Customer Churn Analysis.ipynb

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  {
   "cell_type": "markdown",
   "id": "5fbbdae8",
   "metadata": {
    "id": "5fbbdae8"
   },
   "source": [
    "# Customer Churn Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "11948efa",
   "metadata": {
    "id": "11948efa"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from matplotlib import rcParams\n",
    "from sklearn.utils import resample\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import  confusion_matrix , plot_roc_curve, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01fee939",
   "metadata": {
    "id": "01fee939"
   },
   "source": [
    "### Content \n",
    "*   RowNumber—corresponds to the record (row) number and has no effect on the output.\n",
    "*   CustomerId—contains random values and has no effect on customer leaving the bank.\n",
    "*   Surname—the surname of a customer has no impact on their decision to leave the bank.\n",
    "*   CreditScore—can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.\n",
    "*   Geography—a customer’s location can affect their decision to leave the bank.\n",
    "*   Gender—it’s interesting to explore whether gender plays a role in a customer leaving the bank.\n",
    "*   Age—this is certainly relevant, since older customers are less likely to leave their bank than younger ones.\n",
    "*   Tenure—refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.\n",
    "\n",
    "\n",
    "\n",
    "```\n",
    "1.   Balance—also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.\n",
    "2.   NumOfProducts—refers to the number of products that a customer has purchased through the bank.\n",
    "3.   HasCrCard—denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.\n",
    "4.   IsActiveMember—active customers are less likely to leave the bank.\n",
    "5.   EstimatedSalary—as with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.\n",
    "6.   Exited—whether or not the customer left the bank.\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4c3e60e9",
   "metadata": {
    "id": "4c3e60e9",
    "outputId": "43228d0d-3522-42a6-88a0-90090a770020"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3646</th>\n",
       "      <td>3647</td>\n",
       "      <td>15610900</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>770</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
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       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>4257</th>\n",
       "      <td>4258</td>\n",
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       "      <td>Onwuka</td>\n",
       "      <td>635</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>7305</td>\n",
       "      <td>15764444</td>\n",
       "      <td>Pan</td>\n",
       "      <td>679</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>8</td>\n",
       "      <td>125850.53</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>87008.17</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8954</th>\n",
       "      <td>8955</td>\n",
       "      <td>15581626</td>\n",
       "      <td>Mancini</td>\n",
       "      <td>664</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162719.69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>986</td>\n",
       "      <td>15756475</td>\n",
       "      <td>Kenniff</td>\n",
       "      <td>551</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>82293.82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>91565.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>1900</td>\n",
       "      <td>15806880</td>\n",
       "      <td>Boyle</td>\n",
       "      <td>627</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113408.47</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>2361</td>\n",
       "      <td>15670205</td>\n",
       "      <td>Boyd</td>\n",
       "      <td>518</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>110624.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>89327.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8189</th>\n",
       "      <td>8190</td>\n",
       "      <td>15613292</td>\n",
       "      <td>Ch'eng</td>\n",
       "      <td>715</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68666.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376</th>\n",
       "      <td>3377</td>\n",
       "      <td>15721582</td>\n",
       "      <td>Hale</td>\n",
       "      <td>644</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>77270.08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>115800.10</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>489</td>\n",
       "      <td>15731744</td>\n",
       "      <td>Carslaw</td>\n",
       "      <td>692</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130486.57</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "      RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "3646       3647    15610900  Thompson          770    France  Female   70   \n",
       "4257       4258    15756894    Onwuka          635    France    Male   29   \n",
       "7304       7305    15764444       Pan          679   Germany    Male   58   \n",
       "8954       8955    15581626   Mancini          664    France    Male   54   \n",
       "985         986    15756475   Kenniff          551   Germany    Male   31   \n",
       "1899       1900    15806880     Boyle          627     Spain  Female   30   \n",
       "2360       2361    15670205      Boyd          518   Germany  Female   41   \n",
       "8189       8190    15613292    Ch'eng          715    France    Male   21   \n",
       "3376       3377    15721582      Hale          644   Germany  Female   40   \n",
       "488         489    15731744   Carslaw          692    France    Male   30   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "3646       9  110738.89              1          1               0   \n",
       "4257       1       0.00              1          0               1   \n",
       "7304       8  125850.53              2          1               1   \n",
       "8954       8       0.00              1          1               1   \n",
       "985        9   82293.82              2          1               1   \n",
       "1899       6       0.00              1          1               1   \n",
       "2360       5  110624.99              1          1               0   \n",
       "8189       8       0.00              2          1               0   \n",
       "3376       4   77270.08              2          1               1   \n",
       "488        2       0.00              2          0               1   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "3646         22666.77       1  \n",
       "4257         24865.54       0  \n",
       "7304         87008.17       0  \n",
       "8954        162719.69       1  \n",
       "985          91565.25       0  \n",
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      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"churn.csv\")\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "673e54ad",
   "metadata": {},
   "source": [
    "# Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "79bfc9ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b6c124b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7860e80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "128f5956",
   "metadata": {
    "id": "128f5956",
    "outputId": "04b0279d-fe54-4c39-cefc-aedc67735800"
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
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       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CreditScore           Age        Tenure        Balance  NumOfProducts  \\\n",
       "count  10000.000000  10000.000000  10000.000000   10000.000000   10000.000000   \n",
       "mean     650.528800     38.921800      5.012800   76485.889288       1.530200   \n",
       "std       96.653299     10.487806      2.892174   62397.405202       0.581654   \n",
       "min      350.000000     18.000000      0.000000       0.000000       1.000000   \n",
       "25%      584.000000     32.000000      3.000000       0.000000       1.000000   \n",
       "50%      652.000000     37.000000      5.000000   97198.540000       1.000000   \n",
       "75%      718.000000     44.000000      7.000000  127644.240000       2.000000   \n",
       "max      850.000000     92.000000     10.000000  250898.090000       4.000000   \n",
       "\n",
       "         HasCrCard  IsActiveMember  EstimatedSalary        Exited  \n",
       "count  10000.00000    10000.000000     10000.000000  10000.000000  \n",
       "mean       0.70550        0.515100    100090.239881      0.203700  \n",
       "std        0.45584        0.499797     57510.492818      0.402769  \n",
       "min        0.00000        0.000000        11.580000      0.000000  \n",
       "25%        0.00000        0.000000     51002.110000      0.000000  \n",
       "50%        1.00000        1.000000    100193.915000      0.000000  \n",
       "75%        1.00000        1.000000    149388.247500      0.000000  \n",
       "max        1.00000        1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['RowNumber', 'CustomerId']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "58e38cd1",
   "metadata": {
    "id": "58e38cd1",
    "outputId": "9599e0c5-4956-4c9b-da5a-2aaa5e4f075e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Surname</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2932</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Smith</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>32</td>\n",
       "      <td>5014</td>\n",
       "      <td>5457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Surname Geography Gender\n",
       "count    10000     10000  10000\n",
       "unique    2932         3      2\n",
       "top      Smith    France   Male\n",
       "freq        32      5014   5457"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ba34bc",
   "metadata": {
    "id": "a3aa8962"
   },
   "source": [
    "#### Report dataset info and summary statistic\n",
    "\n",
    "1. Tidak ada missing value pada dataset\n",
    "2. Semua tipe data terlihat baik\n",
    "3. Kita memiliki data 10000 customer\n",
    "4. Kita memiliki 3 jenis tipe data yaitu integer,object and float\n",
    "5. Kolom yang possible untuk di drop adalah RowNumber, CustomerId, dan Surname karena tidak akan memberikan informasi lebih banyak.\n",
    "6. Tidak ada kolom yang memiliki missing value ataupun duplikat, namun kita akan lihat lagi data yang disimpan\n",
    "\n",
    "\n",
    "7. Credit Score customer berada pada rentang 350 sampai 850\n",
    "8. Umur customer termuda adalah 18 tahun dan paling tua ada di 92 tahun\n",
    "9. Maksimal tenure/lama kerja sama customer menggunakan bank dari data customer tersebut adalah 10 tahun, dan rata-rata 5 tahun\n",
    "10. Setiap pelanggan setidaknya menggunakan 1 produk dan maksimal 4 produk\n",
    "11. Terdapat customer yang balace pada akun bank nya adalah 0\n",
    "12. Ada customer yang tidak memiliki kartu kredit, namun lebih banyak memilikinya\n",
    "13. sebagian besar customer adalah member aktif\n",
    "14. Terlihat outlier di kolom EstimatedSalary karena ada customer yang di prediksi hanya berpenghasilan 11\n",
    "15. Customer tersebar di tiga tempat berbeda dan sebagian besar berasal dari France\n",
    "16. Kebanyakan customer adalah Male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cf2b09d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7662</th>\n",
       "      <td>809</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>118098.62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100720.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3727</th>\n",
       "      <td>781</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>186558.55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>175071.29</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5161</th>\n",
       "      <td>842</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17268.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6167</th>\n",
       "      <td>657</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>75</td>\n",
       "      <td>7</td>\n",
       "      <td>126273.95</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>91673.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9638</th>\n",
       "      <td>744</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>121498.11</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>106061.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>731</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>96950.21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>97877.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7613</th>\n",
       "      <td>634</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>51582.50</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>184312.88</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>358</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>52</td>\n",
       "      <td>8</td>\n",
       "      <td>143542.36</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>141959.11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7373</th>\n",
       "      <td>663</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>143625.83</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>176487.05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9082</th>\n",
       "      <td>768</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>180169.44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17348.56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "7662          809   Germany    Male   50      10  118098.62              1   \n",
       "3727          781    France  Female   27       7  186558.55              1   \n",
       "5161          842     Spain    Male   46       9       0.00              1   \n",
       "6167          657     Spain    Male   75       7  126273.95              1   \n",
       "9638          744   Germany  Female   50       1  121498.11              2   \n",
       "1863          731     Spain    Male   25       8   96950.21              1   \n",
       "7613          634    France  Female   37       7   51582.50              2   \n",
       "1962          358     Spain  Female   52       8  143542.36              3   \n",
       "7373          663   Germany    Male   37       7  143625.83              2   \n",
       "9082          768    France  Female   36       5  180169.44              2   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n",
       "7662          1               1        100720.02       1  \n",
       "3727          1               1        175071.29       1  \n",
       "5161          0               0         17268.02       0  \n",
       "6167          0               1         91673.60       0  \n",
       "9638          0               1        106061.47       1  \n",
       "1863          1               0         97877.92       0  \n",
       "7613          1               1        184312.88       0  \n",
       "1962          1               0        141959.11       1  \n",
       "7373          0               1        176487.05       0  \n",
       "9082          1               0         17348.56       0  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(columns=['RowNumber', 'CustomerId','Surname'])\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0737e5de",
   "metadata": {},
   "source": [
    "# Descriptive Analytics\n",
    "\n",
    "#### Descriptive Analytics basically the analysis done on the historical data in this case we have 10k customers data that's been collected over a period of time. Descriptive Analytics is useful in order to understand the trend from past."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b722ed25",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9cea2d2",
   "metadata": {},
   "source": [
    "### Check unique value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "04049bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique of CreditScore columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "850    233\n",
       "678     63\n",
       "655     54\n",
       "705     53\n",
       "667     53\n",
       "684     52\n",
       "670     50\n",
       "651     50\n",
       "683     48\n",
       "652     48\n",
       "648     48\n",
       "660     48\n",
       "682     47\n",
       "663     47\n",
       "640     47\n",
       "637     46\n",
       "714     45\n",
       "686     45\n",
       "710     45\n",
       "645     45\n",
       "633     45\n",
       "687     45\n",
       "679     45\n",
       "706     44\n",
       "627     44\n",
       "646     44\n",
       "619     44\n",
       "584     43\n",
       "625     43\n",
       "628     43\n",
       "608     43\n",
       "613     42\n",
       "635     42\n",
       "632     42\n",
       "701     41\n",
       "554     41\n",
       "712     41\n",
       "698     41\n",
       "601     40\n",
       "699     40\n",
       "597     40\n",
       "634     40\n",
       "606     40\n",
       "680     40\n",
       "644     39\n",
       "711     39\n",
       "681     39\n",
       "656     39\n",
       "592     38\n",
       "593     38\n",
       "Name: CreditScore, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of Geography columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "France     5014\n",
       "Germany    2509\n",
       "Spain      2477\n",
       "Name: Geography, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of Gender columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Male      5457\n",
       "Female    4543\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of Age columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "37    478\n",
       "38    477\n",
       "35    474\n",
       "36    456\n",
       "34    447\n",
       "33    442\n",
       "40    432\n",
       "39    423\n",
       "32    418\n",
       "31    404\n",
       "41    366\n",
       "29    348\n",
       "30    327\n",
       "42    321\n",
       "43    297\n",
       "28    273\n",
       "44    257\n",
       "45    229\n",
       "46    226\n",
       "27    209\n",
       "26    200\n",
       "47    175\n",
       "48    168\n",
       "25    154\n",
       "49    147\n",
       "50    134\n",
       "24    132\n",
       "51    119\n",
       "52    102\n",
       "23     99\n",
       "22     84\n",
       "54     84\n",
       "55     82\n",
       "57     75\n",
       "53     74\n",
       "56     70\n",
       "58     67\n",
       "60     62\n",
       "59     62\n",
       "61     53\n",
       "21     53\n",
       "62     52\n",
       "63     40\n",
       "20     40\n",
       "64     37\n",
       "67     37\n",
       "66     35\n",
       "71     27\n",
       "19     27\n",
       "18     22\n",
       "Name: Age, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of Tenure columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2     1048\n",
       "1     1035\n",
       "7     1028\n",
       "8     1025\n",
       "5     1012\n",
       "3     1009\n",
       "4      989\n",
       "9      984\n",
       "6      967\n",
       "10     490\n",
       "0      413\n",
       "Name: Tenure, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of Balance columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.00         3617\n",
       "130170.82       2\n",
       "105473.74       2\n",
       "85304.27        1\n",
       "159397.75       1\n",
       "144238.70       1\n",
       "112262.84       1\n",
       "109106.80       1\n",
       "142147.32       1\n",
       "109109.33       1\n",
       "146587.30       1\n",
       "79896.00        1\n",
       "132371.30       1\n",
       "85859.76        1\n",
       "69365.25        1\n",
       "197715.93       1\n",
       "95502.21        1\n",
       "79857.64        1\n",
       "142643.54       1\n",
       "120923.52       1\n",
       "126872.60       1\n",
       "124213.36       1\n",
       "138497.84       1\n",
       "141952.07       1\n",
       "125385.01       1\n",
       "155271.83       1\n",
       "172381.80       1\n",
       "197200.04       1\n",
       "127120.62       1\n",
       "105103.33       1\n",
       "98848.19        1\n",
       "157274.36       1\n",
       "104339.11       1\n",
       "142946.18       1\n",
       "109908.06       1\n",
       "135847.47       1\n",
       "108935.39       1\n",
       "150300.65       1\n",
       "131762.94       1\n",
       "114453.58       1\n",
       "128940.24       1\n",
       "138106.75       1\n",
       "143656.55       1\n",
       "127406.50       1\n",
       "107511.52       1\n",
       "154377.30       1\n",
       "125903.22       1\n",
       "163085.79       1\n",
       "144837.79       1\n",
       "106116.56       1\n",
       "Name: Balance, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of NumOfProducts columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    5084\n",
       "2    4590\n",
       "3     266\n",
       "4      60\n",
       "Name: NumOfProducts, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of HasCrCard columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    7055\n",
       "0    2945\n",
       "Name: HasCrCard, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of IsActiveMember columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    5151\n",
       "0    4849\n",
       "Name: IsActiveMember, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of EstimatedSalary columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "24924.92     2\n",
       "101348.88    1\n",
       "55313.44     1\n",
       "72500.68     1\n",
       "182692.80    1\n",
       "4993.94      1\n",
       "124964.82    1\n",
       "161971.42    1\n",
       "39488.04     1\n",
       "187811.71    1\n",
       "121505.61    1\n",
       "184514.01    1\n",
       "121311.12    1\n",
       "12873.39     1\n",
       "157238.05    1\n",
       "123242.11    1\n",
       "184399.76    1\n",
       "57323.18     1\n",
       "89874.82     1\n",
       "31422.69     1\n",
       "18988.58     1\n",
       "19842.18     1\n",
       "62627.55     1\n",
       "197266.58    1\n",
       "199992.48    1\n",
       "16403.41     1\n",
       "78854.57     1\n",
       "189310.27    1\n",
       "78088.17     1\n",
       "103305.01    1\n",
       "143301.49    1\n",
       "116124.28    1\n",
       "142730.27    1\n",
       "58076.52     1\n",
       "186123.57    1\n",
       "29664.45     1\n",
       "147963.07    1\n",
       "38466.39     1\n",
       "68293.93     1\n",
       "10288.24     1\n",
       "179446.31    1\n",
       "118929.64    1\n",
       "115037.67    1\n",
       "187280.51    1\n",
       "183540.22    1\n",
       "28531.81     1\n",
       "42730.12     1\n",
       "104721.50    1\n",
       "65219.85     1\n",
       "70563.90     1\n",
       "Name: EstimatedSalary, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "Unique of Exited columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    print(\"Unique of {} columns\".format(column))\n",
    "    display(df[column].value_counts().head(50))\n",
    "    print(\"====\"*20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a9b115",
   "metadata": {},
   "source": [
    "# Vizualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d77ab4d",
   "metadata": {},
   "source": [
    "### Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "48a43303",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "data = [df.Exited[df['Exited'] == 1].count(), df.Exited[df['Exited'] == 0].count()]\n",
    "\n",
    "ax.pie(data, explode=(0, 0.1), labels = ['Exited', 'Retained'], autopct='%1.1f%%', shadow=True)\n",
    "ax.axis('equal')\n",
    "plt.title(\"Churned and Retained proportion\", size = 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5c2080",
   "metadata": {},
   "source": [
    "Kita tahu bahwa sekitar 20% pelanggan bank berhenti untuk menggunakan jasa bank, dan ini timpang jauh dan kemungkinan kira memerlukan overlaping data pada saat modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162ad2d5",
   "metadata": {},
   "source": [
    "## Variable Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae32bd1",
   "metadata": {},
   "source": [
    "### Numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "011e80d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x792 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,11))\n",
    "num = df.select_dtypes(include=['int64', 'float64']).columns\n",
    "for i in range(0, len(num)):\n",
    "    plt.subplot(3, len(num)/3, i+1).set_title(num[i])\n",
    "    sns.boxplot(y=df[num[i]], color='green')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7e3579fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "for i in range(0, len(num)):\n",
    "    plt.subplot(3,3, i+1)\n",
    "    sns.distplot(df[num[i]], color='green')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65570e03",
   "metadata": {},
   "source": [
    "#### Report :\n",
    "\n",
    "1. Sejauh ini tidak ada outlier pada setiap kolom\n",
    "2. CreditScore terlihat memiliki distribusi normal\n",
    "3. Kebanyakan customer memiliki kredit <700\n",
    "4. Variabel umur terlihat skewed kanan yang berarti customer didominasi oleh muda-mudi sekitar 20-40 tahun\n",
    "5. Customer yang memiliki balance terdistribusi normal kecuali yang tidak memiliki saldo sama sekali\n",
    "6. EstimatedSalary juga berdistribusi normal sehingga customer memiliki range gaji yang mirip satu sama lain\n",
    "7. Rata-rata customer sudah menggunakan dan memiliki hubungan yang baik dengan bank dari `1 to 9` tahun\n",
    "8. Semua customer minimal menggunakan `1 product` bank dan dilanjutkan dengan `2 product`\n",
    "9. Kebanyakan customer (kurang lebih 70%) memiliki `credit card`\n",
    "10. Sangat dikejutkan bahwa kita memiliki jumlah customer yang hampir sama di kondisi `aktif sebagai member` dan `non aktif`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b43a04",
   "metadata": {},
   "source": [
    "### Categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "692b1772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(8,5)})\n",
    "fig,axs = plt.subplots(1,2)\n",
    "sns.color_palette(\"hls\", 8)\n",
    "sns.countplot(data = df,x = 'Gender',ax=axs[0])\n",
    "sns.countplot(data = df,x = 'Geography',ax=axs[1])\n",
    "# sns.countplot(data = df,x = 'Tenure',ax=axs[0,2])\n",
    "# sns.countplot(data = df,x = 'NumOfProducts',ax=axs[1,0])\n",
    "# sns.countplot(data = df,x = 'HasCrCard',ax=axs[1,1])\n",
    "# sns.countplot(data = df,x = 'IsActiveMember',ax=axs[1,2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d64ca622",
   "metadata": {},
   "source": [
    "#### Report :\n",
    "\n",
    "1. Lebih banyak customer `male`\n",
    "2. Demografi customer lebih didominasi asal `France` sedangkan `Spain` dan `Germany` hampir sama"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3e2b5e",
   "metadata": {},
   "source": [
    "### Check Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051d5b91",
   "metadata": {},
   "source": [
    "#### All variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "baf1c8d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6R48eJTo6mqeeesr+Wmlpadf4zoiIiIhcO4cld40aNeLzzz/HNE0M43xG/eabb9KuXTs8PT3t22w2G9OnT7dfaJGQkECZMmX48ssvsdls9n6urpcfzptvvkm/fv0IDw8nPDycwYMH88ADD7Bx48aLnhsTE0OZMmUuSkBN08RqtQLYY7TZbFSuXJlly5YBYLVaSUhIuNq3RERERApBaVtz57DRtmjRgrJlyzJ79mx7svTzzz+zePFiEhMTC/Rt06YNX3zxBQDx8fH06NGDkydP0rZtW1auXInNZiM2NpbffvvtotexWCz2/cfFxfHOO++QnZ0NQHJyMklJSdSpU4eWLVvy3XffkZOTQ2ZmJv/+979JSEigSpUq9unfHTt2kJCQQO3atQu8Ro0aNTh79izbt28HYNGiRYwcOfIGvlsiIiIiV8ZhlTvDMHj33XeZPHky3bp1w9XVlcDAQObMmUNqamqBvkOHDmX8+PF069YNq9XKqFGjqFq1Kg899BBRUVF07dqVSpUqUadOnYtep127drz55pv4+fnx8ssvM3XqVLp06YKXlxdubm6MGDGCmjVrUrNmTXbv3k3v3r2x2Ww88sgjhIWFMX36dMaPH8+sWbNwc3Nj1qxZuLu7F3gNd3d33nrrLSZNmsS5c+fw9fVl6tSphfr+iYiIyJUpbZU7w7zcwjcpEqdPp/5zJxEREScRHOxXpK9XocHoQtnvqb3Fs5BTulJZERERESen75YVERERJ1e6almla7QiIiIiTk6VOxEREXFqpe2CCiV3IiIi4tRKW3JXukYrIiIi4uRUuRMRERGnZpSyWlbpGq2IiIiIk1PlTkRERJxaaVtzp+ROREREnJphGI4OoUiVrlRWRERExMmpciciIiJOrbRNy5au0YqIiIg4OVXuRERExKnpVigiIiIiUmKpciciIiJOrbStuVNyJyIiIk6ttCV3pWu0IiIiIk5OlTsRERFxarqgQkRERERKLFXuioG+WyMdHcIN8XXrcEeHICIicrFStuZOyZ2IiIg4NV1QISIiIiIllip3IiIi4tQMw3B0CEVKlTsRERERJ6LKnYiIiDi10nYrFCV3IiIi4tR0QYWIiIiIlFiq3ImIiIhz0wUVIiIiIlJSqXInIiIizq2UlbJK2XBFREREit6KFSu4++67ueuuu5g/f/5F7d9//z09e/akR48eDBkyhLNnz17zaym5ExEREedmGIXzc4Xi4uKYOXMmn3/+OcuWLWPhwoUcPHjQ3p6Wlsb48eOZM2cOy5cvp27dusyaNeuah6vkTkRERJybg5O7TZs20aZNGwICAvD29qZz586sWbPG3p6Tk8P48eMpX748AHXr1uXkyZPXPFytuRMRERG5BikpKaSkpFy03d/fH39/f/vj+Ph4goOD7Y9DQkKIjIy0Pw4MDOTOO+8EICsrizlz5tC/f/9rjkvJnYiIiDi3Qpqn/PTTT5k9e/ZF24cOHcqwYcPsj03TvKjPpb7vNjU1lSFDhlCvXj169ep1zXEpuRMRERG5Bo8++uglk7D8VTuA8uXLs337dvvj+Ph4QkJCCvSJj49nwIABtGnThv/85z/XFZeSOxEREXFqZiHdxPjC6de/065dO2bNmkViYiJeXl58++23TJw40d5utVoZPHgwXbt2ZciQIdcdl5I7ERERcW4O/oKK8uXLM3z4cB555BFycnLo27cv4eHhDBw4kGeeeYZTp06xd+9erFYra9euBaBRo0ZMmjTpml7PMC81ESxF6raVGx0dwg3xdetwR4cgIiIlQHCwX5G+Xu32/1co+4366clC2e/1UuXuMv744w+6d+/O22+/TefOnR0dzmW1Cwnk0TpVsJomq6LjWXk8rkB7JW9PxjStDabJkdQMZu4+jAk8WrsKbcsHYrWZzNp7hP3JaY4ZgIiISGFx0XfLyp8WL15M586dWbBggaNDuSyLYfB0wzBGbN3DM5t2071qeQLd3Qr0ebpBGB8dOMawzbvBMLilQhC1/X1oWtafwb9EMuG3AwxvVMNBIxAREZEbRcnd38jNzWX58uUMHz6cvXv3cvz4cQC2bt1K9+7duffeexk/frz9PjTHjh3j8ccfp1evXjz44IPs3bu3yGKt5utFbHoWaTlWck2TyMRUmpQtuMCzToAPO87k3Ytna3wSzcsFEB7kT8TpZADis7KxGAZl3FXMFRERJ+PgmxgXNSV3f2PDhg2EhoYSFhbGnXfeyYIFC8jJyeGFF15g+vTpLF26FFfX84nQ6NGjGTVqFEuWLGHixIkMHz68yGL1cbWQnmu1P87MteLjWjBJM/KtJs3IteLjasH7gudl5FrxdVVyJyIiTsYopJ9iSr/J/8bixYvp1q0bAHfffTcjR46kS5culC1blnr16gHQt29fJk2aRHp6Ort37+bFF1+0Pz8jI4OkpCQCAwMLLcYBdavSOMifmn7e7Mu3Vs7L1UJabm6BvrZ81814u1pIy7WSkWvF29VScHtOweeJiIhIyaLk7hLOnDnDTz/9xO7du/nss88wTZOUlBR++uknbDbbRf1tNhvu7u4sW7bMvu3UqVMEBAQUapwfHcibKrYYBp91aIafmyuZuVaaBPmz8HBsgb4HU9JpWtafHWdSaB0SyO8JZ4nNyGRw/eosOBRLsKc7hmFwVsmdiIg4m1J2QYWSu0tYvnw5bdq04cMPP7RvmzVrFr/88gspKSkcOHCAunXrsmLFCgD8/PyoXr06y5Yto2fPnmzcuJFx48bx/fffF0m8VtPknT1HmNG6AQYGq6LjSMjKppqvF72rV2Tm7sO8s/cIo8Jr4eZicCw1kx9PJmADIhNTePfmcFwM+O+uQ0USr4iIiBQe3efuErp3787w4cPp2LGjfduZM2fo2LEjH330Ea+99houLi6EhYWRkpLCBx98wKFDhxg/fjzJycm4ubkxfvx4wsOv7L5vus+diIiUJkV+n7u7PiqU/UZ9N6BQ9nu9VLm7hL8qcvmVLVuW33//nRkzZvD555/j7e3N3LlziYvLu59czZo1mTdvXlGHKiIiIv+kdM3KKrm7Gi4uLgQEBNC3b1/c3NyoVKnSNX81iIiIiEhhUHJ3lQYNGsSgQYMcHYaIiIhcqVJ2QYXucyciIiLiRFS5ExEREedWugp3qtyJiIiIOBNV7kRERMSpmcX4e2ALg5I7ERERcW66oEJERERESipV7kRERMS5la7CnSp3IiIiIs5ElTsRERFxbrqgQkRERMSJ6IIKERERESmpVLkTERER51a6Cneq3ImIiIg4E1XuRERExLnpggoRERERJ1LKkjtNy4qIiIg4EVXuRERExLmVslJWKRuuiIiIiHNT5a4YsNlMR4dwQzywPdLRIVy3BS3CHR2CiIjcaFpzJyIiIiIllSp3IiIi4txKV+FOyZ2IiIg4N1PfLSsiIiIiJZUqdyIiIuLcdEGFiIiIiJRUqtyJiIiIcytdhTsldyIiIuLkdEGFiIiIiJRUqtyJiIiIc9MFFSIiIiJSUqlyJyIiIs6tdBXulNyJiIiIk9MFFSIiIiJSUqlyJyIiIs5NlTsRERERKalUuRMRERGnZpauwp0qdyIiIiLOxGkrdxMmTOC3334jJyeH48ePU7NmTQAeeeQR+vTp4+DoREREpMiUsjV3TpvcvfLKKwDExMTwyCOPsGzZMgdHVHgM4PnwmtTy9yHHZjJ1ZxSx6Vn29u5Vy9OjegWsNpPPoqLZFJdEGXdXxt1UFw+LCwlZ2UzeEcU5q81xgwDahgTycM0q2EyT1THxrIqJK9Ae6u3JC41rAyZH0jJ4e89hzHxtE26qx8BfdhR12CIiUtyVsm+ocNrk7lKOHTvG+PHjSU5OxtPTk5dffpkGDRowZswYfH192bNnD3FxcTz99NP06dOHWbNmATBs2DAAOnbsyGeffca2bdtYsmQJycnJ3H777TzyyCOMGzeOU6dOYRgGI0aMoF27dkU2rlsrlsXDxYWnfomkQaAfTzcI4z8R+wAI8nCjT41QBv60A3cXF965JZyI08k8Vqcq38eeZnV0PP1qVaZntQp8efhEkcV8IYth8FS9MIZs2kmW1cZbbRqzOT6RpOwce5+n6oUxN+oYOxNTeK5hTdqVD2JjXCJ3hgbTu3ooAe5uDotfRESkuChVyd3o0aMZN24cDRo04ODBgzz99NOsXbsWgFOnTvH555/zxx9/XNHUbVxcHKtWrcLV1ZXhw4fTp08f7rjjDuLj43nooYdYunQpvr6+RTEswoP82RqfBMDepFTqBZx/3foBfuxKTCHHZpJjsxKbnkVNfx/Cg/yZFxUNwJb4JJ6sX82hyV01Xy9iM7JIy7UCsDsplcZB/vx06oy9T50yPuxMTAFg2+kkWpQLYGNcImk5uTy/dRfzbmvukNhFRKSY07Ssc0pPT2f37t28+OKL9m0ZGRkkJeUlRTfffDOGYVCnTh2Sk5P/cX8NGjTA1TXv7du0aROHDx/m7bffBiA3N5fo6Gjq169/4wdyCT6uFntSBGAzTSwGWE3wcbOQnnO+LSPXio+rBW83C2l/bs/IzcXH1VIksf4db1cL6fnGkGm14ut64cfz/Mn51zgAtpxOKooQRURESoRSk9zZbDbc3d0LrL07deoUAQEBAHh4eABg5JuXNwwDm+38OrScnPNThJ6engX2/emnn9r3FRcXR7ly5QpjGJeUnmvFO19yZhgG1j8Xo6XnFGzzds1L6jL+3J6dbcPb1dWe6BW1x2tXpVGgPzX8vNl/Ns2+3ctiIS03t0Bf077C7s9x5DomZhERKWFK2b1BSs1w/fz8qF69uj2527hxI/369bvscwIDAzl48CAAkZGRnD59+pL92rRpw+effw7AwYMH6dGjB5mZmTcw+svblZhC25BAABoE+nE4Jd3eti85lfCy/ri7GPi4Wqjm68WR1PS855TPe06bkEB2Jp4tsnjzmxt1nBHbdtN3XQSh3p74ubniahiEB/mzNym1QN+DKek0CfIHoFVwILv+nKIVERG5LMMonJ9iqtRU7gCmT5/O+PHj+fDDD3Fzc2PmzJkFKnUXuvvuu1m7di133303DRs2pEGDBpfsN3bsWMaNG0f37t0BmDZtWpGttwP46eQZWgQH8O4t4RjA5B1R3F8jlJj0LDbGJbLo8Alm3xyOi2Hwwf5jZNtMPo2K5qVmdeherQLJ53J49bcDRRbvpVhNk/f3H2FKiwa4GAZrYuJIOJdNNV8velatyNt7D/P+viM837gWbobBsfRMfjqV4NCYRUREiiPDNE3zn7tJYbp1+S+ODuGGcHcrvn/FXKkFLcIdHYKIiNMLDvYr0ter8czSQtnv4bfvLZT9Xq9SMy0rIiIiUhqUqmlZERERKX3MYrw+rjAouRMRERHnVsrmKUvZcEVEREScmyp3IiIi4txK2TdUqHInIiIi4kRUuRMRERHnVsouqFDlTkRERMSJKLkTERER5+ZiFM7PVVixYgV33303d911F/Pnz//bfqNHj2bx4sXXN9zreraIiIhIcWcU0s8ViouLY+bMmXz++ecsW7aMhQsX2r+7Pn+fwYMHs2bNmmsf55+U3ImIiIgUok2bNtGmTRsCAgLw9vamc+fOFyVxK1as4I477qBr167X/Xq6oEJEREScmllIt0JJSUkhJSXlou3+/v74+/vbH8fHxxMcHGx/HBISQmRkZIHn/Pvf/wbg119/ve64lNyJiIiIXINPP/2U2bNnX7R96NChDBs2zP7YNM2L+hiFeAWvkjsRERFxboVUuXv00Ufp1avXRdvzV+0Aypcvz/bt2+2P4+PjCQkJKZSYQMmdiIiIOLtCqpJdOP36d9q1a8esWbNITEzEy8uLb7/9lokTJxZKTKALKkREREQKVfny5Rk+fDiPPPII9957L926dSM8PJyBAweya9euG/56hnmpiWApUrcu/8XRIdwQ7m4l/w7gC1qEOzoEERGnFxzsV6SvV33c6kLZ79FXr//K1sKgyp2IiIiIE9GaOxEREXFupey7ZZXciYiIiHMrpKtliysld8WAzeroCG6MhBSbo0O4bg/9GvnPnUqAz5tr7aCISGml5E5EREScWymr3OmCChEREREnosqdiIiIODWzlF1QocqdiIiIiBNR5U5EREScWykrZSm5ExEREeemaVkRERERKalUuRMRERHnpluhiIiIiEhJpcqdiIiIOLdSVrlTciciIiLOrXTldpqWFREREXEmqtyJiIiIUzNL2bSsKnciIiIiTkSVOxEREXFupewmxkruRERExLlpWlZERERESipV7kRERMS5la7CXcmv3G3dupVmzZrRs2dPevToQdeuXXnvvfcu+5z+/fuzdevWIopQREREpOg4ReWuUaNGzJs3D4D09HTuvvtu7rrrLmrVquXgyIqGAYxsWpNaZXzItppM+T2K2PQse3v36uW5t3oFrKbJJwei2XQqyd52X81QgjzdeH/PMQdEfp4BvNSqFnUCfci22ZiwOYrotPNj6F2rAn1rV8BqM/lgdzQ/xSZSzsuN19vVw81icPZcLv/ZeICMXKvjBvGnNsGBPFyrClbTZE1MPKtj4gq0h3p7MqpxbUzT5GhaBrP2HsbM1za+WT0GbdxR5HGLiDgrlxJfyro6TpHc5ZeVlYXFYsHPz4/Vq1czd+5csrKyOHfuHK+99hotW7a0983NzWX8+PFERUWRkJBAWFgYs2fPJiEhgaFDh1K7dm327dtH2bJleeuttwgICGDFihW89957GIZB48aNmThxItnZ2bz66qtERUVhtVoZOHAg3bp1K7Ixtw8ti7uLC0/+GEnDQD+GNQ5jzJZ9AAR5uPGvGqEM2LADdxcX3msfTkR8MgYGL95Ui/qBfmw4kVBksf6djlXK4m5x4ZG1O2lczo8RzWvw3I97ASjr6cZDdUN5cPXveFhc+KRTEzafTOLxBlVYfjiOlUfiGRxeld61yvO//SccOg6LYTC4XhhDN+8ky2rjv20aszk+keTsHHufwfXCmBt1jMjEFJ5tUJN2IUFsjE/kztBgelULpYy7mwNHICIiJZ1T5LK7d++mZ8+edO/enY4dO9KqVSvKlSvHggULeP/991m+fDkDBw7ko48+KvC833//HTc3NxYuXMh3333HuXPn+PHHHwHYv38/jz/+OCtXrsTf358VK1YQFxfH5MmT+fjjj/nmm2+wWq38+OOPvPfeezRs2JDFixczf/583n//faKjo4ts/OFl/dkSl1eN25OUSr0AX3tbg0A/diWmkGMzSc+1EpOeRU1/HzwsBquPx/PZgaKL83Kahfiz6UTeGHYlpNKw7PkxNCrnx47TeWNIy7ESnZpJnUAfpv96mG+OxGMAFbw9SM12fNWuqo8XJzKySMu1kmua7E5KJTzQv0Cf2v4+RCamALAtIYmbygYAkJqTy4htu4o6ZBERp2cYhfNTXDlF5e7CadnBgwfz4Ycf8s4777Bu3TqOHDnCtm3bcLmgLtuyZUsCAgKYP38+hw8f5ujRo2RkZABQtmxZGjRoAEDt2rU5e/Ysv//+OzfddBMVKlQAYPr06QC8++67ZGVlsWjRIgAyMjKIioqiSpUqRTJ+H1cL6fmmI62micUAqwnebhbScs63ZeRa8XWzkJpjZVt8MndXDSmSGP+Jj5srqTm59sf5x+DrZiEtX1t6rhVft7yPrsUw+PKem/CwGPzfruNFHveFvC84Fpm5VnzcCp5mRr6VvZm5VrzdLABsPZ2EiIjceMU5ESsMTpHc5efj48Odd97J+vXrWbx4MT179qRly5bUrVuX+fPnF+j7ww8/8Pbbb/PII4/Qu3dvkpKSMM281U8eHh72foZhYJomrq4F367ExEQAbDYb06dPp2HDhgAkJCRQpkyZwhxmAem5VrxdLfbHLoaB9c9FXBk5Bdu8XQsme8VFek4uPvnHwPkxpF0wBh9XC6nZeclermnSe+WvtK4QwGvt6jLgu8gijfsvj9WuSqNAf8J8vdl/Ns2+3cu1YGIKYLOvsMtrTy+Gx0NEREoup5iWzc9qtbJt2zY8PT1xcXFh8ODBtGnThp9++gmrteAv0c2bN9O1a1f69OlDuXLliIiIuKhPfo0bN2bnzp2cPn0agNdff50ffviBNm3a8MUXXwAQHx9Pjx49OHnyZOEN8gK7zqTQtnwgAA0D/Th0Nt3etjcplSZl/XF3MfBxtVDdz4vDKel/tyuH+T0+hVsqBQHQuJwfUcnnY9ydkMpNIWVwdzHwdbMQVsabg8np/KdlTVqWz0uiM3Ks9sTcET6JOs7Ibbu5b30Elbw98XNzxdUwaBzoz97k1AJ9D6akEx6UN1Xbqlwgu5JSHBGyiEipYRhGofwUV05RuftrzR1AZmam/UKHsWPH0rVrVzw9PWnZsiUnThRcbP+vf/2LkSNHsmbNGtzd3WnatCkxMTF/+zrly5fnpZdeYsCAAdhsNpo2bUrv3r3JzMxk/PjxdOvWDavVyqhRo6hatWqhjjm/H0+coWVIAO+3D8cwYNKvUdxfK5TYtCx+OZXIV4dP8G77cAwM5uw9RrbNcUnQ31kXfYa2FQP5tHMTDGDc5j/oX78Sx1Mz+TEmkc8PnGBu5ya4ALN2HCXbZvL5gROMbV2LJ02wmTBp20FHDwOrafL+/iNMbtEAA4O1sXGcOZdNVR8velaryKy9h/m//Ud4vlEtXF0Mjqdl8vMpx1/QIiIizsMwHVnuEABuXvKLo0O4IdLSS/5HKaSccxSzP28e7ugQRET+VnCwX5G+Xq33fyqU/R4c3L5Q9nu9nKJyJyIiIvJ3ivEMaqFwjjKFiIiIiACq3ImIiIiTM0pZKauUDVdERETEualyJyIiIk6ttK25U3InIiIiTs2llCV3mpYVERERcSKq3ImIiIhTK23TsqrciYiIiDgRVe5ERETEqalyJyIiIiIllip3IiIi4tSMUla6U3InIiIiTk3fUCEiIiIiJZYqdyIiIuLUStmsrCp3IiIiIs5ElTsRERFxaqWtcqfkTkRERJxaaUvuNC0rIiIi4kRUuRMRERGn5lLKKndK7oqBXKujI7gxaoeajg7hukUnlvwxAAQHn3R0CNft9OmKjg5BRKREUnInIiIiTq20rblTciciIiJOrbQld7qgQkRERMSJqHInIiIiTs0oZVdUqHInIiIi4kRUuRMRERGnVtrW3Cm5ExEREadW2pI7TcuKiIiIOBFV7kRERMSpqXInIiIiIiWWKnciIiLi1ErZnVBUuRMRERFxJqrciYiIiFPTmjsRERERJ2K4FM7P1VixYgV33303d911F/Pnz7+ofd++ffTp04fOnTvz0ksvkZube83jVXInIiIiUoji4uKYOXMmn3/+OcuWLWPhwoUcPHiwQJ9Ro0bx8ssvs3btWkzT5Msvv7zm11NyJyIiIk7NMArn50pt2rSJNm3aEBAQgLe3N507d2bNmjX29tjYWLKysmjatCkAvXv3LtB+tbTmTkREROQapKSkkJKSctF2f39//P397Y/j4+MJDg62Pw4JCSEyMvJv24ODg4mLi7vmuJTciYiIiFMzCumKik8//ZTZs2dftH3o0KEMGzbM/tg0zcvG9E/tV0vJnYiIiDi1wrpa9tFHH6VXr14Xbc9ftQMoX74827dvtz+Oj48nJCSkQHtCQoL98enTpwu0X60rTu5iYmK44447+Pjjj7n55pvt2zt27Mhnn31G5cqVrzkIgOXLl/Phhx9itVpxcXGhS5cuDB48GIvFgtVqZdCgQZw6dYoBAwYwbtw4atasiWEY5OTkEBISwuTJk6lQocI1vfbixYvZtm0bU6ZMuarnpaamMnr0aN59991ret0bxQBeaFaT2gE+ZNtMXt8eRUx6lr29Z1h5eoVVwGqafLw/mo0nk+xtD9QKJcjTjXd3H3NA5AWlRO4kftUKcLEQ1O5mgm5pX6D9XHw8MZ/NBcAztBKhDzyE4eLCqWVLSNu/D4AKvfrgW6dukcf+F2c5FgDr1m3jnXe+wNXVQp8+d3HffZ0LtB88eJyXX56NaUL16hV57bVncHW18OWXa1mwYA2urhaeeuo+br+9lYNGICJSuC6cfv077dq1Y9asWSQmJuLl5cW3337LxIkT7e2VKlXCw8ODX3/9lebNm7N06VLat29/mT1e3lVdUOHm5sbLL79MWlraNb/gpSxevJgPPviA2bNn88033/DFF1+wb98+Xn75ZSDvKpMDBw7wzTff0KpVK0JCQli2bBlLly7lm2++oVGjRgXepKJy9uxZ9u/fX+Sve6HbQsvibnHh3+sjeXfXUZ5tEmZvC/Jw475aoQzcEMkzP+9hSKPquLkYeLi4MKFVHfrWrOjAyM8zrbmc/HohYcOGU+P5UST+8hM5F6xjOPn1l5TvcS81R44GTFIid5AZfZyMI4ep+cKLVP33IE5+ucAxA/iTMxwLgJycXCZP/pCPP57IvHmTWbhwDQkJSQX6vPnmZzz//CMsWDANgPXrt3H6dBLz5q1gwYJpfPTRBN588zOys3McMQQRETtHX1BRvnx5hg8fziOPPMK9995Lt27dCA8PZ+DAgezatQuAGTNmMHnyZLp27UpmZiaPPPLINY/3qpK7kJAQ2rVrx9SpUwts37ZtG/3797c/HjNmDIsXLyYmJoaePXsydOhQOnXqxPPPP8+CBQu4//776dKlC4cOHQJg9uzZjB07lqpVqwLg6+vLpEmTWLlyJbGxsTz55JMkJyfTu3fvS8bVokULjh49CuRVEp977jk6d+7MmTNnWLRoEd26daN79+6MGTOG9PR0AJYuXUrnzp3p06cPGzZssO+rY8eOxMTEALB161b7uPbt28e//vUvunfvzsMPP8ypU6d47bXXiI+P5+mnnyYtLY1BgwbRu3dvevfuzQ8//HA1b+11aVLOny2n8n7x7k5MpV6gr72tYZAfkQkp5NhM0nOtxKRlUauMD+4Wg1XH4pm7P7rI4rycrJOncA8OweLjg4urK941a5MR9UeBPpnHj+FTuw4Afg0bk7ZvH15VqhI27DkMwyD7zBks3t6OCN/OGY4FwKFD0VStWpEyZXxxd3ejefMGRETsKdBn1qwXadmyEdnZOZw+nYyvrzeRkX/QrFl93N3d8PPzoWrViuzff8RBoxARKT66d+/OypUrWbt2LQMHDgTggw8+oHHjxgDUq1ePr7/+mtWrV/PGG2/g7u5+za911bdCGTNmDL/88gsbN268ov4HDhxgyJAhrFmzhl27dhEbG8vChQvp1q0bCxcuJDExkdjYWMLDwws8r0yZMtSqVYs9e/bw3nvvERISwuLFiy/af05ODqtXr+amm26yb2vfvj1r164lISGB999/n3nz5rFixQq8vLyYPXs2cXFxzJgxg/nz57Nw4UJ7wnc5I0eOZMiQIfabEH766aeMHTuWkJAQ3nnnHb777jsqVarE4sWLmT59eoG59cLm42YhLddqf2wzTSzGpdsycq34ullIzbGyNS65yGL8J7asTCxeXvbHFk9PrJmZF/Qy7QtMXTw8sGXltRsWC6eWLeHYu7MIbNuuqEK+JGc4FgBpaRn4+Z1PlH18vEhLK3ieWCwWYmPj6dbtaZKSUqhXL+zP5/lc8LyMIotbRORSHF25K2pXfUGFr68vEydO5OWXX2b58uX/2L9cuXI0aNAAgAoVKtC2bVsAQkND7RUyAKvVetFzc3IuPZ0THx9Pz549AcjOziY8PJwRI0bY25s0aQJAREQEt99+O4GBgQDcf//9vPjiizRp0oRmzZpRrlw5IC+b3rJly9+OITExkdOnT3P77bcD8NBDDwEUiL9Zs2a8+eabxMXF0aFDB55++unLvS03VHqOFW9Xi/2xCwZW89Jt3q55yURxcWrZEjIOHSQrNgav6jXs261ZWXheWIXLdybZzp3Dxet8e4WevQju3JVD017Hu1ZtPIKvfSHq9SjJxwJg5sx5/PbbXg4cOEp4eB379vT0TPz8fC/qX6lSCN9+O4evvlrLlCkf0alTO9LTMy54ns9FzxMRKUouxTgRKwzXdBPjW265pcD0bOXKlQtcxps/KbuwrGixWAo8DgoKomrVqvz+++8FticmJhIdHU2jRo0uev2/1twtW7aM1atXM3XqVAICAuztHh4eANhstgLPM02T3NxcDMMo0Obq6npRP8D+1R9ubm4F2s+dO0d0dMEptOrVq7N69Wq6d+/O9u3b6du37yUvbS4MkWdSaFchL4FtFOTHwZTzFZY9iak0LeePu4uBj6uF6n5eHD77z5XKolKhZy9qPD+K+tPeIPt0PLnp6dhyc0mP+gPvGjUK9PWqUpW0Pw4AkLpnFz61apO2fx+xX+R9jYuLmyuGxVJol7xfiZJ8LACGD+/PvHmT2bhxHsePnyQ5OZXs7By2b99Ds2b1CvQdPHgiR4+eAMDHxxsXF4Pw8Dr8+utezp3LJjU1nUOHoqlTp5ojhiIiUmpd861QxowZQ/fu3Tl9+jSBgYFER0dz7tw5MjMz+fXXXwtcUftPnnvuOV5//XXmzJlDlSpVSE9PZ+zYsdx9990XVfiuRqtWrfjss88YMmQIAQEBfPnll7Ru3ZrmzZszceJE4uLiCA4OZtWqVfarXQIDAzl48CBVqlSxr5vz8/OjQoUKbNy4kZtvvplly5axbds2Ro4caU8A//e//xEdHc2LL75I+/btuf3220lNTb2iq2iu14bYM7QKCeCD28MxgInbo3iwdigxaVn8fDKRLw+e4P86hONiGLy/5xjZtqJJOq+GYXGlYt/7ODprJqbNJKjdLbgFBJJ18gRnNqyn0oP9qNjnX8TOn8ep3Fw8K1SkzE3NATj7268cmj4F02aj7G23414u+B9erfA4w7EAcHNzZcyYfzNgwDhM06RPn7soX74sBw8e53//W8n48UMYNKgvY8b8Fzc3V7y8PHjttWEEBwfSv393HnpoNKZpMnx4fzw8rn3diIjIjVDaKnfXnNz9NT07YMAAvLy8uO2227jnnnuoVKkSzZs3v6p93XPPPVgsFp599lmys7OxWq3cc889DB48+FrDA/IWJz755JP079+fnJwcGjZsyIQJE/D19WXs2LE89thjeHl5UatWLftznnnmGSZOnMjs2bO55ZZb7NunT5/O+PHjmTZtGoGBgfb/hoaG0r9/f9577z2ef/55unfvjqurK0OHDi2SxA7ABKb+fqjAtmOp59erLTsSx7Ijl77T9TfH4gsztKviH94E//AmBbZ5Vgyl0oP9APAoX4Eaz4+66HmVHnq4SOK7Es5yLAA6dmxFx44Fb2NSq1ZVxo8fAsBNN9W3Xymb3333db7otikiIlJ0DLOo5g7lb7X++hdHh3BDVAmy/XOnYi460Tm+bnlrX8esObyRTp8uPreGEZEbKzjYr0hfr/Pawvk9u7bzLf/cyQGc4zeZiIiIiAD6+jERERFxclpzJyIiIuJESts0ZWkbr4iIiIhTU+VOREREnJqLUbquHVXlTkRERMSJqHInIiIiTk0XVIiIiIg4kdI2TVnaxisiIiLi1FS5ExEREadW2qZlVbkTERERcSKq3ImIiIhTM0rZrVCU3ImIiIhT07SsiIiIiJRYqtyJiIiIUyttlazSNl4RERERp6bKnYiIiDg1fbesiIiIiJRYqtyJiIiIUyttV8squRMRERGnVtqmKZXcFQNeXs7xJ0XSOYujQ7huuTk2R4dwQ3ReG+/oEK5belbJHwPAknZNHB2CiJQySu5ERETEqZW2adnSVqkUERERcWqq3ImIiIhTK223QlFyJyIiIk5N07IiIiIiUmKpciciIiJOrbRVskrbeEVEREScmip3IiIi4tR0QYWIiIiIE9EFFSIiIiJSYqlyJyIiIk5NlTsRERERKbFUuRMRERGnVtoqWaVtvCIiIiJOTZU7ERERcWq6FYqIiIiIE9EFFSIiIiJSYqlyJyIiIk6ttFWyisV4Y2Ji6Nix40Xb69ate037W7p0KX369KFnz550796dzz777G/7ZmdnM3PmTLp3707Pnj2577772LRp0zW97l/GjBnD4sWLr2sfIiIiItfC6Sp3CxcuZMGCBfzf//0fISEhpKSk8MQTT+Dl5cW//vWvi/q/+OKLuLu78/XXX+Ph4cGBAwd44okn+PTTT6lVq5YDRnBt2oYE8mjtKlhNk1XR8XwTHVegvZK3J2Oa1MbE5EhqBv/dfRgzX9vE5vV44ucdRR53fm1DAnm4ZhVspsnqmHhWxRQcQ6i3Jy80rg2YHEnL4O0958cQ6u3JhJvqMfCXHUUddgEGMKZFLeoE+pBttTFxWxQxaVn29l41K9C7ZgWspslHe6L5+UQiFbw9GNe6NhbDwDAMJm2L4lhqpuMG8afWwUH0q5n3mfo2No7VlzgeIxrVBuBoagaz9x0qcDzGNa3P4E2/F3HU5xnAiPCa1CrjQ47NZMqOKGLTzx+L7tXK07N6Baw2k0//iGZTXBJl3F15pXldPCwuJGRl8/rvUZyz2hw2BhG5MUrbmrtin9ylpaXxn//8h7i4OOLj42nRogXTpk0jLi6OkSNHkpGRgYuLC2PHjqVp06a89957TJ06lZCQEAD8/f2ZOnUqaWlpAHTs2JHw8HD27dvH9OnTWbduHRs3bsTDwwPIqxa++eabeHp6AjBz5kw2b97M2bNnCQwMZNasWQQHB9OmTRsaNmxIQkICX3/9NTNmzGDDhg2EhIRgtVpp1apVkb1HFsNgaIMwnvxlJ1lWG7PbNWZTXCJJ2Tn2PkMahPHRgWPsSEzh+UY1ubl8EL/EJXJXpWD6Vg8lwN2tyOK9FIth8FS9MIZsyhvDW20aszm+4BieqhfG3Khj7ExM4bmGNWlXPoiNcYncGRpM72IwBoAOlcviYXHh8e920qisH8Ob1WDEz3sBKOvpxgN1Qnl47e94WFz46M4mbDmVxFPh1fjyj5NsiD1D2woBDG1SnVG/7HPoOCyGweB6YQzbvIMsq403W4ezOT6R5HzHY1DdMD6NOk5k0lmeaVCTtiFl2RR/hjsqBnNvtVDKOPh43FqxLO4WFwb/HEnDQD+GNgzjxW1572uQhxt9a4Ty7x934O7iwru3hhNxOpnH6lblu5jTrI6O5+HalelZrQJfHj7h0HGIyPUzStnVssViWhYgPj6enj17FvgB2LBhA/Xr12fhwoWsXbuWHTt2sGfPHr7++ms6dOjA4sWLGTVqFL/++iuJiYmcPHmSJk2aFNh3zZo1C2xr3749a9eu5cSJE9SqVQtvb+8C/Vu3bk3lypU5duwYhw8fZsGCBaxdu5aqVauyYsUKAJKSkhg0aBDLli3jhx9+YO/evaxcuZK33nqL48ePF/K7VVA1Xy9i07NIy7WSa5rsSkwlPMi/QJ86ZXzYkZgCwNbTSTQvFwBAWk4uz27ZVaTxXko1Xy9iM86PYXdSKo0vMYadf45h2+kkmpcNAPLG8PxWx48BoGmwP5tOJgGw+0wqDYJ87W0Ny/qx43QKOTaTtBwr0amZ1A7wYeZvh/nlRCIAFheD7GJQKarq48WJfMdjT1IKjQMLHo/a/r5EJp0FIOJ0Es3KlgEgLTeXkRGOPx7hQf5sjc87FnuSUqkXcP5Y1A/0Y9eZvGORnmslNj2Lmv4+BZ6zJS6JFsEBjghdROS6FJvKXUhICMuWLSuwrW7dunTr1o3IyEg++eQTDh8+THJyMhkZGbRt25Zhw4axb98+brvtNh5++GEyM/Omskzz8hn6X4mei4vLZftWq1aN0aNH89VXX3HkyBF27NhB1apVL9rPtm3b6NSpE25ubgQFBdG+fftreg+ulY+rhbRcq/1xhtWKr1vBQ2twviadkWvF180CwOY/f5E5mrerhfR8Y8i0WvF1vfDjWXAMPq55Y9hyuniMAcDXzZW0nFz7Y5tpYjHAaoKPm6VAW3pu3nFKzs7bVs3Pi+eanq/0OZK3qyvp+WLNtFrxcb3wM3VeRr72rcXkePi4WUjPOf+ZKnAsLvi8/XVO+LhaSPvzORm5ufbzRERKNk3LFjPz5s1j7dq13HfffbRr144//vgD0zRp3rw533zzDRs2bGDVqlUsWbKEuXPnUqVKFXbv3k3Lli3t+9i2bRs//fQTI0eOBLBPwTZq1IhDhw6RlZVln4YF+OSTTwgODqZatWqMGDGCxx57jM6dO1+UDP71HMMwsNnOV1tcL0pKCseAOlVpHORPDT9v9iWn2bd7WwomEVAw4fXO9wvM0R6vXZVGgXlj2H/2/Bi8LBbSci8YAxeMIbd4jCG/tJxce9IJeZ8N659hp+dY8cmXLPi4Wkj98zi1CCnDmBa1eHnLAYeut3u0VlUaBpbJOx7Jqfbtlzoe+euL3hYL6Re0O1p6jhXvvzsWuQXbvF0tpOZY7duzs214u7qSWkzOExGRq1FspmX/zsaNG7n//vvp0aMHhmGwf/9+bDYb06ZNY9myZfTq1Ytx48axd29etWPAgAFMmTKF06dPA5CYmMiUKVOoVq3aRfsODQ2lQ4cOTJw4kXPnzgGwd+9ePvzwQ2rXrk1ERAStWrXiwQcfpFatWmzcuBGr9eJ/7Nu2bcuaNWvIzs7m7Nmz/Pzzz4X4jpz30R/HeW7Lbnp9H0ElH0/83FxxNQzCy/qzJym1QN+olHSa/jnN2To4kMg/pzcdbW7UcUZs203fdRGEeucbQ5A/ey8Yw8GUdJr8OYZWwYHsKiZjyG/n6RRuDg0CoFFZPw4mp9vb9pxJpVlwGdxdDHzdLISV8eZQcjotQsowsnlNhm7Yzb7EtL/bdZH49OBxXojYxf3rtxHq7WU/Ho0Dy7AvueDxOJSSTnhg3lRsy+BAdicVr+OxKzGFNuUDAWgY6MfhlPPHYl9SKuFl/XF3MfBxtVDN14sjKensSkyh7Z/PaVM+kMgzZx0Su4jcWC6F9FNcFfvK3aOPPsr48eP5+OOP8fHxoVmzZsTExNC/f39GjBjBkiVLsFgsvPLKKwA8+OCD5OTk8MQTT2AYBqZpcv/991/ySlmA119/nRkzZtCzZ0/c3d3x8vJi+vTp1KlThzJlyjB06FC6d++Om5sbdevWJSYm5qJ93HnnnezatYtu3bpRrlw5atasWajvyYWspsk7e48wvVUDDMNgdXQcCeeyqebrRa/qFfnv7sO8u+8IIxvXws3F4FhaJj+eTCjSGP+J1TR5f/8RprRogIthsCbm/Bh6Vq3I23sP8/6+IzzfuBZuhsGx9Ex+OlW8xgCwPuYMrSsE8vGdTTAMmLDlD/rVrUR0WiY/xSay4I8TfHhnE1wMeGfnUbJtJiNuqoGbi8GENnUAOJaayesRBx06Dqtp8n8HjjCpeUNcMFgbG8eZc9lU9fGiR9VQZu87xJwDh3muYW1cXQyi0zL5uZgdj59OnqFlcADv3RqOAbz+exT31wwlJj2LjacS+frwCd65JRwXw2DOvmNk/3nV7NhmdeherQJns3OY8OsBRw9DRG6A0vb1Y4b5TwvUpNB1+Gajo0O4ISzF+c+YK5Sc7PiLGW6EckElf4FJvruWlGhL2jX5504ipUxwsF+Rvt7Lv35fKPud2PzOQtnv9Sr2lTsRERGR61HaLqhwglqLiIiIiPxFlTsRERFxaqrciYiIiEiJpcqdiIiIOLXSdjtyJXciIiLi1ErbrVA0LSsiIiLiRFS5ExEREaemCypEREREpMRS5U5EREScWmmr3Cm5ExEREadmKWXJnaZlRURERBzgxIkT9OvXjy5duvDUU0+Rnp7+t303btzIo48+ekX7VXInIiIiTs3FKJyf6zVhwgQeeugh1qxZQ6NGjXj33Xcv6mOz2fj44495/vnnsdlsVzbe6w9NREREpPRJSUkhJibmop+UlJR/fG5OTg4RERF07twZgN69e7NmzZqL+h06dIhDhw4xceLEK45La+5ERETEqRXWTYw//fRTZs+efdH2oUOHMmzYsMs+NykpCV9fX1xd81Kx4OBg4uLiLupXu3ZtJk2axNatW684LiV3IiIi4tQK62rZRx99lF69el203d/fv8Dj1atXM3ny5ALbqlevftHzDOPGBKrkTkREROQa+Pv7X5TIXUrXrl3p2rVrgW05OTm0bt0aq9WKxWLh9OnThISE3JC4tOZOREREnJqlkH6uh5ubGy1atGDVqlUALF26lPbt21/nXvMouRMRERFxgFdeeYUvv/ySu+++m+3bt/Pcc88B8MUXX/DWW29d8341LSsiIiJOrbh+Q0WlSpWYN2/eRdsffPDBi7a1bt2a1q1bX9F+ldwVA8nJV3bfmuIu+XCmo0O4bs3beTk6hBsiObuY/kt2FXJzC+fqtqL20K+Rjg7hhvi8ebijQxCRK6TkTkRERJxaYd0KpbhSciciIiJOTd8tKyIiIiIllip3IiIi4tSK6wUVhUWVOxEREREnosqdiIiIOLXSVrlTciciIiJOrbQld5qWFREREXEiqtyJiIiIU7OUsvvcqXInIiIi4kRUuRMRERGnVtoqWUruRERExKnpggoRERERKbFUuRMRERGnpsqdiIiIiJRYqtyJiIiIU9OtUERERESkxFLlTkRERJya1txdQt26da9oZ3369GHw4MFX1PfFF18kNjYWgIEDBxIXF3dFz8tvzJgx1KtX76LnDhkyhI4dO171/i4UExNzQ/YjIiIijuNiFM5PcXXDKncHDhzAzc2N/fv3c/LkSSpWrHjZ/lu3buXpp58G4IMPPrjm1y1fvjzffvst/fv3ByAtLY29e/fi4lJ6ZpwN4D+talEnwIccm40JW6KITsuyt/euWYE+tStgNU0+2B3Nz7GJlPN0Y9LN9XBzMTh7LpeXNh0gI9fq0DG8dnd96pf3IzvXxuiVeziWlGlvf6BZJR66qTJWm8msXw6zLiqBygFevNGjEYYBsWczeXHlXrJybQ4bw19SIndwetVKsLgQ2PYWgm5pX6D9XHwcsfPmAgaeoaFUvL8fhosLccsWk3ZgH2BQ/t7e+Nap55D4/9I2JJCHa1bBZpqsjolnVUzBP6JCvT15oXFtwORIWgZv7zmMma9twk31GPjLjqIO284ARjWrSe0yPuTYTF7/NYqY9PPnRc/q5bm3RgWsNpO5+6PZeCrJ3nZ/rVDKerrx7u5jDoj8Ym2CA3m4VhWspsmamHhWX+JYjGpcG9M0OZqWway9BY/F+Gb1GLRxR5HHLSKOcVUZ0KlTp3j44Yfp3bs3ffv2ZceOHfa2xYsXc/PNN3PHHXfw5Zdf2rcnJyfz9NNP07VrV3r27MnmzZuZM2cO8fHxDBo0iKSkJDp27EhMTAy9evVi165dAFitVtq3b8+ZM2eIjIzkwQcfpFevXjzxxBNER0fb99+pUyfWrl1rf/z999/ToUMH++P09HRGjx5N79696dmzJytXrrTH+8wzz/DAAw/QoUMH5s6dy6RJk+jevTv9+/fn3LlzAJw7d45nn32WHj16MHToUM6ePQvwtzH179+foUOH0rlzZ/bt23c1b+81u71KWTxcXHj025289ftRnr+phr2trKcbD9YL5bFvdzJk3W6eaVodNxeDxxtWYcXhOJ74LpIDSWn0rlW+SGL9O53qheDh6kLvuduYui6KsXedrxYH+7jzWKuq9P1kG498/hsvdKyNu8XgP3fWYf5v0dz3aQRbjibx7zbVHDiCPKY1l1OLFlL9meGEDX+BpF9+IjflbIE+pxZ9SUj3e6kxYjSmCamRO8iMPk7G0cPUGPUfqgwYxKmvFjhoBHkshsFT9cIYHbGH4Vt3c0+V8gS6uxXo81S9MOZGHeO5rbsxMGhXPgiAO0ODGdu0LgEX9C9qt4XmnRcDN0Tyzu6jPBMeZm8L8nDjX7VCGbQhkmd/2cNTjfLOCw8XFya0rEPfmpf/47QoWQyDwfXCGBOxhxF/HosL39vBfx6L57f9eSxCzh+Ll5rUpYyDj4WIo5W2yt1VJXdff/01HTp0YPHixYwaNYpff/0VgJycHJYvX07Xrl3p2rUrX3/9Nbm5uQC89dZbVK1aldWrVzNt2jT++9//MmjQIEJCQpgzZw6BgYH2/ffs2ZNVq1YBsGXLFurWrYufnx9jx47ljTfeYMmSJTz++OO8/PLL9ufUr1+fM2fOkJCQAMDq1avp2rWrvf29996jYcOGLF68mPnz5/P+++/bE7Fdu3bx4YcfMn/+fKZMmUL79u1ZsWIFAD///DMAZ86coX///ixfvpyqVavyzjvvkJ2dfdmY6taty9q1a6lfv/7VvL3XrFmwPxtP5lUddp1JpWFZX3tbo7J+7DidQo7NJC3HSnRqJnUCfJj+62G+ORKPAZT39iA123FVO4CWVQL48dAZAH6PPUvjiv72tiaVyvBrdDLZVpPUc7kcS8ygXogftcv5sOFg3nHfHpNMyyqBl9x3UTp38iTuwSFYvH1wcXXFu1Yt0g9GFeiTefwYPrXzkle/ho1I278PrypVqT50OIZhkHPmDC5e3o4I366arxexGVmk5VrJNU12J6XSOMi/QJ86ZXzYmZgCwLbTSTQvGwBAWk4uz2/dVdQhX6RJOX82x+WdF3sSU6kXeP68aBjkR+SZvPMiPddKTFoWtcr44G4x+OZYPJ/sj/673Ra5qj5enLjgWIQHFjwWtf19iPzrWCQkcdOfxyI1J5cR2xx/LESkaF3VtGzbtm0ZNmwY+/bt47bbbuPhhx8G4McffyQ4OJhatWphmiYuLi6sX7+eu+66i4iICGbMmAHkJT0LFy782/3fc889PPDAA7zwwgusXLmSHj16cPToUaKjo3nqqafs/dLS0go8r1OnTnz77bfcc889pKWlUalSJXvbpk2byMrKYtGiRQBkZGQQFZX3y/amm27C19cXX19f+/gAKlWqREpK3j+UYWFhtGjRAoAePXowZsyYf4wpPDz8at7W6+bj5kpaTq79sdU0sRhgNcHHzUJa9vm29Bwrvu55h91iGCy85yY8XAzm7D5epDFfyNfDldSsC8dgYDXNvLZz59vSsq34ebqyNy6Vu+qEsCjyBHfVCcbL3eKI0AuwZmXh4ullf+zi4Yk1M+OCXiaGkfcnn4vn+XbDYiFu2WLObPiBivc9VFQhX5K3q4X0fNP0mVYrvq4X/nNx/s/WjFwrPq557/+W00kUBz6uFtJzzo/Blv+8uKAtI9eKr5uF1Bwr2+KTuadaiCNCvqSLjkWuFR+3gsfCyHcsMnOteLvlHYutxeRYiDiapRhX2QrDVSV3zZs355tvvmHDhg2sWrWKJUuWMHfuXBYtWsTJkyftFx+kpaWxYMEC7rrrLlwv+IVw6NAhwsLCLrV7goODqV69Olu3bmXz5s2MGzeOY8eOUblyZZYtWwbkTdf+VaX7S9euXZk8eTLu7u7cddddBdpsNhvTp0+nYcOGACQkJFCmTBlWrFiBm1vBqYoLY73UNldXV2w222Vj8vT0vPQbWEjSc3Ltv1gBXAwDq/lXmxUft/NtPm4WUv9M9nJNkz4rf6V1hQAmtq3Lv7+PLNK480s7l4uPx4VjMM+3uZ8/Dr7uFlKycnntuwO82qU+/2oSyvqDCSRlZBd53H+JW76E9ENRnIuNwav6+Wlx27ksLBdW4YzzBXNbVhYW7/Pt5Xv2plznuzk8/XW8a9XGI7hok4zHa1elUaA/Nfy82X/2/B8sXhYLabm5BfqanL9vlLerhTQHrtm8lPRcK975zwvynRcXtHm7Whxevb7QY38eizDfC46Fq6XAH3MAtnzHwuuCxFVEwEX3uft706ZNY9myZfTq1Ytx48axd+9eEhIS2LhxIytXrmTdunWsW7eOpUuXsmXLFqKjo2nRooV9qvXQoUMMHDgQwzCwWCxYrRf/A9SzZ0+mTp1Kq1at8PLyokaNGpw9e5bt27cDsGjRIkaOHFngOfXq1SMhIYGvvvqKLl26FGhr06YNX3zxBQDx8fH06NGDkydPXvGYDx06xN69e4G8ael27dpdUUxFacfpFG4JzVtj07isH1HJ6fa23WdSaRZSBncXA183C2H+3hxMTuc/LWvSonwZIC8BNE3HfvC3Rydze61yADSrVIYD8ed/me2MPUvLqgF4WFzw83ClVjlf/ohP49YaZZm+PooH5m3Hapr8fDjRUeFTvkcvagx/gXpT3yT7dDy56WnYcnNJj/oD7xo1C/T1qlyFtD/2A5C6Zzc+NWuTdmAfJxbMB8DFzRXDYrFX94rS3KjjjNi2m77rIgj19sTPzRVXwyA8yJ+9SakF+h5MSafJn1O1rYID2fXntGBxEZmQQrsKeVP1DYP8OJRy/rzYk5hKk3L+uLsY+LhaqO7vxeF87cXBJ1HHGbltN/etj6BSvmPRONCfvckXH4vwv45FuUB2JRWvYyEiReuqKnf9+/dnxIgRLFmyBIvFwiuvvMLy5cu57bbbKF/+/IL8KlWq0LFjRxYuXMgzzzzD2LFj6dGjB66urkybNg3DMOjQoQODBg3iww8/LPAad911F6+88gojRowAwN3dnbfeeotJkyZx7tw5fH19mTp16kWx3XXXXWzbto0KFSoQExNj3z506FDGjx9Pt27dsFqtjBo1iqpVq9oTs3/y1zq748ePU6dOHYYPH37FMRWVddFnaFMxkE87NQHglS1/8HC9SkSnZvJjbCJf7D/B3E5NMIDZO4+SbTP5/MAJxraqhdkIbMDrEQcdFj/A2v3x3FqjLIsea4VhwKjluxnQuhrHkjL4/o/TfLLtOF8+1hIXw2D6+ijOWW0cTsjgv/c2Jttq44/T6YxbXTQXsFyOYXGlQp/7ODbrv5imjcB2t+AWEEjWyRMkblhH6IMPU6HPfcTO/4w462I8KlTE/6a8af+U37ZzeMZkTJtJUPvbcS8X7LBxWE2T9/cfYUqLBrgYBmti4kg4l001Xy96Vq3I23sP8/6+IzzfuBZuhsGx9Ex+OpXwzzsuQhtOnKFl+QDmdAjPuxr71ygerB1KTFoWP59M5KuDJ3j/tnBcDIP/232MbFvx/Mv+r2MxuUUDDAzWxsZx5lw2VX286FmtIrP2Hub/9h/h+Ua1cHUxOJ6Wyc/F7FiIOFrpuX9GHsN0dMlGaDr/Z0eHcEMkH878507FXPN2Xv/cqQRIzi75/5SlZzjHP01+Ps6x2Ofz5kW7llicW3CwX5G+3vexqwplv3dWurtQ9nu99A0VIiIi4tSK821LCoOSOxEREXFqpe1q2ZI/dyMiIiIidqrciYiIiFPTrVBEREREpMRS5U5EREScWmm7oEKVOxEREREnosqdiIiIOLXSVrlTciciIiJOrbRNU5a28YqIiIg4NVXuRERExKkZpWxaVpU7ERERESeiyp2IiIg4tVJWuFNyJyIiIs5N07IiIiIiUmKpciciIiJOrbRVskrbeEVEREScmip3IiIi4tQMw3R0CEVKyZ2IiIg4tVJ2PYWmZUVERESciSp3xYC/v3Pk2IHNfRwdwnU7edY5SvdWW8kfh1nyhwCAExwKAB7YHunoEK7bghbhjg5BHES3QhERERGREkuVOxEREXFqpaxwp+ROREREnJtLKcvuNC0rIiIi4kRUuRMRERGnVsoKd6rciYiIiDgTVe5ERETEqelWKCIiIiJSYqlyJyIiIk6tlBXulNyJiIiIcyttyZ2mZUVERESciCp3IiIi4tR0E2MRERERKbFUuRMRERGnVsoKd0ruRERExLkZhunoEIqUpmVFREREHODEiRP069ePLl268NRTT5Genn5Rn/j4eAYMGEDPnj3p1asXmzdv/sf9KrkTERERp2YU0s/1mjBhAg899BBr1qyhUaNGvPvuuxf1mTZtGrfffjvLli3jjTfeYOTIkVit1svuV8mdiIiISBHLyckhIiKCzp07A9C7d2/WrFlzUb9OnTrRvXt3AKpVq8a5c+fIyMi47L615k5EREScWmF9t2xKSgopKSkXbff398ff3/+yz01KSsLX1xdX17xULDg4mLi4uIv6derUyf7/H330EfXr18fPz++y+/7H5C4mJoYuXbpQs2bNAtvvu+8++vXrd1H/devWcezYMR5//HG++OILAB588MF/epnLyr/PK9W/f3+GDh1K69atWbNmDXPmzCE3NxfTNOnZsyf//ve/r/j5JUG78oE8WqcKVpvJquh4Vh4v+AGp5O3Ji81qY5omR1IzmLnrMCbwWJ0qtA0JxGqazNpzhH3JaY4ZANA2JJBHalXBapqsjonnm+iCYwj19mRMeG1M8sbw1p68MfzVNrF5PQb8vKPI487PAEY2rUntMj5k20wm/xZFbHqWvb1H9fL0DKuA1TT5ZH80m04l2dvuqxlKWU833ttzzAGRn2cALzSrSe2AvDG8vj2KmHxj6BlWnl5/juHj/dFsPHl+DA/UCiXI0413dzt2DJA3jtE3/TkOq8mkS4yjd40K5Jomc/dF80v+cdTOOxbv7HL8OCDv3Hi4ZhVsf54bq2IuPjdeaFwbMDmSlsHbF5wbE26qx8BfdhR12AU4wxik5CqsacpPP/2U2bNnX7R96NChDBs2zP549erVTJ48uUCf6tWrX/Q84zJZ6CeffMLChQv53//+949xXVHlLiQkhGXLll1JV/bs2WP//+tN6i61z6sVFxfH1KlTWbx4MYGBgaSnp9O/f3/CwsK44447bkh8jmYxDIY2DGPQzzvJyrXxzi2N2XgqkaTsHHufoQ3D+HD/MXacSWFE45rcUiGIuMxzNC3rz5O/RBLi5c7EFvV48udIh43h6fphDN64kyyrjVltG7MpruAYhtQP46M/jrEzMYXhjWpyc/kgfolL5K7QYPqEhRLg7uaQ2PNrH1oWd4sLg36MpGGgH880DmP0ln0ABHm48a+aoTyxfgfuLi68f1s4EfHJGBi8eFMtGgT5sSE2wcEjgNv+HMO/10fSKMiPZ5uEMWrT+THcVyuUx37IG8Oc28PZFpeMCwb/aVGLhoF+rCsGYwC4rVLeOAasu3gcZT3cuL92KI9+nzeODzqGs/XPcbzUIu9YrC8m47AYBk/VC2PIprxz4602jdkcX/DceKpeGHOj8s6N5xrWpF35IDbGJXJnaDC9qzv+3HCGMYhcyqOPPkqvXr0u2n5h1a5r16507dq1wLacnBxat26N1WrFYrFw+vRpQkJCLvk606ZN48cff2T+/PlUqFDhH+O65mnZnJwc/vOf/xAVFQXAQw89xE033cSCBQsACA0N5cSJEwAMGzaMm2++mdtvv53t27cTHBzMQw89xLx58zh16hRTpkyhVatWbNu2jZkzZ5KVlcXZs2cZNWoUtWvXLrDPLl268OqrrxIVFYXVamXgwIF069aN7OxsXnrpJXbv3k2lSpVISsr7KzwpKYmcnByysvL+Yvfx8WHKlCl4eHgAedn03LlzycrK4ty5c7z22mu0bNnSPs7c3FzGjx9PVFQUCQkJhIWFMXv2bBISEvj3v/9NYGAgHh4eZGdnM2TIEG655RZM06Rz587MmzeP8uXLX+tbfMWq+XoRm55FWk7eAstdiak0KevPhpNn7H3qlPFhx5m80vHW+CRaBgdwPD2TiNPJAMRnZmMxDMq4u3I2O7fQY77kGDKySMv9cwxJqYQH+fPjqYJj2JmYN4Zt8Um0CA7gl7hEUnNzeW7LLuZ3aF7kcV+oSVl/tsblffb2JKVSL9DX3tYgyI/IMynk2ExybFZi0rOoVcaHmLRMVh+PJyI+mWp+Xo4K3a5JOX+2/FlR3J1YcAwNg/yITMg3hrTzY1h1LJ5tccVjDABNy/mzOd846gddcCz+ZhzfHItna1wy1f2LxzguPDd2J6XSOMifn/7u3DidRItyAWyMSyQtJ5fnt+5i3m2OPTecYQxSshXWtKy/3z9Pv/4dNzc3WrRowapVq+jevTtLly6lffv2F/X75JNP2Lp1K1988cUVv9YVJXfx8fH07NmzwLaXXnqJs2fPsnTpUpKSkpg6dSr33XcfDzzwAAB9+vRh1qxZ9v4JCQl06NCB1157jf79+/P999/z+eefs2TJEj799FNatWrF//73P1577TVq1qzJ5s2bef3111mxYkWBfc6YMYOGDRsydepU0tLSeOCBB2jSpAnffvstkJesHT16lB49egBQr1497rjjDu68807q169P69at6d69O9WqVcNms7FgwQLef/99goKC+Prrr/noo48KJHe///47bm5uLFy4EJvNxqOPPsqPP/5Iw4YNOXLkCB9++CGVK1dm0aJFLF++nFtuuYXt27dTtWrVIknsAHzcLKTnnL9yJiPXio9bwUObv9Sb127Bx9VSIJHLzLXi6+qY5M7bteAYMnOt+LheMIZ81yZlWK34uFoA2BKfRHHh7WaxJ9kAVtPEYoDVBJ8LxpiRkzeG1Bwr2+KTubvqpf9iK2o+bhb7L2EAW/4xXNCWkWvF1y1vDFvjkrmnWvEYA+S93/mPxUXjuOBYFNdxeLtaSM/3nmda887Tgi44v/86N04Xj3PDGcYgUhheeeUVxowZw3vvvUfFihV58803Afjiiy+Ij4/nmWee4Z133sHX15f+/fvbnzdnzpzL5hjXPC2blJTEkSNHGDBgAO3bt2fkyJH/uJ+/MtJKlSrRvHneX2GhoaH2xYjTp09n/fr1rFmzhp07d17yfi+bNm0iKyuLRYsWAZCRkUFUVBTbtm3j/vvvB/LmsZs1a2Z/zoQJExgyZAi//PILv/zyC/fddx8zZsygU6dOvPPOO6xbt44jR46wbds2XFwKzsy3bNmSgIAA5s+fz+HDhzl69Kj9KpWyZctSuXJlIK/kOnPmTDIzM1myZAm9e/f+5zf2Ov27blUaB/lT09+bvfnWynm7WkjLKZig2UzzgnYr6blWvP/8BxTAy9VCWm7RJnZP1KlK40B/avh5F1jvd6lYzPxjsBT85VxcZOQUfE9dDAPrn2Gn51rxdjvfdmEiWFykXzgG8o3hgjbvP5PT4ig9X4IAeX8c/O04iuGxeLx2VRr9eW7sP5vv3LBc4tzggvM7t3iMxRnGIM6huH5DRaVKlZg3b95F2/Mva4uIiLjq/V7zGsPAwEC++eYbHn74YY4cOUKvXr0uecVIfu7u7vb/t1gsF7U/9NBDREZG0qhRIwYPHnzJfdhsNqZPn86yZctYtmwZX375JbfeeiuGYWCz2ez9/rr6ZMOGDaxatYry5cvTp08fZs6cydixY/n6669JT0+nT58+xMTE0LJlywJZ8V9++OEHRo4ciaenJ71796Zly5b2JMPT09Pez9vbm/bt27NmzRq2bNnCnXfeedn34kb48MBxnt28m57fRlDZ2xM/N1dcDYMmQf7sSUot0DfqbDpNy+aVc1uHBBKZmMKuxBRaBQdgACFe7rhgFHnV7uM/jjN86256/xBBJZ+CY9h74RhS0mkSlDeGViGB7Eq6/OfNESLPpNC2QiAADQP9OHT2/B8oe/+cLnd3MfBxtVDdz4vDKRf/AeNokWdSaPfnGBoF+XEwX4x7ElNpWu6CMZwtfmMA2JmQQruK58dx4bFoGlxwHIeK2TjmRh1nxLbd9F0XQWi+8zv8EufGwfznRnAguxKLx7nhDGMQKYmuec3dDz/8wLJly3jrrbe49dZb2bx5MydPnsRisXDu3Lmr3l9ycjJHjx7l888/x8PDg1mzZtlv0pd/n23atOGLL77gtddeIz4+nnvvvZcFCxbQtm1bVq5cSceOHTl58iS//fYbkJeATZw4kfDwcCpXroxpmhw8eJD69etz9OhRXFxc7Ink2LFjL7ox4ObNm+natSt9+vQhLi6OiIgI2rZte8kx9OnTh+HDh3P77bcXSGQLm9U0mb33CDPaNMAFg1XRcSRkZVPN14veYRWZuesw7+w9wgtNauFqGBxLy2TDiQRsQGRiCu/dEo5hwMzdh4os5kuN4d19R5jWsgEuhsHqmDgSzuWNoVe1ivx3z2He23+EkY1q4epicDwtkx9PFo8F7/n9eOIMLUMC+L/bwjGASb9G8UCtUGLSs/jlZCJfHTrBe7eFY2Dwf3uOkW0rfl+JsyH2DK1CAvjg9rwxTNwexYO1Q4lJy+Lnk4l8efAE/9chHBfD4P1iOgbIG0fr8gF8eHve5/vViCgeqh1K9J/jWBh1gjm3h2MYBu/tLr7jsJom7+8/wpQWeefGmnznRs+qFXl772He33eE5xvXws0wOJaeyU+nite54QxjkJKtsNbcFVeGmX+u6xL+7lYoTZs2JTMzk927d+Ph4cEtt9zCiBEjiIiIYPTo0Tz++OMkJycDeRdU1K1blwMHDgAwZswYWrVqRe/evdm6dSuzZ89m3rx5TJkyhe+//x5fX1+aNm3K6tWrWb9+PXv27LHvs1evXowfP579+/djtVoZNGgQvXr1IicnhwkTJhAREUGlSpVIT0/n+eefp3Xr1ixZsoSPPvqInJy8K7NuvfVWXnjhBSwWC6NGjWLPnj14enrSsmVLvv/+ezZs2GC/FUpAQAAjR47EYrHg7u5O+fLlqVGjBv/617945JFHWLduXYH35bbbbmPWrFmEh4df8UFov2LjFfctzi5RjC1xzmUVz1/wV8tq++c+xd3l/2UqOfx8S9lvlWJsQYsr/3dZCldw8OXv03ajxaSvKJT9VvbpXij7vV7/mNzJlTFNkz/++IPRo0ezdOnSq3qukrviQ8ld8eEs/zIpuSs+lNwVH0ruCpe+oeIG+fTTT/nwww956623HB2KiIiI5ONSyv7GUnJ3gzz22GM89thjjg5DRERESjkldyIiIuLUSlnhTsmdiIiIODfDcJJFvFeosL5LV0REREQcQJU7ERERcWqlbVpWlTsRERERJ6LKnYiIiDi10vYNFUruRERExKmVstxO07IiIiIizkSVOxEREXFqpa2SVdrGKyIiIuLUVLkTERERp1baLqhQ5U5ERETEiahyJyIiIk6udJXulNyJiIiIUzNKWXKnaVkRERERJ6LKnYiIiDg1wyhdtazSNVoRERERJ6fKnYiIiDi50rXmTsldMVDWy+boEG6INU+86+gQrtstc4Y6OoQb4tDKeEeHcN063B/o6BBuiCOpbo4O4YZ4vUWyo0O4bt1+3OnoEG6Ilbc1cXQIJY4uqBARERGREkuVOxEREXFyqtyJiIiISAmlyp2IiIg4tdJ2KxQldyIiIuLkNC0rIiIiIiWUKnciIiLi1HQrFBEREREpsVS5ExEREaemyp2IiIiIlFiq3ImIiIiTK121LCV3IiIi4tQMQ9OyIiIiIlJCqXInIiIiTk6VOxEREREpoVS5ExEREadW2m6FouROREREnFzpmqgsXaMVERERcXKlpnIXExNDly5dqFmzZoHt9913H/369buo/xdffAHAgw8+yIsvvsjQoUOpVKnSFb/WI488wrp1664/8CuUumsHp1etxLC4END2FgJvbl+gPTs+jth5c8Ew8KwYSoX7+2G4uBC/fDFp+/dhGAYhPXvjU6dekcWcn2EYvDXpCcLrV+Vcdi5PvTCHw8fi7O3DBnTlXz3aAbBm/Q5e/+8iAsv48PFbT+Pv58WZpDSeHv0Bp8+kOCT+/NoEB/JwrSpYTZM1MfGsjokr0B7q7cmoxrUxTZOjaRnM2nsYM1/b+Gb1GLRxR5HH/RfDgIn3N6VepTJk51p5cf7vHEtIL9AnyNedr56/ja6v/0B2rg0/T1fefLQFvp5uuLm6MGnxLn4/kuigEZyXuGMn0Su/wXBxIeSWm6nQ/tYC7Zlx8UTN/QTDMPAODaVGvwcxXFzYN/tdclLTMCwWXNzdaPjcMw4aQZ62IYE88udnanVMPN9EX/yZGhNeGxOTI6kZvLWn4GdqYvN6DPh5R5HHnd/vG/ew/JNvcbG4cOvdrejQo+0l+33+9lIqVA2h471553vkln0snbsWTKhetzL9n+/jsNtaGMALzWpSO8CHbJvJ69ujiEnPsrf3DCtPr7AKWE2Tj/dHs/Fkkr3tgVqhBHm68e7uYw6IXDQt68RCQkJYtmzZFfV98MEH7f+/detWnn766cIK67qZ1lxOfb2QGqPH4uLuwZE3puDXuAmu/mXsfU4t/pKQ7vfiU6ceJ7+YR2rkDtzKliPjyGHCRv2HnMQzRP/fbGr+Z7xDxtCjcws8Pdzo0OsVWjWrxZSXH+a+f78BQPWqIdzf6xba9xiLzWaybvF4lq+JoF+fW9kUcYDp7yzj9lsaMeGF+xky+gOHxP8Xi2EwuF4YQzfvJMtq479tGrM5PpHk7Bx7n8H1wpgbdYzIxBSebVCTdiFBbIxP5M7QYHpVC6WMu5sDRwCdwkNxd3Wh7xs/0rR6IP/p3Zgn52yxt99aP4QXejSknJ+HfduAjrXYdOA0czccIizEl7ceb0mPqesdEb6dLdfKkYVf0WTsi7h4eLBryjSCmjTBvYy/vc+RL7+i2r09KVOvLgfnzSdxx07K3tSMzLh4mr36SrG4N5bFMHi6fhiDN+Z9pma1bcymuESS8n2mhtQP46M/jrEzMYXhjWpyc/kgfolL5K7QYPqEhRLg4M9Ubq6VL2Yt5ZUPhuPh6c6kIbNodksjygT52fukJKXxwaTPORV9mq5VQwDIzMhi4bsrGPP2EPwCfFk1fx2pyen4B/o6ZBy3hZbF3eLCv9dH0ijIj2ebhDFq0z4AgjzcuK9WKI/9sAN3Fxfm3B7OtrhkXDD4T4taNAz0Y11sgkPiltKn1E/L7tmzh3bt2pGYmEhycjK33347+/btY9asWcyaNYs5c+YQHx/PoEGDSEpKIjIykgcffJBevXrxxBNPEB0dDcDevXvp1asXvXr14p133inSMZw7dRL34BAs3j4Yrq5416xFxsGoAn2yjh/Du3ZdAHwbNCL9wD68qlSl2tDhGIZBTuIZLF7eRRp3fu1a1uW7DTsB2Pb7QZqH17C3xZw4Q8/+U7DZ8moRbq4Wss5lU692Jb798zmbIw7QrmXdog/8AlV9vDiRkUVarpVc02R3Uirhgf4F+tT29yEyMa/CuC0hiZvKBgCQmpPLiG27ijrki7SoWZaf9uVVhnYcTaJx1YAC7aZp0n/2Rs5mZNu3fbT+EJ9vPAKAq4vBuRxrkcX7dzJPnsQzJBhXHx9cXF3xq1WLlKiC50X6seP4160DQGCjhiTv20f22RRyMzLYN+sdIqdMI3FnpCPCt6vm60Vsvs/UrqRUwoMKfqbqlPFh51+fqfgkmpcLACA1N5fntjj+M3XyaBwhlcrh4+eNq5srtRuHcWDnoQJ9zmWe497HO9Ouc3P7toO7jlK5RkUWvLOc15+ehX+Qn8MSO4Am5fzZciqvGrc7MZV6+WJpGORHZEIKOTaT9FwrMWlZ1Crjg7vFYNWxeObuj3ZU2ELe7FBh/BRXpSq5i4+Pp2fPngV+XF1deeCBB5g2bRoTJ07kwQcfpH79+vbnDBo0iJCQEObMmYOPjw9jx47ljTfeYMmSJTz++OO8/PLLAIwePZpRo0axZMkSKleuXKTjsmVmYfHysj928fTEmplxQS/T/kF08fTE9me7YbEQv3wx0e+9TUCbm4sq5Iv4+XpxNvV8zFarDYsl7+OZm2vlTFIqAJNf6seOPUc5eOQUkXuPcc9deb8IunVqjreXx8U7LmLerhbSc88nNpm5VnzcChbI808PZOZa8XazALD1dBJZVlvRBHoZvp6upGbm2h/bbCYWl/Mx/7L/NMnp2QWek5qZw7kcG+X8PHjz0RZMX76nyOL9O9asgueFxdOT3IzMAn1M8/x5YfH0xJqRiWnNpVKnu6j/9FPUGzKYIwu/IjvFcdP93q4W0nMu+Ey5/v1nKsNqxcc17zO1Jb54fKYyM7Lw9j1/LDy9PchMyyrQJzi0LDUbViuwLfVsOvt+P8i/Bnfj+emD+Parnzh1PL5IYr4UHzcLafnOb5tpYjEu3ZaRa8XXzUJqjpWtcclFHKlczCikn+JJ07JAjRo16NOnD56enkyfPv1vn3/06FGio6N56qmn7NvS0tJITEwkPj6edu3y1oj07t2bRYsW3fgBXCB+xRIyDkWRFRuDV/XzlS5bVtbFVTjDpUC7S772kB69Kdvpbo5Ofx3vWrVxDw4p9NgvlJqWiV++f/xdXAys+X4peXi48X/TnyQ1PZNnX/oYgOnvLOONCY/y3VfjWLPud2JOninyuP/yWO2qNAr0J8zXm/1n0+zbvVwtpOXkFuhrs6+GymtPLwZVrvzSsnLx8Tj/T4NhGFht5mWekaduqD9vPd6SyUt2s+2g447FsSVLSYk6REZMDL41wuzbrVlZuHp7Fehr5EtarVlZWLy9cfMvQ4UO7TEsFtz9/fGpWoXMU3G4+xeslhW2J+pUpXGgPzX8vNmXfMFnKrfgZ8o0zx8fb4uFtGLymVr0wSr+iDxCzKET1GhwPnHLyjhXINn7O75lvAmrV4WAsnnvfd0mNTh+8AQVqhb9v1EA6TlWvP9MnAFcMLCal27zds1L7EQcoVQld38nNTWV9PR00tPTSU5OJigo6JL9bDYblStXtieIVquVhIQEDMMo8I+rxWK55PNvtJDuvYC8NXeHJo7Dmp6Gi4cnGQf/oOydnQv09axchfQ/9uNTpx5pe3fjXacu6Qf2kbLjNyre3w8XN1ewWPJW0zvA5u1/cPedN7Fo5RZaNavF7gumML76cAQ/btrDG++tsG+7pXV95n6xji2/RnFv11Zs3v5HUYdt90nUcSBvfdRHtzTDz82VzFwrjQP9+epIbIG+B1PSCQ/yJzIxhVblAtmReNYRIf+tXw+foWOjCqz6PZam1QM5cOKf46tVwY/ZT7Ri2Nxt7I917EUt1XrdC+Stuft93Hhy0tKxeHqQ8kcUlTrfVaCvT5UqnN1/gDL16pK0ew9l6tbh7L59nPxhPQ2eG4Y1K4uM2BN4V6xY5OP4+I/zn6lP2p//TDUJ8ufLwwU/U1Ep6TQJ8mdnYgqtQgLZcaZ4fKb6DLwbyKu+v9R/Kmkp6Xh6eXBg52G6PtjhH59fvU5lYo+cIjU5DW9fLw7tOcZt3dsUctR/L/JMCrdUDOKHmAQaBflxMOX8hUZ7ElMZ3Kga7i4Gbi4uVPfz4vDZ9MvsTYqSUbomKpXcAUyYMIGHH34Ym83GhAkTeOuttwq0WywWrFYrNWrU4OzZs2zfvp0WLVqwaNEiVqxYwbx58wgNDWXDhg106NCBlStXFmn8hsWV8n3u49js/4JpI6DtLbgFBHLu5AkSf1xHxQcepnzv+zj5+WfEL1+MR/mK+DdrAUDKb9s58sZksJkE3XY77uWCizT2vyxbE0HHWxuzfvEEDAMGjfw/nvn33Rw6FofFxeDW1vXxcHejU4emAIybuoA/Dp3go5lDADgRl8jgUXMcEnt+VtPk/f1HmNyiAQYGa2PjOHMum6o+XvSsVpFZew/zf/uP8HyjWri6GBxPy+TnU8VrkfXanSe4pV4IXz3fHsMweOF/vzKgYy2Onk7jh12nLvmcUT0a4uHmwri+4QCkZuYWuAjDEVxcLYTd15e9/30L02ZS/pZ2eAQGknHiBCfXbaDmww8Rdt+/OPjZPMzFS/CqWJFyLZpjuLiQtGcPO1+fgmEYVOt9L25+jlvnZTVN3t13hGktG+BiGKyOiSPhXDbVfL3oVa0i/91zmPf2H2Fkvs/UjyeL12fK1dXCA0N78saIOdhsJrfe04rA4ABij5zih8W/8MiIvpd8nn+gH30H3cOMEXnndquOTahco+gT7b9siD1Dq5AAPrg9HAOYuD2KB2uHEpOWxc8nE/ny4An+r0M4LobB+3uOkX0FFW+RwmCY+UtOTuzvboWSk5ODp6cnX331FaZp0qdPH5588kkOHcpb7Dts2DAmTZrETz/9xIcffkhCQgKTJk3i3Llz+Pr6MnXqVKpWrUpUVBQvvvgiubm5NG3alJ9++umKb4XS6/ufb/h4HWHNE+86OoTrdsucoY4O4YY4tNJx65JulA73Bzo6hBviSKpjr1S9UV5vkezoEK7bcz+V+edOJcDK25o4OoTrFhzs98+dbqBz1ohC2a+HpWWh7Pd6lZrkrjhTcld8KLkrPpTcFS9K7ooPJXdXr7Qld5qWFREREadWnG9bUhiU3ImIiIiTK13JXem6fERERETEyalyJyIiIk6ttN0KpXSNVkRERMTJqXInIiIiTq50rblTciciIiJOzShlyZ2mZUVERESciCp3IiIi4tRK233uVLkTERERcSKq3ImIiIiTK121LCV3IiIi4tR0QYWIiIiIlFhK7kRERMTJGYX0c31OnDhBv3796NKlC0899RTp6ekX9YmPj+exxx6jR48e3Hfffezbt+8f96vkTkRERMQBJkyYwEMPPcSaNWto1KgR77777kV9Zs6cSefOnVm+fDlDhw5lwoQJ/7hfJXciIiLi1AzDKJSf65GTk0NERASdO3cGoHfv3qxZs+aifpMmTeL+++8HICYmBn9//3/cty6oEBEREbkGKSkppKSkXLTd39//H5OwpKQkfH19cXXNS8WCg4OJi4u7qJ+LS14drkuXLsTGxl6yunchJXciIiLi5ApnovLTTz9l9uzZF20fOnQow4YNsz9evXo1kydPLtCnevXqFz3vctXANWvWsG/fPp544glWr15NQEDA3/ZVciciIiJOrbBuhfLoo4/Sq1evi7ZfWLXr2rUrXbt2LbAtJyeH1q1bY7VasVgsnD59mpCQkIv2tWHDBlq2bImPjw/169cnNDSU6OhoJXciIiIiN9qVTL/+HTc3N1q0aMGqVavo3r07S5cupX379hf1W7JkCUeOHOHxxx/n4MGDJCQkUKNGjcvu2zBN07ymqERERETkmsXGxjJmzBjOnDlDxYoVefPNNylTpgxffPEF8fHxPPvss8TFxfGf//yH06dP4+HhwejRo2nRosVl96vkTkRERMSJ6FYoIiIiIk5EyZ2IiIiIE1FyJyIiIuJElNyJiIiIOBEldyIiIiJORMmdiIiIiBNRciciIiLiRJTciYiIiDgRJXdS7J09e9bRIYiIiJQYSu6k2Nq3bx9dunShZ8+exMXFcdddd7Fnzx5Hh3VNMjIy2L9/P6ZpkpGR4ehwJJ+0tDRHh1Bq7d+/39Eh3BCnT592dAgiBejrx5xUbGwsY8eOJTY2lv/973+MHDmS119/ncqVKzs6tCvWr18/Xn31VUaMGMHSpUvZuHEjM2fO5Ouvv3Z0aFdl8+bNjBs3DqvVyoIFC+jRowczZszglltucXRoVy0mJoaDBw9y6623cuLECapUqeLokK7a+vXr2b59O0OGDKFv374kJibyzDPP0K9fP0eHdkWWLl162fZ77723SOK4Ebp27crq1asdHcZ169y5M9WqVaNXr17ceeeduLm5OTqkq9KxY0cMw/jb9h9++KEIo5EbQZU7JzVu3DgGDBiAj48PwcHBdOvWjdGjRzs6rKuSmZlJzZo17Y9vvvlmsrOzHRjRtXnzzTf5/PPP8ff3JyQkhP/9739MmzbN0WFdtVWrVvHUU0/x2muvkZyczAMPPMCyZcscHdZVmz17Nr1792bVqlWEh4ezbt06Fi1a5OiwrtjWrVvZunUrX331FTNmzCAiIoLffvuNt99+m1WrVjk6vKtSq1YtZs+ezc8//0xERIT9p6RZu3YtgwYN4pdffqFLly68+uqr7Nq1y9FhXbF58+bx6aef0qpVK/r06cP8+fNZsGAB/fr147bbbnN0eHINXB0dgBSOpKQkbrnlFmbMmIFhGNx3333Mnz/f0WFdlYCAAPbv32//i3L58uWUKVPGwVFdPZvNRnBwsP1xrVq1HBjNtfvggw/44osvePjhhylbtixLlizh8ccfp2fPno4O7arVrFmTN998kx49euDj40NOTo6jQ7pikydPBqB///4sX76coKAgIG9t6tNPP+3I0K5acnKyPVn9i2EYfPbZZw6M6tq0aNGCxo0bs3r1ambOnMm6desICgpi3LhxNG3a1NHhXValSpUAOHDggP3zBfDEE0/Qu3dvR4Ul10HJnZPy9PTk1KlT9sRo+/btuLu7OziqqzN+/HhGjx5NVFQULVq0oFq1akyfPt3RYV21ChUqsH79egzDICUlhfnz5xMaGurosK6ai4sLvr6+9schISG4uJS84n+5cuWYOHEiu3fvZvr06UyZMqVEHo/4+HgCAgLsj728vErc2q958+Y5OoQbYtOmTSxbtoxNmzZx2223MXPmTG666SYOHDjAwIED+emnnxwd4hXbsmULbdq0AeDHH3/EYrE4OCK5Flpz56R27drF2LFjOX78OFWrVuXs2bO89dZbNGnSxNGhXbWMjAxsNluBxKIkOXPmDJMmTWLTpk2Ypknr1q0ZO3YsISEhjg7tqowZM4ZGjRqxYMECpk+fzueff05WVlaJS7jT0tL4/vvvadasGdWqVWP+/Pnce++9+Pj4ODq0qzJ58mT2799Pp06dsNlsrFmzhpYtW/Lcc885OrQrln9t8Pz58xkxYkSJWxsMeeuD+/btS5cuXfDy8irQ9sknn/DYY485JrCrtHfvXkaPHk18fDyQV9GbNm1aiZ1tKM2U3DmpDRs2cPPNN3P06FGsVis1atQocZW7/v37F1jkaxgGnp6e1KhRg8GDB5eYKdqZM2cyfPhwR4dx3TIyMnjvvfcKJKlPP/10iUu6hw0bxqxZswpse/TRR/n0008dFNG1sVqtfP/992zbtg3DMGjbti133HGHo8O6KgMGDODxxx9nxowZLFmyhK+++oply5aVuCUkjz/+OHPnznV0GDdMUlIShmEUqAxLyaJpWSc1ffp0OnToQO3atR0dyjWrVasWrq6u9OnTB4CVK1dy6tQpypcvz0svvcTs2bMdHOGVWb9+Pc8999xlr0YrCTw8PGjatCkjRowgMTGRdevWlahq19NPP83+/fuJi4srkARZrVYqVKjgwMiuTd++fVmyZAmdO3d2dCjXzBnWBgNkZ2dz8uRJKlas6OhQrsuFldRHHnmkRFZSRcmd06pSpQovvvgiTZo0wdPT0769JN0mYefOnSxevNj+uF69evTp04cZM2b84+0gipOAgAC6dOlCw4YN8fDwsG/Pv3C5JBg7diw2m82eGG3dupXIyEheffVVB0d2ZaZOnUpycjKTJk3i5Zdf5q9JC1dXV8qWLevg6K5e2bJl2b59O+Hh4SWuKv8XZ1gbDHlLLzp27EjZsmXx8PDANE0MwyhxtxD56y4LM2bMoFy5cva7LJTEhLu0U3LnpAIDA4G8BCm/kpTc5eTkEBUVZa8+/vHHH9hsNrKyskrU1Y29evVydAg3xO7du1mxYgUAQUFBTJ8+ne7duzs4qivn6+uLr68v48eP57PPPmPUqFFER0fzxhtvMHr06BKX4O3evZuHH364wDbDMNi3b5+DIrp6L774Ik8++STHjx+nZ8+e9rXBJc1HH33k6BBuCGeppIqSO6c1efJkcnJyOHLkCFarldq1a+PqWrIO99ixYxk4cCBly5bFNE3Onj3L9OnTmTVrVom6/Ubr1q0dHcINYbPZiI+Pt18IcubMmRJ5tezIkSO55557AChfvjwtWrRg1KhRfPzxxw6O7Ops2bLF0SFct8aNG/P1119z9OhRbDYbYWFhJbJyFxwczI8//kh6ejqQN9UfExPDs88+6+DIro6zVFJFF1Q4rd27d/PMM88QEBCAzWYjISGBd955p8RdLZubm8vevXv56aef+OWXXzhw4AC///67o8O6Kn/d/d00TXJzc0lISKB+/fol6sa5ACtWrGDKlCk0b94c0zSJjIzkpZdeolOnTo4O7ap0797dXoH8S69evViyZImDIro2Z86cYcWKFaSnp2OaJjabjZiYmBJ1g+zU1FTeeecdtm3bhqurK+3atePJJ5+86IrT4m7QoEFkZmZy/PhxWrRoQUREBE2bNuXtt992dGhXJTIykpdfftkp7rJQ2pWsUo5csddee42ZM2faT8odO3YwceLEEvXVXdHR0SxcuJDFixeTkpLC4MGDeffddx0d1lVbt25dgceRkZElcqqje/futGrVih07duDq6srLL79c4m7nAnn3g/vxxx/td97fvHlziUsmAIYOHUrVqlXZsWMHd955Jxs3bqRevXqODuuqvPTSS1SpUoXJkydjmiaLFi3i5ZdfZsaMGY4O7aocOXKEb7/9lkmTJtGnTx9eeOGFEle1A6hWrZq9kvrXXRZK2r0TJU/Jm1ORK5KRkVHgr62mTZty7tw5B0Z05b777jsGDBjAv/71L/tUbEhICEOHDrXfjb8kCw8PZ8+ePY4O46qlpKTw/fffc/DgQfbt28eXX35ZYq5Yzm/ChAlMnz6d1q1b07p1a6ZOncr48eMdHdZVS0pKYurUqXTs2JFOnToxb948oqKiHB3WVTl27BijRo2ibt261KtXj5deeokDBw44OqyrVrZsWQzDICwsjAMHDlC+fPkS9VWJJ0+e5MSJE/Tr14+EhAR8fHzw9/cnLi6OAQMGODo8uQaq3DmpMmXK8P3333PnnXcCeQlTSbln0bBhw+jSpQsLFy6kWrVqACX6NiIXJkAHDx4scYv3AZ599ln8/PyoXbt2iT4e9evXZ+XKlSQlJeHm5lbi7tP3l7/u8xgWFsb+/ftp0qQJubm5Do7q6oSFhfH777/TrFkzAPbv30/16tUdG9Q1qF27NhMnTuTBBx9k5MiRxMfHl6iLvt5++222bt1KfHw8/fr1s293dXWlQ4cOjgtMrpnW3Dmpo0ePMmrUKI4fPw7k3Rpl2rRp1KhRw8GR/bM//viDJUuWsHz5cipVqsQ999zD3Llz2bBhg6NDuyYXJneBgYHcc889JSbZ/sul1qqVRBfeHPsvJe37TGfOnMmRI0cYPXo0TzzxBK1bt2b//v18+eWXjg7tH/21DvXcuXOcOXOGGjVq4OLiwuHDh6lWrRqrVq1ydIhXxWq18vvvv9OiRQvWrVvHpk2buO+++6hTp46jQ7sqc+bMYdCgQY4OQ24AJXdOLCEhAW9vb2w2G2fOnLFXwUoKq9XK+vXrWbJkCT/++CPt2v1/e3ca1eSZ9gH8HwwoUpXaCi51aKVapqJnZARZrApEwA2IVmREVGi1OqU6joO4lFiBWuNarS2jiBVxQSgGQRGt1XZG2eLOIDjq2KqAgLKVRSEk7wdeUgOOTbDD7fM81+8cz0nufPkfCeHKvVy3MwICArR7pUjnWrZsGYKDgzm3r6ut3Nxc7WOVSoXvvvsOPXv25OQeqdaN7/n5+VAqlZgwYQIsLS1Zx/pVRUVFz3y99SL7F51SqXzm6/b29p2U5PkcOnQIM2bM+K/bLEJCQjo5EXletCzLU3v37oVCoYBCoUBRUREWLFiAuXPnYsaMGayj6a1Lly6QSCSQSCSoqKjAkSNHsGnTJs4VdwqFAuvWrUNNTQ0AaBuccqkfGQDcuHEDUqmU841aHRwcdJ47Oztj+vTpnCrubt26BVNTU/zud78D0LJ3bfTo0Zwo7IBfirfGxkacPXtW+7vR9vUX3bNOw4pEIs7MBtMcD//QzB1PTZ48GYmJiejevTsAoKGhAX5+frxYVuMad3d3REdHc26Jpq3/NtvClT/ErYqLi7WPNRoNbt68iaioKHz77bcMU+kvKysLoaGh2LJli3ZmKC4uDrt27cLGjRs51Vdxzpw50Gg07d5DXLu9hS8KCwvbzcxnZGTAy8uLUSLSUTRzx1NNTU06zSeNjY0ZphE2S0tLzhd2AH8atT55q4NIJELv3r3x8ccfM0xkmK1bt2L37t0676k5c+bA3t4eERERSEhIYJjOMJWVlUhNTWUd47mdP38esbGxqK+v1/YcLC4ubtcG6UX35z//GTNnzsT777+PqqoqfPLJJ/jpp5+ouOMgKu54SiKRYM6cOZgwYQIA4OTJkzqXpZPOM3ToUCxatAguLi46d8ty6So4oGXfzdMatXIN1/7gtvX48eOnfll4++238ejRIwaJOs7R0RGZmZlwdHTk5G0nrVpv01EoFAgMDMQ//vEPvP3226xjGezw4cOIioqCv78/Hj58iJkzZ2LTpk2sY5EOoOKOp0JDQ5GRkQGlUgmxWIzZs2dr26KQzlVbWwszMzNcvnxZZ5xrxR3XG7WuWLHima9zZSlQpVKhsbGx3bVQjY2NnOll2ap///4IDg7Wnl7m6n7Ubt26Ydq0aSgqKkLPnj0RFRWFqVOnso5lMI1GA2NjYzQ0NGh/FlwuuoWMijseam5uRnNzM7y8vODi4oLMzExYW1uzjiVYrUVDdXW1tjcZF7Vt1Orr68upRq2tBynOnDmDuro6eHt7QywWIz09HT169GCcTn/u7u5Ys2YNZDKZdia4sbERkZGRcHFxYZzOMHv37sXp06fRv39/1lGeS9euXVFVVYU33ngDV65cgZOTE+rr61nHMtjkyZPxpz/9CZGRkaipqUFERASOHj3KqZuNSAsqyXkmLy8P48aNQ25uLmprayGVSrFnzx4sXLgQp06dYh1PkAoLC+Hl5QUfHx+UlpZi/PjxnLyhorVR66hRo7Bnzx7s3LmTU41apVIppFIpSkpKEBMTAx8fH0yaNAlffPEFbt68yTqe3j788EM8evQITk5OmDFjBvz8/LTFRGhoKOt4BrGwsOBcv8enmTt3LpYsWQJXV1ekpKRg0qRJsLW1ZR1LbwcOHAAAxMTEwNPTE2KxGL1798bnn3+OhoYGxulIR9BpWZ4JDAzEkiVLYGdnh/j4eKSkpCA5ORlVVVUIDg7G4cOHWUcUnICAAERERGDp0qVISUnBuXPnsGXLFs59G+ZLo1YvLy8cOHBAe5VdWVkZ5s6dy7nGuVevXkVJSQmMjIxga2uLfv36sY5ksMWLFyMvLw92dnY6h764skT+pNZlzPr6evz444+wsbHhzJKmVCqFQqFo9/hpzwk30LIsz1RXV8POzg5AS8sET09PAIC5uTmnZln4pKGhQWdZ3MXFBXK5nGEiw7Rt1KpUKtGjRw94enqiurqaUaqOW7BgAby9vWFnZwe1Wo0rV64gPDycdSyDhYWF4fjx46xjPJdx48Zx/nqrM2fO4M0338TAgQNx6tQpfPPNN/j973+PIUOGcKa4e3KOh+Z7+IGKO55p/cVsamqCUqnEwoULtc9bW1iQzqFQKCCVSmFubo7CwkLtpvHU1FRO7b3jS6PWVr6+vnB2dsalS5dgZGSENWvWcPKuXxsbG6SkpGD48OHo1q2bdpxL+9ekUinu3buHmzdvYvTo0SgpKcHAgQNZx9JbbGws0tPTIZfLUVhYiL/97W9YtWoVbt68CblcjlWrVrGOaDAu3xtNfkHFHc/Y29tjzZo1aGpqgqWlJYYNG4bS0lJER0dj9OjRrOMJyt69eyGVSrF69WosX74cN27cwB//+Ee8/vrr2LBhA+t4eouPj2cd4TfV0NCAuLg4ZGVlobm5GY6Ojli8eLG24TdXXLlyBVeuXNEZ49qNIenp6YiOjsajR4+QkJAAf39/LFu2DD4+Pqyj6eXIkSM4dOgQTE1NsXHjRri5uWH69OnQaDSYOHEi63h6o4KOf6i445nly5cjLi4ODx48wI4dOwC0bJZ99OgRZDIZ43TCZGVlhYMHD6K+vh5qtRovvfQS60gdwpdGrRERETA1NcXatWsBAImJiVi9ejWnCm6A+/36gJYN/AcPHsSsWbPwyiuvQKFQICgoiDPFnUgkgqmpKQAgJycHM2fO1I5zyY0bN7R9UEtLS7WPNRoNysvLWUYjHUTFHc+YmJhg3rx5OHfunPZKnyVLlgBoaWTs4eHBMp6gPPmB+TRcmmEB+NOoNT8/X+dWBJlMxqlZFgBITk7G4MGDMXz4cADA5s2bYWVlhWnTpjFOZhgjIyOdLzsWFhac2acGtNx/XVNTg/r6ehQUFGhb0RQVFUEs5s6f1xMnTrCOQH5j3Hn3Eb2kp6ejsbER27Ztw6JFi7TjKpUKO3bsoOKuE1lZWWHnzp2sY/xm+NSotaamBj179gQA1NTUoEuXLoxT6S8+Ph6pqak6h3JGjx4NuVyOx48fa2ePuGDw4MHYt28fVCoVCgoKcODAgXZ3m77I5s+fD19fX6hUKrz77ruwsLBAeno6tmzZgg8//JB1PL1x7X5o8uuouOOZ2tpaXLp0CXV1dcjJydGOd+nSRTuDRzqHsbExrz40+dKode7cuZg+fTpcXV0BtCxvzp8/n3Eq/X3zzTfYv3+/zoyXg4MDYmJiMHfuXE4VdzKZDNHR0ejatStWrlwJR0dHhIWFsY6lNy8vL4wYMQKVlZXaotTMzAxRUVEYNWoU43REyKjPHU9lZWXBycmJdQxBi4iI4NU+x+PHjyMxMRHbt2/HtGnT0KVLF9jY2HDu7smKigo8ePAASqUSarUaDg4OeOutt1jH0tuz+o75+voiJSWlcwMJWNs2QW3Z29t3UhJCdFFxxzPh4eGIjIxEYGDgUzf1cq1tBXkx3Lp1Cz169MCrr76KXbt2ISsrC6ampti8ebNOGw4umDBhAqf7w7377rvYsWNHu/YtDx48QFBQENLS0hgl05+bm9szDx1wZT9qYGAgAKCqqgp37tyBnZ0djIyMcOnSJQwZMgQJCQmMExKhomVZnpkxYwYA4KOPPmKchPDF3//+dyQkJKBLly5wcHDAvXv3MH78eOTm5kImk2H9+vWsIxqE6/3hZs2ahXnz5mHlypUYPnw4NBoN8vLyIJfLtb//LzobGxsUFBRg3LhxmDhxImf+79tqbRM0b948bN++HVZWVgBaDlTwadaecA8VdzzT0NAApVLJuaP45MWVlpaG48ePo76+HhKJBJmZmTA1NUVAQADnTpkC3O8P5+vri8ePHyM0NBQlJSUQiUQYOHAggoOD4e/vzzqeXr766ivU1tbi1KlTiI2NRV1dHSQSCby8vGBpack6nsGKi4u1hR3Q8kWhuLiYYSIidLQsyzNPLhPcvXsXI0aMoGUC8lx8fHxw5MgRAIC3t7dOGxHa48VWZWUljIyMOHXjydPU1NTg22+/RVJSEsRiMfbt28c6kkGWLVsGkUiECRMmQK1W4+jRozAzM0NkZCTraESguNNQiOglPj4e8fHx6Nu3L44cOYKvv/4asbGxSEtLg5mZGet4hIOe7DvWtmUIl2aIS0tLERISgilTpmD16tWoqalhHem5XL16FSkpKTA1NUVwcDAcHR052a+soqIC6enpSEtLQ21tLScPIURFReGtt95CQkICEhMT8Yc//AGrV69mHYsIGM3c8dSkSZNw7Ngx7fPW63C4vJGcsDFixAgMGzYMAJCXl6d9rNFokJ+fj4sXL7KMp7f33nsPQ4cOxciRI7W/B5999hnjVB3n5+eH0NBQ3L9/H+np6ZDJZAgJCUFycjLraL+qvLwcJ0+eREZGBioqKuDh4QFPT09O9bhri8t35BL+oT13PDV06FCEhYXpLBOMHDmSdSzCQa3X2HFdaWkpYmNjAQBOTk7w9fVlG+g5qdVq2NvbY+nSpfD09ES/fv3Q3NzMOpZexowZg759+8LDwwM2NjYQiUQoLCxEYWEhAHDuZ8P1O3IJ/1Bxx1NRUVHYt2+fdo+ds7Mzp5qbkheHg4MD6wi/CWNjY53HTz7nIlNTU+zevRvZ2dmQyWSIi4vjzNYLHx8fiEQi1NTUIDc3t93rXCvuuH5HLuEfKu54ysTEBB4eHhg0aJB2mYBLdx0S8r/Gpf2CT7Nx40YkJSXhiy++QK9evVBWVobNmzezjqWXdevWsY7wm+L6HbmEf+jdx1Pp6elYuHAhPv30U1RXV8Pf31974pEQIbpx4wbc3d21/1qfu7m5wd3dnXU8g1laWsLNzQ1qtRpKpRLvvPMO/vnPf7KOZZCioiIEBQXBw8MD5eXlmD17Nu7du8c6lsHa3pEbHh7O6f2DhPvoQAVPSaVSxMfHY9asWUhJSUFZWRmCgoJ0DlkQIiQKheKZS8xcuwc4LCwMly5dQnV1NQYNGoTCwkLY2dlp9xVywXvvvYegoCBs3LgRCoUCSUlJOHLkCPbv3886mkHq6+sRHR2NzMxMqNVqODo6IiQkhDPL5IR/aOaOp2iZgBBdu3fvxoABA7B48WIMGDCg3T+uUSqVOHbsGDw9PREZGYnExEQ0NjayjmWQyspKjB49GkDLMrmfnx9qa2sZpzLciRMnsHTpUiQnJ0OhUCAsLIz6PxKmaBMWT7VdJjhw4AAtExBBs7CwwJgxY1BZWamzDKvRaDh1Q0UrCwsLGBsbw9raGtevX8ekSZNQV1fHOpZBunXrhvv372v3P54/fx4mJiaMU+lvz549qK2tRUJCAoqKirTjzc3NSEtLQ0BAAMN0RMiouOMpmUyG6OhodO3aFStXroSjoyPCwsJYxyKEmZiYGNy/fx8LFixAdHQ06zjPzdLSEjt27ICTkxM2bNgAoGV5kEuWL1+ODz74AHfu3IGPjw+qq6uxdetW1rH0ZmVlhfz8/HbjJiYmvDs0QriF9tzx1IoVKzjdoJWQ/5WSkhLtH2RbW1v07duXcaKOqaurww8//ICJEydi3759yMzMxJw5czBq1CjW0fRWUVGBHj164Mcff0RzczMGDRrEqZm7Vrdu3YK1tbXO2KNHj9CtWzdGiYjQUXHHU9OmTcPevXtpQy8h/0+tViM8PBzHjh2DtbU1mpqacPfuXUyePBlr1qzhzJ7U1qa/T2r9GBeJRCgoKGARq0PGjh0LGxsbeHt7QyKRoGvXrqwjdciJEyfw5Zdfor6+HhqNBmq1Gg0NDcjOzmYdjQgUFXc8NX36dPz000944403dD4w9+7dyzAVIex89dVXKCgowKeffoqePXsCaJk5Cg8Ph62tLRYuXMg4oeF8fX05vXFfrVYjOzsbR48eRXZ2NhwcHODj4wMnJyfW0QwikUgQFRWFr7/+GgsWLMDZs2dRWVkJmUzGOhoRKNpzx0PV1dUIDg7GK6+8AqDlW/3Dhw/x6quvMk5GCDsZGRlISEhA9+7dtWO9e/fG+vXr4efnx8nijuuNmI2MjODs7AxnZ2fk5ORALpcjJCQEFy5cYB3NID179oSjoyMuXryIn3/+GR999BGmTp3KOhYRMG6sQxC9Xbt2DZMmTYKZmRkcHBzg4OCAzMxMfPbZZ9rZCkKESKPR6BR2rczMzDizJNsW1xde8vPzIZfLMX78eOzatQtBQUE4d+4c61gG69atG27fvg1ra2vk5uaisbERP//8M+tYRMC4+YlG/iu5XI5NmzZhzJgx2rElS5Zg7dq1dHqLCJqRkdFTbz+4e/cuJzfxA9yfuQsPD0ffvn1x8OBBxMTEYMqUKZw8hPCXv/wFn3/+OVxdXZGVlQUXFxdIJBLWsYiA0Z47npFKpVAoFE99zcfHh64gI4KVmpqK2NhYhIeHY9iwYVCpVLh8+TLWrl2LxYsXw8PDg3VEvbi5uWmLutLSUlhaWgLgVr++8vJy9OnTB0VFRU8tUPv3788g1W+nuroavXr1Yh2DCBjtueMZlUoFtVrdbplJrVajqamJUSpC2PP29oZKpcKyZctQXFwMoKVPGZcKOwCIj49nHeG5ffzxx9ixYwcCAwMhEol0lpe5UqA+6fz584iLi0N1dbXOOB1gI6zQzB3PREREwNzcHIsWLdIZ3759O+7cuYP169czSkbIi6OiogIikQgvv/wy6yiCVlVVBXNzc52xe/fu4bXXXmMTqIMkEglCQkLazTg+6y5jQv6XqLjjmdraWsyfPx/l5eUYNmwYNBoNrl27ht69eyM6OrrdBykhQvOf//wHiYmJ7WZZqOl35ykpKYFGo8H8+fMRExOjnblrbm7GvHnzkJGRwTihYQICArB//37WMQjRouKOhzQaDbKzs1FQUAAjIyPY2tpi5MiRrGMR8kKYOHEiJk6ciAEDBuiMS6VSRomEZ8WKFcjJyUFZWRksLCy042KxGOPGjcPKlSsZpjNcRkYGTp06BUdHR4jFv+x28vX1ZReKCBoVd4QQQfH390dCQgLrGATAzp07MX/+fAC/HAjhotmzZwNAuy8MNBtMWKEDFYQQQZFKpdiyZUu7WRZ7e3uGqYRp+PDh2mL79u3bmDdvHjZs2AA7OzvW0QxSXl6O48ePs45BiBYVd4QQQcnNzUVeXh4uXryoHROJRHSykQG5XA65XA4AGDRoEHbu3Illy5YhOTmZcTLDjBw5EmfOnME777yj84WBEFboXUgIEZR//etfOHnyJOsYBMDjx48xZMgQ7XNra2uoVCqGiTrmzJkzSEpK0hkTiUQoKChglIgIHRV3hBBBGTJkCAoLC2FjY8M6iuANGjQIGzZsgI+PDwDg2LFjeP3119mG6oCzZ8+yjkCIDjpQQQgRFF9fX1y/fh19+vSBsbExp2524Jvq6mps3boVSqUSYrEY9vb2WLRoEV566SXW0fRy6NAhzJgxA9u3b3/q6yEhIZ2ciJAWNHNHCBGUL7/8knUE8v969eoFmUymfX737l3s3LkTf/3rXxmm0h/NjZAXFRV3hBBBUSqVTx1v28aCdA61Wo3Tp08jISEB2dnZcHNzYx1Jb/7+/gBa3jtt+yRSU2PCEhV3hBBBycnJ0T5uamrChQsXMHLkSGo428lKS0tx6NAhJCcnQyQSoa6uDsePH8fAgQNZR9Pbnj17UFtbi4SEBBQVFWnHm5ubkZaWhoCAAIbpiJBRcUcIEZS2jWWrqqqwZMkSRmmEaeHChbh+/Trc3NywefNm2NnZwd3dnVOFHQBYWVkhPz+/3biJiQnWrVvHIBEhLai4I4QIWvfu3XVmXcj/XllZGSwtLWFubo6XX34ZIpGIk7dTuLq6wtXVFRMmTIC1tTWAlvu9S0pKMHjwYMbpiJBRcUcIEZTAwEBtIaHRaHD37l2MHTuWcSphSU5Oxr///W8cPnwYs2bNgoWFBWpra1FeXo4+ffqwjmewixcvYteuXQgNDYWvry/MzMzg4eFBM8KEGWqFQggRhOLiYgAte+5ai7vXXnsN5ubm6N69O/r3788ynmCpVCp8//33SE5ORmZmJsaOHYtt27axjmWQqVOnYvfu3UhNTcXt27exatUq+Pn54fDhw6yjEYGimTtCiCDMmjULIpFIp32FSCRCWVkZVCoV3SbAiFgshkQigUQiwcOHD5Gamso6UoeYm5vjhx9+wOzZsyEWi/H48WPWkYiAUXFHCBGE06dP6zyvq6uDXC7H2bNnERkZySiVsF29ehUXLlxAQEAAFixYgGvXruGTTz5hHctgb775Jj744APcu3cPTk5OWLx4MYYNG8Y6FhEwI9YBCCGks2VlZcHb2xsAkJqaChcXF8aJhCkqKgq2trY4ceIEunbtCoVCgZiYGNax9HbgwAEAwNq1a/H+++/j0KFDMDExgbe3N0xNTRmnI0JGxR0hRDDq6+shk8mwatUqREREICIigjNXXfGRWq2Gvb09vv/+e3h6eqJfv35obm5mHUtvSUlJAFqWlteuXQtzc3MAgLu7Oy5fvswuGBE8Ku4IIYKQlZWFKVOmAADS0tJotu4FYGpqit27dyMnJweurq6Ii4uDmZkZ61h6e3L/ZtuziXRWkbBEe+4IIYIQFBQEsViMs2fP4ty5c9pxjUYDkUiE7777jmE6Ydq4cSOSkpKwbds29OrVC2VlZdi0aRPrWB3Stk8fF/v2Ef6g4o4QIghUvL14LC0tERISon0eGhrKMI3hqIAjLyrqc0cIIaRT2djYPLUwap1F5UpbGltbW1haWgJouSu39bFGo0F5eTny8vJYxiMCRsUdIYQQ0gG/dm3dgAEDOikJIbqouCOEEEII4RE6LUsIIYQQwiNU3BFCCCGE8AgVd4QQQgghPELFHSGEEEIIj1BxRwghhBDCI/8HGEYQcNY6rH8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax = sns.heatmap(corr,mask=mask, \n",
    "                 annot=True, annot_kws={'size':10}, fmt=\".2f\", cmap='YlGnBu')\n",
    "plt.title(\"Pearson Correlation Heatmap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e26b07",
   "metadata": {},
   "source": [
    "#### Dependent and Independent Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "361f6605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dNWvWAJCWlkZcXBz79u2jX79+AFSvXp1HHnnEvs6MGTMYNWoUO3fuZOfOnfz9739n3rx5dO/enbfeeostW7Zw4sQJ9u3bh5NTzmk4zZs3p1y5cixfvpzjx49z8uRJ0tLSAKhQoQIPPPAAAH5+fixcuJBr166xdu1a+vTpk3tjfyc5OVUf430bPj5enD+vu2k4Sn5/kOi9+XP09e1Y6rdj3arfTk6WXP8gL/DVOt7e3mzYsIGBAwdy4sQJevfubQ8Zt+Pm5mb/t7Oz802vDxgwgO+//56GDRsycuTIW45hs9mIiIggOjqa6OhoPv74Y9q3b4/FYsFms9mXc3G5nru2bdvGxo0bqVy5Mn379mXhwoWEhoayevVqrl69St++fYmPj6d58+YEBwfftL0vv/ySiRMn4uHhQZ8+fWjevDmGcT1EeHh42JcrXbo0HTp0YPPmzezdu5euXbvesRciIiJyawUOJzd+aXfs2JHQ0FBKly7N2bNncXZ2Jjs7O9/jpaSkcPLkScaNG8djjz3Grl27sFqtADnGbNWqlf0KoMTERAIDAzl79iytW7dm/fr12Gw2fv31V7799lvgeoCYP38+8fHxABiGwS+//EK9evU4efIkTk5OjBw5klatWrF9+3b7Nm/Ys2cPfn5+9O3bl4oVK7J///6blrnhRvhp3759jiAmIiIieVfgOSdNmjTBw8ODXr164e7uTvfu3fH19eXy5ctMnjyZihUr5quQcuXK8eSTT9KrVy88PT1p0qQJ6enppKWl0bx5c/uYY8aMISwsDH9/f6xWK5MmTaJatWoMGDCAuLg4/Pz8qFq1KnXq1AGuh5kxY8YwcuRIsrKyAGjfvj2jR4/G2dmZevXq4efnh4eHB82bN7dP4r3hySefZOLEiWzevBk3NzeaNGliDzp/1LRpUywWC3379s3XvouIiMj/WIwb5yjkTzEMg59//pnJkyezbt26fK+vOSe3p3PEjuXj45Wvz9bRe/Pn6OvbsdRvxyronBN98N9d8uGHH7JkyRIWLVpU2KWIiIgUaQond8kzzzzDM888U9hliIiIFHn6bB0RERExFR05EZEcMrOsxMwPyn1BID0j/1fmiYjkRuFERHJwc3XWhEERKVQ6rSMiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqbiUtgFyN3nVaYUHu7F66318fEq7BJKjMwsa2GXICIlXPH6DSYAeLi7EDAhurDLkCIqZn5QYZcgIiWcTuuIiIiIqSiciIiIiKkonIiIiIip5Cmc+Pr65mmwvn37MnLkyDwt+9JLL/Hrr78CMGzYMBISEvK03u+FhIRQt27dm9YdNWoUnTt3zvd4fxQfH39XxhEREZG8u2tHTo4ePYqrqys//fQTZ8+ezXX52NhYDMMA4J///CeVK1cu0HYrV67MZ599Zn+cmprK4cOHCzSWiIiIFL58hZNz584xcOBA+vTpwxNPPMHBgwftr0VFRdG2bVu6dOnCxx9/bH8+JSWF0aNH4+fnR1BQEHv27OG9994jMTGR4cOHc/HiRTp37kx8fDy9e/fmhx9+AMBqtdKhQweSk5P5/vvveeqpp+jduzdDhgzhzJkz9vG7d+/Op59+an/8xRdf0LFjR/vjq1evMnnyZPr06UNQUBDr16+31/v888/Tv39/OnbsyNKlS5k9ezYBAQEEBweTkZEBQEZGBuPGjSMwMJAxY8Zw6dIlgNvWFBwczJgxY+jRowdHjhzJT3tFRESEfF5KvHr1ajp27Mizzz5LbGws33zzDU2aNCErK4tPPvmEZcuWkZKSwvjx4xk9ejQuLi4sWrSIatWq8dZbb3H06FGmTZvGqlWrWLlyJe+99x7e3t728YOCgti4cSMPP/wwe/fuxdfXFy8vL0JDQ3nnnXeoUqUKO3bsYOrUqXzwwQcA1KtXj507d5KUlETFihXZtGkTQ4YMYfv27QAsXryYBg0aEB4eTmpqKv3796dx48YA/PDDD8TExHDp0iU6d+7MkiVLmDJlCsHBwezYsYO6deuSnJxMcHAwzZo1Y+7cubz11ltMnDjxjjX5+voSGRmZrzeiQgXPfC0vci/pvjKOpX47lvrtWAXpd77CSevWrRk7dixHjhzhscceY+DAgQB89dVX+Pj4UKtWLQzDwMnJia1bt9KtWzf279/PvHnzgOu/tFetWnXb8Xv16kX//v158cUXWb9+PYGBgZw8eZIzZ87w3HPP2ZdLTU3NsV737t357LPP6NWrF6mpqVStWtX+2u7du0lPT2fNmjUApKWlERcXB8Cjjz6Kp6cnnp6e9v0DqFq1KpcvXwagRo0aNGvWDIDAwEBCQkJyralRo0b5aSsAycmp2GxGvte7FX3jyZ91/vyVwi6hxPDx8VK/HUj9dqxb9dvJyZLrH+T5CidNmzZlw4YNbNu2jY0bN7J27VqWLl3KmjVrOHv2rH3yaGpqKitXrqRbt264uOTcxLFjx6hRo8ZtdsKH6tWrExsby549e5g2bRqnTp3igQceIDr6+k3FrFYrSUlJOdbz8/Njzpw5uLm50a1btxyv2Ww2IiIiaNCgAQBJSUmULVuWmJgYXF1dczbD5eZ2/PE5FxcXbDbbHWvy8PC4dQNFREQkV/maczJ37lyio6Pp3bs306ZN4/DhwyQlJbFr1y7Wr1/Pli1b2LJlC+vWrWPv3r2cOXOGZs2asXHjRuB6MBk2bBgWiwVnZ2es1ptvkx0UFER4eDgtWrSgVKlS/PWvf+XSpUt8/fXXAKxZs4aJEyfmWKdu3bokJSXx3//+l8cffzzHa61atWLFihUAJCYmEhgYmKcJuzccO3bMPsF29erVtGnTJk81iYiISMHk68hJcHAwEyZMYO3atTg7OzN9+nQ++eQTHnvssRxX2zz44IN07tyZVatW8fzzzxMaGkpgYCAuLi7MnTsXi8VCx44dGT58OEuWLMmxjW7dujF9+nQmTJgAgJubG4sWLWL27NlkZGTg6elJeHj4TbV169aNffv2cf/99xMfH29/fsyYMYSFheHv74/VamXSpElUq1bNHixyc2O+zOnTp6lTpw7jx4/Pc00iIiKSfxbjxvW8Uqju9pwTfbaOFFTM/CCdk3cgzYFwLPXbsQo650R3iBURERFTUTgRERERU1E4EREREVPJ14RYKRrSM7KJmR9U2GVIEZWZdfNVdCIijqRwUgxduXyN4jTdSxPYHEs38RORwqbTOiIiImIqCiciIiJiKgonIiIiYioKJyIiImIqCiciIiJiKgonIiIiYioKJyIiImIqCiciIiJiKgonIiIiYioKJyIiImIqCiciIiJiKgonIiIiYioKJyIiImIqCiciIiJiKgonIiIiYiouhV2AiDieV5lSeLjf+ts/M8vq4GpERHJSOBEpgTzcXQiYEH3L12LmBzm4GhGRnHRaR0RERExF4URERERMReFERERETMUU4SQ+Pp7OnTvf9Lyvr2+Bxlu3bh19+/YlKCiIgIAA/v3vf9922czMTBYuXEhAQABBQUH8/e9/Z/fu3QXa7g0hISFERUX9qTFERERKqmI3IXbVqlWsXLmSd999l0qVKnH58mWGDBlCqVKlePLJJ29a/qWXXsLNzY3Vq1fj7u7O0aNHGTJkCB9++CG1atUqhD0QEREp2UwfTlJTU3n55ZdJSEggMTGRZs2aMXfuXBISEpg4cSJpaWk4OTkRGhpKkyZNWLx4MeHh4VSqVAmAMmXKEB4eTmpqKgCdO3emUaNGHDlyhIiICLZs2cKuXbtwd3cHrh+tWbBgAR4eHgAsXLiQPXv2cOnSJby9vXnzzTfx8fGhVatWNGjQgKSkJFavXs28efPYtm0blSpVwmq10qJFi8JpmIiISBFnmnCSmJhIUNDNlzBu27aNevXq8cYbb5CZmUmvXr348ccf2bZtGx07duTZZ58lNjaWb775hmrVqnH27FkaN26cY4yaNWvmeNyhQwdef/11Nm/eTK1atShdunSO11u2bAnAqVOnOH78OCtXrsTJyYkXX3yRmJgYhgwZwsWLFxk+fDgtW7Zk8+bNHD58mPXr13PlyhUCAwPzvf8VKnjme52SxMfHq7BLKFHUb8dSvx1L/XasgvTbNOGkUqVKREfnvO+Cr68v/v7+fP/993zwwQccP36clJQU0tLSaN26NWPHjuXIkSM89thjDBw4kGvXrgFgGMYdt3UjvDg5Od1x2YceeojJkyfz3//+lxMnTnDw4EGqVat20zj79u2je/fuuLq6Ur58eTp06JDv/U9OTsVmu3PdJZWPjxfnz18p7DKKldx+WKjfjqOvb8dSvx3rVv12crLk+ge5KSbE3smyZcuYO3cu5cuXZ+DAgdSsWRPDMGjatCkbNmygXbt2bNy4kZEjR1KuXDkefPBBDh06lGOMffv2MW/ePPvjG6dwGjZsyLFjx0hPT8+x/AcffMCGDRs4dOgQQ4cOxWaz0aNHD7p27ZojzNw49WOxWLDZbPbnXVxMk/lERESKHNOHk127dtGvXz8CAwOxWCz89NNP2Gw25s6dS3R0NL1792batGkcPnwYgKFDh/Laa69x/vx5AC5cuMBrr73GQw89dNPYVapUoWPHjsycOZOMjAwADh8+zJIlS6hduzb79++nRYsWPPXUU9SqVYtdu3Zhtd58a+/WrVuzefNmMjMzuXTpEjt27LiHHRERESneTP8n/uDBgwkLC+Nf//oX9913H4888gjx8fEEBwczYcIE1q5di7OzM9OnTwfgqaeeIisriyFDhmCxWDAMg379+t3ySh2AV199lXnz5hEUFISbmxulSpUiIiKCOnXqULZsWcaMGUNAQACurq74+voSHx9/0xhdu3blhx9+wN/fn4oVK940x0VERETyzmLkNkFDHEJzTm5P54jvPh8frzt+to767Tj6+nYs9duxiu2cExERESlZFE5ERETEVBRORERExFRMPyFWRO6+9IxsYubffNNDgMysm69IExFxJIUTkRLoyuVr3G5KoO6eKSKFTad1RERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVFwKuwCR4s6rTCk83IvOt1pmlrWwSxCREq7o/MQUKaI83F0ImBBd2GXkWcz8oMIuQURKOJ3WEREREVNROBERERFTUTgRERERU8lzOImPj8fX15ddu3bleL5z587Ex8f/6UI++eQTAgMD6dWrFwEBAbz11ltYrdcn5lmtVoYOHUqvXr2IioqiYcOGBAUF8be//Y1evXrxf//3f5w7d67A246KiiIkJCTf6125coVRo0YVeLsiIiJys3wdOXF1dWXq1Kmkpqbe1SKioqL45z//SWRkJBs2bGDFihUcOXKEqVOnApCQkMDRo0fZsGEDLVq0oFKlSkRHR7Nu3To2bNhAw4YNmTlz5l2tKS8uXbrETz/95PDtioiIFGf5CieVKlWiTZs2hIeH53h+3759BAcH2x+HhIQQFRVFfHw8QUFBjBkzhu7du/OPf/yDlStX0q9fPx5//HGOHTsGQGRkJKGhoVSrVg0AT09PZs+ezfr16/n1118ZMWIEKSkp9OnT55Z1NWvWjJMnTwLXj+S88MIL9OjRg+TkZNasWYO/vz8BAQGEhIRw9epVANatW0ePHj3o27cv27Zts4/1+yNBsbGx9v06cuQITz75JAEBAQwcOJBz584xa9YsEhMTGT16NKmpqQwfPpw+ffrQp08fvvzyy/y0VkRERP6/fF9KHBISQkBAALt27aJt27a5Ln/06FHmzJlD3bp16dGjB1WrVmXVqlVERkayatUqRo4cya+//kqjRo1yrFe2bFlq1arFjz/+yOLFixk0aJA98PxeVlYWmzZt4tFHH7U/16FDB15//XWOHj3KO++8w8cff4y3tzczZswgMjKSZ555hnnz5rFu3TrKlSvHiBEjKF269B33Y+LEiUycOJFOnTrx0Ucf8eGHHxIaGsqgQYN46623WLt2LVWrVuW9997j2LFjrF69mi5duuS5rxUqeOZ52ZLIx8ersEsoUdRvx1K/HUv9dqyC9Dvf4cTT05OZM2cydepUPvnkk1yXr1ixIvXr1wfg/vvvp3Xr1gBUqVIlR9C4Mb/k97Kysm45ZmJiIkFB1+/FkJmZSaNGjZgwYYL99caNGwOwf/9+OnXqhLe3NwD9+vXjpZdeonHjxjzyyCNUrFgRgICAAPbu3Xvbfbhw4QLnz5+nU6dOAAwYMAAgR/2PPPIICxYsICEhgY4dOzJ69Og7teUmycmp2GxGvtYpKXx8vDh//kphl1FgRfEHYVHud1FT1L++ixr127Fu1W8nJ0uuf5AX6CZs7dq1y3F654EHHsAw/veL9fehws3NLce6zs7OOR6XL1+eatWqceDAAdq3b29//sKFC5w5c4aGDRtis9lyrHNjzsntuLu7A9y0nmEYZGdnY7FYcrzm4uJy03IA2dnZwPW5Nr+XkZFBYmIiFovF/lz16tXZtGkTO3bsYOvWrfzrX/9i06ZNOZYRERGR3BX4UuKQkBB27txJYmIi3t7enDlzhoyMDFJSUvjmm2/yNdYLL7zAq6++ypkzZwC4evUqoaGh9OzZkypVqhS0RFq0aMGWLVtISUkB4OOPP6Zly5Y0bdqU7777joSEBGw2Gxs3brSv4+3tzS+//AJgnzfi5eXF/fffb79SKTo6mkWLFuHi4mIPMP/5z39488038fPzY/r06Vy4cIErV5TORURE8qvAt6+/cXpn6NChlCpViscee4xevXpRtWpVmjZtmq+xevXqhbOzM+PGjSMzMxOr1UqvXr0YOXJkQcsDoG7duowYMYLg4GCysrJo0KABM2bMwNPTk9DQUJ555hlKlSpFrVq17Os8//zzzJw5k8jISNq1a2d/PiIigrCwMObOnYu3t7f9/1WqVCE4OJjFixfzj3/8g4CAAFxcXBgzZgxlypT5U/WLiIiURBbj9+djpNBozsntFfVzxD4+XkXus3WKcr+LmqL+9V3UqN+OVdA5J7pDrIiIiJiKwomIiIiYisKJiIiImEqBJ8SKSN6kZ2QTMz+osMvIs8ysm+85JCLiSAonIvfYlcvXKErT74riTeNEpHjRaR0RERExFYUTERERMRWFExERETEVhRMRERExFYUTERERMRWFExERETEVhRMRERExFYUTERERMRWFExERETEVhRMRERExFYUTERERMRWFExERETEVhRMRERExFYUTERERMRWFExERETEVl8IuQETMJTPLio+PV2GXUaL82X6nZ2Rz5fK1u1SNSOFTOBGRHNxcnQmYEF3YZUg+xMwP4kphFyFyF+m0joiIiJiKwomIiIiYisKJiIiImEqRDyexsbE88sgjBAUFERgYiJ+fH4sXL77jOsHBwcTGxjqoQhEREcmPYjEhtmHDhixbtgyAq1ev0rNnT7p160atWrUKuTIRERHJr2IRTn4vPT0dZ2dnvLy82LRpE0uXLiU9PZ2MjAxmzZpF8+bN7ctmZ2cTFhZGXFwcSUlJ1KhRg8jISJKSkhgzZgy1a9fmyJEjVKhQgUWLFlGuXDliYmJYvHgxFouFhx9+mJkzZ5KZmckrr7xCXFwcVquVYcOG4e/vX4hdEBERKbqKRTg5dOgQQUFB2Gw2Tp8+jZ+fHxUrVmTlypW88847lC9fntWrV/P+++/nCCcHDhzA1dWVVatWYbPZGDx4MF999RUNGjTgp59+4tVXX6V+/fqMHTuWmJgYunfvzpw5c4iKiuL+++9n0qRJfPXVVxw8eJAGDRoQHh5Oamoq/fv3p3Hjxjz44IN53ocKFTzvRWuKDd13Q+TO9D2Sd+qVYxWk38UinPzxtM7IkSNZsmQJb731Flu2bOHEiRPs27cPJ6ecU2yaN29OuXLlWL58OcePH+fkyZOkpaUBUKFCBerXrw9A7dq1uXTpEgcOHODRRx/l/vvvByAiIgKAt99+m/T0dNasWQNAWloacXFx+Qonycmp2GzGn2tEMeXj48X587qLg6PoB3fRpO+RvNHPE8e6Vb+dnCy5/kFeLMLJ791333107dqVrVu3EhUVRVBQEM2bN8fX15fly5fnWPbLL7/kjTfeYNCgQfTp04eLFy9iGNcDgru7u305i8WCYRi4uORs14ULFwCw2WxERETQoEEDAJKSkihbtuy93E0REZFiq8hfrfNHVquVffv24eHhgZOTEyNHjqRVq1Zs374dq9WaY9k9e/bg5+dH3759qVixIvv3779pmd97+OGH+e677zh//jwAr776Kl9++SWtWrVixYoVACQmJhIYGMjZs2fv3U6KiIgUY8XiyMmNOScA165ds09UDQ0Nxc/PDw8PD5o3b85vv/2WY70nn3ySiRMnsnnzZtzc3GjSpAnx8fG33U7lypWZMmUKQ4cOxWaz0aRJE/r06cO1a9cICwvD398fq9XKpEmTqFat2j3dZxERkeLKYtw4jyGFSnNObk/niB3Lx8dLn61TxMTMD9L3SB7p54ljFXTOSbE7rSMiIiJFm8KJiIiImIrCiYiIiJhKsZgQKyJ3T2aWlZj5QYVdhuRDekZ2YZcgclcpnIhIDm6uzpow6ECaoClyM53WEREREVNROBERERFTUTgRERERU1E4EREREVNROBERERFTUTgRERERU1E4EREREVNROBERERFTUTgRERERU1E4EREREVNROBERERFTUTgRERERU1E4EREREVNROBERERFTUTgRERERU3Ep7AJE5N7xKlMKD/f8fZtnZlnvUTUiInmjcCJSjHm4uxAwITpf68TMD7pH1YiI5I1O64iIiIipKJyIiIiIqSiciIiIiKkU2zknM2bM4NtvvyUrK4vTp09Ts2ZNAAYNGkTfvn0LuToRERG5nWIbTqZPnw5AfHw8gwYNIjo6f5MCRUREpHAU23ByK6dOnSIsLIyUlBQ8PDyYOnUq9evXJyQkBE9PT3788UcSEhIYPXo0ffv25c033wRg7NixAHTu3Jl///vf7Nu3j7Vr15KSkkKnTp0YNGgQ06ZN49y5c1gsFiZMmECbNm0Kc1dFRESKrBIVTiZPnsy0adOoX78+v/zyC6NHj+bTTz8F4Ny5c3z00Uf8/PPPeTr1k5CQwMaNG3FxcWH8+PH07duXLl26kJiYyIABA1i3bh2enp55rq1ChbwvWxL5+HgVdgklivrtWOq3Y6nfjlWQfpeYcHL16lUOHTrESy+9ZH8uLS2NixcvAtC2bVssFgt16tQhJSUl1/Hq16+Pi8v19u3evZvjx4/zxhtvAJCdnc2ZM2eoV69enutLTk7FZjPysUclh4+PF+fPXynsMoqkgv4QVr8dR1/fjqV+O9at+u3kZMn1D/ISE05sNhtubm455p6cO3eOcuXKAeDu7g6AxWKxv26xWLDZbPbHWVlZ9n97eHjkGPvDDz+0j5WQkEDFihXvxW6IiIgUeyXmUmIvLy+qV69uDye7du3i6aefvuM63t7e/PLLLwB8//33nD9//pbLtWrVio8++giAX375hcDAQK5du3YXqxcRESk5SsyRE4CIiAjCwsJYsmQJrq6uLFy4MMeRkj/q2bMnn376KT179qRBgwbUr1//lsuFhoYybdo0AgICAJg7d26+5puIiIjI/1gMw9BEBxPQnJPb0znigvPx8SrQZ+uo346jr2/HUr8dq6BzTkrMaR0REREpGhRORERExFQUTkRERMRUStSEWJGSJj0jm5j5QflaJzPLeo+qERHJG4UTkWLsyuVr5Hfqn+6eKSKFTad1RERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVBRORERExFQUTkRERMRUFE5ERETEVFwKuwARMZfMLCs+Pl6FXUaJon47lvqdf+kZ2Vy5fM1h21M4EZEc3FydCZgQXdhliIiJxMwP4ooDt6fTOiIiImIqCiciIiJiKgonIiIiYioKJ3fw888/4+vry6efflrYpYiIiJQYCid3EBUVRY8ePVi5cmVhlyIiIlJi6Gqd28jOzuaTTz5h+fLl9O/fn9OnT1OtWjViY2OZNWsWzs7ONGnShGPHjrFs2TJOnTpFWFgYKSkpeHh4MHXqVOrXr1/YuyEiIlLkKJzcxrZt26hSpQo1atSga9eurFy5kvHjx/Piiy/y7rvvUrduXWbNmmVffvLkyUybNo369evzyy+/MHr06HydDqpQwfNe7EaxofsSiIgUroL+HC7IegontxEVFYW/vz8APXv2ZOLEiTz++ONUqFCBunXrAvDEE08we/Zsrl69yqFDh3jppZfs66elpXHx4kW8vb3ztL3k5FRsNuPu70gx4OPjxfnzjrzCvmRTEBSRWynIz+Fb/fx2crLk+ge5wsktJCcns337dg4dOsS///1vDMPg8uXLbN++HZvNdtPyNpsNNzc3oqP/d+Oqc+fOUa5cOQdWLSIiUjxoQuwtfPLJJ7Rq1Yrt27ezZcsWtm7dysiRI9m5cyeXL1/m6NGjAMTExADg5eVF9erV7eFk165dPP3004VWv4iISFGmIye3EBUVxfjx43M8N2DAAJYsWcL777/P5MmTcXJyokaNGnh4eAAQERFBWFgYS5YswdXVlYULF2KxWAqjfBERkSJN4eQWbhwR+b0KFSpw4MAB5s2bx0cffUTp0qVZunQpCQkJANSsWZNly5Y5ulQREZFiR+EkH5ycnChXrhxPPPEErq6uVK1aldmzZxd2WSIiIsWKwkk+DR8+nOHDhxd2GSIiIsWWJsSKiIiIqejIiYjkkJllJWZ+UGGXISImkp6R7dDtKZyISA5urs666Z0D6SaDjqV+Fw06rSMiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqaicCIiIiKmonAiIiIipqJwIiIiIqbiUtgFiEjeeZUphYf7vf22zcyy3tPxRURyo3AiUoR4uLsQMCH6nm4jZn7QPR1fRCQ3Oq0jIiIipqJwIiIiIqaicCIiIiKmUqhzTlJTU5k/fz779+/H2dmZMmXKEBISQoMGDQo0XnBwMGPGjKF06dKsXLmS2bNns2rVKu677z78/f3JzMxkzpw57N+/H4vFQpkyZZg8eTKNGjW6y3smIiIiBVVo4cRmszFs2DBatmzJunXrcHFxYe/evQwbNowNGzbg7e1d4LEffvhhHn74YQAOHDhAixYtAPjggw+w2WzExMRgsVj45ptvGDVqFFu3bsXV1fWu7JeIiIj8OYUWTmJjY0lMTOT555/Hyen62aVWrVoxZ84c9uzZw7/+9S9sNhu1a9dm2rRpvPLKK8TFxWG1Whk2bJj9SMiUKVM4dOgQVatW5eLFi/axIyMjee6559iyZQt79+7Fx8eHpKQksrKyyMrKws3NjaZNm/Lqq69is9kwDIN58+bxxRdf4OzsTL9+/Rg8eDAnTpxg2rRppKSkULp0aaZMmUKjRo0ICQkhJSWFU6dOMWnSJCpWrMicOXNIT0/H29ubGTNm8OCDDxZWe0VERIqsQgsnhw8f5uGHH7YHkxsee+wxYmNjOXnyJFu3bsXLy4t58+bRoEEDwsPDSU1NpX///jRu3JjPPvsMgE2bNnHy5EkCAwNzjNWmTRs6d+5MixYtaN++PTVq1GDEiBG0bt2aFi1a0Lp1a3r37o27uzubNm3i22+/JSYmhqysLAYMGEDPnj2ZNGkSw4cPp3v37hw8eJBx48bx6aefAlCuXDneeecdMjMzeeKJJ3jnnXeoUqUKO3bsYOrUqXzwwQd57keFCp5/rqHFnI+PV2GXUKKo346lfjuW+u1YBel3oYUTJycnDMO47es1atTAy+v6Du3evZv09HTWrFkDQFpaGnFxcezbt49+/foBUL16dR555JE7bvOBBx5g/fr1/PDDD+zevZt169bxwQcfsG7dOvbv34+fnx9ubm64ubkRHR3N1atXOX36NN27dwegSZMmlC1bluPHjwPY56qcPHmSM2fO8Nxzz9m3lZqamq9+JCenYrPdvh8lmY+PF+fPXynsMkzBUT9U1W/H0de3Y6nfjnWrfjs5WXL9g7zQwknDhg356KOPMAwDi8Vif37BggW0adMGDw8P+3M2m42IiAj7RNmkpCTKli3Lxx9/jM1msy/n4nLn3VmwYAFPP/00jRo1olGjRowcOZL+/fuza9eum9aNj4+nbNmyNwUowzCwWq/fQfNGjTabjQceeIDo6Os3x7JarSQlJeW3JSIiIkIhXkrcrFkzKlSoQGRkpP2X/Y4dO4iKiuLChQs5lm3VqhUrVqwAIDExkcDAQM6ePUvr1q1Zv349NpuNX3/9lW+//fam7Tg7O9vHT0hI4K233iIzMxOAlJQULl68SJ06dWjevDmff/45WVlZXLt2jWeffZakpCQefPBB++mjgwcPkpSURO3atXNs469//SuXLl3i66+/BmDNmjVMnDjxLnZLRESk5Ci0IycWi4W3336bOXPm4O/vj4uLC97e3rz33ntcuZLzENCYMWMICwvD398fq9XKpEmTqFatGgMGDCAuLg4/Pz+qVq1KnTp1btpOmzZtWLBgAV5eXkydOpXw8HAef/xxSpUqhaurKxMmTKBmzZrUrFmTQ4cO0adPH2w2G4MGDaJGjRpEREQQFhbGm2++iaurK2+++SZubm45tuHm5saiRYuYPXs2GRkZeHp6Eh4efk/7JyIiUlxZjDtN/BCH0ZyT29M54v/x8fFyyGfrqN+Oo69vx1K/Haugc050h1gRERExFYUTERERMRWFExERETGVQv1sHRHJn/SMbGLmB93TbWRmWe/p+CIiuVE4ESlCrly+xr2eyqe7Z4pIYdNpHRERETEVhRMRERExFYUTERERMRWFExERETEVhRMRERExFV2tYxJOTpbcFyrB1B/HUr8dS/12LPXbsf7Y77z0X5+tIyIiIqai0zoiIiJiKgonIiIiYioKJyIiImIqCiciIiJiKgonIiIiYioKJyIiImIqCiciIiJiKgonIiIiYioKJyIiImIqCidiOr/99htPP/00jz/+OM899xxXr1697bK7du1i8ODBDqyu+IiJiaFnz55069aN5cuX3/T6kSNH6Nu3Lz169GDKlClkZ2cXQpXFR279vmHy5MlERUU5sLLiKbd+f/HFFwQFBREYGMioUaO4dOlSIVRZPOTW688//5yAgAB69epFSEgImZmZuQ9qiJjM8OHDjfXr1xuGYRiRkZHG3Llzb1rGarUa77//vtGiRQtj4MCBji6xyDt37pzRqVMn4+LFi8bVq1eNgIAAIy4uLscyvXr1Mg4cOGAYhmG89NJLxvLlywuh0uIhL/0+d+6cMWLECKNRo0bGmjVrCqnS4iG3fl+5csVo27atce7cOcMwDOP11183Zs6cWVjlFmm59frq1atGu3btjPPnzxuGYRgvvPCCsXLlylzH1ZETMZWsrCz2799Pjx49AOjTpw+bN2++abljx45x7NgxZs6c6egSi4Xdu3fTqlUrypUrR+nSpenRo0eOPv/666+kp6fTpEkT4Pbvg+RNbv2G6399dunSBT8/v0KqsvjIrd9ZWVmEhYVRuXJlAHx9fTl79mxhlVuk5dbr0qVLs2XLFipWrEhaWhrJycmUKVMm13EVTsRULl68iKenJy4u1z8w28fHh4SEhJuWq127NrNnz6Zs2bKOLrFYSExMxMfHx/64UqVKOfr8x9dv9z5I3uTWb4Bnn32WJ5980tGlFUu59dvb25uuXbsCkJ6eznvvvWd/LPmTl69tV1dXvvrqKzp16sTFixdp165druO63PVKRfJo06ZNzJkzJ8dz1atXv2k5i0Ufb363Gbf4MPLf9zm31yV/1E/Hymu/r1y5wqhRo6hbty69e/d2RGnFTl57/dhjjxEbG8uCBQsICwtj/vz5dxxXR06k0Pj5+bF9+/Yc/73//vukpqZitVoBOH/+PJUqVSrkSoufypUrk5SUZH+cmJiYo89/fF3vw5+TW7/l7spLvxMTExkwYAB169Zl9uzZji6x2Mit1ykpKezcudP+OCAggKNHj+Y6rsKJmIqrqyvNmjVj48aNAKxbt44OHToUclXFT5s2bdizZw8XLlzg2rVrfPbZZzn6XLVqVdzd3fnmm28AvQ9/Vm79lrsrt35brVZGjhyJn58fU6ZM0VGsPyG3XhuGwaRJk/jtt9+A60fMH3300VzH1WkdMZ3p06cTEhLC4sWL+ctf/sKCBQsAWLFiBYmJiYwbN66QKyz6KleuzPjx4xk0aBBZWVk88cQTNGrUiGHDhvH888/z8MMPM2/ePEJDQ7l69Sr169dn0KBBhV12kZWXfsvdk1u/z507x+HDh7FarXz66acANGzYUEdQCiAvX9szZ85kxIgRWCwWatWqxYwZM3Id12Lc6oSRiIiISCHRaR0RERExFYUTERERMRWFExERETEVhRMRERExFYUTERERMRWFExERETEVhRMRERExFYUTERERMZX/B03gSoRhYaxbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df.drop(columns=['Exited'], axis=1)\n",
    "temp.corr()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "temp.corrwith(df['Exited']).plot(kind='barh', title=\"Correlation with 'Exited' column -\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092bc361",
   "metadata": {},
   "source": [
    "#### Report :\n",
    "\n",
    "1. Jika standar threshold korelasi adalah 0.2, Kolom `Age` memiliki kekuatan korelasi yang baik dengan `Exited` \n",
    "2. `Balace` dan `NumOfProduct` memiliki power korelasi negatif  \n",
    "2. Tidak ada korelasi antara variabel independen yaitu tidak ada multikolinearitas\n",
    "4. `isActiveMember` bisa diperhitungkan\n",
    "\n",
    "- Age has the strongest relation with Exited (0.35). Here we can make the following comment: As the age of the customer increases, the rate of losing the customer increases. (Positive strong relationship)\n",
    "- Exited and Balance variable have a relatively strong relationship (0.12).\n",
    "- Exited and the variable NumOfProducts have a moderately strong relationship (-0.11). They have a strong negative relationship."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb843bd1",
   "metadata": {},
   "source": [
    "### Continuous Variable Exploration with respect to Exit Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "9e758371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Exited', ylabel='Tenure'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x2160 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(25,30)})\n",
    "fig,ax = plt.subplots(3,2)\n",
    "sns.set_theme(palette=\"Set2\")\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.boxplot(data = df,x = \"Exited\",y = \"Age\",ax = ax[0,0])\n",
    "sns.boxplot(data = df,x = \"Exited\",y = \"CreditScore\",ax = ax[0,1])\n",
    "sns.boxplot(data = df,x = \"Exited\",y = \"Balance\",ax = ax[1,0])\n",
    "sns.boxplot(data = df,x = \"Exited\",y = \"EstimatedSalary\",ax = ax[1,1])\n",
    "sns.boxplot(data = df,x = \"Exited\",y = \"Tenure\",ax=ax[2,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc3e74d",
   "metadata": {},
   "source": [
    "#### Report :\n",
    "1. Kolom `Age` benar merupakan sesuatu yang signifikan untuk mengidentifikasi churn, terlihat bahwa customer muda lebih memiliki kecendrungan untuk keluar ketimbang dengan customer berumur/tua. Hal ini juga sesuai dengan plot korelasi tadi\n",
    "2. Sangat sulit melihat churn dari `credit score` karena memiliki behaviour yang sama, jadi kolom ini tidak bisa dijadikan faktor penentu\n",
    "3. `Balance` juga memperlihatkan behaviour yang sama kecuali customer dengan saldo tinggi sulit untuk churn\n",
    "4. `EtimateSalary` dan `Tenure` juga memperlihatkan behaviour yang sama , namun bisa dibilang bahwa customer dengan rata-rata tenure lebih mungkin untuk stay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3470adaa",
   "metadata": {},
   "source": [
    "### Variable exploration data distribution with respect to `Exit Status`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ea1f3c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = df.drop(columns=['Exited'], axis=1)\n",
    "\n",
    "plt.figure(figsize=(25,25))\n",
    "for i in range(0,len(temp.columns)):\n",
    "    plt.subplot(5,2,i+1)\n",
    "    sns.histplot(df, x = df[df.columns[i]], hue = 'Exited')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c52dd87",
   "metadata": {},
   "source": [
    "#### Report\n",
    "1. Customer dengan credit score rendah cenderung 'churn'\n",
    "2. Customer yang berasal dari `France` lebih tinggi namun di `German` lebih tinggi untuk kemungkinan keluar dari pemakaian bank\n",
    "3. `Female` lebih memikili kecendrungan churn ketimbang `male`\n",
    "4. Customer dengan rentang umur 40-60 memiliki tendency untuk exit\n",
    "5. Yang mendarik pada `Balance`. Customer yang memiliki `Balance` 0 malah jarang untuk exit\n",
    "6. customer yang sudah menggunakan lebih dari 2 product lebih sering untuk exit meskipun sangat sedikit yang menggunakannya\n",
    "7. Customer yang tidak aktif menjadi member tentu lebih tinggi kecendrungan untuk 'churn'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f31c340",
   "metadata": {},
   "source": [
    "### Focus on Variable who have power correlation with `Exited`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8691067",
   "metadata": {},
   "source": [
    "### `Age` distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c07427d6",
   "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>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Age Grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "      <td>Gen X (41-56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>Millennials (25-40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "      <td>Gen X (41-56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "      <td>Millennials (25-40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "      <td>Gen X (41-56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "      <td>Millennials (25-40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "      <td>Millennials (25-40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "      <td>Millennials (25-40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "      <td>Gen X (41-56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "      <td>Millennials (25-40)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619    France  Female   42       2       0.00              1   \n",
       "1             608     Spain  Female   41       1   83807.86              1   \n",
       "2             502    France  Female   42       8  159660.80              3   \n",
       "3             699    France  Female   39       1       0.00              2   \n",
       "4             850     Spain  Female   43       2  125510.82              1   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9995          771    France    Male   39       5       0.00              2   \n",
       "9996          516    France    Male   35      10   57369.61              1   \n",
       "9997          709    France  Female   36       7       0.00              1   \n",
       "9998          772   Germany    Male   42       3   75075.31              2   \n",
       "9999          792    France  Female   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  Exited          Age Grouped  \n",
       "0             1               1        101348.88       1        Gen X (41-56)  \n",
       "1             0               1        112542.58       0  Millennials (25-40)  \n",
       "2             1               0        113931.57       1        Gen X (41-56)  \n",
       "3             0               0         93826.63       0  Millennials (25-40)  \n",
       "4             1               1         79084.10       0        Gen X (41-56)  \n",
       "...         ...             ...              ...     ...                  ...  \n",
       "9995          1               0         96270.64       0  Millennials (25-40)  \n",
       "9996          1               1        101699.77       0  Millennials (25-40)  \n",
       "9997          0               1         42085.58       1  Millennials (25-40)  \n",
       "9998          1               0         92888.52       1        Gen X (41-56)  \n",
       "9999          1               0         38190.78       0  Millennials (25-40)  \n",
       "\n",
       "[10000 rows x 12 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age Grouped'] = pd.cut(x=df['Age'], \n",
    "                            bins=[9, 25, 41, 57, 67, 76], \n",
    "                            labels=['Gen Z (9-24)', 'Millennials (25-40)', 'Gen X (41-56)', 'Boomers II (57-66)', 'Boomers I (>67)'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "34d45225",
   "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>Age Grouped</th>\n",
       "      <th>Exited</th>\n",
       "      <th>user_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gen Z (9-24)</td>\n",
       "      <td>Retained</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gen Z (9-24)</td>\n",
       "      <td>Exited</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Millennials (25-40)</td>\n",
       "      <td>Retained</td>\n",
       "      <td>5465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Millennials (25-40)</td>\n",
       "      <td>Exited</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gen X (41-56)</td>\n",
       "      <td>Retained</td>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gen X (41-56)</td>\n",
       "      <td>Exited</td>\n",
       "      <td>1076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Boomers II (57-66)</td>\n",
       "      <td>Retained</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Boomers II (57-66)</td>\n",
       "      <td>Exited</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Boomers I (&gt;67)</td>\n",
       "      <td>Retained</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Boomers I (&gt;67)</td>\n",
       "      <td>Exited</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age Grouped    Exited  user_count\n",
       "0         Gen Z (9-24)  Retained         565\n",
       "1         Gen Z (9-24)    Exited          46\n",
       "2  Millennials (25-40)  Retained        5465\n",
       "3  Millennials (25-40)    Exited         709\n",
       "4        Gen X (41-56)  Retained        1484\n",
       "5        Gen X (41-56)    Exited        1076\n",
       "6   Boomers II (57-66)  Retained         280\n",
       "7   Boomers II (57-66)    Exited         183\n",
       "8      Boomers I (>67)  Retained         136\n",
       "9      Boomers I (>67)    Exited          22"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_age = df.groupby(['Age Grouped','Exited']).agg({'Gender' : 'count'}).reset_index()\n",
    "df_age.columns = ['Age Grouped','Exited','user_count']\n",
    "df_age['Exited'] = df_age['Exited'].apply(lambda x : 'Exited' if x == 1 else 'Retained')\n",
    "df_age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b7849a",
   "metadata": {},
   "source": [
    "https://www.beresfordresearch.com/age-range-by-generation/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ce4e1647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total User')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(data = df_age, x = 'Age Grouped', y = 'user_count', hue = 'Exited', palette = 'YlOrBr_r')\n",
    "plt.ylabel('Total User')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c0bfb4",
   "metadata": {},
   "source": [
    "### `Geography` distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7abf8c3e",
   "metadata": {
    "scrolled": true
   },
   "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>Geography</th>\n",
       "      <th>Exited</th>\n",
       "      <th>user_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>4204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "      <td>1695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>1</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>2064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Geography  Exited  user_count\n",
       "0    France       0        4204\n",
       "1    France       1         810\n",
       "2   Germany       0        1695\n",
       "3   Germany       1         814\n",
       "4     Spain       0        2064\n",
       "5     Spain       1         413"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_geo = df.groupby(['Geography','Exited']).agg({'Gender':'count'}).reset_index()\n",
    "df_geo.columns = ['Geography','Exited','user_count']\n",
    "\n",
    "df_geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c9cedc41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.barplot(data=df_geo, x = 'Geography',y='user_count', hue='Exited',palette = 'YlGnBu_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f492534",
   "metadata": {},
   "source": [
    "### `Membership` status distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "77d2bdf5",
   "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>Membership</th>\n",
       "      <th>Exited</th>\n",
       "      <th>user_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Membership  Exited  user_count\n",
       "0           0       0        3547\n",
       "1           0       1        1302\n",
       "2           1       0        4416\n",
       "3           1       1         735"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_member = df.groupby(['IsActiveMember', 'Exited']).agg({'Gender':'count'}).reset_index()\n",
    "df_member.columns = ['Membership','Exited','user_count']\n",
    "df_member"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2746d67d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "sns.barplot(data=df_member, x = 'Membership',y='user_count', hue='Exited',palette = 'YlOrBr_r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1e54be1",
   "metadata": {},
   "source": [
    "### `Product` uses distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "be894774",
   "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>NumOfProducts</th>\n",
       "      <th>Exited</th>\n",
       "      <th>user_count</th>\n",
       "      <th>total_user</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Retained</td>\n",
       "      <td>3675</td>\n",
       "      <td>5084</td>\n",
       "      <td>72.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Exited</td>\n",
       "      <td>1409</td>\n",
       "      <td>5084</td>\n",
       "      <td>27.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Retained</td>\n",
       "      <td>4242</td>\n",
       "      <td>4590</td>\n",
       "      <td>92.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Exited</td>\n",
       "      <td>348</td>\n",
       "      <td>4590</td>\n",
       "      <td>7.58%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Retained</td>\n",
       "      <td>46</td>\n",
       "      <td>266</td>\n",
       "      <td>17.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Exited</td>\n",
       "      <td>220</td>\n",
       "      <td>266</td>\n",
       "      <td>82.71%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>Exited</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>100.0%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NumOfProducts    Exited  user_count  total_user Percentage\n",
       "0              1  Retained        3675        5084     72.29%\n",
       "1              1    Exited        1409        5084     27.71%\n",
       "2              2  Retained        4242        4590     92.42%\n",
       "3              2    Exited         348        4590      7.58%\n",
       "4              3  Retained          46         266     17.29%\n",
       "5              3    Exited         220         266     82.71%\n",
       "6              4    Exited          60          60     100.0%"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_product = df.groupby(['NumOfProducts','Exited']).agg({'Gender' : 'count'}).reset_index()\n",
    "\n",
    "df_product.columns = ['NumOfProducts','Exited','user_count']\n",
    "\n",
    "df_product['total_user'] = df_product.groupby(['NumOfProducts'])['user_count'].transform('sum')\n",
    "\n",
    "df_product['Percentage'] = round(df_product['user_count']/df_product['total_user']*100,2)\n",
    "df_product['Percentage'] = df_product['Percentage'].astype(str)\n",
    "df_product['Percentage'] = df_product['Percentage']+'%'\n",
    "\n",
    "df_product['Exited'] = df_product['Exited'].apply(lambda x : 'Exited' if x == 1 else 'Retained')\n",
    "df_product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fe7ecde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3.07, 160, '100.0%')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(data= df_product, x = 'NumOfProducts', y ='user_count', hue='Exited', palette='Purples')\n",
    "\n",
    "plt.title('Product Types and the Churn Rate', loc = 'center', fontweight = 'bold', fontsize=20)\n",
    "\n",
    "plt.xlabel('Number of Products', fontsize = 16, fontdict = {'weight' :'bold'})\n",
    "plt.ylabel('User Count', fontsize=16, fontdict = {'weight' :'bold'})\n",
    "\n",
    "plt.legend(loc = 'center right')\n",
    "\n",
    "plt.text(x = -0.32, y = df_product['user_count'][0] - 150, s = df_product['Percentage'][0], fontsize = 12)\n",
    "plt.text(x = 0.07, y = df_product['user_count'][1] - 150, s = df_product['Percentage'][1], fontsize = 12)\n",
    "plt.text(x = 0.68, y = df_product['user_count'][2] - 150, s = df_product['Percentage'][2], fontsize = 12)\n",
    "plt.text(x = 1.1, y = df_product['user_count'][3] + 100, s = df_product['Percentage'][3], fontsize = 12)\n",
    "plt.text(x = 1.68, y = df_product['user_count'][4] + 100, s = df_product['Percentage'][4], fontsize = 12)\n",
    "plt.text(x = 2.07, y = df_product['user_count'][5] + 100, s = df_product['Percentage'][5], fontsize = 12)\n",
    "plt.text(x = 3.07, y = df_product['user_count'][6] + 100, s = df_product['Percentage'][6], fontsize = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95582a82",
   "metadata": {},
   "source": [
    "### `Balace` distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "06001522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 765.725x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col = \"Exited\", row = \"Gender\", height = 5, hue='Exited', sharey=False, sharex = False)\n",
    "g.map(plt.hist, \"Balance\",bins =10)\n",
    "g.add_legend()\n",
    "g.set(xlabel=\"Balance\")\n",
    "plt.subplots_adjust(hspace=.5, wspace=.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "845dbccc",
   "metadata": {},
   "source": [
    "## Business Recomendation\n",
    "\n",
    "1. Mengingat geografi, pelanggan di `Germany` dan `France` memiliki kecenderungan untuk churn. Bank perlu fokus pada peningkatan fasilitas di sana, misalnya : apakah jumlah cabang bank di sana sudah cukup? apakah ada cukup mesin ATM di sana? apakah ada iklan atau promosi untuk pengguna baru dan pengguna tetap?, Bagaimana regulasi bank di sana?, dsb\n",
    "\n",
    "2. Kita memerlukan campaign yang meraik untuk Millenials, Gen X dan para orang tua Boomers I. Boomers mungkin membutuhkan produk atau promosi yang lebih baik, seperti rencana pensiun yang lebih baik, sedangkan para Millenials dan Gen X bisa diberikan promosi bunga yang lebih tinggi atau memaksimalkan penggunaan teknologi automasi dan fitur baru pada aplikasi mobile banking. \n",
    "\n",
    "3. Promosi untuk bergabung menjadi membership sangat kami rekomendasikan karena ini dapat menurunkan tingkat churn rate. Kita bisa melakukan peningkatan benefit dan kemudahan yang lebih besar kepada customer yang menjadi member namun tetap mempertahankan esensi kemudahan transaksi\n",
    "\n",
    "4. Bank perlu berhenti menggunakan produk nomor 3 dan 4, karena kemungkinan yang terpilih lebih kecil dan churn rate yang tinggi. Oleh karena itu, bank dapat fokus untuk mempromosikan produk nomor 1 dan 2. Untuk produk nomor 1, perusahaan dapat meninjau kepuasan dan rate dari customer, karena churn rate mencapai hingga 27%.\n",
    "\n",
    "5. Bisa dikatakan customer yang sudah menyimpan banyak uangnya di bank malah cenderung exit the bank baik wanita maupun pria, bisa jadi ada masalah tentang kemudahan bertransaksi?."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b31df9b",
   "metadata": {},
   "source": [
    "# Build Model for Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b55730b",
   "metadata": {},
   "source": [
    "#### feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6f343b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "build_df = pd.get_dummies(df, columns = ['Geography', 'Age Grouped'], drop_first=True)\n",
    "build_df['Gender'] = build_df['Gender'].apply(lambda x: 0 if x == 'Male' else 1)\n",
    "build_df.drop(columns=['EstimatedSalary', 'HasCrCard', 'Tenure', 'CreditScore', 'NumOfProducts', 'Age'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "fa50c4e4",
   "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>Gender</th>\n",
       "      <th>Balance</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Age Grouped_Millennials (25-40)</th>\n",
       "      <th>Age Grouped_Gen X (41-56)</th>\n",
       "      <th>Age Grouped_Boomers II (57-66)</th>\n",
       "      <th>Age Grouped_Boomers I (&gt;67)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>159660.80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender    Balance  IsActiveMember  Exited  Geography_Germany  \\\n",
       "0       1       0.00               1       1                  0   \n",
       "1       1   83807.86               1       0                  0   \n",
       "2       1  159660.80               0       1                  0   \n",
       "3       1       0.00               0       0                  0   \n",
       "4       1  125510.82               1       0                  0   \n",
       "\n",
       "   Geography_Spain  Age Grouped_Millennials (25-40)  \\\n",
       "0                0                                0   \n",
       "1                1                                1   \n",
       "2                0                                0   \n",
       "3                0                                1   \n",
       "4                1                                0   \n",
       "\n",
       "   Age Grouped_Gen X (41-56)  Age Grouped_Boomers II (57-66)  \\\n",
       "0                          1                               0   \n",
       "1                          0                               0   \n",
       "2                          1                               0   \n",
       "3                          0                               0   \n",
       "4                          1                               0   \n",
       "\n",
       "   Age Grouped_Boomers I (>67)  \n",
       "0                            0  \n",
       "1                            0  \n",
       "2                            0  \n",
       "3                            0  \n",
       "4                            0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "9392146f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                           Non-Null Count  Dtype  \n",
      "---  ------                           --------------  -----  \n",
      " 0   Gender                           10000 non-null  int64  \n",
      " 1   Balance                          10000 non-null  float64\n",
      " 2   IsActiveMember                   10000 non-null  int64  \n",
      " 3   Exited                           10000 non-null  int64  \n",
      " 4   Geography_Germany                10000 non-null  uint8  \n",
      " 5   Geography_Spain                  10000 non-null  uint8  \n",
      " 6   Age Grouped_Millennials (25-40)  10000 non-null  uint8  \n",
      " 7   Age Grouped_Gen X (41-56)        10000 non-null  uint8  \n",
      " 8   Age Grouped_Boomers II (57-66)   10000 non-null  uint8  \n",
      " 9   Age Grouped_Boomers I (>67)      10000 non-null  uint8  \n",
      "dtypes: float64(1), int64(3), uint8(6)\n",
      "memory usage: 371.2 KB\n"
     ]
    }
   ],
   "source": [
    "build_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9dc158f",
   "metadata": {},
   "source": [
    "#### Suffle, Splitting, Scaling dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3ffb9816",
   "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>Gender</th>\n",
       "      <th>Balance</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>Exited</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "      <th>Age Grouped_Millennials (25-40)</th>\n",
       "      <th>Age Grouped_Gen X (41-56)</th>\n",
       "      <th>Age Grouped_Boomers II (57-66)</th>\n",
       "      <th>Age Grouped_Boomers I (&gt;67)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6252</th>\n",
       "      <td>0</td>\n",
       "      <td>96709.07</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4684</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</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",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4742</th>\n",
       "      <td>0</td>\n",
       "      <td>119152.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4521</th>\n",
       "      <td>1</td>\n",
       "      <td>124995.98</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender    Balance  IsActiveMember  Exited  Geography_Germany  \\\n",
       "6252       0   96709.07               0       0                  1   \n",
       "4684       0       0.00               1       0                  0   \n",
       "1731       1       0.00               0       0                  0   \n",
       "4742       0  119152.10               1       0                  1   \n",
       "4521       1  124995.98               1       0                  0   \n",
       "\n",
       "      Geography_Spain  Age Grouped_Millennials (25-40)  \\\n",
       "6252                0                                1   \n",
       "4684                0                                0   \n",
       "1731                1                                0   \n",
       "4742                0                                0   \n",
       "4521                1                                1   \n",
       "\n",
       "      Age Grouped_Gen X (41-56)  Age Grouped_Boomers II (57-66)  \\\n",
       "6252                          0                               0   \n",
       "4684                          1                               0   \n",
       "1731                          1                               0   \n",
       "4742                          0                               1   \n",
       "4521                          0                               0   \n",
       "\n",
       "      Age Grouped_Boomers I (>67)  \n",
       "6252                            0  \n",
       "4684                            0  \n",
       "1731                            0  \n",
       "4742                            0  \n",
       "4521                            0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#resuffle dataset\n",
    "build_df = resample(build_df, replace=False, n_samples=None, random_state=42)\n",
    "build_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "16829b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split dataset\n",
    "X = build_df.drop(columns=['Exited'], axis=1)\n",
    "y = build_df['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bc742424",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c5bacf30",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling dataset\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fd193c",
   "metadata": {},
   "source": [
    "### Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5a6257b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 10, 5\n",
    "rcParams['lines.linewidth'] = 3\n",
    "rcParams['xtick.labelsize'] = 'x-large'\n",
    "rcParams['ytick.labelsize'] = 'x-large'\n",
    "def evaluate_model(classifier):\n",
    "    cf_matrix = confusion_matrix(y_test, classifier.predict(X_test))\n",
    "    sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
    "                fmt='.2%', cmap='Blues')\n",
    "\n",
    "    print(classification_report(y_test, classifier.predict(X_test),zero_division=0))\n",
    "\n",
    "def score_model(classifier):\n",
    "    print(f\"Test accurarcy {classifier.score(X_test,y_test)}\")\n",
    "    val = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)\n",
    "    print(f\"cross validation Mean : {val.mean()} and STD of {val.std()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3e73f3",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ef9cc7c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accurarcy 0.791\n",
      "cross validation Mean : 0.797625 and STD of 0.000374999999999992\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      1.00      0.88      1582\n",
      "           1       0.00      0.00      0.00       418\n",
      "\n",
      "    accuracy                           0.79      2000\n",
      "   macro avg       0.40      0.50      0.44      2000\n",
      "weighted avg       0.63      0.79      0.70      2000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2LZYvX46oqCh4eXmhR48emDBhgm6dOXPm4PTp04iLiwMAVKlSBT/++COWLl2KBQsWIC8vDzVq1MCECRN0D9IlIiIq7R7mZJkMa7Obd+dz1MoYs4Ht0KFDmDx5MurXrw+tVos7d+5g2bJlaNmyZYF2EBISojuaZszmzZsN2qpXr45ly5YVaPtERESlRZpCjkOK+/jrwj7EP3f609PBGRpBi1EN2zKslUFmA9uKFSvw3Xff6e7MvHLlCmbOnInt27dbvDgiIqKy5Lsbp3FdkwVk6bdXcHLDglY9bVMU2QWzgU0kEuk9RqNRo0YQBHMzlBEREVFB3MxMwWcX90EEEQQTM4DOaRFu5arI3pgMbE/n7WzcuDG++eYbDBo0CGKxGNu3b0dQUJC16iMiIip1rqTfw0+3zkEQBDzIfXI47fmwNqJBG/i5l+PpTwKQT2ALCgqCSCTSHU17+uBc4MlRt8LOOEBERFQWaQQttIKAc4+TEJt+HyceJZjtE1q5Llr61LJ8cVRimAxs165ds2YdREREpYYgCLiV/Rhbbp5Bkjy9QH1qupfH5Cav49zZs2hRv4WFK6SSxuw1bEqlEocPH9ZNMaXRaHDnzh2MHz/e4sURERGVRH/di8NPt8xPvN60gh+612gMDwdnlHNyBWB6rm0q28wGtvHjxyMpKQkpKSl46aWXcOHCBbRq1coatREREZVI+YW1dpXroY5nRbxUrgq8HF2sWBWVZGYD29WrV/Hnn39i9uzZGDZsGARBwJw5c6xRGxERUYnzQ/wZvdceDk74oPGrqO5ezkYVUWlgdi5RHx8fSKVS1KpVC9evX0e9evWQm5trjdqIiIhKlG+uHceBe9f12j5p1YthjV6Y2cDm6uqKXbt2ISAgAL///jvi4uJ0j/wgIiIiQKXV4NzjJJxOua3X3q92IBzEEtsURaWK2cA2c+ZMXL16FSEhIRCLxYiMjMTw4cOtURsREZHdS8h+jMmnduDLq0f12t+s2xKd/BraqCoqbcxew1a7dm1MmjQJALB8+XJL10NERFQiPMzNwrnHydhx+7zBMj83b7SvWt+wE1ERmQxsERER+XbctWtXsRdDRERUEmy7dRb77hp/XmlD78qIatzByhVRaWcysM2cOdOadRAREZUIuWqVybC2LLgfXKWOVq6IygKTgY3PWiMiItKXqczFpFM79Np8XDwQ7FMbXaq/BLHI7KXhREVi9ho2IiKiskYrCLgrz4Ba0CBTmYfdiZcAwOg0U/Na5H8JEVFxYGAjIiJ6hlbQ4tPzf+K2LM3surOadbNCRUSFCGxZWVnw9PS0ZC1EREQ29WfyVfyccK5A665qMwCOEh73IOsw+z/t1q1bGDduHLKysrBt2zYMHToUn3/+OerWrWuN+oiIiCzmcZ4Myy4dgEQkwsPcbKPr1HIvDwGAq9QRPWu9AjepE3xcPKxbKJV5ZgPb/PnzMW3aNHz22Wfw9fVFZGQkZs2ahejoaGvUR0REZBFqrQbT//nV5HIxRBjZMBTNKla3YlVExpm9nSUjIwMhISG612+99RZkMplFiyIiIrK0xHyuUetR8xV80fYNhjWyGwU6+a5QKCASiQAAKSkp0Gq1Fi2KiIjIkmSqPCy6sE+v7aNm3SEWieDt5AJniYONKiMyzmxge+ONN/DOO+8gNTUVS5YswW+//YYRI0ZYozYiIiKLmB3zm97r8k6uqOrmZaNqiMwzG9j69++PWrVq4dChQ1Cr1Zg7dy5CQ0OtURsREVGxy1Erka1S6LX9X+NXbVQNUcGYDWxLlizBgAEDMHHiRGvUQ0REZDGCIGD8iW16bVObdoavKx9bRfatQHNoREZGYsiQIdi9ezeUSqWlayIiIip2SbJ0vPf3FoP2am7e1i+GqJDMBrYPP/wQBw8exDvvvIN9+/ahU6dOWLBggTVqIyIieiEqrQaH79/A9H9+wfxzvxssn98iAg5iiQ0qIyqcAt0lKhaL0bhxYyQkJCAhIQFnzpyxdF1EREQvJD4rxeBO0GfNatYNlfgAXCohzAa2P//8E9u3b8f58+fRpUsXfPLJJ2jUqJE1aiMiIiqS65mPsOTifqPLfF08MKtZN0h5ZI1KELOBbf369RgwYACWLVsGFxcXa9RERERUZGqtxmhY612rCZpWqI7KvMGASiCTgU0mk8Hd3R1ffvklgCcPz1Uo/rsN2tvb2+LFERERFZRMlYcPT243aHcSS7G8TX+I/30APFFJZDKwDR48GDt27EBQUBBEIhEEQdAtE4lEuHr1qlUKJCIiKghjYQ0AlrXpx7BGJZ7JwLZjxw4AQGxsLMRi/ZtJMzIyLFoUERFRYSRmG58XdKh/ECSiAj3Bisiumf1f3LdvX4O2N9980yLFEBERFZZGq8WC83v12la06Y+v2r6JYN86NqqKqHiZPML29ttv49KlS8jLy0OzZs107VqtFg0bNrRKcURERPkRBAHvH9uq11bDvRwnb6dSx2RgW716NTIyMjBt2jR88skn/3WQSlGpUiWrFEdERJSfr68dM2ib2rSLDSohsqx8T4n6+flh5cqVcHNz0/1xcnJCVlZWgXdw9uxZREZGIjAwEMHBwZg9ezZkMlm+fTQaDb788ku8/vrrePnll9GxY0esXr0aGo2mwPslIqLSLfrGacQ8vqPXNrd5OG8woFLJoneJxsbGYtiwYWjdujWWL1+OpKQkLF26FImJidiwYYPJfjNmzMDu3bsxbtw4vPzyyzhz5gxWr14NmUyGyZMnF/ItEhFRaaMVBBx5cFOvbXD9VpzEnUots3eJXrt2rcgbX7FiBXx9fbFmzRpIpU92VblyZYwZMwbHjx9HmzZtDPpcuHAB27dvx6effopevXoBAIKDg5GdnY1jx45Bq9Ua3LVKRERlh0KjRtTxH/XaBtVtjtDK9WxUEZHlmU0+jx8/xl9//QUAWLBgAYYMGVKgEKdUKnHixAl07NhRF9YAICwsDK6urjhw4IDRfnv27IG3tzd69Oih1z5t2jT8+uuvDGtERGXc6Ue3Ddo6VG1g/UKIrMhs+pkyZQqSkpJw4sQJnDx5Er169cL8+fPNbjgpKQkKhQK1a9fWa5dKpfDz80N8fLzRfrGxsahfvz6OHDmCPn36oFGjRmjbti1Wr14NrVZbwLdFRESljVYQEJt+HxfSkvXaF7bqZZuCiKzIbGDLyMjA0KFDceTIEYSHh6NPnz7Izc01u+Hs7GwAgIeHh8EyNzc3kzcepKam4vbt25g6dSr69u2Lb775Bt26dcOqVauwYMECs/slIqLSadaZXVhx+SAupd3TtXWv3hjlnFxtWBWRdZid/F2lUkGlUuHo0aNYuHAhcnNzkZOTY3bD5o6GiUzcxaNSqZCSkoJvvvkGoaGhAICgoCDIZDJ8//33GDVqVKEeK3L58uUCr1tUMTExFt8HFR7Hxf5wTOxTSRiXRLUMKUrDX/QVD9MQk2r/9RdWSRiTssiW42I2sL322msIDg5Gw4YN0bhxY4SHhyM8PNzshj09n9ypI5fLDZbJ5XL4+PgY7efu7g4XFxeEhITotYeFhWHbtm24du1aoQJb48aN4eTkVOD1CysmJgbNmze32PapaDgu9odjYp/seVwUGjXOpSYhQ5GLP27H6S1r7VMLdT0rIbRy3VI39ZQ9j0lZZulxUSgU+R5kMhvYoqKiMGDAAFSuXBkAsHjxYgQEBJjdcY0aNSCVSpGYmKjXrlarkZycbPQOUQCoXbs2bty4AbVaDQeH/55UrVKpAAAuLi5m901ERCVbXMZDLL30l9FlbXzr4G3/ICtXRGRbZn8t0Wq12LVrFwYPHow33ngD+/fvh1qtNrthR0dHBAUFYd++fXrrHzp0CDk5OWjXrp3RfmFhYVCpVNizZ49e+19//QU3Nzc0atTI7L6JiKhk+znhnMllQ+q3tmIlRPbB7BG2JUuW4Nq1a3j77beh1Wrxww8/YNGiRZg2bZrZjY8dOxaRkZEYOXIkhgwZgnv37mHJkiUIDg7WnfK8efMmZDIZmjZtCgDo3r07tm7ditmzZyMtLQ0NGjTA/v37sXv3bkydOpVH2IiISjFBEHAt4yESZWl67WFV/FHZ1QMhvnVNXgNNVJqZDWxHjx7Fzz//rDs9GRYWhh49ehQosAUGBmLt2rVYvnw5oqKi4OXlhR49emDChAm6debMmYPTp08jLu7J9QkSiQTr1q3DqlWrsHHjRqSmpqJWrVqYP38++vfvX9T3SUREJcCltHtYHXtYr+2Tlj1R3tnNRhUR2QezgU0QBL1ryRwdHfVemxMSEmJwA8GzNm/ebNDm6uqKyZMncxoqIqIy5PzjJHxx9ahem6eDM7wceWaFyGxgCwgIwIIFCxAZGQkAiI6Ohr+/v8ULIyKisiEtT441sUeQJE/Xay/n5Ir/a9wBEs5wQ2T+poOPPvoImZmZGDRoEAYOHIi0tDTMnDnTGrUREVEppxG0mPrPLwZhDQDmNg9HZVcvG1RFZH/yPcKm1WqhVqvx6aefWqseIiIqA/YmXcGltHu4mZVisCy0cl1E1mvFmwuInmEysJ0/fx5jx45Famoq6tevj88//xw1atSwZm1ERFQK/Rgfg7/uxRldNrNZV/i5lbNyRUT2z+Qp0UWLFmHevHk4d+4c+vTpg8WLF1uzLiIiKmViUu5g1NHvTYa1uS3CGdaITDB5hC0nJwcdOnQAAAwdOhTbtm2zWlFERFR6qLUa7LpzCXuTYg2WdawWgFfKV0NNj/JwlhT8CQREZY3JwCZ+7q6cwjzKg4iIyjatoMWDnGzsu3sVxx/eMrpO+yr10b9OMytXRlQymQxsgiBYsw4iIiolNsSdwKlHtyHA+M+Raq7emBbYGVKxxMqVEZVcJgNbUlIS3nvvPZOvv/zyS8tWRkREJUqeWoWPYnYjQ5lrcp3Ieq3Qtko9K1ZFVDqYDGzTp0/Xe925c2eLF0NERCXTzcxH+OzifqPLKjq74+36rVHfy4eP6iAqIpOBrX379ihfvny+nVNTU1GhQoViL4qIiEoOtVaDL2KPGrS/Va8l2lWpb4OKiEofk4/1mDZtGjZs2IDMzEyDZTKZDOvWrcOUKVMsWhwREdm/HLUKMrVCr21O8+4Ma0TFyOQRtjVr1mD9+vUIDw9H7dq1UbNmTWg0GiQlJSEhIQFDhgzBmjVrrFkrERHZoedvLlgW3A+uUkcbVUNUOuX7WI8RI0YgMjISJ0+exK1btyAWi/H666+jTZs2cHTkFyMRUVml1mpw9nES7udkYk/SFV27m9SRYY3IAvKdSxQAnJ2dERYWhrCwMCuUQ0RE9k4raDHm2A9Gl8nVSitXQ1Q2mA1sRERET516lID1cSdMLp/S9HUrVkNUdjCwERFRgWQqc42GtU7VGsJV6oCXy1dDdXfOBUpkCQxsRERkVLoiB9cyHiBXrcIPt2KMrrOwVS+Uc3K1cmVEZY/ZwCaXy7F48WLcunULK1aswNKlSzF58mS4ublZoz4iIrIirSDgfk4mfrp1FlczHuS77hehgyAWmXw6FBEVI7NfafPnz4enpydSU1Ph5OQEmUyGWbNmWaM2IiKyIq0g4JPzf2Du2T1mw9rcFuEMa0RWZPYI29WrV/HJJ5/g8OHDcHFxweLFixEeHm6N2oiIyIq23PwHd2RpBu0OYglaVKwBHxcPtKtSH25SR04xRWRlZgObWKz/G5RGozFoIyKikis1T47HeTIceXBTr72KqxfeqNsCDbx9bVQZET1lNrC1bNkSn332GfLy8nD06FFER0ejVatW1qiNiIgs6Mj9G4jOiQP+iTNYNiIgBC0r1bRBVURkjNlDZRMmTICrqys8PDywbNkyNGjQgHOIEhGVYGqtBqOOfo/om/8YXe4kkTKsEdkZs0fYDh8+jDFjxmDMmDG6tp07d6JXr16WrIuIiCzE1CwFVV294OPigfAaL1u5IiIyx2RgO3DgANRqNRYtWgRBECAITyb3VavVWLZsGQMbEVEJI1MpsOn6SYP2zn4voWO1AHg6OtugKiIqCJOB7erVqzh58iRSU1OxadOm/zpIpXjnnXesUhwRERWPddeO4Z+URIP2N53roH3tptYviIgKxWRge3oaNDo6Gm+99ZY1ayIiomK0/+41o2FtRIM2ECel2qAiIioss9ew9e/fH/v27YNcLgfw5LEed+7cwfjx4y1eHBERFV62Mg+5GhUA4Gr6A/x066zecmeJFG0r10NLn1qIYWAjKhHMBrbx48cjKSkJKSkpeOmll3DhwgU+1oOIyI5oBQFnH99BQnYq9t+9lu+64TUaI6LmK1aqjIiKS4FmOvjzzz8xe/ZsDBs2DIIgYM6cOdaojYiICuDnhHNmgxoAVHcrx7BGVEKZDWw+Pj6QSqWoVasWrl+/jq5duyI3N9catRERkRFaQYt7OZl4lJuNP5OvIiHb+GnNis5uEEEEjaCFv5cvhjUItnKlRFRczAY2V1dX7Nq1CwEBAfjxxx9Rp04dZGRkWKE0IiICAI2gxcmHCbidnQqVVoMTjxJMrlvHoyJa+dRCs4rV4eXoYsUqiciSzM50MGvWLFy9ehUhISEQi8WIjIzkYz2IiKzo7OMkbLpxCkce3Mw3rFVx9cLEJh3Roao/wxpRKWP2CFutWrUwadIkAMDy5csBADdv3synBxERFZVaq8HnVw4jQ5EDsejJ79R3czJMrl/V1QvlnFzRo+YrqO5eTteHiEoXk4EtOTkZy5Ytg7e3NyZMmAAXFxfIZDKsWrUK0dHRuHz5sjXrJCIq9VRaDcaamDbqqXJOruhavREqOLnhpXJVIBaJrFQdEdmSycA2bdo0+Pv749GjR/jqq68QGhqK8ePHw9PTE+vWrbNmjUREpV6eRoVllw6YXW9+iwhIxRIrVERE9sRkYHvw4AE2bdqEvLw89OnTBz/++COGDh2K4cOHQyo1eyZV5+zZs1i6dCmuXLkCZ2dndO7cGRMmTIC7u3uB+qelpSEiIgJt27bFwoULC7xfIiJ7lJIrw8orB6EVtHrtj/PkBuu+/1I7VHB2AwBIRGJUdvGEiEfUiMokk8nL1dUVAODs7IzMzEwsWrQIoaGhhdp4bGwshg0bhtatW2P58uVISkrC0qVLkZiYiA0bNhRoG9OnT8fjx48LtV8iInujEbSQqRSYcebXAq3fp3ZTNKngZ+GqiKikKNChsgoVKhQ6rAHAihUr4OvrizVr1uiOylWuXBljxozB8ePH0aZNm3z7//DDDzh37hycnJwKvW8iInugFQQcuncdP9yKKXCf919qh1fKV7NgVURU0pgMbM8edpdICn+9hFKpxIkTJxAZGal3CjUsLAyurq44cOBAvoHt9u3bWLhwIRYtWoSpU6cWev9ERLYmCAJ+T7qCXxMvGl0+v0UEgP++10rFYpRzcrVSdURUkpgMbHFxcWjWrBkAIC8vT/dvQRAgEolw9uxZU10BAElJSVAoFKhdu7b+DqVS+Pn5IT4+3mRftVqNiRMn4vXXX0enTp0Y2IioRPojOdZoWHOROOCDxh1QycXDBlURUUlkMrDt27fvhTacnZ0NAPDwMPyG5ObmBplMZrLv6tWrkZKSgvXr179QDQCs8viRmJiCn+og6+G42J+yNiYn8u7ova4j8cBrjlUgEomQdiMRaUi0UWX6ytq4lAQcE/tky3ExGdiqVXux6ye0Wm2+y03d6XT+/Hl8/fXX+Prrr42GvcJq3LixRa+Bi4mJQfPmzS22fSoajov9Ke1jIlMpEJt+H1pBAADsv3sND7T/zbv8UrkqGN2wLRwlBb/L3hpK+7iURBwT+2TpcVEoFPkeZLLYdw5PT08AgFxueKu6XC6Hj4+P0faJEydiwIABaNmyJdRqtW6ZIAhQq9WQSCS8rZ2I7EqWMhcTT+3Id53etZrYXVgjopLDYt89atSoAalUisRE/UP+arUaycnJRm84uHz5Mu7cuYPo6GhER0frLdu5cyd27tyJTZs2oXXr1pYqm4goXw9ysnDsYTzy1Cpd25EH+U/X16SCH6q7lbN0aURUilkssDk6OiIoKAj79u1DVFSU7k7RQ4cOIScnB+3atTPo06hRI2zbts2gfciQIWjdujXGjBljcBMDEZE1KDVqjDv+Y4HWbe1TCwDgIJagbeV6qOVRwYKVEVFZYDawpaSkYPr06UhMTER0dDQmT56MTz75xOgpzeeNHTsWkZGRGDlyJIYMGYJ79+5hyZIlCA4ORkhICIAnE8nLZDI0bdoU7u7uePnllw22I5FI4O3tbXQZEZGlyVVK/O+k4S+TxiwP7g8XqYOFKyKissZsYJszZw46duyI7777Dl5eXggICMCMGTOwdu1asxsPDAzE2rVrsXz5ckRFRcHLyws9evTAhAkT9LZ/+vRpxMXFvdg7ISIqRjczH+Hvh7eQmJ2KezmZBssrOrvh9Wov6V5LxGI0KV+NYY2ILMJsYLt79y4GDBiA77//Hg4ODpg4cSIiIiIKvIOQkBDd0TRjNm/ebHYbZ86cKfD+iIiKSiNocTntHg7ci8O1jIcm1/u0VS948wG3RGRFZgObSCTSe0SHTCYz+8gOIqKS4q48A5nKXPySeBG3s1PzXddBLMEw/2CGNSKyOrOB7fXXX8eECROQnZ2NrVu34qeffkLXrl2tURsRUbHLVaug0j55ZNBnF/bhUZ7ph3gDQFVXL3T2ewm1PMqjsquXNUokIjJgNrC999572LlzJ7RaLY4fP46BAweif//+1qiNiKhY/Zp4Eb/fuQItBLPrtqtcDy19asHfy/wNVkRElmY2sG3duhXh4eHo1auXFcohIip+eWoV5p7dg1SF4YO8n/L38oGjWIqO1QLQwNsXYj6gm4jsiNnAdvr0aSxfvhwdOnTAwIED0bRpUyuURUT04nLUSuxLvoo9SVcMlrlJHSEWiZGnUWGYfzCaV6phgwqJiArGbGBbunQpMjMzsXv3bsyfPx8KhQL9+vXD22+/bY36iIiK5MTDW9h4/aTRZa/7NUTf2oFWroiIqOgKNNOBl5cXBg4cCB8fH93E7AxsRGQvZKo8xKY/gBYCErJScej+dZPrftSsO6q68eYBIipZzAa2K1euYPv27di7dy9eeukljBgxAq+++qo1aiMiypcgCLgjS8eC83vNrhtZrxXaVqlnhaqIiIqf2cA2ZswY9O3bFz/99BOqVq1qjZqIiMwSBAGLL+7HzayUfNdrVrE63g0IhYg3ERBRCWY2sB06dMgKZRARFZxcpcDPCeeNhrVWlWoBAAK8fdGyUk04Sgp05QcRkV0z+Z3sjTfewJYtWxAYGKj3m6kgCBCJRDh79qxVCiQiela6IgdTTu80aG9crgr61A5ENTdvq9dERGRpJgPbihUrAAC7d+82WCYI5h86SURUnJLl6VhycT9y1CqDZc0r1sC7DUNtUBURkXWITS3w8XnydO+PPvoI1apV0/vzv//9z2oFEhHFZTzEvLO/Gw1rkfVa4Z0GbWxQFRGR9Zg8whYVFYWEhAQkJSUhIiJC165WqyEWm8x5RERFptJqsPD8H1Bo1Hj2FgFj833W9qiAfrWboZ5XJesVSERkIyYD26RJk3D37l3MnDkTM2fO1LVLJBLUr1/fKsURUdmh0Wox9tgPBVr301a94O3kauGKiIjsh8nA5ufnBz8/P/zxxx+8HZ6ILOpCajLWxB4xu56ngzM+C+pjhYqIiOyL2btEmzVrxrtEiciijIW1SU06wd3BSffaUSxFOR5VI6Iyqkh3iRIRFSdHsQRKrUb3eliDYNT15LVpRERPmb1LtHz58khJSUG1atWwf/9+fP755zxFSkTFRqFR64W1FW36I8intg0rIiKyP2Zv95w6dSr++usvXLx4EZs2bULVqlX1bkIgInoRUcd/1HstFfEudCKi55mdsyUpKQnLly/HihUr0Lt3b4wdOxZ9+/a1Rm1EVMpoBC02xp1AqiIHIsDo1FJSscT6hRER2TmzgU2levKgyr///huTJ0+GRqNBTk6OxQsjotJn4sntkKuVJpd/2qqX9YohIipBzAa2Zs2aoVu3bpBIJGjWrBnefvtttGnDp4oTUcEdvHcdP+Vchwamp7Ub5h/MZ6sREZlgNrDNnDkT586dQ0BAAMRiMd555x20a9fOGrURUSmQocjB1vgzBu0jA0Lg5egCAPBzKwcXqYO1SyMiKjHMBjaJRIJHjx7h559/hkqlQkhICKemIqICO3T/ht5rqUiMfnWaoUWlmjaqiIio5DEb2L755hv8+uuv6N27NwRBwMaNG/HgwQOMHj3aGvURkR3TaLXIUuUhU5mLDGUuMpW5yFQ8829lLpLk6Xp9VoUMhJiPBiIiKhSzgW3nzp3YsmUL3N3dAQD9+vXDgAEDGNiIyphctQrxWSm4kfkIN7JS8Cg3GzJVXj5XpRl6NyCUYY2IqAjMBjYAurAGAB4eHpBKC9SNiEowmSoPNzKfBrRHSJJlQChUPPuPCEBVsSsal69avEUSEZURZpNXtWrV8O233+LNN98EAERHR6NqVX7TJSpt0hU5uPnv0bMbmY9wLyfTbB8RAA8HZ3g5usDL0QXeTi7wcnCBl9O/r/9t93Rwxvlz5+Ak4S97RERFYfa755w5czBhwgQsWrQIANCkSRMsXrzY4oURkeVoBQH3czIRn5WC+KzHiM9KQUqeLN8+IohQ3d0b9T19UN/LBzU9ysPL0QUSzkxARGRxZgObr68vNm/ejNzcXGi1Wri5uVmjLiIqRkqNGrezU3EzKwXxWSm4lf0YOWpVvn0kIjFqeZTXBbS6nhXhInW0UsVERPQsk4Ht9u3b+PDDD5GQkICgoCDMmzcPFSpUsGZtRFREmcpcxGel/BvQHuOOLA1aIf/rzxzEEtTxqIj6XpXg7+WL2h4V4MhTmEREdsHkd+O5c+eid+/eCA4OxrZt27Bo0SJ8+umn1qyNiApA//RmCm5mPcZjM6c3gSfXntX1rIh6npVQ17MSariX4zyeRER2ymRge/z4MSIjIwEAEyZMQM+ePa1WFFFJkq7IgUylgEbQ/vtHgEar1b3WCkLBlmmfW68AfdRaLe7lZJg9vQkAVVy99AJaJWd3iPiIDSKiEsFkYHv20R0SiYSP8iB6Rq5aiX9SEnHs4S3czk61dTkGHMQS1HKvgLpeTwJaHY+KcHNwsnVZRERURCZTmPDc9S78TZzKOkEQcD3zEY4/jEfM4ySotBpbl6Tj6eCMup6VdEfQqvP0JhFRqWIysD148ADz5883+XrGjBkF2sHZs2exdOlSXLlyBc7OzujcuTMmTJig9zDe5927dw8rVqzAqVOnkJmZierVq2PQoEEYNGgQ5zElq8lTq3A/NxOCAFzLeIDjD28ZffSFVCSGr4snJGIRJCKx7o9Y9O9rsRgSkf4yiUgEsUhs0Eei12bYRyIWP+n3zLLyTq6oyNObRESlmsnA9tZbb+X7uiBiY2MxbNgwtG7dGsuXL0dSUhKWLl2KxMREbNiwwWifrKwsREZGQiwW44MPPoCvry+OHj2KuXPn4tatWwUOikTGaAUBuWoVcjXKf/9WIe+Z13kaFXLVKqQrc3DucRKU+RxF83PzRohvXbTyqQV3nm4kIiILMhnYxo4d+8IbX7FiBXx9fbFmzRrdNXCVK1fGmDFjcPz4cbRp08agz44dO3D37l3s2bMHdevWBQC0adMGMpkM33//PaKiouDp6fnCtVHZohW0OHo/HrvvXEKWKq/I23GVOqBlpVoIrVwX1d3K8agWERFZhcXuJFAqlThx4gQiIyP1blgICwuDq6srDhw4YDSwVapUCYMHD9aFtafq1asHjUaDlJQUBjYqlDuyNETfOI3bsrRC93WTOqKSszs8HV3QslJNNK3gx2eTERGR1VnsJ09SUhIUCgVq166tv0OpFH5+foiPjzfar1u3bujWrZtB+x9//AE3NzdUr17dIvVS6ZOrVuHXxAs4eO+G3qTljmIJ3Byc4CJxgIvUES4S6b9/O8BZ6vBvuwPKO7mhcbmqkPC6SSIisjGLBbbs7GwAgIeHh8EyNzc3yGTmH+z51BdffIGYmBiMHz8ejo6Fmxrn8uXLhVq/KGJiYiy+DzJNIwjIFJTI0CqRLiiQrlUiXatA5ok4aJ9ZTwIRmjqURxNpeUifzn+p/veP4ula2n9fKKCGDOdvP7TiOyn9+LVinzgu9odjYp9sOS5mA5tWq8X69etx48YNzJw5E9HR0RgxYgQkkvwfGaDVavNdXtBrf1auXInVq1eja9euePfddwvU51mNGzeGk5PlLgiPiYlB8+bNLbZ9+o9Kq8Gj3Gzck2fgfk4W7udk4n5OJh7mZZuddqmhd2W8Ua8FfF14Ot1W+LVinzgu9odjYp8sPS4KhSLfg0xmA9uiRYuQlpaGS5cuAQCOHj2KlJQUs3drPr3OTC6XGyyTy+Xw8fHJt39eXh6mTp2KPXv2oG/fvpg3bx4f6VFGKDVqPMzNxv2cTNz7N5Tdz8nEo1yZ3qnNgvB18UREzZfRomIN3iBAREQlltnAduLECezYsQN9+vSBu7s71q9fX6BpqmrUqAGpVIrExES9drVajeTkZKM3HDyVlpaGkSNH4sqVKxg/fjzee++9ArwVKqkylbk4fP8GkmXpuJ+TiZQ8eaGDWQUnN1Rx9fr3jyey7txHWLPWcJE6WKhqIiIi6zEb2KRSqd6RLUdHxwJNU+Xo6IigoCDs27cPUVFRuj6HDh1CTk4O2rVrZ7RfTk4Ohg4dilu3bmHp0qVGb0CgkkcQBFxJv4/jD28hLuMhNMKTU+YCgDyN+XkwAUAEoIKzO6q4eqKKqxeq/hvQKrt6wlmiH8xi7mYwrBERUalhNnn5+/sjOjoaGo0Gt27dwsaNGxEQEFCgjY8dOxaRkZEYOXIkhgwZgnv37mHJkiUIDg5GSEgIAODmzZuQyWRo2rQpAGDVqlWIi4vD4MGDUbVqVZw/f15vmw0aNICLi0vh3iXZVI5aic3XT+FsalKB1hcBqOTs/uRomZuXLpxVdvHkIzWIiKhMMvvTb/r06ViwYAFSU1PxxhtvIDQ0tMCzDQQGBmLt2rVYvnw5oqKi4OXlhR49emDChAm6debMmYPTp08jLi4OAPD7778DADZv3ozNmzcbbHPnzp1o2LBhgfZPtiMIAuKzHuPkowScSUlErpmjaBWd3RBWxR8B3pXh6+LBYEZERPQMsz8V3d3dsWDBgiLvICQkRHc0zZjnQ9mhQ4eKvC+yPkEQoNCqka3MQ5YqD9nKPCTJM3DqUYLReTdDfOvitWoNUM7JVa/dWeIAMW8KICIiMspsYHt2wvdncU7P0ksraCFXKZ8EMFUespT//q3KQ7ZS8cy/n/ytyme+zacqObujb+1ABFbkg4+JiIgKy2xg8/b21v1bpVLh77//RmBgoCVrIgtQaTX/HQUzCGF5yFYpdG3ZKkWh79I0xlnigBaVaiDYpw7qelbkYzWIiIiKyGxge34S+FGjRmHUqFEWK4iKV5pCjh0J53Hm8R2zD5ctKqlIDE9HZ3g4OMPT0RmeDi5oWK4ympSvxmvRiIiIikGhf5q6urri0aNHlqiFipFSo8YfyVfxR3JsgU5ZPs9V6gAPBxd4ODj9F8YcnOHh+O/f/4YzDwdnOEukPHpGRERkQWYD27x583Q/jAVBwJUrV1CnTh2LF0aG8jQqaMxM+QUAsen38fPt80hX5Oi1e+qOgBkPXk//9nBwglSc/9RjREREZD1mA1u5cuX0Xvfo0QM9evSwWEFk6I4sDdsTzuNqxoMi9a/uVg4D6jaHv1f+04ERERGRfTIb2O7cuYNFixZZoxZ6TlqeHL8kXsDJR7eL1N/DwQk9azZBSOU6EIs4DysREVFJZTawxcXFQRAEXqNkRblqJfYmxWL/3WtQC/+dAhVBVKDplpzEUrSoVBPdazSCi9TRkqUSERGRFZgNbBUrVkT37t3RpEkTuLm56dr5HDbLOPYgHtsTzkOmVui1NylfDX1qN0VlVy8bVUZERES2YjKwKZVKODo6IjAwkM9ds5JLaXex6cYpvbaa7uXRr3Yg/L19bVQVERER2ZrJwDZw4EDs2LHD4DlsZDlH79/U/buCkxt61WqCFpVqcsomIiKiMs5kYBMs9JBVMk6uUuJy+n3d6/97+VX4uHjYsCIiIiKyFyYDm0KhQGxsrMng1qhRI4sVVRadT02C5t8bDGq6l2dYIyIiIh2TgS0pKQnjxo0zGthEIhH++usvixZW1vyTkqj7d8tKNW1YCREREdkbk4GtXr162LlzpxVLKbuylLm4lvFQ97oFAxsRERE9gzNz21hKbjY+v3IYAp4cyaznWQnlnFxtXBURERHZE5OBrUWLFtaso0ySqRSYc3aP3uTszSpWt2FFREREZI9MzlfEB+Na3h1Zml5YA4CXylWxUTVERERkrzjBpA09H9bGNmqPKpzJgIiIiJ7DwGZDF9Pu6v4dVqU+Xi5fzYbVEBERkb1iYLORJFk6/n4Qr3vNO0OJiIjIFN4lamVaQcCBe3HYkXBe1yYRiVHTvbztiiIiIiK7xsBmRRmKHGy8fhJXMx7o2hzEEgyu3wqOEg4FERERGceUYCUaQYtVVw4hWZ6ha6vhXg7vNGiDyrzRgIiIiPLBwGYlt7NTdWFNBKCz30uIqPkypGKJTesiIiIi+8fAZiWx6fd1/w72rYPetZvarhgiIiIqUXiXqJU8e90aH45LREREhcHAZgW5aiUSslIBPDkd2tDb17YFERERUYnCwGYFcRkPof13cvfq7uXh7uBs44qIiIioJGFgs4LYZ0+Hele2YSVERERUEvGmAyu4+swNB7x+jYjKCpVKheTkZOTl5dm6lBJFKpXi6tWrti6DnlNc4+Ls7Aw/Pz84ODgUbv8vvGfK1+M8GR7lyQAAjmIJ6nhWtHFFRETWkZycDA8PD9SqVQsikcjW5ZQYcrkcbm5uti6DnlMc4yIIAlJTU5GcnIzatWsXqi9PiVpYbPp/p0P9vXzgwOeuEVEZkZeXhwoVKjCsEf1LJBKhQoUKRTrqzCNsFnBXnoG9SbHI06hw95mZDRrydCgRlTEMa0T6ivo1wcBWzOQqBVZfOYxUhdxgGW84ICIioqLgKdFilKdWYeWVQ0bDWl3PiqjCOUOJiGzi1KlTGDx4cLFsq2fPnvkuf3Y/5tZ99dVX0a1bN/Ts2RM9e/bEq6++iokTJyInJ6dYan1RDx8+xMiRI4tlWzKZDOPGjYMgCLq2qKgoRERE6K1nbKySk5Px6quv6l7funUL7733HiIiIhAREYEPP/wQaWlpRa7t+PHjiIiIwOuvv45ly5blu25sbCwaN26se33v3j0EBgbqxvCdd94BAGzcuBEHDx4sck3P4xG2YqLSarAm9ghuZ6fq2oJ8aqFZxRpwEEtQz7MSTw0QEZUCv/zyS77LT58+XeB1AWDt2rXw8/MDACiVSgwaNAg7d+7Em2+++WKFFgNfX198/fXXxbKt1atXY8CAAbqfhenp6YiNjUXFihURExOD5s2bF2g7Dx8+xJAhQzB37ly8+uqrEAQBX331FcaOHYvvv/++0HXl5eVh2rRp2Lx5M6pUqYJRo0bh8OHDaN++vcG6ubm5mDt3LlQqla7t0qVLiIiIwNy5c/XWffPNNxEZGYmQkBA4OjoWuq7nWTywnT17FkuXLsWVK1fg7OyMzp07Y8KECXB3dzfZR6lUYuXKldi9ezfS0tJQv359REVFGf3w7IFGq8XX144hLvOhru2Nui0QVtXfhlUREVFBfPnll/j1118hkUgQEhKCiRMnQiKRYNOmTfjuu+/g4eGBOnXqoEaNGhg3bhwaNGiAuLg4nDhxAp999hkAwMvLC0uWLMGaNWsAAP3798dPP/2kWzcjIwPTp0/HrVu34OjoiClTpiA4ONigluzsbMhkMnh7ewMAjhw5gpUrV0KtVsPPzw/z5s1DuXLlcOrUKcyfPx8SiQRNmzZFfHw8Nm/ejMGDB8PLyws3btzA8uXLkZKSYrT/p59+imPHjkEikeC1117D2LFjjb6fnJwcDBkyBAcOHMDjx48xffp03Lt3D1KpFOPHj0e7du2watUqPHz4EImJibh79y769++P0aNH670vmUyGAwcOYOLEibq2Xbt2oUWLFvD398cPP/xQ4MC2ZcsWhIaG6o64iUQijBw5En5+flCr1ZBK/4s2y5Ytw6FDh/T6R0REYMSIEbrXFy9eRM2aNVG9enXd8r179xrNHAsXLsTQoUNx7tw5XdulS5dw/fp19OnTB+7u7pg+fToaNGgAR0dHNG/eHLt27ULfvn0L9N7yY9HAFhsbi2HDhqF169ZYvnw5kpKSsHTpUiQmJmLDhg0m+02ZMgUHDx7Ehx9+CD8/P2zduhWjR4/Gxo0b0apVK0uWXGiCIODbGydxITVZ19azZhOGNSKiZ+xLvopddy5BoVEX+7adJFJE1HgZnfwaFrrv4cOHceDAAWzfvh1SqRTjxo3D1q1b0bx5c0RHR2P79u1wcHDA4MGDUaNGDb2+a9aswezZs/HKK69g06ZNiI2NxYwZM7B582b89NNPeuuuWLECNWrUwOrVqxEXF4dZs2bpAtu7774LiUSC1NRUVK5cGQMHDkTXrl2RlpaGJUuWYNOmTfDy8sLWrVuxePFizJ49G5MmTcJXX32FgIAAzJ8/X29fDRo0wOeff460tDRMmTLFoP/777+PI0eO4LfffoNCocD06dOhUCiMvp9atWrptjtv3jwEBQVh2LBhSEpKwhtvvIGdO3cCAOLi4hAdHY3s7Gx07NgRb731Fjw9PXV9T548iYCAAIjF/12JtX37dowfPx7+/v5YsWIFpk2bpguq+bl69apBmJJIJAgPDzdYd/z48Rg/fny+23v06BEqVaqke+3j44OHDx8arHf48GHk5eWhS5cueu1OTk7o1asXBg0ahMOHD2PMmDHYs2cPHB0d0aJFC2zfvt3+A9uKFSvg6+uLNWvW6BJv5cqVMWbMGBw/fhxt2rQx6HPp0iX89ttvWLhwIXr37g0AaNeuHfr164dly5Zhy5Ytliy5UARBwDHVI8Q+ytC1ve7XEF2rv2S7ooiI7NC+u9csEtYAQKFRY9/da0UKbCdPnkT37t3h7PxkysC+ffti586dUCqV6NChg+5sUPfu3ZGVlaXX9+mRqY4dO+K1115DSEiIyf38888/WLx4MYAngeqHH37QLXt6SvSPP/7AJ598gnbt2kEkEuHChQu4f/8+hgwZAgDQarXw8vLC9evXUaFCBQQEBAAA+vXrh48//li3vVdeeQUATPb39fWFk5MTBg0ahA4dOuD//u//4OTkZPT9JCf/dzDi5MmTunBYvXp1NGnSBBcuXAAAtG7dGo6OjqhQoQK8vb2RnZ2tF9hu376NypX/u/Hu6tWruH//Ptq0aQMHBwc0bNgQO3fuxNChQ/VC3VOCIOhOpYpEIr3r4PJTkCNsxrb1/CVMKSkpWLduHTZt2mSw7rhx43T/bt++PZYsWYJbt24hICAA1apVQ2JiYoFqNcdigU2pVOLEiROIjIzUOzwZFhYGV1dXHDhwwGhgO3z4MMRiMTp16qRrE4vF6NKlC5YsWYL09HSUK1fOUmUXyq7ES4hVZ+heh1auiz61mvJaNSKi53SqFmDRI2ydqgUUqa9WqzVoU6vVEIvFRpc9a+jQoejQoQMOHjyIzz77DBcvXjQ4FfjUsz8HASA+Pt7gwamdO3fGsWPHMH/+fHz77bfQaDRo1qwZvvzySwCAQqGAXC7Ho0eP8q3tafg01V8qleKnn37C6dOnceTIEQwaNAibN282+n6evSHg+WAjCAI0Gg2AJ0eZnjIWqMRiMSSS/55D+vPPP0OpVKJz584AnjyUduvWrRg6dCg8PT0NwnFaWhq8vJ7cuNe4cWNcvnxZb7lWq0VUVBRmz56NihX/e0B9QY6w+fr64vHjx7rXjx49go+Pj946hw4dQmZmJt566y1dW8+ePREdHY0dO3YgPDxcl00EQdCNt1QqLbZMYLHAlpSUBIVCYfAfUiqVws/PD/Hx8Ub7xcfHw8fHx+Aat5o1awJ4cmdIQc9zW1K6Igd7kv77D9OiYg28Va8lwxoRkRGd/BoW6QiYpQUFBeGLL77AwIEDIZVK8fPPPyMoKAjBwcEYN24coqKi4OjoiD///NPgmrP+/ftjzpw5GDp0KLy9vfHXX38BeHJ67vlrqVq0aIE9e/YgICAA8fHxGDlypG79Z33wwQfo1KkTDh48iCZNmmDGjBlISEhA7dq1sWbNGjx8+BBz585FVlYW4uLi0KBBA+zatcvoezPVf8iQIZg3bx42b96M4OBgxMbGIiEhAZMmTTJ4P88GtqCgIGzbtk13SvTs2bOYPXs24uLizH7ONWrUwMWLFwE8OaCza9cubNy4EU2aNAHw5Bq39u3b49SpU2jRogUyMzNx4cIFNGnSBFqtFj/99JPu8x84cCB69uypuzFAEASsWbMGqampemGtoJo0aYKEhAQkJibCz88Pu3fvNjiF2b9/f3Tr1k0300GDBg10N5T8888/yMvLw8iRI3H69GlotVrUqVMHwJO7W5/mlxdlscCWnZ0NAPDw8DBY5ubmBplMZrRfVlaW0T5PA5ypfqY8n8KLi0LQQAIR1BBQXeyGJnJnnDt7znxHspqYmBhbl0DP4ZjYJ0uNi1QqhVxu+JgjW8jLy8OZM2fQtGlTXVu3bt0wffp0XLx4Eb1794ZGo0FwcDB69+4NqVSKgQMHon///nB1dYW3tzdEIpHu/cjlcowePRqTJk2CRCKBs7Mzpk2bBrlcjvbt2yMiIgLR0dG6dUeMGIF58+YhPDwcEokEc+bMQU5ODrRaLXJzc3XbdXZ2xttvv42FCxfixx9/xKxZsxAVFQWtVgsfHx/Mnz8fKpUKc+fOxcSJEyESiVCrVi04ODhALpdDo9EgLy8Pcrkcrq6uRvt7e3ujUaNGulPBDRo0QPPmzY2+n9zcXGi1Wsjlcvzvf//DvHnzsG3bNgDAzJkz4ebmBqVSqXufAAzeE/AkFC1YsADZ2dk4ePAgKleujHr16unWEYlE6NWrF7777js0btwYn3zyCebNmweFQgGFQoGWLVti2LBhuve1cuVKLF++HIsWLYJGo0FAQAA+++yzIv9/++ijjzBmzBgolUqEhISgbdu2kMvlmDt3Ltq3b6+7Zu7Z7T/99/jx4/HRRx9hx44dcHJywvz585GbmwsA+PvvvxEaGmpQl1KpLPzXnWAhMTExgr+/v/D7778bLBs4cKDQv39/o/2GDRsmdO/e3aD977//Fvz9/YXDhw8XaP95eXnCmTNnhLy8vMIVXgipeTJh56nDgkartdg+qGjOnDlj6xLoORwT+2TJcYmNjbXYti3t1q1bwoYNG3Sv33vvPeGvv/6yyr5lMlm+yzUajfDpp58KcrlcEARBWL9+vfDJJ59Yo7QXsmDBAuHAgQO2LqPIzI3L8xQKhdC7d29BoVAYLDP2tWEut1jswblPLzY0lnblcrnRo2jAkyNyxvo8PbL27EWMtlbeyQ1+EjeIeRqUiKhUqVatGi5duoTw8HBERESgVq1a6NChg63LAvDkejBvb2/069cPPXv2xD///INRo0bZuiyzxo4di23bthX4hoGS7rvvvsP7779fLM9gAyx4SrRGjRqQSqUGd0eo1WokJycbveEAAOrUqYN9+/YhNzcXLi4uuvbExESIRCLdeWEiIiJLcXR0xJIlS2xdhknvvvsu3n33XVuXUSgeHh5YvXq1rcuwmuHDhxfr9ix2hM3R0RFBQUHYt28f1Or/7go6dOgQcnJy0K5dO6P92rVrB41Ggz///FPXptVqsXfvXjRt2tSujrARERERWYNFn8M2duxYREZGYuTIkRgyZAju3buHJUuWIDg4WPe8mps3b0Imk+kuBA0MDET79u0xa9YspKeno3bt2tiyZQvi4uLwzTffWLJcIiIqZsIzz88iIuPPfSsIiwa2wMBArF27FsuXL0dUVBS8vLzQo0cPTJgwQbfOnDlzcPr0ab3bgpctW4YlS5Zg7dq1kMvlqF+/Pr744gsEBQVZslwiIipGzs7OSE1NRYUKFRjaiPAkrKWmpuqelVcYFp9LNCQkJN+nP2/evNmgzc3NDbNmzcKsWbMsWRoREVmQn58fkpOTkZKSYutSShSlUllsF6pT8SmucXF2doafn1+h+1k8sBERUdnk4OBg8PB0Mi8mJkb3QFmyH7YeF4vddEBERERExYOBjYiIiMjOldpTok/vwng6ZYYlKRQKi++DCo/jYn84JvaJ42J/OCb2yZLj8jSvmLqLVCSU0kcOZ2dn4/r167Yug4iIiKjA/P39jc4GVWoD29PJah0cHHg7OREREdk1QRCgUqng5uYGsdjwirVSG9iIiIiISgvedEBERERk5xjYiIiIiOwcAxsRERGRnWNgIyIiIrJzDGxEREREdo6BjYiIiMjOMbARERER2TkGtnycPXsWkZGRCAwMRHBwMGbPng2ZTJZvH6VSicWLFyMsLAyvvPIK+vbti8OHD1up4tKvKGNy7949TJ48GWFhYQgMDESPHj3w/fffQ6vVWqnq0q8o4/KstLQ0hISEYMqUKRassmwpyphoNBp8+eWXeP311/Hyyy+jY8eOWL16NTQajZWqLv2KMi5ZWVmYP38+Xn31VQQGBqJv377Yv3+/lSouO2QyGTp16oSNGzeaXdcWP+sZ2EyIjY3FsGHD4OrqiuXLl2PMmDH49ddfMW7cuHz7TZkyBdHR0RgxYgRWrlyJSpUqYfTo0Th9+rSVKi+9ijImWVlZiIyMRExMDD744AOsXr0aISEhmDt3LhYsWGDF6kuvon6tPGv69Ol4/PixBassW4o6JjNmzMDq1avRr18/rF27Fj179sTq1auxePFiK1VeuhV1XKKiorB9+3YMHz4cq1evRp06dTBmzBiGtmKUnp6Od999F3fu3CnQ+jb5WS+QUe+++67QqVMnQaVS6dr27dsn+Pv7C8eOHTPa5+LFi4K/v7+wfft2XZtGoxF69+4tDBo0yOI1l3ZFGZONGzcK/v7+ws2bN/XaZ8yYITRs2FDIzMy0aM1lQVHG5Vlbt24VWrduLbz88svC5MmTLVlqmVGUMTl//rzg7+8v7NixQ6/9448/FiIiIgSNRmPJksuEoozL7du3BX9/f2HdunW6Nq1WK3Tt2lUYPHiwxWsu7bRarbBnzx6hXbt2QqtWrQR/f39hw4YN+fax1c96HmEzQqlU4sSJE+jYsSOkUqmuPSwsDK6urjhw4IDRfocPH4ZYLEanTp10bWKxGF26dMHZs2eRnp5u8dpLq6KOSaVKlTB48GDUrVtXr71evXrQaDRISUmxaN2lXVHH5anbt29j4cKFmDdvHhwdHS1dbplQ1DHZs2cPvL290aNHD732adOm4ddffzU6tyEVXFHHJS8vDwDg7u6uaxOJRPD09ERGRoZFay4L7t69iw8//BChoaFYv359gfrY6mc9vwKNSEpKgkKhQO3atfXapVIp/Pz8EB8fb7RffHw8fHx89L6wAKBmzZoAgFu3blmm4DKgqGPSrVs3zJgxw6D9jz/+gJubG6pXr26ResuKoo4LAKjVakycOBGvv/663jc+ejFFHZPY2FjUr18fR44cQZ8+fdCoUSO0bdsWq1ev5vWexaCo49KgQQOEhoZi7dq1uHjxIrKzsxEdHY1z586hT58+1ii9VCtXrhz++OMPfPzxx/Dy8ipQH1v9rJeaX6Xsyc7OBgB4eHgYLHNzczN5gWhWVpbRPk8HtTAXYZO+oo6JMV988QViYmIwfvx4HtV5QS8yLqtXr0ZKSkqBf6ulginqmKSmpiIrKwtTp07F2LFjUbduXRw8eBCrVq1Cenq60V98qOBe5Gtl9uzZGDVqFPr3769rGzx4MIYOHVrsdZY1bm5ucHNzK1QfW/2sZ2AzwtxvkyKRyGi7IAhF6kfmFXVMnrdy5UqsXr0aXbt2xbvvvlscpZVpRR2X8+fP4+uvv8bXX39t9BsfFV1Rx0SlUiElJQXffPMNQkNDAQBBQUGQyWT4/vvvMWrUKFSqVKnY6y0rijouCQkJGDRoEMqVK4fFixfDx8cHx44dw/r16+Hg4IDJkydbolzKh61+1jOwGeHp6QkAkMvlBsvkcjl8fHyM9vPw8EBCQoJB+9O0/XS7VHhFHZOn8vLyMHXqVOzZswd9+/bFvHnzeE1OMSjKuMjlckycOBEDBgxAy5YtoVardcsEQYBarYZEIuEvOEVU1K8Vd3d3uLi4ICQkRK89LCwM27Ztw7Vr1xjYXkBRx2XDhg2Qy+X45ZdfULlyZQBA69at4eTkhJUrV6JPnz6oX7++5QonA7b6Wc+fWEbUqFEDUqkUiYmJeu1qtRrJycmoV6+e0X516tTBw4cPkZubq9eemJgIkUiEOnXqWKzm0q6oYwI8ecbXW2+9hd9//x3jx4/HggULIJFILF1ymVCUcbl8+TLu3LmD6OhoNGrUSPcnOzsbO3fuRKNGjfgYnBdQ1K+V2rVrQ61W6wVo4MmRNwBwcXGxTMFlRFHH5e7du6hWrZourD3VunVrAMCNGzcsUzCZZKuf9QxsRjg6OiIoKAj79u3T++Z16NAh5OTkoF27dkb7tWvXDhqNBn/++aeuTavVYu/evWjatCmPsL2Aoo5JTk4Ohg4diri4OCxduhTvvfeetUouE4oyLo0aNcK2bdsM/ri6uqJDhw7Ytm0bGjVqZM23UaoU9WslLCwMKpUKe/bs0Wv/66+/4ObmxjF5QUUdlzp16uDu3bt48OCBXvvZs2cBgDdO2YCtftbzlKgJY8eORWRkJEaOHIkhQ4bg3r17WLJkCYKDg3WnDG7evAmZTIamTZsCAAIDA9G+fXvMmjUL6enpqF27NrZs2YK4uDh88803Nnw3pUNRxmTVqlWIi4vD4MGDUbVqVZw/f15vmw0aNOCRgxdU2HFxd3fHyy+/bLAdiUQCb29vo8uocIrytdK9e3ds3boVs2fPRlpaGho0aID9+/dj9+7dmDp1Kr9OikFRxmX48OHYvXs3hg4ditGjR8PHxwenTp3CunXr0LlzZ369WIG9/KwXCeaunivDjh07huXLl+PatWvw8vJCx44dMWHCBN2dIIMHD8bp06cRFxen6yOXy7FkyRLs3bsXcrkc9evXR1RUlMnfnqhwCjsmYWFhuH//vsnt7dy5Ew0bNrRK7aVZUb5WnteiRQt07NgRCxcutFbZpVpRxiQnJwerVq3Cnj17kJqailq1auHtt9/WuzuRXkxRxiUpKQlLly7F8ePHkZeXhxo1aqBv376IjIzUe6YbvZjk5GS89tprmDp1qt4duPbys56BjYiIiMjO8Ro2IiIiIjvHwEZERERk5xjYiIiIiOwcAxsRERGRnWNgIyIiIrJzDGxEREREdo6BjYiIiMjOMbARlSANGjRAREQEevbsqfszffr0fPts374do0aNKpb9r1q1CkFBQejZsyd69eqFiIgIDB061OhEyAXx8OFDDBo0CMCTh4OOGzfOoP1FJScno2HDhnqfWadOnTB48GAkJSWZ7f/5559j//79hd6vRqPBqFGj8PjxY2zfvh3NmzfXfW49e/bEoEGDcO7cuaK8JQBAz549kZWVhezsbAwZMsSg/UWdOnUK4eHhZtdr0KAB0tLSCrXtKVOmFPiJ8IIgGKy/f/9+fP7554XaJ1FJx0ckE5Uw3377LcqXL2+z/Xfr1g2zZs3Svd68eTM+/PBDbN++vdDb8vX1xdatWwEA9+7d0wW/Z9uLg7OzM3755Rfda0EQMH/+fCxbtgxLly7Nt++pU6dMTsydn/Xr16NVq1aoWLEigCczOXz11Ve65QcOHMC4ceNw6NChIj2t/un7SU5OxqVLlwzaS4P4+HjMmTMHFy5cQP369XXtHTt2RHR0NK5evcqZSqjM4BE2olJi27Zt6N+/P3r16oUOHTrg+++/N1jnzz//RO/evdGnTx/0798f//zzDwAgOzsbU6ZMQZ8+fRAREYEFCxboTVCdn+DgYF3QevDgAd577z1EREQgPDwc69atAwCo1Wp89NFHiIiIQJ8+fRAVFQW5XI7k5GQEBgZCo9FgxowZuHPnDt555x1du1arRfv27fUCyfjx43Xv7YsvvkDv3r3Rs2dPvP/++3j48GGBalYoFEhJSYGXlxcAICEhAcOGDcPAgQPRoUMHjB49GgqFAtHR0bh8+TIWLVqEffv2QalUYsGCBejduzd69OiBKVOmQCaTGWw/NzcX3377Lfr06ZPv55aSkqI7SjZhwgSEh4cjIiICixYt0n3+K1eu1H1u77zzDh49egTgvyNbU6dORV5eHnr27AmNRqNrHzRoEPbu3avb3+LFi/HZZ58BAH766Sf06dMHvXr1wtChQxEfH5/v52Xq83lq+fLlunE4ePCgrr0g+1mxYgVWrFhhdL/R0dHo06cPunbtarCsX79+PMpGZYtARCWGv7+/EB4eLvTo0UP35/Hjx4JMJhMGDBggpKWlCYIgCOfOnROaNm0qCIIg/Pzzz8K7774rCIIgvPbaa8K5c+cEQRCEo0ePCqtWrRIEQRCmTJkibNq0SRAEQVCr1cKECROEtWvXGux/5cqVwpw5c3SvVSqV8MknnwijRo0SBEEQ3nrrLWH9+vWCIAhCVlaWEBERIezevVv4559/hC5dugharVYQBEFYtGiREBMTIyQlJenqPHnypNC9e3dBEAS99hUrVuj2mZGRIbRq1UrIysoSduzYIfzf//2foFKpBEEQhK1btwojRowwqDkpKUkICAgQevToIYSHhwvBwcFCly5dhKVLlwoymUwQBEFYuHChsHPnTkEQBEGpVArh4eHC3r17BUEQhMjISOH3338XBEEQVq1aJSxcuFD3PpYsWSJ89NFHBvs8cOCAEBkZqXv97BgIgiBotVphw4YNQnh4uCAIgjBp0iRh3rx5glarFRQKhTB8+HDhq6++Eu7duyc0a9ZMUCgUgiAIwjfffCPs27dPEIQn/xdSU1P1Pqtn27dt26bbp1qtFtq2bSskJCQIp06dEt58800hJydHEIQn/w+6du1q8B6eHY/8Ph9/f3/hq6++EgRBEOLi4oRWrVoJqamp+e5n8uTJwrp16wz2aYqx9bOzs4VXXnlFyM3NLfB2iEoynhIlKmFMnRL98ssvcfjwYdy+fRvXrl1DTk6OwTrdu3fH2LFj0b59e4SEhGDkyJEAgEOHDuHSpUvYtm0bACAvL8/k/vfs2YOYmBgAgEqlQqNGjTBv3jzk5OTg7NmzWL9+PQDAw8MDffr0wZEjRzB9+nRIJBL0798foaGh6Ny5M1555RUkJyebfb99+/ZFv379MGXKFOzevRsdOnSAh4cHDh48iEuXLqFv374AAK1Wi9zcXKPbePaU6NGjRzFx4kSEhITAzc0NADBx4kQcO3YMX3/9NW7fvo1Hjx4Z/fwOHTqE7OxsHD9+XPf+K1SoYLDerVu3UKNGDb22M2fOoGfPnhCJRFAqlahTpw5WrlwJADhy5Ai2bNkCkUgER0dHDBo0CN9++y1GjBiBgIAA9O7dG+3atUO7du0QHBxs9jMDgK5du2LRokVISUlBbGwsatasiVq1auHHH39EYmKi3jWCmZmZyMjIgLe3t9Ftmft83njjDQCAv78/6tati3PnziEmJsbkfoqDu7s73N3dcffuXdStW7dYtklkzxjYiEqBBw8eYODAgRgwYACaN2+OLl266J2aemr8+PHo168f/v77b2zfvh1r167F9u3bodVqsWLFCt0PvqysLIhEIqP7ev4atqdkMhkEQdBr02q1UKvV8PT0xC+//IKzZ8/i5MmT+L//+z8MGTIEHTt2NPveqlWrhpdeegmHDh3C9u3bMW3aNN22R4wYgTfffBMAoFQqkZmZaXZ7bdu2xbBhw/C///0Pv//+Ozw8PPC///0PGo0GXbt2RVhYGO7fv2/wXp7uc9q0aWjfvj0AQC6X650afEosFkOr1eq1PX8N2/Pbff61Wq2GWCzGd999h0uXLuHEiRNYsGABWrdujRkzZph9n66urujcuTN2796Nc+fOoX///rpt9+zZExMnTtS9fvToke70sDHmPh+x+L+rawRBgFQqLdJ+Ckuj0UAikRTb9ojsGa9hIyoFLl++jPLly+P9999H27ZtdWFNo9Ho1lGr1Xj11VeRk5ODN954Ax999BHi4+OhVqsRGhqKjRs3QhAEKJVKjB49Gt99912hanB3d0eTJk0QHR0N4Ml1cTt37kSbNm1w8OBBDB06FIGBgRg3bhx69eqFa9eu6fWXSCRQqVRGtz1gwAB8/fXXyMvLQ/PmzQEAoaGh2LZtm+4ashUrVmDSpEkFqnX48OHw9PTUHeH6+++/MWbMGHTr1g0ikQgXLlzQfXYSiUR3PVloaCiio6OhVCqh1Woxc+ZMozct1KpVq0B3oD71dLtPP/8ff/wRbdq0wbVr1xAeHo66deti1KhRGDp0KOLi4vT6SqVSaDQaowFzwIAB2L59O86dO4fOnTsDAEJCQvDbb7/proXbsmUL3n777Xzry+/zAYAdO3YAAK5cuYLExEQ0adKkSPspjOzsbCgUClStWrXYtklkz3iEjagUCAkJwbZt29ClSxe4uLjglVdeQfny5ZGYmKhbRyqVYtq0aZgwYQKkUilEIhEWLFgAR0dHTJ8+HR9//DEiIiKgUqnQpk0bjBgxotB1LF68GHPnzsX27duhVCp1F8trtVocOXIE4eHhcHV1hZeXF+bNm6fXt379+pBIJOjXrx+WLVumt+zVV1/FnDlzdKdwAaB///54+PAhBgwYAJFIhCpVqmDhwoUFqtPBwQEzZ87EiBEj0L9/f4wfPx5jxoyBl5cXXFxc0LJlS9y5cwcA0KFDB3z66adQqVR4//338emnn6J3797QaDRo2LAhpkyZYrD9Nm3aYPr06cjKyoKnp6fZembMmIH58+frPv+2bdvivffeg6OjI7p27Yq+ffvC1dUVzs7OBkfXKlWqhJdeegldu3bFli1b9JY1btwYUqkUnTt3hpOTE4AnRxhHjhyJ4cOHQyQSwd3dHZ9//rnJI6oA8v18gCePZOnVqxdEIhGWLl0Kb2/vAu/n6Q0HH3zwgdnP6Vl///03wsLC4OjoWKh+RCWVSDD2axkREb2QL7/8EhKJRC9kUvEZMmQIpk2bhoCAAFuXQmQVPCVKRGQBw4cPx8mTJ5GSkmLrUkqdffv2oUWLFgxrVKbwCBsRERGRneMRNiIiIiI7x8BGREREZOcY2IiIiIjsHAMbERERkZ1jYCMiIiKyc/8PS1XWoQzwUusAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_clsf = LogisticRegression(max_iter=10000)\n",
    "log_clsf.fit(X_train,y_train)\n",
    "\n",
    "score_model(log_clsf)\n",
    "evaluate_model(log_clsf)\n",
    "\n",
    "plot_roc_curve(log_clsf, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9412c7c8",
   "metadata": {},
   "source": [
    "#### Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "9cc4e364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accurarcy 0.822\n",
      "cross validation Mean : 0.8188749999999999 and STD of 0.009477770043633683\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.96      0.89      1582\n",
      "           1       0.65      0.32      0.43       418\n",
      "\n",
      "    accuracy                           0.82      2000\n",
      "   macro avg       0.75      0.64      0.66      2000\n",
      "weighted avg       0.80      0.82      0.80      2000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PH374AWfOnMHUqVNLfX0ioqeh1mlxLiMVWco8bE86U+b6zlI7tKzVQL/t5+KJCJ9ADhYgonIx+QxbUlISxo8fj5ycHGzfvh3Dhw/HmjVrEBgYWGK93NxcAICrq2uRfTKZDHK58cWDZ8+ejdGjR2PAgAH6sqFDh2L48OGmwi3i4sWLZa5TVvHx8Wa/BpUd28X22HKbCIKAh4ISl9XZSNbKkQ9tqet6ix2hFQTUlTgjSOoGmUgKJ5EUyHnioJwcnL1T9sTPEmy5Xaortoltsma7mEzY5s2bh2nTpuGTTz6Bj48PYmJiMGvWLMTGxpZYT6fTlbjf2JxBSUlJGDx4MDw9PbFkyRJ4e3vj6NGj2LhxI+zs7PDBBx+YCtlAaGgoHBwcylSnLOLj49GyJSedtDVsF9tj620y7o9t0Agl/94CAG9HF7ziFwoAcJLaI9SzDqRPTERb2dh6u1RHbBPbZO52USqVJXYymUzYsrOzERERgU8++QQAMGTIEHz77bcmL1x4+1ShUBTZp1Ao4O3tXWy9TZs2QaFQ4Pvvv0ft2rUBAG3atIGDgwNWrVqF6OhoBAUFmbw+EZEx1x89wJXs+/rtH29fKPH4F+sGw8PBGRE+gXCxM99/AImIjCnVSgdKpVLfI5aenm6y9wwA/Pz8IJVKkZycbFCu0WiQmppa7IADAEhLS0O9evX0yVqhNm3aAACuXbvGhI2IykUn6BB7/U/8ca/kNTmD3b3R2qshGnvURi1HGVcRICKrM/n062uvvYY33ngDGRkZWLp0KQYNGoRXX33V5Int7e3Rtm1b7Nu3DxqNRl9+8OBB5OXloUOHDsXWCwgIQFpaGu7du2dQfvr0aQBA/fr1TV6biOjfknIfYuwf20wma/8X+iLea94ZL9RpBC8nFyZrRGQTTPaw9e/fHw0aNMDBgweh0Wgwd+5ctG/fvlQnHz9+PGJiYvDmm29i2LBhuHPnDpYuXYrw8HBEREQAAK5fvw65XI4WLVoAAEaOHIndu3dj+PDhGDt2LLy9vXHy5El88cUX6Nq1K5o1a1b+d0tE1daWq6eKlHnYOyHC5/EAKqlYgue9/ODtVHSgFBGRtZXqlmijRo0gl8shlUrRvHnzUp88LCwMGzZswIoVKzBhwgS4u7ujV69emDRpkv6YOXPm4NSpU0hMTAQA1KlTB99++y2WLVuGBQsWoKCgAH5+fpg0aZJ+Il0ion/TCTpsTDyOC5l3ABRdPq9AqzHYfi2wFTrW5eMVRFQ5mEzYDh48iA8++ABBQUHQ6XS4ffs2li9fjlatWpXqAhEREfretOJs2bKlSFn9+vWxfPnyUp2fiKovQRDwsEABlU6DUw9u4c/0ZNOVAKxqNxAOklL9f5WIyCaY/I21cuVK/Pe//9U/6H/p0iXMnDkTcXFxZg+OiKg4Oap8nHhwC8fu3cDd/BzTFf4mFonQuV5jJmtEVOmY/K0lEokMRmU2bdoUglD0dgMRkTlpBR0uZt7Bsfs3cT4zDboSfg+18mqAIY1aFymXiESwZ7JGRJWQ0d9chet2hoaG4ssvv8TgwYMhFosRFxeHtm3bWio+IqqmdIKA9IJcZCvzcTHrDk7cT0KOuqDIcQ5iKWo6ygA8vkVaV+aBkSHhEIu4BBQRVR1GE7a2bdtCJBLpe9MKJ84FHve6lXXFASIiYzQ6Le7m5eC2PBMpiizclmchVZGlX2i9OI3cvBBROxDP1aoPR4mdBaMlIrI8ownblStXLBkHEVUTSq0GqX8nZSnyLKQoMnFH8ahUy0K52zsh3Nsf7XwC4OPsZoFoiYhsg8mHOVQqFQ4dOqRfYkqr1eL27duYOHGi2YMjospNoVY+TswUWTirvIMf/rqL+/k5xUy6UTxXO0fUdHBGTUcXtPX2R9MadSDhrU4iqoZMJmwTJ05ESkoK0tPT8cwzz+DcuXNo3brow7xEVL1lK/NwW56lv62ZIs9ChvJfawnnG69f00EGP5caqO/iCT8XT9SXecLd3okrDRARoRQJ2+XLl/Hrr79i9uzZGDFiBARBwJw5cywRGxHZuBxVAU6l38KJ+0lIUWSVqo4IItR2dkN92ePEzM+lBnxlnpDZ2Zs5WiKiystkwubt7Q2pVIqGDRvi6tWriIqKQn5+Cf9NJqIqLU+jwvnMNPyVnoxLmXehK+EGp1QkRl2ZB/xcPIGMXEQ80wK+Mg9OrUFEVEYmf2s6Ozvjxx9/ROPGjfHtt98iICBAP+UHEVUfOkHAvrTL2HXrXLFzoElFYjR0rfn4dqZLDfi5eKKOkzsk4sfPnMXnxCPArZalwyYiqhJMJmwzZ87Ed999h8mTJ2P79u2IiYnhgAOiauZ8RhrWJhwqdl8jNy+E+/jjuVp+cJbytiYRkTmYTNj8/f3x/vvvAwBWrFhh7niIyMY8yM8tNlnr4ReKtt7+8HJytUJURETVi9GErWfPniVW/PHHHys8GCKyPrm6ADkqpX77TMbtIsf855kOeLamryXDIiKq1owmbDNnzrRkHERkRecz0pCQfRfH7t2EUmd8dQExRFjTfhDnQiMisjCjCRvnWiOqHo7cvY7/Xj9VqmOj/VswWSMisgKOrSeqpnJUBdiRdBonHtwqdr+ngzMcxf/8igh090Jk3WALRUdERE9iwkZUTWh1OoM50z67fATXc9INjmlZyw8h7j5oVqMuajjKLB0iEREZUeqELScnB25uXGyZqLJ5WCDHdzdP43xGWomT3Lb19seIkHALRkZERKVl8mGUmzdvonv37ujevTvu37+PqKgo3LhxwxKxEdFT0Oi02JtyCbPjf8LZjNQSk7WpLboyWSMismEmE7b58+dj2rRpqFmzJnx8fBATE4NZs2ZZIjYiKiNBECBXK3El+x7mn96LXbfOQa3T6vdLRWKDPwAwMiQcDV1rWitkIiIqBZO3RLOzsxEREYFPPvkEADBkyBB8++23Zg+MiEomVyvxZ3oy8jUqAECOugAH7lwt9lhfmQeGNGrNpaGIiCqpUj3DplQqIRKJAADp6enQ6XRmDYqISiYIAt47scPkcQ4SKXo1aI5OdYM5HQcRUSVmMmF79dVX8cYbbyAjIwNLly7FTz/9hFGjRlkiNiJ6Qr5GhXyNGnfyHuGb63+aPL6Nd0P0bdgCng7OFoiOiIjMyWTCNmDAADRs2BAHDx6ERqPB3Llz0b59e0vERkR4PMrz+1vn8Gd6stFhA1H1m+pf+8o8EFarPnvUiIiqEJMJ29KlSzFw4EBMnjzZEvEQ0d/k6gL8dPsSDt29Bq1g/DGEQQEt8WK9EAtGRkREllaqZ9hiYmLQoEEDDBw4EC+//DLs7e3NHRdRtSMIAh4WKKDWaXE2IxW/pCagQKs2OMbD3gkikQgqrRb1ZO4YHPg86sk8rBMwERFZjMmE7b333sPEiRNx5MgRxMXF4ZNPPkHXrl0xbdo0S8RHVGWl58tx9P4NfVJmbIQnAAS6eaGffwsEunlZKjwiIrIhpephE4vFCA0NRVJSEpKSkvDXX3+ZOy6iKkmr02HKqV3IUReU6vjaTm7o698Cz9aopx+pTURE1Y/JhO3XX39FXFwczp49i27dumHhwoVo2rSpqWpEVIz/HN1WquP8XWsiwicQ7WoHcPAAERGZTtg2btyIgQMHYvny5XBycrJETERVik4Q8Me9G4i9fqrY/a52DnilfigAwMXOAS1q+sJeUuplfomIqBow+q0gl8vh4uKC9evXA3g8ea5SqdTv9/DwMHtwRJVVvkaNMxkpyFbm4/vkc8Ues6h1H7jbO0LMHjQiIjLBaMI2dOhQ7Ny5E23btoVIJIIg/DMDlEgkwuXLly0SIFFlIggCjt6/iS3XTpZ43AfPvswJbYmIqNSMJmw7d+4EACQkJEAsNuwByM7ONmtQRJXRpaw72Jl0DimKLKPHdPcLxYt1g+Fi52jByIiIqLIz+aBMv3799Mlboddeew179uwxW1BElU2+RoVVFw8Wu+/FusGo7eyOcG9/PptGRETlYvTb4/XXX8eFCxdQUFCA5557Tl+u0+nQpEkTiwRHVFlsuWY4oCDCJwAv+z6D2s5uVoqIiIiqEqMJ29q1a5GdnY1p06Zh4cKF/1SQSuHlxck7iQBArlbibt4jxD+8bVA+LLitlSIiIqKqqMT7M76+vli1alWR8pycnFKPEj19+jSWLVuGS5cuwdHREV27dsWkSZPg4uJitI5Wq8Xnn3+OuLg43L17Fz4+Pujbty/GjBkDiURSqusSmdPdvEeYHf9TsfvGNHnBwtEQEVFVZ9ZRogkJCRgxYgTatGmDFStWICUlBcuWLUNycjI2bdpktN6MGTOwe/duvP3222jWrBn++usvrF27FnK5HB988EEZ3yJRxckoUGBj4jFcz0k3ekxYrfoWjIiIiKoDk6NEr1y5Uu6Tr1y5Ej4+Pli3bh2k0seXql27NsaNG4djx46hXbt2ReqcO3cOcXFx+Pjjj9GnTx8AQHh4OHJzc3H06FHodLoio1aJzK1wYfadt84Wm6y52TmitrMb3m32khWiIyKiqs7kkLWHDx/i3LlzeOmll7BgwQJcuXIF06ZNQ+PGjUusp1KpcPz4ccTExOiTNQCIjIyEs7Mz9u/fX2zCtmfPHnh4eKBXr14G5VxsnqwhT6PC0Xs3sD3pTLH7+/uHoY13Q7jZcxUQIiIyH5NdVVOmTEFKSgqOHz+OEydOoE+fPpg/f77JE6ekpECpVMLf39+gXCqVwtfXFzdu3Ci2XkJCAoKCgnD48GFER0ejadOmeOGFF7B27VrodLpSvi2ip6PSahCffhtTT31vNFkbFdIOXXybMFkjIiKzM9nDlp2djeHDh+Pjjz9Gjx49EB0djdjYWJMnzs3NBQC4uroW2SeTySCXy4utl5GRgZycHEydOhXjx49HYGAgDhw4gNWrVyMrKwszZswweW2isii83ZmnUWH/nStIVWQjVZFt9HgfJzc0cvNCCz6rRkREFmIyYVOr1VCr1Thy5AgWLVqE/Px85OXlmTyxqd4wkUhk9Hrp6en48ssv0b59ewBA27ZtIZfL8c0332D06NFlmlbk4sWLpT62vOLj481+DSq74tpF98TgGQDIF7TYpUyGQtCYPF9ru1oIlXpCKhIDucD5M2crKtRqgz8rtontYnvYJrbJmu1iMmF76aWXEB4ejiZNmiA0NBQ9evRAjx49TJ7Yze3xhKEKhaLIPoVCAW9v72Lrubi4wMnJCREREQblkZGR2L59O65cuVKmhC00NBQODg6lPr6s4uPj0bJlS7Odn8rn3+2i1mmxPuEwLmbdLfO52tcORIuavmhWo15Fhljt8GfFNrFdbA/bxDaZu12USmWJnUwmE7YJEyZg4MCBqF27NgBgyZIlJgccAICfnx+kUimSk5MNyjUaDVJTU4sdcAAA/v7+uHbtGjQaDezs7PTlarUaAODkxOeFqGwSsu5i5cUDpTq2vswTOkHAS/VCEOBWC7Wd3Iz2BhMREVmKyYRNp9Phxx9/xOHDh6HRaBAREYFGjRoZjPwsjr29Pdq2bYt9+/ZhwoQJ+uMPHjyIvLw8dOjQodh6kZGR+Omnn7Bnzx707t1bX/77779DJpOhadOmZXl/VA0l52bigPIuzlw5ivv5ubgtzyz2OPHfiZhOEGAnlmBJm2g4Su2KPZaIiMiaTCZsS5cuxZUrV/D6669Dp9Phf//7HxYvXlyqaTbGjx+PmJgYvPnmmxg2bBju3LmDpUuXIjw8XH/L8/r165DL5WjRogUAoHv37ti2bRtmz56NzMxMhISE4LfffsPu3bsxdepU9rBRiW7lZmDh2V8eb6TnFHvM9LBu8HOpYcGoiIiIno7JhO3IkSPYsWOH/vZkZGQkevXqVaqELSwsDBs2bMCKFSswYcIEuLu7o1evXpg0aZL+mDlz5uDUqVNITEwEAEgkEnzxxRdYvXo1Nm/ejIyMDDRs2BDz58/HgAEDyvs+qQo59eAW9qZcQoFWXWRfptL4gJiGLjUwpUVX3uIkIqJKx2TCJgiCwbNk9vb2BtumREREFBlA8KQtW7YUKXN2dsYHH3zAZaioiEeqfHyZeKzUx78R8vhZSV+ZJ+rK3M0VFhERkVmZTNgaN26MBQsWICYmBgAQGxuL4OBgswdG9G+H715D7PU/S3Wsm8gOc9r2hrPU3sxRERERmZ/JhO3DDz/EvHnzMHjwYAiCgPbt22PmzJmWiI1IL1dVUGyytqBV72KOBpIuXmayRkREVUaJCZtOp4NGo8HHH39sqXiIinhYIMfMv34sUr4mYhDsxJJi69zic2pERFSFGE3Yzp49i/HjxyMjIwNBQUFYs2YN/Pz8LBkbVXNpimzMPb2nSLmjRIqV7QZaISIiIiLrMLr4++LFizFv3jycOXMG0dHRWLJkiSXjomru6qMHxSZrAPDBsy9bOBoiIiLrMpqw5eXloVOnTnB0dMTw4cNx8+ZNS8ZF1Zhap8XS878VKa/n7IFXA59HXZmH5YMiIiKyIqO3RMViw1yuLFN5EJVXtjIPH5zaVaR8VbuBcJCYHCNDRERUJRn9BhQEwZJxUDUnCAKuPnqAZRd+L7KvpMEFRERE1YHRhC0lJQVjxowxur1+/XrzRkbVyrQ/vy92lYIFrXozWSMiomrPaMI2ffp0g+2uXbuaPRiqfm7lZmDlxQPI06iK7Jv5XBRqOsqsEBUREZFtMZqwdezYETVqlLxAdkZGBmrWrFnhQVHVl63Mw65b53D8QVKRfWE16yPcxx++Mk8rREZERGR7jCZs06ZNQ5s2bRAdHQ13d8M1GOVyObZt24aTJ0/i888/N3uQVHWodVrsS72Cn1MuQanTFNn/enBbtPMJsEJkREREtstowrZu3Tps3LgRPXr0gL+/Pxo0aACtVouUlBQkJSVh2LBhWLdunSVjpUoqV1UAjaDDkXvXceTudeSoCwz2N6tRF2E16yPY3RteTq5WipKIiMh2lTitx6hRoxATE4MTJ07g5s2bEIvFePnll9GuXTvY23OdRjIuPV+OP9OT8X3yOaPH1HV2x8CAlmjiWduCkREREVU+Jie2cnR0RGRkJCIjIy0QDlUFOkGH2fG7oRF0Ro95NfB5vFCnESQio3M3ExER0d84EylVKK1Oh1/TLhebrLnbO0EmtceE0E7wdHC2QnRERESVExM2qjDXHz3AJ8UsKTWkUSu08moIJylXyyAiIioPJmxUbo9U+Zh3ei9y1QWQisTF9qq18W6IDnWCrBAdERFR1WHyASKFQoE5c+bg9ddfR3Z2NmbNmgWFQmGJ2MhGCYKAI3ev4/2TO5H794hPY8naiOBwS4dHRERU5ZjsYZs/fz68vb2RkZEBBwcHyOVyzJo1C0uXLrVEfGRj5OoCbLl2CmczUovdX8fJDX39W+DZmr4WjoyIiKjqMpmwXb58GQsXLsShQ4fg5OSEJUuWoEePHpaIjWxMQtZdbL56Ao9U+foyNztHPO/lh8g6wfB0cIa9hHfZiYiIKprJb1ex2PCuqVarLVJGVV98+m18fuUoBAj6ssg6QejnH8YkjYiIyMxMftO2atUKn3zyCQoKCnDkyBHExsaidevWloiNbMTFzDv4MvGYPllztXPE68Ft0KxGPStHRkREVD2Y7CqbNGkSnJ2d4erqiuXLlyMkJARTpkyxRGxkA64/eoD1l49A+/egAh8nN8x8LorJGhERkQWZ7GE7dOgQxo0bh3HjxunLdu3ahT59+pgzLrIBt+WZWH3pENQ6LQCgpoMME5u9CHd7JytHRkREVL0YTdj2798PjUaDxYsXQxAECMLj22EajQbLly9nwlbF3cvLwaqLB1CgVQN4PLjgnWZcoYCIiMgajCZsly9fxokTJ5CRkYGvv/76nwpSKd544w2LBEfWkVmgwIqL+5GrVgIAnKV2eKdZJ/g4uVk5MiIiourJaMJWeBs0NjYWQ4YMsWRMZCUqrQbT//wBOX9PhgsA9mIJxjeNhK/M04qRERERVW8mn2EbMGAA9u3bp1/dQKvV4vbt25g4caLZgyPLOfswBZ9ePmJQJhWJMfaZDgh087JSVERERASUImGbOHEiUlJSkJ6ejmeeeQbnzp3jtB5VzI/JF7D79oUi5W82jsAznnWsEBERERE9qVQrHfz666+YPXs2RowYAUEQMGfOHEvERma2/eZpHLl3Qz+w4EmL2/TlaFAiIiIbYTJh8/b2hlQqRcOGDXH16lVERUUhPz/fVDWycZlKBfalXSlS3rFOEAYHPg+xSGSFqIiIiKg4JhM2Z2dn/Pjjj2jcuDG+/fZbBAQEIDs72wKhkTkp1CqDbQexFMOC2+B5rwZWioiIiIiMMbnSwaxZs3D58mVERERALBYjJiaG03pUcjmqfMw/s1e/XdNBhuXt+jNZIyIislEme9gaNmyI999/HwCwYsUKAMD169fNGhSZT3q+HDP++sGgLFddAInIZO5OREREVmL0Wzo1NRXvvfce5s2bp39mTS6XY+HChVzloJI69eBWkWQNAD5s2d0K0RAREVFpGU3Ypk2bBk9PT6Snp+Ozzz7DX3/9haioKPzxxx/44osvLBkjVQCFWokvE48VKV/dbiBqObpYISIiIiIqLaO3RO/du4evv/4aBQUFiI6Oxrfffovhw4dj5MiRkEpN3knVO336NJYtW4ZLly7B0dERXbt2xaRJk+DiUrokITMzEz179sQLL7yARYsWlfq6ZGje6b0G21KRGIta94G9pPRtSURERNZh9Nva2fnxIt+Ojo549OgRFi9ejPbt25fp5AkJCRgxYgTatGmDFStWICUlBcuWLUNycjI2bdpUqnNMnz4dDx8+LNN16R9aQYf/3YhHlirPoHx1xCBO3UFERFRJlKp7pWbNmmVO1gBg5cqV8PHxwbp16/S9crVr18a4ceNw7NgxtGvXrsT6//vf/3DmzBk4ODiU+drVmSAIuJR1F8fu30T8w9tF9n/UqheTNSIiokrE6DNsoie+0CUSSZlPrFKpcPz4cXTu3NngFmpkZCScnZ2xf//+EuvfunULixYtwrx582Bvb1/m61c3WkGHhKy7OHE/CWP+2IrVlw4Wm6wNadSaz6wRERFVMkZ72BITE/Hcc88BAAoKCvSvBUGASCTC6dOnSzxxSkoKlEol/P39DS8olcLX1xc3btwwWlej0WDy5Ml4+eWX0aVLF0ydOrXUb6g6upx1DysulpwAA0APv1B0qNPIAhERERFRRTKasO3bt++pTpybmwsAcHV1LbJPJpNBLpcbrbt27Vqkp6dj48aNTxUDAFy8ePGpz2FKfHy82a9hzH1tPr5XFu1JKxQocUWI1B31xM4QPVQj/qH1YrU0a7YLFY9tYpvYLraHbWKbrNkuRhO2evXqPdWJdTpdiftFRp6hOnv2LD7//HN8/vnnxSZ7ZRUaGmrWZ+Di4+PRsmVLs52/JHK1EhtO7ChS3qKmL9ztndCn4bNwllbP28nWbBcqHtvENrFdbA/bxDaZu12USmWJnUxmm9PBzc0NAKBQKIrsUygU8Pb2LrZ88uTJGDhwIFq1agWNRqPfJwgCNBoNJBKJ0WSvOlGoVXjvX8maGCKsbT+YAwqIiIiqGLOtR+Tn5wepVIrk5GSDco1Gg9TUVDRqVPRZqosXL+L27duIjY1F06ZN9X9yc3Oxa9cuNG3aFKdOnTJXyJXGyQdJePfE9iLln7Tty2SNiIioCjJbD5u9vT3atm2Lffv2YcKECfqRogcPHkReXh46dOhQpE7Tpk2xfXvRRGTYsGFo06YNxo0bV2QQQ3VyLiMV6xIOF7vv/We7wMXO0cIRERERkSWY7GFLT0/HW2+9ha5du+Lhw4d444038ODBg1KdfPz48bh9+zbefPNNHDhwALGxsXj//fcRHh6OiIgIAI8Xkj979iwAwMXFBc2aNSvyRyKRwMPDA82aNSv1CglVjSAIxSZrIogwPawbAt28rBAVERERWYLJhG3OnDno3LkzHBwc4O7ujsaNG2PGjBmlOnlYWBg2bNgAuVyOCRMm4NNPP0WvXr2wZs0ag/MPGjSo/O+gmpCrlUXKXqwbgvUvvAo/lxpWiIiIiIgsxeQt0bS0NAwcOBDffPMN7OzsMHnyZPTs2bPUF4iIiND3phVny5YtJs/x119/lfp6VdW1HMNezc9eeM1KkRAREZGlmexhE4lEBlN0yOVyk1N2UMVLyLqnfx1Vv6kVIyEiIiJLM9nD9vLLL2PSpEnIzc3Ftm3b8N133yEqKsoSsdHfBEFAQtZd/fYznnWsGA0RERFZmsmEbcyYMdi1axd0Oh2OHTuGQYMGYcCAAZaIjf6WXiBHhvLxfHYOYikCXGtaOSIiIiKyJJMJ27Zt29CjRw/06dPHAuFQcS4/cTs02MMbUrHEitEQERGRpZl8hu3UqVPo3Lkzpk6dqp9+gywrIfuJ26EevB1KRERU3ZjsYVu2bBkePXqE3bt3Y/78+VAqlejfvz9ef/11S8RX7WkFHa5k39dvP+NZ24rREBERkTWUamkqd3d3DBo0CKNHj4aTkxM+//xzc8dFf7uVm4ECrRoA4GnvDB8nNytHRERERJZmsoft0qVLiIuLw88//4xnnnkGo0aNwosvvmiJ2AiG03k841mbC98TERFVQyYTtnHjxqFfv3747rvvULduXUvERH/LLFBg9+0L+u0mHrwdSkREVB2ZTNgOHjxogTDo3wRBwNzTewzKgty9rRQNERERWZPRhO3VV1/F1q1bERYWZnAbThAEiEQinD592iIBVke3cjOw8OwvBmXOUnu42ztZKSIiIiKyJqMJ28qVKwEAu3fvLrJPEATzRVTNXXv0AEvO/1akfHbL7nx+jYiIqJoyOkrU2/vx7bcPP/wQ9erVM/jz7rvvWizA6uRcRmqxydqbjSPYu0ZERFSNGe1hmzBhApKSkpCSkoKePXvqyzUaDcTiUs0GQmWg1emwLuFwkfIV4f3hJLW3QkRERERkK4wmbO+//z7S0tIwc+ZMzJw5U18ukUgQFBRkkeCqk4tZdwy2ZVIHzHu+B5M1IiIiMp6w+fr6wtfXF7/88gufnbKAjAKFwfYnbftCImJPJhEREZVilOhzzz3HUaIWcCnrn/VC+zZswWSNiIiI9Mo1SpQqllytNFjg/XkvPytGQ0RERLbG5CjRGjVqID09HfXq1cNvv/2GNWvW8BZpBTvzMAW6v6dK8XetiVqOLlaOiIiIiGyJyftuU6dOxe+//47z58/j66+/Rt26dQ0GIdDT+zM9Wf+6lVcDK0ZCREREtshkwpaSkoL33nsPBw4cQN++ffH2228jOzvbAqFVD49U+Uh8dB8AIALQshZvhxIREZEhkwmbWq0GAPzxxx9o27YttFot8vLyzB5YdfH+yZ3618HuPvBwcLZiNERERGSLTC7+/txzz+GVV16BRCLBc889h9dffx3t2rWzRGxVXqbScCqPFjV9rRQJERER2TKTCdvMmTNx5swZNG7cGGKxGG+88QY6dOhgidiqvHt5OQbbHetwQmIiIiIqymTCJpFI8ODBA+zYsQNqtRoRERFcmsoM/FxqQMLPlYiIiIphMmH78ssv8cMPP6Bv374QBAGbN2/GvXv3MHbsWEvEV6UduXtd/1rGJaiIiIjICJMJ265du7B161a4uDyeG6x///4YOHAgE7anlKMqwOmMFP0257YjIiIiY0p1D64wWQMAV1dXSKUm8zwyYcafPxhsd6oTbKVIiIiIyNaZTNjq1auHr776Cmq1Gmq1Gps3b0bdunUtEVuVptRpDLab16xnpUiIiIjI1plM2ObMmYPffvsNLVq0QIsWLfDrr7/iww8/tERsVVaBVm2wPbdlDytFQkRERJWByXubPj4+2LJlC/Lz86HT6SCTySwRV5X2zrHvDLZrOvIzJSIiIuOM9rDdunUL/fr1w3PPPYf//Oc/yMvLY7JWARRqpcG2s9QeUrHEStEQERFRZWA0YZs7dy769u2L7777Dg0aNMDixYstGVeVJAgCYq//aVA2pcXLVoqGiIiIKgujCdvDhw8RExODwMBATJo0CZcuXbJkXFXSnpRLiH9426DMx8nNStEQERFRZWE0YXty6g6JRMKpPCrA5ax7BtuDA1taKRIiIiKqTIwmbIIgGGxzYtenoxV0uJbzQL/drEZdtK/dyIoRERERUWVhtNvs3r17mD9/vtHtGTNmlOoCp0+fxrJly3Dp0iU4Ojqia9eumDRpksFkvP92584drFy5EidPnsSjR49Qv359DB48GIMHD66U65jqBAEfnNxlUNarQXPYcbABERERlYLRhG3IkCElbpdGQkICRowYgTZt2mDFihVISUnBsmXLkJycjE2bNhVbJycnBzExMRCLxXjnnXfg4+ODI0eOYO7cubh582apE0Vbkph9H7nqAoOy+jJPK0VDRERElY3RhG38+PFPffKVK1fCx8cH69at0z8DV7t2bYwbNw7Hjh1Du3btitTZuXMn0tLSsGfPHgQGBgIA2rVrB7lcjm+++QYTJkyAm1vlelD/Srbhs2vzn+/JW8xERERUama7v6hSqXD8+HF07tzZYMBCZGQknJ2dsX///mLreXl5YejQofpkrVCjRo2g1WqRnp5urpDNIiHrLn5OTdBvt/ZqCC8nVytGRERERJWN2YZ+pqSkQKlUwt/f3/CCUil8fX1x48aNYuu98soreOWVV4qU//LLL5DJZKhfv75Z4jUHpVaDlRcPGJT5unhYJxgiIiKqtMyWsOXm5gIAXF2L9ibJZDLI5fJSn+vTTz9FfHw8Jk6cCHt7+zLFcfHixTIdXx7x8fHFlqdqFQbbDhDD454C8feLP54qlrF2Iethm9gmtovtYZvYJmu2i8mETafTYePGjbh27RpmzpyJ2NhYjBo1ChJJySMcdTpdiftL+wzXqlWrsHbtWkRFReGtt94qVZ0nhYaGwsHBocz1Sis+Ph4tWxY/n5pD5h3gUqp+e2X7QXx2zUJKaheyDraJbWK72B62iW0yd7solcoSO5lMJmyLFy9GZmYmLly4AAA4cuQI0tPTTY7WLBwYoFAoiuxTKBTw9vYusX5BQQGmTp2KPXv2oF+/fpg3b16lnNKjUFPPOkzWiIiIqFxMZkDHjx/HokWL4ODgABcXF2zcuBFHjx41eWI/Pz9IpVIkJycblGs0GqSmpqJRI+OTxmZmZmLIkCHYu3cvJk6ciAULFpjs0bNFSbkZ1g6BiIiIqgCTCZtUKjXo2bK3ty/VMlX29vZo27Yt9u3bB41Goy8/ePAg8vLy0KFDh2Lr5eXlYfjw4UhMTMSyZcswZsyY0rwPm5OjKsDu2xesHQYRERFVASYzr+DgYMTGxkKr1eLmzZvYvHkzGjduXKqTjx8/HjExMXjzzTcxbNgw3LlzB0uXLkV4eDgiIiIAANevX4dcLkeLFi0AAKtXr0ZiYiKGDh2KunXr4uzZswbnDAkJgZOTU9nepRXczXtksO3vWstKkRAREVFlZzJhmz59OhYsWICMjAy8+uqraN++falXGwgLC8OGDRuwYsUKTJgwAe7u7ujVqxcmTZqkP2bOnDk4deoUEhMTAQB79+4FAGzZsgVbtmwpcs5du3ahSZMmpbq+LelW/xlrh0BERESVlMmEzcXFBQsWLCj3BSIiIvS9acX5d1J28ODBcl/LlmiFf0bJBrt7c91QIiIiKjeTCduTC74/qTKu6WlJW66dtHYIREREVEWYHHTg4eGh/yOTyXDmzBlLxFXp6QRB/9pZWrbJfomIiIieZLKH7d+LwI8ePRqjR482W0BVUXe/UGuHQERERJVYmWeidXZ2xoMHD8wRS5Xlaudo7RCIiIioEjPZwzZv3jz9DP2CIODSpUsICAgwe2BERERE9JjJhM3T09Ngu1evXujVq5fZAqoKBEFAtirf2mEQERFRFWEyYbt9+zYWL15siViqjDv/mjTXQWJ6ZQgiIiIiY0w+w5aYmAjhiRGPZFpC1l2DbY4SJSIioqdhsuunVq1a6N69O5599lnIZDJ9OedhM+7RE7dDZUzWiIiI6CkZTdhUKhXs7e0RFhaGsLAwS8ZU6YlF/3RchtWqb8VIiIiIqCowmrANGjQIO3fuLDIPG5WNl6OLtUMgIiKiSs7oM2x8bo2IiIjINhjtYVMqlUhISDCauDVt2tRsQVV2ZzNSrB0CERERVSFGE7aUlBS8/fbbxSZsIpEIv//+u1kDq8xy1QX611r2VBIREdFTMpqwNWrUCLt27bJgKJWfTtDh2P0k5GnU+rJgd28rRkRERERVQZnXEiXjLmXdxZZrJw3K3LiOKBERET0lownb888/b8k4qoRTD24ZbHs6OKOWE0eJEhER0dMxmrBxYtyyO5WerH/tKLHDjLAoSETsxCQiIqKnw2yigsQlnTXYjm7YAi52DtYJhoiIiKoUJmwV4I4iG7+kJhiUtasdYKVoiIiIqKphwlYBzmakGmz3btAcdmKJlaIhIiKiqoYJ21PSCDp8n3xev+3j5IpX/EKtGBERERFVNUzYnlKqVmGw/WxNXytFQkRERFUVE7andEXzyGA7umEL6wRCREREVRYTtqckEYn0r0PcfSB6YpuIiIioIjBhe0pJWrn+dctaflaMhIiIiKoqJmwVyMPBydohEBERURXEhO0pZCnzDLZDPHysFAkRERFVZUzYnsKvT0yW62rnAHux1IrREBERUVXFhO0p7L9zVf+6sUdtiDnggIiIiMyAXUJPwUEihVKrAQC8ULuRlaMhIiodtVqN1NRUFBQUQCqV4vLly9YOiZ7ANrFNFdUujo6O8PX1hZ2dXdmu/9RXrsae7E/zc6lhtTiIiMoiNTUVrq6uaNiwIfLy8iCTyawdEj1BoVCwTWxQRbSLIAjIyMhAamoq/P39y1SXt0SJiKqZgoIC1KxZk/NGElmYSCRCzZo1UVBQUOa6TNiIiKohJmtE1lHenz0mbEREREQ2jgkbERFZTWpqKkJDQ9G7d2/07t0bPXv2xIsvvohVq1ZVyPnj4uIwZcqUCjlP69at9XH27t0bb7zxRgVEWLzz58/jk08+0W8/ePAAkyZNQvfu3dGrVy+MHj0aKSkp+tgq4j0W6t27NwBALpcjOjoavXv3xtdff42VK1c+1Xk//vhjJCT8Mx3WgQMHEBISgosXLxoc9+KLLyI1NdWgbOjQoTh58iQAQKVSYfny5ejZsyd69+6NgQMH4tixY+WOKycnB2+99RaioqIwZMgQpKenFzlm1qxZGDx4sL7tmzRpgp9//tngmAkTJmD16tUAHj/vNn78eGi12nLH9W8cdEBERFbl7e2N77//Xr99//59dO3aFd27d0dgYKAVIzP04osvYtGiRRa51vXr15GRkQEAyMvLw9ChQzFy5Eh88sknEIlE+OGHHzBixAjs3bu3wq9d2BaXL1+Gvb09tm3b9tTnvHLlCtLT0/HMM8/oy+Li4tC1a1ds27YN8+fPL/W5pk6dCnt7e2zfvh0ODg5ITEzEyJEj8dVXX6FRo7LP2LBixQo8//zz2LBhA3bt2oWPPvoIK1asMDhm7ty5+kEH27dvx969e9G1a1f9/u3bt+PkyZMICgoCAMhkMoSHh2Pbtm0YMmRImWMqjtkTttOnT2PZsmW4dOkSHB0d0bVrV0yaNAkuLi5G66hUKqxatQq7d+9GZmYmgoKCMGHCBHTs2NHc4RIRkZWlp6dDEATIZDJoNBrMnj0b165dw8OHD+Hv7481a9bg4cOHGD9+PIKCgnD58mXUrFkTK1euhIeHB3bt2oVPP/0ULi4uqFevHpydnQEAZ8+exUcffQSlUglPT0/MnTsXDRo0wNChQ9GkSRMcP34cBQUFmDFjBrZs2YLr169j+PDhGD58eInxlnRed3d3XLt2DStWrEB6ejpWrVoFjUYDX19fzJs3D56envj4449x9OhRSCQSvPTSS+jfvz9WrVqFvLw8fPrpp6hVqxa8vb0xaNAg/TV79eoFe3t7qFQqg1j27t2LTZs2oaCgAEqlEvPnz0erVq2wadMm7Ny5E2KxGM2bN8fcuXNx5coVzJo1CxqNBg4ODli4cCEaNmyIkJAQHDt2DNOmTcPDhw8xZswYvPzyyzh16hQWLVqE8+fPY+HChSgoKICnpyfmzJmD+vXrF3m/TZo00ce1ceNG9OrVS7+dmZmJ48ePY9euXejTpw+mTJlSYl5QKDk5Gfv378fRo0fh4OAAAAgJCcGyZcvg6OhocOy5c+cwa9YsgzKZTIZvvvnGoOzgwYOIjY0FAPTo0QNz586FWq0udtqNrKwsrFq1Clu3btU/i5acnIydO3di8ODBBsd2794dgwYNwmuvvVYhz4yaNWFLSEjAiBEj0KZNG6xYsQIpKSlYtmwZkpOTsWnTJqP1pkyZggMHDuC9996Dr68vtm3bhrFjx2Lz5s1o3bq1OUMuNUEQUPD3HGxERJXVwfvX8cu9RP2ckhXJQSJFT79m6OLbpMTjHjx4gN69e0OpVCIrKwvNmjXDmjVrULt2bfz555+ws7PD//73P+h0Orz++us4dOgQmjZtiitXrmDBggV45pln8Pbbb+PHH3/Eyy+/jCVLlmDXrl3w8PDA6NGj4ezsDJVKhXfffRcrVqxA8+bNsXfvXrz77rvYsWOHPo4ff/wRa9aswfz58/HDDz8gMzMTffr00Sds+/fv198uBB739Dz33HMlnjckJARr1qxBZmYmpkyZgq+//hru7u7Ytm0blixZgv/85z84fPgwfvrpJyiVSkyfPh329vaYMGECTp06hbFjx2Lu3Llo3rx5kc+tW7duBts6nQ7btm3D+vXrUaNGDWzfvh1ffvklwsLC8Nlnn+HIkSOQSCSYM2cO7t+/j6+++gojRoxAVFQU9uzZg7Nnz6Jhw4YAgJo1a2L+/PlYs2YN1q9fj7i4OACPO1RmzJiB9evXo27dujhy5AhmzpyJzZs3G7zfJwmCgIMHD2L27NkGn3VERAR8fX0RGhqK77//vlQ9UZcvX0ajRo30SXihNm3aFDn22WefNei5NebBgwfw8vIC8HiuNRcXF2RmZsLHp+hyk5s3b0b37t1Rr149AIBGo8GMGTMwZ86cIr2dHh4ecHZ2RmJiIho3bmwyDlPMmrCtXLkSPj4+WLduHaTSx5eqXbs2xo0bh2PHjqFdu3ZF6ly4cAE//fQTFi1ahL59+wIAOnTogP79+2P58uXYunWrOUMutVvyDINtCUdcEVEldPDBdbMkawCg1GqwL+2KyYSt8JaoTqfDokWLkJiYiLZt2wIAWrVqBQ8PD8TGxuLmzZu4desW8vIer+Ncs2ZN/S22oKAgPHr0CGfOnEFYWBhq1aoFAOjZsydOnDiBW7duwc3NTZ/4REVFYdasWcjNzQXw+HsGAOrWrYtnn30WTk5OqFevHnJycvRxFndL9OrVqyWet7D83LlzuHv3LoYNGwbgcXLl7u4OHx8fODg4YPDgwejUqRP+7//+T99zVEgsFkMQBJOft1gsxtq1a7F//34kJSXh1KlTEIvFkEqlCAsLQ//+/fHSSy9hyJAh8PHxQceOHTF37lwcOXIEnTp1MrjFZ8ytW7eQkpKCsWPH6svkcrn+dXGJZVZWFgAYJFlxcXEYP348AOCVV17Bf//7X33CVlxvlCAIEIvFpf4sgNL3sBVHLC76iL9Op8OOHTsMkvzVq1ejS5cuRm/F1q1bF7du3bLthE2lUuH48eOIiYnRJ2sAEBkZCWdnZ+zfv7/YhO3QoUMQi8Xo0qWLvkwsFqNbt25YunQpsrKy4Onpaa6wS61AY/gLzl7CxwGJqPKJ9G5k1h62LvVK/0UlFovx/vvvo0+fPti4cSNGjx6N33//HatWrcKwYcMQHR2NrKws/Rf2k4mNSCSCIAgQiUTQ6XT68sLvnyfLCgmCoH8o/MnbX09+Z5li6ryFt+m0Wi2ee+45rF+/HgCgVCqhUCgglUrx3Xff4dSpUzh8+DAGDx6MDRs2GJwvNDRU38P1pOnTpxvcrlUoFOjXrx969+6NVq1aISQkRH+rb926dTh79iwOHz6MUaNGYcmSJejWrRvCwsJw4MABfPXVVzh06JDJZ8l0Oh18fX31PVdarRYPHz7U7//3bUngcdtIJBL9dkJCAq5evYqPPvoICxcuhFarxYMHD/TJtru7uz7hLZSRkQE3NzfUq1cPN27cQEFBgcG1Nm/eDC8vL3Tv3l1fVtoeNm9vbzx8+BC1a9eGRqOBXC6Hh4dHkePOnz+Phg0bGvS8/fLLL7C3t8eOHTv0n4OTkxNGjRoF4PG/peKSv/IwW5aRkpICpVJZZCZfqVQKX19f3Lhxo9h6N27cgLe3d5F72Q0aNAAA3Lx5Ey1btjRP0OXU2KNotykRUWUQ6dMI3QOetXYYelKpFO+//z7eeecdREdH4/jx44iKikK/fv1w//59/PnnnwgPDzdav2XLlpg3bx7u378PLy8v7NmzB25ubggICEB2djbOnz+P5s2bY8+ePahbt26xX8xlUdrzPvvss5gxYwaSkpLg7++PdevW4f79+xg2bBjmzZuHLVu2IDw8HAkJCbh16xYkEgk0f3cMdOvWDatXr8Z3332HAQMGAAB27NiBU6dO4cMPP8SFCxcAPO79EovFGDNmDABgxowZ0Gq1yMzMxGuvvYYdO3YgLCwM9+7dQ2JiIr755ht0794dgwcPRmBgIBYuXFiq9/vo0SP89ddfeP7557Fjxw78+OOP2LJli9E6np6e0Ol0+of24+LiMHDgQMyZM0d/zJQpU/C///0PYWFhaNu2LXbs2IHp06dDJBLh1KlTyMvLQ2BgIKRSKSIjIzFv3jzMmjULDg4OSEhIwBdffIGNGzeWqe0KdezYEbt27cKYMWOwZ88ePP/888U+v3b+/Pki+ceTI0ULR4gWJmvA41HQfn5+5Yrr38yWsBVmx66urkX2yWQygy7UJ+Xk5BRbpzCBM1bPmH8PF64oSkELKUTQQICLQoP4+HizXIfKj21ie9gmtkEqlUKhUOi3n3xtafn5+fov80ItW7ZEs2bNsGTJErz66quYPn069uzZAzs7O4SGhiIpKQktWrQwqFf48L2TkxMmT56MYcOGwcnJCQEBAdBoNFCr1ViwYAHmzJmD/Px8uLm5YcGCBVAoFNBqtSgoKIBCoYBSqYRGoyny+RRXXqg053V2dsasWbMwYcIE6HQ6eHt7Y/78+fDw8EDTpk3RvXt3ODo6IiQkBBEREUhLS8OqVauwcOFCTJgwAevWrcPSpUuxceNGiEQi1KtXD2vWrIFardbHVr9+fQQFBaFr165wdHREy5YtkZaWBgcHB/Tt2xfR0dFwdHREnTp10LVrV4SGhmLu3LlYs2YNJBIJ/u///k///hQKBQoKCqDVag3ev1qtxqJFi7BgwQIolUq4uLhgzpw5Rd7vv7Vr1w5//PEH2rRpgx9//BGfffaZwXGDBg3C8OHD8c4772D48OFYvHgxXnnlFYhEIri5uWHp0qVQKpX65/xWrVqFXr16wc7ODo6Ojpg7dy7q1atXrn/Lb775Jj788ENERUXB1dUVH330ERQKBQ4dOoRDhw7pb6umpaUhKCjI6DUK/w0W7s/NzUVOTg7q169fpI5KpSrz70ORUNqbwWV0+vRpvPrqq1i5cmWRByMHDx4MnU6Hb7/9tki9kSNH4sGDB9i9e7dB+dGjRzFy5Eh8/vnn+mcNSqJUKnHx4kWEhoYWeR6gomQqFThyLh49W7WHWMQp7WxJfHy8zfXEVndsE9tx+fJl/Qg+rltpe6pim1y5cgXr1q2rsPn1rKGs7fLVV19BKpUWO5jiyZ/BQqbyFrNlGW5ubgCK/5+bQqEothcNeNwjV1ydwp61wvPaghoOMvhKZEzWiIiIStC4cWPUqVPHYOLcqkyhUOD48eMGU7E8LbNlGn5+fpBKpUhOTjYo12g0SE1NNTqiIiAgAPfv30d+fr5BeXJyMkQiEQICAswVMhEREZnJ1KlTDSbOrcpkMhnWr19fpgEsppgtYbO3t0fbtm2xb98+/YOTwOMJ6vLy8oze1uzQoQO0Wi1+/fVXfZlOp8PPP/+MFi1a2FQPGxEREZElmHUuivHjxyMmJgZvvvkmhg0bhjt37mDp0qUIDw9HREQEgMfLb8jlcrRo0QIAEBYWho4dO2LWrFnIysqCv78/tm7disTERHz55ZfmDJeIqNoonAKDiCyrvEMHzJqwhYWFYcOGDVixYgUmTJgAd3d39OrVC5MmTdIfM2fOHJw6dQqJiYn6suXLl2Pp0qXYsGEDFAoFgoKC8Omnn+onUiQiovJzdHRERkYGatasae1QiKoVQRCQkZFR7Hx1pph9tteIiAh9b1pxipu7RSaTYdasWUVmKCYioqfn6+uL1NRUpKenQ6VSwd7e3toh0RPYJrapotrF0dERvr6+Za7H6fmJiKoZOzs7/aTm8fHxePZZ25k4l9gmtsra7cL5KIiIiIhsHBM2IiIiIhtXZW+JFo7CKFwqwpyUSqXZr0Flx3axPWwT28R2sT1sE9tkznYpzFeMjSI129JU1pabm4urV69aOwwiIiKiUgsODi52Nagqm7AVLgpsZ2fHuYaIiIjIpgmCALVaDZlMBrG46BNrVTZhIyIiIqoqOOiAiIiIyMYxYSMiIiKycUzYiIiIiGwcEzYiIiIiG8eEjYiIiMjGMWEjIiIisnFM2IiIiIhsHBO2Epw+fRoxMTEICwtDeHg4Zs+eDblcXmIdlUqFJUuWIDIyEs2bN0e/fv1w6NAhC0Vc9ZWnTe7cuYMPPvgAkZGRCAsLQ69evfDNN99Ap9NZKOqqrzzt8qTMzExERERgypQpZoyyeilPm2i1Wqxfvx4vv/wymjVrhs6dO2Pt2rXQarUWirrqK0+75OTkYP78+XjxxRcRFhaGfv364bfffrNQxNWHXC5Hly5dsHnzZpPHWuO7ngmbEQkJCRgxYgScnZ2xYsUKjBs3Dj/88APefvvtEutNmTIFsbGxGDVqFFatWgUvLy+MHTsWp06dslDkVVd52iQnJwcxMTGIj4/HO++8g7Vr1yIiIgJz587FggULLBh91VXen5UnTZ8+HQ8fPjRjlNVLedtkxowZWLt2Lfr3748NGzagd+/eWLt2LZYsWWKhyKu28rbLhAkTEBcXh5EjR2Lt2rUICAjAuHHjmLRVoKysLLz11lu4fft2qY63yne9QMV66623hC5dughqtVpftm/fPiE4OFg4evRosXXOnz8vBAcHC3FxcfoyrVYr9O3bVxg8eLDZY67qytMmmzdvFoKDg4Xr168blM+YMUNo0qSJ8OjRI7PGXB2Up12etG3bNqFNmzZCs2bNhA8++MCcoVYb5WmTs2fPCsHBwcLOnTsNyj/66COhZ8+eglarNWfI1UJ52uXWrVtCcHCw8MUXX+jLdDqdEBUVJQwdOtTsMVd1Op1O2LNnj9ChQwehdevWQnBwsLBp06YS61jru549bMVQqVQ4fvw4OnfuDKlUqi+PjIyEs7Mz9u/fX2y9Q4cOQSwWo0uXLvoysViMbt264fTp08jKyjJ77FVVedvEy8sLQ4cORWBgoEF5o0aNoNVqkZ6ebta4q7rytkuhW7duYdGiRZg3bx7s7e3NHW61UN422bNnDzw8PNCrVy+D8mnTpuGHH34odm1DKr3ytktBQQEAwMXFRV8mEong5uaG7Oxss8ZcHaSlpeG9995D+/btsXHjxlLVsdZ3PX8Ci5GSkgKlUgl/f3+DcqlUCl9fX9y4caPYejdu3IC3t7fBDxYANGjQAABw8+ZN8wRcDZS3TV555RXMmDGjSPkvv/wCmUyG+vXrmyXe6qK87QIAGo0GkydPxssvv2zwi4+eTnnbJCEhAUFBQTh8+DCio6PRtGlTvPDCC1i7di2f96wA5W2XkJAQtG/fHhs2bMD58+eRm5uL2NhYnDlzBtHR0ZYIvUrz9PTEL7/8go8++gju7u6lqmOt73qp6UOqn9zcXACAq6trkX0ymczoA6I5OTnF1ils1LI8hE2Gytsmxfn0008RHx+PiRMnslfnKT1Nu6xduxbp6eml/l8tlU552yQjIwM5OTmYOnUqxo8fj8DAQBw4cACrV69GVlZWsf/xodJ7mp+V2bNnY/To0RgwYIC+bOjQoRg+fHiFx1ndyGQyyGSyMtWx1nc9E7ZimPrfpEgkKrZcEIRy1SPTytsm/7Zq1SqsXbsWUVFReOuttyoitGqtvO1y9uxZfP755/j888+L/cVH5VfeNlGr1UhPT8eXX36J9u3bAwDatm0LuVyOb775BqNHj4aXl1eFx1tdlLddkpKSMHjwYHh6emLJkiXw9vbG0aNHsXHjRtjZ2eGDDz4wR7hUAmt91zNhK4abmxsAQKFQFNmnUCjg7e1dbD1XV1ckJSUVKS/MtgvPS2VX3jYpVFBQgKlTp2LPnj3o168f5s2bx2dyKkB52kWhUGDy5MkYOHAgWrVqBY1Go98nCAI0Gg0kEgn/g1NO5f1ZcXFxgZOTEyIiIgzKIyMjsX37dly5coUJ21Mob7ts2rQJCoUC33//PWrXrg0AaNOmDRwcHLBq1SpER0cjKCjIfIFTEdb6ruc3VjH8/PwglUqRnJxsUK7RaJCamopGjRoVWy8gIAD3799Hfn6+QXlycjJEIhECAgLMFnNVV942AR7P8TVkyBDs3bsXEydOxIIFCyCRSMwdcrVQnna5ePEibt++jdjYWDRt2lT/Jzc3F7t27ULTpk05Dc5TKO/Pir+/PzQajUECDTzueQMAJycn8wRcTZS3XdLS0lCvXj19slaoTZs2AIBr166ZJ2Ayylrf9UzYimFvb4+2bdti3759Br+8Dh48iLy8PHTo0KHYeh06dIBWq8Wvv/6qL9PpdPj555/RokUL9rA9hfK2SV5eHoYPH47ExEQsW7YMY8aMsVTI1UJ52qVp06bYvn17kT/Ozs7o1KkTtm/fjqZNm1rybVQp5f1ZiYyMhFqtxp49ewzKf//9d8hkMrbJUypvuwQEBCAtLQ337t0zKD99+jQAcOCUFVjru563RI0YP348YmJi8Oabb2LYsGG4c+cOli5divDwcP0tg+vXr0Mul6NFixYAgLCwMHTs2BGzZs1CVlYW/P39sXXrViQmJuLLL7+04rupGsrTJqtXr0ZiYiKGDh2KunXr4uzZswbnDAkJYc/BUypru7i4uKBZs2ZFziORSODh4VHsPiqb8vysdO/eHdu2bcPs2bORmZmJkJAQ/Pbbb9i9ezemTp3Kn5MKUJ52GTlyJHbv3o3hw4dj7Nix8Pb2xsmTJ/HFF1+ga9eu/HmxAFv5rhcJpp6eq8aOHj2KFStW4MqVK3B3d0fnzp0xadIk/UiQoUOH4tSpU0hMTNTXUSgUWLp0KX7++WcoFAoEBQVhwoQJRv/3RGVT1jaJjIzE3bt3jZ5v165daNKkiUVir8rK87Pyb88//zw6d+6MRYsWWSrsKq08bZKXl4fVq1djz549yMjIQMOGDfH6668bjE6kp1OedklJScGyZctw7NgxFBQUwM/PD/369UNMTIzBnG70dFJTU/HSSy9h6tSpBiNwbeW7ngkbERERkY3jM2xERERENo4JGxEREZGNY8JGREREZOOYsBERERHZOCZsRERERDaOCRsRERGRjWPCRkRERGTjmLARVSIhISHo2bMnevfurf8zffr0EuvExcVh9OjRFXL91atXo23btujduzf69OmDnj17Yvjw4cUuhFwa9+/fx+DBgwE8nhz07bffLlL+tFJTU9GkSRODz6xLly4YOnQoUlJSTNZfs2YNfvvttzJfV6vVYvTo0Xj48CHi4uLQsmVL/efWu3dvDB48GGfOnCnPWwIA9O7dGzk5OcjNzcWwYcOKlD+tkydPokePHiaPCwkJQWZmZpnOPWXKlFLPCC8IQpHjf/vtN6xZs6ZM1ySq7DhFMlEl89VXX6FGjRpWu/4rr7yCWbNm6be3bNmC9957D3FxcWU+l4+PD7Zt2wYAuHPnjj7xe7K8Ijg6OuL777/XbwuCgPnz52P58uVYtmxZiXVPnjxpdGHukmzcuBGtW7dGrVq1ADxeyeGzzz7T79+/fz/efvttHDx4sFyz1Re+n9TUVFy4cKFIeVVw48YNzJkzB+fOnUNQUJC+vHPnzoiNjcXly5e5UglVG+xhI6oitm/fjgEDBqBPnz7o1KkTvvnmmyLH/Prrr+jbty+io6MxYMAA/PnnnwCA3NxcTJkyBdHR0ejZsycWLFhgsEB1ScLDw/WJ1r179zBmzBj07NkTPXr0wBdffAEA0Gg0+PDDD9GzZ09ER0djwoQJUCgUSE1NRVhYGLRaLWbMmIHbt2/jjTfe0JfrdDp07NjRICGZOHGi/r19+umn6Nu3L3r37o3//Oc/uH//fqliViqVSE9Ph7u7OwAgKSkJI0aMwKBBg9CpUyeMHTsWSqUSsbGxuHjxIhYvXox9+/ZBpVJhwYIF6Nu3L3r16oUpU6ZALpcXOX9+fj6++uorREdHl/i5paen63vJJk2ahB49eqBnz55YvHix/vNftWqV/nN744038ODBAwD/9GxNnToVBQUF6N27N7Rarb588ODB+Pnnn/XXW7JkCT755BMAwHfffYfo6Gj06dMHw4cPx40bN0r8vIx9PoVWrFihb4cDBw7oy0tznZUrV2LlypXFXjc2NhbR0dGIiooqsq9///7sZaPqRSCiSiM4OFjo0aOH0KtXL/2fhw8fCnK5XBg4cKCQmZkpCIIgnDlzRmjRooUgCIKwY8cO4a233hIEQRBeeukl4cyZM4IgCMKRI0eE1atXC4IgCFOmTBG+/vprQRAEQaPRCJMmTRI2bNhQ5PqrVq0S5syZo99Wq9XCwoULhdGjRwuCIAhDhgwRNm7cKAiCIOTk5Ag9e/YUdu/eLfz5559Ct27dBJ1OJwiCICxevFiIj48XUlJS9HGeOHFC6N69uyAIgkH5ypUr9dfMzs4WWrduLeTk5Ag7d+4U/u///k9Qq9WCIAjCtm3bhFGjRhWJOSUlRWjcuLHQq1cvoUePHkJ4eLjQrVs3YdmyZYJcLhcEQRAWLVok7Nq1SxAEQVCpVEKPHj2En3/+WRAEQYiJiRH27t0rCIIgrF69Wli0aJH+fSxdulT48MMPi1xz//79QkxMjH77yTYQBEHQ6XTCpk2bhB49egiCIAjvv/++MG/ePEGn0wlKpVIYOXKk8Nlnnwl37twRnnvuOUGpVAqCIAhffvmlsG/fPkEQHv9byMjIMPisnizfvn27/poajUZ44YUXhKSkJOHkyZPCa6+9JuTl5QmC8PjfQVRUVJH38GR7lPT5BAcHC5999pkgCIKQmJgotG7dWsjIyCjxOh988IHwxRdfFLmmMcUdn5ubKzRv3lzIz88v9XmIKjPeEiWqZIzdEl2/fj0OHTqEW7du4cqVK8jLyytyTPfu3TF+/Hh07NgRERERePPNNwEABw8exIULF7B9+3YAQEFBgdHr79mzB/Hx8QAAtVqNpk2bYt68ecjLy8Pp06exceNGAICrqyuio6Nx+PBhTJ8+HRKJBAMGDED79u3RtWtXNG/eHKmpqSbfb79+/dC/f39MmTIFu3fvRqdOneDq6ooDBw7gwoUL6NevHwBAp9MhPz+/2HM8eUv0yJEjmDx5MiIiIiCTyQAAkydPxtGjR/H555/j1q1bePDgQbGf38GDB5Gbm4tjx47p33/NmjWLHHfz5k34+fkZlP3111/o3bs3RCIRVCoVAgICsGrVKgDA4cOHsXXrVohEItjb22Pw4MH46quvMGrUKDRu3Bh9+/ZFhw4d0KFDB4SHh5v8zAAgKioKixcvRnp6OhISEtCgQQM0bNgQ3377LZKTkw2eEXz06BGys7Ph4eFR7LlMfT6vvvoqACA4OBiBgYE4c+YM4uPjjV6nIri4uMDFxQVpaWkIDAyskHMS2TImbERVwL179zBo0CAMHDgQLVu2RLdu3QxuTRWaOHEi+vfvjz/++ANxcXHYsGED4uLioNPpsHLlSv0XX05ODkQiUbHX+vczbIXkcjkEQTAo0+l00Gg0cHNzw/fff4/Tp0/jxIkT+L//+z8MGzYMnTt3Nvne6tWrh2eeeQYHDx5EXFwcpk2bpj/3qFGj8NprrwEAVCoVHj16ZPJ8L7zwAkaMGIF3330Xe/fuhaurK959911otVpERUUhMjISd+/eLfJeCq85bdo0dOzYEQCgUCgMbg0WEovF0Ol0BmX/fobt3+f997ZGo4FYLMZ///tfXLhwAcePH8eCBQvQpk0bzJgxw+T7dHZ2RteuXbF7926cOXMGAwYM0J+7d+/emDx5sn77wYMH+tvDxTH1+YjF/zxdIwgCpFJpua5TVlqtFhKJpMLOR2TL+AwbURVw8eJF1KhRA//5z3/wwgsv6JM1rVarP0aj0eDFF19EXl4eXn31VXz44Ye4ceMGNBoN2rdvj82bN0MQBKhUKowdOxb//e9/yxSDi4sLnn32WcTGxgJ4/Fzcrl270K5dOxw4cADDhw9HWFgY3n77bfTp0wdXrlwxqC+RSKBWq4s998CBA/H555+joKAALVu2BAC0b98e27dv1z9DtnLlSrz//vulinXkyJFwc3PT93D98ccfGDduHF555RWIRCKcO3dO/9lJJBL982Tt27dHbGwsVCoVdDodZs6cWeyghYYNG5ZqBGqhwvMWfv7ffvst2rVrhytXrqBHjx4IDAzE6NGjMXz4cCQmJhrUlUql0Gq1xSaYAwcORFxcHM6cOYOuXbsCACIiIvDTTz/pn4XbunUrXn/99RLjK+nzAYCdO3cCAC5duoTk5GQ8++yz5bpOWeTm5kKpVKJu3boVdk4iW8YeNqIqICIiAtu3b0e3bt3g5OSE5s2bo0aNGkhOTtYfI5VKMW3aNEyaNAlSqRQikQgLFiyAvb09pk+fjo8++gg9e/aEWq1Gu3btMGrUqDLHsWTJEsydOxdxcXFQqVT6h+V1Oh0OHz6MHj16wNnZGe7u7pg3b55B3aCgIEgkEvTv3x/Lly832Pfiiy9izpw5+lu4ADBgwADcv38fAwcOhEgkQp06dbBo0aJSxWlnZ4eZM2di1KhRGDBgACZOnIhx48bB3d0dTk5OaNWqFW7fvg0A6NSpEz7++GOo1Wr85z//wccff4y+fftCq9WiSZMmmDJlSpHzt2vXDtOnT0dOTg7c3NxMxjNjxgzMnz9f//m/8MILGDNmDOzt7REVFYV+/frB2dkZjo6ORXrXvLy88MwzzyAqKgpbt2412BcaGgqpVIquXbvCwcEBwOMexjfffBMjR46ESCSCi4sL1qxZY7RHFUCJnw/weEqWPn36QCQSYdmyZfDw8Cj1dQoHHLzzzjsmP6cn/fHHH4iMjIS9vX2Z6hFVViKhuP+WERHRU1m/fj0kEolBkkkVZ9iwYZg2bRoaN25s7VCILIK3RImIzGDkyJE4ceIE0tPTrR1KlbNv3z48//zzTNaoWmEPGxEREZGNYw8bERERkY1jwkZERERk45iwEREREdk4JmxERERENo4JGxEREZGN+38lmtFf6AHk2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_clsf = RandomForestClassifier(random_state = 42, max_depth = 10, n_estimators = 1000)\n",
    "rf_clsf.fit(X_train, y_train)\n",
    "\n",
    "score_model(rf_clsf)\n",
    "\n",
    "evaluate_model(rf_clsf)\n",
    "\n",
    "plot_roc_curve(rf_clsf, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c938683b",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "170dc37f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accurarcy 0.791\n",
      "cross validation Mean : 0.797625 and STD of 0.000374999999999992\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      1.00      0.88      1582\n",
      "           1       0.00      0.00      0.00       418\n",
      "\n",
      "    accuracy                           0.79      2000\n",
      "   macro avg       0.40      0.50      0.44      2000\n",
      "weighted avg       0.63      0.79      0.70      2000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_clsf = SVC()\n",
    "svm_clsf.fit(X_train, y_train)\n",
    "\n",
    "score_model(svm_clsf)\n",
    "\n",
    "evaluate_model(svm_clsf)\n",
    "\n",
    "plot_roc_curve(svm_clsf, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e437a9",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "14ffca13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n_neighbours = 17\n",
      "best n_neighbours = 19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_knn = []\n",
    "error = [] \n",
    "for K in range(20):\n",
    "    K = K+1\n",
    "    model = KNeighborsClassifier(n_neighbors = K)\n",
    "\n",
    "    model.fit(X_train, y_train)  \n",
    "    pred=model.predict(X_test) \n",
    "    error.append(np.mean(pred != y_test))\n",
    "    best_knn.insert(K, model.score(X_test,y_test))\n",
    "\n",
    "# Get the best fitting number of neighbours \n",
    "for i,v in enumerate(best_knn):\n",
    "    if v == max(best_knn):\n",
    "        print(f'best n_neighbours = {i}')\n",
    "        \n",
    "curve = pd.Series(error) #elbow curve \n",
    "curve.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "caf30e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accurarcy 0.7925\n",
      "cross validation Mean : 0.7926249999999999 and STD of 0.005287071495639144\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.98      0.88      1582\n",
      "           1       0.53      0.06      0.12       418\n",
      "\n",
      "    accuracy                           0.79      2000\n",
      "   macro avg       0.66      0.52      0.50      2000\n",
      "weighted avg       0.74      0.79      0.72      2000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cDvdvxeC+kWKsbfhdMU3G7Be9Cdvy5cuxYMECfPLJJ3Bzc0NoaCiWLFmC8PDwUuup1SVvS/KYrinfcXFxGDduHJydnbF69Wq4urrizJkz2LFjB8zNzTF37lx9IWtp1aoVLC0NN3QfGRmJDh06GKx9qhj2i+lhn5imvy/8gwf1LHA0+Q6UT2wnZSmWYKBXS/RxbwZzsZkRI6x9+F0xTYbuF7lcXuogk96ELTs7G0FBQfjkk08AAC+99BL27t2r98KPb5/KZLJix2QyGVxdXUus9+WXX0Imk+HHH39E/fpFM486d+4MS0tLbNy4ESNGjICfn5/e6xMRkW6CIOCftATsLYiDLEmpdSzQ1RvDvNvB2dLGSNER0dPKtNOBXC7XjIilpaXpHT0DAC8vL0gkEiQkJGiVK5VKJCcnlzjhAABSUlLg7u6uSdYe69y5MwDgzp07TNiIiJ5BfG4G9sZGIiYnXavc264OxjTugMYOLkaKjIh00Tsf+8UXX8Qrr7yCjIwMrFmzBmPHjsX48eP1NmxhYYHAwEAcO3YMSuV/f72dOnUKeXl56NGjR4n1fH19kZKSgvv3tZ+UuHjxIgDA09NT77WJiKi4nMJ8fH37b6y8/KtWsuZgboVJ/oGY264/kzUiE6V3hG3UqFFo1KgRTp06BaVSiffffx/dunUrU+MzZ85EaGgopk2bhokTJyI1NRVr1qxBly5dEBQUBAC4e/cupFIp2rVrBwB4+eWXcfDgQUyePBmvv/46XF1dce7cOXzxxRfo378/WrduXfF3S0RUCynVKpxIvY1DiVEoUP33B7SZSIxWZk6Y8tzzsJaYGzFCItKnTLdEmzRpAqlUColEgjZt2pS58YCAAGzbtg3r16/HrFmz4OjoiJCQEISFhWnOWbZsGc6fP4/o6GgAQIMGDbB3716sXbsWK1asQEFBAby8vBAWFqZZSJeIiMomKjMFe2Mv4mF+rlZ52zruGOkbgOQbd5isEVUDehO2U6dOYe7cufDz84NarUZiYiLWrVuHjh07lukCQUFBmtG0kuzatatYmaenJ9atW1em9omIqLj7eY/wfexFXMu6p1XewNoBoxu3R0vnhgCAZGMER0Tlpjdh27BhA7755hvNg/7Xr1/H4sWLERERYfDgiIiofPKUhTiUeA0nUqOhFv7bTsrazBxDGrVGcAN/mIm5nRRRdaM3YROJRFqzMlu2bAnhiR8CRERkfGpBjTP3Y/FjwhXkKuSachGA7vWbIKRRG9hbWBkvQCJ6JjoTtsf7drZq1Qrbt2/HuHHjIBaLERERgcDAwKqKj4iI9Ljz6CG+i4lEkixLq9zPwRVjG3eAp52zkSIjosqiM2ELDAyESCTSjKY9XjgXKBp1K++OA0REVLky5TJExF3GP2na6106W9pglE8AOtTz0rmrDBFVLzoTtlu3blVlHEREVEaFKiWOJt/EkeQbUKhVmnJzsRn6e7RAf4/msDAr0yIARFRN6P1GFxYW4vTp05otplQqFRITEzF79myDB0dERP8RBAEX05OwL+4iMuV5Wseeq+eFkT4BqGNla6ToiMiQ9CZss2fPRlJSEtLS0tCiRQtcuXIFnTp1qorYiIjoX0nSLOyNjcTtRw+1yj1tnTG2cQf4OZa8PzMR1Qx6E7abN2/i6NGjWLp0KaZMmQJBELBs2bKqiI2IqNaTKgrwY/xV/HE/BgL+m6FvJ7HEMO+2CKrvC7GIy3QQ1XR6EzZXV1dIJBJ4e3vj9u3bGDhwIPLz86siNiKiWkulVuPUvds4mBiFPKVCUy4WidCroT8Ge7WGjcTCiBESUVXSm7DZ2Njg559/RrNmzbB37174+vpqlvwgIqLKdyPrHvbGROJefo5WeQvnBhjj2x4NbByNFBkRGYvecfTFixfj5s2bCAoKglgsRmhoKF5++eWqiI2IqFZ5mJ+LrddPY8O1k1rJmquVHWa06IlZLYOZrBHVUnpH2Hx8fPDuu+8CANavX2/oeIiIap0CpQKHk67jeMotKAW1ptzKTIIXvFqhd8OmMBebGTFCIjI2nQnbkCFDSq34888/V3owRES1iVoQcO5hHCLiLiNHUaB1rKubL4Z5t4WjhbWRoiMiU6IzYVu8eHFVxkFEVKvE5aRjT2wk4nMztMp97ethbOMO8Lava6TIiMgU6UzYuNYaEVHly5bnYX/8Zfz9MF6r3MnCGsN92qGzize3kyKiYrh3CRFRFVCoVfgt5RZ+SbwOuVqpKZeIxOjr0RwDPFvAyszciBESkSljwkZEZECCIOBKRjK+j7uE9AKp1rGAup4Y6RMAF2s7I0VHRNVFmRO2nJwcODg4GDIWIqIaJVWWje9iI3Er+4FWeUMbR4zx7YDmzvWNFBkRVTd612GLjY3FoEGDMGjQIDx48AADBw5ETExMVcRGRFQtyRRy7L57Acsv/qKVrNlILDCu8XNY1H4gkzUiKhe9CdsHH3yABQsWoG7dunBzc0NoaCiWLFlSFbEREVUrKkGN06l3sPjCQZy6dxvqf/f+FEGE4AZ+WP7cEPRq6A8z7v1JROWk96dGdnY2goKCNK9feuklSKXSUmoQEdU+0dkP8OHFI/g25h/IlHJNeVNHNyxuPxDjm3SEnbmlESMkouqsTM+wyeVyzTTztLQ0qNVqPTWIiGqH9AIpfoi9hIsZSVrl9axsMcqnPdrV9eAyHUT0zPQmbOPHj8crr7yCjIwMrFmzBocOHcLUqVOrIjYiIpMlVylxJOk6jibf1NpOykJshoGerdDXoxm3kyKiSqM3YRs9ejS8vb1x6tQpKJVKvP/+++jWrVtVxEZEZHIEQcA/aQn4Ie4SsgvztY51cvHGCJ92cLa0MVJ0RFRT6U3Y1qxZgzFjxmDOnDlVEQ8RkcmKy03H97EXEZOTrlXeyK4OxjbugMYOLkaKjIhqujI9wxYaGopGjRphzJgx6NevHywsLAwdFxGRycgskGF//GWcT0vQKncwt8Iw77bo4uYLMZ9TIyID0puwvfPOO5g9ezb++OMPRERE4JNPPkH//v2xYMGCqoiPiMhoCpQKHEm+gd9SbkGhVmnKzURiPO/eFC94toK1hNtJEZHhlWmETSwWo1WrVoiLi0NcXBwuXLhg6LiIiIxGLahx5n4sfky4ilxFgdax9nU9McKnHVys7Y0UHRHVRnoTtqNHjyIiIgKXL1/GgAED8NFHH6Fly5ZVERsRUZW7kXUP+2IvISUvW6u8kV0djPZtDz9HV+MERkS1mt6EbceOHRgzZgzWrVsHa2vrqoiJiKjKpcoe4Ye4S7iWlapV7mxhg2E+bdHJxZvPqRGR0ehM2KRSKezs7PDZZ58BKFo8Vy7/b/VuJycngwdHRGRouYUF+DkxCn/cu6vZSgoALMUS9Pdsgb7uzWBhVqanR4iIDEbnT6EJEyZg//79CAwMhEgkgiD894NMJBLh5s2bVRIgEZEhKNQqnEiNxuHE6yhQKTTlIgBd3RpjqHcbOFrwrgIRmQadCdv+/fsBADdu3IBYrL3laHZ2tkGDIiIyFEEQcDE9CRHxl5BeINM61tTRDaN928PTztlI0RERlUzvOP/IkSM1ydtjL774Ig4fPmywoIiIDEHXwrdu1g4Y6dMObeq4c99PIjJJOhO2SZMmISoqCgUFBWjfvr2mXK1Wo3nz5lUSHBFRZdC18K2txAJDGrVGj/p+MHvqTgIRkSnRmbBt2bIF2dnZWLBgAT766KP/KkgkcHHh9itEZPpKW/i2d0N/DPRsBVtz7txCRKav1FuiHh4e2LhxY7HynJycMs8SvXjxItauXYvr16/DysoK/fv3R1hYGOzs7HTWUalU+PzzzxEREYF79+7Bzc0Nw4cPx2uvvQYzM7MyXZeIaq/HC9/+lHAVOVz4lohqAIPOEr1x4wamTJmCzp07Y/369UhKSsLatWuRkJCAL7/8Ume9RYsW4eDBg3jzzTfRunVrXLhwAVu2bIFUKsXcuXPL+RaJqDbhwrdEVBPpnSV669atCje+YcMGuLm5YevWrZBIii5Vv359zJgxA3/99Re6du1arM6VK1cQERGBjz/+GMOGDQMAdOnSBbm5uThz5gzUanWxWatERPfyHmFfLBe+JaKaSe8s0fT0dFy5cgXPP/88VqxYgVu3bmHBggVo1qxZqfUKCwtx9uxZhIaGapI1AAgODoaNjQ1OnDhRYsJ2+PBhODk5ISQkRKucm80TUUlyCwtwMDEKv3PhWyKqwfQOVc2bNw9JSUk4e/Ys/v77bwwbNgwffPCB3oaTkpIgl8vh4+OjVS6RSODh4YGYmJgS6924cQN+fn74/fffMWLECLRs2RLdu3fHli1boFary/i2iKimU6hVOJp8E4sv/IxT9+5okjURgCC3xljecQgGebViskZENYLen2TZ2dmYPHkyPv74YwwePBgjRoxAeHi43oZzc3MBAPb2xR/stbW1hVQqLbFeRkYGcnJyMH/+fMycORONGzfGyZMnsWnTJmRlZWHRokV6r01ENRcXviWi2khvwqZQKKBQKPDHH39g5cqVyM/PR15ent6G9Y2G6VqcUqFQIC0tDdu3b0e3bt0AAIGBgZBKpfj2228xffr0ci0rcu3atTKfW1GRkZEGvwaVH/vF9DxrnzxU5eOsIg0P1Pla5Y4icwSau8Kr0BYPo2Px8JmuUvvwu2J62CemyZj9ojdhe/7559GlSxc0b94crVq1wuDBgzF48GC9DTs4OAAAZDJZsWMymQyuriXP1LKzs4O1tTWCgoK0yoODg7Fv3z7cunWrXAlbq1atYGlpWebzyysyMhIdOnQwWPtUMewX0/MsffLfwreJWuW2EgsM9mqNng248G1F8btietgnpsnQ/SKXy0sdZNKbsM2aNQtjxoxB/fr1AQCrV6/WO+EAALy8vCCRSJCQoL2yuFKpRHJycokTDgDAx8cHd+7cgVKphLm5uaZcoSjanNnampsxE9UWpS1826uhP17gwrdEVEvo/ZNUrVbj559/xoQJEzB+/Hj89ttvUCqVehu2sLBAYGAgjh07pnX+qVOnkJeXhx49epRYLzg4GAqFothepcePH4etrS1atmyp99pEVL2pBTX+uHcXiy/8jF+Srmsla+3remJZh0EY7dueyRoR1Rp6R9jWrFmDW7duYdKkSVCr1fjuu++watWqMi2zMXPmTISGhmLatGmYOHEiUlNTsWbNGnTp0kVzy/Pu3buQSqVo164dAGDQoEHYs2cPli5diszMTDRt2hS//fYbDh48iPnz53OEjaiG48K3RETF6U3Y/vjjD/zwww+a25PBwcEICQkpU8IWEBCAbdu2Yf369Zg1axYcHR0REhKCsLAwzTnLli3D+fPnER0dDQAwMzPDF198gU2bNmHnzp3IyMiAt7c3PvjgA4wePbqi75OITBwXviUi0k1vwiYIgtazZBYWFlqv9QkKCio2geBJu3btKlZmY2ODuXPnchsqolqAC98SEemn96dgs2bNsGLFCoSGhgIAwsPD4e/vb/DAiKhmU6hVOJl6G4cTryFfpdCUiwB0dWuMod5t4GjBRyCIiIAyJGzvvfceli9fjnHjxkEQBHTr1g2LFy+uitiIqAbiwrdEROVXasKmVquhVCrx8ccfV1U8RFSDxeWm4/vYS4jJSdMqd7O2x0ifALSp465zUW0iotpMZ8J2+fJlzJw5ExkZGfDz88PmzZvh5eVVlbERUQ2RWSDDCXkq7l6O1irnwrdERGWjM2FbtWoVli9fji5dumDPnj1YvXo1Nm7cWJWxEVE1x4VviYgqh86ELS8vD7169QIATJ48Gfv27auyoIioelMLavz1IBY/xl9FjqJA61hAXU+M8GkHV2t7I0VHRFT96EzYxE/dnijPUh5EVHvdzLqP72MvFlv4tp7YEpNadYc/F74lIio3nQmbIAi6DhERFXMv7xF+iLuEqMySF76VJGYwWSMiqiCdCVtSUhJee+01na8/++wzw0ZGRNWCVFGAnxP0L3wbmZRpxCiJiKo3nQnbwoULtV7379/f4MEQUfXBhW+JiKqOzoStZ8+eqFOnTqmVMzIyULdu3UoPiohMFxe+JSKqejoTtgULFqBz584YMWIEHB0dtY5JpVLs2bMH586dw+eff27wIInINHDhWyIi49CZsG3duhU7duzA4MGD4ePjg0aNGkGlUiEpKQlxcXGYOHEitm7dWpWxEpGRZBbIsD/+Cs6nxWuVc+FbIqKqUeqyHlOnTkVoaCj+/vtvxMbGQiwWo1+/fujatSssLLjYJVFNx4VviYhMg97N362srBAcHIzg4OAqCIeITAEXviUiMi16EzYiql10LXzrZVcHo33bcy01IiIjYMJGRAB0L3zrZGGN4d7t0MnVG2JOKCAiMgombES1XOkL3zZHX/fmsDDjjwoiImPS+1NYJpNh9erViI2NxYYNG7B27VrMnTsXtra2VREfERkIF74lIqo+9CZsH3zwAVxdXZGRkQFLS0tIpVIsWbIEa9asqYr4iKiSceFbIqLqR2/CdvPmTXz00Uc4ffo0rK2tsXr1agwePLgqYiOiSsaFb4mIqie9CZv4qcUwVSpVsTIiMm1c+JaIqHrTm7B17NgRn3zyCQoKCvDHH38gPDwcnTp1qorYiOgZceFbIqKaQW/CFhYWhm3btsHe3h7r1q1D9+7dMWPGjKqIjYgqiAvfEhHVLHoTttOnT2PGjBlaSdqBAwcwbNgwQ8ZFRBUgCAKuZaXiQPwVJMuytY5x4VsioupLZ8J24sQJKJVKrFq1CoIgQBCK1mdSKpVYt24dEzYiE3P70UMciL9SbEIBF74lIqr+dCZsN2/exN9//42MjAx8/fXX/1WQSPDKK69USXBEpF+iNBMH4q/getY9rXIufEtEVHPo/Cn++DZoeHg4XnrppaqMiYjK4H7eI/yYcBUX05O0ys1EYvRo0AQveLaEAxe+JSKqEfT+2T169GgcO3YMMlnRApsqlQqJiYmYPXu2wYMjouIyC2Q4mBiFvx7EQXhiKykRRAh088Fgr1aoZ2VnxAiJiKiy6U3YZs+ejaSkJKSlpaFFixa4cuUKl/UgMoKcwgL8knQdv9+7A6Wg1jrWvq4nhjRqg4a2jkaKjoiIDKlMOx0cPXoUS5cuxZQpUyAIApYtW1YVsRERgHxlIY4m38TxlGjI1UqtYy2c6mOod1t429c1UnRERFQV9CZsrq6ukEgk8Pb2xu3btzFw4EDk5+dXRWxEtVqhSomTqbdxJPkG8pSFWsd87OtiuHc7NHVyM1J0RERUlfQmbDY2Nvj555/RrFkz7N27F76+vsjOzq6C0IhqJ6VahTP3Y3Eo6RoeFWr/ceRu44Sh3m245ycRUS2jd/PAJUuW4ObNmwgKCoJYLEZoaCiX9SAyALWgxt8P4/Be5CF8G/OPVrLmYmWHV5p2xaL2A9G2rgeTNSKiWkbvCJu3tzfeffddAMD69esBAHfv3jVoUES1iSAIuJKZgh/jryA175HWMScLawzyaoUgt8bcnJ2IqBbTmbAlJydj3bp1cHJyQlhYGKytrSGVSrFp0yaEh4fj2rVrVRknUY0Unf0A++MvIy43Q6vcVmKBAZ4tEdzAj4veEhGR7oRtwYIF8Pf3x8OHD/G///0P3bp1w+zZs+Hg4IAvvviiKmMkqnHictPxY/xV3My+r1VuaSZBH/dm6OveDNYSCyNFR0REpkZnwnb//n18/fXXKCgowIgRI7B3715MnjwZL7/8MiSSsv/Ff/HiRaxduxbXr1+HlZUV+vfvj7CwMNjZlW1hz8zMTAwZMgTdu3fHypUry3xdIlOUKsvGjwlXcTkjWatcIhKjZ0M/DPRoCXsLKyNFR0REpkpn5mVjYwMAsLKywqNHj7Bq1Sp069atXI3fuHEDU6ZMQefOnbF+/XokJSVh7dq1SEhIwJdfflmmNhYuXIj09PRyXZfI1KQXSPFzQhTOPYx7Ym8CQAwRutb3xSCvVqhjaWu0+IiIyLSVaaisbt265U7WAGDDhg1wc3PD1q1bNaNy9evXx4wZM/DXX3+ha9eupdb/7rvvcOnSJVhaWpb72kSm4FFhPg4nXsMf92Ogemp3gg71vBDSqA3q2zgYKToiIqoudE47e3LZADMzs3I3XFhYiLNnz6JPnz5at1CDg4NhY2ODEydOlFo/Pj4eK1euxPLly2FhwWd5qHqRKQqxP+4yFv7zE07du6OVrLVyboCFAQPwavNuTNaIiKhMdI6wRUdHo3379gCAgoICzb8FQYBIJMLFixdLbTgpKQlyuRw+Pj7aF5RI4OHhgZiYGJ11lUol5syZg379+qFv376YP39+md8QkTHJVUocT4nG0eQbyFcptI41cXDBMO+28HN0NVJ0RERUXelM2I4dO/ZMDefm5gIA7O3tix2ztbWFVCrVWXfLli1IS0vDjh07nikGAFWy/EhkZKTBr0HlV5X9ohLUuKl8hEuKDORDpXWsrsgSHS3qwVNhi5y7SYhEUpXFZWr4XTFN7BfTwz4xTcbsF50Jm7u7+zM1rFarSz2ua6X2y5cv4/PPP8fnn39eYrJXXq1atTLoM3CRkZHo0KGDwdqniqmqflELapx7GI+fE6KQoZBpHXO1tkdIozboUM8LYu5MwO+KiWK/mB72iWkydL/I5fJSB5kMtiKng0PRszkymazYMZlMBlfX4reFZDIZ5syZgzFjxqBjx45QKpWaY4IgQKlUwszMjNvykNEJgoBLGcn4Kf4K7uXnaB1ztrDB4Eat0MXNF2Yi7k5ARETPzmAJm5eXFyQSCRISErTKlUolkpOTS5wheu3aNSQmJiI8PBzh4eFaxw4cOIADBw7g66+/RufOnQ0VNlGpBEHAzez7OBB/BQnSTK1jdhJLDPRqiZ4N/GAuLv9EHSIiIl0MlrBZWFggMDAQx44dw6xZszQzRU+dOoW8vDz06NGjWJ2WLVti3759xconTpyIzp07Y8aMGcUmMRBVlZicNByIv4Lbjx5qlVuZSdDXvTn6uDeDlcTcSNEREVFNpjdhS0tLw8KFC5GQkIDw8HDMnTsXH330UYm3NJ82c+ZMhIaGYtq0aZg4cSJSU1OxZs0adOnSBUFBQQCKNpKXSqVo164d7Ozs0Lp162LtmJmZwcnJqcRjRIaWIsvGgfgruJqZolVuLjZDcAN/DPBsDjtz7k5ARESGo/cBm2XLlqFPnz6wtLSEo6MjmjVrhkWLFpWp8YCAAGzbtg1SqRSzZs3Cp59+ipCQEGzevFmr/bFjx1b8HRAZSFp+LrbfOoPlFw9rJWtiiNCjfhMsf24IRvkGMFkjIiKD0zvClpKSgjFjxuDbb7+Fubk55syZgyFDhpT5AkFBQZrRtJLs2rVLbxsXLlwo8/WInlWWPA+HE6/hzwcxUAv/bSQlAtDRpRGGNGoDV+tnn8FMRERUVnoTNpFIpLVEh1Qq1btkB1F1JFXIcSTpBk7duw2FWnsttTZ13DHUuw08bJ2NFB0REdVmehO2fv36ISwsDLm5udizZw++//57DBw4sCpiI6oSBUoFjqfewtHkWyh4ancCf0dXDPNui8YOLkaKjoiIqAwJ22uvvYYDBw5ArVbjr7/+wtixYzF69OiqiI3IoBRqFU7fu4MjSdeRq5BrHfOyq4Ph3m3R3Kk+1/0jIiKj05uw7dmzB4MHD8awYcOqIBwiw1MJapx9EIeDiVHIkudpHatv7YCh3m0QUNeTiRoREZkMvQnb+fPnsX79evTq1Qtjx45Fu3btqiAsosqnFgRcTE/ETwlX8SA/V+tYHUsbDGnUBp1dvbk7ARERmRy9CdvatWvx6NEjHDx4EB988AHkcjlGjRqFSZMmVUV8RM9MEARcz7qHA/FXkCTL0jpmb26FFzxbonuDJtydgIiITFaZdjpwdHTE2LFj4erqqtmYnQkbVQd3Hz3E/vgruJuTplVubWaOfh4t0NvdH1Zm3J2AiIhMm96E7fr164iIiMCRI0fQokULTJ06Fb17966K2IgqLF1dgE3XTuFaVqpWubnYDM83bIp+Hs1ha25ppOiIiIjKR2/CNmPGDIwcORLff/89GjZsWBUxEVXYg7wc/JRwFRcKEoGC/8rFIhG612+CQV6t4GhhbbwAiYiIKkBvwnbq1KkqCIPo2WTKZTiUeA1/3Y+FGtq7E3R29cZgrzZwsbYzXoBERETPQGfCNn78eOzevRsBAQFayxsIggCRSISLFy9WSYBEpcktLMAvyddxOvUOlIL2Dhzt6npgaKM2aGjrZJzgiIiIKonOhG3Dhg0AgIMHDxY7JjyxvyKRMeQrFTiWchO/pdyCXKXUOtbMyQ3N8i0xsEU3I0VHRERUuXQmbK6urgCA9957D1988YXWsTFjxmDv3r2GjYyoBIUqJU79uzuBTFmodczbvi6GNWqL5s71ERkZaaQIiYiIKp/OhG3WrFmIi4tDUlIShgwZoilXKpUQi7mwKFUtlVqNMw9icCjxGrIL87WONbRxxNBGbdC2rgd3JyAiohpJZ8L27rvvIiUlBYsXL8bixYs15WZmZvDz86uS4IgAIEWWjS+jzxZb9LaelS2GeLVBJ9dGEHN3AiIiqsF0JmweHh7w8PDAr7/+ylELMgqVoMbR5Jv4OSEKqicmFDiYW2GQVyt0q98YEu5OQEREtYDeWaLt27fnLFGqcvfyHmHn7b8Rn5uhKZOIxBjk1Rp93JvCwqxMm3QQERHVCBWaJUpkKGpBjd9SovFj/BWtZTq87etiin8g6ts4GjE6IiIi49D54M/jWaJ16tRBWloa3N3d8dtvv2Hz5s28RUoG8SA/B6uv/oYf4i5pkjWJSIzh3m3xbtu+TNaIiKjW0ntfaf78+fD09IRYLMbXX3+NYcOGYfHixdi+fXtVxEe1gFoQcCr1NiLiL0OhVmnKveycMdm/C9y58C0REdVyeqfWJSUl4Z133sHJkycxfPhwvPnmm8jOzq6C0Kg2SMuXYl3UcXwXG6lJ1sQiEYZ4tca8tv2ZrBEREaEMI2wKhQIA8Oeff2Lu3LlQqVTIy8szeGBUswmCgN/v38UPsZcgV/+3U4G7jRMmNw2El10dI0ZHRERkWvQmbO3bt8cLL7wAMzMztG/fHpMmTULXrl2rIjaqoTILZPj6zjnczL6vKRNDhP6eLTDYqxWX6iAiInqK3oRt8eLFuHTpEpo1awaxWIxXXnkFPXr0qIrYqIYRBAF/PYjF3tiLKFApNOUNrB0wuWkXeNvXNWJ0REREpktvwmZmZoaHDx/ihx9+gEKhQFBQELemonLLkufhmzvncS0rVVMmAtDXozlCGrWBOUfViIiIdNKbsG3fvh0//fQThg8fDkEQsHPnTty/fx+vv/56VcRH1ZwgCDj3MB7fxV5AnvK/UTVXa3tM9g9EYwcXI0ZHRERUPehN2A4cOIDdu3fDzs4OADBq1CiMGTOGCRvp9agwH+F3/8GVjGRNmQhA74ZNMcy7LXcrICIiKqMy/cZ8nKwBgL29PSQS/qKl0v2TloDddy9AppRryupZ2WGSfyD8HV2NGBkREVH1ozfzcnd3x1dffYUXX3wRABAeHo6GDRsaPDCqnnILC7A75gIi0xO1yns28MMIn3awMjM3UmRERETVl96EbdmyZQgLC8OqVasAAG3btsXq1asNHhhVPxfTk/Dt3fPIVfw3qlbH0gYT/QLR3Lm+ESMjIiKq3vQmbG5ubti1axfy8/OhVqtha2tbFXFRNSJTyLEn5gLOpyVolXer3xijfNrDWsJRNSIiomehM2GLj4/HO++8g7i4OAQGBmL58uWoW5frZJG2qxkp2HXnHHIUBZoyJwtrTPDrjFZ1eOuciIioMuhcUO3999/H8OHD8f3336NRo0aaW6JEAJCnLMTO239jy43TWslaoKsPlrQfxGSNiIioEukcYUtPT0doaCgAICwsDEOHDq2yoMi0Xc9Kxa7b55FV+N+esg7mVgj164S2dT2MGBkREVHNpDNhe3LpDjMzMy7lQShQKvB93EX8eT9Gq7yjSyOMa/wc7MwtjRQZERFRzaYzCxMEQeu1SCQyeDBkum5l38fXt88hQy7TlNlJLPGSX0e0r+dlxMiIiIhqPp0J2/379/HBBx/ofL1o0aIyXeDixYtYu3Ytrl+/DisrK/Tv3x9hYWFai/E+LTU1FRs2bMC5c+fw6NEjeHp6Yty4cRg3bhz3Ma1icpUSEXGXcOreHa3ygLqeeLFJRzhYWBkpMiIiotpDZ8L20ksvlfq6LG7cuIEpU6agc+fOWL9+PZKSkrB27VokJCTgyy+/LLFOTk4OQkNDIRaL8dZbb8HNzQ1//PEH3n//fcTGxpY5UaRnd+fRQ+y8/TfSC6SaMhuJBcY3fg4dXRpx1JWIiKiK6EzYZs6c+cyNb9iwAW5ubti6davmGbj69etjxowZ+Ouvv9C1a9didfbv34+UlBQcPnwYjRs3BgB07doVUqkU3377LWbNmgUHB4dnjo10K1QpcSDhCk6kROPJG+Nt6rgj1K8THC2sjRYbERFRbWSw+4uFhYU4e/Ys+vTpozVhITg4GDY2Njhx4kSJ9VxcXDBhwgRNsvZYkyZNoFKpkJaWZqiQCUBsTjo+uPQLjj+RrFmbmWOyfyDeaNGDyRoREZERGGzqZ1JSEuRyOXx8fLQvKJHAw8MDMTExJdZ74YUX8MILLxQr//XXX2FrawtPT0+DxFvbKdQq/JwQhaPJNyE8Ma7WwrkBJvp1hrOljRGjIyIiqt0MlrDl5uYCAOzt7Ysds7W1hVQqLVauy6efforIyEjMnj0bFhYW5Yrj2rVr5Tq/IiIjIw1+DUNKUxXgVOE9ZAmFmjJziNHFwgVNC+wRe+2mEaOruOreLzUR+8Q0sV9MD/vENBmzX/QmbGq1Gjt27MCdO3ewePFihIeHY+rUqTAzM9NbrzRlfWB948aN2LJlCwYOHIhXX321THWe1KpVK1haGm59sMjISHTo0MFg7RuSUq3CocRrOJKUCPUTo2pNHd0wyT8Qda2q776x1blfair2iWliv5ge9olpMnS/yOXyUgeZ9CZsq1atQmZmJqKiogAAf/zxB9LS0vTO1nw8MUAmkxU7JpPJ4OrqWmr9goICzJ8/H4cPH8bIkSOxfPlyLulRSQRBwKWMJETEXUbaEzNALcRmGOkTgB4N/CDmDFAiIiKToTdhO3v2LPbv348RI0bAzs4OO3bsKNM2VV5eXpBIJEhISNAqVyqVSE5OLnGG6GOZmZmYNm0arl+/jtmzZ+O1114rw1uhsojLScf3cZcQk6M9eaOJgwsm+wfCxbr4LWwiIiIyLr0Jm0Qi0RrZsrCwKNM2VRYWFggMDMSxY8cwa9YsTZ1Tp04hLy8PPXr0KLFeXl4eJk+ejNjYWKxdu7bECQhUfukFUuyPu4wL6Yla5TYScwzxaoPghv4cVSMiIjJRejMvf39/hIeHQ6VSITY2Fjt37kSzZs3K1PjMmTMRGhqKadOmYeLEiUhNTcWaNWvQpUsXBAUFAQDu3r0LqVSKdu3aAQA2bdqE6OhoTJgwAQ0bNsTly5e12mzatCmsrbm0RFnJFIX4Jek6TqZGQyn891yhmUiM4IZ+GOTZCrbcA5SIiMik6U3YFi5ciBUrViAjIwPjx49Ht27dyrzbQEBAALZt24b169dj1qxZcHR0REhICMLCwjTnLFu2DOfPn0d0dDQA4JdffgEA7Nq1C7t27SrW5oEDB9C8efMyXb82U6pVOH3vDg4lXoNMWah1rH09T4zwbsfbn0RERNWE3oTNzs4OK1asqPAFgoKCNKNpJXk6KTt16lSFr0W6JxQAgI99XYz2bY/GDi5Gio6IiIgqQm/C9uSG70/inp6mR9eEgnpWthjhHYD29Ty5/ycREVE1pDdhc3Jy0vxboVDgzz//REBAgCFjonIqbULBC56tENzQH+bi0tfNIyIiItOlN2F7ehP46dOnY/r06QYLiMqOEwqIiIhqh3JvTWVjY4OHDx8aIhYqI04oICIiql30JmzLly/XPPckCAKuX78OX19fgwdGxRVNKEjG/rhLeMgJBURERLWG3oTN2dlZ63VISAhCQkIMFhCVjBMKiIiIai+9CVtiYiJWrVpVFbFQCdILpDgQfwX/pGlv8cUJBURERLWH3oQtOjoagiBw9MYIYnPSseHaCRSolJoyTiggIiKqffQmbPXq1cOgQYPQtm1b2Nraasq5DpthxedmYMO1k1rJGicUEBER1U46E7bCwkJYWFggICCA665VsURp5r8jawoAgL25JV5r3h1NHF2NHBkREREZg86EbezYsdi/f3+xddjIsFJk2VgfdRJ5yqJkzVZigf9r3Rsets56ahIREVFNJdZ1QBCEqoyDAKTKHmFd1HHIlHIARRMLmKwRERGRzhE2uVyOGzdu6EzcWrZsabCgaqMHeTlYF3UcuYqiZM3KzBxvteoNL7s6Ro6MiIiIjE1nwpaUlIQ333yzxIRNJBLh+PHjBg2sNknLz8XaqOPIURQAACzNJJjVKhje9nWNHBkRERGZAp0JW5MmTXDgwIEqDKV2Si+QYm3UcWQX5gMALMRmmNkymDsWEBERkYbOZ9jI8DLlMqyLOo5MeR4AwFxshhkte8Kfs0GJiIjoCToTtueee64q46h1suV5WHf1ONILZAAAiUiMN1r0QDOn+kaOjIiIiEyNzoSNC+MaTk5hPtZFndBs4G4mEuO1Ft3RwrmBkSMjIiIiU8RbolUst7AA66JO4H5+DgBALBLh1ebd0LqOu5EjIyIiIlPFhK0KyRRyrL92Aql5jwAAYogwtWkQ2tX1MHJkREREZMqYsFWRPGUh1l87iWRZNgBABBGmNO2CDi5exg2MiIiITB4TtiqQr1Rg47WTSJRmAgBEACb5d0YnV2+jxkVERETVAxM2AytQKbDp+inE5WZoykL9OqGLm68RoyIiIqLqhAmbARWqlNhy/TRictI0ZS827ohu9ZsYMSoiIiKqbpiwGYhCrcLWG7/j9qOHmrIxvu3Rs6GfEaMiIiKi6ogJm4GE3/0HN7Pva16P9AnA8+7NjBgRERERVVdM2AzgzP0YnH0Qq3k9tFEb9PNobsSIiIiIqDpjwlbJkmVZ2B1zQfM60NUHAz1bGjEiIiIiqu6YsFWy3XcvQKFWAQAa2jjixSYdIRKJjBwVERERVWdM2CpRqiwbd/+dEWomEuPV5t1gaSYxclRERERU3TGbqER/3I/R/LttXXc0sHE0YjRERJVPrVYjPT0d2dnZUKlUxg6nRpJIJLh586axw6CnVFa/WFlZwcPDA+bm5uW7/jNfmQAULeNx7mGc5nV3rrVGRDVQcnIyRCIRvL29YW5uzkc+DEAmk8HW1tbYYdBTKqNfBEFARkYGkpOT4ePjU666vCVaSS6lJ0GmLAQA1LW0RTOn+kaOiIio8slkMri7u8PCwoLJGlE5iUQi1K1bFwUFBeWuy4Stkvxx/67m30H1G0PMH2REVEOJxfzVQVRRFf1Dh9+6SvAgP0ezo4EIInTlPqFERERUiZiwVYI/n5hs0LpOQzhb2hgxGiKi2uPcuXOYMGGC5rVUKsXYsWOxcuVKAEDv3r2xbt06rTrz5s1DREREqe1OmzYNDx48KPN1H0tOTkbv3r3L8xbK7PLly5g0aRJCQkIwePBgLF26VHNrrSzvqayioqKwcOFCAMD169fRu3dvTJgwARs2bMDx48cr3K5KpcLMmTORn5+vKfv4448RGBiIwsJCTZmuz7Bp06aafz98+BBhYWEYNGgQQkJCMH36dCQlJVU4tps3b2LkyJHo378/Fi5cCKVSWewcqVSKd955B8OGDcOwYcNw/fp1TSyvvPIKhg4diuHDh+Ps2bMAgGPHjuGbb76pcExPY8L2jFSCgLMP/pts0K1+YyNGQ0RUe8lkMkydOhUdO3bEvHnzNOVfffUVrl27Vq62Pv/8c7i5uVV2iBV269YtzJw5E2+//TZ++uknHDhwAIIgYPHixZV+rdatW+PDDz8EAJw8eRKDBw/Grl278NZbb+H555+vcLu7d+9Gt27dYG1tDQBQKpX45ZdfEBAQgCNHjpS5nby8PEyYMAEdO3bEwYMH8dNPP2HQoEGYMmUKFApFhWKbM2cOFi9ejF9//RWCIGDv3r3Fzlm7di0aNGiAAwcO4O2338bSpUsBAKtWrUKvXr3w448/Ys2aNQgLC4NKpULfvn1x9OhRZGRkVCimpxl8lujFixexdu1aXL9+HVZWVujfvz/CwsJgZ2ens05hYSE2btyIgwcPIjMzE35+fpg1axZ69uxp6HDLLUElRa6i6C8cJwtrtKrT0MgRERHVPnl5eXj11VcRGBiI//u//9M6Nn36dMyfPx8//PADLCwstI79/vvv2LhxI5RKJTw8PLB8+XI4Ozujd+/e+Prrr+Hm5ob33nsPkZGRcHNzg0gkwhtvvAEAyMzMxLRp05CYmAgfHx9s3LgRACCXy/HWW28hLi4OXl5e+PDDD+Ho6IjLly/jww8/hFwuh7OzM95//300atQIEyZMgKOjI+7cuYNPPvkEX375JeLiigYCXnzxRYwZMwbbt2/H2LFj0bZtWwBFS0zMmTMHZ86cKfZZrFu3DmfPnsWjR4/g7OyMTZs2wcnJCQsWLMCdO3e02v3555/xxRdfwMzMDB4eHvjkk09w+fJlbN68GVOnTsXu3bsBABYWFkhOTkanTp0wYsQIHDhwAF999RXUajVatmyJ9957D5aWlggMDETLli2Rnp6Offv2aZauEAQBu3btwr59+zRxnj59Gp6enhg2bBi+/vprhISElKmvDx06BFdXV4wdO1ZTFhISAgsLCxQWFmotlxEeHl4s+ercuTMWLFigeZ2SkoKCggK0a9cOADBixAhs3LgRL774ouYcQRBw/PhxnDhxAgDQo0cPNGjQAADQr18/dO7cGQDQqFEjyOVy5OXlwd7eHv369UN4eDhmzZpVpvdWGoMmbDdu3MCUKVPQuXNnrF+/HklJSVi7di0SEhLw5Zdf6qw3b948nDx5Eu+88w48PDywZ88evP7669i5cyc6depkyJDL7ZYyW/Pvrm6+MBNx0JKIao9jyTfxc2IU5Krit5CelaWZBEO8WqOvnr2Y8/PzMX36dNy5cwdbtmwpdnzIkCGIiorCli1bMHv2bE15ZmYm1qxZg6+//hqOjo7Ys2cPVq9erRldAoA9e/YgPz8fR44cQWpqKoYMGaI5lpqais8++wzu7u4YM2YM/vrrLzRp0gQZGRmYMGECnnvuOaxatQpbtmxBWFgY3n77baxfvx5t2rTBL7/8grfffhs//PADgKLbfZs3b8b58+eRk5ODAwcOICsrCx9//DHGjBmDmzdvYvDgwVrvy87ODv3799cqS0hIQGxsLPbs2QOxWIx3330XP//8M1q1aoVHjx4Va3f9+vXYu3cv6tati3Xr1iE29r99sHv27Ilx48YBAGbOnKkZtbxz5w727t2LPXv2wNLSEmvWrMH27dvxxhtvICsrC6+++qomgXns1q1bsLe3h729vaYsIiICAwYMQM+ePTF//nzcvXsXTZroXxLr5s2baNOmTbHyAQMGFCt76aWX8NJLL5Xa3sOHD+Hi4qJ57eLiUux2eEZGBiwsLPDNN9/g6NGjcHBw0CR9/fr105y3fft2NG/eXPM+n3vuOcybN8/0E7YNGzbAzc0NW7duhURSdKn69etjxowZ+Ouvv9C1a9didaKionDo0CGsXLkSw4cPB1CUyY4aNQrr1q3TZPumIL1AimR1HgBAhKLZoUREtcmxlFsGSdYAQK5S4ljKLb0JW1RUFN566y34+vpi0aJF2Lx5c7Fzli1bhqFDh6Jv376asitXruDevXuYOHEigKJFgR0dtRc8P3PmDMaMGQORSAR3d3d06dJFc6xZs2bw9PQEADRu3BhZWVkAAB8fHzz33HMAikZ+5s2bh/j4eDg4OGgSjYEDB2LJkiXIzc0FAE25n58f4uPj8corr6BHjx4ICwsDUPaZhY0aNcLcuXPx/fffIy4uDpcvX4aXlxf8/PwQFxdXrN1evXph/PjxeP7559G/f380b94c586dK/Ua586dQ0JCAsaMGQMAUCgUaNGiheb441HAJ8XHx6N+/f+Wu8rMzMSff/6J5cuXw8rKCr169cKePXuwaNGiEmchC4Kg+QzEYjEEQSjT51GWEbaS2nr681apVMjIyICjoyMOHDiAM2fOYMaMGVrP9O3cuRPfffed1nNr7u7uSEhIKFOs+hgsYSssLMTZs2cRGhqqSdYAIDg4GDY2Njhx4kSJCdvp06chFou1vlRisRgDBgzAmjVrkJWVBWdnZ0OFXS5f3f5b8+/mTvVRz0r3bV4iopqor3szg46w9XVvpve8du3a4Y033kB+fj6GDRuG3bt3Y/z48VrnuLi4YN68eZg/fz78/f0BFP0Sbt++PT777DMARbcyZTKZVj0zMzOo1eoSr/vk7zaRSKT5xf9k+ePXJbUhCIJmtwgrKysAgLOzM/bt24fLly/j9OnTGD58OA4dOoRWrVohKipK69EgqVSKsLAwza1YALh27RreeecdTJ48Gf3799ckN87Ozjh06BDOnDmj1e6iRYtw69YtnD59GnPmzMHMmTO1EquSqFQqDBw4EIsWLQJQ9Ozgk7tePH4vTxKLxTAzM9O8/umnnyAIAkaNGgUAKCgogEKhQFhYGBwcHDSJ7GOPkyUAaNWqVYkTLBYuXIjJkyfDz89PU1aWETY3Nzekp6drXqelpcHV1VXrHGdnZ0gkEs0oZ1BQEPLy8pCRkYG6deti1apVOH36NMLDw7U+P4lEUmnrFRosYUtKSoJcLi+2kq9EIoGHhwdiYmJKrBcTEwNXV9diz7g1atQIABAbG4sOHToYJuhyyJTLNEt5AEA37mxARLVQX4/mekfADO3xc2nW1tZYtWoVpkyZgo4dOxa7vRYSEoIjR47g119/RVBQENq2bYtFixYhLi4OPj4+2Lp1Kx48eKCZYQoAXbt2xeHDh/H888/j4cOHOH/+PCZNmlTqCE9MTAxu3LiBFi1aYN++fejatSt8fX2RnZ2Nq1evok2bNjh8+DAaNmwIJycnrbrHjx9HREQENm/ejO7du+Ps2bO4d+8eJk+ejJdffhk9evRAmzZtoFAosHLlStjZ2Wk9l/fPP/+gU6dOGD9+PHJzc7F06VL06tULx48fx48//ogNGzZotTtq1Cjs2rUL06dPh0KhwM2bN/UmbJ07d8aOHTvw+uuvo06dOli6dCm8vLzw5ptv6qzj5eWF1NRUzesffvgBK1euxAsvvACgaHSzX79+OHz4MEaMGIFGjRrh119/1dzy/e677zSjmwMGDMCmTZvw/fffY/To0Zr2zp8/j/fee6/U2Evi7u4OS0tLREZGokOHDjhw4AB69OihdY6FhQU6d+6MQ4cO4cUXX8Tly5dhbW0NZ2dn7Ny5E+fOncPu3bvh4OCgVS85OVmTvzwrgyVsj7PjJ+9XP2ZrawupVFpivZycnBLrPE7gdNXTpbwzg8pKLqgggQhKCHAUmUOV8BCRiWkGuRZVTGRkpLFDoKewT0xTefpFIpEUG4UypoKCAqhUKk1MTZo0wYsvvoi33noLu3btglqtRn5+vub43LlzceHCBcjlctjY2GDJkiWYNWsW1Go1XF1d8cEHH0Amk2nqDRo0CFFRURg0aBDq1auH+vXrQxCEYtdVKpWQy+XIz8+Hp6cnNm7ciKSkJDRp0gSLFi2CQqHAihUrsGzZMuTn58PBwQErVqzQjE4VFBRAJpOhQ4cOOHToEAYOHAhLS0v06tULHh4eAIDly5fj/fffR0FBAZRKJTp16oT58+dDJpNprh8cHIyDBw9i0KBBkEgkaNy4MeLj4zF58uQS250+fTomTZoEKysr2Nvb4/3330diYqLmvT1ebuPJa3h6emLatGmYMGEC1Go1mjVrhpdeeknzWZT0/w9PT09kZmbi/v37SEpKQmZmJrp27ap17vjx4xEeHo7+/fvj/fffx4oVK7Bp0yYoFAr4+flh3rx5mvO3bt2KNWvWYMeOHZrb1Zs3b4ZCoajQTNHly5dj+fLlkMlkaNasGUaOHAmZTIZPP/0ULi4uGDVqFJYsWYIPP/wQ33zzDSQSCVasWIG8vDxs3rwZtra2WiN5mzZtgouLC37//Xd079692GdSWFhY/p+HgoFERkYK/v7+wi+//FLs2NixY4XRo0eXWG/KlCnCoEGDipX/+eefgr+/v3D69OkyXb+goEC4cOGCUFBQUL7AyyGjQCrs/vu48EieZ7BrUMVcuHDB2CHQU9gnpqm8/XLjxg0DRWKaTp48KZw4cUIQBEHIyckRevfuLWRlZRn0mlKp1KDtG8tXX30l7Nq1y9hhVFhF+mXcuHFCenp6sfKSvkf68haDTWl8PCxYUqYtk8lKHEUDikbkSqrzeGTt6eFGY6pjaQs/iSMcLKyNHQoRERlA48aNsW3bNgwdOhShoaGYNWtWsduYVDbjx4/HmTNntBbOrcmOHDmC/v37o27dupXSnsFuiXp5eUEikRSbHaFUKpGcnFzihAMA8PX1xbFjx5Cfn69ZXA8omqosEong68ttn4iIqGp4enqa1OoE1Zm5uTk+/fRTY4dRZUpaZuRZGGyEzcLCAoGBgTh27JjWFg+nTp1CXl5esQf6HuvRowdUKhWOHj2qKVOr1Thy5AjatWtnUiNsRERERFXBoOuwzZw5E6GhoZg2bRomTpyI1NRUrFmzBl26dEFQUBAA4O7du5BKpZoVhgMCAtCzZ08sWbIEWVlZ8PHxwe7duxEdHY3t27cbMlwiIioDtVpd4lpZRKSfUMY15J5m0IQtICAA27Ztw/r16zFr1iw4OjoiJCREs2AfULSY4fnz5xEdHa0pW7duHdasWYNt27ZBJpPBz88Pn376KQIDAw0ZLhER6WFra4uUlBS4ubnB3Ny80taYIqoNBEFARkZGiWvV6WPwvUSDgoI0o2kl2bVrV7EyW1tbLFmyBEuWLDFkaEREVE4eHh5IT09HQkKC1uMuVHkKCwuL7XlKxldZ/WJlZaVZqqU8DJ6wERFRzSEWi+Hq6lpsJXiqPJGRkSVu70TGZex+4UMIRERERCaOCRsRERGRiauxt0Qfz8J4vK2GIcnlcoNfg8qP/WJ62Cemif1ietgnpsmQ/fI4X9E1i1QkVHR+qYnLzc3F7du3jR0GERERUZn5+/uXuBtUjU3Y1Go1ZDIZp50TERGRyRMEAQqFAra2tiWuc1hjEzYiIiKimoKTDoiIiIhMHBM2IiIiIhPHhI2IiIjIxDFhIyIiIjJxTNiIiIiITBwTNiIiIiITx4SNiIiIyMQxYSvFxYsXERoaioCAAHTp0gVLly6FVCottU5hYSFWr16N4OBgtGnTBiNHjsTp06erKOKaryJ9kpqairlz5yI4OBgBAQEICQnBt99+C7VaXUVR13wV6ZcnZWZmIigoCPPmzTNglLVLRfpEpVLhs88+Q79+/dC6dWv06dMHW7ZsgUqlqqKoa76K9EtOTg4++OAD9O7dGwEBARg5ciR+++23Koq49pBKpejbty927typ91xj/K5nwqbDjRs3MGXKFNjY2GD9+vWYMWMGfvrpJ7z55pul1ps3bx7Cw8MxdepUbNy4ES4uLnj99ddx/vz5Koq85qpIn+Tk5CA0NBSRkZF46623sGXLFgQFBeH999/HihUrqjD6mqui35UnLVy4EOnp6QaMsnapaJ8sWrQIW7ZswahRo7Bt2zYMHToUW7ZswerVq6so8pqtov0ya9YsRERE4OWXX8aWLVvg6+uLGTNmMGmrRFlZWXj11VeRmJhYpvON8rteoBK9+uqrQt++fQWFQqEpO3bsmODv7y+cOXOmxDpXr14V/P39hYiICE2ZSqUShg8fLowbN87gMdd0FemTnTt3Cv7+/sLdu3e1yhctWiQ0b95cePTokUFjrg0q0i9P2rNnj9C5c2ehdevWwty5cw0Zaq1RkT65fPmy4O/vL+zfv1+r/MMPPxSGDBkiqFQqQ4ZcK1SkX+Lj4wV/f3/hiy++0JSp1Wph4MCBwoQJEwwec02nVquFw4cPCz169BA6deok+Pv7C19++WWpdYz1u54jbCUoLCzE2bNn0adPH0gkEk15cHAwbGxscOLEiRLrnT59GmKxGH379tWUicViDBgwABcvXkRWVpbBY6+pKtonLi4umDBhAho3bqxV3qRJE6hUKqSlpRk07pquov3yWHx8PFauXInly5fDwsLC0OHWChXtk8OHD8PJyQkhISFa5QsWLMBPP/1U4t6GVHYV7ZeCggIAgJ2dnaZMJBLBwcEB2dnZBo25NkhJScE777yDbt26YceOHWWqY6zf9fwGliApKQlyuRw+Pj5a5RKJBB4eHoiJiSmxXkxMDFxdXbW+WADQqFEjAEBsbKxhAq4FKtonL7zwAhYtWlSs/Ndff4WtrS08PT0NEm9tUdF+AQClUok5c+agX79+Wj/46NlUtE9u3LgBPz8//P777xgxYgRatmyJ7t27Y8uWLXzesxJUtF+aNm2Kbt26Ydu2bbh69Spyc3MRHh6OS5cuYcSIEVUReo3m7OyMX3/9FR9++CEcHR3LVMdYv+sl+k+pfXJzcwEA9vb2xY7Z2trqfEA0JyenxDqPO7U8D2GTtor2SUk+/fRTREZGYvbs2RzVeUbP0i9btmxBWlpamf+qpbKpaJ9kZGQgJycH8+fPx8yZM9G4cWOcPHkSmzZtQlZWVol/+FDZPct3ZenSpZg+fTpGjx6tKZswYQImT55c6XHWNra2trC1tS1XHWP9rmfCVgJ9f02KRKISywVBqFA90q+iffK0jRs3YsuWLRg4cCBeffXVygitVqtov1y+fBmff/45Pv/88xJ/8FHFVbRPFAoF0tLSsH37dnTr1g0AEBgYCKlUim+//RbTp0+Hi4tLpcdbW1S0X+Li4jBu3Dg4Oztj9erVcHV1xZkzZ7Bjxw6Ym5tj7ty5hgiXSmGs3/VM2Erg4OAAAJDJZMWOyWQyuLq6lljP3t4ecXFxxcofZ9uP26Xyq2ifPFZQUID58+fj8OHDGDlyJJYvX85ncipBRfpFJpNhzpw5GDNmDDp27AilUqk5JggClEolzMzM+AdOBVX0u2JnZwdra2sEBQVplQcHB2Pfvn24desWE7ZnUNF++fLLLyGTyfDjjz+ifv36AIDOnTvD0tISGzduxIgRI+Dn52e4wKkYY/2u52+sEnh5eUEikSAhIUGrXKlUIjk5GU2aNCmxnq+vLx48eID8/Hyt8oSEBIhEIvj6+hos5pquon0CFK3x9dJLL+GXX37B7NmzsWLFCpiZmRk65FqhIv1y7do1JCYmIjw8HC1bttT8l5ubiwMHDqBly5ZcBucZVPS74uPjA6VSqZVAA0UjbwBgbW1tmIBriYr2S0pKCtzd3TXJ2mOdO3cGANy5c8cwAZNOxvpdz4StBBYWFggMDMSxY8e0fnidOnUKeXl56NGjR4n1evToAZVKhaNHj2rK1Go1jhw5gnbt2nGE7RlUtE/y8vIwefJkREdHY+3atXjttdeqKuRaoSL90rJlS+zbt6/YfzY2NujVqxf27duHli1bVuXbqFEq+l0JDg6GQqHA4cOHtcqPHz8OW1tb9skzqmi/+Pr6IiUlBffv39cqv3jxIgBw4pQRGOt3PW+J6jBz5kyEhoZi2rRpmDhxIlJTU7FmzRp06dJFc8vg7t27kEqlaNeuHQAgICAAPXv2xJIlS5CVlQUfHx/s3r0b0dHR2L59uxHfTc1QkT7ZtGkToqOjMWHCBDRs2BCXL1/WarNp06YcOXhG5e0XOzs7tG7dulg7ZmZmcHJyKvEYlU9FviuDBg3Cnj17sHTpUmRmZqJp06b47bffcPDgQcyfP5/fk0pQkX55+eWXcfDgQUyePBmvv/46XF1dce7cOXzxxRfo378/vy9VwFR+14sEfU/P1WJnzpzB+vXrcevWLTg6OqJPnz4ICwvTzASZMGECzp8/j+joaE0dmUyGNWvW4MiRI5DJZPDz88OsWbN0/vVE5VPePgkODsa9e/d0tnfgwAE0b968SmKvySryXXnac889hz59+mDlypVVFXaNVpE+ycvLw6ZNm3D48GFkZGTA29sbkyZN0pqdSM+mIv2SlJSEtWvX4q+//kJBQQG8vLwwcuRIhIaGaq3pRs8mOTkZzz//PObPn681A9dUftczYSMiIiIycXyGjYiIiMjEMWEjIiIiMnFM2IiIiIhMHBM2IiIiIhPHhI2IiIjIxDFhIyIiIjJxTNiIiIiITBwTNqJqpGnTphgyZAiGDh2q+W/hwoWl1omIiMD06dMr5fqbNm1CYGAghg4dimHDhmHIkCGYPHlyiRshl8WDBw8wbtw4AEWLg7755pvFyp9VcnIymjdvrvWZ9e3bFxMmTEBSUpLe+ps3b8Zvv/1W7uuqVCpMnz4d6enpiIiIQIcOHTSf29ChQzFu3DhcunSpIm8JADB06FDk5OQgNzcXEydOLFb+rM6dO4fBgwfrPa9p06bIzMwsV9vz5s0r84rwgiAUO/+3337D5s2by3VNouqOSyQTVTNfffUV6tSpY7Trv/DCC1iyZInm9a5du/DOO+8gIiKi3G25ublhz549AIDU1FRN4vdkeWWwsrLCjz/+qHktCAI++OADrFu3DmvXri217rlz53RuzF2aHTt2oFOnTqhXrx6Aop0c/ve//2mOnzhxAm+++SZOnTpVodXqH7+f5ORkREVFFSuvCWJiYrBs2TJcuXIFfn5+mvI+ffogPDwcN2/e5E4lVGtwhI2ohti3bx9Gjx6NYcOGoVevXvj222+LnXP06FEMHz4cI0aMwOjRo/HPP/8AAHJzczFv3jyMGDECQ4YMwYoVK7Q2qC5Nly5dNInW/fv38dprr2HIkCEYPHgwvvjiCwCAUqnEe++9hyFDhmDEiBGYNWsWZDIZkpOTERAQAJVKhUWLFiExMRGvvPKKplytVqNnz55aCcns2bM17+3TTz/F8OHDMXToULzxxht48OBBmWKWy+VIS0uDo6MjACAuLg5TpkzB2LFj0atXL7z++uuQy+UIDw/HtWvXsGrVKhw7dgyFhYVYsWIFhg8fjpCQEMybNw9SqbRY+/n5+fjqq68wYsSIUj+3tLQ0zShZWFgYBg8ejCFDhmDVqlWaz3/jxo2az+2VV17Bw4cPAfw3sjV//nwUFBRg6NChUKlUmvJx48bhyJEjmuutXr0an3zyCQDg+++/x4gRIzBs2DBMnjwZMTExpX5euj6fx9avX6/ph5MnT2rKy3KdDRs2YMOGDSVeNzw8HCNGjMDAgQOLHRs1ahRH2ah2EYio2vD39xcGDx4shISEaP5LT08XpFKpMGbMGCEzM1MQBEG4dOmS0K5dO0EQBOGHH34QXn31VUEQBOH5558XLl26JAiCIPzxxx/Cpk2bBEEQhHnz5glff/21IAiCoFQqhbCwMGHbtm3Frr9x40Zh2bJlmtcKhUL46KOPhOnTpwuCIAgvvfSSsGPHDkEQBCEnJ0cYMmSIcPDgQeGff/4RBgwYIKjVakEQBGHVqlVCZGSkkJSUpInz77//FgYNGiQIgqBVvmHDBs01s7OzhU6dOgk5OTnC/v37hf/7v/8TFAqFIAiCsGfPHmHq1KnFYk5KShKaNWsmhISECIMHDxa6dOkiDBgwQFi7dq0glUoFQRCElStXCgcOHBAEQRAKCwuFwYMHC0eOHBEEQRBCQ0OFX375RRAEQdi0aZOwcuVKzftYs2aN8N577xW75okTJ4TQ0FDN6yf7QBAEQa1WC19++aUwePBgQRAE4d133xWWL18uqNVqQS6XCy+//LLwv//9T0hNTRXat28vyOVyQRAEYfv27cKxY8cEQSj6/0JGRobWZ/Vk+b59+zTXVCqVQvfu3YW4uDjh3Llzwosvvijk5eUJglD0/4OBAwcWew9P9kdpn4+/v7/wv//9TxAEQYiOjhY6deokZGRklHqduXPnCl988UWxa+pS0vm5ublCmzZthPz8/DK3Q1Sd8ZYoUTWj65boZ599htOnTyM+Ph63bt1CXl5esXMGDRqEmTNnomfPnggKCsK0adMAAKdOnUJUVBT27dsHACgoKNB5/cOHDyMyMhIAoFAo0LJlSyxfvhx5eXm4ePEiduzYAQCwt7fHiBEj8Pvvv2PhwoUwMzPD6NGj0a1bN/Tv3x9t2rRBcnKy3vc7cuRIjBo1CvPmzcPBgwfRq1cv2Nvb4+TJk4iKisLIkSMBAGq1Gvn5+SW28eQt0T/++ANz5sxBUFAQbG1tAQBz5szBmTNn8PnnnyM+Ph4PHz4s8fM7deoUcnNz8ddff2nef926dYudFxsbCy8vL62yCxcuYOjQoRCJRCgsLISvry82btwIAPj999+xe/duiEQiWFhYYNy4cfjqq68wdepUNGvWDMOHD0ePHj3Qo0cPdOnSRe9nBgADBw7EqlWrkJaWhhs3bqBRo0bw9vbG3r17kZCQoPWM4KNHj5CdnQ0nJ6cS29L3+YwfPx4A4O/vj8aNG+PSpUuIjIzUeZ3KYGdnBzs7O6SkpKBx48aV0iaRKWPCRlQD3L9/H2PHjsWYMWPQoUMHDBgwQOvW1GOzZ8/GqFGj8OeffyIiIgLbtm1DREQE1Go1NmzYoPnFl5OTA5FIVOK1nn6G7TGpVApBELTK1Go1lEolHBwc8OOPP+LixYv4+++/8X//93+YOHEi+vTpo/e9ubu7o0WLFjh16hQiIiKwYMECTdtTp07Fiy++CAAoLCzEo0eP9LbXvXt3TJkyBW+//TZ++eUX2Nvb4+2334ZKpcLAgQMRHByMe/fuFXsvj6+5YMEC9OzZEwAgk8m0bg0+JhaLoVartcqefobt6Xaffq1UKiEWi/HNN98gKioKZ8+exYoVK9C5c2csWrRI7/u0sbFB//79cfDgQVy6dAmjR4/WtD106FDMmTNH8/rhw4ea28Ml0ff5iMX/PV0jCAIkEkmFrlNeKpUKZmZmldYekSnjM2xENcC1a9dQp04dvPHGG+jevbsmWVOpVJpzlEolevfujby8PIwfPx7vvfceYmJioFQq0a1bN+zcuROCIKCwsBCvv/46vvnmm3LFYGdnh7Zt2yI8PBxA0XNxBw4cQNeuXXHy5ElMnjwZAQEBePPNNzFs2DDcunVLq76ZmRkUCkWJbY8ZMwaff/45CgoK0KFDBwBAt27dsG/fPs0zZBs2bMC7775bplhffvllODg4aEa4/vzzT8yYMQMvvPACRCIRrly5ovnszMzMNM+TdevWDeHh4SgsLIRarcbixYtLnLTg7e1dphmojz1u9/Hnv3fvXnTt2hW3bt3C4MGD0bhxY0yfPh2TJ09GdHS0Vl2JRAKVSlVigjlmzBhERETg0qVL6N+/PwAgKCgIhw4d0jwLt3v3bkyaNKnU+Er7fABg//79AIDr168jISEBbdu2rdB1yiM3NxdyuRwNGzastDaJTBlH2IhqgKCgIOzbtw8DBgyAtbU12rRpgzp16iAhIUFzjkQiwYIFCxAWFgaJRAKRSIQVK1bAwsICCxcuxIcffoghQ4ZAoVCga9eumDp1arnjWL16Nd5//31ERESgsLBQ87C8Wq3G77//jsGDB8PGxgaOjo5Yvny5Vl0/Pz+YmZlh1KhRWLdundax3r17Y9myZZpbuAAwevRoPHjwAGPGjIFIJEKDBg2wcuXKMsVpbm6OxYsXY+rUqRg9ejRmz56NGTNmwNHREdbW1ujYsSMSExMBAL169cLHH38MhUKBN954Ax9//DGGDx8OlUqF5s2bY968ecXa79q1KxYuXIicnBw4ODjojWfRokX44IMPNJ9/9+7d8dprr8HCwgIDBw7EyJEjYWNjAysrq2Kjay4uLmjRogUGDhyI3bt3ax1r1aoVJBIJ+vfvD0tLSwBFI4zTpk3Dyy+/DJFIBDs7O2zevFnniCqAUj8foGhJlmHDhkEkEmHt2rVwcnIq83UeTzh466239H5OT/rzzz8RHBwMCwuLctUjqq5EQkl/lhER0TP57LPPYGZmppVkUuWZOHEiFixYgGbNmhk7FKIqwVuiREQG8PLLL+Pvv/9GWlqasUOpcY4dO4bnnnuOyRrVKhxhIyIiIjJxHGEjIiIiMnFM2IiIiIhMHBM2IiIiIhPHhI2IiIjIxDFhIyIiIjJx/w/vjWZhz4sWJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_clsf = KNeighborsClassifier(n_neighbors=15)\n",
    "knn_clsf.fit(X_train, y_train)\n",
    "\n",
    "score_model(knn_clsf)\n",
    "\n",
    "evaluate_model(knn_clsf)\n",
    "\n",
    "plot_roc_curve(knn_clsf, X_test, y_test)  \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3655fb4",
   "metadata": {},
   "source": [
    "# Summary for the model\n",
    "\n",
    "Model terbaik di antara yang diterapkan adalah Random Forests dengan akurasi 85,6% seperti untuk model lainnya, meskipun akurasinya berkisar sekitar 80%, AUC mereka sangat buruk dan karena itu tidak boleh digunakan untuk skenario dunia nyata."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Copy of Customer Churn Analysis.ipynb",
   "provenance": [
    {
     "file_id": "https://github.com/bayuwira/Project-Churn-for-Bank-Customers/blob/main/Customer%20Churn%20Analysis.ipynb",
     "timestamp": 1632553595677
    }
   ]
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}