FIN5622_Project_3_neil_yejjey_tuk12930 (1).ipynb

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   "source": [
    "<h2>FIN5622 Project #3\n",
    "    <p class=\"text-primary\">Loan Default Prediction with Random Forests</p>\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<div class = \"panel panel-success\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Project Introduction</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    In this project we will build a random forest model to predict borrower defaults on a set of loan performance data.\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "<div class = \"panel panel-info\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Instructions</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Please code you solutions directly in this Notebook under each task in the cells provided below. Tasks are intended to be done in order with results from one task informing work in later tasks. Include all code used to produce your results, and support your answers with textual explanations if neccessary. <b>Any written text must be typed in a markdown cell to receive credit!</b> You may feel free to add additional Code and/or Markdown cells as needed. Code must be written using <b>Python 3</b>.\n",
    "    <br><br>Please order the cells in your Notebook in the order in which they are intended to run! It is expected that your Notebook is able to run in sequence from start to finish without any errors (before submitting check by: <code>Kernel > Restart & Run All</code>).\n",
    "    <br><br>Once ready, please submit your assignment as an <b>.ipynb file</b> (<code>File > Download as > Notebook</code>). You must name your files in the following format: <b><code>FIN5622_Project_3_Richard_Lee_tuc12345.ipynb</code></b>.\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "---\n",
    "<div class = \"alert alert-block alert-warning\">\n",
    "    <b>Note</b>: Import all libraries that you plan on using in the code block below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
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   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 1</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Read in the dataset \"loan_performance_data_2016.csv\" into a dataframe called <code>loans</code> using the pandas <code>pd.read_csv()</code> function. Check that there are 405,180 loans present in the dataframe.\n",
    "    <br><br>\n",
    "    Note that there are 16 feature columns and 1 target column (i.e. \"loan_default\") in this dataset. Please review the file \"Loan_Data_Dictionary.xlsx\" for definitions on each column.\n",
    "    <p style=\"text-align:right;\">[0pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>loan_term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>fico</th>\n",
       "      <th>dti</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>income_verification</th>\n",
       "      <th>home_mortgage</th>\n",
       "      <th>home_own</th>\n",
       "      <th>home_rent</th>\n",
       "      <th>debt_consolidation</th>\n",
       "      <th>coborrower</th>\n",
       "      <th>num_accounts</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>derogatory_records</th>\n",
       "      <th>loan_default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.32</td>\n",
       "      <td>750</td>\n",
       "      <td>19.05</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>9.309</td>\n",
       "      <td>18.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.99</td>\n",
       "      <td>670</td>\n",
       "      <td>6.59</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12.152</td>\n",
       "      <td>50.6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.425</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.53</td>\n",
       "      <td>730</td>\n",
       "      <td>35.56</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2.096</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.500</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.67</td>\n",
       "      <td>665</td>\n",
       "      <td>38.29</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>18.847</td>\n",
       "      <td>62.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.875</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.27</td>\n",
       "      <td>670</td>\n",
       "      <td>11.75</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>25.200</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>405175</th>\n",
       "      <td>16.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.79</td>\n",
       "      <td>665</td>\n",
       "      <td>12.25</td>\n",
       "      <td>150.0</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>12</td>\n",
       "      <td>7.700</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405176</th>\n",
       "      <td>24.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.79</td>\n",
       "      <td>665</td>\n",
       "      <td>19.61</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>49.431</td>\n",
       "      <td>84.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405177</th>\n",
       "      <td>40.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.49</td>\n",
       "      <td>705</td>\n",
       "      <td>12.75</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.633</td>\n",
       "      <td>64.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405178</th>\n",
       "      <td>24.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.49</td>\n",
       "      <td>660</td>\n",
       "      <td>18.30</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>17.641</td>\n",
       "      <td>68.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405179</th>\n",
       "      <td>14.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.49</td>\n",
       "      <td>660</td>\n",
       "      <td>23.36</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>7.662</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>405180 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loan_amnt  loan_term  int_rate  fico    dti  annual_income  \\\n",
       "0           7.200        3.0      5.32   750  19.05           49.0   \n",
       "1          12.000        5.0     11.99   670   6.59           49.0   \n",
       "2          11.425        3.0     19.53   730  35.56           26.0   \n",
       "3          17.500        3.0     13.67   665  38.29           46.0   \n",
       "4          22.875        5.0     17.27   670  11.75           65.0   \n",
       "...           ...        ...       ...   ...    ...            ...   \n",
       "405175     16.000        5.0     12.79   665  12.25          150.0   \n",
       "405176     24.000        5.0     12.79   665  19.61           95.0   \n",
       "405177     40.000        5.0     10.49   705  12.75          227.0   \n",
       "405178     24.000        5.0     14.49   660  18.30          110.0   \n",
       "405179     14.000        5.0     14.49   660  23.36           95.0   \n",
       "\n",
       "        income_verification  home_mortgage  home_own  home_rent  \\\n",
       "0                         0              1         0          0   \n",
       "1                         0              1         0          0   \n",
       "2                         0              0         0          1   \n",
       "3                         0              0         0          1   \n",
       "4                         0              0         0          1   \n",
       "...                     ...            ...       ...        ...   \n",
       "405175                    0              0         0          1   \n",
       "405176                    0              1         0          0   \n",
       "405177                    1              1         0          0   \n",
       "405178                    0              0         0          1   \n",
       "405179                    1              1         0          0   \n",
       "\n",
       "        debt_consolidation  coborrower  num_accounts  revol_bal  revol_util  \\\n",
       "0                        1           0            11      9.309        18.4   \n",
       "1                        0           0            10     12.152        50.6   \n",
       "2                        0           0            11      2.096         7.2   \n",
       "3                        1           0            33     18.847        62.6   \n",
       "4                        1           0             6     25.200        90.0   \n",
       "...                    ...         ...           ...        ...         ...   \n",
       "405175                   0           0            12      7.700        55.0   \n",
       "405176                   0           0             5     49.431        84.4   \n",
       "405177                   0           0             5      8.633        64.9   \n",
       "405178                   1           0            10     17.641        68.1   \n",
       "405179                   0           0             8      7.662        54.0   \n",
       "\n",
       "        derogatory_records  loan_default  \n",
       "0                        0             0  \n",
       "1                        0             1  \n",
       "2                        0             0  \n",
       "3                        0             0  \n",
       "4                        0             0  \n",
       "...                    ...           ...  \n",
       "405175                   1             0  \n",
       "405176                   0             0  \n",
       "405177                   0             0  \n",
       "405178                   1             1  \n",
       "405179                   0             0  \n",
       "\n",
       "[405180 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loans = pd.read_csv(\"loan_performance_data_2016.csv\")\n",
    "loans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 2</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Use the function <code>train_test_split()</code> from the <code>sklearn.model_selection</code> module to split <code>loans</code> into training and test datasets. Provide the function with 2 separate numpy arrays, storing the features and target separately (like we did in class). Additionally, set the following optional paramters for the function to the values listed below:\n",
    "    <ul>\n",
    "        <li><code>test_size = .2</code></li>\n",
    "        <li><code>random_state = 5622</code></li>\n",
    "    </ul>\n",
    "    <br>\n",
    "    The function should return 4 outputs, representing the features and target for the training and test datasets, and store them as numpy arrays in variables called <code>X_train</code>, <code>X_test</code>, <code>y_train</code>, and <code>y_test</code>. The training data should contain 80%, while the test data should contain 20%, of the original <code>loans</code> dataset. \n",
    "    <br><br>\n",
    "    Check that each array shoud have the following shapes: \n",
    "    <ul>\n",
    "        <li><code>X_train.shape = (324144, 16)</code></li>\n",
    "        <li><code>X_test.shape = (81036, 16)</code></li>\n",
    "        <li><code>y_train.shape = (324144,)</code></li>\n",
    "        <li><code>y_test.shape = (81036,)</code></li>\n",
    "    </ul>\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['loan_amnt',\n",
       "  'loan_term',\n",
       "  'int_rate',\n",
       "  'fico',\n",
       "  'dti',\n",
       "  'annual_income',\n",
       "  'income_verification',\n",
       "  'home_mortgage',\n",
       "  'home_own',\n",
       "  'home_rent',\n",
       "  'debt_consolidation',\n",
       "  'coborrower',\n",
       "  'num_accounts',\n",
       "  'revol_bal',\n",
       "  'revol_util',\n",
       "  'derogatory_records'],\n",
       " ['loan_default'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_target = [\"loan_default\"]\n",
    "loan_features = loans.drop(\"loan_default\", axis = 1).columns.tolist()\n",
    "loan_features, loan_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        loans[loan_features], loans[loan_target], test_size = .2 , random_state = 5622)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(324144, 16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81036, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(324144, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81036, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 3</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Fit a random forest model using the training dataset, <code>X_train</code> and <code>y_train</code>. Recall that this is done by calling <code>RandomForestClassifier()</code> from the <code>sklearn.ensemble</code> module, and using the <code>.fit()</code> method. Set <code>random_state = 5622</code> when initializing the random forest model. \n",
    "    <br><br>\n",
    "    We will accept all default settings for this first random forest model. Specifically, the random forest will include 10 decision tree classifiers, no limit for tree depth, and no minimum leaf size. Store the fitted model in a variable called <code>rf_model_1</code>.\n",
    "    </ul>\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model_1 = RandomForestClassifier(n_estimators = 10, random_state = 5622).fit(X_train, y_train.squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 4</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Create a second random forest model, but changing the default settings to the below parameter values when calling <code>RandomForestClassifier()</code>: \n",
    "    <ul>\n",
    "        <li><code>n_estimators = 50</code></li>\n",
    "        <li><code>max_depth = 5</code></li>\n",
    "        <li><code>min_samples_leaf = 5</code></li>\n",
    "        <li><code>random_state = 5622</code></li>\n",
    "    </ul>\n",
    "    Fit the model using the <code>fit()</code> method on the training data, <code>X_train</code> and <code>y_train</code>, and save the model in a variable called <code>rf_model_2</code>.\n",
    "    <br><br>\n",
    "    Explain what each of the following parameters controls in the random forest model: \n",
    "    <ul>\n",
    "        <li><code>n_estimators</code></li>\n",
    "        <li><code>max_depth</code></li>\n",
    "        <li><code>min_samples_leaf</code></li>\n",
    "    </ul>\n",
    "    <p style=\"text-align:right;\">[20pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model_2 = RandomForestClassifier(n_estimators = 50, max_depth = 5, min_samples_leaf = 5, random_state = 5622).fit(X_train, y_train.squeeze())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin, the n_estimators paramteter establishes/controls the number of trees in the forest. \n",
    "max_depths, meanwhile controls the maximum depth of the trees.  Finally, min_samples_leaf is the minimum number of samples requierd to split an internal code. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 5</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "     Assess the performance of both <code>rf_model_1</code> and <code>rf_model_2</code> on the test dataset <code>X_test</code> and <code>y_test</code> by:\n",
    "    <ul>\n",
    "        <li>Calculating the accuracy of the both model's predictions using the <code>.score()</code> method.</li>\n",
    "        <li>Comparing the mean predicted probability of default, using the <code>.predict_proba()</code> method, against the actual observed rate of default for both models.</li>\n",
    "        <li>Calculating the AUC for both models using the <code>roc_auc_score()</code> function from the <code>sklearn.metrics</code> module.</li>\n",
    "    <p style=\"text-align:right;\">[20pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9425761389999506"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model_1.score(loans[loan_features], loans[loan_target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2078890863320006,\n",
       " loan_default    0.195061\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model_1.predict_proba(X_test)[:, 1].mean(), y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_1 = rf_model_1.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6499845811865892"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_prob_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.804119156917913"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model_2.score(loans[loan_features], loans[loan_target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.19648078233427232,\n",
       " loan_default    0.195061\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model_2.predict_proba(X_test)[:, 1].mean(), y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prob_2 = rf_model_2.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7122833152969601"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_prob_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 6</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Review the performance assessment results calculated in the previous tasks for both models. \n",
    "    <br><br>\n",
    "    Which model, <code>rf_model_1</code> or <code>rf_model_2</code>, performed better under each of the performance metrics calculated in the previous task (accuracy, actual vs. expected default probability, and AUC)? Explain which model was expected to perform better and why.\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rf_model_1 a higher score than rf_model_2 in terms of accuracy with a score of 0.9425761389999506 compared to 0.804119156917913.  This is because there are less parameters established in the first model, creating a higher accuracy.  However, in terms of actual vs expected default probability, the rf_model_2 performs better (20.7% vs 19.6%), and auc score (71% vs 65%) due to the fact that it implements the max_depth function and the min_samples_leaf function, unlike rf_model_1.  These functions in the second model control the depth of the trees as well as establish the minimum number of samples required to split the internal code - which allows this model to be a more effective indicator of default probability and AUC score. \n"
   ]
  },
  {
   "attachments": {
    "Task_7_Plot.PNG": {
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YknIP/zX77o1nXn7HQDpDjmdHSPKdhwD5R8nJyjb6bp8nC1mrwJdzbfKqfsWxTLEmsUpbHBY0lEsljiWae0I6t8+B6EYpaY0UGyYd/Vra/ra0+glp+UPSykf1w9J/6MqqJ9S6+TVd3jFKl7Z/roQNX2nXim+1ZNEsTaXu2+z5em/ePD06/w09smawxhz5UAezwlXfXOdsAvdPgEG2K0uWZMDc/OQSJckxvbl8kyN31LV8spdb2nQg7ZLWx+VpXWzfhk2nCpSYV9nlYQRy5PCkPAdJrYEXdZYoqIdF6QsxeqcBSKoNkCCbKqnsB79lYN85RuRqJZE0/mtyM3B86O9MgmXmw19NwmaOD9NZHqVWeAPg9/i+qVvVGUA50bwhsCwScVvIWgWduC5bm5wbqtCxSlNcmMYvlY585Vqk656RlvxdWnSXNP/P0vf3SCsekdY/K+12pgNbrNcz29Vy4aiKzsboWEKqvj+arjGbU/T0ohg9Nv+EXl4epy93p2htXJZ2nk3U9PjZWpG2Qvm1+brSA1+sJ79DFgsKOFACmpwDNG4BGAZ8tYDKq6uEFcvrNnDpSteCbF1TqybuTtMry+PNgerLMGJ1gjbE53cJWe/AURKbonhkjaf2EYmfgaYvqLBs8TXTj5lCdPihKfVBo59XneDChQsmqz2lVAAx8AR6ZKrn4YTfGsCyHgALiDmW1HOi4RB/Ne6AriDL9rDN1O3noWYhaxVw4rozFmq7z7Sp1rFOz0pnd7ogPfi5Y5G+JW143nm9f8yB6N3SMscyBaKbX5Iip0vJ65z5d0nn90vZkfqhMFFtxWfVXFmo+tpqnc0v1ea4bH2967TeWBmrZxZF68VlsfpiR6pWxeTo4NmL2nrmpMYfn6qI/CjVNTeouL5ENU01asOv2wv5HbJYWGTNx5IDJmSjp9MAFpr3uoyPELiSXQt4XusmvxZkecXPKKlTSkGVkvP7Npy+WK3CqsYuIcu2sz8U8gOqgBYY0imio8h+Ty0oAMr+UsIDQGLxYqGyP1ijlCqhgY8HDw8dxlEgj+oEWMNYrkCWcik8tKgkCqRvv/12U8qbqrBdQRbx8LKQtbpxcq4rri2sQe+V3ww0SDlDQ4VzEca4r/kHP3Pg6cAUy3Tun6QFf3E+3ydteVU69IV0coELX+YHxOWZ+qGx2llsm2Nt/mBcfQz1juHF/bzsRI7eXZdoLNX7vjumF5bE6pt96dpzukinHV4UVjWovKFG69LX6fm9z2v4vuGKuxTXK8u1o/wOWW5mbm5WBJj4zDgGbmb+ev5Fzw95LV0LsvyUA9vqDW29HNp/z8nqSuwLB46aUKwbV8e1IEvtKaqfsn/4boEi4CTqAquWhxDAJJkEy6CGE9EWFP8DvIzHmgWy1JuiFhZQBYzsP+VWruUuQBayVjdMXFPGSnVe9wmJqikylqbil0g735PC/uq84t8qzfmDNOs37qs+Fmv0PKkgTqrIdqMA6opdGGPlYu3il3UAfcVZdqvDD1yGpbVNOpxeosl70/VUWLTumR6hB2ZG6v31SVodk6fUQteAqmxoNvO3YpxVZuqT45/q4a0Pa2rcVOXU5KixFSOr7/eC3yHb37oWZAdSHDgsTVwdQIkifZTCBogdRUMe04Ajwm2wf/9+YwHTIIiVi7VP5VbqM9G4h+uEzxRABLZAlEYvOmwQQUDhPyxptgM4EtHAeArodXYs2EYeDEAWnyzA7QhTC1mrvsu5doyV6limXsMUftTGKgeW8dKxadLyh6UZv5Cm/qc05X9J0/7L9Z3uGimlbnGAmuXMX+mAtM4Fqc9rOlemZ6liEHmN3BX1zdqXWqSPt6Torm8j9LuJB3W3A9cPN6VoV0qhgSruRObl91iorc42tjlwxg2QW5Orz098ruMXj6vBeQj0hwXryUK2lwJSWI6UhQZKhEYtXbrUdKroKMAL3Eh3hqjJD6DZB3y0NP4BSSrE0oBGhVROCtNxoTAOCxUYY/Xiy6boHpVRBw8ebMCK3xdXDH7bjpDkO9tLTf1bbrnFlI+muim/8ZWFrFWfxHUDVAFkdYH7Kn/gU2np/dLMXzlg/b/Oq/8tbis/PtYzWx0LJLXdInVAzG/bLVNj9QI6A7sfr0fAWnu5VcU1l5WUX6nvI7NMG8pfvjms26cc0ZAFJ/TdofOKzS5XdWOLgTC/AcouXN1cKYA0uSRZYSlhiimKUbOzbgNdB7gAtj8sWE8Wsr0U6+PgAS+Eq8PzMTOOv74D05gHMd1znTDOc5cwjv3Cj800LgbGE6bFeP76/o7vXs4H7/f89dbvffcG5sN69n7DfL6ykLW6vpxrA/B5lmpLo1Rf5rzK5zrAPO2Ac5u0b6y06G/S7N+7r/20+kdMkdL3OlZqjtSMhepAlY4BHSDqiUsQMAJJLNCi6kadu1Sro+dKNDP8vF5ZEa+HZh/XI3Oj9NaaBC12YJuUX6W6y8517lir/LazyxjLtaC2QBvPb9QLe1/QW+Fv6cTFE2phW/wk7ikL2V4KGPkCic+81ntlm7Fc+UsHAt/t837X8bcdh67GM3Q1DXgSaYArgfUzYDXj02U62+E7v68sZK2uK6CIpUpMKi34SWul8C/dgH6ASvjU2qccS3WSlLDSsWgvukDmlZ/fmsG5vsw11vl1hrWJtXqhpE7HL5RpZXSO89qf7Fip0Xp4znG9tCxOk3anaWdyoXLK640LwLNUry66EzlTVd1Urc+iPtNTu57Sd6e+U2ZVprFc/SkL2X4WURMrV640LgBC1wjFwv86UODiuBCpMWfOHLN+tqMrN0ZHWchaXRXgMdaq8wpP99PyC1LmEReqhyc6UB0uhd3pNlYRQrVvjBQ915nnsBvHioWLpWv8qZ1fT4wFjLzO0wB1qfqy4nIqtC3xomYeytB765L03JKTetGB6idbTyvsWJZp0MqraFBjs/P251i5/P5aAqy4AvC5NrU1qaqpStPjp+tAzgEDXCxbf8tC1uqqLGStDPo4/yacKlo6tcwNp8JSXXyv6wag1R/rNWGFlLrZDaGi11UXMO1MzIn1WVDZYFwACx2A0khFh4DH55/Q6ytPaer+c9rqAJcQrOoGrkv3t90VgK1prtHe7L365PgnSi1LNVZrfctP49j9LQtZq6uykL3JxHnGYsVaba53w6TSdrg9oza/7EYB0J+f2FRe/wn0x1ItPedat/hVTUNV19YqYjUAldf66sYWpV6s1sb4fE3YmWZgOmRhtGm84vva2DxFZpTqfHGtqhyw+roCuiuuX3ysZyvO6tv4bzVs7zBNOjlJeTV5Brz8G0hZyPaTOLG+g790reVfbxrHiKGr+SxkQ13OeeXcGleAA0Za9ck8FbNA2vqatOwf0tw/uh0Ato+QElc5F0WMm6GK3AC4D64DKKYCRAbgSLw5HQIS86q0+Hi23lmbqMHzonT/zGN6YWmsvj1wXofOFiutsMZEDNBo1dvLz7mqzbVLhMDZ8rMaeXSkAeyGcxtMYxfRAzdCFrL9JFrq2Sav9b+/t8eDJCfMi1LwFdOZxro7QtKbxvYRWcBfltFxPgvZUJZzTg1YHeuzrsTtVbXtTWne/0hzHLASpxoxVcqJcoP+60td69azVLvROOTrXy2ta9LBtGJN3JWmxxecMLGrD84+rlEbk7U+Lt9Ataj6srFsAavX+ae3Vx7XLBDFz0p41qX6Szqcd9i4CHAPAN7eL71vCjHIOgfRe0r3x3CNC4uTyjqBFWCj4wA9th555BGTW8AXsh3n5a/33YOd7zzedOSNA95k+qK7LZ+Z33de/qakpJgODUzzFd9jY2P16KOP6p577jE14JOTk822+MpCNhTEPcDANUzDlQNJ3AH4TAuT3M4AC++UpvxvF7B8Lz3/Yw8qrvvrwIipVy3VNrc7e1Nrm4qqGrUruVCjHZDeMeWIfvvlQd059YjGbEnR3tQiA9WGbjZYdVfmPnD2lcato/lH9eL+F7Uzc6ezfNbhDjcKrp5CC7JcVMTs0WWPFs6+DCTzvVzjrKHzE8T6iDmlyyy5GCitQ4IXOg8QRmW2p10cZPIUEPxPJiw6J9DaTyQCvwd2DCTT4TvjWTbLIGaWcfyObrWjRo0y1ijT6UnGeDoysM5r9fgi/wHrB9CEd5ESkWX7ykI2BIRhQLdVLNHKXDf13+FJbiQAvazotrpthHNBRLjxqsZSde6bq0bFtc89UwFrzeUWA00y1S08lqnhzqv/X6cd1R0OVJ9bfFKzD2foVG6lGptb/6lDQH9eXpedBwNhWIRl3bPpHtPAlV2dfcPB6qvQgizd8PZ/Iq19WloztPfDWmfY8IIb59fFyQKKbMsDDzyg999/X7///e9NbyqyZlHY0RdUwJAUjmTpIgkMoCTPAb2u6M2VmZlp8hWQfYt6aPTkIs8B0CVZOYlgSCrDeshdwLxM5zvz0xOMcVir18rC5VnEpFTkd4DaVxayQSagCBxNmFV7Yur8WCnuewekb7gxqyRXISKAVH9EAlQ7BgRWLWDtxnlmDmbjdR6wZpfVmzCqbw+cM6FVD8yKNNEAJF8hCUtKQbVqGn9ssPLHpcT1yYCVer7yvN4+/LbxvW46v8m4Bm6U77UrhRZkeYIDRnxL9DDpy3B8hhsX2AVkgRUdDQYNGmQsRLrEkgSmqwQxdL8lZpVOAW+88YZJnI1FS7IWcsgCSF71sTi97rpYprzikwgGyxdQA1ncBoCa32HdkseWbrbXy8IFZNkWuvWS95aT7ysL2SAS54hOAcVnpAuHpCjnBia0asFt0rIHXH/ridluBYCyDNcXC5TN0P3zi+UJNOlNtTQqWy8tj9M/HLDylwQsZLHKLa+/6gLwLNUerKJH4toEpCRwqbhcodKGUq1PX6+c6hzX7+qvFfdBoQVZLiBef7ig+mO4xhMRyJI5C6sTeF0v1SHVHMj9ykVAkheSxWCprl692pTT4SR0zMLFa31nWbjIdwBMKdUDuAE2CWauZcmyjbgV2EYgi/uCcb6ykA1QcT64Frkm68uliwkuPHEDEL9KqNXqx6Vd70unljoXXKID4Ap3fu4H/LPdPKfMBSixRGntd7uxZpgczbgEyNeMf/ViVaNp4OpP/+q1hNWK3xWYrkxbqfHR43Wy6OTVnAOB4HvtSqEF2QEUkMV65JUfKF0r1SGWLtOAI/OSTYucsICTihEkfaErLsDGx0oDFpYsSbixlCkrAyDpwQVkOWGkOvTqeTGNV3/cAJ1BlnlYFyeZ37GOzo6XhWwgyjkXgJLaVKeWS3s+lFYOdlMDkhMA0KasdzNckemqj+cOYJbUNmlHcqHGbjltYlgJtZpz+IJissqNy+BGXB301orIj9DYyLF6ds+zmpM4x/heB6LHVl9lIdtL4ZOl+yq+UqCEj5SurB0r7SJ8tEyj7AzycsXSgEX1XdwAgA93A2kQyTcLfFkWf6l8gN+V9IYAGh8vCbxpaCMlIpBmHYmJiV3W+CKHwR/+8AfjkuA3JBC3PtkAFG9jgAMrlFCqtO3S3jHSsgfdxit8rXRfJW9AcZrrMsAn200fa2fCd0p0QEZJrZZF5ejN1Qkm7IpwKzoN0HmgurHFNF4NhNXqievQs1BLGko04tAIjYkcoyP5R1TWWHZDw7J6IgvZXor18cpN2RguBl7pgZQXWeA7sK1M8wCM35XPHHws39zcXDMPUQW89jMwnd/iUgCeWLMkfmF9WNGsG4sXa5dwLOp+YdGmp6f/0/pxCwBxHgq4FLB4CTNjW31lIXsDxLH2wAoogSaxqrQrUGKFaADKrFBaJee427hFA28f4MIvgSVwpWho+qUazQrP0NCwGN0345g+3pyi8LPFJkcALoGBFuAErlivCcUJ5i9+2IiCCJODINAatq4nC9leyjxl2yHmfQdmAJBt88KsvLSCTPPm5a/33Xc5jPPCuXyneeP56/2OgXGcQG+8Nx/rIzzLWz+f2S5vfm9ZLMNXFrIDLOCKK4BQKvysRMbgBpj9W2nh7dKe0W75aixaurGacKvu+1c7E7/8sRdWpcZtSzVVA0gbSMTAmcJqldc1XzNdoL9Fl1i6wE6JnaLB2wcrujD6aixssFivvrKQ7WeR1pBY1XvvvdcE/lM8koamgQIXvleKLFLgkfWzHc8884yJ5b2W2D4LWT+L40ojFLlLcQfgQ03Z6CaxnvL/St/9Utr6upuTtbbIuTsbnXmx2vp2PlgtwKSRCssUuH6wIVm3fBWue2dEaO6RCyZlINOwcG/U2WfNADayIFIv7HtB/9jyD61NX2t6cAXzNRkykA0UOLANbJOvdTmQD4DO1s9wvWPDPBay/pRzTOlRRSeB/Dg3sxU9rmb+WlrzpJS42u0Eg3/1qsXqXDd9PBdAs9GBJ72xdqcUmdjWP39z2LgGSMZCBAHwBcI3ErC4B2jEii2K1asHXtVHxz7SmfIzxnplWjAr6CELEPBr4hP1Xo3t0PMBHy8NasDWqj/k4MpzBzTVuJmrsFq3v+12Dlh8n3RgnJQbLTdHgAPXfoAJkASW+Fqpe3W2qEZrTuaauFZyB7y6Ml5bEi6aYoOEafWR4X0S9y7+VRqxkkuTVVRfpPLGcp0pO6Pi+uKg8712paCHLAIMNBQRj2qH3g1EJ+DLtVZsPwlg1jlWK41VMfNca3XhHW7awNhFUnnWj2A1x7x/jjsWKQA9nlGm6QfPuflZF5zQZ9tTdeRciUl2bSxWZ3U3+kzToJVUmmSSaOMeIKGLF03AdXjj7Or+VUhAFnFSeE22Q+8Gc1Fz51n1ToZazltAc4NUlOL2PNz/qbRikNtZgG6t5Golrwb+WObth+PNEkyUgGOV5pTVm8qsdBgYtiRWr608pdnhGYrO/LGoYCCcY+BJT60tGVv02sHXNCJ8hOkSy7hQAauvQgayVlY3Tg4YACcVAohh9axWEl/HzHdDsvDF9oM7wFfgiEgBElyviM4xBQW9woLUxSKdIJZroAnQn6s4Z4oYUmcLV0GouAY6k4WslVVvhEUIGPCnFiW5ca1ECSx/SAqfIGUcdJNdM535DGD7bqV5YK273GqyXE0/eN4UFnxiwQl9uStNB85cUmZJnRqaW42FGwCGq7PNbtRAXm2eDuUeMj21appqDFzJPwBgQ9GC9TRgkPVe5zt7LfWm+b66diULWasbJ+e6BJamR1az25V19wfSor+6A+6B7EjXamWefhK3A35UoAk8Y7Mr9MWOMxo0J8pUGZh+4LzinHElNU0GwIEk7mXSEZJn4IOjH+jVg68q6mKUX0twB5r8DlkPoATDExzvBeb7gpTvXis38CRYvitZyFrdMGGRkiYQy5UsV7N/55bBjp7jjEv+sYtrP1mtCLiSv7X2covC04s1Yk2C/vbtUdOgtSI611itnr/1RoZgdaXaplqFpYTp0e2P6t3D7yqmMEYNrcEd99pT+R2yAJaWfxKk3HrrrRoxYoTp+umBlunkUyWv6p133mnSAKamprb/+p9lIWs1IAICpuOAA1bACVypdwVcp/2XNOOX0gkHrnQa6KfwKwR6DFgdi5QwLHpm7U29ZOJafzfxoB51LNdNpwpURgiWachyfxcoAvP0ysIFQKTAhaoLemT7IyahCw1boex77Up+h6yXrYr0fsSykihlyZIlpk8+kGU6iVHCwsLMdNL28Rn4diYLWSu/C3LRkEV1jELHQj250K2BNes30pL7XMuVChy+nQb6yYbEJVB7uVUXHAt1yfFsA1WqDRApQM0soOtVGQhEwOIGuFh3UevPrTd+V74DV/6a8KxA2+gBkN8hSzdPMkaR95QDTA5WupnSu8iDLND94osvTKYo/m7evPknJ4PPBMpj4bIsEl1byFr1q7jeACZdWUlyTfarjcPdHAJ0HiC+NWmt2yvLhGD1D1hZLcBsamnTperLis+p0LwjF4w7AH/rJ1tPK/J8qRocuLohWO0/DCB51isdCaiz9d6R9/TcnueUUppi7l3+3czyO2SxWLFMly5dag44WaBItZednW2+4zZgHMX9SNOHuyAyMvKqJevNA4D5HTlXSXJtIWvVr6LLK3BNXi+tHiItcOAKZKkuQBJsMl8B1n60WhGApbLAvtQifb491ZRyecYB7IyD55WcX/VjPgFnlYGKKixUrNd5SfP09O6n9XHkx4q9FHtTugY6k98hW1NTYwC7YMECA0wACiRJ7+c1iFE1gKTX9DqaO3euKUpIIxnzI/562aWwhJnfQtaqTzLUcixXQqwupUrxS1xrle6uO9+Tzmz9seOAgUX/II6l4G/ltZ88reQP+GhTioY6YH1/fZKpk8V4pgeiS8CTZ73SiEXPLUpvT4ufZqoWAFzcA979e7PL75AFhnv27DGZ/sl5SrJo/LL4X7FQyTdA4msqBXhWL6DFzdDZSbI+Wau+y7mu8KeWX/ix88D3d7nhWMC1rtiZpf8BwSLpHBCfU2l6Yr2wJFaD5h7Xp1tTtSOp0LFoG0wkQaDLAyxgXZK6xLgFyJSF77WxtdFMt/pRfocsIMU1QPZ+SqeQ9R9QUvSPFWPJAmHqW5H5n8oANJTxu85kIWvVa/GqTyQA+VnjFrtViZfe7yZqOb/PsVwvutOxcPsJFIAVXyrJWqjySnwr/lZT0uXIBUVdKFNhVaPpFuu5BQJVwBMXAB0IVqWtMjGvI4+ONLDFIPL+Wf1UfocsB5+V0NCF75WM/Fi0WLLUvsJlgNVKln8qAnjTu5KFrFWPBVy9DgRnd0orH5Xm3yptec0t44LlimXbTwKUXhgW8a17TxeZktlUeX3m+xhT4uX0xWpVNfBK3f6jAJfBp7OxlOCmBAy5XnEP8B2XgVXX8jtkEScHy5SV8RewegPT+IvP1Rv43pUsZK26L4dgAJZGraLT0q4PpJm/ktY/K2Ufc8OwmNaPjVnAFcu1rK7JpBgcsiDadB54bWW8dqcUmiqvbhiWa7kGuoArDVte7CvVYilmSN7X6qZqM4572KprDQhk+1MWslbXlbFcW91GK/yux6a5Ma7f/C/nAnIudCIFTGNW3wVfjNXaRjHCKyqqbjTxrdTK+u2XB03+1mPnSw1YgW+wCLgCVhq1yO/6/envlVaeZhq0rsa8Ov+sri8LWasQk3PjtzivryRnoVFr/p/dYoRUIqgucN0C+Fz7yfry/K2UdPlmX7oemBWpe6ZHGN8rUQL4WnEbBLq/taMAKVEC+F4f3/G43jz0ppJKkq5arhaw3ZeFrFUIyLnhAaexXB24Jq1xY10pSrjrfbdIIXle+yFpC6AktIr41bzyBmOlkr+VxNhPhcWYYoTAtaGZ1+vgAqtBZztAKcH9xYkvNHT3UM04NcNkzrKugd7JQtYq+MWNX1cind0lbXxBmnuLtH2EW+n1aieCvsOBJVAvi5Ium08V6J21iRo057heX3VKS6OylV1Wb/IJBJvV6okaW0V1RcYVUFBboMmxk021gsaWRjPOqneykLUKTnl+V+BKhABhWKb76ytSynqpMqc9YqD3tOOXwBKXQHl9s2KyyrXwWJbecKCK1Uo57a0JBQau+GODFa5YqNTZ2p+zX+NPjNfFWrczwdWGrT4cQysLWaugk0Gf1FAhZR6W9o2RFt4prXpcivvebejqB6uLtQDNqoZmHT5brIm7zxqw0jNr6v5zOpJeYiIIghGqvqJxi6qw1NkaumuoPov6zFix1i3Qf7KQtQoOcdNjuTaUu7GudH39/m5p7VMOXJe4+Vxp8DJZsXoPCH5KpEBJbZM2O1bqBxuSHbhGmzyuK6NzlZBXaRq6sG6BcDALFwDVCT489qFp2Np8frMBLOC11mv/yULWKoDl3OjGLUCjlnO+yee64x1p9u/dMi+xYVJxqpt/oI/WK7ykQQt4UnjwlRXxun9mpN5cfUrbEi8qs7TOhGEFM3oAJ2DFBVDXXGcqFmRWZWpX5i4lliSqvqXezGPVv7KQtQpceXA9t1daM8SNdSWBS8IKt2usAwpj3fbRouTXNFgVVDZo/M403T7liOn2GplRqvK6JtPYFcjJWrorLFRyDOzM3Kl3Dr9jGrUA7uW2y1djX636XxayVgEmh2SAk3CsylzXLTD1Px243iMlrnITaZtY1z5ars7A6z49r+ocC3VnSqEemh2pW78O16JjWaY7LG6DIOfqVQHTovoik4bw7o13a1zUOOXWOMfXyu+ykLUKHGEqer20YhZIi+6W5v2P6xYgFAu49tHn6umKY5kCV5Jk43elA8GojcnKKKkzLoNQsFw90YiFO4CGLfK97s7abaxXa7kOjCxkrW6wHJJ5boHqi26qwXXPuOFYu0a6HQkAbz8BAeuVdIMZxbUmtpWMWM8vPmniXmnQogNBKIiYV0KwaMjC/0rvrflJ801Z7ua2ZgNeq4GRhazVDZRzowNPemmlbnbzuS68Q9r+lpvABZ+rgWv/AAGulNY2mdytI1YnaPDcKE0/eN7U08J6DRXu4BogkcuyM8tM3Cv5XvG5er25QscJEhyykLW6AXJucl7960pduG59XVryd7cyQez3UkV2v/hdPfHqT2QAuVvdLrDRpnZW+Nlik24wWDsRdKa6ljodyT9i4l1fPfCqFiYvvBqWZXVjZCFrNcByaEZe1+xIae/H0rxbpQ3DHNhucS1a00urfwQ3ASjhV/OOXtBj80/oucUntT4u30QShApYr9qnzg6R3/XlAy+b2NfwvHBVXq5sn8vqRslC1mqA5BANy7Sxyq2nRZzrMmcgHKv0nOt3xTfLfH0U8MR6xcdKjCvdYEngsjAiU2cKq41PlunBLsBK4xXxrdGF0cYlQMXYQ7mHlFWdZdIU2satGy8LWSs/qx2uhGUR23p4ojTnj9K+se3ZserdefpBLAXLlfSCJHH5bHuqyes6emOyTlwoU3Vj8FQiuJ4ALI1bhGGRyGXIriEm76vX2cAqcGQha+Vf4QsEpFkR0uon3JCsqFlSTWG/+l1hJ5EBWK+rYnJNXtchC6O11bFkS2ouX62hFQoCpBQs3Ju9V8/uedbke6WDAZYr8LUKLFnIWvlHwBOIkmuAagQzf+26CDIOOlddgwvffhB+SF79ye+K9frqinj95ZvDGrv1tIkaCJYyL9eT5xrAesVSPZBzQEN2DtGHER+arrGEZVnXQGDKQtaq/wXUmmqlnONuzOvcP0lHvpZqi1y3gYFB/4AP/2pOeb2mHzhvusPSsHUwrdhAN1Q6FHiApRHrXMU543clNWF6RbqqmqrchC4hYqWHoixkrfpPwJNOBRVZbi8tyr5QdpvIAZM8GxD0DQZmCc5/5G8tqGzUjuRCk2fg0XlRmnkoQ0VVjW7Mqzt70MvLN0A6wu8SvtNb4W8ppjDGQNUbrAJbFrJW/af6UjeZy6aXpAV/kQ5NkGoc6xXXQD/BAKjUNLaYsi8fbU5x4HpCY7ak6FRupbFcTcxr+7zBLva1uKFYWzK2aNjeYXr14KsmHWHF5Yr2OayCQRayVn0T8CT8qjhNipgiLf2HC9mUDY65WdNvYVnAE9dAWmGN5hzOMNbr22sTTc7XktrLbjKXEKCr5xow/lfn4USF2FcOvGLqbJH71fpeg08Wsla9F1QjcoDyL3QoWPagFDndjXs1YUR9px5LYDXVjvW6IT7fNGwNWRCteUcu6HxxrXEbhIoALGDNr83XsYJjpphh1eUqnSg8odrmWjPdKvhkIWvVCzk3OxYqJWBi5kmL7nLzvabvcaMJ+gmwvP43tbbpZFaFyZD16PwoU2o7OrPMhGqFSsMWAq6ANCI/QqMiRmnk0ZE6W3HWRBJguVrABq/8Dln8SleuOK8/ztDW1nb1s6/Dns9M8wbfaR1lIXuDxasqEMXXume0Wxl270dS8Rnnampon6lvwjVAzOul6suaFZ5hKhRgve5KKTTjgGuoCHjyr7Sh1GTJenLXk3r/6Ps6fvG4yZ5lXQPBL79DFmiWlZVp3rx5uv/++zV69Gilp6ertfXHXikNDQ1av369Bg0apCeeeEJ79jgWUReykL2B4uGH/zU3WlpwmzT7d259LbrKXg3N6r1AJ4Alz+ve1CJTuPCOqUeMa6C45vLVsKxQked7ZUguSdawfcO0/MxyXaq/dNX3ai3Y4JffIcsKoqKi9N577yk3N1fTpk3TsmXLVFNTc9XKTUtL0/PPP6+srCwVFxerurq6/deuvPkAc0REhIXsjRDugZZGB6qLpe/+W1p8r1QQ7zxFm/sOV4cjrmvgirJK6zRh1xnd9s0R439Nyq9SkwPXUOhQ4Al4AlHiXjee22g+k0QbHyyfLVhDS36HbF1dnVavXq2ZM2caSIaHh2vy5MnKz8834GxubtacOXPMdCzcnJwc1dfTn92VB1is4QsXLmjDhg2aOnWqhexACojSDZaeW/NvlXa975zYknbrte9AIK71YlWjadh6dP4JM6w5mXc1iXYoAZbrGbiSjvDF/S/qke2PmBhYz6K9lqvMKjjld8hilYaFhWnp0qXme2xsrMaPH2+sVuAJLD/++GONGDFCH3zwgcaMGWNcAmyYd8Ex3+7du/XRRx9p+PDhmjRpkoWs3+Uce+BKzy0SueB3pYHr8JduPKxp3Oq9OLVeroGojDJ9vDlFj8yNMkldkh3rFddAKMEVAdHCukItSF6gZ/Y8o9ERo5VQkmAat6z1GrryO2RxCyxZskQLFy4033EdTJw40VisHmTHjRunzz//XKWlpVqxYoVmzZplLFem+wroRkZGWnfBQAjANlZIyeukFY+44VmnljnjKl1C9lFEDaQUVJvKBA/OOq43VydoZ3KhKWAIXEMJOVy3pCFsvtJsGrS+OPGFVp1dZbrHWr9r6MvvkAWGNGQB0crKSq1atcq4By5dumTcB7gLcAF88803Ki8v19q1a43roKSk5Kol6yvb8OVnccxN9ECh80ScKS19QNr+tlsOhs4F9N7qpTibuAZyyuq15mSuXloep2cXn9SiyCyTzIVMWf3A74ARAKVLbEJxgvG9ZlRmmLpb1NuiggFhW1ahL79DFpCeP39en332mXEFjBo1yvhlcRtg1RJ9kJ2dbVwBgPjDDz/Uzp071dTUZCE74AKwzo1Ptdjdo90IgiNfubkITM+t3otTSQmYw+kl+mBDkqmv9eWuNMXlVJgGrxBiqxHXLr7XlWkr9frB1/XB0Q+UWpbaPtXqZpLfIcvFBhAzMjJ07NgxJSUlqaqqSkVFRabxi+lYs0QYMD0hIcFYvJ0BFlnI+km4B+i9dXantOoxKexOt2oBmbOwuLo4H9cTr/5A9NylWk3Zl27iXUeuT9L+1EsmLMvtDhtaiGV/yJA1NnKshu4eqjmJc0z32IbW/okjtgou+R2y/S0L2f6WAzisVBqzDn7upiVc86Rbg8tUi+0dAPkZYVl1l1tNCZihYTEaNCdKiyOzlVVar6aWn/rbg12Aldd/XATkfAWybx1+S8cLjhvfa6g9SKy6LwvZm1nc+ACWeNe1T0vf/Uo6udDtGtuH+Fes1+a2K8bP+vaaRNOhgIxZ1NdqaG41UQWhxBwari63XlZsUayJHMipyTFxrzXNNabBC/Ba3byykL0ZBeFwATQ7r69ZR6XlD0vf3y1dOOxcEVQt6ANcW6+osr5ZS6Oy9efJ4Xpg5jFjyeKPDbWoAUT4FV1iV5xZocHbB+uNQ28YK9Y2all5spC9GUX0QO0lx2oNc/O+rn9eKkzysV57jkIAWl7XpPCzxSYN4d3TI/T13rO61O53ZXqoCRcApV9o1KLOFrkHiuqKjOVqw7KsPPUZslxoA+lvspDtgzhPALYyWzo8SQr7q3RovFSe6Y7v4XlkdvyuJNFOyKs0DVuU3n5rTYIizpeaBi+mhxJuACgFC+lUgCuAiIHx0eN1NP+oDcuy6lR9gixwpQssHQcGShayfRAAqMqX9n8izf+zFD2vvXNB79wDWKjEvC4/kaNnvj+p5xfHms9EDYSia4DrnaoE+3P26/Ooz02uAUBLLKy1Xq26UrchC9RSUlJ06NAhk2MAuBJyNXv2bO3YsaN9Nv/LQrY3wuRskaoLpH1jpAW3S/FL3HywPbVenQHrtNqxXvecLtKHm5L1zKKTxoqlBAy+V6aHkjzrNaU0Rd+d+s6U4Z4YM1FF9UUWrFbXVbchSweCTz/91HSJ/eKLL0w2rbfeektz585VcnJy+2z+l4VsT+VAAAuW9ITb35Jm/kqKXSRT2LCHgGBuLNS88npN2JVmcg28uy5R+89cMjkIQtLv6vzDBXCy6KTePfKuXj7wsjac22DSEdqoAavuqNuQZYaVK1ea3lvx8fG65ZZbTNpBOg745ob1tyxkeyiqx1Iehg4GRBGc2dYrFwEAbW27otjscr2+Ml4PzT6u9XF5JjUhyVzgayghFtcA1isltxtbG01nAgoaJpYkqr6l3oDXWrFW3VG3ITt9+nQdPnzYwI0ur/fcc49J/sICOiZy8acsZLspIEqC7dObpcX3SOufdSMIWjhu3YcDcwJYQIp7gIatJ8OildDuGgg14xW4AlBKwQDVcVHjdKr4lEnuQiysbdiy6qm6DVlyC/z85z/Xr3/9a/3mN7/Rv/7rv+pXv/qV+UxegoGShez15FAPwNId9tg0twfX1tfdfAT4ZXtowXr+18XHs3XXtxHGB5tf0WASvYSaewDAkjQ7pzrHhGU9uPVBTY+fblwD1mq16q26DVlKxJAZi+xZ/CX/ALlisWYHEngWstcQACXWFaCSWHvGL6VDX0hVeY452jOXDvwEpIVVjZq2/5z+Nu2opu5PN+W3TVhWiDAHeAJXz4KNKYwxcB2ya4j25eyz1qtVn9VtyF68eFGLFy82mbSAHPlgSeyCP9a6CwJAUA/3QH6stOF5twfX6U1uAxeQ6CEViXFNKagyybTptbU6JtdYtKEY94qPtayxzACVctwzTs0w1qutFGvVH+o2ZBctWmSiCzZt2qQpU6Zo5MiRBrJYAAMpC9mOco4/+QfI9ZpxwE2uTSNX2g7n7DY6k3v2AMQFgK+VzgSvrzylYUtitSu50BQ3HOBT7VcBT6CaXZ2tVWmr9NXJr5RXm2fiXknwMtDXtVXoqtuQBWw0fAFWhgcffNBUmR1IKxZZyHYQMKjIdsOy6GCw+SXp4qkeuwcQi6psaNbak3l6dN4JvbcuSSezyt2ELu3zhILYG5JnH847rDGRY/Tcnuc0N3GuiuuLDXytrPpT3YYsCbWpwwXgZsyYod/+9rcmVpYfHjjgWFADJAtZTw72sFRJSbjrPSnsb1L4BKkk3QVsDywxZsUNQArC6QfO6YGZkaZzwfniWuOXDRVhnQJRhtOlp/XO4Xc07vg4heeFmwTbNizLyh/qEWSpWoDbgJpd3l8G6m4NlCxkHUFF/K+pW93YV9wD+F970YML0T02Ma9SIzckGf/rqphcldZSmaJ9hiAX4ASsxLsSjoWvFd/r7qzdxl1gG7as/KluQxY/7N69e01UQW1t7U8G4mYHShayDvmoIEuBw6X3Szvfdxu7eul/bWxpMz22yJw1fGmcKQ+D/zVUGrjYC8Kyzlee16SYSRq+b7gBK6C1kQNWA6FuQ3bChAmm4YteXxQ79B1iYmLaZ/O/bmrIAlHyvZ7e6Nbf2jNaKk13w7Z6IOAJYKsbW0yvrUFzj5vusZTiDqVihgAWkO7M3Klh+4bp8Z2Pa9uFbSZblnULWA2UeuQueP311zV58mTji/UdsHAHSjcvZB0oUIMrdYs07xZpx7u9TlFIQxaZsmaHZ+jOqUf0zb505Ve6HQxCCbBECWzO2KwhO4doQvQE5dXkma6ytnHLaiDVbcgCNpLEkH0L94DvYHMX+FlAgXpb6buluQ5gt735I2B7IKxXQJpXUa9x207rnukRmn80U+V1hOIZjAe1aNgiBMsru03X2ILaAp0pP2PGWdeA1Y1QtyG7fPlyxcXFmR8QtuU7DGRM4U0HWY4tMbAJK90QLTJplWW44O3BcWdO8g+QXPulZXF6bP4JUxaGcaHQPRbLlYYtkmhPjp2sEeEjFHcpzlybXkSBdRFY3Qh1G7KNjY0GbAMdF9tRNxVkAellB7Cnlktz/uDmgq3M6ZEFC1aAKKkISfBCesJXV8br+IUyNbWGhmUHSOmxhe/1lQOv6M1Db5rkLlWXq9rnsLK6ceo2ZNv/3nDdPJB18IiLAAuWHLBUM6grdsHbAwHYgspGzTtyQfdOj9CnW1NN/CvjQ8Wuww1Apdindz+tb+O/Ne4B02vLWq5WASAL2UAVUQTJ66VFf5X2fuRWNTAWbPfA4flfiRj4fMcZPT7/hBYczTQNXsHeg4utJyzrQtUFRRZEmpyvpY2lplMBnwnPsrIKFFnIBpwc/JGL4Nw+afG9bpgWdbl60GgDQIl/jcxw8w88832MtiddVO3l4M8/gGuAhC50iR0VMUqjI0YrtybXNmpZBawsZANNdCoAsCsHS5tfkUrSXAu2m3Q0+V8bWrQ18aKeXBhtenGdyCwzSV+C3UUAYOmpNSthlp7f+7w+i/pM0YXRppDhQDa+Wln1RH6HrGndbY9CaGtrM0NnEQl8Z9r1UieGLGQ5Hm0OTC8ckpb8XdrUnuilrXu96czPHcBW1jcr7FiW/jErUp9sPa30S27+gQ6HO2jEY4HIACxV3AARBRF6Ye8LWpG2woRn4Tawsgpk+R2ygLO0tNRUtb3rrrv03nvv6ezZsz+JrQWqJACnKOOwYcN07ty59in/rJCFLLDIPymtelTaOEy6dNoZ1/1KBvhZS2sv69NtqSbBNh0NyuuaTF2uYLXxACxgrWis0Obzmw1QcRVQitvrEmstWKtAl98hywoAI3DNy8sTtcKWLl1qKip4Yp5Tp07piSeeMElozpw50z7FlWcNA2aKN4YUZI0J6sC0yIHqmiHSSgeyOcdd6HZDuABIsJ1dVq9hS07qvu+OaV1cnuqbW4MargCU7q/HC45r6K6hemzHYzpXcc5cC8Ht9LC62eR3yNbV1Wn16tWaOXOmsWrpNUbX3Pz8fDOdmwZLl/SJbAQ5ErB0PXmALS4uVlpamtasWWOShocMZPG3ljqW+7qn3YxaF8Ldhi/gex0xC41Zh84WG/cAPljiX8mqBXyDVQAW3+u0uGkatG2QqbeVXpFu3AZWVsEmv0MWN0BYWJixXlFsbKzGjx+vrKwsA1A2YNeuXZo6daqOHDkiciRgyTLNE5Ddt2+fxo0bp1deeUWTJk0Kfsiyf1iwph7XSNcPm3GwWxas+emVH0z9rRXROXp4jvOmsC7xav7XYOSr5xqg1xZugT3Ze0z0wOqzq02XWKZZC9YqGOV3yOIWIOfswoULzXdcBxMnTlRubq6xbMmF8Pjjj5tEM8CXz5S4wQL2BS3iO7lrQ8JdgLVamOhWkl14h5S0th2w1wcJIM1wgPrV7rMaNCdK0w6cMwUOg9V6BZ74WBNLEk2OVxq0SKJNpQJyEXS8Dqysgkl+hywwJEsXZcMrKiq0YsUK08DF6z8rJ8HM999/b6zdsWPHatCgQcYlQJ7azm6ukGj4Mi6CdGnTi27Bw7TtUnODa6JeQ0C0oblNMVnlJjRr+NJYU+CwvL7JWLbBxiLPei2qLzIW67uH39WXMV8qsyrTTPP+WVkFs/wOWRqrLly4YNwAo0aN0gcffGDcAuSgxSr1DdeKj483Vq6vT7ajgh+yDjToHrt/rBR2p1v8sJsuApJp70t1E2y/tDzO+GKJfw02uHrCx3qm7IzGHh+rJ3c9qe8SvlNaeZqxaq2sQkV+hyzWKEDMzs42YMXfiguhpKREhYWFZron/LeZmZnGVdCVghqyNNyQ8CXiG2ne/7iJX1qc/bhOg47pYNDYoqVR2SZ71pgtKUrKrzJWbdBZr84G07BFbgGs2LPlZ02vrYj8CJU2lJrx1j1gFUryO2T7W0ENWRq6cA0sukuKmiXVl7kmahdikgfYL3aeMeFZX+9NV35FgxkfTOK1H8uVpNkxRTFafHqxKi5XqKG1wcS92oYtq1CVheyAyQFIUYq05klp88vXTboNYOlgQPzrc4tjdduUw278a3v9rWCT53udlzRP/9j6D71/9H1dqr90tUOBBaxVqMpCdiBEJAFW65GvpbC/SplHXcB2YcUylmTaNHANnhele2dEKMrkfw2u8CwXna4Fi+/1vSPv6ZFtj2hB8gJjvdp0hFY3gyxkB0KXq6WTYW7xw5h5UiPJpLsArDO6yQEsCbaJf31tZbwyS+uCroMB8MR69VIPnig8oc9PfK6TRSdNwxbgtYC1uhlkIetPAcXWJik7UlrxyI+Jt7FsuxDdYY+kl+j5xSc1amOycsvrDWCDha8eXMnvejT/qMmYRWcCfLHU3GKaldXNJAtZfwqg0GWWjFokfsk94YzsmpZ0MsAtQA6CkeuTdK7YgZID2GBSS1uLUkpTNOPUDA3ePthYr4V1bhSJtVytbkZZyPpLhGU11bnW6/f3uDliyRXbhfC3UuTw7bWJGrE6QYnO5+Yg8sECUBqxyO9KrgEKGa5PX28Aa10DVjezLGT9Ic9NEBsmzf6dFLPAOdJdby++1oySWo1Yk6BXVsQrOrPcNHwFg4Cnl4IQlwCQXZm2UgnFCcaqtbK62WUh298CsLgJkte6gD34hVRX4o7vRIRjkehl9KZkUybm6LkSUzom0Bu5gCv+VaIE1p5dqymxU0zHAuJe8b3anANWVq4sZPtbAJaKBvP/LG0c7gC21CFS5z26vFwEYzan6KHZx02X2YbmwG8Y8lwDpB+kt9YTO58wDVzEvVpZWf1UFrL9KSy3qjxp9ROuHxYL1rSm/7NF5wK2VRN3p+m2KUe0NeGi8cEGQ5gWgA3PDdeDWx/UC/te0LGCYyZFIeOtrKx+KgvZ/hJhWQ3l0q4PpLm3SLlRbjfaTqCJi4CeW/OOXNAtX4WbnAT4YAOZr3QcoGAhrgB8sGTKmpkw82o6Qisrq85lIdtfIvHLiTluZq2k1VJzfaeARdWNLVoRnat7pkdo5qHzamoNXB8srgEgmleTp6WpSzXhxASVNZQZfyxRAzZywMrq2rKQ7auAIy6BrAhpxSDpwDippsi1bDsIkFIuZtOpAj0yN0pT9qWrzvkeyIAleTadCsZGjtXwfcO1MHmh6cUFXK2srK4vC9m+CthU50u7R0krB0sXE7q0YMkHuzulSEPDYvT59lSV1FwOeBcB/lbg+vGxj006QkK1rKysui8L2b6qqVZKWCkte1BKdP7yvYOwVBub2xR+tljPLT5pAJtl8hEEljXoRQ3QDZZeW8S5Epa1NWOrsquzjS/WWrBWVj2ThWyvhZugTcqPc6vM7hkt1RS6lm0H0ZuLjFovLovTu+sSlVZYoysBlq7QA+z5yvP66uRXptcWvbWwZhmsrKx6JwvZ3grwVBdIW1518xLkx7b7YX+EJ64A8hGcKaw25WLeWpNgKhoA3UAS1mlNc402ntuoF/e/qOf3Pm+qxeIasI1aVlZ9k4Vsr+SAhzpUcYvdaILUrc6RbGif5go0EapVUNmgN1af0tOLYnQyu9zNqOXOckPFVtAjC8ASKbAqbZUe3fGoJkZPVFZVlol7tYC1suq7LGR7LAc8WKxEEMz9k7TjHecoNjqjf7ROPcASC0s+ggdnHzfZtUzCF3eWGyrgCVyJeaWXFl1hc2tyTRFDrFcvPMvKyqrvspDtqYBPU420/W1p4e1SYdI/ARZ/a1VDs8ZtS9Vd30Zo9+lCB1xYju48N0rAFb8rVipdYifFTNLIoyOVWpZqrFrvn5WVVf/JQranIrtWwnLp2587f1c5JivlvH8EE5EEFfXN+mZfuukuuzE+3/hlbzRgEdYpFWGJFnhm9zN69cCr2pu9V3XNXVcHtrKy6pssZHsiOh0UxLvlvHe+64Zr+VqxDkjL6pq04Gim7p95TEuOZ5tSMjeyswGWKXD1fK/EvdKwNTVuqgnPojeXdQ1YWflPFrLdlgPK2iJp2xvS8oekyhzXN9suOFpzucWA9YGZkVoQkWnyEdzo3lyEX+VU5yj2UqxxE+CDPZJ3xI0cCATz2soqxGUh2x0BI4CauMpNYZi23XUTtEOK/6sbW7Q2Nk9DFkZrxqHzKq9vMoC9URgDrhWXK3Qw56DGRI4xZWDoZOA1alnfq5XVwGjAIGsaVtqHjvKd1tl0X90YyDqApVbX2qekLa+55b3bxeZise5IumjKd+OLJQk30QU3SkCULFnkeH12z7Mm70BkQaSpEnu942tlZdW/8jtkuamvXLliVtTQ0KDm5ma1tZHWz73ZvWmNjY0GnN70rnRDIEsJ78OTpCX3u5VnTWOXK6oYhJ8tMXGwdJfNLiMEiodF+wwDJGeNBq6e73Vf9j69Ff6WqbNFBi1Kw1jfq5XVwMvvkAWiFRUVWrx4sQYPHqxx48YpMzPzKmj5u3nzZg0dOlTPPvus5syZo9LS0vZf/7MGFrIOKQFT5hE3CTepDC9Xm3FAlNwDFDzERTByQ5LOF9eaSIKBlgdWIgcO5R4yn8sby02+Adtry8rqxsrvkGUFJ06c0MiRI1VYWKjp06dr+fLlqq11E6kA4cTERAPi7OxsTZgwQZs2bTLTOtOAQhY3QUOFdGiCm5+gMNGFrgMtrFWSvJDwZfiyOJ0prDEugoG2YAEsbgDSET6y7RE9vftp4ypgPH5Za71aWd1Y+R2ydXV1Wr16tVkBlmt4eLgmT56s/Px8M51x3lBVVWUACoR9xbT6+npj4e7evVvTpk0bGMjSdZYus3SdjZnvdkJwRINWdWOLPtycYqzY2OzyG2bBEi3wTew3un/z/Xr/6Ps6V3HOdDiw1quVVWDI75Ctrq5WWFiYli5damB58uRJjR8/XllZWe1zuBBtbW0103AnYPl6YhrW7tatWzVixAjjVpg4ceLAQLYiW9r6mrR9hFSZ62yMWyIGP+y62Dzd9e1RbU4oGHAL1nMPEONK+e3XD76uTec3OdvVaKZZwFpZBY78DtmamhotWbLEgBZgRkVFGUjm5OS0z+FuRHp6urFwV61aZaxWXwFZIAxYDx8+7H9LFmKSjyB1i7Tor1LyOmcjXB8yboLozHID2Ml701V7mdLX7b/zs7BQyZaVUJJgogXKGstU3FCsgtoC27BlZRWg8jtkiRrYsWOH8bXyed26dZo5c6aKi4vNdMBVUFBgpi9YsMD4ahnXlSIjI/3vk3WsRFVkuiW9N7/iVqBltLNdueX1xg87bEmsiqoar7mt/SkAerHuorFc3zj0hsZHjzcNW1ZWVoEtv0OW6IHz588bF8GUKVM0duxY45dNSkpSfHy8sVCB62233aYvv/zSfE5OTm7/9T9rQBq+sGKj5zpW7F3S2Z0mZAuUkpPgqz1n9fCc44rNrjBW7UAgFsBSqWBC9AS9vP9lzU2aa5K60OBlZWUV2PI7ZLH0mpqalJqaqu3btxt/Kw1ceXl5ysjIMHGx0dHR2rJli3bt2mUatnz9tR3ld8jyyk2X2bC/utUOmlzLuvXKFeOHvXfGMW2Iz3fTFvqRsOCb9Xr+1xOFJ/ThsQ8Vnhdu3ARWVlbBIb9DFgELLFqAiuWKj5WBcd5fpnkD37uSXyELYLEOw7900xiSDMYZR+RAfE6FcROM33HGJIHxV5dZD64NLQ0mx8CS1CXmM75Y63u1sgo+DQhk+1N+hSz5CcoypO/+W4qYIjW7SVToJjtmy2m9sDRWqRer/RquReNWUX2R6RI7ePtgfXL8E9U01ViwWlkFqSxkr8qxS+kuu/lV6dufSVW5DnNbTarCZVHZumd6hPalXjL1uRzu+kV0HogujNYLe1/Qg1sf1Jqza4wFC2D9YzdbWVn5WxaynogoyD8pzfi/UmyYQ7wWNbe2KTKj1OQlmLr/nEprm9pn7h8BTgCKC6Cupc6U3N5+YbvJmpVQnGAatqwFa2UV3LKQNXKsRBJw09C16G9SXal+cKCbWVqn99cnmTpd6ZfcbrP9KQBKOkK6xC4+vdiAFuB6cMVVYWVlFdyykEXALC9GWniHFDVLV1qaVFXfpIXHMvXo/CjjJjBVZvuJeQAUkBKGNTtxtp7a9ZTpGkvHAusWsLIKLVnIInyx+z9142Ir3ZIx+x2wPrHghOYdvaCqhpb2GftHhGRF5EeYGlvD9w3X2vS1KmkosYC1sgpBWcgSUVB8RloxSD8cnay2xlpll9WZSIIPNiQpv6Khz24Cz/fa0NpgqhPgEgjPDdfC5IWmiyzfiSqwsrIKPVnIUn325EJpyd91JTdadQ0NppS326ur3IFj3wELQLFUiRaYnzTfJNEm9tXmerWyCn1ZyFZkmRwFP+z5UC1VhVp5Iku3fXNYWxMKTLatviAQ65XihfGX4jU6YrSpErsibYVp7AKuFrBWVqGvmxiyDuCwUs9skxbeqdYzu5ScW6q/TTuqT7edVn1Ta68aujx48g/f6+6s3Xpsx2N65cArOpJ/xMS9WteAldXNo5sXso6VaUp87/5AP6x9WtW5p/XK8jg9Oi9KlQ3NvfbDYr3SqaClrcXke00rS1NYSpixXoGuCc1y/llZWd0cunkh60CQ2l0/LB+k1tilWhd1TndPj9Cx86Wm22xPrVjgSQMWXWKXnVmmL6O/NN8BLZarBauV1c2pmxeyl2tMTGzb6ieVm3xMLy6J1uiNyapp7HkSbgCKpUpY1ntH3tOQnUO0IHnBj50KLGCtrG5a3aSQdaBXek7a8Lxqd47Rgr2xemphlGKyytXczeQvgNMA1CEyboCDuQf12sHX9PGxj3Ws4JgJ17KAtbKyugkh60APV0H6blNaJm7nQg1dFK1Z4Rmm00F3Q7aAK41YWdVZBrJxl+K08fxGU70AuFpZWVmhmw+ytOzXFuvKno9Uuexpfbd+ryklk5hXaRq7rodYAEoil9NlpzUtfpomx05WeSMWcLNxD9jIASsrK1/dfJDFir2YoJZF9+n46kl6fsFRhR3LMiW+rycQXNtcq/Xp6/XS/pc0bO8w7cjcYcZZt4CVlVVnuvkg29IgxcxXyey79fWSDXpnTZzJsEW9rmsJiBKatSx1mWnY+jb+W6VXpLu9tnraUmZlZXXT6CaDrIPKxkpdWTVEJ6Y/qYdmhGvtyVw3ZKt9Dl/hGgCs+F6JHsAdcKb8jOnBVdVUZX2vVlZW19VNBFkHoz+06UrxOVVP/oUWzRzvWLGndJGy3u1z+ArLlTjXM2Vn9NGxj/T+kfeVUZnhgpflOH+ti8DKyup6unkgyyt9W7NaI6br3Jd/0Csz1mtPSmGn9boAaPnlcm29sFXP7HnGdIndn7PfuAasrKyseqKbCLJtUmOlmhberd2Tn9HotSdVe7nzjgdECmzJ2KKhu4dq+qnpyqrKsr22rKyseqWbB7JXWvVDVoRqv/r/9Pm0b7U7Kc80WJl/zl9cA5lVmUoqSTKfAWt4XrhN6GJlZdUn3TyQbWvRlW1vK+urP2rUskMqqblsrFggSyLtAzkHNOroKI07Ps7kerWNWlZWVv2hmwOyNFLVl6l51h+07qvh2hqfY3yxWKiEYc1KmGXKwEyInqCoi1Emg5Z1DVhZWfWHBgyy5tW8fehM15vuqVeQJcVgxiFd/ua/NH7mPJXX1Kvtittza136OlNri6oFVCwgZMvKysqqv+R3yALNKwCtuVkNDQ1qampSW1vbT2DKd6DJwHx870o9h6yzntYm1e37UgVf/1Yrj8Qpq+KCSeJCGkJyDWRUZpheW7Zxy8rKqr/ld8gC2IqKCi1cuFAPPvigxowZo4yMjKsgBbbl5eX6+OOP9fDDD2vKlCkqLi420zpTjyEL5JsbVL3sCR346kGtT9msJ3cMMXGvANZYzxasVlZWfpLfIcsKoqKiNHLkSANPVrRs2TLV1tYawDF9y5Yt+uyzz5Sfn6+pU6dq48aNP7F0fdUbyDZVFehi2O16ael9un3tX0w6QlwDFq5WVlb+lt8hW1dXp9WrV2vmzJkGnOHh4fr6668NULFygSVW7JEjR8y8AHjBggXGteCBlr81NTW6ePGitm/fbkDcE8jmpx7TpFUP6InND2nL+c0mesD6Xq2srAZCfodsdXW1wsLCtHTpUgPL2NhYjR8/XllZWcZlAExff/11paSkqLGx0Vix8+bNMy4G5mcAxjt27NB7772n5557ThMnTuyRT7YkL11xe75R1sUUW2fLyspqQOV3yGKBLlmyxPhkASav+0AyJyfHwBOwvvPOOwa+9fX1WrNmjebPn2/gzPyI+VpbWw1YDx8+rGnTpvUAsvy+TVcIy3KW4y3TysrKaiDkd8gC0Z07dxrrFat17dq1mjVrlkpKSgw8iTbgOyAuLS3V7NmzjcuADetMkZGRPQ/hsrKysrpB8jtkcQkQTQBksWCJLsD/mpCQoJMnTxoLNTk5We+//74mTJhghlOnTrX/+p/VqzhZKysrqxskv0OW13Os1bNnz2rv3r2Ki4szroCCggJlZ2dfdRkA3IMHDxrg4jboShayVlZWwSS/QxYBWixaOhpguQJWb2AafxnvDXzvShayVlZWwaQBgWx/ykLWysoqmGQha2VlZeVHBR1kjx07djXOlg4N3Rny8vKM/7ezaaEwEA6Xm5vb6bRgHux5C87BnrefDsy/efNmYxxeSwEDWeJpX3zxRb377rumc0J3hhEjRujPf/6z+dvZ9GAeOA5/+ctfTIcO4o07mydYB++8vfXWW51OD+aB88a+vfHGGyF13tgvztutt96qt99+u9N5gnnozXnjN9yfixcvbqdY5woYyNIwRjRCdwdic6uqqvTss8+aONzO5gnmgf2jF1xRUZGJyuhsnmAcvPP29NNPmwRCnc0TzAP798wzz5h8HqF43p566ilVVlZ2Ok8wD+zf0KFDDUt6et5wG1xLAQPZnoroBQ7M8OHDTVKaUBORGC+99JLKysrMAyhU5J23F1544ZohfcEqzhvXJCAKxfP2/PPPG7CEmjhvw4YNM2Go10rJ2hsFNWQJHSNBTSiedPaPHnMk17lW6FuwyTtvNBYQZx1qYv+4JnmAhOJ5mzFjhvkbamL/2DdY0t/nLWghizgYvE6H0sXsK/YNa4gLIJR0M5w3rKFQPG+FhYX2vPVQQQ1ZDkaoXci+CtX9s+ctOGXPW+8UFJBlx3l68pTBsuNvxycO05nmDcH0tL3Wtne273z3x8XQ3+rOefOdhwYE330PdLGtN+N586Z7+x1M541tZ1/YZv52lO9+MfTHfgUFZDkYNADho7zjjjv06quvmkQ0HATEgaDlk5Cg22+/XaNGjTLxbsEicuxOnz7dhGzR2EWeB++CZt9Ijn7ffffp73//u7744gsTo9fZBRJo8s4bPkrO22uvvabExMSr5w2xfzQ2zJ07V4899phSU1PbpwS+iI4gRpLz9vLLL5vcyb7nDX86KT45dw899JCJqQyW80YrO35z7ifClLgmOW8epIhnJ9Lgrrvu0iuvvGLux0AX54Rzxrn69a9/bXywvmI6naFIanXbbbfpzTffVGZmZvvU3isoIMtThwQ0xOkBGJKFk5vWSwDOyScp+NixY810L8UiBy3QxTZygXIhc+GyX99//715aLBvTE9KSjIXPfvGTU2i9OuFjQSC2EYSBBFXybZTGYOBlndPzMP+E9L1wQcfGFAFgzgv8fHx5kbkvPGQIF7Sy5cMiAATsd8ErXNzk4M5GCDLOYmOjjZxoCR8Itk+9xznzbvfMAqAFFVNuCZJZRro9xvbTsNWTEyM5syZ85NOBN5+UaGF/cY/y/nkQdPX/QoKyHJg1q9fb8rTcJHSBZdSNxcuXDAHgKcPtcZI/E2r7qZNm8zB8eqNBarYNrZ969atptAklg/7xsXLvnk3JPPxGfBysTMEQwsvIT/r1q0zFzPbT85gzhtQQuwXD0rOK/D9/PPPdfr0aTMtkMV2c01Sw45E85w39g3wYPmwr9yw7PfkyZNNLTz2C8gGOogQ9xD5obkO2Rd6Y3Le6O3FvrNvK1as0OjRo7Vt2zZz3jZs2ND+68AW2895WL58+U+6wzLes2L3799vrt0DBw7oyy+//Emhgd4oKCDLSccy5UZkZ7F82Pn09HRz0XLBYzHwKspnkolzAHlV7cvB8bfYNk4m7gAsWPaTfQA6aWlpP4EsFwAXO7l7seq50ANdwAdrgIcC+0A+Yc7buXPnzHcsJh4wwIiHy6effnrVVcIQqGLbOFerVq0ylUH4zDXJg5JrkvPGvvHgx0UCaOkyvmfPHnN9BrowTnibWrRokdlXLHbyQZ8/f/7qeWMcri3eUnDTkUvau14DXewfDwlfyHocIVaW+4vP5CXg4XLp0qU+XY9BAVlAxAXNQWFnMfe/+uorkzTcOzicaCwGPnPjMq/3yh2oYtvYXlwdWA3crLxeYxEBIu+i5S++ShKjYz1xkQeD2J+VK1deLbbJKyjnzbPSmY6vkorGFN184IEHjCukr5aDv8W2cU1ivXkxsVyTPCy8svicI9xX7BvzYvFNmjTJvH4Hung4AiFcAOzriRMnzLZjpXO/Ed/MA4VXa1wIGEDMG+hvjp6uBVncP8CVzxSHZb89t2RvFRSQ5dU4IiLC+OzwTdKYgN+VmD0uZqZjLXGR89TBumBgWqDfrFikPBx4TcG/BZSwatkPprEPvF7zauaV8mEcF0Wgi/OChcO2s908KLkZ8Xd55w3XDrDCeh8yZIj5zE0eDOcNFwHWKueN10/etOhOyzT2jQciDZXc1Dz4sYq4ZgNdQBTAfPTRR+a8ASR8mFyTnDem03efhjzeFrF6AVYwnDcegN6DgWuOfWGfvOuR8bwtst+8YTIP0/qyX0EBWQ4MFgA7TwMRJxj/K0/YQ4cOmQOFv4jkDryeYfEFQ2unJ25SbkZaab1XL15Z2EesIF6xf/Ob35j9Y9+4ATgmgS62kQYv3/PGvnnnzdflQYMXr6RnzpxpHxP44prEH+mdNwwBEh1x3rgm2XdcIDxAedDs2rXLnM9AF+eN6ByuO++8Yd1hDFDlBNcV/kpa6Ummwv6xz4F+TQJK3pJ4m7rnnnt05513GoOGfWG/eEjwEMSaZb+JUuLtq68KCshycHiaYCXg86K1lgPCAaPVFquOG5YLg3I4pGTjIg8WsW9Yd2w7ViuvnzjneU1hv9hf3AW4EHgd7evry0CJbcQ66HjecON03AfgwwUeDBDydK3zBnCYzj5x3th3LNpgeAPxzhuWq3fe2DfOG/cb+8Z5wu3DdAwcpge6PI6wvTzUaYzkQcg+MXCvsW/wg3PKfvNA6auCArJWVlZWwSoLWSsrKys/ykLWysrKyo+ykLWysrLyoyxkrYJaXiMNPcv27t2rffv2mcgSQnDovEHHlN27d5txNGLQWENoFR0DiHSgYSrQW8WtglsWslZBLQBJq/1//Md/mD72xEcDWvI90BuJkDdiOOmdRI8lYqwHDRpk4nU//PBDEwNKRICVlb9kIWsV1AKyQPJnP/uZCTECuFishE2NGzfOdNclbIxyN/RQorMD4wlB4jMxk8HQC8sqeGUhaxXU8izZf/u3fzPB4wAUVwBWK11a6e4KhAcPHmzcA4CVDgTEUdOjB4uWGEkrK3/JQtYqqOVB9uc//7mxXukUADTptEGPJODK32+++cZ0AQWsf/rTn0wXXtIr0tunPwLOray6koWsVVDLg+wvfvEL89kTDVyffPKJSVtXUlJiXAZeMh4sXixdMmORT8Baslb+lIWsVVCrK8iSMQp3AX3PGe9ljyKpCePpXkl+CICLS8G3i6+VVX/KQtYqqOWlqHvkkUd+khfAy+jvmwScvukkMiEpCFDld2SXIsSLMDArK3/IQtYq6AVcOyZeAaKM87VQO47rbB4rq/6WhayVlZWVH2Uha2VlZeVHWchaWVlZ+VEWslZWVlZ+lIWslZWVlR9lIWtlZWXlR1nIWllZWflRFrJWVlZWfpP0/wNVavfy38TSRgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 7</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Calculate the ROC curve for both <code>rf_model_1</code> and <code>rf_model_2</code>. Recall that ROC curves can be created using the function <code>roc_curve()</code> from the <code>sklearn.metrics</code> module.\n",
    "    <br><br>\n",
    "    Plot both the ROC curves on a single line plot. Recall that the ROC curve should be a upward-sloping and concaved curve above the 45-degree line. Provide axis labels, a legend, and a title for the plot. \n",
    "    <br><br>\n",
    "    Your final plot should look like this:\n",
    "        \n",
    "![Task_7_Plot.PNG](attachment:Task_7_Plot.PNG)\n",
    "    <br>\n",
    "    Which model has better performance according to the ROC plot? Why?\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, th = roc_curve(y_test, y_prob_1)\n",
    "fpr_2, tpr_2, th_2 = roc_curve(y_test, y_prob_2)\n",
    "\n",
    "plt.plot(fpr, tpr, label = \"rf_model_1\")\n",
    "plt.plot(fpr_2, tpr_2, label = \"rf_model_2\")\n",
    "plt.plot(fpr, fpr, linestyle = \"--\", color = \"green\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.title(\"Random Forest ROC Curves\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rf_model_2 has better performance according to the ROC plot, thi i because rf_model_2 has a steeper curve.  By the ROC model,curves that are steeper towards the top left are better performers. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}