihWNdRFIvFoo8//lgVKlRQ27ZtFRMTozp16mjJkiUlOg4AAAAAlAmLh3nNDVnsdrvd7CDgOr74PufKg64zN/j+YXYIhhs6qviLEF4vajasY3YIhnuj0mSzQzDchmc+MzsEw9k3/2h2CGXi/U9+MzsEw91QOcDsEAz3y/6TZodguBp1K5kdguFuuMH9HiX877ajZodguPDIymaHUCbG9vE2O4Sr8sc3q0w7d/mWcaadu6y41OJ0AAAAAIDrn53F6QzlnvMIrqBjx44KCAgosI0fP97s8AAAAAAAcPhHVtznzJmj8+fPF7gtJCTkGkcDAAAAAEDh/pGJe/Xq1c0OAQAAAADcl5suEmcW7iYAAAAAAC7sH1lxBwAAAACUHbtYnM5IVNwBAAAAAHBhVNwBAAAAAIay84y7obibAAAAAAC4MBJ3AAAAAABcGFPlAQAAAADGYqq8obibAAAAAAC4MCruAAAAAABD2S28Ds5IVNwBAAAAAHBhJO4AAAAAALgwpsoDAAAAAAzFe9yNReIOJz8c9DY7BMP12vKU2SEYruej88wOwXDHT140OwTD2Rp1NjsEw9k3TzE7BMNZWjUyO4Qy0fT9nWaHYDhPN/w3YM0aNc0OwXAebvh7Kuflfs/qTuj/h9khGM734q9mh1BGbjE7ALgAEncAAAAAgLFYnM5QbvidKAAAAAAA7oOKOwAAAADAUDzjbizuJgAAAAAALozEHQAAAAAAF8ZUeQAAAACAoexicTojUXEHAAAAAMCFUXEHAAAAABiKxemMxd0EAAAAAMCFkbgDAAAAAODCmCoPAAAAADCWhcXpjETFHQAAAAAAF0bFHQAAAABgKDs1YkNxNwEAAAAAcGEk7gAAAAAAuDCmyl8j8fHxOn36tJYtW2Z2KAAAAABQpuwsTmeoa1Jxj4+Pl8VikcVikbe3t8LDwzV27FhdunTpWpz+utG+fXvHffLx8VHDhg01a9Yss8MCAAAAAJjomk2Vj4uL09GjR7Vnzx6NHDlSSUlJmjx5cr5xubm51yqkYruWMQ0cOFBHjx7VTz/9pG7duunxxx/XokWLrtn5AQAAAKC07BYP05o7umZXZbVaFRoaqlq1aunRRx9VTEyMPvnkE8XHx6tr1656+eWXVa1aNUVEREiSfv75Z3Xr1k3BwcEKCQlRly5ddPDgQcfx0tLSdMstt8jf31/BwcG69dZbdejQIUnS9u3b1aFDB5UvX16BgYFq0aKFvvnmG0lSUlKSoqKinGKbMWOGwsLCHJ+vNqbimDJliqpWraobbrhBjz/+uC5evOi03c/PT6GhoapTp46SkpJUr149ffLJJ5KkZ555RvXr15efn5/q1KmjF198Md/+AAAAAAD3Ytoz7r6+vvrtt98kSampqQoMDNSaNWskSRcvXlRsbKyio6O1YcMGeXl56aWXXlJcXJy+//57eXh4qGvXrho4cKAWLVqk3NxcbdmyRZb//xxFr1691Lx5c73xxhvy9PRURkaGypUrV6L4ShqTt7f3FY+5bt06Va1aVevWrdPevXvVvXt3RUVFaeDAgUXep8sV//LlyyslJUXVqlXTf//7Xw0cOFDly5fX008/XaJrAwAAAICyZBfPuBvpmifudrtdqampWr16tZ544gmdOHFC/v7+mjNnjiP5/d///V/ZbDbNmTPHkYzPmzdPwcHBSktLU8uWLZWVlaV///vfqlu3riQpMjLScY7Dhw/rqaeeUoMGDSRJ9erVK3GcJY3pzjvvvOIxK1SooNdff12enp5q0KCB7r77bqWmphaYuOfl5WnRokX6/vvvNWjQIEnSCy+84NgeFhamUaNGafHixVeduOfk5CgnJ8ep79JFb3mVs17V8QAAAAAAxrtmU+WXL1+ugIAA+fj4qGPHjurevbuSkpIkSU2aNHGqWG/fvl179+5V+fLlFRAQoICAAIWEhOjChQvat2+fQkJCFB8fr9jYWHXq1EmvvPKKjh496th/xIgRGjBggGJiYjRhwgTt27evxPGWNKbiaNSokTw9PR2fq1atquPHjzuNmTVrlgICAuTr66uBAwdq+PDhevTRRyVJS5Ys0a233qrQ0FAFBATohRde0OHDh0t8bZclJycrKCjIqX3xfvJVHw8AAAAAYLxrVnHv0KGD3njjDXl7e6tatWry8vq/U/v7+zuNPXPmjFq0aKEFCxbkO06lSpUk/VntHjp0qFatWqUlS5bohRde0Jo1a/Q///M/SkpKUs+ePbVixQp99tlnSkxM1OLFi3XPPffIw8NDdrvd6ZgFPSd+NTFdyd+n61ssFtlsNqe+Xr166fnnn5evr6+qVq0qD48/v1tJT09Xr169NGbMGMXGxiooKEiLFy/W1KlTi3XugiQkJGjEiBFOfW+tufKUfwAAAAAoirsuEmeWa5a4+/v7Kzw8vFhjb7rpJi1ZskSVK1dWYGBgoeOaN2+u5s2bKyEhQdHR0Vq4cKH+53/+R5JUv3591a9fX8OHD1ePHj00b9483XPPPapUqZIyMzNlt9sdU94zMjIMi6m0goKCCrxPmzZtUq1atfT88887+i4vxne1rFarrFbnafFe5eyFjAYAAAAAmMElvwbp1auXKlasqC5dumjDhg06cOCA0tLSNHToUP3yyy86cOCAEhISlJ6erkOHDunzzz/Xnj17FBkZqfPnz2vIkCFKS0vToUOHtHHjRm3dutXxDHz79u114sQJTZo0Sfv27dPMmTP12WeflTqmslavXj0dPnxYixcv1r59+/Tqq6/qo48+KvPzAgAAAEBJ2S0W05o7csnE3c/PT19++aVq1qype++9V5GRkerfv78uXLigwMBA+fn5aefOnbrvvvtUv359DRo0SI8//rgeeeQReXp66rffflPv3r1Vv359devWTR07dtSYMWMk/bmI3axZszRz5kw1a9ZMW7Zs0ahRo0odU1nr3Lmzhg8friFDhigqKkqbNm3Siy++WObnBQAAAAB3NnPmTIWFhcnHx0etWrXSli1bCh379ttvq02bNqpQoYIqVKigmJiYIscbxWL/+wPf+Eeb8Yn7/XHotaWf2SEY7v1/zTM7BMMdP5l/rYnr3bBGG8wOwXBf2duaHYLhLK0amR1Cmdj1/k6zQzCcp0uWG/B3Hm74eyrn5X4VvLvq7DA7BMP5XvzD7BDKRKWGt5gdwlU5svu/pp27ev0mxR67ZMkS9e7dW7Nnz1arVq00Y8YMvffee9q1a5cqV66cb3yvXr106623qnXr1vLx8dHEiRP10Ucf6ccff1T16tWNvAwnbvhXKwAAAADATHZZTGs5OTnKzs52an9/DfZl06ZN08CBA9W3b181bNhQs2fPlp+fn+bOnVvg+AULFuixxx5TVFSUGjRooDlz5shmsyk1NbUsbyeJu1EuvyKuoLZhQ9lX3Ro1alTo+QtaCR8AAAAA3FFBr71OTs7/2uvc3Fx9++23iomJcfR5eHgoJiZG6enpxTrXuXPndPHiRYWEhBgWf0Gu2ary7q6olenLcsrEZStXrizwtXaSVKVKlTI/PwAAAABcZubr4Ap67fXf36YlSSdPnlReXl6+fKlKlSraubN4j5w988wzqlatmlPyXxZI3A1S3FfdlZVatWqZen4AAAAAcAUFvfa6LEyYMEGLFy9WWlqafHx8yvRcJO4AAAAAAEPZ5fqLOlasWFGenp46duyYU/+xY8cUGhpa5L5TpkzRhAkT9MUXX6hp06ZlGaYknnEHAAAAAPwDeXt7q0WLFk4Ly11eaC46OrrQ/SZNmqRx48Zp1apVatmy5bUIlYo7AAAAAOCfacSIEerTp49atmypW265RTNmzNDZs2fVt29fSVLv3r1VvXp1x+J2EydO1OjRo7Vw4UKFhYUpMzNT0v8tVl5WSNwBAAAAAIYyc3G6kujevbtOnDih0aNHKzMzU1FRUVq1apVjwbrDhw/Lw+P/ruWNN95Qbm6u7r//fqfjJCYmKikpqcziJHEHAAAAAPxjDRkyREOGDClwW1pamtPngwcPln1ABSBxBwAAAAAY6npYnO56cn3MXwAAAAAA4B+KxB0AAAAAABfGVHkAAAAAgKGul8XprhfcTQAAAAAAXBgVdwAAAACAoViczlhU3AEAAAAAcGFU3OGkdf1ss0Mw3GLPuWaHYLi++581OwTD5Z763ewQDPdIxnNmh2A434DfzA7BcE3f32l2CGUi4v4GZodgON/qVrNDMJxXgKfZIRguL8dmdgiGqxgRYnYIhtuW+IXZIRjOZnfPCu+9ZgdwlewW9/x9mIWKOwAAAAAALozEHQAAAAAAF8ZUeQAAAACAoexu+uiCWai4AwAAAADgwqi4AwAAAAAMZadGbCjuJgAAAAAALozEHQAAAAAAF8ZUeQAAAACAoexicTojUXEHAAAAAMCFUXEHAAAAABiKiruxqLgDAAAAAODCqLgDAAAAAAxFxd1YVNwBAAAAAHBhJO4AAAAAALgwpsoDAAAAAAzFVHljUXEHAAAAAMCFkbhfpfj4eFksFlksFnl7eys8PFxjx47VpUuXlJaWJovFogoVKujChQtO+23dutWx32UXLlxQfHy8mjRpIi8vL3Xt2rXE8aSlpemmm26S1WpVeHi4UlJSSnmFAAAAAHB17HaLac0dkbiXQlxcnI4ePao9e/Zo5MiRSkpK0uTJkx3by5cvr48++shpn3feeUc1a9Z06svLy5Ovr6+GDh2qmJiYEsdx4MAB3X333erQoYMyMjL05JNPasCAAVq9evXVXRgAAAAAwGWQuJeC1WpVaGioatWqpUcffVQxMTH65JNPHNv79OmjuXPnOj6fP39eixcvVp8+fZyO4+/vrzfeeEMDBw5UaGhoieOYPXu2ateuralTpyoyMlJDhgzR/fffr+nTp1/9xQEAAAAAXAKJu4F8fX2Vm5vr+Pzwww9rw4YNOnz4sCTpgw8+UFhYmG666SZDz5uenp6vUh8bG6v09HRDzwMAAAAAxWGXxbTmjkjcDWC32/XFF19o9erVuu222xz9lStXVseOHR3Pm8+dO1f9+vUz/PyZmZmqUqWKU1+VKlWUnZ2t8+fPF7pfTk6OsrOznVpubo7h8QEAAAAArh6JeyksX75cAQEB8vHxUceOHdW9e3clJSU5jenXr59SUlK0f/9+paenq1evXuYEW4Dk5GQFBQU5tflvTTM7LAAAAADXOSruxiJxL4XLi8Ht2bNH58+f1/z58+Xv7+80pmPHjjp//rz69++vTp066YYbbjA8jtDQUB07dsyp79ixYwoMDJSvr2+h+yUkJCgrK8up9Rk0wvD4AAAAAABXz8vsAK5n/v7+Cg8PL3KMl5eXevfurUmTJumzzz4rkziio6O1cuVKp741a9YoOjq6yP2sVqusVqtTn7e33fD4AAAAAPyzuGvl2yxU3K+BcePG6cSJE4qNjS10zE8//aSMjAydOnVKWVlZysjIUEZGRrGOP3jwYO3fv19PP/20du7cqVmzZmnp0qUaPny4QVcAAAAAADALFfdrwNvbWxUrVixyzF133aVDhw45Pjdv3lzSnwvfXUnt2rW1YsUKDR8+XK+88opuvPFGzZkzp8gvCgAAAAAA1wcS96t0eaX4grRv377IhLtr1675th88eLBU8bRv317fffddqY4BAAAAAEaw25kqbySmygMAAAAA4MJI3K8DjRo1UkBAQIFtwYIFZocHAAAAAE5sspjW3BFT5a8DK1eu1MWLFwvcVqVKlWscDQAAAADgWiJxvw7UqlXL7BAAAAAAACYhcQcAAAAAGIr3uBuLZ9wBAAAAAHBhVNwBAAAAAIbidXDGouIOAAAAAIALo+IOAAAAADAUz7gbi4o7AAAAAAAujMQdAAAAAAAXxlR5AAAAAIChWJzOWFTcAQAAAABwYVTcAQAAAACGYnE6Y1FxBwAAAADAhZG4AwAAAADgwpgqDyc2swMoAwF+7jdNx6v6jWaHYDgPq7fZIRiuok+Q2SEYzsevnNkhGM7TTb/C9q1uNTsEw50/kmN2CIZr0CPc7BAMt3PRXrNDMFxm9gmzQzCcp4fd7BAM54aXdF1jcTpjuek/VwAAAAAAcA9U3AEAAAAAhnLHmbxmouIOAAAAAIALI3EHAAAAAMCFMVUeAAAAAGAoFqczFhV3AAAAAABcGBV3AAAAAICh7KLibiQq7gAAAAAAuDAq7gAAAAAAQ/GMu7GouAMAAAAA4MJI3AEAAAAA/1gzZ85UWFiYfHx81KpVK23ZsqXI8e+9954aNGggHx8fNWnSRCtXrizzGEncAQAAAACGsstiWiuJJUuWaMSIEUpMTNS2bdvUrFkzxcbG6vjx4wWO37Rpk3r06KH+/fvru+++U9euXdW1a1f98MMPRty2QpG4AwAAAADcRk5OjrKzs51aTk5OgWOnTZumgQMHqm/fvmrYsKFmz54tPz8/zZ07t8Dxr7zyiuLi4vTUU08pMjJS48aN00033aTXX3+9LC+JxB0AAAAAYCyb3byWnJysoKAgp5acnJwvxtzcXH377beKiYlx9Hl4eCgmJkbp6ekFXld6errTeEmKjY0tdLxRWFUeAAAAAOA2EhISNGLECKc+q9Wab9zJkyeVl5enKlWqOPVXqVJFO3fuLPDYmZmZBY7PzMwsZdRFI3EHAAAAALgNq9VaYKJ+PWOq/FWKj4+XxWKRxWKRt7e3wsPDNXbsWF26dElpaWmyWCyqUKGCLly44LTf1q1bHftdlpaWpi5duqhq1ary9/dXVFSUFixYUOxYUlJSHMe83Hx8fAy7VgAAAAAoiethcbqKFSvK09NTx44dc+o/duyYQkNDC9wnNDS0ROONQuJeCnFxcTp69Kj27NmjkSNHKikpSZMnT3ZsL1++vD766COnfd555x3VrFnTqW/Tpk1q2rSpPvjgA33//ffq27evevfureXLlxc7lsDAQB09etTRDh06VLqLAwAAAAA35u3trRYtWig1NdXRZ7PZlJqaqujo6AL3iY6OdhovSWvWrCl0vFFI3EvBarUqNDRUtWrV0qOPPqqYmBh98sknju19+vRxWo3w/PnzWrx4sfr06eN0nOeee07jxo1T69atVbduXQ0bNkxxcXH68MMPix2LxWJRaGioo/39uQsAAAAAuFbsdotprSRGjBiht99+W/Pnz9eOHTv06KOP6uzZs+rbt68kqXfv3kpISHCMHzZsmFatWqWpU6dq586dSkpK0jfffKMhQ4YYev/+jsTdQL6+vsrNzXV8fvjhh7VhwwYdPnxYkvTBBx8oLCxMN9100xWPlZWVpZCQkGKf+8yZM6pVq5Zq1KihLl266Mcffyz5BQAAAADAP0j37t01ZcoUjR49WlFRUcrIyNCqVaschdDDhw/r6NGjjvGtW7fWwoUL9dZbb6lZs2Z6//33tWzZMjVu3LhM42RxOgPY7XalpqZq9erVeuKJJxz9lStXVseOHZWSkqLRo0dr7ty56tev3xWPt3TpUm3dulVvvvlmsc4fERGhuXPnqmnTpsrKytKUKVPUunVr/fjjj7rxxhsL3S8nJyff+wxzc3Pk7e1eCzkAAAAAuLbsdrMjKL4hQ4YUWjFPS0vL1/fAAw/ogQceKOOonFFxL4Xly5crICBAPj4+6tixo7p3766kpCSnMf369VNKSor279+v9PR09erVq8hjrlu3Tn379tXbb7+tRo0aFSuO6Oho9e7dW1FRUWrXrp0+/PBDVapU6YqJf0HvN3z3rWnFOicAAAAA4NogcS+FDh06KCMjQ3v27NH58+c1f/58+fv7O43p2LGjzp8/r/79+6tTp0664YYbCj3e+vXr1alTJ02fPl29e/e+6rjKlSun5s2ba+/evUWOS0hIUFZWllPrPWhEkfsAAAAAAK4tpsqXgr+/v8LDw4sc4+Xlpd69e2vSpEn67LPPCh2Xlpamf//735o4caIGDRpUqrjy8vL03//+V3fddVeR4wp6v6G393U0pwUAAACAS7KV4LVsuDIq7tfAuHHjdOLECcXGxha4fd26dbr77rs1dOhQ3XfffcrMzFRmZqZOnTpVrOOPHTtWn3/+ufbv369t27bpoYce0qFDhzRgwAAjLwMAAAAAYAIS92vA29tbFStWlMVS8LdO8+fP17lz55ScnKyqVas62r333lus4//+++8aOHCgIiMjdddddyk7O1ubNm1Sw4YNjbwMAAAAACiW6+V1cNcLi91+Pa33h7L29c4ss0Mw3I6jQWaHYLge2a+bHYLhbCePmx2C4Z763f3WjPDxK2d2CIarWcPP7BDKRJMnmpkdguHOH8m58qDrTIMeRT9ydz3auajoNXauR94h7vd334WVGWaHYDh3Tdg6t/Q0O4Sr8sX35v2dHdPU/d6SRcUdAAAAAAAXxuJ014GAgIBCt3322Wdq06bNNYwGAAAAAIrGvG5jkbhfBzIyMgrdVr169WsXCAAAAADgmiNxvw5c6ZVzAAAAAOBK7LwOzlA84w4AAAAAgAuj4g4AAAAAMJSNZ9wNRcUdAAAAAAAXRuIOAAAAAIALY6o8AAAAAMBQdjuL0xmJijsAAAAAAC6MijsAAAAAwFB2FqczFBV3AAAAAABcGIk7AAAAAAAujKnyAAAAAABD2cTidEai4g4AAAAAgAuj4g4AAAAAMBSL0xmLxB1O3HEKxolTNrNDMNy5Hf81OwTDXTqfY3YIhjt0/FezQzCcf5C/2SEYrmaNmmaHUCa8AjzNDsFwDXqEmx2C4XYu2mt2CIZr+HCE2SEY7obGdcwOwXBrbe43jTn3kvtdE3AZiTsAAAAAwFB2O1+kGMkdC6wAAAAAALgNEncAAAAAAFwYU+UBAAAAAIaysTidoai4AwAAAADgwqi4AwAAAAAMxevgjEXFHQAAAAAAF0biDgAAAACAC2OqPAAAAADAUHbxHncjUXEHAAAAAMCFUXEHAAAAABiK18EZi4o7AAAAAAAujIo7AAAAAMBQvA7OWFTcAQAAAABwYSTuAAAAAAC4MKbKAwAAAAAMxVR5Y1FxL4X4+HhZLBZNmDDBqX/ZsmWyWP7vvYV2u11vvfWWWrVqpYCAAAUHB6tly5aaMWOGzp075xj33nvvqUGDBvLx8VGTJk20cuXKEsfy1xYXF1f6iwQAAAAAmIrEvZR8fHw0ceJE/f7774WOefjhh/Xkk0+qS5cuWrdunTIyMvTiiy/q448/1ueffy5J2rRpk3r06KH+/fvru+++U9euXdW1a1f98MMPxY4lLi5OR48edbRFixaV+voAAAAAoKRsdotpzR0xVb6UYmJitHfvXiUnJ2vSpEn5ti9dulQLFizQsmXL1KVLF0d/WFiYOnfurOzsbEnSK6+8ori4OD311FOSpHHjxmnNmjV6/fXXNXv27GLFYrVaFRoaasBVAQAAAABcBRX3UvL09NT48eP12muv6Zdffsm3fcGCBYqIiHBK2i+zWCwKCgqSJKWnpysmJsZpe2xsrNLT04sdS1pamipXrqyIiAg9+uij+u2330p4NQAAAAAAV0PiboB77rlHUVFRSkxMzLdtz549ioiIuOIxMjMzVaVKFae+KlWqKDMzs1gxxMXF6d1331VqaqomTpyo9evXq2PHjsrLyyt0n5ycHGVnZzu13NycYp0PAAAAAApjt5vX3BGJu0EmTpyo+fPna8eOHU799mv0J+fBBx9U586d1aRJE3Xt2lXLly/X1q1blZaWVug+ycnJCgoKcmrz35p2TeIFAAAAABQPibtB2rZtq9jYWCUkJDj1169fXzt37rzi/qGhoTp27JhT37Fjx676mfU6deqoYsWK2rt3b6FjEhISlJWV5dT6DBpxVecDAAAAgMuouBuLxN1AEyZM0Keffur0XHrPnj21e/duffzxx/nG2+12ZWVlSZKio6OVmprqtH3NmjWKjo6+qlh++eUX/fbbb6patWqhY6xWqwIDA52at7f1qs4HAAAAACgbJO4GatKkiXr16qVXX33V0detWzd1795dPXr00Pjx4/XNN9/o0KFDWr58uWJiYrRu3TpJ0rBhw7Rq1SpNnTpVO3fuVFJSkr755hsNGTLkiuc9c+aMnnrqKX399dc6ePCgUlNT1aVLF4WHhys2NrbMrhcAAAAACmKzm9fcEYm7wcaOHSubzeb4bLFYtHDhQk2bNk3Lli1Tu3bt1LRpUyUlJalLly6OxLp169ZauHCh3nrrLTVr1kzvv/++li1bpsaNG1/xnJ6envr+++/VuXNn1a9fX/3791eLFi20YcMGWa1U0AEAAADgesZ73EshJSUlX19YWJhycpxXZvfw8NDgwYM1ePDgIo/3wAMP6IEHHihxHL6+vlq9enWJ9wMAAAAAuD4SdwAAAACAoex2i9khuBWmyl8HNmzYoICAgEIbAAAAAMB9UXG/DrRs2VIZGRlmhwEAAAAAxeKur2UzC4n7dcDX11fh4eFmhwEAAAAAMAFT5QEAAAAAcGFU3AEAAAAAhnLX96mbhYo7AAAAAAAujIo7AAAAAMBQLE5nLCruAAAAAAC4MBJ3AAAAAABcGFPlAQAAAACGYqq8sai4AwAAAADgwkjcAQAAAACGstnNa2Xl1KlT6tWrlwIDAxUcHKz+/fvrzJkzRY5/4oknFBERIV9fX9WsWVNDhw5VVlZWic9N4g4AAAAAwBX06tVLP/74o9asWaPly5fryy+/1KBBgwod/+uvv+rXX3/VlClT9MMPPyglJUWrVq1S//79S3xunnEHAAAAABjK3Z5x37Fjh1atWqWtW7eqZcuWkqTXXntNd911l6ZMmaJq1arl26dx48b64IMPHJ/r1q2rl19+WQ899JAuXbokL6/ip+NU3AEAAAAAbiMnJ0fZ2dlOLScnp1THTE9PV3BwsCNpl6SYmBh5eHho8+bNxT5OVlaWAgMDS5S0S1Tc8Tc2swMoA5VvcL/vp/yioswOwXC245lmh2C4sBuqmx2C4Xx8y5kdguE83O+vCElSXo77/Y2+c9Fes0MwXMOHI8wOwXA//WeX2SEYzitwn9khGM7zczcrh0ry9Xa/a/qTm/6PqgwlJydrzJgxTn2JiYlKSkq66mNmZmaqcuXKTn1eXl4KCQlRZmbx/h178uRJjRs3rsjp9YXhTwEAAAAAwFA2m3ktISFBWVlZTi0hIaHAOJ999llZLJYi286dO0t9P7Kzs3X33XerYcOGV/UFAhV3AAAAAIDbsFqtslqtxRo7cuRIxcfHFzmmTp06Cg0N1fHjx536L126pFOnTik0NLTI/f/44w/FxcWpfPny+uijj1SuXMlnMJK4AwAAAAAMdb0sTlepUiVVqlTpiuOio6N1+vRpffvtt2rRooUkae3atbLZbGrVqlWh+2VnZys2NlZWq1WffPKJfHx8ripOpsoDAAAAAFCEyMhIxcXFaeDAgdqyZYs2btyoIUOG6MEHH3SsKH/kyBE1aNBAW7ZskfRn0n7nnXfq7Nmzeuedd5Sdna3MzExlZmYqLy+vROen4g4AAAAAwBUsWLBAQ4YM0e233y4PDw/dd999evXVVx3bL168qF27duncuXOSpG3btjlWnA8PD3c61oEDBxQWFlbsc5O4AwAAAAAMdb1MlS+JkJAQLVy4sNDtYWFhsv/lwtu3b+/0uTSYKg8AAAAAgAuj4g4AAAAAMJTNDSvuZqLiDgAAAACAC6PiDgAAAAAwlFHPdl8di4nnLhtU3AEAAAAAcGEk7gAAAAAAuDCmygMAAAAADOWOr4MzExV3AAAAAABcGBV3AAAAAIChbDazI3AvVNwBAAAAAHBhJO5XKT4+XhaLRRMmTHDqX7ZsmSyW/3v9gN1u11tvvaVWrVopICBAwcHBatmypWbMmKFz585Jkn788Ufdd999CgsLk8Vi0YwZM0ocz8yZMxUWFiYfHx+1atVKW7ZsKdX1AQAAAABcA4l7Kfj4+GjixIn6/fffCx3z8MMP68knn1SXLl20bt06ZWRk6MUXX9THH3+szz//XJJ07tw51alTRxMmTFBoaGiJ41iyZIlGjBihxMREbdu2Tc2aNVNsbKyOHz9+1dcGAAAAAFfLbjevuSMS91KIiYlRaGiokpOTC9y+dOlSLViwQIsWLdJzzz2nm2++WWFhYerSpYvWrl2rDh06SJJuvvlmTZ48WQ8++KCsVmuJ45g2bZoGDhyovn37qmHDhpo9e7b8/Pw0d+7cUl0fAAAAAMB8JO6l4OnpqfHjx+u1117TL7/8km/7ggULFBERoS5duuTbZrFYFBQUVOoYcnNz9e233yomJsbR5+HhoZiYGKWnp5f6+AAAAABQUja7ec0dkbiX0j333KOoqCglJibm27Znzx5FRESU6flPnjypvLw8ValSxam/SpUqyszMLHLfnJwcZWdnO7Xc3JyyDBcAAAAAUEIk7gaYOHGi5s+frx07djj12138AYvk5GQFBQU5tXffmmZ2WAAAAACuczzjbiwSdwO0bdtWsbGxSkhIcOqvX7++du7cWabnrlixojw9PXXs2DGn/mPHjl1xobuEhARlZWU5td6DRpRluAAAAACAEiJxN8iECRP06aefOj1X3rNnT+3evVsff/xxvvF2u11ZWVmlPq+3t7datGih1NRUR5/NZlNqaqqio6OL3NdqtSowMNCpeXuXfHE8AAAAAEDZIXE3SJMmTdSrVy+9+uqrjr5u3bqpe/fu6tGjh8aPH69vvvlGhw4d0vLlyxUTE6N169ZJ+nOBuYyMDGVkZCg3N1dHjhxRRkaG9u7dW6xzjxgxQm+//bZjuv6jjz6qs2fPqm/fvmVyrQAAAABQFLvNblpzR15mB+BOxo4dqyVLljg+WywWLVy4UG+99Zbmzp2rl19+WV5eXqpXr5569+6t2NhYSdKvv/6q5s2bO/abMmWKpkyZonbt2iktLe2K5+3evbtOnDih0aNHKzMzU1FRUVq1alW+BesAAAAAANcfEverlJKSkq8vLCxMOTnOq7J7eHho8ODBGjx4cKHHCgsLK/VCdkOGDNGQIUNKdQwAAAAAMIKbFr5Nw1R5AAAAAABcGIm7izt8+LACAgIKbYcPHzY7RAAAAABAGWKqvIurVq2aMjIyitwOAAAAAK7EXd+nbhYSdxfn5eWl8PBws8MAAAAAAJiExB0AAAAAYCgbq9MZimfcAQAAAABwYVTcAQAAAACG4hl3Y1FxBwAAAADAhZG4AwAAAADgwpgqDwAAAAAwFFPljUXFHQAAAAAAF0bFHQAAAABgKBsld0NRcQcAAAAAwIWRuAMAAAAA4MKYKg8AAAAAMJTdZnYE7oWKOwAAAAAALoyKO5zY7RazQzCcv4/7fd13yfdGs0MwnJfF/f7sVbb6mx2C4cqVc7/fUzkv97smSaoYEWJ2CIbLzD5hdgiGu6FxHbNDMJxX4D6zQzDcpexLZodgOE+L+y0c5uHhftd0PbOzOJ2hqLgDAAAAAODCqLgDAAAAAAxlc79Jr6ai4g4AAAAAgAsjcQcAAAAAwIUxVR4AAAAAYCgWpzMWFXcAAAAAAFwYFXcAAAAAgKFsFNwNRcUdAAAAAAAXRuIOAAAAAIALY6o8AAAAAMBQdubKG4qKOwAAAAAALoyKOwAAAADAULwNzlhU3AEAAAAAcGFU3AEAAAAAhrLxjLuhqLgDAAAAAODCSNwBAAAAAHBhJO6lEB8fL4vFogkTJjj1L1u2TBaLxfHZbrfrrbfeUqtWrRQQEKDg4GC1bNlSM2bM0Llz5yRJb7/9ttq0aaMKFSqoQoUKiomJ0ZYtW0ocy19bXFycMRcKAAAAACVgt9tNa+6IxL2UfHx8NHHiRP3++++Fjnn44Yf15JNPqkuXLlq3bp0yMjL04osv6uOPP9bnn38uSUpLS1OPHj20bt06paenq0aNGrrzzjt15MiRYscSFxeno0ePOtqiRYtKfX0AAAAAAHOxOF0pxcTEaO/evUpOTtakSZPybV+6dKkWLFigZcuWqUuXLo7+sLAwde7cWdnZ2ZKkBQsWOO03Z84cffDBB0pNTVXv3r2LFYvValVoaGgprgYAAAAASs9uMzsC90LFvZQ8PT01fvx4vfbaa/rll1/ybV+wYIEiIiKckvbLLBaLgoKCCjzuuXPndPHiRYWEhBQ7lrS0NFWuXFkRERF69NFH9dtvvxX/QgAAAAAALonE3QD33HOPoqKilJiYmG/bnj17FBERUeJjPvPMM6pWrZpiYmKKNT4uLk7vvvuuUlNTNXHiRK1fv14dO3ZUXl5eofvk5OQoOzvbqeXm5pQ4VgAAAABA2SFxN8jEiRM1f/587dixw6n/ahZHmDBhghYvXqyPPvpIPj4+xdrnwQcfVOfOndWkSRN17dpVy5cv19atW5WWllboPsnJyQoKCnJq7741rcTxAgAAAMBf2ex205o7InE3SNu2bRUbG6uEhASn/vr162vnzp3FPs6UKVM0YcIEff7552ratOlVx1OnTh1VrFhRe/fuLXRMQkKCsrKynFrvQSOu+pwAAAAAAOORuBtowoQJ+vTTT5Wenu7o69mzp3bv3q2PP/4433i73a6srCzH50mTJmncuHFatWqVWrZsWapYfvnlF/3222+qWrVqoWOsVqsCAwOdmre3tVTnBQAAAABeB2csEncDNWnSRL169dKrr77q6OvWrZu6d++uHj16aPz48frmm2906NAhLV++XDExMVq3bp2kP6fav/jii5o7d67CwsKUmZmpzMxMnTlz5ornPXPmjJ566il9/fXXOnjwoFJTU9WlSxeFh4crNja2zK4XAAAAAP4pTp06pV69eikwMFDBwcHq379/sfI16c8vMjp27CiLxaJly5aV+Nwk7gYbO3asbLb/e/eBxWLRwoULNW3aNC1btkzt2rVT06ZNlZSUpC5dujgS6zfeeEO5ubm6//77VbVqVUebMmXKFc/p6emp77//Xp07d1b9+vXVv39/tWjRQhs2bJDVSgUdAAAAAEqrV69e+vHHH7VmzRotX75cX375pQYNGlSsfWfMmCGLxXLV5+Y97qWQkpKSry8sLEw5Oc4rs3t4eGjw4MEaPHhwocc6ePDgVcfh6+ur1atXX/X+AAAAAGAkm829pqzv2LFDq1at0tatWx2PNb/22mu66667NGXKFFWrVq3QfTMyMjR16lR98803RT7KXBQq7gAAAAAAt1HQa6//XlwtqfT0dAUHBzutRRYTEyMPDw9t3ry50P3OnTunnj17aubMmQoNDb3q85O4Xwc2bNiggICAQhsAAAAAuBK73bxW0Guvk5OTS3U9mZmZqly5slOfl5eXQkJClJmZWeh+w4cPV+vWrdWlS5dSnZ+p8teBli1bKiMjw+wwAAAAAMDlJSQkaMQI59dcF7b217PPPquJEycWebwdO3ZcVRyffPKJ1q5dq+++++6q9v8rEvfrgK+vr8LDw80OAwAAAACKxW7iM+5Wq7XYi3SPHDlS8fHxRY6pU6eOQkNDdfz4caf+S5cu6dSpU4VOgV+7dq327dun4OBgp/777rtPbdq0UVpaWrFilEjcAQAAAAD/UJUqVVKlSpWuOC46OlqnT5/Wt99+qxYtWkj6MzG32Wxq1apVgfs8++yzGjBggFNfkyZNNH36dHXq1KlEcZK4AwAAAABQhMjISMXFxWngwIGaPXu2Ll68qCFDhujBBx90rCh/5MgR3X777Xr33Xd1yy23KDQ0tMBqfM2aNVW7du0SnZ/EHQAAAABgKJvdvV4HJ0kLFizQkCFDdPvtt8vDw0P33XefXn31Vcf2ixcvateuXTp37pzh5yZxBwAAAADgCkJCQrRw4cJCt4eFhcl+hS8srrS9MCTuAAAAAABDmbk4nTviPe4AAAAAALgwEncAAAAAAFwYU+UBAAAAAIZiqryxqLgDAAAAAODCqLgDAAAAAAxFwd1YVNwBAAAAAHBhVNwBAAAAAIbiGXdjkbjDyYmz/maHYLj25beaHYLhvroYa3YIhjtXyf3+Orq1Zq7ZIRjunQUnzQ7BcBP6/2F2CGViW+IXZodgOE8P9/tH4FqbxewQDOf5ufv9njwt7ndN9laNzQ7BcDe/2M7sEMpG1FtmRwAXwFR5AAAAAABcmPuVuAAAAAAAprLb3W+mipmouAMAAAAA4MKouAMAAAAADGVjcTpDUXEHAAAAAMCFkbgDAAAAAODCmCoPAAAAADAUi9MZi4o7AAAAAAAujIo7AAAAAMBQdhanMxQVdwAAAAAAXBgVdwAAAACAoai4G4uKOwAAAAAALozEHQAAAAAAF8ZUeQAAAACAoWy8Ds5QVNwBAAAAAHBhVNwBAAAAAIZicTpjUXE3WWZmpp544gnVqVNHVqtVNWrUUKdOnZSamipJCgsLk8Vi0ddff+2035NPPqn27ds7jSmsxcfHX+OrAgAAAAAYhYq7iQ4ePKhbb71VwcHBmjx5spo0aaKLFy9q9erVevzxx7Vz505Jko+Pj5555hmtX7++wONs3bpVeXl5kqRNmzbpvvvu065duxQYGChJ8vX1vTYXBAAAAAAwHIm7iR577DFZLBZt2bJF/v7+jv5GjRqpX79+js+DBg3S7NmztXLlSt111135jlOpUiXHzyEhIZKkypUrKzg4uOyCBwAAAIBC2FmczlBMlTfJqVOntGrVKj3++ONOSftlf026a9eurcGDByshIUE2m+0aRgkAAAAAMBuJu0n27t0ru92uBg0aFGv8Cy+8oAMHDmjBggWGxZCTk6Ps7GyndjE3x7DjAwAAAPhnstnspjV3ROJukpJOHalUqZJGjRql0aNHKzc315AYkpOTFRQU5NTeT5lgyLEBAAAAAMYgcTdJvXr1ZLFYHAvQFceIESN0/vx5zZo1y5AYEhISlJWV5dTuj3/WkGMDAAAA+Oey2+ymNXdE4m6SkJAQxcbGaubMmTp79my+7adPn87XFxAQoBdffFEvv/yy/vjjj1LHYLVaFRgY6NTKeVtLfVwAAAAAgHFI3E00c+ZM5eXl6ZZbbtEHH3ygPXv2aMeOHXr11VcVHR1d4D6DBg1SUFCQFi5ceI2jBQAAAACYgcTdRHXq1NG2bdvUoUMHjRw5Uo0bN9Ydd9yh1NRUvfHGGwXuU65cOY0bN04XLly4xtECAAAAQPHY7XbTmjviPe4mq1q1ql5//XW9/vrrBW4/ePBgvr4ePXqoR48eBY5v37692/5hBQAAAIB/IhJ3AAAAAICh7Dab2SG4FabKAwAAAADgwkjcAQAAAABwYUyVBwAAAAAYyuam71M3CxV3AAAAAABcGBV3AAAAAICheNOVsai4AwAAAADgwqi4AwAAAAAMZecZd0NRcQcAAAAAwIWRuAMAAAAA4MKYKg8AAAAAMBRT5Y1FxR0AAAAAABdGxR0AAAAAYCib3WZ2CG6FijsAAAAAAC6MxB0AAAAAABfGVHkAAAAAgKFYnM5YJO5wcvqsp9khGC7gzH6zQzDcwZxWZodguNCQPLNDMFxV31Nmh2C48MjKZodgON+Lv5odQpmw2S1mh2A4Dzf8N2DuJff7Pfl6u98vysMN//Dd/GI7s0Mw3Nfj1psdQpm4e7TZEcAVkLgDAAAAAAxFxd1YPOMOAAAAAIALo+IOAAAAADCU3U7F3UhU3AEAAAAAcGEk7gAAAAAAuDCmygMAAAAADGWz2cwOwa1QcQcAAAAAwIVRcQcAAAAAGIrXwRmLijsAAAAAAC6MxB0AAAAAABdG4g4AAAAAMJTdbjOtlZVTp06pV69eCgwMVHBwsPr3768zZ85ccb/09HTddttt8vf3V2BgoNq2bavz58+X6Nwk7gAAAAAAXEGvXr30448/as2aNVq+fLm+/PJLDRo0qMh90tPTFRcXpzvvvFNbtmzR1q1bNWTIEHl4lCwVZ3E6AAAAAIChzFycLicnRzk5OU59VqtVVqv1qo+5Y8cOrVq1Slu3blXLli0lSa+99pruuusuTZkyRdWqVStwv+HDh2vo0KF69tlnHX0RERElPj8VdwAAAACA20hOTlZQUJBTS05OLtUx09PTFRwc7EjaJSkmJkYeHh7avHlzgfscP35cmzdvVuXKldW6dWtVqVJF7dq101dffVXi85O4AwAAAADcRkJCgrKyspxaQkJCqY6ZmZmpypUrO/V5eXkpJCREmZmZBe6zf/9+SVJSUpIGDhyoVatW6aabbtLtt9+uPXv2lOj8bp+4t2/fXk8++aTZYSglJUXBwcFmhwEAAAAAZc5us5vWrFarAgMDnVph0+SfffZZWSyWItvOnTuv6h7YbH8ulPfII4+ob9++at68uaZPn66IiAjNnTu3RMdym2fc4+PjNX/+/Hz9mzdvVmRk5DWJYdGiRXrooYc0ePBgzZw585qcEwAAAABwdUaOHKn4+Pgix9SpU0ehoaE6fvy4U/+lS5d06tQphYaGFrhf1apVJUkNGzZ06o+MjNThw4dLFKfbJO6SFBcXp3nz5jn1VapUSZ6enoXuk5ubK29vb0PO/8477+jpp5/Wm2++qalTp8rHx8eQ415mZKwAAAAAUFZsZfhaNiNVqlRJlSpVuuK46OhonT59Wt9++61atGghSVq7dq1sNptatWpV4D5hYWGqVq2adu3a5dS/e/dudezYsURxutVUeavVqtDQUKd2++23O02VDwsL07hx49S7d28FBgY6lu//6quv1KZNG/n6+qpGjRoaOnSozp49W+xzHzhwQJs2bdKzzz6r+vXr68MPPyxy/L59+/5fe3ceV3Pa/w/8dU6UltOCIlmKydJGhrlxI0uorNkp28RkBpMl28ygmLGTYWwzEWMLYxnbIFGWuG0pxpKIkBpjS2j//P7wdX6OE9pOnz6n1/Px8Lid8zkz87ru03Len+u63he6d++OKlWqwMjICE2bNsWRI0dUXpNX1rdL7vft24d69erBwMAAvXv3xqtXr7B+/XpYW1vDzMwM3377LXJycvL/fx4RERERERHlqUGDBnBzc8OIESNw9uxZnDp1CqNHj0b//v2VHeUfPHiA+vXr4+zZswAAmUyGiRMnYunSpfjjjz8QHx+PadOm4fr16/Dx8SnQf1+rCvf8WrhwIRo2bIjo6GhMmzYNt27dgpubG3r16oXY2Fhs3boVJ0+exOjRo/P97wwJCUHnzp1hYmICb29vrFmz5qOvT0tLg4eHB8LDwxEdHQ03Nzd07dpVbcnE+1kB4NWrV1i6dClCQ0Nx8OBBREREwNPTEwcOHMCBAwewYcMGrF69Gn/88UfB/88hIiIiIiIqIjH3uGvKpk2bUL9+fbRv3x4eHh5o2bIlfv31V+X1rKws3LhxA69evVI+N3bsWEydOhXjxo1Dw4YNER4ejrCwMNSpU6dA/22tWiq/b98+GBkZKR9/aPlBu3btMGHCBOXj4cOHw8vLSzkzb2tri6VLl8LFxQUrV6785JL33NxcrFu3DsuWLQMA9O/fHxMmTEBCQgJsbGzy/GcaNmyIhg0bKh/PmjULu3btwp49e1RuGLyf9cSJE8jKysLKlSuVb3bv3r2xYcMGpKSkwMjICHZ2dmjbti2OHTuGfv36fTB3XucbZmXqobxu4c83JCIiIiIi0kYVK1bE5s2bP3jd2toagqB+42DKlCkq57gXhlbNuLdt2xaXLl1S/lm6dGmer3v37D0AiImJwbp162BkZKT806lTJ+Tm5iIhIeGT/92wsDC8fPkSHh4eAIDKlSujQ4cOH+0UmJaWBn9/fzRo0ACmpqYwMjLCtWvX1Gbc388KAAYGBip3aKpUqQJra2uVmxZVqlRRa57wvrzON9y3qWjnGxIREREREVHx0qoZd0NDQ3z22Wf5et270tLS4Ovri2+//VbttTVr1vzkv2/NmjV48uQJ9PX1lc/l5uYiNjYWgYGBkMvV74/4+/sjLCwMCxcuxGeffQZ9fX307t0bmZmZH80KAOXLl1d5LJPJ8nzu7fEDHzJ16lSMHz9e5bnt/+NsOxERERERFY3wiVqECkarCvfCaty4Ma5evZqvov99jx8/xp9//onQ0FDY29srn8/JyUHLli1x+PBhuLm5qf1zp06dwtChQ+Hp6Qngzc2DO3fuFHoMhaGnp6d2nmF5Nq0nIiIiIiIqVVi4A5g8eTKaNWuG0aNHY/jw4TA0NMTVq1cRFhaGX3755aP/7IYNG1CpUiX07dsXMplM5ZqHhwfWrFmTZ+Fua2uLnTt3omvXrpDJZJg2bdonZ8iJiIiIiIikQJNN4soirdrjXlhOTk6IjIxEXFwcWrVqBWdnZ0yfPl3Z1v9j1q5dC09PT7WiHQB69eqFPXv24N9//1W7tnjxYpiZmaFFixbo2rUrOnXqhMaNGxfLeIiIiIiIiEh7yIS82t5RmfV7pNgJil8fYYvYEYrd+owBYkcodlUr5ogdodjVNUsRO0KxCz1VWewIxW5M00tiR9CIE2nqzU2lrpxc+z6ypGep3/iXOn1d7VtBWF5H+8bUdE/+jz2WijOztPCDLIDOWTfEjlAorgPOi/bfPrJFC38Hih2AiIiIiIiItIsgaN8NLzFxqfwnnDhxQuWYuPf/EBEREREREWkSZ9w/oUmTJrh06ZLYMYiIiIiIiCQjl83pihUL90/Q19cv1DFxRERERERERMWBhTsREREREREVK4FHXRcr7nEnIiIiIiIiKsVYuBMRERERERGVYlwqT0RERERERMVKYHO6YsUZdyIiIiIiIqJSjDPuREREREREVKwEgc3pihNn3ImIiIiIiIhKMRbuRERERERERKUYl8oTERERERFRsWJzuuLFGXciIiIiIiKiUowz7kRERERERFSshFw2pytOnHEnIiIiIiIiKs0EohKWnp4uzJgxQ0hPTxc7SrHhmKRDG8fFMUkDxyQNHJN0aOO4OCZp0MYxUeknEwSBXQOoRKWmpsLExATPnz+HsbGx2HGKBcckHdo4Lo5JGjgmaeCYpEMbx8UxSYM2jolKPy6VJyIiIiIiIirFWLgTERERERERlWIs3ImIiIiIiIhKMRbuVOL09PQwY8YM6OnpiR2l2HBM0qGN4+KYpIFjkgaOSTq0cVwckzRo45io9GNzOiIiIiIiIqJSjDPuRERERERERKUYC3ciIiIiIiKiUoyFOxEREREREVEpxsKdiIiIiIiIqBRj4U5EBXb//n3k5uaKHYOIiIiIqExg4U6lhrGxMW7fvi12DMoHOzs73LlzR+wYxerUqVPIyMgQOwYRERERkRoW7lRqaPPJhNo2Q62N75W7uzsePHggdgwqo7Zs2YKXL1+KHYOIqEi+/PJLvHjxQuwYRFqJhTtRCdDGGWpto403I0g6fH19kZKSInaMAsvIyOBKFSJSWr9+PV6/fi12DCKtVE7sAERlAYtCKm65ubmQy9Xvvebm5uL+/fuoWbOmCKkKztnZGTKZLF+vvXjxoobTiEdKPyPCwsIQFBSE06dPIzU1FcCbrU7NmzfH+PHj4erqKnLCoklKSsLq1asRHx8PS0tLDB8+HPXr1xc7VpEIgoCIiAjlmDp16oTy5cuLHavA/v33X6xduxanT59GcnIyAKBq1apo0aIFhg4dCnNzc5ETFo02vE9S+llWUIIg4I8//sCxY8fwzz//qK2k3Llzp0jJqKxg4U5EJCGpqakYPnw49u7dC2NjY/j6+mLGjBnQ0dEBADx69Ag2NjbIyckROWn+9OjRQ+wIVADr16/H8OHD0bt3bwQFBaFKlSoAgJSUFBw+fBgeHh5Ys2YNBg0aJHLS/DMwMMDdu3dhbm6Oq1evokWLFjA3N4ezszP279+PlStX4vTp03BychI7ar55eHhgy5YtMDExwZMnT+Dh4YGzZ8+icuXKePz4MerWrYvjx49LqtA9d+4cOnXqBAMDA7i6uqJu3boA3nztLV26FHPnzsWhQ4fQpEkTkZPmnza+TwDw4sULVKhQ4aOvMTY2LqE0xWfs2LFYvXo12rZtiypVquT7pjNRcZEJ2nxrjCTF2NgYly5dQu3atcWOUuwUCgViYmK0Zmza+F5J5T3y8/PDwYMH8dNPP+HZs2f48ccf4eDggJ07d0JXVxcpKSmwtLTUqp4KZYFUvv7q1q0LPz8/jBo1Ks/rK1asQFBQEG7evFnCyQpPLpcjOTkZFhYW6NGjB3Jzc7Fz506UK1cOubm58PLyQlpaGvbu3St21Hx7d0zffPMNIiMjsW/fPtjY2OD+/fvo0aMHmjZtipUrV4odNd+aNWuGhg0bYtWqVWoFkyAIGDlyJGJjY3H69GmREhacNr5Pcrn8owWtIAiQyWSSubn8rooVK2Ljxo3w8PAQOwqVUZxxp1KD95CkQxvfK6ncOd+9ezfWr1+PNm3aAHgzY925c2d07doVe/bsASCdsXzIhQsXcO3aNQCAvb09nJ2dRU5EbyUmJn50KXz79u0xYcKEEkxUvC5evIhNmzahXLk3H4/kcjkmTZqEzp07i5ys8I4ePYr58+fDxsYGAFC9enXMmzcPI0aMEDlZwcTExGDdunV5/nyTyWQYN26cpH9WaMv7BAB//PEHKlasKHaMYmdiYlLqb66SdmPhThqVmpoKIyMjtb24OTk5ePnypcpSqb/++gtWVlYlHbFESLGQunfvHgCgRo0aateuXr2KatWqlXQkjZLKzYhHjx6hVq1ayseVK1fGkSNH0KlTJ3h4eCA4OFjEdEXzzz//oH///oiIiICpqSkA4NmzZ2jbti1CQ0Mlt1xUG9nb22PNmjWYP39+ntfXrl0LOzu7Ek5VNDKZTPkzWi6Xw8TEROW6qakpnj59Kka0Ink7pqdPn6JOnToq1z777DMkJSWJEavQqlatirNnz36w38DZs2eVWzekRNveJwD473//CwsLC7FjFLuAgAAEBgZi7dq10NfXFzsOlUEs3Eljdu3ahcmTJ+PSpUswMDBQuZaeno6mTZti4cKF6Nq1KwCgZcuWYsQsEVIpCrOzsxEYGIilS5ciLS0NAGBkZIQxY8ZgxowZyiY5eRXzUieV42tq1qyJa9euKWdlgDfLrA8fPoyOHTvC09NTxHRFM2bMGLx48QJ///03GjRoAODNTaIhQ4bg22+/xZYtW0ROqDm1atWSRBOqRYsWoUuXLjh48CBcXV1V9riHh4fj9u3b2L9/v8gpC0YQBNStWxcymQxpaWmIjY1V2c8eHx+PqlWripiwcIYOHQo9PT1kZWUhISEB9vb2ymvJycnKm2NS4e/vj6+++goXLlxA+/bt1b72fvvtNyxcuFDklAWnbe+TNuvbty+2bNkCCwsLWFtbq/3M1uYGqlQ6sHAnjVm5ciUmTZqkVrQDgKGhISZPnoxffvlFWbhLUUhICPr165fnGN8llRnqMWPGYOfOnZg/fz6aN28OADh9+jQCAgLw+PFjSe2ze+vx48eYPn36B7vAPnnyRKRkhdOxY0eEhISo7bEzMjLCoUOH0KFDB5GSFd3Bgwdx5MgRZdEOvDlKcfny5ejYsaOIyTTvypUrYkfIlzZt2uDKlStYuXIlzpw5o9LZ293dHSNHjoS1tbW4IQsoJCRE5fFnn32m8vjMmTOSuyE2ZMgQ5d+7d++OV69eqVzfsWMHGjVqVMKpimbUqFGoXLkygoKCsGLFCuUeaR0dHXz++edYt24d+vbtK3LKgtHG96lWrVrKZqnaZsiQIbhw4QK8vb3ZnI5EweZ0pDHVqlXD8ePH1T4EvRUfH4/WrVtLchnYW1WqVMHr16/Rp08f+Pj4oEWLFmJHKhITExOEhobC3d1d5fkDBw5gwIABeP78uUjJCs/DwwPx8fHw8fHJ8xftux+cpODp06dISkpSmZV514sXL3Dx4kW4uLiUcLKiUygUOHHihNoH1ejoaLi4uCiPHpOSnJwcBAUFYdu2bUhMTERmZqbKdandOCqoLVu2oFu3bjA0NBQ7SrE5deoUmjRpAj09PbGjFNrLly+ho6Pzyc7fpVVWVhb+/fdfAG+2C+W1WuX+/fuoVq1ansdmSoXU3ydtY2hoiEOHDmn1ClEq3TjjThrz9OlTZGdnf/B6VlaWJPcNvuvBgwfYu3cv1q1bhzZt2qB27doYNmwYhgwZIsmllXp6ennOltnY2EBXV7fkAxWDEydO4OTJk2jYsKHYUYqFmZkZzMzMPnhdoVBIsmgHgHbt2sHPzw9btmxRrlB58OABxo0bh/bt24ucrnACAwMRHByMCRMm4IcffsD333+PO3fuYPfu3Zg+fbrY8TTO19cX//nPf7SqoZO7u7vkT9V4/0aK1E4KKV++PCwtLT/6Gjs7O0mNKS9SfJ/atWv3ydfIZDKEh4eXQJriVaNGDUkeY0fag4U7aYy1tTXOnz//wUYy58+fV2myJUXlypWDp6cnPD09kZKSgo0bN2L9+vWYNm0a3Nzc4OPjg65du0rmjv/o0aMxa9YshISEKGeTMjIy8NNPP2H06NEipyuc+vXr4/Xr12LHKDZLly7N1+u+/fZbDScpfr/88gu6desGa2trZR+Fe/fuwcHBARs3bhQ5XeFs2rQJv/32Gzp37oyAgAAMGDAAderUgZOTE86cOSPJ96kgtHFRH8ckDRyTOD52k/zFixfYvHkzMjIySjBR8Vm0aBEmTZqEVatWSW5LEGkHFu6kMT179sT333+PDh06qHV6TU5Oxg8//ABvb2+R0hW/KlWqoGXLloiLi0NcXBwuX76MIUOGwMzMDCEhIcrju0qbnj17qjw+cuQIqlevrvzlGxMTg8zMTMnOeK5YsQJTpkzB9OnT4eDgoLakUmp3z4OCglQe37t3D5aWlsrjq4A3sxlSLAhr1KiBixcv4siRI7h+/ToAoEGDBh89fqy0S05OhqOjI4A3fQjebjfp0qULpk2bJmY0IqJi9/7vKOBN49vly5fjp59+gpWVFWbNmiVCsqLz9vbGq1evUKdOHRgYGKh9ntD2rU8kPhbupDFTpkzBn3/+CVtbW3h7e6NevXoAgOvXr2PTpk2oUaMGpkyZInLKoktJScGGDRsQEhKC27dvo0ePHti3bx9cXV3x8uVLzJw5E0OGDMHdu3fFjpqn948+6tWrl8pjqXeQNzU1RWpqqtryPUEQIJPJlA2OpCIhIUHlsUKhQGRkZKleOlkQMpkMHTp0kHSTvXdVr14dDx8+RM2aNVGnTh0cPnwYjRs3xrlz5yS9R5qIKD82bdqE6dOn4/Xr1wgICMBXX32lcqNZSpYsWSJ2BCrjpPmdQ5KgUChw6tQpTJ06FVu3blXuZzc1NYW3tzd++uknKBQKkVMWTdeuXXHo0CHUrVsXI0aMwODBg1GxYkXldUNDQ0yYMAELFiwQMeXHvd9RWdt4eXmhfPny2Lx5M7vASkB4eDjCw8PzPAFg7dq1IqUqPE9PT4SHh+M///kPxowZA29vb6xZswaJiYkYN26c2PGIiDTi4MGDmDJlChISEuDv74/x48dLukllVlYWIiMjMW3aNJXjWIlKEgt30igTExOsWLECy5cvx7///gtBEGBubp5n8STFTr0WFhaIjIxUHp2WF3Nzc7VZ0tLun3/+wY0bNwAA9erVg4WFhciJCu/KlSuIjo5Wrvig0iswMBAzZ85EkyZNYGlpqRU3WebOnav8e79+/VCrVi1ERUXB1tZW0kdhlmXa8HX5Po5JGqQwprNnz2Ly5Mk4c+YMRo4ciSNHjqBy5cpixyqy8uXLY8eOHdziRKJi4U4lQiaTwdzc/KOvkWKnXhcXFzRu3Fjt+czMTISGhmLw4MGQyWSSacKXmpqKUaNGITQ0VOWM3H79+mH58uVqy+qloEmTJrh37x4LdwlYtWoV1q1bh0GDBokdpVhs3boVe/bsUfaIGDlyJJo1a4ZmzZqJHa3E1KpVK8+juqRMCg3CCopjkgYpjKlZs2bQ19fHyJEjYWNjg82bN+f5Oin2YenRowd2797N1VIkGp7jTqWGQqFATEyMpAp3HR0dPHz4UG1G+vHjx7CwsJDc/ul+/fohOjoay5YtU64iOH36NPz8/NCoUSOEhoaKnLDgtm/fjoCAAEycOBGOjo5qRYSTk5NIyQrn/bPMq1evjpMnT6p1uJVa0z0AqFSpEs6ePYs6deqIHaXIVq5ciVGjRsHW1hb6+vq4fPkyxo8fX6q3zVDZdfLkSTRt2lRSK94+5d69e6hWrRp0dHTEjlJspPA+WVtbf3JlgEwmw+3bt0soUfH58ccfsWjRIrRv3x6ff/652tJ/Kd6MIGlh4U6lhhQLd7lcjpSUFLXVBDExMWjbtq3kOowaGhri0KFDaNmypcrzJ06cgJubG16+fClSssLL6yg+mUwm2eZ0crlc5UPR23G8/1hq4wKAyZMnw8jISCuWItrb26Nv376YMWMGAGDjxo3w9fWV5PdQQcTExKBx48aS+/o7cOAAdu7ciYoVK+LLL79UOcb06dOn6NWrF44ePSpiwoK7evUqfvnlF5w+fRrJyckAgKpVq6J58+YYPXo07OzsRE5YvG7duoURI0ZI7n16+PAhwsPDUbFiRbi6ukJXV1d57eXLl1i0aBGmT58uYkJ662N726V6M4KkhUvliQrB2dkZMpkMMpkM7du3V+mQmpOTg4SEBLi5uYmYsHAqVaqU53J4ExMTmJmZiZCo6KTWX+BTjh07JnYEjUlPT8evv/6KI0eOwMnJSW11xOLFi0VKVnC3b9/GkCFDlI8HDhwIHx8fPHz4EJaWliIm0zypzQds3rwZgwcPhpubG27cuIFly5YhODgYXl5eAN5sfYqMjBQ5ZcH89ddf6NGjBxo3bozu3bsrj2RNSUlBWFgYGjdujD///BOdOnUSOWnxSUtLk9z7dO7cOXTs2BG5ubnIysqClZUVdu/eDXt7ewBvxhQYGCipwv3o0aMYPXo0zpw5o7by6/nz52jRogVWrVqFVq1aiZSw8LTt8wRJDwt3okLo0aMHAODSpUvo1KkTjIyMlNd0dXVhbW2tdqyaFPzwww8YP348NmzYgKpVqwJ4cw71xIkTJTsLKpX+Avnl4uJSoNfPnTsXI0eOhKmpqWYCFaPY2Fg0atQIwJumgu+SQlOmd2VkZKgso5TL5dDV1cXr169FTFV0PXv2/Oj158+fS+69WrBgARYvXqxc5rpt2zZ8+eWXSE9Ph4+Pj8jpCmfKlCmYPHkyZs6cqXYtICBAuX1ISoX70qVLP3r9wYMHJZSk+Hz33Xfw9PREcHAwXr58icmTJ8PFxQVhYWFwdnYWO16hLFmyBCNGjMhzu5aJiQl8fX2xePFiSRbuRGLjUnkqNYyNjSXXnG79+vXo168fKlSoIHaUYuHs7Iz4+HhkZGSgZs2aAIDExETo6enB1tZW5bUXL14UI2KhvJ1Fu3btGgCgQYMGGDNmTJloWCfF7yttIJfL8dVXX8HAwED53PLly+Ht7a2yqkVKqwiAN52VO3TooJzBfd+TJ0+wb98+SS2VNzIywuXLl1WWwR47dgzdunXDggUL4OnpiWrVqklqTPr6+rh06dIHf8bduHEDjRo1ktSNJLlcDktLS5Wl5O/KzMxEcnKypN6nihUr4syZM6hbt67yublz52L+/Pk4dOgQatasKbmvvVq1auHgwYNo0KBBntevX7+Ojh07IjExsYSTFY/79+9jz549SExMRGZmpso1qf08J+nhjDuVGlK8h/TuUlht8HYlgTbZsWMH+vfvjyZNmigb7p05cwYODg4IDQ2V5MqIgpDi95U2aN26tfJIxbdatGihsgdSajPTwJubXr169frgTPSlS5ewb9++Ek5VNMbGxkhJSVEp3Nu2bYt9+/ahS5cuuH//vojpCsfa2hr79+//YOG+f/9+ya1GqlWrFubNm4e+ffvmef3SpUv4/PPPSzhV0aWnp6s8njJlCsqVK4eOHTti7dq1IqUqvJSUlI+eJFGuXDk8evSoBBMVn/DwcHTr1g21a9fG9evX4eDggDt37kAQhDxPGCIqbizcqURkZ2cjIiICt27dwsCBA6FQKJCUlARjY2PlMvMXL16InDJ/KlasiLi4OFSuXBlmZmYf/fAtteZ0bxtpaZNJkyZh6tSpaktGZ8yYgUmTJml94S4158+fx7Zt2/Kczdi5c6dIqQouIiJC7Aga8fnnn+PixYsfLNz19PSUq3Wk4osvvsBff/2ldkyfi4sL9u7diy5duoiUrPBmzpyJgQMHIiIiAq6urip73MPDw3Hw4MEPHtNVWn3++ee4cOHCBwv3t01HpcTBwQFRUVFqp5v4+/sjNzcXAwYMEClZ4VlZWeHKlSv47LPP8rweGxsr2T4fU6dOhb+/PwIDA6FQKLBjxw5YWFjAy8tLkn2NSHq4VJ407u7du3Bzc0NiYiIyMjIQFxeH2rVrw8/PDxkZGVi1apXYEQtk/fr16N+/P/T09LBu3bqPFu5SnZE/f/68clm5nZ2dJGcx3jIwMEBsbKzah4ibN2+iYcOGePXqlUjJSoaUTmsIDQ3F4MGD0alTJxw+fBgdO3ZEXFwcUlJS4OnpiZCQELEjlnkZGRnIyclR2QIgdZGRkYiKisLUqVPzvH7s2DH8/vvvkvv6i4qKwtKlS/PsKu/n56dcgSQVV69exatXr9CkSZM8r2dlZSEpKUlSKwmCg4MRERGBjRs35nl93rx5WLVqlaSaoo0ZMwYRERE4d+6c2jbC169f44svvkDbtm0/2bOgNFIoFLh06RLq1KkDMzMznDx5Evb29oiJiUH37t1x584dsSOSlmPhThrXo0cPKBQKrFmzBpUqVVIWERERERgxYgRu3rwpdkT6P/fv38eAAQNw6tQpZTOzZ8+eoUWLFggNDUX16tXFDVgIHh4e6NOnD4YNG6byfEhICEJDQ3Ho0CGRkpUMKRXuTk5O8PX1xahRo5S5bWxs4OvrC0tLSwQGBoodscBycnKwbt06hIeH459//kFubq7KdakdXUVE9DEpKSlo3LgxdHR0MHr0aOV2jevXr2P58uXIycnBxYsXP9gnozSrWrUqjh07hgYNGsDOzg5z585Ft27dEBMTg//+979IS0sTOyJpOS6VJ407ceIEoqKi1BrKWFtbS7ILbGpqar5fm1dX1dJs+PDhyMrKwrVr15S/bG/cuIFhw4Zh+PDhOHjwoMgJC65bt26YPHkyLly4oFwKe+bMGWzfvh2BgYHYs2ePymtJPLdu3ULnzp0BvDmd4eXLl5DJZBg3bhzatWsnycLdz88P69atQ+fOneHg4CDJfe0kPa9fv0ZYWBjatm0LhUKhci01NRURERHo1KkT9PT0REpYcNo4pvT0dBw+fFirxlSlShVERUXh66+/xtSpU5XbF2QyGTp16oTly5dLsmgHgGbNmuHkyZNo0KABPDw8MGHCBFy+fBk7d+5U22pDpAks3EnjcnNz8+yIev/+fbVfVFJgamqa7w/fUuoEC/z/JaPvNjSqV68eli1bJtmjW7755hsAwIoVK7BixYo8rwFvPlRI7f3Kj1atWkFfX1/sGPliZmam7HXxdp+ko6Mjnj17JtktDaGhodi2bRs8PDzEjlIsdHR08vU6KX0vaeOYfv31V+zZsyfPm5HGxsZYunQp7t27h1GjRomQrnC0cUyrV6/+5JgSExMxevRoEdIVXq1atXDgwAE8ffoU8fHxEAQBtra2MDMzEztakSxevFg5qx4YGIi0tDRs3boVtra27ChPJYKFO2lcx44dsWTJEvz6668A3hRIaWlpmDFjhiQ/zB47dkz59zt37mDKlCkYOnSocr/g6dOnsX79esyZM0esiIVWo0YNZGVlqT2fk5ODatWqiZCo6N5fmqwtBg8ejLZt26J169aoU6fOB1934MCBEkxVNK1bt0ZYWBgcHR3Rp08f+Pn54ejRowgLC0P79u3Fjlcourq6H2zSJEWCIKBWrVoYMmSIZM+Zfp82jmnTpk2YNm3aB6+PHTsWM2fOlFSRW5bHJLXC/S0zMzM0bdpU7BjF5t0tZ4aGhpLr0URaQCDSsMTERMHOzk5o0KCBUK5cOaFZs2ZCpUqVhHr16gkpKSlixyuSdu3aCZs3b1Z7ftOmTYKLi0vJByqi3bt3C1988YVw7tw55XPnzp0TmjVrJuzatUu8YKTGx8dHsLW1FWQymVC9enXBy8tL+O2334S4uDixoxXa48ePhQcPHgiCIAg5OTnCnDlzhK5duwrjx48Xnjx5InK6wlm4cKHwzTffCLm5uWJHKRbnzp0TRo4cKZiamgrOzs7CsmXLJPvevKWNYzI1NRXu3r37wet3794VTE1NSzBR0XFMRFTWsTkdlYjs7Gxs3boVMTExSEtLQ+PGjeHl5SWZJbwfYmBggJiYGNja2qo8HxcXh0aNGkluea+ZmRlevXqF7OxslCv3ZkHO278bGhqqvFZKR92Fh4cjKChI2Sm/QYMGGDt2LFxdXUVOVnQPHjzA8ePHERkZicjISMTFxcHS0lKSZ09ri549e6o8Pnr0KCpWrAh7e3u1842ldMTdu9LT0/HHH38gJCQEZ86cQdeuXeHj44MOHTqIHa3QtGlMCoUCERERHzwR5MKFC2jTpo1kjmEFOCYSx6eO/X2XlD4XkTRxqTxpVFZWFurXr499+/bBy8sLXl5eYkcqVjVq1MBvv/2G+fPnqzwfHByMGjVqiJSq8JYsWSJ2hGK3YsUK+Pn5oXfv3vDz8wPwpjmdh4cHgoKCJLWsMi9mZmaoVKkSzMzMYGpqinLlysHc3FzsWIWWm5uL+Pj4PDuwt27dWqRUBWNiYqLy2NPTU6QkmlOhQgV4e3vD29sbCQkJ8PHxgZubGx49eoSKFSuKHa9QtGlM9vb2OHLkyAcLwsOHD8Pe3r6EUxUNx0Ri0MbPRSRhYk/5k/arVq2acPXqVbFjaMT+/fuFChUqCA4ODoKPj4/g4+MjODo6ChUqVBD2798vdjyNmTNnjvD06VOxY+SLlZWVsGzZMrXnf/nlF6FatWoiJCoeU6dOFZo3by5UqFBBcHZ2FsaOHSvs3r1b0kt8T58+LdjY2AhyuVyQyWQqf+RyudjxNOrkyZNCenq62DEK5N69e8KsWbOEOnXqCJaWlsLkyZOFrKwssWMVibaMafXq1YKhoaGwd+9etWt79uwRDA0NhdWrV4uQrPA4JirNHj9+LHYEKgO4VJ40bvbs2YiLi0NwcLBy+bU2uXfvHlauXInr168DeLMMe+TIkZKccc8vY2NjXLp0SRJngxsZGeHSpUtqDcJu3rwJZ2dnyZ67KpfLYW5ujnHjxqFnz56oW7eu2JGKrFGjRqhbty4CAwNhaWmptjzx/ZlsbSKV76nMzEzs2rULa9aswYkTJ+Du7o4vv/wS7u7u+e7OXtpo45gAwNvbG5s3b0b9+vVVztKOi4tD3759sWXLFpETFhzHRKXN4cOHERwcjL179+L169dixyEtx8KdNM7T0xPh4eEwMjKCo6Oj2l5pqe7xLMsUCgViYmJKfZEBAAMHDoSzszMmTpyo8vzChQtx/vx5hIaGipSsaGJiYhAZGYmIiAicOHECurq6cHFxQZs2bdCmTRtJFvKGhoaIiYnRqi7s+SWV76lKlSpBoVBgyJAhGDRoECwsLPJ8nbGxcQknKzxtHNNb27Ztw6ZNm5RHctWtWxcDBw5E3759xY5WaBwTie3u3btYu3Yt1q9fj6dPn8Ld3R29evVCnz59xI5GWo6FO2ncsGHDPno9JCSkhJIUj9jYWDg4OEAulyM2Nvajr3VyciqhVCVLKkUGAPz4449YuHAh/vvf/yqP7Dtz5gxOnTqFCRMmqHwY//bbb8WKWWQxMTEICgrCpk2bkJubK6kzp99q164dJk2aBDc3N7GjlDipfE/J5XLl3/Nq2CQIAmQymaS+/rRxTERUvDIzM7Fz504EBwfj1KlTcHV1xV9//YXo6Gg4OjqKHY/KCBbuRAUkl8uRnJwMCwsLyOVyyGQy5PVtpM0f9KRSZACAjY1Nvl4nk8lw+/ZtDacpPoIgIDo6GhEREYiIiMDJkyeRmpoKJycnuLi4ICgoSOyI+fLuza9bt27hhx9+wMSJE+Ho6KjWgV1bb4QB0vmeioyMzNfrXFxcNJyk+GjjmN7+bvoYmUyG7OzsEkpUdBwTiWXMmDHYsmULbG1t4e3tjf79+6NSpUooX748YmJiYGdnJ3ZEKiNYuBMV0N27d1GzZk3IZDLcvXv3o6+tVatWCaUqWVIpMrSZmZkZ0tLS0LBhQ+US+VatWsHU1FTsaAXysZtfAJTXtPlGGCCd76mcnBwsXLgQe/bsQWZmJtq3b48ZM2ZI+mhPbRzTn3/++cFrp0+fxtKlS5Gbm4v09PQSTFU0HBOJpVy5cpg8eTKmTJkChUKhfJ6FO5U07esURqWOjY3NR+8oS2mWE1AtxrW1MC+LpNIc7K2NGzeiVatWktx3+66EhASxI5QK+T0nWGyzZ89GQEAAXF1doa+vj59//hn//PMP1q5dK3a0QtPGMXXv3l3tuRs3bmDKlCnYu3cvvLy8MHPmTBGSFR7HRGLZsGED1q5dC0tLS3Tu3BmDBg2Cu7u72LGoDGLhTho3duxYlcdZWVmIjo7GwYMH1RqGSVFSUhJOnjyZ57nTUt4z/TGtWrWS9GxUXqS2+Khz585iRygW7978On78OFq0aKF2+kR2djaioqK0+kaZVL7+fv/9d6xYsQK+vr4AgCNHjqBz584IDg5W2SsuJdo4pnclJSVhxowZWL9+PTp16oRLly7BwcFB7FhFwjFRSRowYAAGDBiAhIQErFu3DqNGjcKrV6+Qm5uLq1evcsadSgyXypNoli9fjvPnz0uuOd271q1bB19fX+jq6qJSpUoqs2ZS2jOdlJSExYsXY/r06WozuM+fP8ePP/4If39/VKlSRaSEmieVpcrvOn/+PLZt24bExERkZmaqXJPiaQ06Ojp4+PChWlfvx48fw8LCQrJL5bOzsxEREYFbt25h4MCBUCgUSEpKgrGxMYyMjMSOVyB6enqIj49XOe6yQoUKiI+PR/Xq1UVMVnjaOCbgzc/u2bNnY9myZWjUqBHmzZuHVq1aiR2rSDgmKg0EQcDhw4exZs0a7NmzB5UrV0bPnj2xdOlSsaORlpP+rWSSLHd3d+zYsUPsGEUybdo0TJ8+Hc+fP8edO3eQkJCg/COVoh0AFi9ejNTU1DyXXZuYmODFixdYvHixCMnoQ0JDQ9GiRQtcu3YNu3btQlZWFv7++28cPXpUsuedv93L/r7Hjx+rHSMpFXfv3oWjoyO6d++OUaNG4dGjRwCAefPmwd/fX+R0BZednY0KFSqoPFe+fHlkZWWJlKjotHFM8+fPR+3atbFv3z5s2bIFUVFRki8GOSYqLWQyGTp16oRt27YhKSkJ/v7++W5ySVQUnHEn0cyfPx8rVqzAnTt3xI5SaJUqVcLZs2dRp04dsaMUiYODA1atWoWWLVvmeT0qKgojRozA33//XcLJSo7UZtydnJzg6+uLUaNGKbPb2NjA19cXlpaWCAwMFDtivvXs2RPAm0ZNbm5u0NPTU17LyclBbGws6tWrh4MHD4oVsdB69OgBhUKBNWvWoFKlSsqvsYiICIwYMQI3b94UO2KByOVyuLu7q7xHe/fuRbt27VRurkhpxYe2jklfXx+urq7Q0dH54Os4JnFp45hIej1zSDq4x500ztnZWWUWTRAEJCcn49GjR1ixYoWIyYrOx8cH27dvx5QpU8SOUiQJCQmoWbPmB69Xr15d0jdY8kMqzcHeunXrlnKfu66uLl6+fAmZTIZx48ahXbt2kirc364QEAQBCoVCpX+Crq4umjVrhhEjRogVr0hOnDiBqKgo6OrqqjxvbW2NBw8eiJSq8IYMGaL2nLe3twhJio82jmnw4MGS+5n2KRwTSQXnRElTWLiTxnXv3l3lF5NcLoe5uTnatGmD+vXri5is6ObMmYMuXbrg4MGDeZ47LZXl5fr6+rhz584Hi/c7d+5oXTO690ntF62ZmRlevHgBALCyssKVK1fg6OiIZ8+e4dWrVyKnK5i3fS6sra3h7+8v2WXxecnNzc1zb/79+/dVjhWSCin3JPkQbRzTunXrxI5Q7DgmIirrWLiTxgUEBIgdQWPmzJmDQ4cOoV69egCg1pxOKv7zn/9gw4YNaN26dZ7Xf//9d3zxxRclnKpk/fXXX7CyshI7Rr61bt0aYWFhcHR0RJ8+feDn54ejR48iLCwM7dq1Eztegfzzzz+wsLDAjBkz8ryenZ2NixcvSvJrsGPHjliyZAl+/fVXAG9+LqSlpWHGjBnw8PAQOR0RERFJBfe4k8Zpa6do4M2sZ1BQEIYOHSp2lCI5duwYOnTogLFjx2LixInK7vEpKSmYP38+fv75Zxw+fFhyBeHDhw8RHh6OihUrwtXVVWW58suXL7Fo0SJMnz5dxISF9+TJE6Snp6NatWrIzc3F/PnzERUVBVtbW/j7+8PS0lLsiPn2/s8IR0dHHDhwQNnlOyUlBdWqVZPkz4p79+7Bzc0NgiDg5s2baNKkCW7evInKlSvj+PHjaj8XiYhI2qTWM4ekg4U7aZxcLkdycrLaB9SkpCTUqVMHr1+/FilZ0VWtWhUnTpyAra2t2FGKbPXq1fDz80NWVhaMjY0hk8nw/PlzlC9fHkFBQfj666/Fjlgg586dQ8eOHZGbm4usrCxYWVlh9+7dsLe3ByDtYvBD0tPTsXz5cixYsADJyclix8m3939GvP+hJyUlBZaWlsjNzRUzZqFlZ2dj69atiImJQVpaGho3bgwvLy+t335CRFQWsTkdaQqXypPGvD3PUiaTITg4WOW84pycHBw/flzye9z9/PywbNkyrTi709fXF126dMG2bdsQHx8PQRBQt25d9O7dW5JnGX/33Xfw9PREcHAwXr58icmTJ8PFxQVhYWFwdnYWO16hZWRkICAgAGFhYdDV1cWkSZPQo0cPhISE4IcffoCOjg7GjRsndsxiJ6WtJ29lZWWhfv362LdvH7y8vODl5SV2JCIi0jDOiZKmsHAnjQkKCgLw5gfYqlWrVI460dXVhbW1NVatWiVWvGJx9uxZHD16FPv27YO9vb1aczqpHeFiZWWlNUXfhQsXsHz5csjlcigUCqxYsQI1a9ZE+/btcejQoY920S/Npk+fjtWrV8PV1RVRUVHo06cPhg0bhjNnzmDRokXo06fPR48VopJTvnx5pKenix2DiIiKUWZmJhISElCnTh2UK6deSkmtZw5JBwt30piEhAQAQNu2bbFz506YmZmJnKj4mZqaKs+g1gbbt2/Hli1bEBcXBwCoW7cuBg4ciN69e4ucrHDeL5qmTJmCcuXKoWPHjli7dq1IqYpm+/bt+P3339GtWzdcuXIFTk5OyM7ORkxMjCRnpYE3s+kvXrxAhQoVIAiCsoFbamoqACj/V4pGjRqFefPmITg4OM8PeEREJA2vXr3CmDFjsH79egBAXFwcateujTFjxsDKykp5NHDLli3FjElajHvciQi5ubkYMGAAtm/fjrp16yq3MFy7dg3x8fHo06cPtmzZIqnCsHXr1hg4cCBGjhypdm3+/PmYPn06srKyJLfHXVdXFwkJCcq7+fr6+jh79iwcHR1FTlZ4crlc5WvrbfH+/mOpvVcA4OnpifDwcBgZGcHR0VHtqDuprcohIiqr/Pz8cOrUKSxZsgRubm6IjY1F7dq18eeffyIgIADR0dFiRyQtx9v/VCLu37+PPXv2IDExEZmZmSrXpHLWuTb7+eefceTIEezZswddunRRubZnzx4MGzYMP//8M8aOHStOwEIYPHgwIiMj8yzcJ02apNzCITU5OTkq3fHLlSun0j9Cio4dOyZ2BI0xNTVFr169xI5BRERFtHv3bmzduhXNmjVTublsb2+PW7duiZiMygrOuJPGhYeHo1u3bqhduzauX78OBwcH3LlzB4IgoHHjxjh69KjYEQvNxsbmo7PQt2/fLsE0hefk5ISxY8fiyy+/zPP6mjVr8PPPPyM2NraEk9H75HI53N3doaenBwDYu3cv2rVrV6ZmcufOnYuRI0fC1NRU7ChERFRGGBgY4MqVK6hdu7bK6ScxMTFo3bo1nj9/LnZE0nKccSeNmzp1Kvz9/REYGAiFQoEdO3bAwsICXl5ecHNzEztekbw/A52VlYXo6GgcPHgQEydOFCdUIdy8eROurq4fvO7q6orRo0eXYKLiFRsbq7Jv38nJSeREhTdkyBCVx97e3iIlEc/s2bPRt29fFu5ERFRimjRpgv3792PMmDEA/v9pJ8HBwWjevLmY0aiMYOFOGnft2jVs2bIFwJtlva9fv4aRkRFmzpyJ7t27S+588Hf5+fnl+fzy5ctx/vz5Ek5TePr6+nj27NkHO62npqaiQoUKJZyq6M6ePQsfHx9cvXpVeTyLTCaDvb091qxZg6ZNm4qcsOBCQkLEjiA6KS0U05ZVOUREZd3s2bPh7u6Oq1evIjs7Gz///DOuXr2KqKgoREZGih2PygC52AFI+xkaGir3tVtaWqrsA/r333/FiqVR7u7u2LFjh9gx8q158+ZYuXLlB68vX75ccneTr169ivbt20NfXx8bN27ExYsXcfHiRWzYsAF6enpo3749rl69KnZM0nJjx46Fn5+f8s8333yD5s2b4/nz5/jqq6/EjkdERPnUsmVLxMTEIDs7G46Ojjh8+DAsLCxw+vRpfP7552LHozKAM+6kcc2aNcPJkyfRoEEDeHh4YMKECbh8+TJ27tyJZs2aiR1PI/744w9UrFhR7Bj59v3336NNmzZ4/Pgx/P39Ub9+fQiCgGvXrmHRokX4888/JddALCAgAB06dMCOHTtUZjwbNWqEAQMGoGfPnggICMC2bdtETEnaTltW5RARlWVZWVnw9fXFtGnT8Ntvv4kdh8ooNqcjjbt9+zbS0tLg5OSEly9fYsKECYiKioKtrS0WL16MWrVqiR2x0JydndWOrUpOTsajR4+wYsUKSc2o7dq1C1999RWePHmi8ryZmRlWr14tuc7Y5ubm+Ouvv9CkSZM8r587dw4eHh549OhRCSejonq3KZBU3b59G40aNZL0GfVERGWJiYkJLl26BBsbG7GjUBnFGXfSqJycHNy/f1/ZDMzQ0FCSR3B9SPfu3VUKd7lcDnNzc7Rp00Z5FrpUeHp6olOnTjh06BBu3rwJ4E0jt44dO8LAwEDkdAX34sULVKlS5YPXq1atihcvXpRgIqL/T2qrcoiIyroePXpg9+7dGDdunNhRqIxi4U4apaOjg44dO+LatWta1QH67SzZ+PHjP/oaY2PjkopUJL1798bw4cPRqVMneHp6ih2nWNSqVQtnz55FjRo18rz+v//9T9KrPcqyVq1aQV9fX+wY+fKpVTlERCQNtra2mDlzJk6dOoXPP/9c7RjWb7/9VqRkVFawcCeNc3BwwO3bt7VqaZGpqelHO0ULggCZTIacnJwSTFV4T58+RefOnVGtWjUMGzYMw4YNk/z71b9/f4wfPx716tWDg4ODyrXLly/D398fgwcPFikd5WXw4MFo27YtWrdujTp16nzwdQcOHCjBVEXTo0cPlcdSXpVDRFSWrVmzBqamprhw4QIuXLigck0mk7FwJ43jHnfSuIMHD2Lq1KmYNWtWnncopTIr/a53j/0QBAEeHh4IDg6GlZWVyutcXFxKOlqh3b17FyEhIfj9999x9+5duLi4YPjw4ejVqxf09PTEjldg6enpaN++Pf73v/+hQ4cOaNCggbLh3pEjR/DFF1/g6NGjkjzmTlsNHz4cx48fR3x8PKysrODi4oI2bdrAxcUFtra2YscjIiIiEg0Ld9I4ufz/nzr4/pJRKc1Kf4w2NMt619GjR7F27Vrs2rULenp6GDBgAL788kvJHXeSmZmJoKAgbNmyBXFxcQDe7Nvv378/xo0bJ8kbEmXBgwcPcPz4cURGRiIyMhJxcXGwtLTE/fv3xY5WYBcvXkT58uXh6OgIAPjzzz8REhICOzs7BAQEQFdXV+SEREREJAUs3Enj3p2dzouUZqU/RNsK97devHiBzZs347vvvsPz58+RnZ0tdiQqA169eoWTJ0/i2LFjiIiIwMWLF2FnZ4fo6GixoxVY06ZNMWXKFPTq1Qu3b9+GnZ0devbsiXPnzqFz585YsmSJ2BGJiOgDxo8fj1mzZsHQ0PCjfY0AYPHixSWUisoq7nEnjdOGwrwsSkhIwLp167Bu3To8f/4crq6uYkcqkKdPn2Ljxo0YMmSI2naM58+f4/fff8/zGonnu+++Q0REBKKjo9GgQQO4uLhgypQpaN26NczMzMSOVyhxcXFo1KgRAGD79u1wcXHB5s2bcerUKfTv35+FOxFRKRYdHY3r16/D2dn5ozePP9b3iKi4sHCnEnHixAmsXr0at2/fxvbt22FlZYUNGzbAxsYGLVu2FDtesdCGH9rp6en4448/sHbtWhw/fhw1atSAj48Phg0b9sHu7KXVL7/8gtjYWIwZM0btmomJCU6cOIHU1FR8//33IqSjvMydOxfm5uaYMWMGevbsibp164odqcgEQUBubi4A4MiRI+jSpQsAoEaNGvj333/FjEZERJ9w7Ngx6Ojo4OHDhzh27BgAoF+/fli6dOlHj5wl0gQW7qRxO3bswKBBg+Dl5YWLFy8iIyMDwJtZz9mzZ0uqQ/RbPXv2VHmcnp6OkSNHqjXe27lzZ0nGKrSzZ89i7dq12Lp1K9LT0+Hp6YmDBw+iffv2kr0hsWPHDixatOiD1319feHv78/CvRSJjo5GZGQkIiIisGjRIujq6iob1LVp00aShXyTJk3w448/wtXVFZGRkVi5ciWANyta+KGPiKj0e39X8V9//YWXL1+KlIbKMu5xJ41zdnbGuHHjMHjwYJW94NHR0XB3d0dycrLYEQts2LBh+XpdSEiIhpMUD7lcjoYNG8LHxwdeXl6SXZb8LoVCgb///hs1a9bM83piYiIcHByQmppawskov2JiYhAUFIRNmzYhNzdXko0sY2Nj4eXlhcTERIwfPx4zZswAAIwZMwaPHz/G5s2bRU5IREQfI5fLkZycDAsLCwDa29eISj/OuJPG3bhxA61bt1Z73sTEBM+ePSv5QMVAKgV5fnXp0gWhoaEwMDAQO0qx0dHRQVJS0gcL96SkJJUTD0h8giAgOjoaERERiIiIwMmTJ5GamgonJyfJ9spwcnLC5cuX1Z5fsGABdHR0REhEREQFIZPJ1FYfSnU1IkkbC3fSuKpVqyI+Ph7W1tYqz588eZJ3K0uJ/fv3Iy0tTasKd2dnZ+zevRvNmjXL8/quXbvg7OxcwqnoYypWrIi0tDQ0bNgQLi4uGDFiBFq1agVTU1OxoxXavXv3IJPJUL16dQBvtqVs3rwZdnZ2+Oqrr0ROR0REnyIIAoYOHao8Qlbq2yNJuli4k8aNGDECfn5+WLt2LWQyGZKSknD69Gn4+/tj2rRpYscjqO/f0gajR49G//79Ub16dXz99dfK2c2cnBysWLECQUFBXKZcymzcuBGtWrXSqk7/AwcOxFdffYVBgwYhOTkZHTp0gL29PTZt2oTk5GRMnz5d7IhERPQRQ4YMUXns7e0tUhIq67jHnTROEATMnj0bc+bMwatXrwAAenp68Pf3x6xZs0ROR8Cb/VspKSkwNzcXO0qx+v777zFnzhwoFArl6o7bt28jLS0NEydOxNy5c0VOSNrOzMwMZ86cQb169bB06VJs3boVp06dwuHDhzFy5Ejcvn1b7IhEREQkASzcqcRkZmYiPj4eaWlpsLOzg5GRkdiR6P/I5XKYmJh8cs/WkydPSihR8Xm7NPnmzZsQBAF169bFwIED8cUXX4gdjfJw/vx5bNu2DYmJicjMzFS5JsVliEZGRrhy5Qqsra3RrVs3/Pe//8XkyZORmJiIevXq4fXr12JHJCIiIgngUnkqMbq6ulAoFFAoFCzaS6HAwECYmJiIHaPYffHFF8jIyMCqVatw+/ZtTJw4EVZWVtiwYQNsbGzQsmVLsSPS/wkNDcXgwYPRqVMnHD58GB07dkRcXBxSUlLg6ekpdrxCsbe3x6pVq9C5c2eEhYUpVxklJSWhUqVKIqcjIiIiqWDhThqXnZ2NwMBALF26FGlpaQDezEKNGTMGM2bMQPny5UVOSADQv39/5VEn2mTHjh0YNGgQvLy8EB0djYyMDADA8+fPMXv2bBw4cEDkhPTW7NmzERQUhFGjRkGhUODnn3+GjY0NfH19YWlpKXa8Qpk3bx48PT2xYMECDBkyBA0bNgQA7Nmzh6s+iIiIKN+4VJ407uuvv8bOnTsxc+ZMNG/eHABw+vRpBAQEoEePHli5cqXICUlHRwcPHz7UysLd2dkZ48aNw+DBg1XOXo2Ojoa7uzuSk5PFjkj/x9DQEH///Tesra1RqVIlREREwNHREdeuXUO7du3w8OFDsSMWSk5ODlJTU2FmZqZ87s6dOzAwMNDK7zkiIiIqfpxxJ43bvHkzQkND4e7urnzOyckJNWrUwIABA1i4lwLafP/uxo0baN26tdrzJiYmePbsWckHog8yMzPDixcvAABWVla4cuUKHB0d8ezZM2VjSykSBAEXLlzArVu3MHDgQCgUCujq6mrV8YtERESkWSzcSeP09PTUznAHABsbG+jq6pZ8IFKTm5srdgSNqVq1KuLj49W+Bk+ePKnsNE+lQ+vWrREWFgZHR0f06dMHfn5+OHr0KMLCwtCuXTux4xXK3bt34ebmhsTERGRkZKBDhw5QKBSYN2+esvcCERER0afIxQ5A2m/06NGYNWuWcm8xAGRkZOCnn37C6NGjRUxGZcGIESPg5+eH//3vf5DJZEhKSsKmTZvg7++Pr7/+Wux49I5ffvkF/fv3B/DmKL/x48cjJSUFvXr1wooVK0ROVzh+fn5o0qQJnj59Cn19feXznp6eCA8PFzEZERERSQn3uJPGvf2Aqqenp2zMFBMTg8zMTLRv317ltVI87olKN0EQMHv2bMyZM0e53FpPTw/+/v7KDt9UeqWnp2P58uVYsGCBJPsRVKpUCVFRUahXr55Kj4U7d+7Azs5O0lsAiIiIqORwqTxpnKmpKXr16qXyXI0aNURKQ2WNTCbD999/j4kTJyI+Ph5paWmws7PjkYSlSEZGBgICAhAWFgZdXV1MmjQJPXr0QEhICH744Qfo6Ohg3LhxYscslNzcXOTk5Kg9f//+fSgUChESERERkRRxxp2IiEQ1efJkrF69Gq6uroiKisKjR48wbNgwnDlzBt999x369OkDHR0dsWMWSr9+/WBiYoJff/0VCoUCsbGxMDc3R/fu3VGzZk2EhISIHZGIiIgkgIU7ERGJqnbt2liyZAm6deuGK1euwMnJCUOHDsWaNWsgk8nEjlck9+7dg5ubGwRBwM2bN9GkSRPcvHkTlStXxvHjx3kcHBEREeULC3fSuMePH2P69Ok4duwY/vnnH7UO5k+ePBEpGRGVBrq6ukhISICVlRUAQF9fH2fPnoWjo6PIyYpHdnY2tm7dipiYGKSlpaFx48bw8vJSaVZHRERE9DHc404aN2jQIMTHx8PHxwdVqlSR/AwaERWvnJwclaMhy5UrpxU9CLKyslC/fn3s27cPXl5e8PLyEjsSERERSRQLd9K4EydO4OTJk8qO8kRE7xIEAUOHDoWenh6AN53kR44cCUNDQ5XXSe3UifLlyyM9PV3sGERERKQFWLiTxtWvXx+vX78WOwYRlVJDhgxReezt7S1SkuI3atQozJs3D8HBwShXjr9yiYiIqHC4x5007ty5c5gyZQqmT58OBwcHlC9fXuW6sbGxSMmIiDTL09MT4eHhMDIygqOjo+RXERAREZE4ePufNM7U1BSpqalo166dyvOCIEAmk+V5xjERkTYwNTVFr169xI5BREREEsfCnTTOy8sL5cuXx+bNm9mcjojKhNzcXCxYsABxcXHIzMxEu3btEBAQwE7yREREVChcKk8aZ2BggOjoaNSrV0/sKEREJWLWrFkICAiAq6sr9PX1cejQIQwYMABr164VOxoRERFJkFzsAKT9mjRpgnv37okdg4ioxPz+++9YsWIFDh06hN27d2Pv3r3YtGkTcnNzxY5GREREEsQZd9K47du3IyAgABMnToSjo6NaczonJyeRkhERaYaenh7i4+NRo0YN5XMVKlRAfHw8qlevLmIyIiIikiIW7qRxcrn6wg6ZTMbmdESktXR0dJCcnAxzc3PlcwqFArGxsbCxsRExGREREUkRm9ORxiUkJIgdgYioRAmCgKFDh0JPT0/5XHp6OkaOHKlyJByPgyMiIqL84Iw7ERFRMRs2bFi+XhcSEqLhJERERKQNWLhTidiwYQNWrVqFhIQEnD59GrVq1cKSJUtgY2OD7t27ix2PiIiIiIio1GJXedK4lStXYvz48fDw8MCzZ8+Ue9pNTU2xZMkSccMRERERERGVcizcSeOWLVuG3377Dd9//z10dHSUzzdp0gSXL18WMRkREREREVHpx8KdNC4hIQHOzs5qz+vp6eHly5ciJCIiIiIiIpIOFu6kcTY2Nrh06ZLa8wcPHkSDBg1KPhAREREREZGE8Dg40piZM2fC398f48ePx6hRo5Ceng5BEHD27Fls2bIFc+bMQXBwsNgxiYiIiIiISjV2lSeN0dHRwcOHD2FhYYFNmzYhICAAt27dAgBUq1YNgYGB8PHxETklERERERFR6cbCnTRGLpcjOTkZFhYWyudevXqFtLQ0leeIiIiIiIjow7hUnjRKJpOpPDYwMICBgYFIaYiIiIiIiKSHM+6kMXK5HCYmJmrF+/uePHlSQomIiIiIiIikhzPupFGBgYEwMTEROwYREREREZFkccadNCavPe5ERERERERUMDzHnTTmU0vkiYiIiIiI6NNYuJPGcDEHERERERFR0XGpPBEREREREVEpxhl3IiIiIiIiolKMhTsRERERERFRKcbCnYiIiIiIiKgUY+FOREREREREVIqxcCciIiIiIiIqxVi4ExEREREREZViLNyJiIiIiIiISrH/B/RvOAFqkSykAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.heatmap(df.corr(), cmap=\"coolwarm\", annot=False)\n",
+    "plt.title(\"Correlation Heatmap\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e0da765d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.279770Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.279466Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.291049Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.290222Z"
+    },
+    "papermill": {
+     "duration": 0.022361,
+     "end_time": "2025-11-09T21:03:13.292805",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.270444",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X = df.drop(columns=[\"Fire_Alarm\"])\n",
+    "y = df[\"Fire_Alarm\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9da9984e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.309578Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.309255Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.545083Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.543845Z"
+    },
+    "papermill": {
+     "duration": 0.246164,
+     "end_time": "2025-11-09T21:03:13.547011",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.300847",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b3286833",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.563831Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.562995Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.605038Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.604194Z"
+    },
+    "papermill": {
+     "duration": 0.052379,
+     "end_time": "2025-11-09T21:03:13.607014",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.554635",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "minmax_scaler = MinMaxScaler()\n",
+    "X_minmax = minmax_scaler.fit_transform(X)\n",
+    "X_minmax = pd.DataFrame(X_minmax, columns=X.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ebdad4d9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.623461Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.623053Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.652861Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.651861Z"
+    },
+    "papermill": {
+     "duration": 0.039933,
+     "end_time": "2025-11-09T21:03:13.654551",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.614618",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temperature_C_</th>\n",
+       "      <th>Humidity_</th>\n",
+       "      <th>TVOC_ppb_</th>\n",
+       "      <th>eCO2_ppm_</th>\n",
+       "      <th>Raw_H2</th>\n",
+       "      <th>Raw_Ethanol</th>\n",
+       "      <th>Pressure_hPa_</th>\n",
+       "      <th>PM1_0</th>\n",
+       "      <th>PM2_5</th>\n",
+       "      <th>NC0_5</th>\n",
+       "      <th>NC1_0</th>\n",
+       "      <th>NC2_5</th>\n",
+       "      <th>CNT</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.512692</td>\n",
+       "      <td>0.723239</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.522488</td>\n",
+       "      <td>0.525685</td>\n",
+       "      <td>0.986014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.512875</td>\n",
+       "      <td>0.712535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.534928</td>\n",
+       "      <td>0.547185</td>\n",
+       "      <td>0.987013</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.513046</td>\n",
+       "      <td>0.701520</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.544179</td>\n",
+       "      <td>0.565731</td>\n",
+       "      <td>0.986347</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.513229</td>\n",
+       "      <td>0.690971</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.549282</td>\n",
+       "      <td>0.579682</td>\n",
+       "      <td>0.986125</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.513412</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.553429</td>\n",
+       "      <td>0.591498</td>\n",
+       "      <td>0.987013</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Temperature_C_  Humidity_  TVOC_ppb_  eCO2_ppm_    Raw_H2  Raw_Ethanol  \\\n",
+       "0        0.512692   0.723239        0.0        0.0  0.522488     0.525685   \n",
+       "1        0.512875   0.712535        0.0        0.0  0.534928     0.547185   \n",
+       "2        0.513046   0.701520        0.0        0.0  0.544179     0.565731   \n",
+       "3        0.513229   0.690971        0.0        0.0  0.549282     0.579682   \n",
+       "4        0.513412   0.681818        0.0        0.0  0.553429     0.591498   \n",
+       "\n",
+       "   Pressure_hPa_  PM1_0  PM2_5  NC0_5  NC1_0  NC2_5      CNT  \n",
+       "0       0.986014    0.0    0.0    0.0    0.0    0.0  0.00000  \n",
+       "1       0.987013    0.0    0.0    0.0    0.0    0.0  0.00004  \n",
+       "2       0.986347    0.0    0.0    0.0    0.0    0.0  0.00008  \n",
+       "3       0.986125    0.0    0.0    0.0    0.0    0.0  0.00012  \n",
+       "4       0.987013    0.0    0.0    0.0    0.0    0.0  0.00016  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_minmax.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c769db30",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.672003Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.671667Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.933724Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.932680Z"
+    },
+    "papermill": {
+     "duration": 0.273076,
+     "end_time": "2025-11-09T21:03:13.935619",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.662543",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4ee3b6e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:13.952615Z",
+     "iopub.status.busy": "2025-11-09T21:03:13.952259Z",
+     "iopub.status.idle": "2025-11-09T21:03:13.994485Z",
+     "shell.execute_reply": "2025-11-09T21:03:13.993276Z"
+    },
+    "papermill": {
+     "duration": 0.052908,
+     "end_time": "2025-11-09T21:03:13.996539",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:13.943631",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X_minmax, y, test_size=0.2, random_state=42, stratify=y\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "dc40cf89",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.013011Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.012689Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.017374Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.016481Z"
+    },
+    "papermill": {
+     "duration": 0.014651,
+     "end_time": "2025-11-09T21:03:14.018873",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.004222",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "log_reg = LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "5bf88923",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.035605Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.035249Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.828082Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.826463Z"
+    },
+    "papermill": {
+     "duration": 0.806094,
+     "end_time": "2025-11-09T21:03:14.832676",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.026582",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "log_reg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f42f186a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.865519Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.865129Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.877009Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.873061Z"
+    },
+    "papermill": {
+     "duration": 0.028268,
+     "end_time": "2025-11-09T21:03:14.878819",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.850551",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = log_reg.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "29f5fd1e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.907578Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.906802Z",
+     "iopub.status.idle": "2025-11-09T21:03:14.911713Z",
+     "shell.execute_reply": "2025-11-09T21:03:14.910672Z"
+    },
+    "papermill": {
+     "duration": 0.018673,
+     "end_time": "2025-11-09T21:03:14.913286",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.894613",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b0c49095",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:14.930787Z",
+     "iopub.status.busy": "2025-11-09T21:03:14.930450Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.154379Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.153087Z"
+    },
+    "papermill": {
+     "duration": 0.235271,
+     "end_time": "2025-11-09T21:03:15.156555",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:14.921284",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
+    "            xticklabels=[\"No Alarm\", \"Fire Alarm\"],\n",
+    "            yticklabels=[\"No Alarm\", \"Fire Alarm\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "130fc2d1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.175438Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.175038Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.521626Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.520496Z"
+    },
+    "papermill": {
+     "duration": 0.358078,
+     "end_time": "2025-11-09T21:03:15.523589",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.165511",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from xgboost import XGBClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "19ded50e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.541793Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.541457Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.546690Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.545575Z"
+    },
+    "papermill": {
+     "duration": 0.016439,
+     "end_time": "2025-11-09T21:03:15.548509",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.532070",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xgb_clf = XGBClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "877ed54c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.571036Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.570712Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.905784Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.904931Z"
+    },
+    "papermill": {
+     "duration": 0.346471,
+     "end_time": "2025-11-09T21:03:15.907710",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.561239",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+       "              colsample_bylevel=None, colsample_bynode=None,\n",
+       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "              gamma=None, grow_policy=None, importance_type=None,\n",
+       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
+      ],
+      "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=None, max_bin=None,\n",
+       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
+       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "              num_parallel_tree=None, random_state=None, ...)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xgb_clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6b23e21b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.928243Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.927846Z",
+     "iopub.status.idle": "2025-11-09T21:03:15.945072Z",
+     "shell.execute_reply": "2025-11-09T21:03:15.944333Z"
+    },
+    "papermill": {
+     "duration": 0.028884,
+     "end_time": "2025-11-09T21:03:15.947225",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.918341",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_xgb = xgb_clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4a74df94",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:15.971009Z",
+     "iopub.status.busy": "2025-11-09T21:03:15.969821Z",
+     "iopub.status.idle": "2025-11-09T21:03:16.167310Z",
+     "shell.execute_reply": "2025-11-09T21:03:16.166091Z"
+    },
+    "papermill": {
+     "duration": 0.210109,
+     "end_time": "2025-11-09T21:03:16.169032",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:15.958923",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm_xgb = confusion_matrix(y_test, y_pred_xgb)\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(cm_xgb, annot=True, fmt=\"d\", cmap=\"Greens\",\n",
+    "            xticklabels=[\"No Alarm\", \"Fire Alarm\"],\n",
+    "            yticklabels=[\"No Alarm\", \"Fire Alarm\"])\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"Actual\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8eabb07f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:16.189844Z",
+     "iopub.status.busy": "2025-11-09T21:03:16.189509Z",
+     "iopub.status.idle": "2025-11-09T21:03:16.205346Z",
+     "shell.execute_reply": "2025-11-09T21:03:16.204433Z"
+    },
+    "papermill": {
+     "duration": 0.028572,
+     "end_time": "2025-11-09T21:03:16.207316",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:16.178744",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "91e33b07",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:16.227875Z",
+     "iopub.status.busy": "2025-11-09T21:03:16.227536Z",
+     "iopub.status.idle": "2025-11-09T21:03:16.232617Z",
+     "shell.execute_reply": "2025-11-09T21:03:16.231626Z"
+    },
+    "papermill": {
+     "duration": 0.017308,
+     "end_time": "2025-11-09T21:03:16.234251",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:16.216943",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "lr_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('classifier', LogisticRegression(solver='liblinear', random_state=42))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9443b085",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:16.254655Z",
+     "iopub.status.busy": "2025-11-09T21:03:16.254275Z",
+     "iopub.status.idle": "2025-11-09T21:03:16.295511Z",
+     "shell.execute_reply": "2025-11-09T21:03:16.293534Z"
+    },
+    "papermill": {
+     "duration": 0.053956,
+     "end_time": "2025-11-09T21:03:16.297479",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:16.243523",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "53326c44",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:16.390111Z",
+     "iopub.status.busy": "2025-11-09T21:03:16.389260Z",
+     "iopub.status.idle": "2025-11-09T21:03:16.394260Z",
+     "shell.execute_reply": "2025-11-09T21:03:16.393317Z"
+    },
+    "papermill": {
+     "duration": 0.087793,
+     "end_time": "2025-11-09T21:03:16.396133",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:16.308340",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d21b7761",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:16.416338Z",
+     "iopub.status.busy": "2025-11-09T21:03:16.416001Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.009995Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.008887Z"
+    },
+    "papermill": {
+     "duration": 0.605892,
+     "end_time": "2025-11-09T21:03:17.011825",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:16.405933",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "start_train = time.time()\n",
+    "lr_pipeline.fit(X_train, y_train)\n",
+    "end_train = time.time()\n",
+    "lr_train_time = end_train - start_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9e3bef6e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.032105Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.031340Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.036043Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.035105Z"
+    },
+    "papermill": {
+     "duration": 0.016502,
+     "end_time": "2025-11-09T21:03:17.037620",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.021118",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "096c3e2a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.057298Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.057010Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.067813Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.066992Z"
+    },
+    "papermill": {
+     "duration": 0.022636,
+     "end_time": "2025-11-09T21:03:17.069555",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.046919",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_lr = lr_pipeline.predict(X_test)\n",
+    "lr_acc = accuracy_score(y_test, y_pred_lr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8e45f99d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.089537Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.089079Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.094292Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.093057Z"
+    },
+    "papermill": {
+     "duration": 0.017305,
+     "end_time": "2025-11-09T21:03:17.096074",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.078769",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xgb_pipeline = Pipeline([\n",
+    "    ('classifier', XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42))\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9e2fe08e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.115848Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.115533Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.387463Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.386668Z"
+    },
+    "papermill": {
+     "duration": 0.284009,
+     "end_time": "2025-11-09T21:03:17.389366",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.105357",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "start_train = time.time()\n",
+    "xgb_pipeline.fit(X_train, y_train)\n",
+    "end_train = time.time()\n",
+    "\n",
+    "xgb_train_time = end_train - start_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5c752075",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.411498Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.411152Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.428107Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.427385Z"
+    },
+    "papermill": {
+     "duration": 0.02909,
+     "end_time": "2025-11-09T21:03:17.430138",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.401048",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_xgb = xgb_pipeline.predict(X_test)\n",
+    "xgb_acc = accuracy_score(y_test, y_pred_xgb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4a5b9617",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.452458Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.452114Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.456650Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.455632Z"
+    },
+    "papermill": {
+     "duration": 0.01665,
+     "end_time": "2025-11-09T21:03:17.458729",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.442079",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d6a69524",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.478763Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.478356Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.485382Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.483780Z"
+    },
+    "papermill": {
+     "duration": 0.018683,
+     "end_time": "2025-11-09T21:03:17.486878",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.468195",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pickle_lr = pickle.dumps(lr_pipeline)\n",
+    "pickle_xgb = pickle.dumps(xgb_pipeline)\n",
+    "\n",
+    "lr_size_kb = sys.getsizeof(pickle_lr) / 1024\n",
+    "xgb_size_kb = sys.getsizeof(pickle_xgb) / 1024"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "2041a26b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.509513Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.509125Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.528953Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.528250Z"
+    },
+    "papermill": {
+     "duration": 0.032513,
+     "end_time": "2025-11-09T21:03:17.530848",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.498335",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "start_inf = time.time()\n",
+    "_ = lr_pipeline.predict(X_test)\n",
+    "end_inf = time.time()\n",
+    "lr_inf_time_total = end_inf - start_inf\n",
+    "lr_inf_time_single = (lr_inf_time_total / len(X_test)) * 1000\n",
+    "\n",
+    "start_inf = time.time()\n",
+    "_ = xgb_pipeline.predict(X_test)\n",
+    "end_inf = time.time()\n",
+    "xgb_inf_time_total = end_inf - start_inf\n",
+    "xgb_inf_time_single = (xgb_inf_time_total / len(X_test)) * 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "76224a04",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.554374Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.554060Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.559949Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.558941Z"
+    },
+    "papermill": {
+     "duration": 0.019234,
+     "end_time": "2025-11-09T21:03:17.562132",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.542898",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "results_df = pd.DataFrame({\n",
+    "    'Model': ['LR-Pipeline', 'XGB-Pipeline'],\n",
+    "    'Size (KB)': [lr_size_kb, xgb_size_kb],\n",
+    "    'Total Inference (s)': [lr_inf_time_total, xgb_inf_time_total],\n",
+    "    'Single Inference (ms)': [lr_inf_time_single, xgb_inf_time_single],\n",
+    "    'Accuracy': [lr_acc, xgb_acc],\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "885eac4a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.581897Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.581552Z",
+     "iopub.status.idle": "2025-11-09T21:03:17.586452Z",
+     "shell.execute_reply": "2025-11-09T21:03:17.585458Z"
+    },
+    "papermill": {
+     "duration": 0.016835,
+     "end_time": "2025-11-09T21:03:17.588182",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.571347",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "1fff48c4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-09T21:03:17.608172Z",
+     "iopub.status.busy": "2025-11-09T21:03:17.607805Z",
+     "iopub.status.idle": "2025-11-09T21:03:18.141631Z",
+     "shell.execute_reply": "2025-11-09T21:03:18.140462Z"
+    },
+    "papermill": {
+     "duration": 0.54588,
+     "end_time": "2025-11-09T21:03:18.143379",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:17.597499",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='whitegrid')\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.barplot(x='Model', y='Size (KB)', data=results_df)\n",
+    "plt.title('Model Memory Footprint (KB)')\n",
+    "plt.ylabel('Size (KB)')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.barplot(x='Model', y='Single Inference (ms)', data=results_df)\n",
+    "plt.title('Average Single Inference Time (ms)')\n",
+    "plt.ylabel('Time (ms)')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.barplot(x='Model', y='Accuracy', data=results_df)\n",
+    "plt.title('Model Accuracy')\n",
+    "plt.ylabel('Score')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea971d80",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010446,
+     "end_time": "2025-11-09T21:03:18.165723",
+     "exception": false,
+     "start_time": "2025-11-09T21:03:18.155277",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Conclusion\n",
+    "- **Logistic Regression** is more lightweight, faster, and requires minimal resources.\n",
+    "- **XGBoost** may offer higher accuracy but is more memory-heavy."
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [
+    {
+     "datasetId": 2424784,
+     "sourceId": 4100314,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 31089,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 17.106672,
+   "end_time": "2025-11-09T21:03:18.998540",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2025-11-09T21:03:01.891868",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}