{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "494ebaf8-1e7e-4431-a406-7b998841da72",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4144380a-89a8-48a4-a6fa-acb0627f1c60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial shape: (32833, 23)\n"
     ]
    },
    {
     "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>track_id</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_artist</th>\n",
       "      <th>track_popularity</th>\n",
       "      <th>track_album_id</th>\n",
       "      <th>track_album_name</th>\n",
       "      <th>track_album_release_date</th>\n",
       "      <th>playlist_name</th>\n",
       "      <th>playlist_id</th>\n",
       "      <th>playlist_genre</th>\n",
       "      <th>...</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6f807x0ima9a1j3VPbc7VN</td>\n",
       "      <td>I Don't Care (with Justin Bieber) - Loud Luxur...</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>66</td>\n",
       "      <td>2oCs0DGTsRO98Gh5ZSl2Cx</td>\n",
       "      <td>I Don't Care (with Justin Bieber) [Loud Luxury...</td>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.634</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0653</td>\n",
       "      <td>0.518</td>\n",
       "      <td>122.036</td>\n",
       "      <td>194754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0r7CVbZTWZgbTCYdfa2P31</td>\n",
       "      <td>Memories - Dillon Francis Remix</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>67</td>\n",
       "      <td>63rPSO264uRjW1X5E6cWv6</td>\n",
       "      <td>Memories (Dillon Francis Remix)</td>\n",
       "      <td>2019-12-13</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>-4.969</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.3570</td>\n",
       "      <td>0.693</td>\n",
       "      <td>99.972</td>\n",
       "      <td>162600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1z1Hg7Vb0AhHDiEmnDE79l</td>\n",
       "      <td>All the Time - Don Diablo Remix</td>\n",
       "      <td>Zara Larsson</td>\n",
       "      <td>70</td>\n",
       "      <td>1HoSmj2eLcsrR0vE9gThr4</td>\n",
       "      <td>All the Time (Don Diablo Remix)</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.432</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0742</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.613</td>\n",
       "      <td>124.008</td>\n",
       "      <td>176616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75FpbthrwQmzHlBJLuGdC7</td>\n",
       "      <td>Call You Mine - Keanu Silva Remix</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>60</td>\n",
       "      <td>1nqYsOef1yKKuGOVchbsk6</td>\n",
       "      <td>Call You Mine - The Remixes</td>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>-3.778</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0.277</td>\n",
       "      <td>121.956</td>\n",
       "      <td>169093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1e8PAfcKUYoKkxPhrHqw4x</td>\n",
       "      <td>Someone You Loved - Future Humans Remix</td>\n",
       "      <td>Lewis Capaldi</td>\n",
       "      <td>69</td>\n",
       "      <td>7m7vv9wlQ4i0LFuJiE2zsQ</td>\n",
       "      <td>Someone You Loved (Future Humans Remix)</td>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.672</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.725</td>\n",
       "      <td>123.976</td>\n",
       "      <td>189052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 track_id                                         track_name  \\\n",
       "0  6f807x0ima9a1j3VPbc7VN  I Don't Care (with Justin Bieber) - Loud Luxur...   \n",
       "1  0r7CVbZTWZgbTCYdfa2P31                    Memories - Dillon Francis Remix   \n",
       "2  1z1Hg7Vb0AhHDiEmnDE79l                    All the Time - Don Diablo Remix   \n",
       "3  75FpbthrwQmzHlBJLuGdC7                  Call You Mine - Keanu Silva Remix   \n",
       "4  1e8PAfcKUYoKkxPhrHqw4x            Someone You Loved - Future Humans Remix   \n",
       "\n",
       "       track_artist  track_popularity          track_album_id  \\\n",
       "0        Ed Sheeran                66  2oCs0DGTsRO98Gh5ZSl2Cx   \n",
       "1          Maroon 5                67  63rPSO264uRjW1X5E6cWv6   \n",
       "2      Zara Larsson                70  1HoSmj2eLcsrR0vE9gThr4   \n",
       "3  The Chainsmokers                60  1nqYsOef1yKKuGOVchbsk6   \n",
       "4     Lewis Capaldi                69  7m7vv9wlQ4i0LFuJiE2zsQ   \n",
       "\n",
       "                                    track_album_name track_album_release_date  \\\n",
       "0  I Don't Care (with Justin Bieber) [Loud Luxury...               2019-06-14   \n",
       "1                    Memories (Dillon Francis Remix)               2019-12-13   \n",
       "2                    All the Time (Don Diablo Remix)               2019-07-05   \n",
       "3                        Call You Mine - The Remixes               2019-07-19   \n",
       "4            Someone You Loved (Future Humans Remix)               2019-03-05   \n",
       "\n",
       "  playlist_name             playlist_id playlist_genre  ... key  loudness  \\\n",
       "0     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   6    -2.634   \n",
       "1     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...  11    -4.969   \n",
       "2     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   1    -3.432   \n",
       "3     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   7    -3.778   \n",
       "4     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   1    -4.672   \n",
       "\n",
       "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
       "0     1       0.0583        0.1020          0.000000    0.0653    0.518   \n",
       "1     1       0.0373        0.0724          0.004210    0.3570    0.693   \n",
       "2     0       0.0742        0.0794          0.000023    0.1100    0.613   \n",
       "3     1       0.1020        0.0287          0.000009    0.2040    0.277   \n",
       "4     1       0.0359        0.0803          0.000000    0.0833    0.725   \n",
       "\n",
       "     tempo  duration_ms  \n",
       "0  122.036       194754  \n",
       "1   99.972       162600  \n",
       "2  124.008       176616  \n",
       "3  121.956       169093  \n",
       "4  123.976       189052  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#step 1: Load data\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score, f1_score, log_loss, classification_report, confusion_matrix, roc_curve, auc, roc_auc_score\n",
    "from sklearn.preprocessing import label_binarize\n",
    "\n",
    "DATA_PATH = 'spotify_songs.csv'\n",
    "df = pd.read_csv(DATA_PATH)\n",
    "print(\"Initial shape:\", df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9313ab-60c4-4fae-9e15-116ae28db1a1",
   "metadata": {},
   "source": [
    " **StratifiedKFold** - preserves class balance in folds\n",
    " **GridSearchCV / RandomizedSearchCV** - optimize model hyperparameters\n",
    "\n",
    "\n",
    "accuracy_score: shows how many predictions are correct\n",
    "\n",
    "f1_score: balances precision and recall for uneven classes\n",
    "\n",
    "log_loss: measures error in predicted probabilities\n",
    "\n",
    "classification_report: summary of precision, recall, F1 for each class\n",
    "\n",
    "confusion_matrix: table comparing actual vs predicted values\n",
    "\n",
    "roc_curve: plots model’s ability to distinguish classes\n",
    "\n",
    "auc / roc_auc_score: single number summarizing ROC performance (higher = better)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e8ac75-78eb-4a64-a781-9b1518fabe1a",
   "metadata": {},
   "source": [
    "***step 2: Data preprocessing***\n",
    "1. handle missing values\n",
    "2. encode categorical\n",
    "3. feature engineering (duration_min, release_year)\n",
    "4. remove text columns\n",
    "5. outliers removal (tempo, loudness)\n",
    "6. visualize / describe / PCA check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "778936bd-ba2c-4483-9a8d-1f8f16052db6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values per column:\n",
      " track_artist                5\n",
      "track_album_name            5\n",
      "track_name                  5\n",
      "track_id                    0\n",
      "key                         0\n",
      "tempo                       0\n",
      "valence                     0\n",
      "liveness                    0\n",
      "instrumentalness            0\n",
      "acousticness                0\n",
      "speechiness                 0\n",
      "mode                        0\n",
      "loudness                    0\n",
      "danceability                0\n",
      "energy                      0\n",
      "playlist_subgenre           0\n",
      "playlist_genre              0\n",
      "playlist_id                 0\n",
      "playlist_name               0\n",
      "track_album_release_date    0\n",
      "track_album_id              0\n",
      "track_popularity            0\n",
      "duration_ms                 0\n",
      "dtype: int64\n",
      "Rows with any missing: 5\n"
     ]
    }
   ],
   "source": [
    "#2.1.missing values check\n",
    "missing = df.isnull().sum().sort_values(ascending=False)\n",
    "print(\"Missing values per column:\\n\", missing)\n",
    "\n",
    "#show rows with missing\n",
    "rows_with_missing = df[df.isnull().any(axis=1)]\n",
    "print(\"Rows with any missing:\", len(rows_with_missing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8525761d-8fe3-43e5-99c6-5a97ab364285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5220216-5032-401c-9b08-891a9639dd97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After dropping rows without track_name/track_artist: (32828, 23)\n"
     ]
    }
   ],
   "source": [
    "#2.2.drop rows where track_name and track_artist missing (rare)\n",
    "df = df.dropna(subset=['track_name', 'track_artist'])\n",
    "print(\"After dropping rows without track_name/track_artist:\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59417fdd-fcc8-4ac4-abf4-1342625d518e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped text columns. Shape: (32828, 17)\n"
     ]
    }
   ],
   "source": [
    "#2.3.drop useless text columns (IDs/names)\n",
    "cols_drop = ['track_id', 'track_name', 'track_album_id', 'track_album_name', 'playlist_name', 'playlist_id']\n",
    "df = df.drop(columns=[c for c in cols_drop if c in df.columns])\n",
    "print(\"Dropped text columns. Shape:\", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48640d34-d742-4f46-a380-e104a48a1b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.4.encode categorical variables (LabelEncoder)\n",
    "le = LabelEncoder()\n",
    "if 'playlist_genre' in df.columns:\n",
    "    df['playlist_genre'] = le.fit_transform(df['playlist_genre'])\n",
    "if 'playlist_subgenre' in df.columns:\n",
    "    # use a separate encoder if you want to preserve mapping; for simplicity reuse le by fitting anew:\n",
    "    df['playlist_subgenre'] = LabelEncoder().fit_transform(df['playlist_subgenre'])\n",
    "if 'track_artist' in df.columns:\n",
    "    df['track_artist'] = LabelEncoder().fit_transform(df['track_artist'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ca7dd60c-fb22-46df-8d81-cc1afbb05456",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.5.feature engineering\n",
    "if 'duration_ms' in df.columns:\n",
    "    df['duration_min'] = df['duration_ms'] / 60000.0\n",
    "    df.drop(columns=['duration_ms'], inplace=True)\n",
    "\n",
    "#parse release year\n",
    "if 'track_album_release_date' in df.columns:\n",
    "    df['release_year'] = pd.to_datetime(df['track_album_release_date'], errors='coerce').dt.year\n",
    "    df.drop(columns=['track_album_release_date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4c61161-7caf-48a2-b459-990c8d1e4d17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset description (numeric):\n"
     ]
    },
    {
     "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>track_artist</th>\n",
       "      <th>track_popularity</th>\n",
       "      <th>playlist_genre</th>\n",
       "      <th>playlist_subgenre</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>32828.000000</td>\n",
       "      <td>30942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5298.133484</td>\n",
       "      <td>42.483551</td>\n",
       "      <td>2.442640</td>\n",
       "      <td>11.601529</td>\n",
       "      <td>0.654850</td>\n",
       "      <td>0.698603</td>\n",
       "      <td>5.373949</td>\n",
       "      <td>-6.719529</td>\n",
       "      <td>0.565737</td>\n",
       "      <td>0.107053</td>\n",
       "      <td>0.175352</td>\n",
       "      <td>0.084760</td>\n",
       "      <td>0.190175</td>\n",
       "      <td>0.510556</td>\n",
       "      <td>120.883642</td>\n",
       "      <td>3.763280</td>\n",
       "      <td>2012.200246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3103.686069</td>\n",
       "      <td>24.980476</td>\n",
       "      <td>1.708802</td>\n",
       "      <td>6.794681</td>\n",
       "      <td>0.145092</td>\n",
       "      <td>0.180916</td>\n",
       "      <td>3.611572</td>\n",
       "      <td>2.988641</td>\n",
       "      <td>0.495667</td>\n",
       "      <td>0.101307</td>\n",
       "      <td>0.219644</td>\n",
       "      <td>0.224245</td>\n",
       "      <td>0.154313</td>\n",
       "      <td>0.233152</td>\n",
       "      <td>26.903632</td>\n",
       "      <td>0.997275</td>\n",
       "      <td>10.398531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.448000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1957.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2581.750000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-8.171250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092700</td>\n",
       "      <td>0.331000</td>\n",
       "      <td>99.961000</td>\n",
       "      <td>3.130075</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5267.500000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.721000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-6.166000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.080400</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>121.984000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7977.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>-4.645000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132000</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>0.693000</td>\n",
       "      <td>133.918250</td>\n",
       "      <td>4.226354</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10691.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.983000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.275000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918000</td>\n",
       "      <td>0.994000</td>\n",
       "      <td>0.994000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>239.440000</td>\n",
       "      <td>8.630167</td>\n",
       "      <td>2020.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       track_artist  track_popularity  playlist_genre  playlist_subgenre  \\\n",
       "count  32828.000000      32828.000000    32828.000000       32828.000000   \n",
       "mean    5298.133484         42.483551        2.442640          11.601529   \n",
       "std     3103.686069         24.980476        1.708802           6.794681   \n",
       "min        0.000000          0.000000        0.000000           0.000000   \n",
       "25%     2581.750000         24.000000        1.000000           6.000000   \n",
       "50%     5267.500000         45.000000        2.000000          11.000000   \n",
       "75%     7977.000000         62.000000        4.000000          18.000000   \n",
       "max    10691.000000        100.000000        5.000000          23.000000   \n",
       "\n",
       "       danceability        energy           key      loudness          mode  \\\n",
       "count  32828.000000  32828.000000  32828.000000  32828.000000  32828.000000   \n",
       "mean       0.654850      0.698603      5.373949     -6.719529      0.565737   \n",
       "std        0.145092      0.180916      3.611572      2.988641      0.495667   \n",
       "min        0.000000      0.000175      0.000000    -46.448000      0.000000   \n",
       "25%        0.563000      0.581000      2.000000     -8.171250      0.000000   \n",
       "50%        0.672000      0.721000      6.000000     -6.166000      1.000000   \n",
       "75%        0.761000      0.840000      9.000000     -4.645000      1.000000   \n",
       "max        0.983000      1.000000     11.000000      1.275000      1.000000   \n",
       "\n",
       "        speechiness  acousticness  instrumentalness      liveness  \\\n",
       "count  32828.000000  32828.000000      32828.000000  32828.000000   \n",
       "mean       0.107053      0.175352          0.084760      0.190175   \n",
       "std        0.101307      0.219644          0.224245      0.154313   \n",
       "min        0.000000      0.000000          0.000000      0.000000   \n",
       "25%        0.041000      0.015100          0.000000      0.092700   \n",
       "50%        0.062500      0.080400          0.000016      0.127000   \n",
       "75%        0.132000      0.255000          0.004830      0.248000   \n",
       "max        0.918000      0.994000          0.994000      0.996000   \n",
       "\n",
       "            valence         tempo  duration_min  release_year  \n",
       "count  32828.000000  32828.000000  32828.000000  30942.000000  \n",
       "mean       0.510556    120.883642      3.763280   2012.200246  \n",
       "std        0.233152     26.903632      0.997275     10.398531  \n",
       "min        0.000000      0.000000      0.066667   1957.000000  \n",
       "25%        0.331000     99.961000      3.130075   2010.000000  \n",
       "50%        0.512000    121.984000      3.600000   2017.000000  \n",
       "75%        0.693000    133.918250      4.226354   2019.000000  \n",
       "max        0.991000    239.440000      8.630167   2020.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genre counts:\n",
      " playlist_genre\n",
      "0    6043\n",
      "4    5743\n",
      "2    5507\n",
      "3    5431\n",
      "1    5153\n",
      "5    4951\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#2.6.basic visualizations and description\n",
    "print(\"\\nDataset description (numeric):\")\n",
    "display(df.describe())\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(df.corr(numeric_only=True), cmap='coolwarm', annot=False)\n",
    "plt.title(\"Correlation heatmap\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.countplot(x='playlist_genre', data=df)\n",
    "plt.title(\"Genre counts\")\n",
    "plt.show()\n",
    "\n",
    "#show class distribution numbers\n",
    "print(\"Genre counts:\\n\", df['playlist_genre'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ba1146f6-e536-4f1f-93cb-abaf039a2381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.7.outliers visualization and removal (tempo, loudness)\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(x=df['loudness'])\n",
    "plt.title('Loudness boxplot')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(x=df['tempo'])\n",
    "plt.title('Tempo boxplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5fcc88be-9804-49d7-aa32-c0b89611fb25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 17 outliers. Remaining: 32811\n"
     ]
    }
   ],
   "source": [
    "#define reasonable limits and remove rows outside them\n",
    "tempo_min, tempo_max = 40, 220\n",
    "loudness_min, loudness_max = -35, 0\n",
    "\n",
    "before = df.shape[0]\n",
    "df = df[(df['tempo'].between(tempo_min, tempo_max)) & (df['loudness'].between(loudness_min, loudness_max))]\n",
    "after = df.shape[0]\n",
    "print(f\"Removed {before - after} outliers. Remaining: {after}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9c9d7659-2618-471a-a9c9-77ad625c7adb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "release_year missing count: 1886\n"
     ]
    }
   ],
   "source": [
    "#2.8.check missing in release_year (coerced dates produce NaN)\n",
    "if 'release_year' in df.columns:\n",
    "    print(\"release_year missing count:\", df['release_year'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3fbc1c0d-0285-4708-8a10-5bc76edc4a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Components needed for 90% variance: 1\n"
     ]
    }
   ],
   "source": [
    "#2.9.optional PCA check (how many components explain variance)\n",
    "from sklearn.decomposition import PCA\n",
    "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n",
    "#remove target from numeric candidate list\n",
    "if 'playlist_genre' in numeric_cols:\n",
    "    numeric_cols.remove('playlist_genre')\n",
    "\n",
    "X_numeric = df[numeric_cols].fillna(df[numeric_cols].median())  #simple fill to do PCA\n",
    "scaler = StandardScaler()\n",
    "for_pca_scaled = scaler.fit_transform(X_numeric)\n",
    "pca = PCA()\n",
    "pca.fit(for_pca_scaled)\n",
    "\n",
    "\n",
    "pca = PCA()\n",
    "pca.fit(X_numeric)\n",
    "explained = np.cumsum(pca.explained_variance_ratio_)\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(np.arange(1, len(explained)+1), explained, marker='o')\n",
    "plt.xlabel('n components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.title('PCA Scree (cumulative)')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "print(\"Components needed for 90% variance:\", np.argmax(explained >= 0.90) + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60b779d-ec1a-40f1-9d6a-6c556a6c02d1",
   "metadata": {},
   "source": [
    "I cleaned and prepared the spotify dataset for analysis by removing incomplete rows, dropping useless text IDs, and encoding categorical columns into numbers. New features were added (duration in minutes, release year), and i removed unrealistic outliers only from tempo (below 40 or above 220 BPM) and loudness (outside –35 to 0 dB) since other features were already normalized. A PCA variance check showed that one component explains nearly all variance, meaning numeric features are highly correlated and consistent. I stopped PCA to keep features interpretable, leaving a clean, structured, and reliable dataset ready for modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "74f6cbdf-dec6-49b0-8ada-aedad17be497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes: X_train, X_test, y_train, y_test -> (26248, 16) (6563, 16) (26248,) (6563,)\n"
     ]
    }
   ],
   "source": [
    "#step 3: prepare X and y, split to train/test\n",
    "y = df['playlist_genre']\n",
    "X = df.drop(columns=['playlist_genre'])\n",
    "\n",
    "#split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n",
    "print(\"Shapes: X_train, X_test, y_train, y_test ->\", X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0f6e8939-b7b9-4092-ade0-85ba9c63610f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any NaN in scaled train? 1515\n",
      "Any NaN in scaled test? 371\n"
     ]
    }
   ],
   "source": [
    "#step 4: scale features SAFELY (fit scaler only on train)\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train), columns=X.columns, index=X_train.index)\n",
    "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=X.columns, index=X_test.index)\n",
    "\n",
    "# quick sanity checks\n",
    "print(\"Any NaN in scaled train?\", X_train_scaled.isnull().sum().sum())\n",
    "print(\"Any NaN in scaled test?\", X_test_scaled.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2463c17c-580c-443a-abc4-e0959407fd20",
   "metadata": {},
   "source": [
    "**step 5:clean scaled train/test safely (no leakage)**\n",
    "\n",
    "1.combine train X + y, fix missing release_year, remove duplicates\n",
    "2.ensure lengths align after cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c0629d46-7872-4438-b44f-e8b50e04505e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#ensure y_train index reset and length sync (safety)\n",
    "y_train = y_train.reset_index(drop=True)\n",
    "X_train_scaled = X_train_scaled.reset_index(drop=True)\n",
    "X_test_scaled = X_test_scaled.reset_index(drop=True)\n",
    "y_test = y_test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d8dcb014-2ac2-4e88-b6a5-64c694c7a05f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine\n",
    "train_combined = X_train_scaled.copy()\n",
    "train_combined['playlist_genre'] = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "95c88302-49ff-457d-9658-7a9832e2fbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fill release_year median (handle NaN created by parse)\n",
    "if 'release_year' in train_combined.columns:\n",
    "    train_combined['release_year'].fillna(train_combined['release_year'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9f014cfc-af99-4bf4-99d6-0a17d30da642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicates before (train): 7\n",
      "Duplicates after (train): 0\n"
     ]
    }
   ],
   "source": [
    "#check foe duplicates\n",
    "print(\"Duplicates before (train):\", train_combined.duplicated().sum())\n",
    "train_combined.drop_duplicates(inplace=True)\n",
    "print(\"Duplicates after (train):\", train_combined.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a4d85dd9-f793-4c03-8bb9-688428f7febb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split back\n",
    "y_train = train_combined['playlist_genre'].reset_index(drop=True)\n",
    "X_train_scaled = train_combined.drop(columns=['playlist_genre']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2e9d546b-ddea-4cf7-b0d9-2b1cee62d622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicates before (test): 0\n",
      "Duplicates after (test): 0\n",
      "Final shapes : X_train_scaled, y_train, X_test_scaled, y_test: (26241, 16) (26241,) (6563, 16) (6563,)\n"
     ]
    }
   ],
   "source": [
    "#clean test features only (do not touch y_test)\n",
    "if 'release_year' in X_test_scaled.columns:\n",
    "    X_test_scaled['release_year'].fillna(X_test_scaled['release_year'].median(), inplace=True)\n",
    "print(\"Duplicates before (test):\", X_test_scaled.duplicated().sum())\n",
    "X_test_scaled.drop_duplicates(inplace=True)\n",
    "X_test_scaled.reset_index(drop=True, inplace=True)\n",
    "print(\"Duplicates after (test):\", X_test_scaled.duplicated().sum())\n",
    "\n",
    "print(\"Final shapes : X_train_scaled, y_train, X_test_scaled, y_test:\", X_train_scaled.shape, y_train.shape, X_test_scaled.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4419030-bf70-4cd2-be69-cfd8fc8f4a91",
   "metadata": {},
   "source": [
    "I split the dataset into training and testing sets (80/20) using stratified sampling to preserve class balance. Then I safely normalized features with StandardScaler, fitting it only on the training data to prevent data leakage. The scaled data was checked for missing values and duplicates, and any NaNs in release_year were replaced with the median. Duplicate rows were removed from both train and test sets to ensure clean and unique records. This process produced standardized, consistent datasets ready for reliable model training and evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7b103c-09ff-41b4-856d-bf630ad02295",
   "metadata": {},
   "source": [
    "**step 6: model training helper and model list**\n",
    "\n",
    "1.train each model with GridSearchCV (min 2 hyperparameters)\n",
    "\n",
    "2.use StratifiedKFold\n",
    "\n",
    "3.handle predict_proba missing gracefully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "df5c6c2c-0731-4d3f-99ca-27844bd8d1ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, ExtraTreesClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3881b7e4-67f6-4947-9018-073dca1697da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def safe_predict_proba(model, X):\n",
    "    #return probabilities if available, otherwise soft one-hot of predictions\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "        return model.predict_proba(X)\n",
    "    else:\n",
    "        preds = model.predict(X)\n",
    "        #create one-hot probabilities (not ideal but avoids crash)\n",
    "        n_classes = len(np.unique(y_train))\n",
    "        proba = np.zeros((len(preds), n_classes))\n",
    "        for i, p in enumerate(preds):\n",
    "            proba[i, int(p)] = 1.0\n",
    "        return proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2011564a-9040-487b-af02-6464ad3369cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(name, estimator, param_grid, X_tr, y_tr, X_val, y_val, cv_splits=cv):\n",
    "    print(f\"\\n>> Training {name} ...\")\n",
    "    if param_grid:\n",
    "        search = GridSearchCV(estimator, param_grid, cv=cv_splits, scoring='f1_macro', n_jobs=-1, verbose=0)\n",
    "        search.fit(X_tr, y_tr)\n",
    "        best = search.best_estimator_\n",
    "        best_params = search.best_params_\n",
    "    else:\n",
    "        # no params to tune\n",
    "        best = estimator\n",
    "        best.fit(X_tr, y_tr)\n",
    "        best_params = {}\n",
    "    # predictions\n",
    "    y_pred = best.predict(X_val)\n",
    "    y_proba = safe_predict_proba(best, X_val)\n",
    "\n",
    "    results = {\n",
    "        'name': name,\n",
    "        'model': best,\n",
    "        'best_params': best_params,\n",
    "        'accuracy': accuracy_score(y_val, y_pred),\n",
    "        'f1_macro': f1_score(y_val, y_pred, average='macro'),\n",
    "        'log_loss': log_loss(y_val, y_proba)\n",
    "    }\n",
    "    print(f\"{name} done. best_params: {best_params}\")\n",
    "    print(f\"Accuracy: {results['accuracy']:.4f}, F1_macro: {results['f1_macro']:.4f}, Log_loss: {results['log_loss']:.4f}\")\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc308982-175f-4f7b-8bec-099484a11c32",
   "metadata": {},
   "source": [
    "Two utility functions were implemented to ensure stable and consistent model evaluation.\n",
    "safe_predict_proba() guarantees that every classifier outputs class probabilities, even if the model does not natively support them, by generating one-hot encoded pseudo-probabilities when necessary.\n",
    "train_model() automates the process of training, hyperparameter tuning (via GridSearchCV), and performance evaluation using stratified 3-fold cross-validation, which maintains class balance across folds. It calculates key metrics such as accuracy, macro-F1, and log loss, returning all results in a unified format. This framework allows fair comparison of different models and prevents bias from random data splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "158c4c83-874f-4417-bc18-3b33d2a54a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#step 7: Define models + param grids (10 models)\n",
    "results_all = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7f34ca03-f79b-4418-a9ea-fdcfb9ab1a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training LogisticRegression ...\n",
      "LogisticRegression done. best_params: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "Accuracy: 0.5353, F1_macro: 0.5345, Log_loss: 1.2100\n"
     ]
    }
   ],
   "source": [
    "#1.Logistic Regression\n",
    "lr = LogisticRegression(max_iter=1000, random_state=42)\n",
    "lr_params = {'C': [0.1, 1, 10], 'solver': ['lbfgs'], 'penalty': ['l2']}\n",
    "results_all['LogisticRegression'] = train_model('LogisticRegression', lr, lr_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bd7805b6-9332-47d7-a2f2-c744085bc64f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training KNN ...\n",
      "KNN done. best_params: {'n_neighbors': 7, 'p': 1, 'weights': 'distance'}\n",
      "Accuracy: 0.6608, F1_macro: 0.6614, Log_loss: 2.7560\n"
     ]
    }
   ],
   "source": [
    "#2.KNN\n",
    "knn = KNeighborsClassifier()\n",
    "knn_params = {'n_neighbors': [3,5,7], 'weights': ['uniform', 'distance'], 'p': [1,2]}\n",
    "results_all['KNN'] = train_model('KNN', knn, knn_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "93fa32a3-a5dc-4b8b-999e-ec5da0986655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training DecisionTree ...\n",
      "DecisionTree done. best_params: {'criterion': 'gini', 'max_depth': 10, 'min_samples_split': 5}\n",
      "Accuracy: 0.9997, F1_macro: 0.9997, Log_loss: 0.0068\n"
     ]
    }
   ],
   "source": [
    "#3.Decision Tree\n",
    "dt = DecisionTreeClassifier(random_state=42)\n",
    "dt_params = {'criterion': ['gini','entropy'], 'max_depth': [5,10,15,None], 'min_samples_split': [2,5,10]}\n",
    "results_all['DecisionTree'] = train_model('DecisionTree', dt, dt_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "eb28771e-0f27-4bf8-8538-37de0aad637a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training RandomForest ...\n",
      "RandomForest done. best_params: {'max_depth': 20, 'min_samples_split': 5, 'n_estimators': 200}\n",
      "Accuracy: 0.9787, F1_macro: 0.9790, Log_loss: 0.3345\n"
     ]
    }
   ],
   "source": [
    "#4.Random Forest (ensemble)\n",
    "rf = RandomForestClassifier(random_state=42)\n",
    "rf_params = {'n_estimators': [100,200], 'max_depth': [10, 20, None], 'min_samples_split': [2,5]}\n",
    "results_all['RandomForest'] = train_model('RandomForest', rf, rf_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "29b8c090-8f01-40c0-acaa-5ae612e1a70d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training GradientBoosting ...\n",
      "GradientBoosting done. best_params: {'learning_rate': 0.05, 'max_depth': 3, 'n_estimators': 100}\n",
      "Accuracy: 1.0000, F1_macro: 1.0000, Log_loss: 0.0175\n"
     ]
    }
   ],
   "source": [
    "#5.Gradient Boosting\n",
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "gb_params = {'n_estimators': [100], 'learning_rate': [0.05, 0.1], 'max_depth':[3,5]}\n",
    "results_all['GradientBoosting'] = train_model('GradientBoosting', gb, gb_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c357313d-0c37-4bad-bbff-07c42013efe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training SVM ...\n",
      "SVM done. best_params: {'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}\n",
      "Accuracy: 0.6887, F1_macro: 0.6887, Log_loss: 0.8081\n"
     ]
    }
   ],
   "source": [
    "#6.SVM\n",
    "svm = SVC(probability=True, random_state=42)\n",
    "svm_params = {'C': [0.1,1,10], 'kernel': ['linear','rbf'], 'gamma': ['scale','auto']}\n",
    "results_all['SVM'] = train_model('SVM', svm, svm_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "e7069167-9e7a-400d-8dd2-65fe56f55eec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training NaiveBayes ...\n",
      "NaiveBayes done. best_params: {}\n",
      "Accuracy: 0.5123, F1_macro: 0.5113, Log_loss: 1.5931\n"
     ]
    }
   ],
   "source": [
    "#7.Naive Bayes\n",
    "nb = GaussianNB()\n",
    "nb_params = {}  #no tuning needed typically\n",
    "results_all['NaiveBayes'] = train_model('NaiveBayes', nb, nb_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "95a16f61-4461-4ea9-a68b-3e936ba29b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training AdaBoost ...\n",
      "AdaBoost done. best_params: {'learning_rate': 0.1, 'n_estimators': 100}\n",
      "Accuracy: 0.4758, F1_macro: 0.4436, Log_loss: 1.3091\n"
     ]
    }
   ],
   "source": [
    "#8.AdaBoost\n",
    "ada = AdaBoostClassifier(random_state=42)\n",
    "ada_params = {'n_estimators': [50,100], 'learning_rate': [0.01, 0.1, 1]}\n",
    "results_all['AdaBoost'] = train_model('AdaBoost', ada, ada_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "984bd229-1a39-436d-84fa-8752db0fb4e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training ExtraTrees ...\n",
      "ExtraTrees done. best_params: {'max_depth': None, 'min_samples_split': 5, 'n_estimators': 100}\n",
      "Accuracy: 0.9933, F1_macro: 0.9933, Log_loss: 0.3066\n"
     ]
    }
   ],
   "source": [
    "#9.ExtraTrees (ensemble)\n",
    "et = ExtraTreesClassifier(random_state=42)\n",
    "et_params = {'n_estimators':[100], 'max_depth':[10, None], 'min_samples_split':[2,5]}\n",
    "results_all['ExtraTrees'] = train_model('ExtraTrees', et, et_params, X_train_scaled, y_train, X_test_scaled, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ae48ba11-260e-4dfd-af08-30a4a335b03e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> Training XGBoost ...\n",
      "XGBoost done. best_params: {'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 100}\n",
      "Accuracy: 1.0000, F1_macro: 1.0000, Log_loss: 0.0167\n",
      "\n",
      ">> Training LightGBM ...\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001646 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 3007\n",
      "[LightGBM] [Info] Number of data points in the train set: 26241, number of used features: 16\n",
      "[LightGBM] [Info] Start training from score -1.692270\n",
      "[LightGBM] [Info] Start training from score -1.852441\n",
      "[LightGBM] [Info] Start training from score -1.785037\n",
      "[LightGBM] [Info] Start training from score -1.798758\n",
      "[LightGBM] [Info] Start training from score -1.743443\n",
      "[LightGBM] [Info] Start training from score -1.891584\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "LightGBM done. best_params: {'learning_rate': 0.1, 'max_depth': -1, 'n_estimators': 100}\n",
      "Accuracy: 1.0000, F1_macro: 1.0000, Log_loss: 0.0000\n"
     ]
    }
   ],
   "source": [
    "#10.XGBoost / LightGBM if available (optional)\n",
    "try:\n",
    "    from xgboost import XGBClassifier\n",
    "    xgb = XGBClassifier(random_state=42, use_label_encoder=False, eval_metric='mlogloss')\n",
    "    xgb_params = {'n_estimators':[100], 'learning_rate':[0.05,0.1], 'max_depth':[3,5]}\n",
    "    results_all['XGBoost'] = train_model('XGBoost', xgb, xgb_params, X_train_scaled, y_train, X_test_scaled, y_test)\n",
    "except Exception as e:\n",
    "    print(\"XGBoost not available or failed:\", e)\n",
    "\n",
    "try:\n",
    "    from lightgbm import LGBMClassifier\n",
    "    lgbm = LGBMClassifier(random_state=42)\n",
    "    lgbm_params = {'n_estimators':[100], 'learning_rate':[0.05,0.1], 'max_depth':[-1,10]}\n",
    "    results_all['LightGBM'] = train_model('LightGBM', lgbm, lgbm_params, X_train_scaled, y_train, X_test_scaled, y_test)\n",
    "except Exception as e:\n",
    "    print(\"LightGBM not available or failed:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9cf19dfe-d03c-4f86-85e0-180513e33ea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#step 8: Neural Network (MLP) - RandomizedSearchCV (optimize many hyperparameters)\n",
    "#use RandomizedSearchCV with proper X_train_scaled, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "99e55fef-cf7d-4fc8-86a1-1916d4fb8dfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 30 candidates, totalling 120 fits\n",
      "\n",
      "Randomized Search completed in 12.90 minutes\n",
      "Best Parameters (MLP): {'solver': 'adam', 'learning_rate_init': 0.001, 'learning_rate': 'adaptive', 'hidden_layer_sizes': (256, 128), 'early_stopping': True, 'batch_size': 32, 'alpha': 0.0, 'activation': 'relu'}\n",
      "Best CV F1-macro: 0.9903\n",
      "\n",
      "Test Results (MLP):\n",
      "  - Accuracy:  0.9942\n",
      "  - F1-macro:  0.9942\n",
      "  - Log Loss:  0.0289\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV, StratifiedKFold\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, f1_score, log_loss\n",
    "import time\n",
    "\n",
    "#define base model\n",
    "mlp = MLPClassifier(max_iter=500, random_state=42)\n",
    "\n",
    "# define parameter grid for random search\n",
    "param_dist = {\n",
    "    'hidden_layer_sizes': [(64,), (128,), (256,), (128, 64), (256, 128)],\n",
    "    'activation': ['relu', 'tanh', 'logistic'],\n",
    "    'solver': ['adam', 'sgd'],\n",
    "    'learning_rate_init': [1e-2, 1e-3, 1e-4],\n",
    "    'alpha': [0.0, 1e-4, 1e-3],\n",
    "    'batch_size': [32, 64],\n",
    "    'early_stopping': [True],\n",
    "    'learning_rate': ['constant', 'adaptive']\n",
    "}\n",
    "\n",
    "#use stratified k-fold cross-validation\n",
    "cv_nn = StratifiedKFold(n_splits=4, shuffle=True, random_state=42)\n",
    "\n",
    "#set up randomized search\n",
    "random_search = RandomizedSearchCV(\n",
    "    estimator=mlp,\n",
    "    param_distributions=param_dist,\n",
    "    n_iter=30,  #moderate number of iterations\n",
    "    scoring='f1_macro',\n",
    "    n_jobs=-1,\n",
    "    cv=cv_nn,\n",
    "    verbose=2,\n",
    "    random_state=42,\n",
    "    return_train_score=True\n",
    ")\n",
    "\n",
    "#fit model\n",
    "start = time.time()\n",
    "random_search.fit(X_train_scaled, y_train)\n",
    "end = time.time()\n",
    "\n",
    "#print search summary\n",
    "print(f\"\\nRandomized Search completed in {(end - start) / 60:.2f} minutes\")\n",
    "print(\"Best Parameters (MLP):\", random_search.best_params_)\n",
    "print(f\"Best CV F1-macro: {random_search.best_score_:.4f}\")\n",
    "\n",
    "#best model\n",
    "best_mlp = random_search.best_estimator_\n",
    "\n",
    "#evaluate on test data\n",
    "y_pred_mlp = best_mlp.predict(X_test_scaled)\n",
    "y_proba_mlp = best_mlp.predict_proba(X_test_scaled)\n",
    "\n",
    "mlp_results = {\n",
    "    'name': 'MLP',\n",
    "    'model': best_mlp,\n",
    "    'best_params': random_search.best_params_,\n",
    "    'accuracy': accuracy_score(y_test, y_pred_mlp),\n",
    "    'f1_macro': f1_score(y_test, y_pred_mlp, average='macro'),\n",
    "    'log_loss': log_loss(y_test, y_proba_mlp)\n",
    "}\n",
    "\n",
    "#print results clearly\n",
    "print(\"\\nTest Results (MLP):\")\n",
    "print(f\"  - Accuracy:  {mlp_results['accuracy']:.4f}\")\n",
    "print(f\"  - F1-macro:  {mlp_results['f1_macro']:.4f}\")\n",
    "print(f\"  - Log Loss:  {mlp_results['log_loss']:.4f}\")\n",
    "\n",
    "#save results\n",
    "results_all['MLP'] = mlp_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7fd1ce98-bc0c-4c0f-836b-0945f669c355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>f1_macro</th>\n",
       "      <th>log_loss</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GradientBoosting</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.745432e-02</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 3, 'n_est...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.668571e-02</td>\n",
       "      <td>{'learning_rate': 0.05, 'max_depth': 5, 'n_est...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.099756e-07</td>\n",
       "      <td>{'learning_rate': 0.1, 'max_depth': -1, 'n_est...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTree</td>\n",
       "      <td>0.999695</td>\n",
       "      <td>0.999694</td>\n",
       "      <td>6.808461e-03</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10, 'min_sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MLP</td>\n",
       "      <td>0.994210</td>\n",
       "      <td>0.994166</td>\n",
       "      <td>2.887806e-02</td>\n",
       "      <td>{'solver': 'adam', 'learning_rate_init': 0.001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ExtraTrees</td>\n",
       "      <td>0.993296</td>\n",
       "      <td>0.993260</td>\n",
       "      <td>3.066437e-01</td>\n",
       "      <td>{'max_depth': None, 'min_samples_split': 5, 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.978668</td>\n",
       "      <td>0.979004</td>\n",
       "      <td>3.344941e-01</td>\n",
       "      <td>{'max_depth': 20, 'min_samples_split': 5, 'n_e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.688709</td>\n",
       "      <td>0.688737</td>\n",
       "      <td>8.081126e-01</td>\n",
       "      <td>{'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.660826</td>\n",
       "      <td>0.661423</td>\n",
       "      <td>2.756007e+00</td>\n",
       "      <td>{'n_neighbors': 7, 'p': 1, 'weights': 'distance'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.535274</td>\n",
       "      <td>0.534515</td>\n",
       "      <td>1.210047e+00</td>\n",
       "      <td>{'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaiveBayes</td>\n",
       "      <td>0.512266</td>\n",
       "      <td>0.511321</td>\n",
       "      <td>1.593148e+00</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.475849</td>\n",
       "      <td>0.443640</td>\n",
       "      <td>1.309054e+00</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  accuracy  f1_macro      log_loss  \\\n",
       "0     GradientBoosting  1.000000  1.000000  1.745432e-02   \n",
       "1              XGBoost  1.000000  1.000000  1.668571e-02   \n",
       "2             LightGBM  1.000000  1.000000  5.099756e-07   \n",
       "3         DecisionTree  0.999695  0.999694  6.808461e-03   \n",
       "4                  MLP  0.994210  0.994166  2.887806e-02   \n",
       "5           ExtraTrees  0.993296  0.993260  3.066437e-01   \n",
       "6         RandomForest  0.978668  0.979004  3.344941e-01   \n",
       "7                  SVM  0.688709  0.688737  8.081126e-01   \n",
       "8                  KNN  0.660826  0.661423  2.756007e+00   \n",
       "9   LogisticRegression  0.535274  0.534515  1.210047e+00   \n",
       "10          NaiveBayes  0.512266  0.511321  1.593148e+00   \n",
       "11            AdaBoost  0.475849  0.443640  1.309054e+00   \n",
       "\n",
       "                                          best_params  \n",
       "0   {'learning_rate': 0.05, 'max_depth': 3, 'n_est...  \n",
       "1   {'learning_rate': 0.05, 'max_depth': 5, 'n_est...  \n",
       "2   {'learning_rate': 0.1, 'max_depth': -1, 'n_est...  \n",
       "3   {'criterion': 'gini', 'max_depth': 10, 'min_sa...  \n",
       "4   {'solver': 'adam', 'learning_rate_init': 0.001...  \n",
       "5   {'max_depth': None, 'min_samples_split': 5, 'n...  \n",
       "6   {'max_depth': 20, 'min_samples_split': 5, 'n_e...  \n",
       "7         {'C': 10, 'gamma': 'auto', 'kernel': 'rbf'}  \n",
       "8   {'n_neighbors': 7, 'p': 1, 'weights': 'distance'}  \n",
       "9       {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}  \n",
       "10                                                 {}  \n",
       "11        {'learning_rate': 0.1, 'n_estimators': 100}  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 9: Results summary and visual comparison\n",
    "#build DataFrame of results\n",
    "rows = []\n",
    "for k, v in results_all.items():\n",
    "    rows.append({\n",
    "        'model': v['name'] if 'name' in v else k,\n",
    "        'accuracy': v['accuracy'],\n",
    "        'f1_macro': v['f1_macro'],\n",
    "        'log_loss': v['log_loss'],\n",
    "        'best_params': v.get('best_params', {})\n",
    "    })\n",
    "res_df = pd.DataFrame(rows).sort_values('f1_macro', ascending=False).reset_index(drop=True)\n",
    "display(res_df)\n",
    "\n",
    "#barplot compare f1_macro\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(x='f1_macro', y='model', data=res_df)\n",
    "plt.title('Models comparison by F1_macro')\n",
    "plt.xlim(0,1)\n",
    "plt.show()\n",
    "\n",
    "#log_loss comparison\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(x='log_loss', y='model', data=res_df)\n",
    "plt.title('Models comparison by Log Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c2f26836-ca2c-4fdd-9b68-79bb43d81633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for GradientBoosting ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1209\n",
      "           1       1.00      1.00      1.00      1029\n",
      "           2       1.00      1.00      1.00      1101\n",
      "           3       1.00      1.00      1.00      1086\n",
      "           4       1.00      1.00      1.00      1148\n",
      "           5       1.00      1.00      1.00       990\n",
      "\n",
      "    accuracy                           1.00      6563\n",
      "   macro avg       1.00      1.00      1.00      6563\n",
      "weighted avg       1.00      1.00      1.00      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for XGBoost ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1209\n",
      "           1       1.00      1.00      1.00      1029\n",
      "           2       1.00      1.00      1.00      1101\n",
      "           3       1.00      1.00      1.00      1086\n",
      "           4       1.00      1.00      1.00      1148\n",
      "           5       1.00      1.00      1.00       990\n",
      "\n",
      "    accuracy                           1.00      6563\n",
      "   macro avg       1.00      1.00      1.00      6563\n",
      "weighted avg       1.00      1.00      1.00      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAGHCAYAAAAk+fF+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABaOUlEQVR4nO3deVxU1fsH8M+wDYswssimuCEqKiLihmYuuOaSmXu55JpbEW6huZaMWrnkvuWaW5lmpaalmYooopiSSyZuCQKKKAjDdn5/+HV+XRmUZRaY+bx73derOffcO88zl5pnzj33XpkQQoCIiIjof8wMHQARERGVLiwOiIiISILFAREREUmwOCAiIiIJFgdEREQkweKAiIiIJFgcEBERkQSLAyIiIpJgcUBEREQSLA5MyJ9//on33nsP1apVg7W1NcqVK4eGDRtiwYIFePjwoU7f+/z582jVqhUUCgVkMhkWL16s9feQyWSYNWuW1vf7Khs3boRMJoNMJsPvv/+eb70QAjVq1IBMJkPr1q2L9R4rVqzAxo0bi7TN77//XmBMpcnQoUMhl8tx8eLFfOvmzZsHmUyGH3/8UdL++PFjzJs3D02bNkX58uVhaWkJNzc3dOrUCdu2bYNKpVL3vXnzpvr4PF8cHBzg7++PxYsXIzc3V+c5vkpxji+RTgkyCWvWrBEWFhaibt26Yvny5eLo0aPi0KFDIjw8XFSrVk306NFDp+/foEED4ePjI/bv3y9OnTol4uPjtf4ep06dEnfu3NH6fl9lw4YNAoCwt7cX7777br71R48eVa9v1apVsd6jbt26Rd42NTVVnDp1SqSmphbrPfUlNTVVVK5cWQQEBIisrCx1+59//imsrKzEkCFDJP2vXbsmqlevLsqVKydCQ0PFDz/8IP744w+xc+dOMXToUCGXy8Unn3yi7h8XFycAiPHjx4tTp06JU6dOiQMHDojRo0cLACI0NFRvuRakOMeXSJdYHJiAiIgIYW5uLjp16iQyMzPzrVepVOKHH37QaQwWFhZi9OjROn0PQ3leHAwfPlzY2Njk+zJ+9913RVBQUIm+AIqybVZWlsjOzi7W+xjK4cOHhUwmEzNmzBBCPMvB399feHl5iUePHqn7ZWdnizp16ojy5cuLv/76S+O+bt68Kfbs2aN+/bw4+Pzzz/P1bdmypfDw8NBuMsXA4oBKGxYHJqBr167CwsJC3L59u1D9c3Nzxfz580WtWrWElZWVqFChghg4cGC+X+WtWrUSdevWFWfOnBGvvfaasLGxEdWqVRNKpVLk5uYKIf7/i/PFRQghZs6cKTQNXj3fJi4uTt3222+/iVatWgknJydhbW0tvLy8RM+ePUV6erq6DwAxc+ZMyb4uXrwounfvLsqXLy/kcrnw9/cXGzdulPR5/st+27ZtYurUqcLDw0PY29uL4OBgceXKlVd+Xs/j/e2334SNjY1YtWqVet2jR4+EjY2NWLt2rcYvgFmzZokmTZoIR0dHYW9vLwICAsS6detEXl6euk+VKlXyfX5VqlSRxL5582YRGhoqPD09hUwmE5cvX1avO3r0qBBCiKSkJFGpUiURFBQk+YUeGxsrbG1tNY566NPo0aOFhYWFOHv2rJg6daoAIA4dOiTps2vXrgK/6AvysuKga9euonLlypK2wv79CyHE+vXrRf369YVcLheOjo6iR48e+YqWf/75R/Tt21d4eHgIKysr4erqKtq2bSvOnz8vhHj58SUyFBYHRi4nJ0fY2tqKpk2bFnqbkSNHCgBi3Lhx4uDBg2LVqlWiQoUKwsvLSyQlJan7tWrVSjg7OwsfHx+xatUqcfjwYTFmzBgBQGzatEkIIURiYqI4deqUACB69eqlHtYVovDFQVxcnLC2thbt27cXe/fuFb///rv45ptvxMCBA0VKSop6uxeLgytXrgh7e3vh7e0tNm/eLH7++WfRv39/AUDMnz9f3e/5l2jVqlXFO++8I37++Wexfft2UblyZeHj4yNycnJe+nk9jzcqKkoMHDhQNGnSRL1u5cqVws7OTjx+/FhjcTBkyBCxfv16cfjwYXH48GHx6aefChsbGzF79mx1n3Pnzonq1auLgIAA9ed37tw5SewVK1YUvXr1Evv27RM//fSTePDgQb7iQAghTpw4ISwsLMRHH30khBAiPT1d1KlTR9SuXVukpaW9NE9dS0tLE9WrVxdVq1YV5ubm4v3338/XZ8SIEQKAuHr1aqH3+7w4mD9/vsjOzhbZ2dkiOTlZrF+/XlhYWIhp06ZJ+hf27z88PFwAEP379xc///yz2Lx5s6hevbpQKBTi2rVr6n61atUSNWrUEFu2bBHHjh0Tu3fvFhMmTFAfl5cdXyJDYXFg5BISEgQA0a9fv0L1v3z5sgAgxowZI2k/ffq0ACCmTp2qbmvVqpUAIE6fPi3pW6dOHdGxY0dJGwAxduxYSVthi4PvvvtOABAxMTEvjf3F4qBfv35CLpfnGzHp3LmzsLW1VQ9XP/8SfeONNyT9nv9KfV7MFOS/xcHzfV26dEkIIUTjxo3V58xfNXScm5srsrOzxZw5c4Szs7Nk9KCgbZ+/3+uvv17guv8WB0IIMX/+fAFA7NmzRwwePFjY2NiIP//886U56su2bdsEAOHu7i6ePHmSb32nTp0EgHynx/Ly8tRf/NnZ2ZKC7nlxoGkZMmSIpG9h//5TUlKEjY1Nvr+Z27dvC7lcLgYMGCCEECI5OVkAEIsXL35p3jytQKUNr1YgiaNHjwIAhgwZImlv0qQJfH198dtvv0na3d3d0aRJE0lb/fr1cevWLa3F1KBBA1hZWWHkyJHYtGkTbty4Uajtjhw5guDgYHh5eUnahwwZgqdPn+LUqVOS9u7du0te169fHwCKlEurVq3g7e2Nr7/+GhcvXkRUVBSGDh360hjbtWsHhUIBc3NzWFpaYsaMGXjw4AESExML/b5vv/12oftOmjQJXbp0Qf/+/bFp0yYsXboUfn5+r9wuJyenWEthrwbIy8vD0qVLYWZmhsTERFy4cKHQOS1ZsgSWlpbqxd/fP1+fDz/8EFFRUYiKisLRo0cRHh6OXbt2oX///uo+hf37P3XqFDIyMvL18/LyQtu2bdX9nJyc4O3tjc8//xwLFy7E+fPnkZeXV+i8iAyFxYGRc3Fxga2tLeLi4grV/8GDBwAADw+PfOs8PT3V659zdnbO108ulyMjI6MY0Wrm7e2NX3/9Fa6urhg7diy8vb3h7e2NJUuWvHS7Bw8eFJjH8/X/9WIucrkcAIqUi0wmw3vvvYetW7di1apVqFmzJlq2bKmx75kzZ9ChQwcAwNq1a3Hy5ElERUVh2rRpRX5fTXm+LMYhQ4YgMzMT7u7uGDhw4Cu3uXnzpuTLtyiLt7d3oeL64osvcOrUKWzbtg0+Pj4YOnRovs+gcuXKAPIXbAMGDFB/8Tds2FDj/itVqoRGjRqhUaNGaN26NcLCwjB9+nR8++23+OWXXwAU/u+/sP1kMhl+++03dOzYEQsWLEDDhg1RoUIFfPDBB3jy5EmhPhciQ7AwdACkW+bm5ggODsaBAwdw9+5dVKpU6aX9n39BxsfH5+t77949uLi4aC02a2trAIBKpVJ/EQNAcnJyvr4tW7ZEy5YtkZubi7Nnz2Lp0qUICQmBm5sb+vXrp3H/zs7OiI+Pz9d+7949ANBqLv81ZMgQzJgxA6tWrcLcuXML7Ldjxw5YWlrip59+Un8WALB3794iv6dMJit03/j4eIwdOxYNGjRAbGwsJk6ciK+++uql23h6eiIqKqrIcQGQHNuC/PXXX5gxYwYGDRqEvn37okqVKmjRogWmTZuGhQsXqvu1b98ea9aswb59+zBx4kR1u6urK1xdXQEA9vb2kvscvMzz0aELFy6gY8eOhf77/2+/F73430mVKlWwfv16AMC1a9ewa9cuzJo1C1lZWVi1alWh4iTSN44cmICwsDAIITBixAhkZWXlW5+dna2+yUzbtm0BAFu3bpX0iYqKwuXLlxEcHKy1uKpWrQrg2c2Z/uvFG978l7m5OZo2bYrly5cDAM6dO1dg3+DgYBw5ckRdDDy3efNm2NraolmzZsWM/OUqVqyISZMmoVu3bhg8eHCB/WQyGSwsLGBubq5uy8jIwJYtW/L11dZoTG5uLvr37w+ZTIYDBw5AqVRi6dKl+P7771+6nZWVlfpXd1GXV52yyMnJweDBg+Hi4qIeDWrWrBlCQ0OxZMkSnDx5Ut33rbfeQp06dRAeHo4rV66U+POIiYkBAHVhUdi//6CgINjY2OTrd/fuXfXpLE1q1qyJTz75BH5+fpK/XW2PthGVFEcOTEBQUBBWrlyJMWPGIDAwEKNHj0bdunWRnZ2N8+fPY82aNahXrx66deuGWrVqYeTIkepzv507d8bNmzcxffp0eHl54aOPPtJaXG+88QacnJwwbNgwzJkzBxYWFti4cSPu3Lkj6bdq1SocOXIEXbp0QeXKlZGZmYmvv/4aANCuXbsC9z9z5kz89NNPaNOmDWbMmAEnJyd88803+Pnnn7FgwQIoFAqt5fKiefPmvbJPly5dsHDhQgwYMAAjR47EgwcP8MUXX2j8pe3n54cdO3Zg586dqF69OqytrQs1T+BFM2fOxPHjx3Ho0CG4u7tjwoQJOHbsGIYNG4aAgABUq1atyPssKaVSibNnz+LAgQMoX768uv3TTz/Fjz/+iKFDhyImJgY2NjYwNzfH3r170bFjRzRp0gQjRoxA69at4ejoiEePHuH06dO4cOECfH19873P7du3ERkZCQBIT0/HqVOnoFQqUaVKFfTs2RMACv33X758eUyfPh1Tp07FoEGD0L9/fzx48ACzZ8+GtbU1Zs6cCeBZ4Ttu3Dj07t0bPj4+sLKywpEjR/Dnn3/i448/VsemreNLpDWGnhFJ+hMTEyMGDx4sKleuLKysrISdnZ0ICAgQM2bMEImJiep+z6/zrlmzprC0tBQuLi7i3XffLfA+By8aPHhwvuu0oeFqBSGEOHPmjGjevLmws7MTFStWFDNnzhTr1q2TXK1w6tQp8dZbb4kqVaoIuVwunJ2dRatWrcS+ffvyvYem+xx069ZNKBQKYWVlJfz9/cWGDRskfZ7P6v/2228l7c9nub/Y/0X/vVrhZTTNSP/6669FrVq1hFwuF9WrVxdKpVKsX78+330ebt68KTp06CDs7e013ufgxdj/u+751QqHDh0SZmZm+T6jBw8eiMqVK4vGjRsLlUr10hy0LSYmRlhaWooRI0ZoXH/q1ClhZmamvvTyudTUVBEeHi4aN24sHBwchIWFhXB1dRXt27cXy5cvl9z/QtPVCtbW1qJmzZoiJCQk3906C/v3L4QQ69atE/Xr1xdWVlZCoVCIN998U8TGxqrX379/XwwZMkTUrl1b2NnZiXLlyon69euLRYsWSa6SKOj4EhmKTAghDFCTEBERUSnFOQdEREQkweKAiIiIJFgcEBERkQSLAyIiIpJgcUBEREQSLA6IiIh07I8//kC3bt3g6ekJmUwmuRNqdnY2pkyZAj8/P9jZ2cHT0xODBg3KdwM3lUqF8ePHw8XFBXZ2dujevTvu3r0r6ZOSkoKBAwdCoVBAoVBg4MCBePToUZHjZXFARESkY+np6fD398eyZcvyrXv69CnOnTuH6dOn49y5c/j+++9x7dq1fA+DCwkJwZ49e7Bjxw6cOHECaWlp6Nq1q+ThZgMGDEBMTAwOHjyIgwcPIiYmplDPT3mRUd7nwCZgnKFDMIiUqPx/dEREZZm1ju/jW5Lvi4zzxft/rkwmw549e9CjR48C+0RFRaFJkya4desWKleujNTUVFSoUAFbtmxB3759ATx7joeXlxf279+Pjh074vLly6hTpw4iIyPRtGlTAEBkZCSCgoJw5coV1KpVq9AxcuSAiIhMl8ys2ItKpcLjx48lS2Ef+vUqqampkMlk6luKR0dHIzs7W/0kV+DZA9Hq1auHiIgIAM8eJa5QKNSFAfDsOSUKhULdp7BYHBARkemSyYq9KJVK9bn954tSqSxxSJmZmfj4448xYMAAODg4AAASEhJgZWUFR0dHSV83NzckJCSo+zx/iNh/ubq6qvsUFh+8REREpktW/N/IYWFhCA0NlbQV5hHlL5OdnY1+/fohLy8PK1aseGV/IYTkke2aHt/+Yp/CYHFARERUDHK5vMTFwH9lZ2ejT58+iIuLw5EjR9SjBgDg7u6OrKwspKSkSEYPEhMT0bx5c3Wf+/fv59tvUlIS3NzcihQLTysQEZHpKsFpBW16Xhj8/fff+PXXX+Hs7CxZHxgYCEtLSxw+fFjdFh8fj0uXLqmLg6CgIKSmpuLMmTPqPqdPn0Zqaqq6T2Fx5ICIiExXCU4rFEVaWhquX7+ufh0XF4eYmBg4OTnB09MTvXr1wrlz5/DTTz8hNzdXPUfAyckJVlZWUCgUGDZsGCZMmABnZ2c4OTlh4sSJ8PPzQ7t27QAAvr6+6NSpE0aMGIHVq1cDAEaOHImuXbsW6UoFgMUBERGZMi2PABTk7NmzaNOmjfr187kKgwcPxqxZs7Bv3z4AQIMGDSTbHT16FK1btwYALFq0CBYWFujTpw8yMjIQHByMjRs3wtzcXN3/m2++wQcffKC+qqF79+4a763wKrzPgRHhfQ6IyNjo/D4HzaYUe9uMyPlajKR04cgBERGZLj2NHJQ1nJBIREREEhw5ICIi06WnCYllDYsDIiIyXTytoBGLAyIiMl0cOdCIxQEREZkujhxoxOKAiIhMF0cONOKnQkRERBIcOSAiItPFkQONWBwQEZHpMuOcA01YHBARkeniyIFG/FRe0KKhN75bPAo3Ds1Fxvll6Na6vnqdhYUZPvvgTUTtmorkiC9x49BcrPt0IDwqKCT7sLK0wMIpvXHnyDwkR3yJbxePQkXX8pI+DWpXwk8rxyH+jwW4e3Q+ln3SH3Y2VvpIUet2bv8GnTu0ReMAP/Tr3RPnos8aOiS9YN7M2xQYfd6l5JHNpQ2LgxfY2chx8dq/+GjernzrbK2t0MDXC/PWHkBQ//noN2EtfCq74tvFoyT9Pp/0Nrq3qY9BYRsQ/N4ilLOxwu6v3ofZ/4avPCoo8POq8fjnThJeH/gF3hy7HHW83bF2zkC95KhNBw/sx4J5SowYORo7v9uLhg0DMWbUCMTfu2fo0HSKeTNv5m0kZGbFX4yYcWdXDIdO/oXZK37CD0cu5Fv3OC0TXUcvw+7D5/H3rUScuXgTofO/RWCdyvBydwQAOJSzxpAeQfh44R4cPX0VF67exdBPNqNeDU+0bVobANC5ZT1k5+QiRLkLf99KRPRftxGi3IW32gWgupeLXvMtqS2bNuCtt99Gz169Ud3bG5PDpsHdwx27dm43dGg6xbyZN/MmY2bQ4uDu3buYNm0a2rRpA19fX9SpUwdt2rTBtGnTcOfOHUOGVmgO9jbIy8vDoycZAIAA38qwsrTAr6cuq/vEJ6Ui9p97aOZfDQAgt7JAdnYu/vu07AxVNgCgeQNvPUZfMtlZWbj8VyyCmr8maQ9q3gIXYs4bKCrdY97MG2DeRoOnFTQyWHFw4sQJ+Pr6Ys+ePfD398egQYPw7rvvwt/fH3v37kXdunVx8uTJV+5HpVLh8ePHkkXk5eohg2df8p9+8CZ2HjiLJ+mZAAB3ZweosrLVxcJziQ+ewM3ZAQDw+5mrcHN2wEeDgmFpYY7y9jaYM777s+1fmL9QmqU8SkFubi6cnZ0l7c7OLkhOTjJQVLrHvJk3wLyNBk8raGSwqxU++ugjDB8+HIsWLSpwfUhICKKiol66H6VSidmzZ0vazN0aw9KjidZi1cTCwgxb5r0HM5kMHyrzz094kUwmw/Nxgss3EjBixhbMm9ATc8Z3R25eHlZsP4aE5MfIy83Tady6IHuhghZC5GszRsz7GeZt3Iw+b2PKRYsMVvpcunQJ77//foHrR40ahUuXLr1yP2FhYUhNTZUsFm6B2gw1HwsLM3wzfxiqVHRG19HL1KMGAJDw4DHkVpYob28j2aaCUzkkPnisfr3z4FlUaz8V3h0/QcXWU/DZqv2o4FgON/99oNPYtcmxvCPMzc2RnJwsaX/48AGcncvW3ImiYN7MG2DeRoMjBxoZLDsPDw9EREQUuP7UqVPw8PB45X7kcjkcHBwki8zMXJuhSjwvDLwrV0CX95fhYWq6ZP35y7eRlZ2D4Ga11W3uLg6o6+2JyAtx+faX+PAJ0jOy0KtjQ2RmZeO3yCs6i13bLK2s4FunLiIjpKd/IiMi4N8gwEBR6R7zZt4A8zYanHOgkcFOK0ycOBHvv/8+oqOj0b59e7i5uUEmkyEhIQGHDx/GunXrsHjxYr3HZWdjBW+vCurXVSs6o37Nikh5/BT3klKx7fPhCKjthZ4froK5mQxuzvYAgIepT5Gdk4vHaZnYuPcU5oX2xIPUdKSkPoXyo7dw6fo9HDn9/1/87/d9HZEXbiDtaRaCm9VGeEgPTF/6A1LTMvLFVJoNHPwepn08GXXq1YO/fwB2f7sT8fHx6N23n6FD0ynmzbyZNxkzgxUHY8aMgbOzMxYtWoTVq1cjN/fZJEJzc3MEBgZi8+bN6NOnj97jalinCg6t+1D9esHEtwEAW/ZF4rNV+9U3RTqzM0yyXYfhS3A8+m8AwOQvdiM3Nw9b5w+DjdwSR89cxcgPtyAv7/+vTmhUrwo+eb8Lytla4erN+xg3dzu2//zy+RWlUafObyD1UQrWrFyBpKRE1PCpieWr1sDTs6KhQ9Mp5s28mbeRMPLTA8UlE/+9ns5AsrOz1ee1XFxcYGlpWaL92QSM00ZYZU5K1DJDh0BEpFXWOv4Ja9Plq2Jvm/HzB1qMpHQpFc9WsLS0LNT8AiIiIq3iyIFGpaI4ICIiMggWBxqxOCAiItNl5FcdFBdLJiIiIpLgyAEREZkunlbQiMUBERGZLp5W0IjFARERmS6OHGjE4oCIiEwXRw40YnFAREQmy6ieMKlFHE8hIiIiCY4cEBGRyeLIgWYsDoiIyHSxNtCIxQEREZksjhxoxuKAiIhMFosDzVgcEBGRyWJxoBmvViAiIiIJjhwQEZHJ4siBZiwOiIjIdLE20IinFYiIyGTJZLJiL0Xxxx9/oFu3bvD09IRMJsPevXsl64UQmDVrFjw9PWFjY4PWrVsjNjZW0kelUmH8+PFwcXGBnZ0dunfvjrt370r6pKSkYODAgVAoFFAoFBg4cCAePXpU5M+FxQEREZksfRUH6enp8Pf3x7JlyzSuX7BgARYuXIhly5YhKioK7u7uaN++PZ48eaLuExISgj179mDHjh04ceIE0tLS0LVrV+Tm5qr7DBgwADExMTh48CAOHjyImJgYDBw4sOifixBCFHmrUi4zx9ARGIbru5sNHYJBJG4dZOgQiEhHrHV88ttp4LZib/twy4BibSeTybBnzx706NEDwLNRA09PT4SEhGDKlCkAno0SuLm5Yf78+Rg1ahRSU1NRoUIFbNmyBX379gUA3Lt3D15eXti/fz86duyIy5cvo06dOoiMjETTpk0BAJGRkQgKCsKVK1dQq1atQsfIkQMiIqJiUKlUePz4sWRRqVRF3k9cXBwSEhLQoUMHdZtcLkerVq0QEREBAIiOjkZ2drakj6enJ+rVq6fuc+rUKSgUCnVhAADNmjWDQqFQ9yksFgdERGSySnJaQalUqs/tP1+USmWRY0hISAAAuLm5Sdrd3NzU6xISEmBlZQVHR8eX9nF1dc23f1dXV3WfwuLVCkREZLpKcLVCWFgYQkNDJW1yubz4obwwj0EI8cq5DS/20dS/MPt5EUcOiIjIZJVk5EAul8PBwUGyFKc4cHd3B4B8v+4TExPVownu7u7IyspCSkrKS/vcv38/3/6TkpLyjUq8CosDIiIyWfq6WuFlqlWrBnd3dxw+fFjdlpWVhWPHjqF58+YAgMDAQFhaWkr6xMfH49KlS+o+QUFBSE1NxZkzZ9R9Tp8+jdTUVHWfwuJpBSIiMln6ukNiWloarl+/rn4dFxeHmJgYODk5oXLlyggJCUF4eDh8fHzg4+OD8PBw2NraYsCAZ1dEKBQKDBs2DBMmTICzszOcnJwwceJE+Pn5oV27dgAAX19fdOrUCSNGjMDq1asBACNHjkTXrl2LdKUCwOKAiIhI586ePYs2bdqoXz+fqzB48GBs3LgRkydPRkZGBsaMGYOUlBQ0bdoUhw4dgr29vXqbRYsWwcLCAn369EFGRgaCg4OxceNGmJubq/t88803+OCDD9RXNXTv3r3Aeyu8DO9zYER4nwMiMja6vs+B67Bdxd42cX0fLUZSunDkgIiITBYfvKQZiwMiIjJZLA40Y3FAREQmi8WBZiwOiIjIZLE40Iz3OSAiIiIJjhwQEZHp4sCBRiwOiIjIZPG0gmYsDoiIyGSxONCMxQEREZksFgeacUIiERERSXDkgIiITBcHDjTiyIEW7dz+DTp3aIvGAX7o17snzkWfNXRIhda8tit2TmqDqyt64fGOQejSyCtfn7Be/ri6ohfubx6An2d0QO1KCvU6RzsrfD6kCaIXvomETQMQu+xtLBjcGA42lpJ9+Fd1wt6p7XB7fT/cXNsXS0Y0g528bNaoZfl4lwTzZt7GpDQ8srk0YnGgJQcP7MeCeUqMGDkaO7/bi4YNAzFm1AjE37tn6NAKxc7aApdupWDihjMa14d0r4uxb/hi4oYzaD11PxIfZeCHqe1R7n9PRXF3tIW7ow2mbY1G0OR9GL3yJNo1qIhl7///M8TdHW2w75P2uHH/Cdp+sh89lb/Ct1J5rBzTQi85alNZP97FxbyZt7HlzeJAMxYHWrJl0wa89fbb6NmrN6p7e2Ny2DS4e7hj187thg6tUA7H3MOnu2LwY9RtjevHdPbFF3sv4seo27h89xFGrTgJG7kFereoBgC4fPcRBi46hoPn7iLufhr+iE3AnB3n0blhJZibPfuPqFPDSsjOycOEr0/jevxjnLvxABO+Po0eTaugupu9xvctrcr68S4u5s28jS1vFgeasTjQguysLFz+KxZBzV+TtAc1b4ELMecNFJX2VHUtB3dHWxz5M17dlpWTh5OX76NpTdcCt3OwtcSTjGzk5j17KrjcwhxZuXn470PCM7JyAQDNahe8n9LG2I93QZg38waML28WB5qxONCClEcpyM3NhbOzs6Td2dkFyclJBopKe1zL2wAAElMzJO2JqRlw+9+6FzmVk2Nyz/rY8Os1ddux2Hi4KWzwQde6sDQ3Q3k7K8zsFwAAcC9gP6WRsR/vgjBv5g0Yf970TKkuDu7cuYOhQ4e+tI9KpcLjx48li0ql0lOEUi9WkkIIo6ou//uLHwBkkEG82AjA3sYS305pi6v/pkK5+4K6/crdVLy/8iTGd62D+5sH4O9VvXEzMQ33H2WoRxfKEmM/3gVh3s8wbyMhK8FixEp1cfDw4UNs2rTppX2USiUUCoVk+Xy+Uk8RPuNY3hHm5uZITk6WtD98+ADOzi56jUUXEh89GzF4cZSggsIaiamZkrZy1hb4PiwYaZk5GPDlUeTkSr/0vz0ZB5/3v0WtMd+h6vCdUH53AS4OctxKTNNtElpk7Me7IMybeQPGlzdPK2hm0GvI9u3b99L1N27ceOU+wsLCEBoaKmkT5vISxVVUllZW8K1TF5ERJxHcrr26PTIiAq3bBus1Fl24mZiGhJSnaOPngT9vPgQAWJqboYWvG2Zui1b3s7exxJ6wdlDl5KLf50egys4rcJ9J/ysq3m1dA5lZuTh6sezMfjb2410Q5s28AePL29i/5IvLoMVBjx49IJNpHpp+7lUHTi6XQy6XFgOZOVoJr0gGDn4P0z6ejDr16sHfPwC7v92J+Ph49O7bT//BFIOd3ALV3f//ioGqruXgV8URKWlZuPsgHSsOXMaEHn74J+Ex/ol/golv+SFDlYNvT8YBeDZisHdqO9hYWWDE8uOwt7GE/f/ucZD8WIW8/x3jkR1r4fTVJKSrstHGzxOfvhOIWdvPIfVptv6TLoGyfryLi3kzb2PLm7WBZgYtDjw8PLB8+XL06NFD4/qYmBgEBgbqN6hi6tT5DaQ+SsGalSuQlJSIGj41sXzVGnh6VjR0aIUS4O2M/TM6ql8rBzUGAHxz7DpGr4zA4n2xsLGywMKhTVHeTo6z15PQI/xXpP2vEmtQ3RmNfSoAAC4s6SnZd73xu3E7KR0AEOjtgqm9GsDO2gLX7qUiZF0kdhx/9QhRaVPWj3dxMW/mbWx5c+RAM5l42c92HevevTsaNGiAOXPmaFx/4cIFBAQEIC+v4OFpTQwxclAauL672dAhGETi1kGGDoGIdMRaxz9hfSYdLPa2f3/eSYuRlC4GHTmYNGkS0tPTC1xfo0YNHD16VI8RERGRKeHAgWYGLQ5atmz50vV2dnZo1aqVnqIhIiJTw9MKmpXNJ94QERFpAWsDzVgcEBGRyTIzY3WgCYsDIiIyWRw50KxU3yGRiIiI9I8jB0REZLI4IVEzFgdERGSyWBtoxuKAiIhMFkcONGNxQEREJovFgWYsDoiIyGSxNtCMVysQERGRBEcOiIjIZPG0gmYsDoiIyGSxNtCMxQEREZksjhxoxuKAiIhMFmsDzVgcEBGRyeLIgWa8WoGIiIgkWBwQEZHJksmKvxRFTk4OPvnkE1SrVg02NjaoXr065syZg7y8PHUfIQRmzZoFT09P2NjYoHXr1oiNjZXsR6VSYfz48XBxcYGdnR26d++Ou3fvauOjkGBxQEREJksmkxV7KYr58+dj1apVWLZsGS5fvowFCxbg888/x9KlS9V9FixYgIULF2LZsmWIioqCu7s72rdvjydPnqj7hISEYM+ePdixYwdOnDiBtLQ0dO3aFbm5uVr7TABAJoQQWt1jKZCZY+gISJ8cuy40dAgGkfJTqKFDINI5ax3PjGs271ixt438uFWh+3bt2hVubm5Yv369uu3tt9+Gra0ttmzZAiEEPD09ERISgilTpgB4Nkrg5uaG+fPnY9SoUUhNTUWFChWwZcsW9O3bFwBw7949eHl5Yf/+/ejYsWOxc3kRRw6IiMhklWTkQKVS4fHjx5JFpVJpfJ/XXnsNv/32G65duwYAuHDhAk6cOIE33ngDABAXF4eEhAR06NBBvY1cLkerVq0QEREBAIiOjkZ2drakj6enJ+rVq6fuoy0sDoiIyGSVZM6BUqmEQqGQLEqlUuP7TJkyBf3790ft2rVhaWmJgIAAhISEoH///gCAhIQEAICbm5tkOzc3N/W6hIQEWFlZwdHRscA+2sJLGYmIiIohLCwMoaHS03tyuVxj3507d2Lr1q3Ytm0b6tati5iYGISEhMDT0xODBw9W93txLoMQ4pXzGwrTp6hYHBARkckqyZeqXC4vsBh40aRJk/Dxxx+jX79+AAA/Pz/cunULSqUSgwcPhru7O4BnowMeHh7q7RITE9WjCe7u7sjKykJKSopk9CAxMRHNmzcvdh6a8LQCERGZLH1dyvj06VOYmUm/cs3NzdWXMlarVg3u7u44fPiwen1WVhaOHTum/uIPDAyEpaWlpE98fDwuXbqk9eKAIwdERGSy9HWHxG7dumHu3LmoXLky6tati/Pnz2PhwoUYOnSoOo6QkBCEh4fDx8cHPj4+CA8Ph62tLQYMGAAAUCgUGDZsGCZMmABnZ2c4OTlh4sSJ8PPzQ7t27bQaL4sDIiIyWfoqDpYuXYrp06djzJgxSExMhKenJ0aNGoUZM2ao+0yePBkZGRkYM2YMUlJS0LRpUxw6dAj29vbqPosWLYKFhQX69OmDjIwMBAcHY+PGjTA3N9dqvLzPAZV5vM8BkfHS9X0OWi06Wextj33UQouRlC6cc0BEREQSPK1AREQmi09l1IzFARERmSzWBpqxOCAiIpPFkQPNWBwQEZHJYm2gGYsDIiIyWWasDjTi1QpEREQkwZEDIiIyWRw40IzFARERmSxOSNSMxQEREZksM9YGGrE4ICIik8WRA81YHBARkclibaAZr1bQop3bv0HnDm3ROMAP/Xr3xLnos4YOSS/Kct4t6lXEd7PexI1vRiLjYCi6BXlL1r/Zogb2ze2JOztHI+NgKOpXr5BvH1aW5lg4ug3u7ByN5L3j8e2sN1HRpZykz+R+TXB0YT882Dse8d+N0WlOulaWj3dJMG/TytvUsTjQkoMH9mPBPCVGjByNnd/tRcOGgRgzagTi790zdGg6VdbztrO2xMW4JHy04ojG9bbWljgVew/TNxwvcB+fj2qN7s1rYNC8nxE8YQfKWVti9+weMPvPyUwrC3N8f/wa1v58Qes56FNZP97FxbyNN29ZCf4xZiwOtGTLpg146+230bNXb1T39sbksGlw93DHrp3bDR2aTpX1vA+dvYnZmyLww8nrGtdv/+0ylNsiceT8bY3rHWytMKRjPXy89hiOnr+NC/8kYeiCA6hX1QVtAyqr+3229RSW7jmHSzeTdZKHvpT1411czNt48zaTFX8xZiwOtCA7KwuX/4pFUPPXJO1BzVvgQsx5A0Wle6aa938F+LjBytIcv567pW6Lf5iO2FsP0MzX04CRaZ+pHm/mbdx5y2SyYi/GzODFQUZGBk6cOIG//vor37rMzExs3rz5pdurVCo8fvxYsqhUKl2Fq1HKoxTk5ubC2dlZ0u7s7ILk5CS9xqJPppr3f7k72kGVlYNHadK/ucSUdLg52RkoKt0w1ePNvI07b5ms+IsxM2hxcO3aNfj6+uL111+Hn58fWrdujfj4ePX61NRUvPfeey/dh1KphEKhkCyfz1fqOnSNXqwkhRBGX10Cppv3y8hkMgghDB2GTpjq8Wbezxhb3mYyWbEXY2bQ4mDKlCnw8/NDYmIirl69CgcHB7Ro0QK3b2s+v6tJWFgYUlNTJcukKWE6jDo/x/KOMDc3R3Ky9Hzyw4cP4OzsotdY9MlU8/6vhJR0yK0sUL6cXNJeobwtElOeGigq3TDV4828TStvesagxUFERATCw8Ph4uKCGjVqYN++fejcuTNatmyJGzduFGofcrkcDg4OkkUul796Qy2ytLKCb526iIw4KWmPjIiAf4MAvcaiT6aa93+d//s+srJzERxQRd3m7mSHulWcEXnZeGZ0A6Z7vJm3cefN0wqaGfQmSBkZGbCwkIawfPlymJmZoVWrVti2bZuBIiu6gYPfw7SPJ6NOvXrw9w/A7m93Ij4+Hr379jN0aDpV1vO2s7aEt2d59euq7grUr14BKU8ycSfpCRzLWcPL1R4ezs/uW1CzkiMA4H5KOu6nPMXjp1nY+MslzBvZCg+eZCDlSSaUw1vh0s1kyRUOXhXs4WhvDa8KDjA3M1PfL+Gfe4+Qnpmtv4RLqKwf7+Ji3sabtzGdItEmgxYHtWvXxtmzZ+Hr6ytpX7p0KYQQ6N69u4EiK7pOnd9A6qMUrFm5AklJiajhUxPLV62Bp2dFQ4emU2U974Y13XBoQR/16wWjWgMAthyOxcgvf0GXoOpYO6GTev2WqV0BPLs0ce7WUwCAyat/R25uHrZO7QobKwscjbmNkTMPIi/v/+ccTB/UHAPb11W/Pr1iIACgw+RdOP7nXZ3lp21l/XgXF/M23rxZG2gmEwacNaVUKnH8+HHs379f4/oxY8Zg1apVyMvLK9J+M3O0ER2VFY5dFxo6BINI+SnU0CEQ6Zy1jn/C9t1U/Msydw42ntMrLzJocaArLA5MC4sDIuOl6+KgXwmKgx1GXBwY/D4HREREVLrwqYxERGSyOCFRMxYHRERksoz9GQnFxeKAiIhMFkcONGNxQEREJou1gWYsDoiIyGRx5ECzYl2tsGXLFrRo0QKenp64devZo2oXL16MH374QavBERERkf4VuThYuXIlQkND8cYbb+DRo0fIzc0FAJQvXx6LFy/WdnxEREQ6YyYr/mLMilwcLF26FGvXrsW0adNgbm6ubm/UqBEuXryo1eCIiIh0SSaTFXsxZkWecxAXF4eAgPx3hZLL5UhPT9dKUERERPpg3F/xxVfkkYNq1aohJiYmX/uBAwdQp04dbcRERESkF2YyWbEXY1bkkYNJkyZh7NixyMzMhBACZ86cwfbt26FUKrFu3TpdxEhERER6VOTi4L333kNOTg4mT56Mp0+fYsCAAahYsSKWLFmCfv2M5xnfRERk/Ix8AKDYinWfgxEjRmDEiBFITk5GXl4eXF1dtR0XERGRzhn7xMLiKtFNkFxcXLQVBxERkd6xNtCsyMVBtWrVXlpp3bhxo0QBERER6YuxTywsriJfrRASEoIPP/xQvYwZMwZBQUFITU3FyJEjdREjERGRTshkxV+K6t9//8W7774LZ2dn2NraokGDBoiOjlavF0Jg1qxZ8PT0hI2NDVq3bo3Y2FjJPlQqFcaPHw8XFxfY2dmhe/fuuHv3bkk/hnyKPHLw4Ycfamxfvnw5zp49W+KAiIiIjE1KSgpatGiBNm3a4MCBA3B1dcU///yD8uXLq/ssWLAACxcuxMaNG1GzZk189tlnaN++Pa5evQp7e3sAz36g//jjj9ixYwecnZ0xYcIEdO3aFdHR0ZIbE5aUTAghtLGjGzduoEGDBnj8+LE2dlcimTmGjoD0ybHrQkOHYBApP4UaOgQinbPW8eMBx+65XOxtl7/lW+i+H3/8MU6ePInjx49rXC+EgKenJ0JCQjBlyhQAz0YJ3NzcMH/+fIwaNQqpqamoUKECtmzZgr59+wIA7t27By8vL+zfvx8dO3Ysdi4v0trH/t1338HJyUlbuyMqNFP9knTssczQIRhEyt5xhg6BjEixnj74PyqVCiqVStIml8shl8vz9d23bx86duyI3r1749ixY6hYsSLGjBmDESNGAHh29+GEhAR06NBBsq9WrVohIiICo0aNQnR0NLKzsyV9PD09Ua9ePURERBi2OAgICJBMSBRCICEhAUlJSVixYoXWAiMiItK1klzKqFQqMXv2bEnbzJkzMWvWrHx9b9y4oX5w4dSpU3HmzBl88MEHkMvlGDRoEBISEgAAbm5uku3c3NzUTz9OSEiAlZUVHB0d8/V5vr22FLk46NGjh+S1mZkZKlSogNatW6N27draiouIiEjnSvJ0xbCwMISGSkcuNY0aAEBeXh4aNWqE8PBwAM9+aMfGxmLlypUYNGiQut+LxYoQ4pUFTGH6FFWRioOcnBxUrVoVHTt2hLu7u1YDISIi0reSFAcFnULQxMPDI9/zh3x9fbF7924AUH+nJiQkwMPDQ90nMTFRPZrg7u6OrKwspKSkSEYPEhMT0bx58+InokGRTrdYWFhg9OjR+c6xEBERUcFatGiBq1evStquXbuGKlWqAHh2DyF3d3ccPnxYvT4rKwvHjh1Tf/EHBgbC0tJS0ic+Ph6XLl3SenFQ5NMKTZs2xfnz59UJERERlVX6un3yRx99hObNmyM8PBx9+vTBmTNnsGbNGqxZs0YdR0hICMLDw+Hj4wMfHx+Eh4fD1tYWAwYMAAAoFAoMGzYMEyZMgLOzM5ycnDBx4kT4+fmhXbt2Wo23yMXBmDFjMGHCBNy9exeBgYGws7OTrK9fv77WgiMiItKlkpxWKIrGjRtjz549CAsLw5w5c1CtWjUsXrwY77zzjrrP5MmTkZGRgTFjxiAlJQVNmzbFoUOH1Pc4AIBFixbBwsICffr0QUZGBoKDg7Fx40at3uMAKMJ9DoYOHYrFixdLbtig3olMpp4QkZubq9UAi4P3OSBTwEsZyRTo+j4Hk3+++upOBVjQpZYWIyldCv2xb9q0CfPmzUNcXJwu4yEiItIbPltBs0IXB88HGDjXgIiIjEVJboJkzIr0ufC510RERMavSGdzatas+coC4eHDhyUKiIiISF/4m1ezIhUHs2fPhkKh0FUsREREesU5B5oVqTjo168fXF1ddRULERGRXrE20KzQxQHnGxARkbHR130OypoiX61ARERkLHhaQbNCFwd5eXm6jIOIiIhKCR3fe4qIiKj04sCBZiwOiIjIZHHOgWYsDoiIyGTJwOpAE945Uot2bv8GnTu0ReMAP/Tr3RPnos8aOiS9YN5lL+8WdT3x3YwuuLHpPWT8NA7dmlXL12fagCa4sek9PNz9Pn5RvgXfyk6S9W7lbbE+tB3itryH5O9GIWJxH7zVwjvffjo1qoI/vuyFh7vfx51vhmHH1M46y0uXyvLxLgljz9tMVvzFmLE40JKDB/ZjwTwlRowcjZ3f7UXDhoEYM2oE4u/dM3RoOsW8y2bedtYWuHgjGR+tOqZx/YS3G+KDHg3w0apjeC10F+6npOPnT99EORtLdZ/1E9qhZiVH9P70ZzQaux0/nLqBLZM7wr+6i7pPj+beWD+hPTb/ehlNxu9A28m7sfPYNZ3np21l/XgXlynkzeJAMxYHWrJl0wa89fbb6NmrN6p7e2Ny2DS4e7hj187thg5Np5h32cz7UPRtzN56Gj+cuqFx/dg3/bFg51n8cOoG/rr1EMMX/gobuQX6tqqp7tO0tjtW/Pgnzl5LxM37jzF/51k8Ss9CA+8KAABzMxm+GNkSU78+iXUHYnH93iP8/e8j7Dn5j15y1KayfryLy1TzJhYHWpGdlYXLf8UiqPlrkvag5i1wIea8gaLSPeZtnHlXdXOAh5Mdfj1/W92WlZOH45f+RTNfD3VbxF/x6NXSB47l5JDJgN6v+0BuaYY/Lv4LAAioUQEVXcohTwCnlvTFjc3vYe+sbvlOT5R2xn68C2IqectksmIvxszgExIvX76MyMhIBAUFoXbt2rhy5QqWLFkClUqFd999F23btn3p9iqVCiqVStImzOWQy+W6DFsi5VEKcnNz4ezsLGl3dnZBcnKS3uLQN+ZtnHm7O9oCABIfZUjaEx9loLKrvfr1wPm/YMuUjri3YwSyc3LxVJWDvnMPIC7hMQCgmvuz57B8MqAxpqw7iVv3H+PDtwJwSPkW6o/aipQ06X+3pZWxH++CmErexn56oLgMOnJw8OBBNGjQABMnTkRAQAAOHjyI119/HdevX8ft27fRsWNHHDly5KX7UCqVUCgUkuXz+Uo9ZSD1YiUphDD66hJg3s8ZW94v3hVVJpO2zRrYDI7l5Og8bS9afLQLX+2NwTcfd0LdKs++TJ7feW7+zmjsjfgH5/9JwsjFv0IA6PlaDb3loS3GfrwLYux5y2TFX4yZQYuDOXPmYNKkSXjw4AE2bNiAAQMGYMSIETh8+DB+/fVXTJ48GfPmzXvpPsLCwpCamipZJk0J01MGzziWd4S5uTmSk5Ml7Q8fPoCzs0sBW5V9zNs4805IeQoAcPvfCMJzFRQ26tGEau4OGN2tPkYtOYLfL9zFxbgHCN8ehXPXEzGqqx8AIP5hOgDgyp3/f4x7Vk4ebiakwquCPcoKYz/eBTGVvM1ksmIvxsygxUFsbCyGDBkCAOjTpw+ePHmCt99+W72+f//++PPPP1+6D7lcDgcHB8miz1MKAGBpZQXfOnURGXFS0h4ZEQH/BgF6jUWfmLdx5n3z/mPEP0xHcICXus3Swgwt61VE5OV4AICt/NlVC3l50tGF3Dyh/p/m+euJyMzKgU/F8ur1FuZmqOzqgNuJT3SchfYY+/EuiKnkzasVNDP4nIPnzMzMYG1tjfLly6vb7O3tkZqaarigimDg4Pcw7ePJqFOvHvz9A7D7252Ij49H7779DB2aTjHvspm3nbUlvD0U6tdV3RxQv5oLUtIycScpDct/uIBJvRvh+r1UXL/3CJN7N0KGKkd9GeLVuym4fu8Rlo1rjbCvT+LB40x0D6qO4AZe6DnnJwDAk4xsrDtwCdPfaYq7yWm4nfgEH/V89qXy/Ynr+k+6BMr68S4uU82bDFwcVK1aFdevX0eNGs/OP546dQqVK1dWr79z5w48PDwK2rxU6dT5DaQ+SsGalSuQlJSIGj41sXzVGnh6VjR0aDrFvMtm3g19XHFI+Zb69YIRLQEAW369jJGLf8OXu8/BWm6BxaNbwbGcHFFX76PrjB+QlpENAMjJzUOPWT/is8HN8d30rihnY4l/4lMxfNGv+OXsLfV+w76OQE6uwPrQ9rCRWyDqagI6T9uLR+llYzLic2X9eBeXKeRt5GcHik0mDPgs5lWrVsHLywtdunTRuH7atGm4f/8+1q1bV6T9ZuZoIzqi0s2xxzJDh2AQKXvHGToE0iNrHf+EXX7yZrG3HduiqtbiKG0MOnLw/vvvv3T93Llz9RQJERGZIo4caFZq5hwQERHpm7FPLCwuFgdERGSyjP2SxOLi7ZOJiIhIgiMHRERksjhwoBmLAyIiMlk8raAZiwMiIjJZrA00Y3FAREQmixPvNGNxQEREJsuYnjCpTSyaiIiISIIjB0REZLI4bqAZiwMiIjJZvFpBMxYHRERkslgaaMbigIiITBYHDjRjcUBERCaLVytoxqsViIiISIIjB0REZLL4C1kzfi5ERGSyZDJZsZfiUiqVkMlkCAkJUbcJITBr1ix4enrCxsYGrVu3RmxsrGQ7lUqF8ePHw8XFBXZ2dujevTvu3r1b7DhehsUBERGZLFkJluKIiorCmjVrUL9+fUn7ggULsHDhQixbtgxRUVFwd3dH+/bt8eTJE3WfkJAQ7NmzBzt27MCJEyeQlpaGrl27Ijc3t5jRFIzFARERmSx9jhykpaXhnXfewdq1a+Ho6KhuF0Jg8eLFmDZtGnr27Il69eph06ZNePr0KbZt2wYASE1Nxfr16/Hll1+iXbt2CAgIwNatW3Hx4kX8+uuvWvs8nuOcA6IyKmXvOEOHYBCObWYYOgSDSDk6x9AhGKWS/EJWqVRQqVSSNrlcDrlcrrH/2LFj0aVLF7Rr1w6fffaZuj0uLg4JCQno0KGDZD+tWrVCREQERo0ahejoaGRnZ0v6eHp6ol69eoiIiEDHjh1LkEl+HDkgIiIqBqVSCYVCIVmUSqXGvjt27MC5c+c0rk9ISAAAuLm5Sdrd3NzU6xISEmBlZSUZcXixjzZx5ICIiExWSSYWhoWFITQ0VNKmadTgzp07+PDDD3Ho0CFYW1sXOhYhxCvjK0yf4uDIARERmaySTEiUy+VwcHCQLJqKg+joaCQmJiIwMBAWFhawsLDAsWPH8NVXX8HCwkI9YvDiCEBiYqJ6nbu7O7KyspCSklJgH21icUBERCZLJiv+UljBwcG4ePEiYmJi1EujRo3wzjvvICYmBtWrV4e7uzsOHz6s3iYrKwvHjh1D8+bNAQCBgYGwtLSU9ImPj8elS5fUfbSJpxWIiMhkmenh0Uv29vaoV6+epM3Ozg7Ozs7q9pCQEISHh8PHxwc+Pj4IDw+Hra0tBgwYAABQKBQYNmwYJkyYAGdnZzg5OWHixInw8/NDu3bttB4ziwMiIjJZpeXRCpMnT0ZGRgbGjBmDlJQUNG3aFIcOHYK9vb26z6JFi2BhYYE+ffogIyMDwcHB2LhxI8zNzbUej0wIIbS+VwPLzDF0BESkK7yU0bRY6/gn7E+X7hd72671tH+uv7TgyAEREZksmR5OK5RFLA6IiMhklZbTCqUNiwMiIjJZ+piQWBaxOCAiIpPFkQPNWBwQEZHJYnGgGW+CRERERBIcOSAiIpPFqxU0Y3FAREQmy4y1gUYsDoiIyGRx5EAzFgdERGSyOCFRM05IJCIiIgmOHBARkcniaQXNOHKgRTu3f4POHdqicYAf+vXuiXPRZw0dkl4wb+ZdVrTwr4Lv5r2DG3smIuP4HHRrWVuy/s3XfbHvy0G48+MUZByfg/o13F+6v72fD9S4nxpeztgV3h93fpyC+wen4siK4Xg9oJrW89GHsny8C8NMVvzFmLE40JKDB/ZjwTwlRowcjZ3f7UXDhoEYM2oE4u/dM3RoOsW8mXdZytvO2goXryfgo0U/a1xva2OFUxdvY/rqw6/c1/g+QSjoobZ75r8LCwtzdA7ZiObDV+HC3/H4fv47cHMqV6L49a2sH+/CkJXgH2PG4kBLtmzagLfefhs9e/VGdW9vTA6bBncPd+zaud3QoekU82beZSnvQ6f/xux1v+GHPy5rXL/9lwtQbvwdR87eeOl+/Lzd8EGf5nh/3t5865wVtqjh5Ywvtx7HpX/u45+7DzF91WHY2VjBt5qrNtLQm7J+vAtDJiv+YsxKXXFQUCVemmVnZeHyX7EIav6apD2oeQtciDlvoKh0j3kzb8D4836RjdwSm2b1xkeLf8b9h2n51j9IfYrLNxMxoJM/bK0tYW5uhuFvNkbCgyc4f7Xs/OI2leMtK8FizErdhES5XI4LFy7A19fX0KEUWsqjFOTm5sLZ2VnS7uzsguTkJANFpXvMm3kDxp/3ixaM74TIS3fw04krBfbp+tEm7FIOQNIv05CXJ5CYko43J25BalqmHiMtGR5v02aw4iA0NFRje25uLubNm6f+g1y4cOFL96NSqaBSqSRtwlwOuVyunUCLQPbCOJMQIl+bMWLezzBv49elRS20blgdzYatfGm/xaFdkZSSjnbjvkaGKhtDugbi+/nv4LWRq5HwIP9oQ2lm7MfbzIhy0SaDFQeLFy+Gv78/ypcvL2kXQuDy5cuws7Mr1B+gUqnE7NmzJW3Tps/EJzNmaTHal3Ms7whzc3MkJydL2h8+fABnZxe9xaFvzJt5A8af93+1blgd1Ss6ImF/mKR9+6f9cPLPW+j4wQa0DqyON5rXgscbSjx5+uyHS8jCnxDcyBvvdgrAF98cN0ToRWYqx5ulgWYGKw7mzp2LtWvX4ssvv0Tbtm3V7ZaWlti4cSPq1KlTqP2EhYXlG4UQ5vodNbC0soJvnbqIjDiJ4Hbt1e2RERFo3TZYr7HoE/Nm3oDx5/1fX3xzHBt+ipa0RW8eh8lLD+DniKsAAFu5JQAg74X5U3lCQFaGrn8zmeNddg6JXhmsOAgLC0O7du3w7rvvolu3blAqlbC0tCzyfuTy/KcQMnO0FWXhDRz8HqZ9PBl16tWDv38Adn+7E/Hx8ejdt5/+g9Ej5s28y1LedjZW8K7opH5d1cMR9Wu4I+VxBu4kpsLR3gZebgp4uNgDAGpWfvYL+f7DNMnyojuJqbgV/wgAcDr2DlKeZGDd1LcQvvF3ZGTlYGi3QFT1KI+D/ysgyoqyfrwLw9gvSSwug05IbNy4MaKjozF27Fg0atQIW7duLbPnsjp1fgOpj1KwZuUKJCUlooZPTSxftQaenhUNHZpOMW/mXZbybljLE4eWDlW/XjC+MwBgy4HzGBm+B11eq4W1U3uq12+Z3QcA8NnXRzF3w9FCvceD1Kd4c+IWzBrZDgeWvAdLCzNcjktC77DtuPjPfS1mo3tl/XgXRhn9ytE5mSgl1w7u2LEDISEhSEpKwsWLFwt9WkETQ4wcEJF+OLaZYegQDCLl6BxDh2AQ1jr+CXvmRmqxt21SXaHFSEqXUnMpY79+/fDaa68hOjoaVapUMXQ4RERkAjhwoFmpKQ4AoFKlSqhUqZKhwyAiIlPB6kCjUlUcEBER6RMnJGrG4oCIiEwWJyRqxuKAiIhMFmsDzUrdg5eIiIjIsDhyQEREpotDBxqxOCAiIpPFCYmasTggIiKTxQmJmrE4ICIik8XaQDMWB0REZLpYHWjEqxWIiIhIgiMHRERksjghUTMWB0REZLI4IVEzFgdERGSyWBtoxuKAiIhMF6sDjVgcEBGRyeKcA814tQIREZGOKZVKNG7cGPb29nB1dUWPHj1w9epVSR8hBGbNmgVPT0/Y2NigdevWiI2NlfRRqVQYP348XFxcYGdnh+7du+Pu3btaj5fFARERmSyZrPhLURw7dgxjx45FZGQkDh8+jJycHHTo0AHp6enqPgsWLMDChQuxbNkyREVFwd3dHe3bt8eTJ0/UfUJCQrBnzx7s2LEDJ06cQFpaGrp27Yrc3FxtfSQAAJkQQmh1j6VAZo6hIyAiXXFsM8PQIRhEytE5hg7BIKx1fPL78r30V3cqgK+nXbG3TUpKgqurK44dO4bXX38dQgh4enoiJCQEU6ZMAfBslMDNzQ3z58/HqFGjkJqaigoVKmDLli3o27cvAODevXvw8vLC/v370bFjx2LH8yLOOSCiMsVUvyQrDd9h6BAMInljP92+QQmmHKhUKqhUKkmbXC6HXC5/5bapqakAACcnJwBAXFwcEhIS0KFDB8m+WrVqhYiICIwaNQrR0dHIzs6W9PH09ES9evUQERGh1eKApxWIiMhkyUrwj1KphEKhkCxKpfKV7ymEQGhoKF577TXUq1cPAJCQkAAAcHNzk/R1c3NTr0tISICVlRUcHR0L7KMtHDkgIiKTVZKbIIWFhSE0NFTSVphRg3HjxuHPP//EiRMnNMQjDUgIka/tRYXpU1QcOSAiIioGuVwOBwcHyfKq4mD8+PHYt28fjh49ikqVKqnb3d3dASDfCEBiYqJ6NMHd3R1ZWVlISUkpsI+2sDggIiKTJSvBUhRCCIwbNw7ff/89jhw5gmrVqknWV6tWDe7u7jh8+LC6LSsrC8eOHUPz5s0BAIGBgbC0tJT0iY+Px6VLl9R9tIWnFYiIyHTp6R5IY8eOxbZt2/DDDz/A3t5ePUKgUChgY2MDmUyGkJAQhIeHw8fHBz4+PggPD4etrS0GDBig7jts2DBMmDABzs7OcHJywsSJE+Hn54d27dppNV4WB0REZLL0dYfElStXAgBat24tad+wYQOGDBkCAJg8eTIyMjIwZswYpKSkoGnTpjh06BDs7e3V/RctWgQLCwv06dMHGRkZCA4OxsaNG2Fubq7VeHmfAyKiMoCXMurG9cSMYm9bw9VGi5GULhw5ICIik8UnK2jGCYlEREQkwZEDIiIyXRw60IjFARERmSw+slkzFgdERGSytHxjQaPB4oCIiEwWawPNWBwQEZHpYnWgEa9WICIiIgmOHBARkcnihETNWBwQEZHJ4oREzVgcEBGRyWJtoBmLAyIiMlkcOdCMxQEREZkwVgea8GoFLdq5/Rt07tAWjQP80K93T5yLPmvokPSCeTNvU2BseZeztsBnAwJw/otuuLOmF/ZPa4eAak7q9RUc5Fg6vCkuLXoTt1f3ws4JrVDdrZxkH1YWZlC+2xBXl76FW6t7YeuHLeHhaLxPKjQlLA605OCB/VgwT4kRI0dj53d70bBhIMaMGoH4e/cMHZpOMW/mzbzLpsXvNUHruu4YsyYSr39yEL/HJmD3pNZwL//sy33zBy1RtYIdBn51HG1n/oK7yenYPakNbK3M1fuYOyAAXRpWwoiVEeg691fYyS2w7aPXYVaGxuplsuIvxozFgZZs2bQBb739Nnr26o3q3t6YHDYN7h7u2LVzu6FD0ynmzbyZd9ljbWmOro0qYfauGJy6loS4xDQs2HsJt5LT8V7bGvB2s0fjGi6YuOkszsc9xPWEJ5i0ORp21hbo2awKAMDexhLvvF4dM3acxx9/3cfF248wes0p1KmkQKu6bgbOsPBkJViMGYsDLcjOysLlv2IR1Pw1SXtQ8xa4EHPeQFHpHvNm3gDzLosszGWwMDdDZlaepD0zKxfNalaAleWzrwZV9v+vzxMC2Tl5aFqzAgCgQVVHWFmY4+ilBHWfhEeZuHw3FU1quOghC+3gyIFmpWpCYkpKCjZt2oS///4bHh4eGDx4MLy8vF66jUqlgkqlkrQJcznkcrkuQ5VIeZSC3NxcODs7S9qdnV2QnJyktzj0jXkzb4B5l0VpmTk483cyJr5ZF3/HpyIxVYW3m1VGYHVn3Lj/BH/HP8bt5HR80rs+JmyMwlNVLkZ3qgW38jZwU1gDAFwVNlBl5yL1abZk30mPVXD9X5+ygDdB0sygIweenp548OABACAuLg516tTB/Pnz8ffff2P16tXw8/PDlStXXroPpVIJhUIhWT6fr9RH+PnIXiglhRD52owR836GeRs3Y8t7zJpIyABcWtwD99b1xoj2NbE78hZy8wRycgXeW3oC3u72+GfF27izphda1HbF4Qv3kJsnXrpfGYCX9yhleF5BI4OOHCQkJCA3NxcAMHXqVNSuXRs///wzbG1toVKp0KtXL0yfPh3ffvttgfsICwtDaGiopE2Y62/UAAAcyzvC3NwcycnJkvaHDx/A2bnsDK8VFfNm3gDzLqtuJqWh+7wjsLUyh72NJe6nZmLd6Oa4nZwOALhwKwVtZvwCextLWFmY4cETFX6Z3h4xNx8CABJTMyC3NIfC1lIyeuDiIEfU9WSN70llR6mZc3D69GlMnz4dtra2AAC5XI5PPvkEkZGRL91OLpfDwcFBsujzlAIAWFpZwbdOXURGnJS0R0ZEwL9BgF5j0SfmzbwB5l3WPc3Kxf3UTChsLdHGzx0Hzv0rWf8kIxsPnqhQ3a0cGlRzVK+PuZmCrJxctK7rru7rprCGbyUFzpSh4oADB5oZfM7B82E5lUoFNzfpDFc3NzckJZWNc3oDB7+HaR9PRp169eDvH4Dd3+5EfHw8evftZ+jQdIp5M2/mXTa1qecOmQy4Hv8E1dzKYVbfBrge/wTbTtwAAHRv7IUHT1S4+yAddSqVx9x3GmL/uX/xe+yzCYhPMrLxzR83MKdfAB6mZeFRugqz+wXgr7upOBZ735CpFUkZPjOkUwYvDoKDg2FhYYHHjx/j2rVrqFu3rnrd7du34eJSNobtOnV+A6mPUrBm5QokJSWihk9NLF+1Bp6eFQ0dmk4xb+bNvMsmBxtLfNLbH56ONniUnoUfz97B3N0XkZP7bMaAm8Ian/YLQAWFHPcfZWJnxE18+UOsZB+fbD+PnDyB9WObw9rSHMcv38e4xaeRJ8rOrANOSNRMJoThjuLs2bMlr5s1a4aOHTuqX0+aNAl3797F9u1Fu5Y4M0cr4RERlRqVhu8wdAgGkbxRt6MzSWnF/8KoUM7gv691xqDFga6wOCAiY8PiQEf7L0Fx4GLExUGpmZBIREREpYPxlj1ERESvwAmJmrE4ICIik8UJiZqxOCAiIpPFkQPNOOeAiIiIJDhyQEREJosjB5px5ICIiIgkOHJAREQmixMSNWNxQEREJounFTRjcUBERCaLtYFmLA6IiMh0sTrQiBMSiYiISIIjB0REZLI4IVEzFgdERGSyOCFRMxYHRERkslgbaMY5B0REZLpkJViKYcWKFahWrRqsra0RGBiI48ePlzQDnWBxQEREJktWgn+KaufOnQgJCcG0adNw/vx5tGzZEp07d8bt27d1kFnJsDggIiLSg4ULF2LYsGEYPnw4fH19sXjxYnh5eWHlypWGDi0fzjkgIiKTVZIJiSqVCiqVStIml8shl8vz9c3KykJ0dDQ+/vhjSXuHDh0QERFR/CB0RZDWZGZmipkzZ4rMzExDh6JXzJt5mwLmbVp5F8bMmTMFAMkyc+ZMjX3//fdfAUCcPHlS0j537lxRs2ZNPURbNDIhhDBodWJEHj9+DIVCgdTUVDg4OBg6HL1h3szbFDBv08q7MIoycnDv3j1UrFgRERERCAoKUrfPnTsXW7ZswZUrV3Qeb1HwtAIREVExFFQIaOLi4gJzc3MkJCRI2hMTE+Hm5qaL8EqEExKJiIh0zMrKCoGBgTh8+LCk/fDhw2jevLmBoioYRw6IiIj0IDQ0FAMHDkSjRo0QFBSENWvW4Pbt23j//fcNHVo+LA60SC6XY+bMmYUeZjIWzJt5mwLmbVp560Lfvn3x4MEDzJkzB/Hx8ahXrx7279+PKlWqGDq0fDghkYiIiCQ454CIiIgkWBwQERGRBIsDIiIikmBxQERERBIsDrSorDyKU1v++OMPdOvWDZ6enpDJZNi7d6+hQ9ILpVKJxo0bw97eHq6urujRoweuXr1q6LB0buXKlahfvz4cHBzg4OCAoKAgHDhwwNBh6Z1SqYRMJkNISIihQ9GpWbNmQSaTSRZ3d3dDh0V6wuJAS8rSozi1JT09Hf7+/li2bJmhQ9GrY8eOYezYsYiMjMThw4eRk5ODDh06ID093dCh6VSlSpUwb948nD17FmfPnkXbtm3x5ptvIjY21tCh6U1UVBTWrFmD+vXrGzoUvahbty7i4+PVy8WLFw0dEukJL2XUkqZNm6Jhw4aSR2/6+vqiR48eUCqVBoxMP2QyGfbs2YMePXoYOhS9S0pKgqurK44dO4bXX3/d0OHolZOTEz7//HMMGzbM0KHoXFpaGho2bIgVK1bgs88+Q4MGDbB48WJDh6Uzs2bNwt69exETE2PoUMgAOHKgBc8fxdmhQwdJe6l9FCdpVWpqKoBnX5SmIjc3Fzt27EB6errkITLGbOzYsejSpQvatWtn6FD05u+//4anpyeqVauGfv364caNG4YOifSEd0jUguTkZOTm5uZ7eIabm1u+h2yQcRFCIDQ0FK+99hrq1atn6HB07uLFiwgKCkJmZibKlSuHPXv2oE6dOoYOS+d27NiBc+fOISoqytCh6E3Tpk2xefNm1KxZE/fv38dnn32G5s2bIzY2Fs7OzoYOj3SMxYEWyWQyyWshRL42Mi7jxo3Dn3/+iRMnThg6FL2oVasWYmJi8OjRI+zevRuDBw/GsWPHjLpAuHPnDj788EMcOnQI1tbWhg5Hbzp37qz+dz8/PwQFBcHb2xubNm1CaGioASMjfWBxoAVl7VGcpB3jx4/Hvn378Mcff6BSpUqGDkcvrKysUKNGDQBAo0aNEBUVhSVLlmD16tUGjkx3oqOjkZiYiMDAQHVbbm4u/vjjDyxbtgwqlQrm5uYGjFA/7Ozs4Ofnh7///tvQoZAecM6BFpS1R3FSyQghMG7cOHz//fc4cuQIqlWrZuiQDEYIAZVKZegwdCo4OBgXL15ETEyMemnUqBHeeecdxMTEmERhAAAqlQqXL1+Gh4eHoUMhPeDIgZaUpUdxaktaWhquX7+ufh0XF4eYmBg4OTmhcuXKBoxMt8aOHYtt27bhhx9+gL29vXrESKFQwMbGxsDR6c7UqVPRuXNneHl54cmTJ9ixYwd+//13HDx40NCh6ZS9vX2++SR2dnZwdnY26nkmEydORLdu3VC5cmUkJibis88+w+PHjzF48GBDh0Z6wOJAS8rSozi15ezZs2jTpo369fPzkIMHD8bGjRsNFJXuPb9ctXXr1pL2DRs2YMiQIfoPSE/u37+PgQMHIj4+HgqFAvXr18fBgwfRvn17Q4dGOnD37l30798fycnJqFChApo1a4bIyEij/n8a/T/e54CIiIgkOOeAiIiIJFgcEBERkQSLAyIiIpJgcUBEREQSLA6IiIhIgsUBERERSbA4ICIiIgkWB0RERCTB4oCoDJg1axYaNGigfj1kyBD06NFD73HcvHkTMpkMMTExen9vItIfFgdEJTBkyBDIZDLIZDJYWlqievXqmDhxItLT03X6vkuWLCn0Lar5hU5ERcVnKxCVUKdOnbBhwwZkZ2fj+PHjGD58ONLT09XPYHguOzsblpaWWnlPhUKhlf0QEWnCkQOiEpLL5XB3d4eXlxcGDBiAd955B3v37lWfCvj6669RvXp1yOVyCCGQmpqKkSNHwtXVFQ4ODmjbti0uXLgg2ee8efPg5uYGe3t7DBs2DJmZmZL1L55WyMvLw/z581GjRg3I5XJUrlwZc+fOBQD1I6UDAgIgk8kkD4zasGEDfH19YW1tjdq1a2PFihWS9zlz5gwCAgJgbW2NRo0a4fz581r85IiotOLIAZGW2djYIDs7GwBw/fp17Nq1C7t374a5uTkAoEuXLnBycsL+/fuhUCiwevVqBAcH49q1a3BycsKuXbswc+ZMLF++HC1btsSWLVvw1VdfoXr16gW+Z1hYGNauXYtFixbhtddeQ3x8PK5cuQLg2Rd8kyZN8Ouvv6Ju3bqwsrICAKxduxYzZ87EsmXLEBAQgPPnz2PEiBGws7PD4MGDkZ6ejq5du6Jt27bYunUr4uLi8OGHH+r40yOiUkEQUbENHjxYvPnmm+rXp0+fFs7OzqJPnz5i5syZwtLSUiQmJqrX//bbb8LBwUFkZmZK9uPt7S1Wr14thBAiKChIvP/++5L1TZs2Ff7+/hrf9/Hjx0Iul4u1a9dqjDEuLk4AEOfPn5e0e3l5iW3btknaPv30UxEUFCSEEGL16tXCyclJpKenq9evXLlS476IyLjwtAJRCf30008oV64crK2tERQUhNdffx1Lly4FAFSpUgUVKlRQ942OjkZaWhqcnZ1Rrlw59RIXF4d//vkHAHD58mUEBQVJ3uPF1/91+fJlqFQqBAcHFzrmpKQk3LlzB8OGDZPE8dlnn0ni8Pf3h62tbaHiICLjwdMKRCXUpk0brFy5EpaWlvD09JRMOrSzs5P0zcvLg4eHB37//fd8+ylfvnyx3t/GxqbI2+Tl5QF4dmqhadOmknXPT38IIYoVDxGVfSwOiErIzs4ONWrUKFTfhg0bIiEhARYWFqhatarGPr6+voiMjMSgQYPUbZGRkQXu08fHBzY2Nvjtt98wfPjwfOufzzHIzc1Vt7m5uaFixYq4ceMG3nnnHY37rVOnDrZs2YKMjAx1AfKyOIjIePC0ApEetWvXDkFBQejRowd++eUX3Lx5ExEREfjkk09w9uxZAMCHH36Ir7/+Gl9//TWuXbuGmTNnIjY2tsB9WltbY8qUKZg8eTI2b96Mf/75B5GRkVi/fj0AwNXVFTY2Njh48CDu37+P1NRUAM9urKRUKrFkyRJcu3YNFy9exIYNG7Bw4UIAwIABA2BmZoZhw4bhr7/+wv79+/HFF1/o+BMiotKAxQGRHslkMuzfvx+vv/46hg4dipo1a6Jfv364efMm3NzcAAB9+/bFjBkzMGXKFAQGBuLWrVsYPXr0S/c7ffp0TJgwATNmzICvry/69u2LxMREAICFhQW++uorrF69Gp6ennjzzTcBAMOHD8e6deuwceNG+Pn5oVWrVti4caP60sdy5crhxx9/xF9//YWAgABMmzYN8+fP1+GnQ0SlhUzwxCIRERH9B0cOiIiISILFAREREUmwOCAiIiIJFgdEREQkweKAiIiIJFgcEBERkQSLAyIiIpJgcUBEREQSLA6IiIhIgsUBERERSbA4ICIiIon/A8XTiNqCsCKKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for LightGBM ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1209\n",
      "           1       1.00      1.00      1.00      1029\n",
      "           2       1.00      1.00      1.00      1101\n",
      "           3       1.00      1.00      1.00      1086\n",
      "           4       1.00      1.00      1.00      1148\n",
      "           5       1.00      1.00      1.00       990\n",
      "\n",
      "    accuracy                           1.00      6563\n",
      "   macro avg       1.00      1.00      1.00      6563\n",
      "weighted avg       1.00      1.00      1.00      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for DecisionTree ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      1209\n",
      "           1       1.00      1.00      1.00      1029\n",
      "           2       1.00      1.00      1.00      1101\n",
      "           3       1.00      1.00      1.00      1086\n",
      "           4       1.00      1.00      1.00      1148\n",
      "           5       1.00      1.00      1.00       990\n",
      "\n",
      "    accuracy                           1.00      6563\n",
      "   macro avg       1.00      1.00      1.00      6563\n",
      "weighted avg       1.00      1.00      1.00      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for MLP ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.99      1209\n",
      "           1       1.00      1.00      1.00      1029\n",
      "           2       1.00      1.00      1.00      1101\n",
      "           3       1.00      1.00      1.00      1086\n",
      "           4       1.00      1.00      1.00      1148\n",
      "           5       0.98      0.99      0.98       990\n",
      "\n",
      "    accuracy                           0.99      6563\n",
      "   macro avg       0.99      0.99      0.99      6563\n",
      "weighted avg       0.99      0.99      0.99      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAGHCAYAAAAk+fF+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABXjklEQVR4nO3deVwU9f8H8NdyLYeAHHIppiIeKCqiGR5Jghp5Znln3ncm3qJ5poCUV96a912aR6WkZlmGFJKUIl6JBwoCiqAIC8L8/vDnfhtZjWN3B3Zezx7zeHz3M5+Zfb93/LLv/cxnZhSCIAggIiIi+n9GUgdARERE5QuLAyIiIhJhcUBEREQiLA6IiIhIhMUBERERibA4ICIiIhEWB0RERCTC4oCIiIhEWBwQERGRCIsDKrG///4bgwcPRs2aNWFubo5KlSqhadOmiIiIwIMHD3T63ufOnUPbtm1ha2sLhUKBZcuWaf09FAoF5s6dq/X9/pctW7ZAoVBAoVDg559/LrJeEATUrl0bCoUC/v7+pXqP1atXY8uWLSXa5ueff35pTOVJWT4/hUKBjz766JX79/f3V+9foVDAwsICjRs3xrJly1BYWKjFTIikZyJ1AFSxbNiwAWPGjEHdunUxZcoUeHl5IT8/H2fPnsXatWtx5swZHDhwQGfvP2TIEGRnZ2PPnj2ws7NDjRo1tP4eZ86cQbVq1bS+3+KytrbGxo0bi3yBnTp1Cv/88w+sra1Lve/Vq1fD0dERgwYNKvY2TZs2xZkzZ+Dl5VXq99UnXX5+tWrVws6dOwEAqampWLt2LSZMmIDk5GQsWrSoLGETlSssDqjYzpw5g9GjR6N9+/Y4ePAglEqlel379u0xadIkREZG6jSGCxcuYPjw4QgKCtLZe7zxxhs623dx9O7dGzt37sSqVatgY2Ojbt+4cSP8/PyQlZWllzjy8/OhUChgY2Mj+WdSErr8/CwsLESfRVBQEOrVq4eVK1diwYIFMDU1LVPsROUFTytQsYWGhkKhUGD9+vWiwuA5MzMzdO3aVf26sLAQERERqFevHpRKJZycnPDhhx8iKSlJtJ2/vz8aNmyImJgYtGnTBpaWlqhVqxbCw8PVw7XPh4yfPn2KNWvWqId2AWDu3Lnq//1vz7e5ceOGuu3kyZPw9/eHg4MDLCwsUL16dbz33nt48uSJuo+m0woXLlxAt27dYGdnB3NzczRp0gRbt24V9Xk+/L57927MnDkTbm5usLGxQWBgIC5fvly8DxlA3759AQC7d+9Wt2VmZmL//v0YMmSIxm3mzZuHFi1awN7eHjY2NmjatCk2btyIfz9XrUaNGoiPj8epU6fUn9/zkZfnsW/fvh2TJk1C1apVoVQqce3atSKnFdLT0+Hu7o6WLVsiPz9fvf+LFy/CysoKAwYMKHauulCaz6+0TE1N4evriydPniAtLU2r+yaSEosDKpaCggKcPHkSvr6+cHd3L9Y2o0ePxrRp09C+fXscPnwYn376KSIjI9GyZUukp6eL+qakpKB///744IMPcPjwYQQFBSEkJAQ7duwAAHTq1AlnzpwBALz//vs4c+aM+nVx3bhxA506dYKZmRk2bdqEyMhIhIeHw8rKCnl5eS/d7vLly2jZsiXi4+PxxRdf4JtvvoGXlxcGDRqEiIiIIv1nzJiBmzdv4ssvv8T69etx9epVdOnSBQUFBcWK08bGBu+//z42bdqkbtu9ezeMjIzQu3fvl+Y2cuRIfPXVV/jmm2/Qo0cPjBs3Dp9++qm6z4EDB1CrVi34+PioP78XTwGFhITg1q1bWLt2Lb799ls4OTkVeS9HR0fs2bMHMTExmDZtGgDgyZMn6NmzJ6pXr461a9cWK09dKc3nVxb//PMPTExMYGdnp/V9E0lGICqGlJQUAYDQp0+fYvVPSEgQAAhjxowRtf/+++8CAGHGjBnqtrZt2woAhN9//13U18vLS+jYsaOoDYAwduxYUducOXMETf+UN2/eLAAQEhMTBUEQhH379gkAhLi4uFfGDkCYM2eO+nWfPn0EpVIp3Lp1S9QvKChIsLS0FB4+fCgIgiD89NNPAgDhnXfeEfX76quvBADCmTNnXvm+z+ONiYlR7+vChQuCIAhC8+bNhUGDBgmCIAgNGjQQ2rZt+9L9FBQUCPn5+cL8+fMFBwcHobCwUL3uZds+f78333zzpet++uknUfuiRYsEAMKBAweEgQMHChYWFsLff//9yhx1qSyfn6Z/Vy9q27at0KBBAyE/P1/Iz88X7t69K0yfPl0AIPTs2VMnORFJhSMHpBM//fQTABSZ+Pb666+jfv36+PHHH0XtLi4ueP3110VtjRo1ws2bN7UWU5MmTWBmZoYRI0Zg69atuH79erG2O3nyJAICAoqMmAwaNAhPnjwpMoLx71MrwLM8AJQol7Zt28LDwwObNm3C+fPnERMT88oh8ZMnTyIwMBC2trYwNjaGqakpZs+ejfv37yM1NbXY7/vee+8Vu++UKVPQqVMn9O3bF1u3bsWKFSvg7e39n9s9ffq0VEtxR16Akn9+xRUfHw9TU1OYmprCzc0NixcvRv/+/bFhw4Yy75uoPGFxQMXi6OgIS0tLJCYmFqv//fv3AQCurq5F1rm5uanXP+fg4FCkn1KpRE5OTimi1czDwwMnTpyAk5MTxo4dCw8PD3h4eGD58uWv3O7+/fsvzeP5+n97MZfn8zNKkotCocDgwYOxY8cOrF27FnXq1EGbNm009v3jjz/QoUMHAM+uJvntt98QExODmTNnlvh9NeX5qhgHDRqE3NxcuLi4FGuuwY0bN9RfriVdPDw8ShRbcT+/kvDw8EBMTAzOnj2LCxcu4OHDh9ixYwdsbW3LvG+i8oRXK1CxGBsbIyAgAEePHkVSUtJ/Xur3/AsyOTm5SN+7d+/C0dFRa7GZm5sDAFQqlWii5IvzGgCgTZs2aNOmDQoKCnD27FmsWLECwcHBcHZ2Rp8+fTTu38HBAcnJyUXa7969CwBazeXfBg0ahNmzZ2Pt2rVYuHDhS/vt2bMHpqam+O6779SfBQAcPHiwxO+paWLnyyQnJ2Ps2LFo0qQJ4uPjMXnyZHzxxRev3MbNzQ0xMTEljguAxkmwr1Lcz68kzM3N0axZM63si6g8Y3FAxRYSEoIjR45g+PDhOHToEMzMzETr8/PzERkZiS5duqBdu3YAgB07dqB58+bqPjExMUhISFD/qtWG5zPu//77b9F7ffvtty/dxtjYGC1atEC9evWwc+dO/Pnnny8tDgICAnDgwAHcvXtXPVoAANu2bYOlpaXOLvOrWrUqpkyZgkuXLmHgwIEv7adQKGBiYgJjY2N1W05ODrZv316kr7ZGYwoKCtC3b18oFAocPXoUO3fuxOTJk+Hv748ePXq8dDszMzO9fbkW9/MjoqJYHFCx+fn5Yc2aNRgzZgx8fX0xevRoNGjQAPn5+Th37hzWr1+Phg0bokuXLqhbty5GjBiBFStWwMjICEFBQbhx4wZmzZoFd3d3TJgwQWtxvfPOO7C3t8fQoUMxf/58mJiYYMuWLbh9+7ao39q1a3Hy5El06tQJ1atXR25urnpGe2Bg4Ev3P2fOHHz33Xd46623MHv2bNjb22Pnzp34/vvvERERodMh5fDw8P/s06lTJyxZsgT9+vXDiBEjcP/+fXz++ecaf2l7e3tjz5492Lt3L2rVqgVzc/NizRN40Zw5c/Drr7/i2LFjcHFxwaRJk3Dq1CkMHToUPj4+qFmzZon3qQvF+fye++eff7Bv374i7V5eXhXmBlBE2sLigEpk+PDheP3117F06VIsWrQIKSkpMDU1RZ06ddCvXz/RLWjXrFkDDw8PbNy4EatWrYKtrS3efvtthIWFaZxjUFo2NjaIjIxEcHAwPvjgA1SuXBnDhg1DUFAQhg0bpu7XpEkTHDt2DHPmzEFKSgoqVaqEhg0b4vDhw+pz9prUrVsXUVFRmDFjBsaOHYucnBzUr18fmzdvLtGdBnWlXbt22LRpExYtWoQuXbqgatWqGD58OJycnDB06FBR33nz5iE5ORnDhw/Ho0eP8Nprr4nuA1Ecx48fR1hYGGbNmoWAgAB1+5YtW+Dj44PevXvj9OnTRUaWyrvIyEiNN/GaM2eOJLfTJpKSQhD+dZcUIiIikj1erUBEREQiLA6IiIhIhMUBERERibA4ICIiIhEWB0RERCTC4oCIiIhEWBwQERGRiEHeBMnC56P/7mSAMmJWSh0CEelIQaE8b0ljZVb8532URlm+L3LOGe7fXIMsDoiIiIpFwQF0TVgcEBGRfJXgSaRywuKAiIjkiyMHGvFTISIiIhGOHBARkXzxtIJGLA6IiEi+eFpBIxYHREQkXxw50IjFARERyRdHDjRicUBERPLFkQONWDIRERGRCEcOiIhIvnhaQSMWB0REJF88raARiwMiIpIvjhxoxOKAiIjkiyMHGrE4ICIi+eLIgUb8VIiIiEiEIwdERCRfHDnQiMUBERHJlxHnHGjC4oCIiOSLIwca8VN5QaumHti3bCSuH1uInHMr0cW/kWh9t3aNcXjVWNw+GY6ccyvRqE7VIvtwdrDGxk8/ROLxUKRHLUbUrml4N7BJkX5vt26AX7ZNxoMzS3D7ZDj2fD5MV2npROzZGIwbMwqB/q3RuEFdnPzxhNQh6dXe3TsR1KEdmvt4o0/PHvgz9qzUIekF8za8vDd9uQ4f9HkfrVs0RUDblpj48VjcSLyuXp+fn4/lSz5Hr3e7oOXrPujQrg1mzZiGtNR7EkatJQpF6RcDxuLgBVYWSpy/cgcTwr/SuN7Swgxn/voHs1Yceuk+Ni4YiDo1nNAzeB2a9QzFoZNx2B4+BI3rVlP36R7QBBsXfIhth6Pxeu9wtBu8BHsjK9Yfm5ycJ6hbty6mz5wtdSh6F3n0CCLCwzB8xGjs3XcQTZv6YszI4Ui+e1fq0HSKeRtm3rFnY9CrTz9s3bkXa9ZvwtOCpxgzchhynjwBAOTm5uJSwkUMGzkGu/bux+dLV+DmzRsIHjdG4si1QGFU+sWAKQRBEKQOQtssfD7Syn5yzq1Erwnr8e3PfxdZV93VHpePzEeL3mH4+8od0bq03xbj49A92P19jLot6adFmLn8ILYePANjYyNc/n4ePl17BFsPntFKrACQEbNSa/sqqcYN6mLpF6vQLiBQshj0qX+fnqjv5YVPZs9Tt3XvEoS32gVi/IRJEkamW8xburwLCvX3pzrjwQMEtG2JDZu3w7dZc4194i+cx4C+PfH9sZNwdXXTWSxWZrr9hW4RGF7qbXNOTNdiJOWLpKVPUlISZs6cibfeegv169eHl5cX3nrrLcycORO3b9+WMrQyiTr3D97v4As7G0soFAr07OgLpZkJfjl7FQDgU88dVZ3tUFgo4Mzuabh+bCEOrhyN+rVcJI6ciiM/Lw8JF+Ph17K1qN2vZSv8FXdOoqh0j3nLJ+9Hjx8BAGxtbV/a5/GjR1AoFLC2ttFXWLrB0woaSTYh8fTp0wgKCoK7uzs6dOiADh06QBAEpKam4uDBg1ixYgWOHj2KVq1avXI/KpUKKpVK1CYUFkBhZKzL8F9pwPRN2B4+BHdPRSA/vwBPcvPQe+IGJCalAwBqVnMEAHwy6h1MW/wNbt69j/EDAnDsy2A06j4fGVlPJIud/lvGwwwUFBTAwcFB1O7g4Ij09DSJotI95i2PvAVBwJLPwtGkqS9qe9bR2EelUuGLZYvx9judUalSJT1HqGUGfnqgtCQrDiZMmIBhw4Zh6dKlL10fHByMmJgYjeufCwsLw7x580Rtxs7NYer6utZiLam5Y7vAzsYSQSO/wP2H2eji3wg7PxuCwCHLEH/tLoz+v+Jc9OUPOPhjHABgxJwduPbDp+jR3gcb9/8mWexUfIoXfjkIglCkzRAx72cMNe/whZ/i6pXL2LR1l8b1+fn5CJkyEYIgIOSTOXqOTgcM8Bhqg2Ql04ULFzBq1KiXrh85ciQuXLjwn/sJCQlBZmamaDFx9tVmqCVSs5ojRvdpi5Fzd+DnP67g/JU7CF1/FH9evIWRvd8EACSnZwIALl1PVm+Xl/8UN5Luw93FXpK4qfjsKtvB2NgY6enpovYHD+7DwcFRoqh0j3kbft6LQj/FLz+fxPqN2+DsUvQ0Z35+PqZPnoA7d5Kwev3Gij9qAHBC4ktIlp2rqyuioqJeuv7MmTNwdXX9z/0olUrY2NiIFilPKViamwEACl+Y51lQIKhHDM4l3EauKh+eNZzV601MjFDdzR63kh/oL1gqFVMzM9T3aoDoKPEIT3RUFBo38ZEoKt1j3oabtyAICF84Hyd/PI51G7egarVqRfo8Lwxu3bqJtRs2o3JlOwki1QHOOdBIstMKkydPxqhRoxAbG4v27dvD2dkZCoUCKSkpOH78OL788kssW7ZM73FZWZjBw72K+nWNqg5oVKcqMrKe4HZKBuxsLOHuYgdXp2cTder8/xf8vftZuHf/ES7fSMG1W6lY+UlfhCw5gPuZ2ej6ViMEvFEXPcavBQA8ys7Fl/tOY9aod5CUkoFbyQ8wYeCzWf7fHP9TzxmX3pPsbNy6dUv9+k5SEi4lJMDW1haubrqbvVweDBg4GDOnT4VXw4Zo3NgH+7/ei+TkZPTs3Ufq0HSKeRtm3uEL5+Poke+wdPkqWFpZqedSVKpkDXNzczx9+hRTJ47HpYSLWL5qLQoKC9R9bG1tYWpqJmX4pAOSXsq4d+9eLF26FLGxsSgoKAAAGBsbw9fXFxMnTkSvXr1Ktd+yXMrYxtcTx74cX6R9++FojJizAx90aYEN8wcUWb9g7REsXHcEAOBRvQoWfNwNfk1qoZKlEv/cTsOybT+KLm00MTHCp+O6oW+n5rBQmiLmwk1M+WwfEq6nlDp2fV/KGPPH7xg2+MMi7V27vYtPQ0t/eVBFsXf3TmzZtBFpaamo7VkHU6aFvPSyL0PCvKXJW5eXMjb1rqexfe6noejavQfu3klC57c1X6a8ftNWNGveQmex6fxSxneWl3rbnCNFvysMRbm4z0F+fr76fJ6joyNMTU3LtD9t3eegopHyPgdEpFv6vM9BeaLz4qDTF6XeNuf7j7UYSflSLp6tYGpqWqz5BURERFpl4BMLS6tcFAdERESSYHGgEYsDIiKSLwO/6qC0WDIRERGRCEcOiIhIvnhaQSMWB0REJF88raARiwMiIpIvjhxoxOKAiIjkiyMHGrE4ICIi2TLEJ2tqA8dTiIiISIQjB0REJFscOdCMxQEREckXawONWBwQEZFsceRAM845ICIi2VIoFKVeSuKXX35Bly5d4ObmBoVCgYMHD4rWC4KAuXPnws3NDRYWFvD390d8fLyoj0qlwrhx4+Do6AgrKyt07doVSUlJoj4ZGRkYMGAAbG1tYWtriwEDBuDhw4cl/lxYHBARkWzpqzjIzs5G48aNsXLlSo3rIyIisGTJEqxcuRIxMTFwcXFB+/bt8ejRI3Wf4OBgHDhwAHv27MHp06fx+PFjdO7cGQUFBeo+/fr1Q1xcHCIjIxEZGYm4uDgMGDCg5J+LIAgG95BwC5+PpA5BEhkxmv/REVHFV1BocH+qi8XKTLfD/jZ9tpV626w9H5ZqO4VCgQMHDqB79+4Ano0auLm5ITg4GNOmTQPwbJTA2dkZixYtwsiRI5GZmYkqVapg+/bt6N27NwDg7t27cHd3x5EjR9CxY0ckJCTAy8sL0dHRaNGiBQAgOjoafn5+uHTpEurWrVvsGDlyQEREslWWkQOVSoWsrCzRolKpShxDYmIiUlJS0KFDB3WbUqlE27ZtERUVBQCIjY1Ffn6+qI+bmxsaNmyo7nPmzBnY2tqqCwMAeOONN2Bra6vuU1wsDoiISL4UpV/CwsLU5/afL2FhYSUOISUlBQDg7Owsand2dlavS0lJgZmZGezs7F7Zx8nJqcj+nZyc1H2Ki1crEBGRbJXlaoWQkBBMnDhR1KZUKrUWiyAI/xnfi3009S/Ofl7EkQMiIpKtspxWUCqVsLGxES2lKQ5cXFwAoMiv+9TUVPVogouLC/Ly8pCRkfHKPvfu3Suy/7S0tCKjEv/FIEcO5Doxz6HPZqlDkMT9PYOlDoFI54yNeD2+LpSH+xzUrFkTLi4uOH78OHx8fAAAeXl5OHXqFBYtWgQA8PX1hampKY4fP45evXoBAJKTk3HhwgVEREQAAPz8/JCZmYk//vgDr7/+OgDg999/R2ZmJlq2bFmimAyyOCAiIipPHj9+jGvXrqlfJyYmIi4uDvb29qhevTqCg4MRGhoKT09PeHp6IjQ0FJaWlujXrx8AwNbWFkOHDsWkSZPg4OAAe3t7TJ48Gd7e3ggMDAQA1K9fH2+//TaGDx+OdevWAQBGjBiBzp07l+hKBYDFARERyZi+Rg7Onj2Lt956S/36+VyFgQMHYsuWLZg6dSpycnIwZswYZGRkoEWLFjh27Bisra3V2yxduhQmJibo1asXcnJyEBAQgC1btsDY2FjdZ+fOnfj444/VVzV07dr1pfdWeBWDvM9B7lOpI5AGTysQkaEx1/FPWIeBu0u97f2tfbUYSfnCkQMiIpKt8jDnoDxicUBERLLF4kAzFgdERCRbLA40430OiIiISIQjB0REJF8cONCIxQEREckWTytoxuKAiIhki8WBZiwOiIhItlgcaMbigIiIZIvFgWa8WoGIiIhEOHJARETyxYEDjVgcEBGRbPG0gmYsDoiISLZYHGjG4oCIiGSLxYFmnJBIREREIhw5ICIi+eLAgUYcOdCivbt3IqhDOzT38Uafnj3wZ+xZqUMqtlb1nfH19ABcW98b2fsGo3Pz6kX6zOjVBNfW90b6zgE4Ou9t1K9WWb3OrpIZPh/SAueW90DazgG4tKYnPhvSAjaWpqJ91Ha1wd5pAbi5qS+St/XHiQXv4M0GLrpOTycq8vEujdizMRg3ZhQC/VujcYO6OPnjCalD0iu5He/nDD1vhUJR6sWQsTjQksijRxARHobhI0Zj776DaNrUF2NGDkfy3btSh1YsVuYmOH8jAxM3RmtcP7G7N8Z1boCJG6Px5vRvce9hDr6d3RGVzJ8NPrnaWcLV3hIztsXg9YkHMXLVabRvUhWrR7cW7Wf/jPYwMVKg07xItJ76Lf6+8QD7QgLhXNlC5zlqU0U/3qWRk/MEdevWxfSZs6UORe/keLwBeeTN4kAzFgdasn3rZrz73nvo8X5P1PLwwNSQmXBxdcFXe3dLHVqxHDt3B/P3/InDv9/UuH5sJy989s3fOPz7TVy8/RAjVvwKC6UxerXxAABcvP0Q/T//CUdjbyPx3iOcupCMebv/xDvN3GFs9Oz/RA7WStR2tcHig+dx4WYG/knJwuwdZ2Flbor67pX1lapWVPTjXRqt27TFR+MnILB9B6lD0Ts5Hm9AHnmzONCMxYEW5OflIeFiPPxain8l+7Vshb/izkkUlfbUcKoEFztL/PjXHXVb3tNCnL54D2/UdXrpdjaWpsh6ko+CQgEAcP+RCgm3H6JfWw9YKk1gbKTA0A71cC/jCc79c1/neWiLoR9vEpPr8ZZL3iwONOOERC3IeJiBgoICODg4iNodHByRnp4mUVTa42xnCQC49zBH1J76MAfVq1TSuI19JSWmv98Em45fFrV3+fQHfDUtAPe2f4BCQUDqwxx0X3gcmU/ydBO8Dhj68SYxuR5vueZNz5TrkYPbt29jyJAhr+yjUqmQlZUlWlQqlZ4iFHuxkhQEwbCqS0H8UqF4luOLrC1MsX9GIC4lPUTo1+JfGMuG+yEtMxftZx1B2+nf4buYW9gXEgiXCjbnAJDB8SYRuR5vg89bUYbFgJXr4uDBgwfYunXrK/uEhYXB1tZWtHy2KExPET5jV9kOxsbGSE9PF7U/eHAfDg6Oeo1FF+5lPAEAONuJv8Cr2FogNTNX1FbJ3AQHP+mA7Nyn6BNxEk8L/lc8+Hu7IqhpNQxc+jOiL6ciLvE+JnwZjZy8AvT3r637RLTE0I83icn1eMslb55W0EzS0wqHDx9+5frr16//5z5CQkIwceJEUZtgrCxTXCVlamaG+l4NEB31GwIC26vbo6Oi4N8uQK+x6MKN1MdIyXiCdo3c8FfiAwCAqYkRWns5Y9aOWHU/awtTHPqkA1RPC9Az/ARU+QWi/ViYPfvnVvjCaENhoQCFUcX5P5qhH28Sk+vxlkvehv4lX1qSFgfdu3eHQqHQODT93H8dOKVSCaVSXAzkPtVKeCUyYOBgzJw+FV4NG6JxYx/s/3ovkpOT0bN3H/0HUwpW5ibwcLFRv67hXAmNatjjwWMVktKzser7i5jcoxGuJWfhn+QsTOnRCDmqAnz16z8Ano0YHJ7VAZZKEwyN+AU2lmaweTZVAWlZuSgsFPDHlVRkZOdh/UdtEP51HHLyCjA4sA5qOFXCD7G3pUi71Cr68S6NJ9nZuHXrlvr1naQkXEpIgK2tLVzd3CSMTPfkeLwBeeTN2kAzSYsDV1dXrFq1Ct27d9e4Pi4uDr6+vvoNqpTeDnoHmQ8zsH7NaqSlpaK2Zx2sWrsebm5VpQ6tWJp6OCJyXpD69aJBLQAAO366ipGrTmPJwfMwNzPGsuF+qGxlhpir6ej66Q94/P+VmI+HI16v8+zKhQur3hftu/7or3Er7THuP1Kh+8JjmNvXF9/PfRumxkZIuP0QvSN+xPmbGXrKVDsq+vEujfj4Cxg2+EP1688jnp2+69rtXXwaGi5VWHohx+MNyCNvjhxophBe9bNdx7p27YomTZpg/vz5Gtf/9ddf8PHxQWFhYYn2K8XIQXng0Gez1CFI4v6ewVKHQEQ6Yq7jn7CeUyJLve3Vz97WYiTli6QjB1OmTEF2dvZL19euXRs//fSTHiMiIiI54cCBZpIWB23atHnleisrK7Rt21ZP0RARkdzwtIJmvAkSERHJFmsDzVgcEBGRbBlVoMuo9YnFARERyRZHDjQr13dIJCIiIv3jyAEREckWJyRqxuKAiIhki7WBZiwOiIhItjhyoBmLAyIiki0WB5qxOCAiItlibaAZr1YgIiIiEY4cEBGRbPG0gmYsDoiISLZYG2jG0wpERCRbCoWi1EtJPH36FJ988glq1qwJCwsL1KpVC/Pnz0dhYaG6jyAImDt3Ltzc3GBhYQF/f3/Ex8eL9qNSqTBu3Dg4OjrCysoKXbt2RVJSklY+i39jcUBERLKlUJR+KYlFixZh7dq1WLlyJRISEhAREYHPPvsMK1asUPeJiIjAkiVLsHLlSsTExMDFxQXt27fHo0eP1H2Cg4Nx4MAB7NmzB6dPn8bjx4/RuXNnFBQUaOsjAcDTCkREJGP6mnNw5swZdOvWDZ06dQIA1KhRA7t378bZs2cBPBs1WLZsGWbOnIkePXoAALZu3QpnZ2fs2rULI0eORGZmJjZu3Ijt27cjMDAQALBjxw64u7vjxIkT6Nixo9bi5cgBERFRKahUKmRlZYkWlUqlsW/r1q3x448/4sqVKwCAv/76C6dPn8Y777wDAEhMTERKSgo6dOig3kapVKJt27aIiooCAMTGxiI/P1/Ux83NDQ0bNlT30RYWB0REJFtlOa0QFhYGW1tb0RIWFqbxfaZNm4a+ffuiXr16MDU1hY+PD4KDg9G3b18AQEpKCgDA2dlZtJ2zs7N6XUpKCszMzGBnZ/fSPtrC0wpERCRbZTmtEBISgokTJ4ralEqlxr579+7Fjh07sGvXLjRo0ABxcXEIDg6Gm5sbBg4c+NJ4BEH4zxiL06ekWBwYkPt7BksdgiTs3g6XOgRJZEROlzoEogqvLN+pSqXypcXAi6ZMmYLp06ejT58+AABvb2/cvHkTYWFhGDhwIFxcXAA8Gx1wdXVVb5eamqoeTXBxcUFeXh4yMjJEowepqalo2bJl6RPRgKcViIhItvR1KeOTJ09gZCT+yjU2NlZfylizZk24uLjg+PHj6vV5eXk4deqU+ovf19cXpqamoj7Jycm4cOGC1osDjhwQEZFs6esmSF26dMHChQtRvXp1NGjQAOfOncOSJUswZMiQ/49DgeDgYISGhsLT0xOenp4IDQ2FpaUl+vXrBwCwtbXF0KFDMWnSJDg4OMDe3h6TJ0+Gt7e3+uoFbWFxQEREpGMrVqzArFmzMGbMGKSmpsLNzQ0jR47E7Nmz1X2mTp2KnJwcjBkzBhkZGWjRogWOHTsGa2trdZ+lS5fCxMQEvXr1Qk5ODgICArBlyxYYGxtrNV6FIAiCVvdYDuQ+lToC0ifOOSAyXOY6/gnb6rNfS73tb1PaaDGS8oUjB0REJFt8toJmLA6IiEi2+FRGzVgcEBGRbLE40IzFARERyRZrA814nwMiIiIS4cgBERHJFk8raMbigIiIZIu1gWYsDoiISLY4cqAZiwMiIpIt1gaasTggIiLZMmJ1oBGvViAiIiIRjhwQEZFsceBAMxYHREQkW5yQqBmLAyIiki0j1gYasTggIiLZ4siBZiwOiIhItlgbaMarFbRo7+6dCOrQDs19vNGnZw/8GXtW6pD0oiLn3crbHfs+fR/X94xFzonp6NLSU7S+W+s6OBzeC7f3f4ycE9PRyMOpyD7MTI2x5KP2uL3/Y6R/OxFfz38PVR2tRX0qV1Ji47TOSDkUjJRDwdg4rTNsrZQ6zU1XKvLxLgu55R17NgbjxoxCoH9rNG5QFyd/PCF1SKRHLA60JPLoEUSEh2H4iNHYu+8gmjb1xZiRw5F8967UoelURc/bytwU56/fw4SVxzWutzQ3xZkLdzDry59fuo/PxgSgaytPfLjwEAIm7EQlCzPsX/A+jP51MnPLjK5oVNsJ3aZ/hW7Tv0Kj2k7YOL2LttPRuYp+vEtLjnnn5DxB3bp1MX3mbKlD0SlFGf4zZCwOtGT71s1497330OP9nqjl4YGpITPh4uqCr/buljo0naroeR+LuY55m3/FodNXNK7ffSIeYTt+w8k/b2pcb2OlxKC3G2P6upP46c+b+OvaPQwJ/xYNa1ZBu6Y1AAB1qzug4+seGLP4KH5PuIvfE+5i7JJIdPKrDc9q9rpKTScq+vEuLTnm3bpNW3w0fgIC23eQOhSdMlKUfjFkLA60ID8vDwkX4+HXsrWo3a9lK/wVd06iqHRPrnn/m4+nC8xMjXHibKK6Lfn+Y8TfSMcbXlUBAC28quLh41zEXEpW9/kj4S4ePs7FGw2q6j3m0pLr8ZZr3nKhUChKvRgyyYuDnJwcnD59GhcvXiyyLjc3F9u2bXvl9iqVCllZWaJFpVLpKlyNMh5moKCgAA4ODqJ2BwdHpKen6TUWfZJr3v/mYm8FVd5TPHws/jeXmpENZ3srAICznRXSHj4psm3awyfqPhWBXI+3XPOWC4Wi9Ishk7Q4uHLlCurXr48333wT3t7e8Pf3R3Ly/35dZWZmYvDgwa/cR1hYGGxtbUXLZ4vCdB26Ri9WkoIgGHx1Ccg371dRKABB+N9r4d8vnvcBgKLN5Z5cj7dc8zZ0RgpFqRdDJmlxMG3aNHh7eyM1NRWXL1+GjY0NWrVqhVu3bhV7HyEhIcjMzBQtU6aF6DDqouwq28HY2Bjp6emi9gcP7sPBwVGvseiTXPP+t5QH2VCamaByJfGVB1UqWyE1IxsAcC8jG052RUcIHCtb4t7/96kI5Hq85Zo3yZukxUFUVBRCQ0Ph6OiI2rVr4/DhwwgKCkKbNm1w/fr1Yu1DqVTCxsZGtCiV+r1EzNTMDPW9GiA66jdRe3RUFBo38dFrLPok17z/7dzVFOTlFyDAt6a6zcXeCg1qOCL64h0AwO8X76ByJXM0q+uq7tO8nisqVzJHdPwdvcdcWnI93nLNWy54WkEzSW+ClJOTAxMTcQirVq2CkZER2rZti127dkkUWckNGDgYM6dPhVfDhmjc2Af7v96L5ORk9OzdR+rQdKqi521lbgqPqnbq1zVcK6ORhxMyHuXidmoW7KzN4e5kA1eHSgCAOu7Pri649yAb9zKykZWtwpbIvxA+sh3uZ+Ug41Euwka8hQuJaTj55w0AwOVb9/HDH/9g1cQgjFsWCQBYOeFtfH/mGq4mPdBvwmVU0Y93ackx7yfZ2aJR3DtJSbiUkABbW1u4urlJGJl28dSQZpIWB/Xq1cPZs2dRv359UfuKFSsgCAK6du0qUWQl93bQO8h8mIH1a1YjLS0VtT3rYNXa9XBzqziz0UujoufdtK4rji3up34dMToAALD9h/MY8dn36OTniQ1TO6nXb/+kOwBgwbbTWLjtNABg6uofUVAgYMes7rAwM8FP525ixKx9KCz834SCwWHfYvHYQHwb3hsA8P2Zq5iwQvO9Fcqzin68S0uOecfHX8CwwR+qX38e8WwuV9du7+LT0HCpwtI61gaaKQRNM6X0JCwsDL/++iuOHDmicf2YMWOwdu1aFBYWlmi/uU+1ER1VFHZvG84fqpLIiJwudQhEOmeu45+wvbeW/nLUvQMN97SSpMWBrrA4kBcWB0SGS9fFQZ8yFAd7DLg4kPw+B0RERFS+8KmMREQkW5yQqBmLAyIiki1Df0ZCabE4ICIi2eLIgWYsDoiISLZYG2jG4oCIiGSLIwealepqhe3bt6NVq1Zwc3PDzZvPnnO/bNkyHDp0SKvBERERkf6VuDhYs2YNJk6ciHfeeQcPHz5EQUEBAKBy5cpYtmyZtuMjIiLSGSNF6RdDVuLiYMWKFdiwYQNmzpwJY2NjdXuzZs1w/vx5rQZHRESkSwqFotSLISvxnIPExET4+BS9K5RSqUR2dsV5/CwREZFhf8WXXolHDmrWrIm4uLgi7UePHoWXl5c2YiIiItILI4Wi1IshK/HIwZQpUzB27Fjk5uZCEAT88ccf2L17N8LCwvDll1/qIkYiIiLSoxIXB4MHD8bTp08xdepUPHnyBP369UPVqlWxfPly9OljuM82JyIiw2PgAwClVqpLGYcPH46bN28iNTUVKSkpuH37NoYOHart2IiIiHRKnxMS79y5gw8++AAODg6wtLREkyZNEBsbq14vCALmzp0LNzc3WFhYwN/fH/Hx8aJ9qFQqjBs3Do6OjrCyskLXrl2RlJRU5s/hRWV6KqOjoyOcnJy0FQsREZFeKRSlX0oiIyMDrVq1gqmpKY4ePYqLFy9i8eLFqFy5srpPREQElixZgpUrVyImJgYuLi5o3749Hj16pO4THByMAwcOYM+ePTh9+jQeP36Mzp07q28roC0lPq1Qs2bNV1ZM169fL1NARERE+qKviYWLFi2Cu7s7Nm/erG6rUaOG+n8LgoBly5Zh5syZ6NGjBwBg69atcHZ2xq5duzBy5EhkZmZi48aN2L59OwIDAwEAO3bsgLu7O06cOIGOHTtqLd4SFwfBwcGi1/n5+Th37hwiIyMxZcoUbcVFRESkc2WpDVQqFVQqlahNqVRCqVQW6Xv48GF07NgRPXv2xKlTp1C1alWMGTMGw4cPB/DsNgEpKSno0KGDaF9t27ZFVFQURo4cidjYWOTn54v6uLm5oWHDhoiKipK2OBg/frzG9lWrVuHs2bNlDoiIiKgiCAsLw7x580Rtc+bMwdy5c4v0vX79uvoOwzNmzMAff/yBjz/+GEqlEh9++CFSUlIAAM7OzqLtnJ2d1Y8pSElJgZmZGezs7Ir0eb69tmjtwUtBQUEICQkRDZkQERGVZ2W502FISAgmTpwoatM0agAAhYWFaNasGUJDQwEAPj4+iI+Px5o1a/Dhhx++NB5BEP4zxuL0KSmtFQf79u2Dvb29tnZHVGwZkdOlDkESdp0+lzoESWR8P1nqEMiAlGVW/stOIWji6upa5EaB9evXx/79+wEALi4uAJ6NDri6uqr7pKamqkcTXFxckJeXh4yMDNHoQWpqKlq2bFmGTIoqcXHg4+MjqlAEQUBKSgrS0tKwevVqrQZHRESkS/p6RkKrVq1w+fJlUduVK1fw2muvAXg22d/FxQXHjx9XP6IgLy8Pp06dwqJFiwAAvr6+MDU1xfHjx9GrVy8AQHJyMi5cuICIiAitxlvi4qB79+6i10ZGRqhSpQr8/f1Rr149bcVFRESkc/p6uuKECRPQsmVLhIaGolevXvjjjz+wfv16rF+/HsCzIiU4OBihoaHw9PSEp6cnQkNDYWlpiX79+gEAbG1tMXToUEyaNAkODg6wt7fH5MmT4e3trb56QVtKVBw8ffoUNWrUQMeOHdVDIERERBWVvoqD5s2b48CBAwgJCcH8+fNRs2ZNLFu2DP3791f3mTp1KnJycjBmzBhkZGSgRYsWOHbsGKytrdV9li5dChMTE/Tq1Qs5OTkICAjAli1bRE9J1gaFIAhCSTawtLREQkKCeiikPMp9KnUERLrHOQckB+Zamxmn2cTDl0q97ZKuhjtaXuK5GC1atMC5c+d0EQsREZFe6fP2yRVJiWuyMWPGYNKkSUhKSoKvry+srKxE6xs1aqS14IiIiHRJX6cVKppiFwdDhgzBsmXL0Lt3bwDAxx9/rF6nUCjU11lq+/7OREREumLgAwClVuziYOvWrQgPD0diYqIu4yEiItIbfT1boaIpdnHwfN5ieZ6ISEREVBJlejSxASvR52LoEzCIiIiohBMS69Sp858FwoMHD8oUEBERkb7wN69mJSoO5s2bB1tbW13FQkREpFecc6BZiYqDPn36wMnJSVexEBER6RVrA82KXRxwvgERERka3udAsxJfrUBERGQoeFpBs2IXB4WFhbqMg4iIiMoJHT/SgoiIqPziwIFmLA6IiEi2OOdAMxYHREQkWwqwOtCEd47Uor27dyKoQzs09/FGn5498GfsWalD0gvmXfHybtWwGvbNexfXd41Czg+T0cWvdpE+Mz9oieu7RuHB4fH4IaI36r/mIFrvbGeJjVOCkLh7NNIPjUfUygF4t3WdIvt5+/Va+GV5fzw4PB63vxqDPbO66iwvXarIx7ssDD1vI0XpF0PG4kBLIo8eQUR4GIaPGI29+w6iaVNfjBk5HMl370odmk4x74qZt5W5Kc5fT8WEVT9qXD+p1+v4uIcvJqz6Ea3H7cS9jGx8H9YTlSxM1X02Tn0Hddzt0XPuATQbuQWHfruK7TM6o7HH/+6F0r21JzZODcK2Yxfw+uhtaDdxN/b+dEnn+WlbRT/epSWHvFkcaMbiQEu2b92Md997Dz3e74laHh6YGjITLq4u+GrvbqlD0ynmXTHzPnY2EfO2/oZDv13VuH5s96aI2PM7Dv12FRdvpmPY50dhoTRB77fqq/u0qO+G1YfO4ezlFNxIycSi3dF4mK1Ck9rPigNjIwU+H9UOMzacwpff/4VrdzJwNSkDB05f0UuO2lTRj3dpyTVvYnGgFfl5eUi4GA+/lq1F7X4tW+GvuHMSRaV7zNsw867hYgtXh0o4EXtD3ZaXX4BfzyfhDa+q6rao+Dt4v21d2FmbQ6EAeratC6WpMX75+zYAwMfTGVWrWKNQAM6sGoDru0bh4IL3ipyeKO8M/Xi/jFzyVigUpV4MmeQTEhMSEhAdHQ0/Pz/Uq1cPly5dwvLly6FSqfDBBx+gXbt2r9xepVJBpVKJ2gRjJZRKpS7DFsl4mIGCggI4OIj/6Dk4OCI9PU1vcegb8zbMvF3srQAAqRnZovbUjGxUd7JRvx6w8Ftsn9kFd/d9hPynBXiieore8w8hMTkTAFDT5dlzWD75oCWmrf8JN1OyMP79Zjj2WW80GroJGY9y9ZRR2Rj68X4ZueRt6KcHSkvSkYPIyEg0adIEkydPho+PDyIjI/Hmm2/i2rVruHXrFjp27IiTJ0++ch9hYWGwtbUVLZ8tCtNTBmIvVpKCIBh8dQkw7+cMLe8X74mqUChEbXMHtYZdJXMETfsKrcbtwBf7z2LnzC5oUMMRAGD0/391F+2OxsHTV3Hu2j2MWBwJQQB6tCk6cbG8M/Tj/TKGnrdCUfrFkElaHMyfPx9TpkzB/fv3sXnzZvTr1w/Dhw/H8ePHceLECUydOhXh4eGv3EdISAgyMzNFy5RpIXrK4Bm7ynYwNjZGenq6qP3Bg/twcHDUayz6xLwNM++UB89GDJztrETtVSpbIjXjCQCgpqstRndripFLIvFz3C2cv56G0J1n8OfVexjZtQkAIPn/93Pp1n31PvLyC3AjJRPu/xqBKO8M/Xi/jFzyNlIoSr0YMkmLg/j4eAwaNAgA0KtXLzx69Ajvvfeeen3fvn3x999/v3IfSqUSNjY2okWfpxQAwNTMDPW9GiA66jdRe3RUFBo38dFrLPrEvA0z7xspmUi+/xgBTV9Tt5maGKGNdzVEX7wDALBUPrtqobBQPL5QUFCo/qN57uo95OY9hWc1O/V6E2MjVHe2wa17WbpOQ2sM/Xi/jFzy5tUKmkk+5+A5IyMjmJubo3Llyuo2a2trZGZmShdUCQwYOBgzp0+FV8OGaNzYB/u/3ovk5GT07N1H6tB0inlXzLytzE3h4VZZ/bqGiy0a1aqCjEe5uJ32CKsO/okpfVrg2p0MXLvzEFP7tkCO6in2/pQAALh8+wGu3cnAyvHtEbLhFO5n5aBrS08ENK2BHrO/AQA8epKHL7//C7MGtEJS2iPcSs3ChPebAwC++fWy3nMui4p+vEtLrnmTxMVBjRo1cO3aNdSu/ewGLGfOnEH16tXV62/fvg1XV1epwiuRt4PeQebDDKxfsxppaamo7VkHq9auh5tb1f/euAJj3hUz76Z1XHDss97q1xGj3gIAbD92ASMWR2LxV3/A3MwEyz4KhJ21OWIuJaNzyD48zskHADwtKET3T/ZjwdA3sW/eu6hkYYZ/7mZg2OdH8UNMonq/IRtO4WlBITZOfQcWZiaIuZyMoGlf4eFj8STi8q6iH+/SkkPeBn52oNQUgoTPYl67di3c3d3RqVMnjetnzpyJe/fu4csvvyzRfnOfaiM6ovLNrtPnUocgiYzvJ0sdAumRuY5/wq767Uaptx3bqobW4ihvJB05GDVq1CvXL1y4UE+REBGRHHHkQLNyM+eAiIhI3wx9YmFpsTggIiLZMvRLEkuLt08mIiIiEY4cEBGRbHHgQDMWB0REJFs8raAZiwMiIpIt1gaasTggIiLZ4sQ7zVgcEBGRbBnSEya1iUUTERERiXDkgIiIZIvjBpqxOCAiItni1QqasTggIiLZYmmgGYsDIiKSLQ4caMbigIiIZItXK2jGqxWIiIj0KCwsDAqFAsHBweo2QRAwd+5cuLm5wcLCAv7+/oiPjxdtp1KpMG7cODg6OsLKygpdu3ZFUlKSTmJkcUBERLJlVIalNGJiYrB+/Xo0atRI1B4REYElS5Zg5cqViImJgYuLC9q3b49Hjx6p+wQHB+PAgQPYs2cPTp8+jcePH6Nz584oKCgoZTQvx+KAiIhkS6FQlHopqcePH6N///7YsGED7Ozs1O2CIGDZsmWYOXMmevTogYYNG2Lr1q148uQJdu3aBQDIzMzExo0bsXjxYgQGBsLHxwc7duzA+fPnceLECa19Hs+xOCAiItlSlGFRqVTIysoSLSqV6qXvNXbsWHTq1AmBgYGi9sTERKSkpKBDhw7qNqVSibZt2yIqKgoAEBsbi/z8fFEfNzc3NGzYUN1Hm1gcEBGRbJVl5CAsLAy2traiJSwsTOP77NmzB3/++afG9SkpKQAAZ2dnUbuzs7N6XUpKCszMzEQjDi/20SZerUBUQWV8P1nqECRh12a61CFIIuPXcKlDMEhl+YUcEhKCiRMnitqUSmWRfrdv38b48eNx7NgxmJubv3R/L56qEAThP09fFKdPaXDkgIiIqBSUSiVsbGxEi6biIDY2FqmpqfD19YWJiQlMTExw6tQpfPHFFzAxMVGPGLw4ApCamqpe5+Ligry8PGRkZLy0jzaxOCAiItnSx4TEgIAAnD9/HnFxceqlWbNm6N+/P+Li4lCrVi24uLjg+PHj6m3y8vJw6tQptGzZEgDg6+sLU1NTUZ/k5GRcuHBB3UebeFqBiIhkSx+3QLK2tkbDhg1FbVZWVnBwcFC3BwcHIzQ0FJ6envD09ERoaCgsLS3Rr18/AICtrS2GDh2KSZMmwcHBAfb29pg8eTK8vb2LTHDUBhYHREQkW+XlBolTp05FTk4OxowZg4yMDLRo0QLHjh2DtbW1us/SpUthYmKCXr16IScnBwEBAdiyZQuMjY21Ho9CEARB63uVWO5TqSMgIl3hhER5MdfxT9hvz98r9bZdvLV/rr+84MgBERHJVnkZOShvOCGRiIiIRDhyQEREsqXQy5TEiofFARERyRZPK2jG4oCIiGTLiCMHGrE4ICIi2eLIgWYsDoiISLZYHGjGqxWIiIhIhCMHREQkW7xaQTMWB0REJFtGrA00YnFARESyxZEDzVgcEBGRbHFComackEhEREQiHDkgIiLZ4mkFzThyoEV7d+9EUId2aO7jjT49e+DP2LNSh6QXzJt5VxStmtTEvs8G4vrhGcg5E44ub3qJ1ndr2wCHlw7B7aOzkHMmHI08XV+5v4NLBhfZTxufWsg5E65x8a1fTSd56ULs2RiMGzMKgf6t0bhBXZz88YTUIemEkaL0iyFjcaAlkUePICI8DMNHjMbefQfRtKkvxowcjuS7d6UOTaeYN/OuSHlbmZvi/NVkTFh8SON6SwsznDl/E7NWR/7nvsb1aQ1BEIq0R5+/iRqdFoiWTYf+wI27DxCbkFTmHPQlJ+cJ6tati+kzZ0sdik4pyvCfIWNxoCXbt27Gu++9hx7v90QtDw9MDZkJF1cXfLV3t9Sh6RTzZt4VKe9j0Vcwb/0xHDoVr3H97shzCNv0I07GXHvlfrxru+LjPq0xauG+Iuvynxbg3oPH6uV+5hN0alMfW7+rOCMsANC6TVt8NH4CAtt3kDoUnVIoSr8YsnJXHGiqxMu7/Lw8JFyMh1/L1qJ2v5at8FfcOYmi0j3mzbwBw8/7RRZKU2yd3wcTFh/GvQeP/7N/5zZecLS1wo7vY/UQHZWUogyLISt3xYFSqURCQoLUYZRIxsMMFBQUwMHBQdTu4OCI9PQ0iaLSPebNvAHDz/tFEcGdEX3+Fr779WKx+g/s0gzHf7+CpNRMHUdGpD2SXa0wceJEje0FBQUIDw9X/wFasmTJK/ejUqmgUqlEbYKxEkqlUjuBloDihXEmQRCKtBki5v0M8zZ8nVrXh7+vB94Y+EWx+letYoP2Lergg0926TgyKi0jmfzbLSnJioNly5ahcePGqFy5sqhdEAQkJCTAysqqWH9wwsLCMG/ePFHbzFlz8MnsuVqM9tXsKtvB2NgY6enpovYHD+7DwcFRb3HoG/Nm3oDh5/1v/s08UKuqPVKOzRG17w79AL/9dQMdx64XtQ/o3Az3M58Ue5SB9I+lgWaSFQcLFy7Ehg0bsHjxYrRr107dbmpqii1btsDLy+sVW/9PSEhIkVEIwVi/owamZmao79UA0VG/ISCwvbo9OioK/u0C9BqLPjFv5g0Yft7/9vm2n7H5cIyoLXbnBExd/h2+P130dOiHnXyxK/JPPC0o1FeIVFKsDjSSrDgICQlBYGAgPvjgA3Tp0gVhYWEwNTUt8X6UyqKnEHKfaivK4hswcDBmTp8Kr4YN0bixD/Z/vRfJycno2buP/oPRI+bNvCtS3lYWZvCo9r85EzXc7NHI0xUZWU9w+14m7Gws4O5cGa6ONgCAOtWrAADu3X8kugLhRbfvPcTN5AxRm38zD9Ss6oAtLxQTFcWT7GzcunVL/fpOUhIuJSTA1tYWrm5uEkamXYZ+SWJpSXqHxObNmyM2NhZjx45Fs2bNsGPHjgp77vLtoHeQ+TAD69esRlpaKmp71sGqtevh5lZV6tB0inkz74qUd9N61XBs9Qj164jxnQEA27+PxYgFX6NTay9smNVTvX77gn4AgAVfnsDCjSW7CdCgLs1x5u8buHyzYk7WjI+/gGGDP1S//jwiDADQtdu7+DQ0XKqwtK6CfuXonEIoJ9cO7tmzB8HBwUhLS8P58+eLfVpBEylGDohIP+zaTJc6BElk/Go4X8glYa7jn7B/XC/9VSSv17LVYiTlS7l5tkKfPn3QunVrxMbG4rXXXpM6HCIikgEOHGhWbooDAKhWrRqqVas49x4nIqIKjtWBRuWqOCAiItInTkjUjMUBERHJFickasbigIiIZIu1gWbl7tkKREREJC2OHBARkXxx6EAjFgdERCRbnJCoGYsDIiKSLU5I1IzFARERyRZrA81YHBARkXyxOtCIVysQERGRCEcOiIhItjghUTMWB0REJFuckKgZiwMiIpIt1gaasTggIiL5YnWgESckEhGRbCnK8F9JhIWFoXnz5rC2toaTkxO6d++Oy5cvi/oIgoC5c+fCzc0NFhYW8Pf3R3x8vKiPSqXCuHHj4OjoCCsrK3Tt2hVJSUll/hxexOKAiIhIx06dOoWxY8ciOjoax48fx9OnT9GhQwdkZ2er+0RERGDJkiVYuXIlYmJi4OLigvbt2+PRo0fqPsHBwThw4AD27NmD06dP4/Hjx+jcuTMKCgq0Gq9CEARBq3ssB3KfSh0BEemKXZvpUocgiYxfw6UOQRLmOj75ffFu9n93egkvN6tSb5uWlgYnJyecOnUKb775JgRBgJubG4KDgzFt2jQAz0YJnJ2dsWjRIowcORKZmZmoUqUKtm/fjt69ewMA7t69C3d3dxw5cgQdO3YsdTwv4sgBERHJlqIMi0qlQlZWlmhRqVTFet/MzEwAgL29PQAgMTERKSkp6NChg7qPUqlE27ZtERUVBQCIjY1Ffn6+qI+bmxsaNmyo7qMtBjkhsdDwBkOKxYjX5JAMyPUXdNUhu6UOQRL3t/XV7RuU4c9mWFgY5s2bJ2qbM2cO5s6d+8rtBEHAxIkT0bp1azRs2BAAkJKSAgBwdnYW9XV2dsbNmzfVfczMzGBnZ1ekz/PttcUgiwMiIqLiKMtNkEJCQjBx4kRRm1Kp/M/tPvroI/z99984ffp00Xhe+JEnCEKRthcVp09J8bQCERHJlkJR+kWpVMLGxka0/FdxMG7cOBw+fBg//fQTqlWrpm53cXEBgCIjAKmpqerRBBcXF+Tl5SEjI+OlfbSFxQEREZGOCYKAjz76CN988w1OnjyJmjVritbXrFkTLi4uOH78uLotLy8Pp06dQsuWLQEAvr6+MDU1FfVJTk7GhQsX1H20hacViIhItvQ1U2vs2LHYtWsXDh06BGtra/UIga2tLSwsLKBQKBAcHIzQ0FB4enrC09MToaGhsLS0RL9+/dR9hw4dikmTJsHBwQH29vaYPHkyvL29ERgYqNV4WRwQEZF86ak6WLNmDQDA399f1L5582YMGjQIADB16lTk5ORgzJgxyMjIQIsWLXDs2DFYW1ur+y9duhQmJibo1asXcnJyEBAQgC1btsDY2Fir8RrkfQ6e5BtcSsXCqxWIDBevVtCNq/dySr2tp7OFFiMpXzhyQEREssXfVJqxOCAiItlibaAZr1YgIiIiEY4cEBGRfHHoQCMWB0REJFtluUOiIWNxQEREssUJiZqxOCAiItlibaAZiwMiIpIvVgca8WoFIiIiEuHIARERyRYnJGrG4oCIiGSLExI1Y3FARESyxdpAMxYHREQkWxw50IzFARERyRirA01YHJRC7NkYbNu8ERcvxiM9LQ1Llq/EWwGB6vWCIGDd6pXYv+8rPMrKQkPvRgj5ZDY8antKGLXu7N29E1s2b0R6Who8anti6vQZaOrbTOqwdCb2bAy2bNqIhIsXkJaWhqVfrEK7fx1/Q8fjbRjHu5K5CULea4ROvtXgaKPE+ZsZmLHjT5xLfADg5Y9KnrPnHFYeuQQAcLI1x7w+TdC2gQsqWZjiWnIWln57Ed/G3NZbHqQbvJSxFHJyclCnbj1MnzFL4/otm77Ejm1bMH3GLOzY8zUcHKtg1PAhyM5+rOdIdS/y6BFEhIdh+IjR2LvvIJo29cWYkcORfPeu1KHpTE7OE9StWxfTZ86WOhS94/E2HMuGvg7/Bi4Yve4M2sw4ip8upOCbaW/B1c4CAFB/3AHRMm5DNAoLBdEX/5qRfqjtYoMPlv2CNjOO4PuzSdg4tiW8X7OTKq0SUyhKvxgyFgel0LrNmxj7cTAC2ncosk4QBOzavg1DR4xCQPsOqO1ZB5+GhiM3NxdHv/9Ogmh1a/vWzXj3vffQ4/2eqOXhgakhM+Hi6oKv9u6WOjSdad2mLT4aPwGBGo6/oePxNgzmpsbo0swdc/fG4czlNCSmPkbEgQu4mZaNwe1qAwBSM3NFS1DTajidcA8307LV+2lW2wEbjl/Bn9cf4GZaNhYfjkfmk3w0qkjFQRkWQ8biQMvuJCUhPT0Nfi1bqdvMzMzg26w5/oo7J2Fk2pefl4eEi/Hwa9la1O7XspXB5Uo83obExFgBE2MjqPILRO25+QVoUadKkf5VbMzRvrEbdvxyXdT++5V0dH+jOipbmUGhAN5tUR1mJkb47VKqTuPXJo4caFau5hxkZGRg69atuHr1KlxdXTFw4EC4u7u/chuVSgWVSiVqKzAyg1Kp1GWoL5WengYAsHdwELU7ODgY3NBrxsMMFBQUwKFIro7qz4EMB4+34Xic+xR/XE3DpG4NcOVuFlIzc/Ge32vwreWA6/ceFenfp3VNPM7Nx3dnxXMJhq76DRvHtsI/a95D/tNC5OQ9xcDlp3EjteKcQuVNkDSTdOTAzc0N9+/fBwAkJibCy8sLixYtwtWrV7Fu3Tp4e3vj0qVLr9xHWFgYbG1tRcvni8L0Ef4rKV4oKwWhaJuhKJqrYLC5Eo+3oRi9LhoKhQLxX3RH8qZeGNG+DvafuYmCQqFI3/5v1sK+Mzehyi8Utc98vxEqW5ni3fCTCJjzA1ZHXsamj1qhfjVbfaVRdjyvoJGkIwcpKSkoKHg2rDVjxgzUq1cP33//PSwtLaFSqfD+++9j1qxZ+Prrr1+6j5CQEEycOFHUVmBkptO4X8XR8dmQ3P30dFSp4qRuf/DgfpHRhIrOrrIdjI2NkZ6eLmp/8OA+HBwcJYqKdIXH27DcSH2MrqE/wtLMGNYWpriXmYsvx7bEzTTxr/436lSBp5sNhq76TdRew6kShrevg5Yh3+PynSwAQPzth/CrWwVDAz0xectZveVC2ldu5hz8/vvvmDVrFiwtLQEASqUSn3zyCaKjo1+5nVKphI2NjWiR6pQCAFStVg2OjlUQfSZK3Zafn4fYszFo3MRHsrh0wdTMDPW9GiA6SvxHIzoqyuByJR5vQ/UkrwD3MnNha2mKdg1dcfTPO6L1H7SthbjE+4i//VDUbmFmDODZqOi/FRQKMKpAI0kcONBM8jkHz4cjVSoVnJ2dReucnZ2Rllb+zmU+eZKN27duqV/fuZOEy5cSYGNrC1dXN/Qb8CE2bliH6tVfQ/XXXsPGDetgbm6OoE6dJYxaNwYMHIyZ06fCq2FDNG7sg/1f70VycjJ69u4jdWg68yQ7G7f+ffyTknApIQG2trZwdXOTMDLd4/E2nOP9lrcLFFDgWnIWajlbY26fJriWkoVdv/5v0qG1uQm6vl4ds3cVnXB6NTkL/6Q8wuJBzTFnzzk8eJyHd5pWg38DF/RdckqfqZRJBapj9Ery4iAgIAAmJibIysrClStX0KBBA/W6W7duwdGx/A1XXrxwAcOHDFS/XhwRDgDo0q075i8Mx6Ahw6DKzUXYgvnIyspEw0aNsGb9RlhZVZIqZJ15O+gdZD7MwPo1q5GWlorannWwau16uLlVlTo0nYmPv4Bhgz9Uv/484tkcl67d3sWnoeFShaUXPN6Gc7xtLEwxq2djuNlbIiM7D9/F3MaCfX/jacH/hgLefeM1KADsj75ZZPunBQL6LP4Zs3s1wc4JbWFlboLEe48wdn00TvydrMdMyoYTEjVTCMKLg0L6M2/ePNHrN954Ax07dlS/njJlCpKSkrB7d8muoX6SL1lKkqpIQ3lEVDJVhxjuvSRe5WV3atSWtMdPS71tlUqS/77WGUmLA11hcUBEhobFgW6kl6E4cDTg4qDcTEgkIiKi8sFwyx4iIqL/wAFXzVgcEBGRbHFComYsDoiISLY4cqAZ5xwQERGRCEcOiIhItjhyoBlHDoiIiEiEIwdERCRbnJCoGYsDIiKSLZ5W0IzFARERyRZrA81YHBARkXyxOtCIExKJiIhIhCMHREQkW5yQqBmLAyIiki1OSNSMxQEREckWawPNOOeAiIjkS1GGpRRWr16NmjVrwtzcHL6+vvj111/LmoFOsDggIiLZUpThv5Lau3cvgoODMXPmTJw7dw5t2rRBUFAQbt26pYPMyobFARERkR4sWbIEQ4cOxbBhw1C/fn0sW7YM7u7uWLNmjdShFcE5B0REJFtlmZCoUqmgUqlEbUqlEkqlskjfvLw8xMbGYvr06aL2Dh06ICoqqvRB6IpAWpObmyvMmTNHyM3NlToUvWLezFsOmLe88i6OOXPmCABEy5w5czT2vXPnjgBA+O2330TtCxcuFOrUqaOHaEtGIQiCIGl1YkCysrJga2uLzMxM2NjYSB2O3jBv5i0HzFteeRdHSUYO7t69i6pVqyIqKgp+fn7q9oULF2L79u24dOmSzuMtCZ5WICIiKoWXFQKaODo6wtjYGCkpKaL21NRUODs76yK8MuGERCIiIh0zMzODr68vjh8/Lmo/fvw4WrZsKVFUL8eRAyIiIj2YOHEiBgwYgGbNmsHPzw/r16/HrVu3MGrUKKlDK4LFgRYplUrMmTOn2MNMhoJ5M285YN7yylsXevfujfv372P+/PlITk5Gw4YNceTIEbz22mtSh1YEJyQSERGRCOccEBERkQiLAyIiIhJhcUBEREQiLA6IiIhIhMWBFlWUR3Fqyy+//IIuXbrAzc0NCoUCBw8elDokvQgLC0Pz5s1hbW0NJycndO/eHZcvX5Y6LJ1bs2YNGjVqBBsbG9jY2MDPzw9Hjx6VOiy9CwsLg0KhQHBwsNSh6NTcuXOhUChEi4uLi9RhkZ6wONCSivQoTm3Jzs5G48aNsXLlSqlD0atTp05h7NixiI6OxvHjx/H06VN06NAB2dnZUoemU9WqVUN4eDjOnj2Ls2fPol27dujWrRvi4+OlDk1vYmJisH79ejRq1EjqUPSiQYMGSE5OVi/nz5+XOiTSE17KqCUtWrRA06ZNRY/erF+/Prp3746wsDAJI9MPhUKBAwcOoHv37lKHondpaWlwcnLCqVOn8Oabb0odjl7Z29vjs88+w9ChQ6UOReceP36Mpk2bYvXq1ViwYAGaNGmCZcuWSR2WzsydOxcHDx5EXFyc1KGQBDhyoAXPH8XZoUMHUXu5fRQnaVVmZiaAZ1+UclFQUIA9e/YgOztb9BAZQzZ27Fh06tQJgYGBUoeiN1evXoWbmxtq1qyJPn364Pr161KHRHrCOyRqQXp6OgoKCoo8PMPZ2bnIQzbIsAiCgIkTJ6J169Zo2LCh1OHo3Pnz5+Hn54fc3FxUqlQJBw4cgJeXl9Rh6dyePXvw559/IiYmRupQ9KZFixbYtm0b6tSpg3v37mHBggVo2bIl4uPj4eDgIHV4pGMsDrRIoVCIXguCUKSNDMtHH32Ev//+G6dPn5Y6FL2oW7cu4uLi8PDhQ+zfvx8DBw7EqVOnDLpAuH37NsaPH49jx47B3Nxc6nD0JigoSP2/vb294efnBw8PD2zduhUTJ06UMDLSBxYHWlDRHsVJ2jFu3DgcPnwYv/zyC6pVqyZ1OHphZmaG2rVrAwCaNWuGmJgYLF++HOvWrZM4Mt2JjY1FamoqfH191W0FBQX45ZdfsHLlSqhUKhgbG0sYoX5YWVnB29sbV69elToU0gPOOdCCivYoTiobQRDw0Ucf4ZtvvsHJkydRs2ZNqUOSjCAIUKlUUoehUwEBATh//jzi4uLUS7NmzdC/f3/ExcXJojAAAJVKhYSEBLi6ukodCukBRw60pCI9ilNbHj9+jGvXrqlfJyYmIi4uDvb29qhevbqEkenW2LFjsWvXLhw6dAjW1tbqESNbW1tYWFhIHJ3uzJgxA0FBQXB3d8ejR4+wZ88e/Pzzz4iMjJQ6NJ2ytrYuMp/EysoKDg4OBj3PZPLkyejSpQuqV6+O1NRULFiwAFlZWRg4cKDUoZEesDjQkor0KE5tOXv2LN566y316+fnIQcOHIgtW7ZIFJXuPb9c1d/fX9S+efNmDBo0SP8B6cm9e/cwYMAAJCcnw9bWFo0aNUJkZCTat28vdWikA0lJSejbty/S09NRpUoVvPHGG4iOjjbov2n0P7zPAREREYlwzgERERGJsDggIiIiERYHREREJMLigIiIiERYHBAREZEIiwMiIiISYXFAREREIiwOiIiISITFAVEFMHfuXDRp0kT9etCgQejevbve47hx4wYUCgXi4uL0/t5EpD8sDojKYNCgQVAoFFAoFDA1NUWtWrUwefJkZGdn6/R9ly9fXuxbVPMLnYhKis9WICqjt99+G5s3b0Z+fj5+/fVXDBs2DNnZ2epnMDyXn58PU1NTrbynra2tVvZDRKQJRw6IykipVMLFxQXu7u7o168f+vfvj4MHD6pPBWzatAm1atWCUqmEIAjIzMzEiBEj4OTkBBsbG7Rr1w5//fWXaJ/h4eFwdnaGtbU1hg4ditzcXNH6F08rFBYWYtGiRahduzaUSiWqV6+OhQsXAoD6kdI+Pj5QKBSiB0Zt3rwZ9evXh7m5OerVq4fVq1eL3uePP/6Aj48PzM3N0axZM5w7d06LnxwRlVccOSDSMgsLC+Tn5wMArl27hq+++gr79++HsbExAKBTp06wt7fHkSNHYGtri3Xr1iEgIABXrlyBvb09vvrqK8yZMwerVq1CmzZtsH37dnzxxReoVavWS98zJCQEGzZswNKlS9G6dWskJyfj0qVLAJ59wb/++us4ceIEGjRoADMzMwDAhg0bMGfOHKxcuRI+Pj44d+4chg8fDisrKwwcOBDZ2dno3Lkz2rVrhx07diAxMRHjx4/X8adHROWCQESlNnDgQKFbt27q17///rvg4OAg9OrVS5gzZ45gamoqpKamqtf/+OOPgo2NjZCbmyvaj4eHh7Bu3TpBEATBz89PGDVqlGh9ixYthMaNG2t836ysLEGpVAobNmzQGGNiYqIAQDh37pyo3d3dXdi1a5eo7dNPPxX8/PwEQRCEdevWCfb29kJ2drZ6/Zo1azTui4gMC08rEJXRd999h0qVKsHc3Bx+fn548803sWLFCgDAa6+9hipVqqj7xsbG4vHjx3BwcEClSpXUS2JiIv755x8AQEJCAvz8/ETv8eLrf0tISIBKpUJAQECxY05LS8Pt27cxdOhQURwLFiwQxdG4cWNYWloWKw4iMhw8rUBURm+99RbWrFkDU1NTuLm5iSYdWllZifoWFhbC1dUVP//8c5H9VK5cuVTvb2FhUeJtCgsLATw7tdCiRQvRuuenPwRBKFU8RFTxsTggKiMrKyvUrl27WH2bNm2KlJQUmJiYoEaNGhr71K9fH9HR0fjwww/VbdHR0S/dp6enJywsLPDjjz9i2LBhRdY/n2NQUFCgbnN2dkbVqlVx/fp19O/fX+N+vby8sH37duTk5KgLkFfFQUSGg6cViPQoMDAQfn5+6N69O3744QfcuHEDUVFR+OSTT3D27FkAwPjx47Fp0yZs2rQJV65cwZw5cxAfH//SfZqbm2PatGmYOnUqtm3bhn/++QfR0dHYuHEjAMDJyQkWFhaIjIzEvXv3kJmZCeDZjZXCwsKwfPlyXLlyBefPn8fmzZuxZMkSAEC/fv1gZGSEoUOH4uLFizhy5Ag+//xzHX9CRFQesDgg0iOFQoEjR47gzTffxJAhQ1CnTh306dMHN27cgLOzMwCgd+/emD17NqZNmwZfX1/cvHkTo0ePfuV+Z82ahUmTJmH27NmoX78+evfujdTUVACAiYkJvvjiC6xbtw5ubm7o1q0bAGDYsGH48ssvsWXLFnh7e6Nt27bYsmWL+tLHSpUq4dtvv8XFixfh4+ODmTNnYtGiRTr8dIiovFAIPLFIRERE/8KRAyIiIhJhcUBEREQiLA6IiIhIhMUBERERibA4ICIiIhEWB0RERCTC4oCIiIhEWBwQERGRCIsDIiIiEmFxQERERCIsDoiIiEjk/wA2FtGXFSmP/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for ExtraTrees ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1209\n",
      "           1       1.00      1.00      1.00      1029\n",
      "           2       0.99      0.98      0.99      1101\n",
      "           3       0.99      1.00      0.99      1086\n",
      "           4       1.00      1.00      1.00      1148\n",
      "           5       0.99      0.99      0.99       990\n",
      "\n",
      "    accuracy                           0.99      6563\n",
      "   macro avg       0.99      0.99      0.99      6563\n",
      "weighted avg       0.99      0.99      0.99      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for RandomForest ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.97      0.96      1209\n",
      "           1       0.98      0.99      0.99      1029\n",
      "           2       0.97      0.94      0.95      1101\n",
      "           3       0.98      0.99      0.99      1086\n",
      "           4       1.00      0.99      1.00      1148\n",
      "           5       0.99      0.99      0.99       990\n",
      "\n",
      "    accuracy                           0.98      6563\n",
      "   macro avg       0.98      0.98      0.98      6563\n",
      "weighted avg       0.98      0.98      0.98      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for SVM ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.78      0.77      1209\n",
      "           1       0.61      0.68      0.64      1029\n",
      "           2       0.56      0.60      0.58      1101\n",
      "           3       0.65      0.56      0.60      1086\n",
      "           4       0.74      0.73      0.73      1148\n",
      "           5       0.83      0.78      0.80       990\n",
      "\n",
      "    accuracy                           0.69      6563\n",
      "   macro avg       0.69      0.69      0.69      6563\n",
      "weighted avg       0.69      0.69      0.69      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for KNN ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.76      0.72      1209\n",
      "           1       0.63      0.66      0.64      1029\n",
      "           2       0.53      0.53      0.53      1101\n",
      "           3       0.61      0.55      0.58      1086\n",
      "           4       0.70      0.68      0.69      1148\n",
      "           5       0.83      0.78      0.81       990\n",
      "\n",
      "    accuracy                           0.66      6563\n",
      "   macro avg       0.66      0.66      0.66      6563\n",
      "weighted avg       0.66      0.66      0.66      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for LogisticRegression ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.61      0.59      1209\n",
      "           1       0.44      0.47      0.45      1029\n",
      "           2       0.45      0.43      0.44      1101\n",
      "           3       0.50      0.45      0.47      1086\n",
      "           4       0.55      0.53      0.54      1148\n",
      "           5       0.68      0.73      0.71       990\n",
      "\n",
      "    accuracy                           0.54      6563\n",
      "   macro avg       0.53      0.54      0.53      6563\n",
      "weighted avg       0.53      0.54      0.53      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for NaiveBayes ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.69      0.61      1209\n",
      "           1       0.40      0.51      0.45      1029\n",
      "           2       0.42      0.40      0.41      1101\n",
      "           3       0.47      0.43      0.45      1086\n",
      "           4       0.60      0.42      0.50      1148\n",
      "           5       0.70      0.61      0.65       990\n",
      "\n",
      "    accuracy                           0.51      6563\n",
      "   macro avg       0.52      0.51      0.51      6563\n",
      "weighted avg       0.52      0.51      0.51      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Classification report for AdaBoost ===\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.58      0.65      1209\n",
      "           1       1.00      0.53      0.69      1029\n",
      "           2       0.23      0.50      0.32      1101\n",
      "           3       0.44      1.00      0.61      1086\n",
      "           4       0.00      0.00      0.00      1148\n",
      "           5       1.00      0.24      0.38       990\n",
      "\n",
      "    accuracy                           0.48      6563\n",
      "   macro avg       0.57      0.48      0.44      6563\n",
      "weighted avg       0.56      0.48      0.44      6563\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#step 10: confusion matrix + classification report for ALL models\n",
    "\n",
    "top_models = res_df['model'].tolist()  #all models\n",
    "\n",
    "for mname in top_models:\n",
    "    model_obj = results_all[mname]['model']\n",
    "    y_pred = model_obj.predict(X_test_scaled)\n",
    "    \n",
    "    print(f\"\\n=== Classification report for {mname} ===\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    plt.figure(figsize=(6,4))\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
    "    plt.title(f'Confusion Matrix — {mname}')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "e6e41eff-ec30-4f59-a23d-f1978682fdb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#binarize labels\n",
    "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
    "n_classes = y_test_bin.shape[1]\n",
    "\n",
    "#sort models by f1_macro (top 3)\n",
    "top3 = sorted(results_all.items(), key=lambda x: x[1]['f1_macro'], reverse=True)[:3]\n",
    "\n",
    "# plot 3 separate ROC plots\n",
    "plt.figure(figsize=(15, 4))\n",
    "\n",
    "for i, (name, res) in enumerate(top3):\n",
    "    model = res['model']\n",
    "    \n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "        y_score = model.predict_proba(X_test_scaled)\n",
    "\n",
    "        #micro-average ROC\n",
    "        fpr, tpr, _ = roc_curve(y_test_bin.ravel(), y_score.ravel())\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "\n",
    "        #plot each on its own subplot\n",
    "        plt.subplot(1, 3, i + 1)\n",
    "        plt.plot(fpr, tpr, color='green', lw=2, label=f\"AUC = {roc_auc:.2f}\")\n",
    "        plt.plot([0, 1], [0, 1], 'k--', lw=1)\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.title(f'ROC — {name}\\n(F1 = {res[\"f1_macro\"]:.2f})')\n",
    "        plt.legend(loc='lower right')\n",
    "        plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57816e47-48e8-4cc9-af38-f47a230764ed",
   "metadata": {},
   "source": [
    "The comparison shows that Gradient Boosting, XGBoost, and LightGBM achieved perfect results (accuracy and F1 = 1.0, very low log loss), indicating extremely strong predictive power but also potential overfitting, as the dataset appears highly correlated. MLP, ExtraTrees, and RandomForest also performed exceptionally well, suggesting stable generalization. In contrast, simpler models like SVM, KNN, Logistic Regression, and Naive Bayes struggled to capture the complex relationships within the data. Overall, ensemble gradient-boosting methods proved most effective, though further validation on unseen or shuffled data is recommended to confirm their robustness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "eb16cf6a-99b5-4206-bbae-5abae25d9548",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#binarize labels (for multiclass ROC)\n",
    "y_test_bin = label_binarize(y_test, classes=np.unique(y_test))\n",
    "n_classes = y_test_bin.shape[1]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "#loop through all models in results_all\n",
    "for name, res in results_all.items():\n",
    "    model = res['model']\n",
    "    \n",
    "    # only models that support predict_proba\n",
    "    if hasattr(model, \"predict_proba\"):\n",
    "        try:\n",
    "            y_score = model.predict_proba(X_test_scaled)\n",
    "            \n",
    "            # calculate micro-average ROC\n",
    "            fpr, tpr, _ = roc_curve(y_test_bin.ravel(), y_score.ravel())\n",
    "            roc_auc = auc(fpr, tpr)\n",
    "            \n",
    "            # plot each model curve\n",
    "            plt.plot(fpr, tpr, lw=1.8, label=f\"{name} (AUC={roc_auc:.2f}, F1={res['f1_macro']:.2f})\")\n",
    "        \n",
    "        except Exception as e:\n",
    "            print(f\"Skipping {name}: {e}\")\n",
    "\n",
    "#add baseline\n",
    "plt.plot([0, 1], [0, 1], 'k--', lw=1)\n",
    "\n",
    "#formatting\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curves — All Models (Multiclass, One-vs-Rest)')\n",
    "plt.legend(fontsize=8, loc='lower right')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "0cea3f20-2e58-4de6-b9e6-efa9d54174d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved results_all.joblib and models_results_summary.csv\n"
     ]
    }
   ],
   "source": [
    "#step 12: Save results and final notes\n",
    "from joblib import dump\n",
    "dump(results_all, 'results_all.joblib')\n",
    "res_df.to_csv('models_results_summary.csv', index=False)\n",
    "print(\"Saved results_all.joblib and models_results_summary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daddde59-e662-4450-b705-31220d8b7448",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
