Luthfi_Arfiansyah-submission.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BRI Financial Data Challenge\n",
    "\n",
    "### Submission by Luthfi Arfiansyah"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "USECASE:\n",
    "Goal: Membuat model klasifikasi yang dapat menentukan apakah seorang nasabah akan churn atau tidak.\n",
    "Model harus dibuat sekurang-kurangnya meliputi :\n",
    "1. Exploratory data analysis (EDA)\n",
    "2. Feature Engineering\n",
    "3. Modeling\n",
    "4. Model Evaluation\n",
    "\n",
    "EVALUASI DALAM KOMPETISI: \n",
    "Evaluasi dilakukan dengan mengukur performa model yang dilakukan oleh peserta, berdasarkan\n",
    "performance AUC yang dihasilkan dari data testing.\n",
    "\n",
    "Data terdiri atas dua dataset, yaitu:\n",
    "- fin_data_challenge_train.csv: 100.000 baris dan 126 kolom (125 kolom fitur dan 1 kolom target 'y’)\n",
    "- fin_data_challenge_test.csv: 25.000 baris dan 125 kolom (tanpa kolom target ‘y’)\n",
    "\n",
    "Deskripsi Kolom\n",
    "Data Nasabah:\n",
    "x0-x124 (125 kolom) : _Merupakan data nasabah yang telah dinormalisasi dan dirahasiakan nama kolomnya._\n",
    "\n",
    "Variabel target:\n",
    "y - Apakah nasabah akan churn? (1 :iya, 0 :tidak)\n",
    "\n",
    "Note : terdapat beberapa Missing Value pada data.\n",
    "\n",
    "File yang dikirimkan memiliki format sebagai berikut:\n",
    "1. Coding script dalam bentuk ‘nama_lengkap-submission.ipynb’\n",
    "2. Nama file output (hasil prediksi terhadap data test): 'nama_lengkap-submission.csv' <br /> Kolom: 'Id' dan 'Predicted'. <br />Mohon perhatikan bahwa nama file dan nama kolom bersifat case sensitive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jawaban : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _Import Necessary Module_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary library\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import tree\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deklarasi data csv kedalam variabel\n",
    "df_train = pd.read_csv('findata_challenge_train.csv') #untuk data training\n",
    "df_test = pd.read_csv('findata_challenge_test.csv') #untuk data testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "# Memastikan data sudah berhasil dibuka\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "\n",
       "[5 rows x 125 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA) & Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah data berhasil masuk kedalam variabel, Langkah selanjutnya adalah melakukan analisa dan eksplorasi data (EDA) & manipulasi data (). Dari soal diketahui bahwa data telah dinormalisasi dan ada beberapa data yang hilang. Selain itu karena nama kolom data dirahasiakan, maka penting untuk mengetahui bahwa kolom data-data yang ada pada variabel df_train merupakan kolom data kategorikal atau numerikal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Classification:  Membagi kolom berdasarkan tipe data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------\n",
      "Info Data Train:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Columns: 126 entries, x0 to y\n",
      "dtypes: float64(120), int64(1), object(5)\n",
      "memory usage: 96.1+ MB\n",
      "None\n",
      "--------------------------\n",
      "Info Data Test:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25000 entries, 0 to 24999\n",
      "Columns: 125 entries, x0 to x124\n",
      "dtypes: float64(120), object(5)\n",
      "memory usage: 23.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('--------------------------')\n",
    "print('Info Data Train:')\n",
    "print(df_train.info())\n",
    "print('--------------------------')\n",
    "print('Info Data Test:')\n",
    "print(df_test.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari Informasi diatas dapat dilihat bahwa:\n",
    "- data train memiliki: 120 kolom data bertipe float, 1 kolom data bertipe integer, dan 5 kolom data bertipe object/ non-numeric\n",
    "- data test memiliki: 120 kolom data bertipe float dan 5 kolom data bertipe object/ non-numeric\n",
    "\n",
    "Selanjutnya, saya klasifikasikan nama kolom berdasarkan tipe data yang dimiliki."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kolom data integer pada df_train:  ['y'] , banyak data: 1\n",
      "Kolom data object pada df_train:  ['x21', 'x79', 'x89', 'x108', 'x112'] , banyak data: 5\n",
      "Kolom data float pada df_train:  ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50', 'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60', 'x61', 'x62', 'x63', 'x64', 'x65', 'x66', 'x67', 'x68', 'x69', 'x70', 'x71', 'x72', 'x73', 'x74', 'x75', 'x76', 'x77', 'x78', 'x80', 'x81', 'x82', 'x83', 'x84', 'x85', 'x86', 'x87', 'x88', 'x90', 'x91', 'x92', 'x93', 'x94', 'x95', 'x96', 'x97', 'x98', 'x99', 'x100', 'x101', 'x102', 'x103', 'x104', 'x105', 'x106', 'x107', 'x109', 'x110', 'x111', 'x113', 'x114', 'x115', 'x116', 'x117', 'x118', 'x119', 'x120', 'x121', 'x122', 'x123', 'x124'] , banyak data: 120\n",
      "-------------------------------------------------------------------\n",
      "Kolom data integer pada df_test:  [] , banyak data: 1\n",
      "Kolom data object pada df_test:  ['x21', 'x79', 'x89', 'x108', 'x112'] , banyak data: 5\n",
      "Kolom data float pada df_test:  ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x22', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50', 'x51', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60', 'x61', 'x62', 'x63', 'x64', 'x65', 'x66', 'x67', 'x68', 'x69', 'x70', 'x71', 'x72', 'x73', 'x74', 'x75', 'x76', 'x77', 'x78', 'x80', 'x81', 'x82', 'x83', 'x84', 'x85', 'x86', 'x87', 'x88', 'x90', 'x91', 'x92', 'x93', 'x94', 'x95', 'x96', 'x97', 'x98', 'x99', 'x100', 'x101', 'x102', 'x103', 'x104', 'x105', 'x106', 'x107', 'x109', 'x110', 'x111', 'x113', 'x114', 'x115', 'x116', 'x117', 'x118', 'x119', 'x120', 'x121', 'x122', 'x123', 'x124'] , banyak data: 120\n",
      "-------------------------------------------------------------------\n",
      "Apakah kolom data int df_train = df_test?  False\n",
      "Apakah kolom data object df_train = df_test?  True\n",
      "Apakah kolom data float df_train = df_test?  True\n",
      "-------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Klasifikasi kolom data berdasarkan tipe datanya\n",
    "\n",
    "df_train_by_type = {\n",
    "  'int': df_train.select_dtypes(include=['int64']).columns.to_list(),\n",
    "  'float': df_train.select_dtypes(include=['float64']).columns.to_list(),\n",
    "  'obj': df_train.select_dtypes(include=['object']).columns.to_list()  \n",
    "}\n",
    "df_test_by_type = {\n",
    "  'int': df_test.select_dtypes(include=['int64']).columns.to_list(),\n",
    "  'float': df_test.select_dtypes(include=['float64']).columns.to_list(),\n",
    "  'obj': df_test.select_dtypes(include=['object']).columns.to_list()  \n",
    "}\n",
    "print('Kolom data integer pada df_train: ', df_train_by_type['int'], ', banyak data:', len(df_train_by_type['int']))\n",
    "print('Kolom data object pada df_train: ', df_train_by_type['obj'], ', banyak data:', len(df_train_by_type['obj']))\n",
    "print('Kolom data float pada df_train: ', df_train_by_type['float'], ', banyak data:', len(df_train_by_type['float']))\n",
    "print('-------------------------------------------------------------------')\n",
    "print('Kolom data integer pada df_test: ', df_test_by_type['int'], ', banyak data:', len(df_train_by_type['int']))\n",
    "print('Kolom data object pada df_test: ', df_test_by_type['obj'], ', banyak data:', len(df_train_by_type['obj']))\n",
    "print('Kolom data float pada df_test: ', df_test_by_type['float'], ', banyak data:', len(df_train_by_type['float']))\n",
    "print('-------------------------------------------------------------------')\n",
    "print('Apakah kolom data int df_train = df_test? ', df_train_by_type['int'] == df_test_by_type['int'])\n",
    "print('Apakah kolom data object df_train = df_test? ', df_train_by_type['obj'] == df_test_by_type['obj'])\n",
    "print('Apakah kolom data float df_train = df_test? ', df_train_by_type['float'] == df_test_by_type['float'])\n",
    "print('-------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari data diatas bahwa pada data train dan data test masing masing memiliki 120 data numerikal (tipe _float64_) dan 5 data kategorikal (tipe _object_). Kolom 'y' (tipe _int_) diabaikan karena merupakan _dependent variable_. Selanjutnya saya akan melakukan eksplorasi lanjut dengan mencari data _unique_ pada masing-masing kolom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Kalkulasi Unique Data perkolom\n",
    "unique_per_column = [ len(df_train['x'+str(x)].unique()) for x in range(len(df_train.loc[:, df_train.columns!='y'].columns))  ]\n",
    "unique_per_column = pd.DataFrame({'Unique Data':unique_per_column, 'Column': ['x'+str(x) for x in range(len(df_train.loc[:, df_train.columns!='y'].columns))]})\n",
    "unique_per_column = unique_per_column.set_index('Column').T\n",
    "\n",
    "# Kelompokan data berdasarkan tipe kolom\n",
    "unique_float = unique_per_column[df_train_by_type['float']].T\n",
    "unique_obj = unique_per_column[df_train_by_type['obj']].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ksb+bobPNkudnDCQ9K8lDJoNHo+PG5XLP8eXGacDmfg4fQyv/laFb0EkZOnJNddwxfHfbJB+YCzyNr/FHSf5ygTqOztCR6cOTK7v7M0nOneL1JEMnqr0ydD/bJcnRNTGN4pyx+9ADMnQaeu68475v3M+1M4QEF3LvJLsnecVc4Gl87i8zBA/PzXCdzfeKudDPOPYz4/pXzevKNNdhaq9Fjj/fo5P8cYb7/Jwkh1fVjRcZe2qG7k2b3UdjPXMhsS2959PcQ3+UoRPTs+d1rTo8QwewSYeOy8dOBo7GkNPzM/zj0EPmPedTk4GnLamqe2UIZX02yT26+2cTmzdmeB8fMXn80XHjcj0+QwEAAGBm6fQEAAAA2799x+UnFtn+qQxTTu2TcSq7+br7jCRvqaoNVfW7SX4nQ1ehfZLcaRx2sQDIMly0wrEnLbDurHG5c4aw1nw3HJefXWDbx6aoZzNVtWuG7j4/SPLsBTrCzHUh2jfJV7b2OBMuMy5/liTd/fkkn6+qXceOO9dKcp0M5/ba2cL56e6LMnQLmguG/W6Saya5fpLbj8OWe44vm80DPz3W+c0MAZOXznX2mfK4NxqXJy5wzBMWeE3HJzm+qi6f4X2/VoZzdIsM065Nc81eKUMA6P+Nx7pFkmdnCNhMum6GaROPH1/bfMdnmHpun/w67DJp33F5sfu1u39UVV9Psm9VXba7z5rYfPK84XPBmm/PWz93T+y8wLEXcuUMU/rdM0No55UZAl/7zZ/OsrtPTfLGqtpUVTfJ8F5cM8mNs4zPiWnuoar6ZoZ7+T/nvQ/p7p9V1RczdLSas2+S0+Z1cpozF8jaZ976+e/dUvZM8vYMf2/9ZHefPa+mczKEtlJV18lwfV8zw/V+x3HY1nyGAgAAwA5L6AkAAAC2f3OdgM5aZPv3xuUuS+2kqu6T5AUZAjTJEGL5zyRfzBBoWHrep4XNBQN2WmLMpeaNnbTQNFtzHV8Wq+ey4/JnC2z78RJ1bMncfv9P5nX4mefyKzhGkqSqdh+P95O5wMfY2eqvkzwqvz6Xp2UIz/woyVWWsd8bZgi17D+uuiBD56wTMwSolnuOv9Pdey1z7DTH3X1c/nSB3fxkgf3unuRvM0xjt1OGa+OUJB9NcpNMd83+SXcfOe730AzTtT17nBLt0xPjVnq/Lef5+47Pnxwzv3vQnAWnopvCCUnuNU6peESGTl37Z5jC7wnzB1fVozJ0evqtcdWZGaaN/GqGqeiWes+nuYeuMP6+WCem7817vNsyxs4/J+fOH7iE3TO8xk1JnlRVR3f3FyYHVNVtM1yPNxlXnZfh8/M/k1wtW/cZCgAAADss09sBAADA9m8uIHLVRbbPBUnOWGwHVXXzJO/M0B3mARk65uzW3fsn+fBiz1uGuZDRlZcYM1f3SgJJk+amvVooBLTHAuvmQlTz/44yPyAxF6L6ZHfXEj+v2sq6J916XE52N3pphunP3pehQ9IVuvu3u/uBWTxA8ytVdZkkx2boYPS0DF1vdu3ufTMEktbElMedO3eXzcXtusC6ozNMgfb/ktwqyWW7e+/ufsRWlPruuV+6+6sZgj2bkrypqi49MW6l99uK79dVduzYpSjjFHIPy3CtP66q7jo5sKrum+S1Ge7VA5Ps2d27d/fdk3x+Gcea5h6ae592W2Rf89f/NGv7nv4owzSij8twXRw5Tt2Y5FddzD6QYVrBR2bo9LRrd98iydtWcFwAAADYYQk9AQAAwPbvC+PyVotsv22GYM9/j497gTH3z/B3hMd299u6+5tjACIZprpLtq5LyX9M1LCYuYDPp5cYM43PZXiNv7/AtpsusO78cXnpeeuvOflg7Lj0P0luUFWXmjc2VXVIVR1WVXtNXfHFPW5cvmVi3QOT/DDJwd19XHf/ZDzupZJcffx9qXN0hwzhsyO6+6Xd/aXunnvtKznHWzLNcT+f4dwtdC3vN/mgqi6X5B5JTuzux3T3Cd3903HbXhmmt1vJ63lpkn/P0PnsbyfWfz1DB5+bVdVCU8jNXeuLTXH4hXF56/kbqmq3DF2eTp54j7ap7v52kqdneO/+oaquMLH5gXPL7n53d393YttC19BmnzXT3EPj9HHfSLLP/Pe5qjZm3vWQ4X297Dg953xbOifL8b3u/kF3H5thGrubZfNOWAdm6Fr3F939uu7+andfOG5by/sLAAAAtltCTwAAALCd6+7/SfKxJPtV1WMmt1XVIzIESD7W3aeOqy8Yl5eYGHreuNysI1NV3TG/DjosNUXdYj6Z5DtJDq2qu8zfWFW3ztBZ6ptJPr4V+7+Y7v5Bkn9LcouqetDEsXZLcvgCT/nauLznxNhLJvnTBcYelWHqrRfM6/Jy/SRHJHlKFpiGbblq8Mwkd80wLdY7JjaflyHIc7mJ8RuTvCK/niJw8hxdkOWd4z3z66nGtuYcb8myjzueuw8kuUNVHTQxdrdcfDq085NclGT3qrrExNhLZTgXv9rv1ujui5I8NMMUaH9cVfca1/8iyVszTO+22fVUVXdLcr8kJ2fzLl2T3pWhM9djq2puGrRU1ab8+lz+49bWvUpem+QjGbqlHTmxfrFzeUiS240P51+DyebX4VFZ/j30Dxk6Oh02r75nZZgib9JR4/IVk525quoaGbp2XZDV67j0lAydpZ5XVVcb1y323twoyZPGh2txfwEAAMB2a9N6FwAAAABsE4/KEDB6dVXdJ8mXktwwyZ2TfC/DdEtzThuXB1fVz5K8Mcnbkzx1fP7tknw/yY0yhG9+nORKSSY7vixLd/+iqg7NMHXY+6vqI/l1p5t9ktwpyZlJHrLKnW0en6Gr05uq6g8zdJe5Z349zdWkN2TorPSKqrpFhtd77wzBlJ/PG/uCDO/JE5PcpqqOyxBCum+GTlEPGjvULMeTq+rM8fdNSa6YIThyvSSnJLnPRKeYZJjK7WlJTqyqd43PuWuS62aYemuPDOfo++P405Jcr6pek2FKvI+O+31IVV0xQ6jqauNrPS9DV56pz/EyHD/lcR+fITD0jvF1nprkgAwBp1/p7nOq6pgkf5jkM1X1oQxT4B2QIRDzv0kuV1UbxgDT1Lr761X17Axdn15fVTfs7tMzdEK6VZJnjPfLCUn2TnKvDGGYB090Spu/z7Or6mEZ7rkTqupfkpyeoSPWDTPcxy/cmnpXS3d3VT08yZeT3KeqHtbdb8hwDd4/yb9U1VuTnJ3k9zJctz/MxT8n5j5r3lBVH+ruV2a6e+hvx/XPHAOSn0ly4wydm87M5lPcvSnD+39Qki9V1fszXA/3Hsc9obu/ufJ3J+nu06rqsAzXxRHjMd4z1vRnVXW9DEHOa2f43JmbfnIt7i8AAADYbun0BAAAADuA7v5GhumeXpfk+hmCI9dO8sokN578P/u7+ztJnp0hbPL4JL/X3V/IMFXYf2aYpumRGYIjf5EhnHTRuH1rajsuQwDp75LsmeQx48+eSV4+1vfvW7PvJY753SQ3zxDSuF2ShyX5bJIHLTD2ixmnSUtycJIHJ/lwkjsm+eW8secmuX2GrkOXTPLYJP83yaeS3L673zpFmU8a9/PcJH+WIUxydoZzs093f2ve+D8fx140HvcPMoSJ7prk+eOYyXP0+CTfHl/7vbv75xlCcMdkOB9PSHKTDO/RjTKEkW5TVbtO8Rq2aNrjjq/7Fhm68tx2rP/zGQIt8z08wzV0uXG/d8twnm+ZIcx3qQznayVeniG4tUeGgFy6+8djjS/NcJ88PsN0Z29MctPuXnKqxu4+JsP0dseONc+FEv80yR3Xa2q7SePnxNPGh6+oqr27+70ZrtNvZrhPDs1wHzwuw+tINr8Gn59h2so7Z3iPprqHuvu8DPfhq5Nca9zHbuMxTkpyzsTYznD/PjFD8OzhGQJw/57kTt396hW+JfO9MkMo7F5VdVB3n5YhxPnRsebHJrnOOO56Sc5IcrctTEEJAAAATKhF/lEZAAAAwA6nqvbNEKB5Y3cfur7VAEupqr2S/GgMzs3f9p0kP+/u62/zwgAAAIBtQqcnAAAAAGAWHZHk7Krae3JlVR2coVPcx9alKgAAAGCb2LTeBQAAAAAAbIW/zzCV3Weq6pgMU8T9TpJ7Jjk1yeHrWBsAAACwxnR6AgAAAABmTnf/W5I7Jvl0kgOS/EmSfZK8JslNu/uH61geAAAAsMaqu9e7BgAAAAAAAAAAgGXT6QkAAAAAAAAAAJgpQk8AAAAAAAAAAMBMEXoCAAAAAAAAAABmitATAAAAAAAAAAAwU4SeAAAAAAAAAACAmSL0BAAAAAAAAAAAzJT/D4NMU8BnH0H3AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2376x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig,[ax1,ax2] = plt.subplots(2,1, figsize=(33,10))\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.countplot(x=\"Unique Data\", data=unique_float, ax=ax1, palette=\"Set2\")\n",
    "sns.countplot(x=\"Unique Data\", data=unique_obj, ax=ax2,  palette=\"Set2\")\n",
    "ax1.set_xlabel('Total Unique Data Pada Kolom Numerikal',fontsize=20)\n",
    "ax2.set_xlabel('Total Unique Data Pada Kolom Kategorikal',fontsize=20)\n",
    "ax1.set_ylabel('Jumlah Kolom',fontsize=20)\n",
    "ax2.set_ylabel('Jumlah Kolom',fontsize=20)\n",
    "for label in (ax1.get_xticklabels() + ax1.get_yticklabels() + ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "\tlabel.set_fontsize(16.5)\n",
    "ax1.set_yticks(np.arange(1,9,1.0))\n",
    "ax2.set_yticks(np.arange(1,4,1.0))\n",
    "fig.suptitle('Total Unique Data pada Data Train', fontsize=25)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Menarik, ada gap total data _unique_ yang besar pada data numerikal. ada kolom data yang memiliki data _unique_ dibawah 200. Hal ini menjadi kontras karena dari 100.000 record data, ada kolom data numerikal yang memiliki nilai data _unique_ dibawah 200. Dengan dasar itu, maka kolom-kolom tersebut saya masukan kedalam kolom data kategorikal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list kolom numerikal yang akan dipindahkan kedalam list kolom kategorikal:\n",
      "['x6', 'x7', 'x22', 'x51', 'x63', 'x80', 'x93', 'x96', 'x104', 'x117', 'x118']\n"
     ]
    }
   ],
   "source": [
    "# List nama kategori kolom numerikal yang akan saya pindahkan ke kategori kolom kategorikal\n",
    "num_cat = unique_float.loc[unique_float['Unique Data'] < 200].index.to_list()\n",
    "print('list kolom numerikal yang akan dipindahkan kedalam list kolom kategorikal:')\n",
    "print(num_cat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Berikut proses pemindahan data kolom numerikal yang akan dipindahkan ke tipe kategorikal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Membuat fungsi untuk memindahkan kolom dari kelompok float (numerikal) ke object (kategorikal)\n",
    "def move_num_to_cat (list_col, target_col, source_col):\n",
    "  def sort_by_length(e):\n",
    "    return len(e)\n",
    "  for i in range(len(list_col)):\n",
    "    if target_col.count(list_col[i]) == 0:\n",
    "      target_col.append(list_col[i])\n",
    "      source_col.remove(list_col[i])\n",
    "  target_col.sort(key=sort_by_length)\n",
    "  source_col.sort(key=sort_by_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data train kategorikal setelah dipindah: ['x6', 'x7', 'x21', 'x79', 'x89', 'x22', 'x51', 'x63', 'x80', 'x93', 'x96', 'x108', 'x112', 'x104', 'x117', 'x118'] banyak data: 16\n",
      "data train kategorikal setelah dipindah: ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60', 'x61', 'x62', 'x64', 'x65', 'x66', 'x67', 'x68', 'x69', 'x70', 'x71', 'x72', 'x73', 'x74', 'x75', 'x76', 'x77', 'x78', 'x81', 'x82', 'x83', 'x84', 'x85', 'x86', 'x87', 'x88', 'x90', 'x91', 'x92', 'x94', 'x95', 'x97', 'x98', 'x99', 'x100', 'x101', 'x102', 'x103', 'x105', 'x106', 'x107', 'x109', 'x110', 'x111', 'x113', 'x114', 'x115', 'x116', 'x119', 'x120', 'x121', 'x122', 'x123', 'x124'] banyak data: 109\n"
     ]
    }
   ],
   "source": [
    "# Eksekusi untuk kelompok data train\n",
    "move_num_to_cat(num_cat, df_train_by_type['obj'], df_train_by_type['float'])\n",
    "print('data train kategorikal setelah dipindah:', df_train_by_type['obj'], 'banyak data:', len(df_train_by_type['obj']))\n",
    "print('data train kategorikal setelah dipindah:', df_train_by_type['float'], 'banyak data:', len(df_train_by_type['float']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data test kategorikal setelah dipindah: ['x6', 'x7', 'x21', 'x79', 'x89', 'x22', 'x51', 'x63', 'x80', 'x93', 'x96', 'x108', 'x112', 'x104', 'x117', 'x118'] banyak data: 16\n",
      "data test kategorikal setelah dipindah: ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'x8', 'x9', 'x10', 'x11', 'x12', 'x13', 'x14', 'x15', 'x16', 'x17', 'x18', 'x19', 'x20', 'x23', 'x24', 'x25', 'x26', 'x27', 'x28', 'x29', 'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50', 'x52', 'x53', 'x54', 'x55', 'x56', 'x57', 'x58', 'x59', 'x60', 'x61', 'x62', 'x64', 'x65', 'x66', 'x67', 'x68', 'x69', 'x70', 'x71', 'x72', 'x73', 'x74', 'x75', 'x76', 'x77', 'x78', 'x81', 'x82', 'x83', 'x84', 'x85', 'x86', 'x87', 'x88', 'x90', 'x91', 'x92', 'x94', 'x95', 'x97', 'x98', 'x99', 'x100', 'x101', 'x102', 'x103', 'x105', 'x106', 'x107', 'x109', 'x110', 'x111', 'x113', 'x114', 'x115', 'x116', 'x119', 'x120', 'x121', 'x122', 'x123', 'x124'] banyak data: 109\n"
     ]
    }
   ],
   "source": [
    "# Eksekusi untuk kelompok data test\n",
    "move_num_to_cat(num_cat, df_test_by_type['obj'], df_test_by_type['float'])\n",
    "print('data test kategorikal setelah dipindah:', df_test_by_type['obj'], 'banyak data:', len(df_test_by_type['obj']))\n",
    "print('data test kategorikal setelah dipindah:', df_test_by_type['float'], 'banyak data:', len(df_test_by_type['float']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengubah tipe data numerikal yang ada pada kolom kategorikal menjadi tipe string agar dideteksi sebagai kolom kategorikal\n",
    "df_train[df_train_by_type['obj']] = df_train[df_train_by_type['obj']].astype(str)\n",
    "df_test[df_test_by_type['obj']] = df_test[df_test_by_type['obj']].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------\n",
      "Data Train Setelah dilakukan pembagian kolom:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Columns: 126 entries, x0 to y\n",
      "dtypes: float64(109), int64(1), object(16)\n",
      "memory usage: 96.1+ MB\n",
      "None\n",
      "--------------------------\n",
      "Data Test Setelah dilakukan pembagian kolom:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25000 entries, 0 to 24999\n",
      "Columns: 125 entries, x0 to x124\n",
      "dtypes: float64(109), object(16)\n",
      "memory usage: 23.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Validasi hasil pemindahan kolom\n",
    "print('--------------------------')\n",
    "print('Data Train Setelah dilakukan pembagian kolom:')\n",
    "print(df_train.info())\n",
    "print('--------------------------')\n",
    "print('Data Test Setelah dilakukan pembagian kolom:')\n",
    "print(df_test.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selanjutnya kita perlu mengetahui unique data pada kolom 'y' untuk menentukan proses pemodelan data selanjutnya."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique data pada kolom \"y\": [0 1]\n"
     ]
    }
   ],
   "source": [
    "# Mencari unique data pada kolom 'y'\n",
    "print('unique data pada kolom \"y\":', df_train['y'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Analysis : Memeriksa Korelasi Antar Kolom pada _Data Train_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada proses ini, saya akan fokus kepada _Data Train_ karena _Data Train_ penting untuk digunakan sebagai pemodelan. Mencari nilai korelasi antar kolom cukup penting dalam pemodelan. ini untuk menentukan seberapa signifikan kolom tersebut dalam data secara keseluruhan. Kolom yang digunakan adalah kolom yang bersifat numerikal. Dengan metode ini kita dapat memastikan bahwa suatu kolom memiliki pengaruh terhadap kolom lain, dengan begitu kolom-kolom yang tidak signifikan dapat dihilangkan untuk akurasi model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data korelasi antar kolom di Data Train\n",
    "corr = df_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot heatmap data korelasi\n",
    "mask = np.triu(np.ones_like(corr))\n",
    "fig, (ax,cbar_ax) = plt.subplots(2,figsize=(30,30), gridspec_kw={\"height_ratios\": (.9, .02), \"hspace\": .05})\n",
    "sns.heatmap(corr, cmap='coolwarm', vmax=1, vmin=-1, linewidths=0.2,ax=ax, cbar_ax=cbar_ax, cbar_kws={\"orientation\": \"horizontal\"}).set_title('Correlation Map Antar Kolom Data Train', fontdict={'size':20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari plot diatas dapat saya interpretasikan bahwa mayoritas korelasi antar kolom tidak memiliki korelasi/ independen. Beberapa kolom memiliki hubungan korelasi yang cukup erat. Gradasi warna merah menunjukan hubungan korelasi positif dan gradasi warna biru menunjukan hubungan korelasi negatif. semakin pekat warnanya, menunjukan semakin kuat hubungan korelasi pada kedua kolom tersebut. Cukup sulit menentukan apakah kolom tersebut berpengaruh signifikan terhadap data dikarenakan nama kolom yang disembunyikan.\n",
    "\n",
    "Saya akan fokus terhadap korelasi antar kolom _independent_ ('y') dan _dependent_ (kolom 'x' numerikal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 25))\n",
    "heatmap = sns.heatmap(corr[['y']].sort_values(by='y', ascending=False), vmin=-1, vmax=1, annot=True, cmap='coolwarm')\n",
    "heatmap.set_title(\"Korelasi Kolom 'y' terhadap Kolom Lain \", fontdict={'fontsize':18}, pad=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari hasil plot diatas, dapat saya interpretasikan bahwa tidak ada kolom numerikal yang memiliki korelasi yang signifikan terhadap kolom 'y' karena nilainya berada di antara 0.14 sampai -0.13 dari skala 1 sampai -1. Dengan kata lain, semua kolom numerikal memiliki korelasi yang hampir sama terhadap kolom 'y'. Atas dasar itu, semua kolom numerikal akan dimasukan kedalam pemodelan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Analysis : Memeriksa Nilai Kosong pada _Data Train_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dalam melakukan pemodelan, setiap baris data pada setiap kolom harus terisi nilai. Maka dari itu, harus dilakukan pemeriksaan terlebih dahulu pada _Data Train_ apakah ada data yang kosong (NaN/Null)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "      <th>...</th>\n",
       "      <th>x116</th>\n",
       "      <th>x117</th>\n",
       "      <th>x118</th>\n",
       "      <th>x119</th>\n",
       "      <th>x120</th>\n",
       "      <th>x121</th>\n",
       "      <th>x122</th>\n",
       "      <th>x123</th>\n",
       "      <th>x124</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total NaN</th>\n",
       "      <td>123.000</td>\n",
       "      <td>131.000</td>\n",
       "      <td>124.000</td>\n",
       "      <td>115.000</td>\n",
       "      <td>137.000</td>\n",
       "      <td>116.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.000</td>\n",
       "      <td>130.00</td>\n",
       "      <td>...</td>\n",
       "      <td>123.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>120.00</td>\n",
       "      <td>129.000</td>\n",
       "      <td>127.000</td>\n",
       "      <td>130.00</td>\n",
       "      <td>123.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentage</th>\n",
       "      <td>0.123</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x0       x1       x2       x3       x4       x5   x6   x7  \\\n",
       "Total NaN   123.000  131.000  124.000  115.000  137.000  116.000  0.0  0.0   \n",
       "Percentage    0.123    0.131    0.124    0.115    0.137    0.116  0.0  0.0   \n",
       "\n",
       "                 x8      x9  ...     x116  x117  x118     x119    x120  \\\n",
       "Total NaN   134.000  130.00  ...  123.000   0.0   0.0  122.000  120.00   \n",
       "Percentage    0.134    0.13  ...    0.123   0.0   0.0    0.122    0.12   \n",
       "\n",
       "               x121     x122    x123     x124    y  \n",
       "Total NaN   129.000  127.000  130.00  123.000  0.0  \n",
       "Percentage    0.129    0.127    0.13    0.123  0.0  \n",
       "\n",
       "[2 rows x 126 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eksplorasi Data NaN pada Data Train \n",
    "null_in_train = df_train.isnull().sum().to_frame('Total NaN')\n",
    "null_in_train['Percentage'] = null_in_train['Total NaN']*100 / 100000\n",
    "null_in_train.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['x6', 'x7', 'x21', 'x22', 'x51', 'x63', 'x79', 'x80', 'x89', 'x93',\n",
       "       'x96', 'x104', 'x108', 'x112', 'x117', 'x118', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Periksa kolom data yang tidak memiliki nilai null / nan\n",
    "df_train_with_null = null_in_train.loc[null_in_train['Total NaN'] <=0].index\n",
    "df_train_with_null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "kolom kategorikal dan kolom 'y' tidak memiliki nilai null atau NaN. Nilai Null atau NaN pada kolom kategorikal sudah diubah menjadi string pada proses sebelumnya. karena itu kolom kategorikal dan kolom 'y' akan di drop sementara karena akan menjadi outliers pada proses pencarian nilai Null/NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total NaN</th>\n",
       "      <td>109.0</td>\n",
       "      <td>128.256881</td>\n",
       "      <td>11.310765</td>\n",
       "      <td>100.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>127.000</td>\n",
       "      <td>137.000</td>\n",
       "      <td>159.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentage</th>\n",
       "      <td>109.0</td>\n",
       "      <td>0.128257</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean        std    min      25%      50%      75%  \\\n",
       "Total NaN   109.0  128.256881  11.310765  100.0  122.000  127.000  137.000   \n",
       "Percentage  109.0    0.128257   0.011311    0.1    0.122    0.127    0.137   \n",
       "\n",
       "                max  \n",
       "Total NaN   159.000  \n",
       "Percentage    0.159  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memeriksa nilai NaN/Null secara statistik\n",
    "null_in_train.loc[null_in_train['Total NaN'] >0].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HEEKUERWuGNm+fTvXrl2TKYCFsLArnn5qRxBCFNP27dsB6NGjh1XbrXCHabZt28b+/fvVjiFEudcx6jgdo46rHUMIUQzbtm1j2zbrz4tT4UZGhBDWMeyvXwD4s04zlZMIIWxdhRsZEUIIIYRtkWJECCGEEKqSYkQIIYQQqpJiRAghhBCqkhNYhRAWMa3Xc2pHEEKUERWuGElJSSExMZFx48apHaXMycjIwNnZWe0YZUpJ+ywyMhKNsWz/eMa7e6kdwWZocjKJjIy06O8d+fksPumzO0VGRuLlZf2fXTlMI4SwiG7hh+gWfkjtGEKIMqBs/+lVApUrV8bBwYHFixerHaXMCQsLk5lri6mkfTZu3DiOXoy3QCLrefLE7wD8Wr+NyknUp+idCKpdxaK/d+Tns/ikz+6k1lEDGRkRQgghhKqkGBFCCCGEqqQYEUIIIYSqpBgRQgghhKoq3AmsQgjrmPLYi2pHEEKUEVKMCCEsItXZTe0IQogyQg7TCCEsos+ZP+lz5k+1YwghyoAKNzLSq1cvrl27pnYMIcq93mf2ArC5UUeVkwghiqpXr16qtFvhipEePXoQFhamdgwhhBDC5vTo0UOVduUwjRBCCCFUJcWIEEIIIVQlxYgQQgghVFXhzhkRQljHuCdeUTuCEKKMkGJECGERWXoHtSMIIcoIOUwjhLCIAcd3MuD4TrVjCCHKABkZEcJG6TKScQrfVuBj2oyWAHd93JIylZwitd3j+O8AbHEyWDyTrdNlJANV1I4hhM2SYkQIGxQUFHTPxy8f1wPQorb1P+B2ZJwEINT53m27HrcHip8xIyMDZ2fnkoWzWVUK/Z4KUZFJMSKEDXrppZfu+fjJvHqAxYsXWyFNfl2+6pLX9shC2v5fyOJmDAsLIyQkpCTRhBBllJwzIoQQQghVSTEihBBCCFVpFEVR1A5RkBMnTuDgIJcGCiGEEOWFwWCgadOmd9xvs8WIEEIIISoGOUwjhBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVSTEihBBCCFVJMSKEEEIIVdmpHeBuTpw4gYODg0X2bTAYLLbv8kz6rfgs1Wfnz+f9X69eqe+68LaT8hqv511I4yUMKe+zkpF+Kz7ps+K73z4zGAw0bdr0jvttthhxcHAgJCTEIvsOCwuz2L7LM+m34rNUn40Zk/f/7t2lvuvC2/4qr/HdHQtpvIQh5X1WMtJvxSd9Vnz322dhYWEF3i+HaYQQQgihKilGhBBCCKEqKUaEEEIIoSqbPWdECFHGLVqkdgJRBuXk5BAbG0tWVpZV2rrbOQyiYEXtM0dHR/z9/dHr9UXarxQjQgjLKOCMeSEKExsbi5ubG7Vq1UKj0Vi0rczMTJycnCzaRnlTlD5TFIUbN24QGxtLYGBgkfYrh2mEEJbx2295/4QohqysLLy8vCxeiAjL0Wg0eHl5FWt0S0ZGhBBmiqKQkGYgOjmDxDQDaVk5ZGYb0Wk12Om0eDjpuZWVg4OdDpNJQau9xwfG7Nl5/z/8sHXCi3JDCpGyr7jfQylGhKjAsnNNHLmczJ+RSRyPTuF07E1uZxvv+Zzr9rcACJn2C3V8XGkSUIlmNSrRvo4X/pWdrRHbJuQaTdjpbGdwuahzP9habltz6NAhRowYwQcffMCjjz5qvr9Pnz40bNiQd9991+qZTpw4wTvvvINOpyM0NJQXX3yxwO2++uorkpKSmDRpEgBbtmzh66+/RqfTERwczIwZMzAajbz22mtcvXoVrVbL22+/TZ06ddizZw8ffvgh1apVY9GiRWi1WmbNmsWoUaPw9/e3+GuUYkSICsZkUjhw8Qbrjsby67l40g256HUaQvzc6d/cn+AqrgR4OlPVwxE3Rz1Oeh0mRSHHaCI1I4chG9wx5JgYWqcm4dfT2HLqGt8djgYg0NuFbg2q8FiTajQEyvPft3Y6LQt/jVA7hllSUiLe3j6FbjehW7AV0pRttWvXZuvWreZi5Pz582RmZqqWZ/r06SxZsoSAgACee+45zp07R4MGDcyPZ2Vl8eabb3L69Gm6d+9uvm/RokVs3rwZJycnXnnlFXbt2oWiKOTm5rJmzRr27dvHokWLWLJkCatXr+aLL77gww8/JDw8HK1Wi6urq1UKEZBiRIgKIyM7lx+OxPLZ3ovEJGfi5mjHow/48VCILx2CvHFxKPzXgZ+HEx5OenCCNx/N+2VoMilEJqaz90ISf0Qk8sXeSyzfc5ENMal4uzqQm3SbWt4uln55orzq0uXO+556CsaOhYwM6NXrzsdHjsz7l5QETz6Z/7EizAhcv359Ll26RFpaGm5ubmzatIk+ffoQFxcHwM8//8xXX32FVqulRYsWTJo0ievXrzNjxgwMBgOJiYmMHz+ehx9+mD59+tC6dWvOnz+PRqNh2bJluLm5mdtatWoV27dvz9f+vHnzqFatGgDp6elkZ2dTo0YNAEJDQ9m/f3++YsRgMNCvXz86dOjAxYsXAbC3t2fNmjXmk01zc3NxcHDAz88Po9GIyWQiPT0dO7u8n3sXFxeysrIwGAw4OTmxdOlSZsyYUWhflRYpRoQo5wy5RlYeuMJHuyJJycihRc3KTO5Rn+4NquCo1933/rVaDcFV3Aiu4sao0EBSbmfz85nr6NdriU3JYNCC3XQO9mFUaCCd6nrL+QCiTOjevTs7duygf//+nDp1imeffZa4uDhSU1NZsmQJP/74I05OTkyePJl9+/ah0Wh45plnaNOmDceOHWPJkiU8/PDD3L59m0cffZSpU6cyceJE9uzZk+/wz7Bhwxg2bNhdc6Snp+Pq6mq+7eLiQkxMTL5tPDw8CA0NZf369eb7tFot3t7eAKxcuZKMjAw6dOjA9evXuXr1Kj179iQlJYVPPvkEgLFjxzJ37lzq1atHdHQ0zZs3Z8uWLYSFhdGvXz+aNWtWKv16N1KMCFFOKYrCz2eu887WMK6mZtKxrjfjHqpLy1qeFm23sos9Q9rUgM3fkZCWxcSbzqw8eIWnvzhMkK8rozoE0q9ZdZzs778QEhXAvUYynJ3v/bi3d4kXcOrTpw8zZswgICCAli1bmu+Pjo4mOTmZ5557DoDbt28THR1Ny5Yt+fjjj1m3bh0ajYbc3Fzzc/4exfDz88NgMORrp7CREVdXV27fvm1+7Pbt27i7uxfpNZhMJubPn8+lS5dYsmQJGo2Gr776itDQUCZOnEhcXBxPP/00mzdvpk6dOixcuBCj0cj48eOZPXs2b7zxBosXL2bMmDGsWLGiGL1XfFKMCFEOxaZkMG3jWX4PTyDEz515TzQmtK63dUPUq4cv8BLwfOc6bDl1jc/3XuKNn07zwa8RjOlSh6FtapTK6IwQpS0gIICMjAxWrlzJK6+8Yh6N8Pf3x8/Pjy+++AK9Xs/69esJCQlh8eLFDBgwgM6dO/Pjjz/y008/mfd1r9HAwkZGXF1d0ev1REdHExAQwN69e+96Auu/TZs2DXt7e5YtW4ZWm3fSsru7u3kiMg8PD3JzczEa//+k9bVr19KvXz8gr5jRaDRWOV9GihEhyhFFUVh/7CrTN53FpCi89WgII9vXUufqic2b8/7v0wd7Oy39m/vTr1l1Dl1KZsnvF3h7yzmW74niha5BDGwVgIOdFCXCtvTq1YuNGzcSGBhoLkY8PT0ZOXIkw4cPx2g0Ur16dXr27MkjjzzCe++9x/Lly6latSopKSmllmPmzJlMmjQJo9FIaGgoTZo0ITU1lbfeeoulS5cW+JyzZ8+ybt06WrZsydNPPw3AiBEjGDlyJG+88QZDhgwhJyeHCRMm4OycdxVceno6hw8fZtH/Zk/28fFh8ODBDBkypNRey91oFEVRLN5KCVhyaWdZNrpkpN+Kz1J99vc5ff8cgU7LyuGNn86w+eQ1Wtfy5P2nmhDgWfqX2nb5Kq/x3SN333O7AkP+w8GLN/hgRwSHLydTzcORV7rXo3+z6pw/H15m3mdyNU3ps+bvGZmBtfiK02cFfS/v9v2VkREhyoHIhHSeX3mEyzcymNQ9mDFdgtDda0IyG9C2thdrn2/L/qgbvLf9PJN+OMmX+y4xvJELZaQWEUKUEpn5Rogy7vfweB5fupfUjBy+fqYVLz5Y1+YLkb9pNBraBHry05j2LB7UlNSMHF7bEcd/vv6LqMR0teMJIaxERkaEKMNWHrzC9I1naFDNnRUjWuLn4WTxQwexKXknsxXWzpP/225dIdtN6BZs3lffptXYd/4qeyKS+D38D5oFVKZ1oCf2drb3d5OtH+4QoiyRYkSIMkkhOjmTqRvO8FB9Xz4c3KxIk5bZOjudloa+jrQK8mN/1A2ORqcQHn+LjkE+BFdxlTlKhCinyv5vLyEqGJNJ4VLSbeJvGZjYpgazHm9kk4dlfnn1vRI/19nejodDqtComge7zifwy9nrnL7qRJd6Pni7OpRiSiGELbC9sU8hxF0ZTQoTfzhJ/C0D1So5MbuvbRYiAOm+fqT7+t3XPqp6ODKwVQAP1vPlRrqB1Yej+SMiEUPuvRfzE0KULVKMCFFGmEwKb6w/zU/HrxJQ2Ykans42fdgiePc2gndvu+/9aDUaHvD3YET7WjT0c+dETCrfHLhCeNwtbHRmAlGGHTp0iHr16rF169Z89/fp04fXXnutSPuIiopi+PDhAEyYMIHs7OxSz/lPv//+O0888QQDBw7k+++/v+t2c+bM4bvvvjPf/uqrrxgwYAADBgwwz1eyfPlyhg8fzvDhw3n88cfp0KEDAEuXLmXgwIF89tlnQN5aNy+//HK+CdPuR6kXIzk5OUyePJkhQ4bw5JNPsnPnTq5cuWKeOGX69OmYTKbSblaIck1RFN7eeo61R2J46cEgqlcu/flDSlvjLd/ReMt3hW9YRE56HQ+FVGFQqwDcHO3Yfi6eH49dJSndUPiThSiGv1ft/dv9rNq7cOFC7O3tSyvaHXJycpg7dy5ffPEFK1euZO3atSQlJeXbJjk5mf/85z/8/vvv5vtiYmLYtGkTa9as4fvvv2fv3r2Eh4fz3HPPsXLlSlauXEnVqlWZN28eAPv372ft2rXs378fyJup9YknnkCnK53JCkv9nJFNmzZRqVIl5s+fT2pqKn379qV+/fqMHz+eNm3aMG3aNHbu3Em3bt1Ku2khyq0Pfo3gy32XeaZDLV7pFszmOWonUk8Vd0cGtgzgzLVb7I9MYvXhaJoFVKJNoJdNXnUjSu6bk9/wxfEvSnWfo5qNYkSTEffcpiSr9iYkJDBp0iQURcHH5/8nn3vwwQf5+eefuXLlCu+++y5Go5GUlBRmzJhB8+bN6d69O82bN+fSpUt4eXmxZMmSfB/wCxcu5NixY/nyff755+YCJyoqiho1auDh4QFAixYt+Ouvv+jZs6d5+9u3b/PSSy+xZ88e831Vq1bls88+M7f196q+f9uxYwfu7u6EhoYCYGdnh9FoRKvVkpaWxrFjxxg6dGjRO74QpV6MPPLII/To0QPI+2tOp9Nx9uxZWrduDUCnTp3Yt2+fFCNCFNHHu6NY8nskg1oFMK13A5s+NGMtGo2GB6p7EOTjyr6oJI5Fp3I+Po1OdX2o6ytX3Yj7V9xVe3fu3Env3r156qmn2LZtW77DIQCRkZFMmTKFevXqsXnzZtavX0/z5s2JiYnh66+/xs/Pj0GDBnH69GmaNm1qft6ECRPumTM9PR03NzfzbRcXF9LT88/RExAQQEBAQL5iRK/X4+npiaIovPfeezRo0IDAwEDz459++ikffPCB+fbw4cOZMGECw4YNY/ny5YwePZr58+eTkZHBCy+8YF4huKRKvRhxcXEB8jro5ZdfZvz48cybN8/8y8HFxYW0tLRC92MwGAgLCyvteABkZWVZbN/lmfRb8d1vn22LuMWSA0l0CXRhWH07wsPDAcjIqAFAWFh0vu1DQkJISkoseeAiyMnJO/5dWDtF3Q6C822Tm5tbrNfQ1EeLv7Mrh69m8vOZ6xx3taNldSc8HC291k2wxfu6OIreb8E2/XOck5NjPiQyIHgAA4IHlHobf+9fUZQ7Dr8YDAaMRiPdunXjnXfewdfXl6ZNm5KdnU1ubi4REREkJyczevRoADIyMoiKiiIqKorHHnuMzMxMGjRogNFoJDMzE5PJRGZmJpUqVWLJkiU4ODiQkZGBi4uL+f5KlSqRmZmJr68vt27dypdp6dKlHD9+PF/GTz75xLzYnV6vz/ecmzdvEhgYWOBhpZycnHz9azAYmD59Oi4uLrzxxhvm+6OionBxccHX19d8X2hoKKGhocTGxrJ//37i4uJwc3Oja9eufPHFF7z00ksFtlfU95pFLu2Ni4vjhRdeYMiQIfTp04f58+ebHyvq8scODg6yNo2NkX4rvvvpsz8vJLLs0CW61PNhxYiW6P+x2N3/1rUqcN9FWZ/kfuiv2xepHb2+aNv9e5uirrGS//kQXEPhdOxN9l+8wbaINJrXyJswTW/BRQIt3dfFUZx+s+Wf47CwMKutF1PQOisODg7odDrq1q2LwWDg+++/N6/aa2dnR1BQEH5+fnz99df5Vu2NiYkhLCyMpk2bcuHCBXQ6HU5OTmi1WpycnJg/fz4LFiygTp06fPjhh1y9ejXf4wA6nQ4HB4d8mSZPnnzP19CgQQNiY2MxGAw4Oztz4sQJnn/++QL7UK/Xo9frcXJyQlEUXnzxRdq0acNzzz2Xb7tjx47RtWvXAvfx2WefMXHiRE6ePImjoyOOjo5kZ2fftb2C1qYpSKkXI0lJSYwaNYpp06bRrl07IK+zDh06RJs2bdizZw9t27Yt7WaFKFciE9IY++0x6vq6snRIc4t+oFrKlqmLrdqeVqOhSUAlgnzzDt0cuZJC+PU0Ogf7UMfHRQ7diGIrzqq9Y8aMYfLkyWzbtg1/f/879vXYY48xbtw43N3dS3VVX71ez2uvvcbo0aNRFIUnnniCKlWqEBkZyapVq5gxY0aBz/vtt984fPgw2dnZ/PnnnwC88sorNGvWjEuXLpmvovmn48eP4+fnh6+vL+3bt2fMmDH8/PPPzJw5875fR6mv2jt79mx+/vlnateubb7vzTffZPbs2eTk5FC7dm1mz55d6Bm4smqv7ZF+K76S9NmNdAN9l+0jM9vEhhfa41/AlTP3WhDX0tPBf3Qm75LFFxqtLJX9/XM6eCjZyEhBrqZksisigRvp2dT0cqZzsA+VnUvvqoZ/51abrNpbfLJqb/GVmVV733rrLd5666077l+1alVpNyVEuWPINfLfVUdJuGVgzXNtCyxEyooGO9YDcK57f1Xar17ZiSGtanAyNpWDF5P59mA0LWpWplWtytiVwZEmIcozmQ5eCBuhKAqvrz/NX5dTWDqkGc1qVFY70n1psOMnQL1iBECr1dCsRmXqVnFj74UkDl9OJvz6LToH+xDoLYduhLAV8ueBEDbiy32XWX/sKhMeDqZ342pqxylXXB3seKRRVfo3q46dVsvmU3FsPHmNlAzLzowphCgaKUaEsAFHryQzZ1sY3RpU4eWHgtSOU24FeDozpE0NOtb1Ji41i1UHr7A3MonsXJkVWgg1yWEaIVSWlG5g7LfHqF7ZiQUDmsihAwvTaTU0r1GZelXc2BeVxNErKYTH3SK0rjf1qrhJ/wuhAhkZEaIAucbS+Uu5sKsCco0mXlp9nNSMHD4e2gIPJ32ptCsK5+JgR/cGVXmqpT8uDnZsPxvPD0djSUjLUjuaEBWOjIwIUQA7nbZULtss7HLLfZF582F0a1CF7Wevs/3s9SLtNzYlbx6Dhb/G5rvfli7b3DB7udoRisTPw4lBrQI4G3eL/ZE3+O5wDA9U96BdHS+c9JaexVXYmkOHDjFixAg++OADHn30UfP9ffr0oWHDhrz77ruF7sNgMNCzZ898C9PdjxMnTvDOO++g0+kIDQ3lxRdfLHC7r776iqSkJCZNmgTAli1b+Prrr9HpdAQHBzNjxgwUReGtt97i0qVLaDQaZs6cSXBwMHv27OHDDz+kWrVqLFq0CK1Wy6xZsxg1alSB86aUNhkZEUIlFxPTOXIlhUbV3GngV/isxGVNrqMTuY5lYw4HjUZDo2oePN2uJk39K3Hm2k2+3n+Zk7GpmEylOhWTKANKc9Xe0jB9+nTef/99vvvuO06ePMm5c+fyPZ6VlcXEiRNZvXp1vvsWLVrEN998w5o1a0hPT2fXrl3s2rULgDVr1jB+/HgWLlwIwOrVq/niiy/w9fUlPDyc8PBwXF1drVKIgIyMCKGKtKwcfj0Xj6+bA52DbWdK8dLUeNO3AJx6rPRW9rQ0B72OzvV8aFjdnT/OJ7L7fCJnrt6kS7Av1SuXjcKqPPnmG/iidBftZdQoGHHvRXsLXbV306ZNfP3119jb21OrVi1mzZpFdnY2kyZN4tatW9SoUcO8r/PnzzN79mwAKlWqxJw5c/ItbLdq1Sq2b9+er/158+ZRrVreFXXp6elkZ2eb9xkaGsr+/ftp0KCBeXuDwUC/fv3o0KEDFy9eBMDe3p41a9aYJyj7e1Xe0NBQuvxv1sRr166Zl2dxcXEhKysLg8GAk5MTS5cuvevsrZYgIyNCWJlJUdh+Nh6jovBIo6rldgKu4D2/ELznF7VjlIi3qwP9m1enV6OqZOWYWHcslp/PxJGelat2NGElf6/aqygKp06dolmzZgCkpKSwZMkSvv76a7777jvc3NxYu3Yta9asITg4mG+//ZZBgwaZ9zN16lSmT5/OypUr6dSpE5999lm+doYNG8bKlSvz/fu7EIG8YsTV1dV8u6DFZj08PAgNDc13n1arNa+ku3LlSjIyMsxTvNvZ2TFlyhTefvtt+vTpA8DYsWOZO3cu1atXJzo6mubNm7NlyxamTZt2x0J9liAjI0JY2ZHLKVxNzaRbgyqlOj25KF0ajYa6Vdyo5e3CkcspHI1O4VLSbVrV8qRZjUpqx6sQRowofBTDUvr06cOMGTMICAigZcuW5vtjYmIICgoyFwitWrVi7969mEwmOnfuDECTJk2ws8v7eI2KijKv3ZKTk0OtWrXytVPYyIirqyu3b982P1bUxWYBTCYT8+fP59KlSyxZsiTflWLz5s1j0qRJPPXUU2zdupU6deqwcOFCjEYj48ePZ/bs2bzxxhssXryYMWPGsGLFiiK1WVJSjAhhRXE3Mzl46QbBVVwJqepW+BOE6vQ6Le3qeNGgmjt7IhLZH3WDc9du0bFu+Ty8JvIEBASQkZHBypUrzav2Avj7+xMVFUVGRgbOzs4cPnyYwMBAIO9E04cffphz586Rm5s3ihYYGGguLo4ePUpiYmK+doYNG8awYcPumsPV1RW9Xk90dDQBAQHs3bv3riew/tu0adOwt7dn2bJlaLV5I7AbNmwgPj7evLKvRqMxPwawdu1a+vXrB+QVMxqNxirny0gxIoSVGHKN/HLmOm4OdjxY31fmsyhjPJz09GlSjSs3brPrfCKDlh+kUTV3QoO8cZCrbsqlu63a+9JLLzFixAi0Wi01atQwX73y6quvMnjwYGrXro1en3eZ/owZM5gyZQq5ubloNBreeeedYueYOXMmkyZNwmg0EhoaSpMmTUhNTeWtt95i6dKlBT7n7NmzrFu3jpYtW/L0008DMGLECLp3787rr7/O0KFDyc3N5Y033sDR0RHIOyR0+PBhFi1aBICPjw+DBw9myJAhxc5cXKW+am9pkVV7bU9F67fSvLRXURR+OXudCwnpDGjhj5/H/Z0M+dGkvDPcX1hw56W9trJq75OT8rZbt+De21lq1V5LyjGaMJoUlu+5iLO9ji71fAnydS38iRYkq/YWn6zaW3xlZtVeIcSdwq+nERGfTrvaXvddiJQVhRUhZZlep+XVR4K5mZnDzrAEtp6Oo66vKw/W98VRRkmEKLbyeRq/EDYkLSuH3ecTqebhSMtaZXslXpFfFXdHBrYKoF0dL6IS0/n2UDTRyRlqxxKizJFiRAgLUhSFHefiUVDo3rAq2gp0nkiLHz6nxQ+fqx3D4nRaDa1refJUywD0Og0/Hb/KnguJpbakgBAVgRQjQljQ+aRsYlMy6VTXp8KtOxN4aDeBh3arHcNqqrg7Mrh1DRr7e3A8OpU1R2JIychWO1aZZKOnMopiKO73UIoRISwk+XY2J+IyqeXlTMNq5W+6d3EnvU5L13q+PNakGhkGI2sOxxCVmK52rDLF0dGRGzduSEFShimKwo0bN8xX6RSFnMAqhAUYTQrbz17HTqvh4ZAqchlvBRPo7cKg1gFsPRXHllNxtKpVmba1vSrUYbqS8vf3JzY29o75OCwhJyfHfAmuKJqi9pmjo2Ox1rWRYkQIC/jrcjIJaQY61nTGxUF+zCoid0c9A1r4szsikb8upxB/y8AjDaviZC9X29yLXq83TyJmaRVtuoLSYKk+k8M0QpSyhLQs/rqcTL2qbtSoVHGne8+1dyDX3kHtGKqy02l5OKQKD9X35WpKJmuPxJAq55EIcQf5k02IUmQ0Kfx6Lh5HvY4uwT6k30xWO5JqNsz5rPCNKohG1T3wcrVn08lrfH8klseaVqOqe9GPpwtR3snIiBCl6K/LySSlZ/OQTH4l/sXPw8l8+e+PR2O5lHS78CcJUUFIMSJEKUlMM5gPz9T2UXdqcFvQZtVHtFn1kdoxbEplZ3ueahmAp4s9m09d48zVm2pHEsImSDEiRCkwmhR+Dcs7PNM52LbXVbGWgBMHCThxUO0YNsfFwY4nmvtTw9OZneEJHItOUTuSEKqTYkSIUnD0SgqJaQa61vPFSQ7PiELY22np07gadX1d+fNCEselIBEVnJzAKsR9Sko3cOjSDYKruKq+cqsoO3RaDT0aVkVRrrPnQhIajYamAZXUjiWEKmRkRIj7YPrf1TMOdjq6BPuqHUeUMTqthkcaVaWOjwt/RCRyMiZV7UhCqEKKESHuw9HoFBLSDHSt5yOTWf1LlnslstwrqR3D5um0Gno28qO2twu7IxI5FZuqdiQhrE4O0whRQjfSDRy6mExdX1fqVnFTO47N2TJtidoRygydVkOvB/zYejqOXecTcba3k0N+okKRkREhSsD0v6tn7O20dKknV8+I+6fTaujVqCpV3R355ex1rqVmqh1JCKuRYkSIEjgWk7fWSJd6PjjbywBjQTp8/j4dPn9f7Rhlip1OS58mfrg62LH51DVSZOp4UUFIMSJEMSXfzubgxWTq+LhQV4bS78ov7AR+YSfUjlHmONvb0bdpNTRo2HjiGhnZuWpHEsLipBgRohhMisJvYfHotRq61vNFI0vCCwuo5GxPnyZ+pBty2XTyGjlGk9qRhLAoKUaEKIYTManE3cyicz0fXBzk8IywHD8PJx5pWJX4WwZ+C4tHURS1IwlhMVKMCFFEKRnZ7I+6QW1vF+rJ1TPCCoJ8XWlXx4uI+HROyBwkohyzWDFy8uRJhg8fDsC5c+fo2LEjw4cPZ/jw4Wzbts1SzQphEcr/Ds/YaTV0rS+HZ4oi3bsK6d5V1I5R5rWqWZna3i78GZnE1RS5wkaUTxYZZ16xYgWbNm3CyckJgLNnz/LMM88watQoSzQnhMWdir3JtdQsuoVUwVUOzxTJL68tUDtCuaDRaOjesApr/oph25k4BreuIe9BUe5YZGSkRo0aLFny/xMenTlzht27dzN06FDeeOMN0tPTLdGsEBZxMzOHfVFJ1PRyJsRPDs8I63Ow09H7AT9yjCa2nY7DaJLzR0T5YpHyukePHsTGxppvN27cmAEDBtCoUSM+/vhjPvroI6ZMmXLPfRgMBsLCwiwRj6ysLIvtuzyrSP0WEhJCUlIiiqLw+8XbKIpCM187btxIKtZ+cnNzSUpKLPV8OTl5hz/u3HewRdrL33b2XdrOr9c3iwHYNmJcIXvMn9lSfVb6LN/X/9bG34m9VzLYcSqaVv7O+R4rer8FV5if48JUpN9ppcVSfWaVsb5u3brh7u5u/vrtt98u9DkODg6EhIRYJE9YWJjF9l2eVbR+8/b24fTVm1xPv8mD9X2pWc2j2PtISkrE27v0Z2jV6/UABe7bEu3la/u6fZHaqXHtSpHz/HMbS/WZJVg7p7c3ZCiJHItOJaiaJ3V9/3+krjj9VpF+ju+lov1OKw3322d3K2SscjXN6NGjOXXqFAAHDhygYcOG1mhWiPuSlpXD3gtJ+Fd2olE1d7XjCAFA+zre+Lo5sDMsgbSsHLXjCFEqrFKMzJgxgzlz5jB8+HCOHTvG2LFjrdGsECWmKAo7wxNQUHg4pIpcPSNshk6r4ZFGVTGaFHack/lHRPlgscM0/v7+fP/99wA0bNiQNWvWWKopIUrduqOxXLmRQZdgHzyc9GrHESKfys72dK7nw86wBI5Gp9CypqfakYS4L3J9mBD/En8ri7e3nKNaJUca+xf/PBGRJ7V6LbUjlGsN/dy5kpTBgagbBFR2Rqd2ICHugxQjQvyDoii8+dMZDLkm+srhmfvy24TCT1QXJafRaHgoxJfrh7L45ex1etR2LvxJQtgomQ5eiH/YdPIav4XFM6l7PSo726sdR4h7ctTr6N6gCqkZORyLk9lZRdklxYgQ/5OYZmD6prM0DajEqNBAteOUeQ8vnMrDC6eqHaPcC/B0plmNSly4kU1McobacYQoESlGhPif6ZvOkGEwMv/Jxui0cnjmflW6eplKVy+rHaNCaFfbCzd7Lb+FxZOda1I7jhDFJsWIEMC203FsO32dcQ/Xpa6syCvKGL1OS9sAZ25l5bI/qnizBAthC6QYERVe8u1spm08wwPVPXi+U2214whRIr6udjTx9+Bk7E1Z3VeUOVKMiApvxqazpGbk8N6TjbHTyY+EKLs6BHnj7mjHr2Hx5BjlcI0oO+Q3r6jQtp6KY9PJa4x7qC4hfjLle2lKrFOfxDr11Y5Roeh1Wh4OqcLNzBwOXLyhdhwhikzmGREVVmKagbc2nKaJvwdjutRRO06588eYN9WOUCEFeDrzQHUPjkenEuzrRlUPR7UjCVEoGRkRFZKiKLy+/jS3s428/1QTOTwjypUOQV64OOj4PTwBk0nWrhG2T34Diwpp/bGr/BYWz6s96hHkK1fPWMIj707ikXcnqR2jQnKw09E52IfEdAMnYlPVjiNEoaQYERXOtdRMZmw+S+tanjzTQSY3sxTXpHhck+LVjlFhBfm4UsvLmYMXb3ArK0ftOELckxQjokJRFIUpP57CaFKYP0AmNxPll0ajoWs9XxQF/jifqHYcIe6pSMXIsmXL8t1+//33LRJGCEv79lA0f15I4o1eIdT0clE7jhAW5e6kp01tTy4m3SYqMV3tOELc1T2vpvnhhx9Yt24dUVFR7NmzBwCj0Uhubi4TJ060SkAhSsuVG7eZsy2MjnW9GdqmhtpxhLCKZgGVCb+exu7ziQRUlpV9hW26ZzHy+OOP065dOz799FP++9//AqDVavHy8rJKOCFKi9GkMPmHU+i0GuY90RiNRg7PWFpcSFO1IwhAp9XwUH1fvj8Sy8FLMveIsE33LEbs7e3x9/dn5syZnDlzBoPBAEBsbCytWrWySkAhSsPney9y+HIy7w9oQrVKTmrHqRD2jZbRU1vh5+FEo2runIhJJSI+jWBZf0nYmCJNevbyyy9z48YN/Pz8gLwTo6QYEWXF2Ws3mb/9PD0aVqF/8+pqxxFCFe2DvIlMSGf6xrOsfraNjA4Km1KkYiQpKYk1a9ZYOosQpS4z28i4NSeo7GzPu/3l8Iw19Z71EgBbpi1ROYkAcNLraFfHi13nE9l6Oo7ejaupHUkIsyJdTRMYGEh8vMwXIMqeuT+HEZmQzvtPNaGyi73acSoUx1upON5KVTuG+IdG1T1o4OfOO1vDyMjOVTuOEGZFKkaOHj1K165dCQ0NNf8TwtbtCk/gmwNXGB0aSMe6PmrHEUJ1Wo2GWY83JO5mFh/tilQ7jhBmRTpMs2PHDkvnEKJUJaUbmLzuJPWrujG5Rz214whhM1rW8qR/8+qs2HOJJ1sEEOgt8+0I9RWpGHn99dfvuG/u3LmlHkaI0qAoClPWneJWVi7f/qctjnqd2pGEsCmv9azPjrPxzNx8li9HtpJzqYTqilSM9OrVC8j7JX/u3DkSEhIsGkqI+/HtoWh2hicwrXcD6lWVSxjVEtO0rdoRxF34ujky/uG6zN4axs6wBB5uUEXtSKKCK1Ix0rFjR/PXnTp1YtSoURYLJMT9iExIZ/bWc3Ss683I9rXUjlOhHRr2gtoRxD083b4Wa/6KYdaWc4TW9ZYRRKGqIhUje/fuNX+dmJhIUlKSxQIJUVLZuSbGrz2Ok17H+wOaoJVF8IS4K71Oy8zHGjL0s0Os2HORlx6qq3YkUYEVqRjZunWr+Wt7e3vmzJljsUBClNT87eGcuXqLT4a1wNfdUe04FV7fN/4DwIY5n6mcRNxNhyBvHn3Aj492R9KveXX8Ze0aoZIiFSNz584lIiKCyMhIAgMDCQkJsXQuIYplZ1g8K/68xPC2NXmkUVW14wjALtugdgRRBG88GsLO8Hje2RrGx8NaqB1HVFBFmmdk5cqVTJ06lePHjzN16lQ+//xzS+cSosiupWYy8YeTNPBz581HpVAWojiqV3LihS5B/HzmOvsi5RC8UEeRipEtW7bw7bff8uabb/Ldd9+xbds2S+cSokhyjCZe/u44ObkmPhraXE7CE6IEnu1UmwBPJ2ZuPkuO0aR2HFEBFakYURQFO7u8Izp6vR69Xm/RUKL8yLXwL7aFv0Zw5EoKc/o/IJM3CVFCjnodUx9tQER8OqsOXlE7jqiAinTOSIsWLXj55Zdp0aIFR48epVmzZpbOJcoJO52Whb9GWGTfV27cZsOJazSq5s7FxNul2s6EbsGltq+K6lKbLmpHEMXQrUEVOtb15oNfI3isSTW8XB3UjiQqkEJHRtauXcsrr7xC//79SUtLo3Xr1kyZMsUa2YS4q3RDLtvPxuPlYk+nYFl3xhYdHTCaowNGqx1DFJFGo2F6nwZkZhtZsOO82nFEBXPPYmTJkiXs27eP3NxcunTpQt++fTl48CAfffSRtfIJcQeTorD9zHVyjCZ6PeCHXleko41CiEIE+box8n+ToZ2Oval2HFGB3PO3+J49e1i8eDFOTk4A+Pv7s3DhQn7//XerhBOiIIcvJRObmknX+r54utirHUfcxZOThvPkpOFqxxDF9PLDdfFysWf6pjMoiqJ2HFFB3LMYcXZ2vmMBJb1ej4tL4ScKnjx5kuHD834RXblyhcGDBzNkyBCmT5+OySRna4uSiU7O4NClZEL83Gjg5652HCHKHXdHPa8+Up9j0alsOHFV7TiigrhnMeLo6EhMTEy++2JiYgpd4XHFihW89dZbGAx5kx7NnTuX8ePHs3r1ahRFYefOnfcZW1REt7Jy+PlMHF4u9nSt56t2HCHKrSeb+9MkoBJzt4WTbshVO46oAO5ZjEyaNImxY8cyZ84cVq5cybx58xg7dmyhJ7DWqFGDJUuWmG+fPXuW1q1bA3kL7e3fv78UoouKJNdoYuupOEwmeLSxnCcihCVptRpmPtaQhDQDS36/oHYcUQHc89LeunXrsnr1anbu3ElCQgINGzbkhRdewNXV9Z477dGjB7GxsebbiqKYR1NcXFxIS0srNJjBYCAsLKwor6HYsrKyLLbv8qwk/RYSEkJSUuJ9t30oJoOEtGw61XLBmHGTpIz73mUhgksld25ubqns599ycvKWfL9z36WT+95tZ9+l7ZJt9+/Mluqz0mf5vi6OovdbcJF+jh2AbkGufP7nRVpWzsbfo/ydnyWfBcVnqT4rdJ4RNzc3+vbte1+NaLX//1fs7du3cXcv/Fi/g4ODxdbACQsLk/V1SqCk/ebtfX+X3p69dpPI5FRa1qxMszre97Wv4rjf3JD3QVwa+/m3vyceLGjflmgvX9vX7YvUzqWHHitynn9uY6k+swRbylmcfivqz/Ec/9o8uGA3q8MMfPlMk/uJZ5Pks6D47rfP7lbIWGWsu0GDBhw6dAjIu0KnZcuW1mhWlAMJt7LYdT6RAE8n2tXxUjuOKIZTjw3l1GND1Y4h7oOPmwPjHq7LrvOJ/B4er3YcUY5ZpRiZMmUKS5YsYeDAgeTk5NCjRw9rNCvKuIzsXLacjsPZXkfPhn5oCzlxWtgWu6xM7LIy1Y4h7tOIdrWo4+PCrM3nMOQa1Y4jyqkiTQdfEv7+/nz//fcABAYGsmrVKks1Jcoho0lh6+k4MrKNDGjhj5O9LIBX1vR96zkA1i1YqXIScT/s7bRM79OQEV8c5ou9lxnTpY7akUQ5JJckCJu0OyKBa6lZdAupQhV3R7XjCFGhdQr2oVuDKiz5/QLxt7LUjiPKISlGhM05FZvKmau3aFmzMvWquqkdRwgBTH20AbkmhXd/Dlc7iiiHpBgRNiU2JYM/IhKp5eUsJ6wKYUNqeDnzfKfa/HT8KkcuJ6sdR5QzUowIm3ErM4dtp6/j4aTnkUZV5YRVIWzMmC518PNwZPqmsxhNsm6NKD1SjAibYMgxsvHkNUyKQp8m1XCwkxNWy7pz3ftxrns/tWOIUuRsb8cbvUI4e+0Wqw9dUTuOKEekGBGqM5oUtp6JIzUjm0cf8KOyc/mb6bEiOte9P+e691c7hihlvRv70SHIi/e2nycxzaB2HFFOSDEiVKUoCrvPJxCTnMlD9asQ4OmsdiRRShxvJuN4U84tKG80Gg2zHm+EIcfEnG0ylbooHVKMCFUdi07lzLVbtKpVmQbVCl8mQJQdvd8eR++3x6kdQ1hAHR9Xnu+cdzLrgagbascR5YAUI0I1FxLS2BuZRLCvK+1qy5UzQpQlL3QNIsDTiakbz5Cda1I7jijjpBgRqriWmsn2s/FUdXekW4Mq5lWdhRBlg6Nex6zHGhGZkM5ney+qHUeUcVKMCKu7kW5g08lruDnY0aeJH3Y6eRsKURZ1re9Lj4ZV+HDnBWKSM9SOI8ow+RQQVpWWlcOGE9fQaTX0bVYdZ3uLLY8khLCCaX0aokHDjE1nURSZe0SUjBQjwmqycoxsOHGN7FwTfZtWx8NJr3YkYUGneg/mVO/BascQFla9khMTuwezMzyBLafi1I4jyigpRoRV5BpNbD55jZsZOfRu7IePm4PakYSFRXTpRUSXXmrHEFYwsn0tGvt7MGPTWVJuZ6sdR5RBUowIizOaFLaduc61m1n0aChziVQUrglxuCbIX8oVgZ1Oy7v9G3MzM4fZW2XuEVF8UowIizKaFLafvc6lpNt0redD3SqyCm9F8ch7r/LIe6+qHUNYSYNq7jzfuTY/HovlzwuJascRZYwUI8JiTCaFKT+e4kJCOh2DvGnsX0ntSEIIC3rpwbrU9nbh9fWnycjOVTuOKEOkGBEWoSgKMzafZd3RWNoEetK8ZmW1IwkhLMxRr2Nu/weITcnkgx0RascRZYgUI6LUKYrCvF/O882BKzzfqTZtAj3VjiSEsJI2tb0Y2qYGX+y7xPHoFLXjiDJCihFRqhRFYf7283zyRxTD2tbgtZ71ZXZVISqY13rWp6q7IxN/OElmtlHtOKIMkGJElBpFUXj3l3CW7Y5iSJsazHqskRQiFdixJ57h2BPPqB1DqMDNUc/8AU24mHibeb+Eqx1HlAEy/aUoFYqiMGdbGCv+vMSwtnmFiFYrhUhFdrHdg2pHECrqEOTNyPa1+Gr/Zbo3qEL7IG+1IwkbJiMj4r4pisKsLedY8eclRravxduPSyEioHLMRSrHyAJqFdmUR+pT29uFyetOcSsrR+04woZJMSLui8mkMG3jWb7cd5lRHQKZ3qeBHJoRADy0eDoPLZ6udgyhIid7He8/1YS4m5nM2nxO7TjChkkxIkosx2hi/NoTrDyYd9XM1N4hUogIIfJpVqMyY7sEse5oLDvOXlc7jrBRUoyIEsnMNvLsN0fYdPIarz5ST66aEULc1csP1aWBnzuvrz9N/K0steMIGyTFiCi2m5k5DP/8EH9EJDKn3wOM7RIkhYgQ4q7s7bQsHtSU29m5jF9zAqNJUTuSsDFSjIhiib+VxcBPD3AyNpWPhjRnSJsaakcSQpQBdau4MevxRhy4eIOPdkWqHUfYGLm0VxTZpZRsRm3Yx83MHL4Y2YqOdX3UjiRs2OEhY9SOIGzMgBb+HIi6waLfImgd6Enb2l5qRxI2QkZGRJH8EZHIxJ+vYlIUfvhvOylERKGim7cnunl7tWMIG6LRaHi7byNqerkwbs1xbqQb1I4kbIQUI6JQqw9FM+qrv/Bz1bPhhQ40rOahdiRRBvhEheETFaZ2DGFjXB3sWDqkGSkZOUz84SQmOX9EIMWIuAejSWHutjDe+Ok0nep6M79nNfw8nNSOJcqIzh/PofPHc9SOIWxQw2oeTH00hN3nE/lkT5TacYQNkGJEFCg1I5tnvvqLT/dcZHjbmqwY0RJnvbxdhBClY1jbmvRpUo3528+zKzxB7ThCZfLpIu4Qfv0Wjy3dx4GoJOb2f4C3+zbCTidvFSFE6dFoNLz3RGNCqrrz8prjRCWmqx1JqEg+YUQ+207H0X/ZfrJyjKx5rh2DW8ulu0IIy3Cy17F8RAv0Oi3PfnNE1q+pwKQYEQBk55qYsy2Msd8eo35VNza/FEqLmpXVjiWEKOf8KzuzbGhzom9kyIRoFZhV5xnp168frq6uAPj7+zN37lxrNi/uIiY5gxe/O87JmFSGt63JW71DcLDTqR1LlHH7npmgdgRRRrSt7cX0xxoydcMZFuw4z5RH6qsdSViZ1YoRg8GAoiisXLnSWk2KIvj5dByv/ngKgI+HNqfnA34qJxLlRVzD5mpHEGXIsDY1OHftFh/vjqKmpzOD5BBxhWK1YiQ8PJzMzExGjRpFbm4ur7zyCk2bNrVW8+JfMrONvLPtHKsORtMkoBJLBzcjwNNZ7ViiHPE7ewyQokQUjUajYdbjDbmamskbP53Gy9WBbg2qqB1LWInVzhlxdHRk9OjRfP7558ycOZNJkyaRm5trrebFPxyLTuHRD/9k1cFonu0YyA/Pt5NCRJS6Dl8upMOXC9WOIcoQvU7Lx0Ob80B1D15cfYyjV5LVjiSsxGojI4GBgdSsWRONRkNgYCCVKlUiMTERP7+CDwsYDAbCwiwze2NWVpbF9m3LcowK355M4YczqXg72zG3ux9N/TREXThfpOeXpN9CQkJISkosSVyVBZdK7tzcXIu8/pycvL8Y79x36eS+d9vZd2m7ZNv9O7Ol+qz0Wb6vi6Po/RZs87//XutQiUk/Z/D054dY0LMaNSvZW6SdivpZcD8s1WdWK0bWrVtHREQEM2bMID4+nvT0dHx87r6+iYODAyEhIRbJEhYWZrF926pz124x5YeThMXdYkALf6b2aYC7o75Y+yhpv3l7l811bEojd1JSokVev16f970raN+W7m/9dfsitaPXF227f29jqT6zBFvKWZx+Kwu//9bUrE3/j/cza3cSP45tb5HZnyviZ8H9ut8+u1shY7XDNE8++SRpaWkMHjyYCRMmMGfOHOzsZNFgS8vMNjL35zD6LN1LYloWK0a0ZP6AJsUuRIQQwpoCPJ35cmQrbmXlMnTFIeJvZakdSViQ1aoBe3t73n//fWs1J8hbafetDaeJSc7kqZb+vN4zhMoulhnuFEKI0taougdfPtOKkV8cZtDyg3z3bFuqejiqHUtYgAxNlEMJt7J4Z1sYG09co7aPC2uea0vb2l5qxxIVzB9j3lA7gigHWtXy5JvRrXn6i78YtPwA3z3XVhbsLIdkBtZyJCvHyLLdkXRdsJufT19n3EN1+XlcRylEhCoS64SQWEeOx4v716JmXkFyIz2bQcsPci01U+1IopRJMVIOKIrC9rPX6b5wD+/9cp72Qd7smNCJCd2CZSZVoZoax/ZT49h+tWOIcqJ5jcp8M7o1yenZDFx+gOgbGWpHEqVIipEy7mRMKkM/O8TzK4/iYKdl5ejWrBjRklreLmpHExVc69Uf03r1x2rHEOVIsxqVWfWfNqRl5dJv2T6OR6eoHUmUEilGypBco8n8dWRCOv9deZTHP9pH+PU0Zj7WkJ/HdaRjXctdaiiXwAkh1NYkoBI/jmmPs4OOwSsOsv3sdbUjiVIgJ7CWIXY6LTM3neXw5WTOXbuFnU5D20BPmtWoTPLtbJb8HmnR9ksy/8OEbsEWSiOEqKjq+Ljy09gOjP76CP9ddZRpvRvwTIdAtWOJ+yDFSBlxOek2y3ZHsu5oLBo0NAmoRKtalXG2l2+hEKLi8XZ1YM2zbRm35jgzN5/jyo0M3nw0BL1OBvzLIvkks3GRCWks/T2STSevoddpeaC6By1qVsZNJi0TQlRwTvY6Ph7Wgne2hvHFvkucvnqTpUOayaW/ZZAUIzZIURQOXUpmxZ6L7AxPwEmvY3RoIM92qs23B6PVjidEkewcN1PtCKIC0Gk1TOvTgKY1KvH6j6d49MO9LBrYlE7BtjNVvyicFCM2JMdoYtvpOD77M6/C93SxZ9xDdRnRriZerg5qxxOiWFICaqsdQVQgjzWpRsNq7oxddYynvzzMSw/WZdxDddFpNWpHE0UgxYgNiLuZyXeHY1j7VzTxtwzU9nZhTr8H6N+8Oo56mSdElE21D/wOwMV2D6qcRFQUdXxc2fBCB97acIYPd15g74VE3nuyCUG+rmpHE4WQYkQlJpPCn5FJrDp4hZ1h8ShA52Af5vSrSdd6vmilmhdlXPMfvwSkGBHW5WSvY8GAxoTW9WLGpnP0+vBPJjwczLMdA7GTk1ttlhQjVpaYZuDHY7GsPhRNdHIGXi72PN+5DkNa1yDA01nteEIIUeZpNBr6NfOnQ5A30zeeZd4v4Ww7Hcd7TzYmxM9d7XiiABWyGAmsXceq7WXlGPktLJ71x67yR0QiRpNCm0BPJvWoR4+GVWTKdiGE1eQaTWVuhKCkmX3dHPl4WAu2nY5j2sYz9F6yl6FtajDuobpWOQ+vLPY1qJO7QhYjjg72LPw1wqJtKIpC3M0swuJuEZGQTnauCVcHO5oFVCLEzx1PF3uiEtJZlpBe5H3KBGJCiPtlp9Na/Pdfabvf3329HvCjXW0vPvg1gm8PRfPTsauM7RpEe29T4U++D2Wxr0Gdz5oKWYxYiqIo3LidzYX4dM7Hp3EzMwc7rYYgX1dC/Nzxr+yEViPnggghhLVVdrHn7b6NeLp9Td79OZx5v4Tj62LHpCx3+jatjr1d2RvBKE+kGCkFN9INRCSkcyE+jZSMHDSAf2UnWgd6EuTjKm9yUSH98up7akcQ4g5Bvm589nQr9kcmMW39cV5dd4qFv0YwOjSQQa1r4OogH4tqkF4voeTb2VyIT+NCQjo3bmcD4F/JiaYBlQjydZVp2kWFl+7rp3YEIe6qfZA3H/auToLOm0/+iGL21jCW/B7JiHY1GdS6BtUrySyu1iSfmEWkKApJ6dlEJaYTmZjOjfS8AqR6JSe6BPsQ5OuKi1TUQpgF794GQESXXionEaJgGo2GLvV86VLPl+PRKXzyRxRLd0WydFckoUHeDGgZQPcGVWS+JyuQT897MJkUrt3MJCrxNhcT07mVlQuAn4cjnf9XgMiQnhAFa7zlO0CKEVE2NKtRmU+HtyQmOYN1R2NZdzSWl787jrujHY829qNbgyq0r+MthYmFyCfpv+QYTUQnZxCVmM6lxNtk5ZrQaTXU8HSmVaAngV4uMgIihBDlVICnMxO6BTPuobocuHiD74/EsOnENb47HIOTXkfHut48HFKF9kFeVK/khEYuSigV8qkKZGYbuZR0m6jEdKKTM8g1KTjYaQn0dqG2jws1PV3kJFQhhKhAtFoNHYK86RDkjSHXyMGLyfx2Lp6dYfHsOBcP5I2St6zlSatalWleozJBvq4yclJCFbYYuZmZw8XEdKISb3MtNRMFcHWwo2E1d2r7uFK9kpMssCSEEDbAUpNwhYSEFGk7BzsdnYN96Bzsw6zHGxJ+PY2/Lifz1+UU/rqUzOaT1wDQaqCWlwv1qroRXMWNWt7OXE3JxM3RDlcHO1nm4x4qXDFy9Eoyb204S1jcLQC8XOxpVcuT2j4u+Lo5yJCbEELYGEtNHpaUlIi3t0+Jn1/b24VAL2fSsnKJv5VF0u1sbqQbOHDxBr+cuY7yj201GnCxt8PZXoejXofT//7Z22nR6zTY6f73v/b/b9tpNej+/qf5/6+1/7it1VAuPrcqXDGSfDsHb1d7OgZ5U9vHhUrO9mpHEqJc2jJ1sdoRhLA4jUaDu5Medyc9df9xf67RRP8W/izZeYG0rNy8f4YcMrONZOWYuJmZ93W28f5ngf13sfL3ba027zG9VouDXoujXve/f1oc7f6/KHJ1tMPFQYedVr3TESpcMdKtQRW6NahSJqfoFaIsyfLwVDuCEKqx02mp4+NKTS+Xe26nKAq5JoVco0KO0USuKe//HKMJo0nJ+6co5q9NJvLd/vfjRpOCScn/WI7RRGpGDlk5WWTlmDAqSoFZnPQ63J3s6PlAVepXte6CghWuGBFCWEeDHesBONe9v8pJhLBdGo0GvU6DXgdOWP7k17+Ln6ycvBGajOxc0g3/+5eVS1auCTsVzm2RYkQIYRENdvwESDEihC35/+JHi5sjwJ2rFwf5ulk9l1yvKoQQQghVSTEihBBCCFVJMSKEEEIIVUkxIoQQQghVyQmsQgiL2DB7udoRhBBlhBQjQgiLyHV0UjuCEKKMkMM0QgiLaLzpWxpv+lbtGEKIMkCKESGERQTv+YXgPb+oHUMIUQZY7TCNyWRixowZnD9/Hnt7e2bPnk3NmjWt1bwQQgghbJTVRkZ+++03srOzWbt2LRMnTuTdd9+1VtNCCCGEsGFWK0aOHj1Kx44dAWjatClnzpyxVtNCCCGEsGFWK0bS09NxdXU139bpdOTm5lqreSGEEELYKI2i3GUt4VI2d+5cmjRpQq9evQDo1KkTe/bsuev2J06cwMHhzgV8hBBCCFE2GQwGmjZtesf9VjuBtXnz5uzatYtevXpx4sQJgoOD77l9QWGFEEIIUf5YbWTk76tpIiIiUBSFOXPmUKdOHWs0LYQQQggbZrViRAghhBCiIDLpmRBCCCFUJcWIEEIIIVQlxYgQQgghVFXuihGTycS0adMYOHAgw4cP58qVK3dsk5ycTI8ePTAYDABkZWXx0ksvMWTIEJ599lmSk5OtHVt1Jek3gCtXrtCnTx9rRrUZJemztLQ0/vvf/zJs2DAGDhzI8ePHrR1bVSXps4yMDMaMGcPQoUMZOXIk8fHx1o6tqpL+bAJERUXRokWLO+6vCErSb4qi0LFjR4YPH87w4cN5//33rR1bVSXpM6PRyOzZsxk0aBD9+/dn165dJWq73BUjhU07/+effzJq1CgSExPN93333XcEBwezevVq+vbty7Jly6wdW3Ul6bcNGzYwYcKEClm8Qcn67Msvv6Rt27asWrWKuXPnMmvWLGvHVlVJ+uz777+nYcOGfPvttzz22GOsWLHC2rFVVZI+g7yJJufNm4e9vb0149qMkvRbdHQ0DRs2ZOXKlaxcuZKJEydaO7aqStJnGzduJDc3lzVr1vDxxx8XWMAURbkrRgqbdl6r1fLll19SqVKlAp/TqVMnDhw4YLW8tqIk/ebh4cGqVausGdOmlKTPRo4cyaBBg4C8vygq2sR+Je2zMWPGAHDt2jXc3d2tltcWlKTPFEVh6tSpvPLKKzg5OVkzrs0oSb+dPXuW+Ph4hg8fzrPPPsvFixetGVl1JemzvXv3UqVKFZ577jneeustHnzwwRK1bbVJz6zlbtPO29nlvdQOHToU+Bw3NzcAXFxcSEtLs05YG1KSfuvatavV8tmikvTZ3x+kiYmJTJ48mTfeeMM6YW1ESfrs7+1GjBhBREQEX375pVWy2oqS9NnSpUvp3Lkz9evXt1pOW1OSfvPx8eG5556jZ8+eHDlyhMmTJ/Pjjz9aLbPaStJnKSkpREdH8+mnn/LXX3/x+uuv8+233xa77XJXjLi6unL79m3zbZPJZO7Iojzn9u3bFe4vLyhZv1V0Je2z8+fP88orr/Dqq6/SunVrS0a0OffzPvvmm2+Iiori+eef57fffrNURJtTkj7btGkTVatW5ccffyQxMZFRo0aV6AOiLCtJvzVq1AidTgdAy5YtSUhIQFEUNBqNRbPaipL0WaVKlejSpQsajYbWrVtz+fLlErVd7g7TNG/e3LzmTVGmnf/7OX/88QcAe/bsoUWLFhbNaItK0m8VXUn6LDIyknHjxvH+++/TuXNnS0e0OSXps08//ZQNGzYAeSOXf39YVBQl6bNff/3VfN6Dj48PX3zxhaVj2pyS9NvSpUv5+uuvAQgPD8fPz6/CFCJQsj5r0aKF+fPz7z4riXL3p2+3bt3Yt28fgwYNMk87/+WXX1KjRg0eeuihAp8zePBgpkyZwuDBg9Hr9RXuDGooWb9VdCXps/fff5/s7GzeeecdIO8vkY8//tiasVVVkj574oknmDJlCj/++CNGo5E5c+ZYObW65GezZErSb8899xyTJ0/mjz/+QKfTMXfuXCunVldJ+uypp55i+vTpPPXUUyiKwsyZM0vUtkwHL4QQQghVlbvDNEIIIYQoW6QYEUIIIYSqpBgRQgghhKqkGBFCCCGEqqQYEUIIIYSqpBgRooI6dOgQ7dq1My8K9tRTT7Fy5UqrtX/+/Hn++usvi+w7JSWFadOmAXlzRwwcOJBPPvkEgNzcXF5++WWMRiOQN3X6lClTyMrKskgWIUThpBgRogJr27ateXKsVatW8eWXX3Lr1i2rtL1jxw4iIyMtsu9FixYxZMgQAPbv38/atWv5888/AVi7di1PPPGEefI0jUZD7969+eyzzyySRQhRuHI36ZkQomTS09PRarXodDrOnz/P7NmzgbzpnufMmcO5c+dYsGABer2ep556Cg8PD5YuXYqiKDRs2JCZM2dy5MgRFi5ciE6nIyAggFmzZrF582b++OMPsrKyiI6O5tlnn6VDhw789NNP6PV6GjZsyLVr1/j222/Jzc1Fo9GwdOlSKleuzMyZMzlz5gze3t5cvXqVjz/+GJ1Ox9SpUzEYDDg4OPD222/nm/UxPT2d06dPmydfsrOzw2g0otVqSUtL49ixYwwdOjTfa2/fvj3vvvsuY8eORauVv9GEsDYpRoSowA4ePMjw4cPRaDTo9XqmTp2Ki4sLU6dOZc6cOQQFBfHDDz/w2Wef0b59ewwGAz/88AO5ubl0796dH374AS8vL1asWEFcXBxTp05l9erVeHl5sWjRIn766Sfs7OxIT0/n888/5/Lly/z3v/+lf//+9OvXD29vbxo3bsz+/ftZvnw5Tk5OTJs2jb179+Ls7Exqairr1q0jOTmZ7t27AzBv3jyGDx9O586dOXDgAAsWLMg3a/KJEycIDAw03x4+fDgTJkxg5MiRLF++nNGjRzN//nwyMjJ44YUX8Pb2RqfT4enpSURERIVeXE4ItUgxIkQF1rZtWxYuXHjH/VFRUeaRhZycHGrVqgVg/pBPSUnB3d0dLy8vAJ599llu3LhBQkIC48ePByArK4v27dtTs2ZN8we8n58f2dnZd7Tn5eXFlClTcHFx4eLFizRt2tT8P4Cnpye1a9cGICIigk8//ZTPPvsMRVHuWMgrJSUFb29v8+1u3brRrVs3YmJi2Lt3Lzdu3MDT05NHHnmElStXMmHCBAB8fX1JTU0tQS8KIe6XFCNCiDsEBgYyb948qlWrxtGjR0lMTAQwH8Lw8vLi1q1bpKamUqlSJWbPns1jjz1G1apVWbZsGW5ubuzcuRNnZ2fi4uIKXGxMo9FgMplIS0vjww8/ZPfu3QA888wzKIpC3bp12bhxIwA3b940rwZau3ZtRo0aRfPmzYmKirrjJNi/s/3bxx9/zPjx4zl58iQ6nQ6NRpNvhdKbN2+aiyshhHVJMSKEuMOMGTOYMmWK+RyOd955h4SEBPPjWq2W6dOn8/zzz6PVamnQoAEPPPAAb775Js899xyKouDi4sJ7771HXFxcgW00atSI9957jzp16tC8eXMGDhyInZ0d7u7uJCQk0L9/f/bs2cOgQYPw9vbG0dERvV7PlClTmDFjBgaDgaysLN588818+23SpAkLFizId9/x48epVq0avr6+tG/fnjFjxvDzzz+bR39MJhPx8fEEBQWVck8KIYpCFsoTQtikqKgowsPDefTRR0lJSaF3797s2rULe3v7Qp87bdo0Bg0aRIMGDYrU1h9//MHZs2cZO3bs/cYWQpSAnDYuhLBJfn5+bNmyhaeeeor//Oc/TJo0qUiFCMC4ceNYvXp1kbZVFIXNmzczcuTI+0grhLgfMjIihBBCCFXJyIgQQgghVCXFiBBCCCFUJcWIEEIIIVQlxYgQQgghVCXFiBBCCCFUJcWIEEIIIVT1fxnftWVJxulqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kolom data y dan yang bertipe kategorikal tidak memiliki nilai NaN, maka tidak dimasukan ke dalam plot\n",
    "f, (ax_box, ax_hist) = plt.subplots(2,sharex=True, gridspec_kw= {\"height_ratios\": (0.21, 1)}, figsize=(9,5))\n",
    "mean = null_in_train.loc[null_in_train['Total NaN'] > 0].Percentage.mean()\n",
    "median = null_in_train.loc[null_in_train['Total NaN'] > 0].Percentage.median()\n",
    "mode = null_in_train.loc[null_in_train['Total NaN'] > 0].Percentage.mode()[0]\n",
    "\n",
    "sns.boxplot(data=null_in_train.loc[null_in_train['Total NaN'] > 0], x='Percentage',  ax=ax_box)\n",
    "ax_box.axvline(mean, color='r', linestyle='--')\n",
    "ax_box.axvline(median, color='g', linestyle='-')\n",
    "ax_box.axvline(mode, color='b', linestyle='-')\n",
    "\n",
    "sns.histplot(data=null_in_train.loc[null_in_train['Total NaN'] > 0], x='Percentage', kde=True, ax=ax_hist)\n",
    "ax_hist.axvline(mean, color='r', linestyle='--', label=\"Mean = \" + str(\"{:.3f}\".format(mean)) + '%')\n",
    "ax_hist.axvline(median, color='g', linestyle='-', label=\"Median = \" + str(\"{:.3f}\".format(median))+ '%')\n",
    "ax_hist.axvline(mode, color='b', linestyle='-', label=\"Mode = \" + str(\"{:.3f}\".format(mode))+ '%')\n",
    "\n",
    "ax_hist.legend()\n",
    "\n",
    "ax_box.set(xlabel='')\n",
    "ax_hist.set(xlabel='Percentage (%)')\n",
    "f.suptitle('Distribusi Data Kosong pada Data Train')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fDari nilai statistik dan hasil plotting, dapat disimpulkan bahwa jumlah rata-rata data NaN/Null pada perkolom pada data numerikal di _data train_ adalah 0.128% dari total data. Cukup kecil dan tidak terlalu signifikan apabila kita rubah data NaN tersebut dengan nilai _mean_ dari kolom yang berkaitan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Preparation : _Label Encoding_ untuk Kolom Kategorikal pada _Data Train_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data dengan tipe kategorikal harus dirubah kedalam nilai numerikal. Untuk itu kita gunakan fungsi _LabelEncoder_ pada modul sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LUTHFI~1\\AppData\\Local\\Temp/ipykernel_14340/1946452041.py:6: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if X[column].dtype == np.number: continue\n"
     ]
    }
   ],
   "source": [
    "# X untuk independent variables\n",
    "# y untuk dependent variable\n",
    "X = df_train.loc[:, df_train.columns != 'y']\n",
    "y = df_train['y']\n",
    "for column in X.columns:\n",
    "    if X[column].dtype == np.number: continue\n",
    "    # Melakukan label encoding untuk setiap kolom non-numerik\n",
    "    X[column] = LabelEncoder().fit_transform(X[column])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Preparation : Membagi _Data Train_ untuk _Training_ dan _Validation_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Data Train_ akan dibagi menjadi dua bagian untuk keperluan _training_ dan _validation_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pembagian data menjadi 2 bagian, Training dan Validation pada variabel X dan y.\n",
    "# Pembagian dilakukan dengan porsi 90% untuk training dan 10% untuk validasi\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.1, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Preparation : Mengisi Nilai Kosong (NaN/Null) pada _Data Train_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah data dibagi menjadi dua bagian, proses selanjutnya adalah mengisi nilai kosong (NaN/Null) pada tiap-tiap kolom. Karena hasil analisis sebelumnya bahwa rata-rata hanya 0.128% data yang kosong dari setiap kolom, maka kita masukan nilai _mean_ dari tiap-tiap kolom untuk mengisi nilai kosong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengisi data kosong dengan nilai rata-rata dari kolom yang bersangkutan\n",
    "for i in df_train_by_type['float']:\n",
    "  mean_to_fill_train = X_train[i].mean()\n",
    "  X_train[i].fillna(mean_to_fill_train, inplace=True)\n",
    "  mean_to_fill_test = X_validation[i].mean()\n",
    "  X_validation[i].fillna(mean_to_fill_test, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modelling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Modelling : Membuat Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari komposisi Data Train dan jumlah unique data pada kolom _dependent variable_ 'y' kita dapat memastikan bahwa pada kasus ini adalah proses pemodelan _classification supervised learning_.\n",
    "\n",
    "Saya telah melakukan pemodelan data dengan berbagai metode seperti _Random Forest_, _Decision Tree_, _SVC_, _Multi-layer Perceptron_ (MLP), dan lain-lain. Dari hasil percobaan saya, Algoritma pemodelan MLP yang paling baik dengan parameter tingkat akurasi prediksi dan waktu proses pemodelan. Dengan algoritma pemodelan yang lain, tingkat akurasi prediksi antara 82% - 88% dan waktu proses pemodelan diatas 1 menit. Ketika menggunakan MLP, tingkat akurasi prediksi yang didapat antara 87% - 92% dengan waktu pemodelan paling cepat sekitar 30s. Atas dasar tersebut, untuk case ini saya akan menggunakan algoritma MLP untuk pembuatan model datanya.\n",
    "\n",
    "Selain itu nilai alpha=1 dan activation _logistic_ juga dipilih karena hasil _trial and error_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.42718472\n",
      "Iteration 2, loss = 0.37027376\n",
      "Iteration 3, loss = 0.35963769\n",
      "Iteration 4, loss = 0.35602649\n",
      "Iteration 5, loss = 0.35350707\n",
      "Iteration 6, loss = 0.35289210\n",
      "Iteration 7, loss = 0.35147045\n",
      "Iteration 8, loss = 0.35059717\n",
      "Iteration 9, loss = 0.35053581\n",
      "Iteration 10, loss = 0.34983516\n",
      "Iteration 11, loss = 0.34980083\n",
      "Iteration 12, loss = 0.34916722\n",
      "Iteration 13, loss = 0.34956497\n",
      "Iteration 14, loss = 0.34924465\n",
      "Iteration 15, loss = 0.34916018\n",
      "Iteration 16, loss = 0.34905078\n",
      "Iteration 17, loss = 0.34865342\n",
      "Iteration 18, loss = 0.34874926\n",
      "Iteration 19, loss = 0.34903150\n",
      "Iteration 20, loss = 0.34893302\n",
      "Iteration 21, loss = 0.34870333\n",
      "Iteration 22, loss = 0.34921152\n",
      "Iteration 23, loss = 0.34837956\n",
      "Iteration 24, loss = 0.34840541\n",
      "Iteration 25, loss = 0.34872463\n",
      "Iteration 26, loss = 0.34881362\n",
      "Iteration 27, loss = 0.34880327\n",
      "Iteration 28, loss = 0.34853929\n",
      "Iteration 29, loss = 0.34854401\n",
      "Iteration 30, loss = 0.34818963\n",
      "Iteration 31, loss = 0.34884482\n",
      "Iteration 32, loss = 0.34861980\n",
      "Iteration 33, loss = 0.34825516\n",
      "Iteration 34, loss = 0.34854224\n",
      "Iteration 35, loss = 0.34858719\n",
      "Iteration 36, loss = 0.34833585\n",
      "Iteration 37, loss = 0.34827003\n",
      "Iteration 38, loss = 0.34831935\n",
      "Iteration 39, loss = 0.34833886\n",
      "Iteration 40, loss = 0.34863393\n",
      "Iteration 41, loss = 0.34830044\n",
      "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
     ]
    }
   ],
   "source": [
    "# Pemodelan dengan menggunakan bantuan library SKlearn\n",
    "mod = MLPClassifier(alpha=1, max_iter=1000, activation='logistic' ,verbose=True)\n",
    "mod = mod.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss per iterasi dari hasil pemodelan.\n",
    "sns.lineplot(data=mod.loss_curve_).set(xlabel='Iterasi', ylabel='Loss', title='Loss per Iterasi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setelah melakukan pemodelan, lalu kita lakukan prediksi dari data 'X_validation' dengan menggunakan model yang kita train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Validasi Model\n",
    "y_pred = mod.predict(X_validation)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nilai prediksi dari 'X_validation' dengan model, kemudian kita bandingkan dengan nilai 'y_validation' dalam bentuk _confussion matrix_. Setelah itu, nilai akurasi dari model dapat kita kalkulasi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>8206</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>961</td>\n",
       "      <td>753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0         8206           80\n",
       "Actual 1          961          753"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confussion Matrix untuk menghitung akurasi dan validasi dari model\n",
    "pred_table = pd.DataFrame(confusion_matrix(y_true=y_validation, y_pred=y_pred), columns=['Predicted 0','Predicted 1'], index=['Actual 0','Actual 1'])\n",
    "pred_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "akurasi model :  89.59 %\n"
     ]
    }
   ],
   "source": [
    "# Menghitung akurasi model\n",
    "mod_acc = ((pred_table.iloc[0,0]+pred_table.iloc[1,1]) / (pred_table.iloc[0].sum()+pred_table.iloc[1].sum()))*100\n",
    "print('akurasi model : ', mod_acc,'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94      8286\n",
      "           1       0.90      0.44      0.59      1714\n",
      "\n",
      "    accuracy                           0.90     10000\n",
      "   macro avg       0.90      0.71      0.77     10000\n",
      "weighted avg       0.90      0.90      0.88     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "print(classification_report(y_validation, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1041"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Menghitung Mean Absolute Error\n",
    "mean_absolute_error(y_true=y_validation, y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediksi Menggunakan _Data Test_ Pada Model dan _Export to CSV_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Analysis : Memeriksa Nilai Kosong (NaN/Null) pada _Data Test_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sama seperti proses pada _Data Train_, _Data Test_ juga perlu dilakukan proses analisis untuk memeriksa nilai-nilai kosong NaN/Null pada data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>x0</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>x5</th>\n",
       "      <th>x6</th>\n",
       "      <th>x7</th>\n",
       "      <th>x8</th>\n",
       "      <th>x9</th>\n",
       "      <th>...</th>\n",
       "      <th>x115</th>\n",
       "      <th>x116</th>\n",
       "      <th>x117</th>\n",
       "      <th>x118</th>\n",
       "      <th>x119</th>\n",
       "      <th>x120</th>\n",
       "      <th>x121</th>\n",
       "      <th>x122</th>\n",
       "      <th>x123</th>\n",
       "      <th>x124</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total NaN</th>\n",
       "      <td>40.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>26.000</td>\n",
       "      <td>27.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>36.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>...</td>\n",
       "      <td>32.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.000</td>\n",
       "      <td>38.000</td>\n",
       "      <td>39.000</td>\n",
       "      <td>34.000</td>\n",
       "      <td>46.000</td>\n",
       "      <td>42.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentage</th>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               x0     x1      x2      x3      x4      x5   x6   x7      x8  \\\n",
       "Total NaN   40.00  30.00  26.000  27.000  39.000  36.000  0.0  0.0  32.000   \n",
       "Percentage   0.04   0.03   0.026   0.027   0.039   0.036  0.0  0.0   0.032   \n",
       "\n",
       "                x9  ...    x115    x116  x117  x118    x119    x120    x121  \\\n",
       "Total NaN   38.000  ...  32.000  29.000   0.0   0.0  26.000  38.000  39.000   \n",
       "Percentage   0.038  ...   0.032   0.029   0.0   0.0   0.026   0.038   0.039   \n",
       "\n",
       "              x122    x123    x124  \n",
       "Total NaN   34.000  46.000  42.000  \n",
       "Percentage   0.034   0.046   0.042  \n",
       "\n",
       "[2 rows x 125 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eksplorasi Data NaN pada Data Train \n",
    "null_in_test = df_test.isnull().sum().to_frame('Total NaN')\n",
    "null_in_test['Percentage'] = null_in_test['Total NaN']*100 / 100000\n",
    "null_in_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['x6', 'x7', 'x21', 'x22', 'x51', 'x63', 'x79', 'x80', 'x89', 'x93',\n",
       "       'x96', 'x104', 'x108', 'x112', 'x117', 'x118'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Periksa kolom data yang tidak memiliki nilai null / nan\n",
    "df_test_with_null = null_in_test.loc[null_in_train['Total NaN'] <=0].index\n",
    "df_test_with_null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kolom kategorikal dan kolom 'y' tidak memiliki nilai null atau NaN. Nilai Null atau NaN pada kolom kategorikal sudah diubah menjadi string pada proses sebelumnya. karena itu kolom kategorikal dan kolom 'y' akan di drop sementara karena akan menjadi outliers pada proses pencarian nilai Null/NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total NaN</th>\n",
       "      <td>109.0</td>\n",
       "      <td>128.256881</td>\n",
       "      <td>11.310765</td>\n",
       "      <td>100.0</td>\n",
       "      <td>122.000</td>\n",
       "      <td>127.000</td>\n",
       "      <td>137.000</td>\n",
       "      <td>159.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Percentage</th>\n",
       "      <td>109.0</td>\n",
       "      <td>0.128257</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean        std    min      25%      50%      75%  \\\n",
       "Total NaN   109.0  128.256881  11.310765  100.0  122.000  127.000  137.000   \n",
       "Percentage  109.0    0.128257   0.011311    0.1    0.122    0.127    0.137   \n",
       "\n",
       "                max  \n",
       "Total NaN   159.000  \n",
       "Percentage    0.159  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memeriksa nilai NaN/Null secara statistik\n",
    "null_in_train.loc[null_in_train['Total NaN'] >0].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# kolom data y dan yang bertipe kategorikal tidak memiliki nilai NaN, maka tidak dimasukan ke dalam plot\n",
    "f, (ax_box, ax_hist) = plt.subplots(2,sharex=True, gridspec_kw= {\"height_ratios\": (0.21, 1)}, figsize=(9,5))\n",
    "mean = null_in_test.loc[null_in_train['Total NaN'] > 0].Percentage.mean()\n",
    "median = null_in_test.loc[null_in_train['Total NaN'] > 0].Percentage.median()\n",
    "mode = null_in_test.loc[null_in_train['Total NaN'] > 0].Percentage.mode()[0]\n",
    "\n",
    "sns.boxplot(data=null_in_test.loc[null_in_test['Total NaN'] > 0], x='Percentage',  ax=ax_box)\n",
    "ax_box.axvline(mean, color='r', linestyle='--')\n",
    "ax_box.axvline(median, color='g', linestyle='-')\n",
    "ax_box.axvline(mode, color='b', linestyle='-')\n",
    "\n",
    "sns.histplot(data=null_in_test.loc[null_in_test['Total NaN'] > 0], x='Percentage', kde=True, ax=ax_hist)\n",
    "ax_hist.axvline(mean, color='r', linestyle='--', label=\"Mean = \" + str(\"{:.3f}\".format(mean)) + '%')\n",
    "ax_hist.axvline(median, color='g', linestyle='-', label=\"Median = \" + str(\"{:.3f}\".format(median))+ '%')\n",
    "ax_hist.axvline(mode, color='b', linestyle='-', label=\"Mode = \" + str(\"{:.3f}\".format(mode))+ '%')\n",
    "\n",
    "ax_hist.legend()\n",
    "\n",
    "ax_box.set(xlabel='')\n",
    "ax_hist.set(xlabel='Percentage (%)')\n",
    "f.suptitle('Distribusi Data Kosong pada Data Train')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari nilai statistik dan hasil plotting, dapat disimpulkan bahwa jumlah rata-rata data NaN/Null pada perkolom pada data numerikal di _data test_ adalah 0.033% dari total data. Cukup kecil dan tidak terlalu signifikan apabila kita rubah data NaN tersebut dengan nilai _mean_ dari kolom yang berkaitan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Preparation : _Label Encoding_ untuk Kolom Kategorikal pada _Data Test_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LUTHFI~1\\AppData\\Local\\Temp/ipykernel_14340/1510193198.py:5: DeprecationWarning: Converting `np.inexact` or `np.floating` to a dtype is deprecated. The current result is `float64` which is not strictly correct.\n",
      "  if X_test[column].dtype == np.number: continue\n"
     ]
    }
   ],
   "source": [
    "# X untuk independent variables\n",
    "# y untuk dependent variable# \n",
    "X_test = df_test\n",
    "for column in X.columns:\n",
    "    if X_test[column].dtype == np.number: continue\n",
    "    # Melakukan label encoding untuk setiap kolom non-numerik\n",
    "    X_test[column] = LabelEncoder().fit_transform(X_test[column])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Data Preparation : Mengisi Nilai Kosong (NaN/Null) pada _Data Test_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nilai kosong pada _data test_ juga perlu diisi sebelum melakukan prediksi dengan cara yang sama seperti yang telah dilakukan pada _data train_ sebelum melakukan prediksi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mengisi data kosong dengan nilai rata-rata dari kolom yang bersangkutan\n",
    "for i in df_test_by_type['float']:\n",
    "  mean_to_fill_test = X_test[i].mean()\n",
    "  X_test[i].fillna(mean_to_fill_test, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - _Prediction Result_ menggunakan _Data Test_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melakukan prediksi menggunakan model yang telah dibuat sebelumnya dengan menggunakan _data test_. hasil prediksi ini yang akan menjadi jawaban akhir dari soal ini."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>24990</th>\n",
       "      <th>24991</th>\n",
       "      <th>24992</th>\n",
       "      <th>24993</th>\n",
       "      <th>24994</th>\n",
       "      <th>24995</th>\n",
       "      <th>24996</th>\n",
       "      <th>24997</th>\n",
       "      <th>24998</th>\n",
       "      <th>24999</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Id         0      1      2      3      4      5      6      7      8      \\\n",
       "Predicted      0      0      0      0      0      0      1      0      1   \n",
       "\n",
       "Id         9      ...  24990  24991  24992  24993  24994  24995  24996  24997  \\\n",
       "Predicted      0  ...      0      0      0      0      0      0      0      1   \n",
       "\n",
       "Id         24998  24999  \n",
       "Predicted      0      0  \n",
       "\n",
       "[1 rows x 25000 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_result = pd.DataFrame(mod.predict(X_test), columns=['Predicted'])\n",
    "pred_result.index.name = 'Id'\n",
    "pred_result.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customer yang akan churn pada data test: 2125\n",
      "customer yang tidak akan churn pada data test: 22875\n"
     ]
    }
   ],
   "source": [
    "print('customer yang akan churn pada data test:', len(pred_result.loc[pred_result['Predicted'] == 1]))\n",
    "print('customer yang tidak akan churn pada data test:', len(pred_result.loc[pred_result['Predicted'] == 0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### - Menyimpan Hasil Prediksi Ke dalam _file_ .csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Menyimpan hasil prediksi ke file .csv\n",
    "pred_result.to_csv('Luthfi_Arfiansyah-submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24995</th>\n",
       "      <td>24995</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24996</th>\n",
       "      <td>24996</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24997</th>\n",
       "      <td>24997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24998</th>\n",
       "      <td>24998</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24999</th>\n",
       "      <td>24999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id  Predicted\n",
       "0          0          0\n",
       "1          1          0\n",
       "2          2          0\n",
       "3          3          0\n",
       "4          4          0\n",
       "...      ...        ...\n",
       "24995  24995          0\n",
       "24996  24996          0\n",
       "24997  24997          1\n",
       "24998  24998          0\n",
       "24999  24999          0\n",
       "\n",
       "[25000 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memastikan file .csv sudah benar\n",
    "pd.read_csv('Luthfi_Arfiansyah-submission.csv')"
   ]
  }
 ],
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