2020-LendingClub_EnsembleMethods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Description:**\n",
    "\n",
    "This is the Jupyter notebook is used for the machine learning part of the project. In this notebook the focus is on ensebmle methods, which are: Random Forest, AdaBoost and XGBoost\n",
    "\n",
    "**Project Name:** Lending Club\n",
    "\n",
    "**Team:** Silas Mederer, Jonas Bechthold\n",
    "\n",
    "**Date:** 2020-10-02 to 2020-10-08"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up & load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = 100           # Ensures modeling results can be replicated\n",
    "pd.set_option('display.max_columns', 50) # Sets maximum columns displayed in tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import our dataframae, which was exported after the EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/df_clean.csv')\n",
    "df.drop(\"Unnamed: 0\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41292 entries, 0 to 41291\n",
      "Data columns (total 14 columns):\n",
      "loan_amnt               41292 non-null float64\n",
      "term_is_36              41292 non-null int64\n",
      "int_rate                41292 non-null float64\n",
      "grade                   41292 non-null object\n",
      "sub_grade               41292 non-null object\n",
      "emp_length              41292 non-null float64\n",
      "annual_inc              41292 non-null float64\n",
      "purpose                 41292 non-null object\n",
      "addr_state              41292 non-null object\n",
      "dti                     41292 non-null float64\n",
      "inq_last_6mths          41292 non-null float64\n",
      "open_acc                41292 non-null float64\n",
      "pub_rec_bankruptcies    41292 non-null int64\n",
      "default                 41292 non-null int64\n",
      "dtypes: float64(7), int64(3), object(4)\n",
      "memory usage: 4.4+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot confusion matrices\n",
    "\n",
    "def scores(y_train, y_test, y_train_pred, y_pred, filename=str):\n",
    "    \n",
    "    sns.set(font_scale=1.5)\n",
    "    print(\"Confusion matrix train\")\n",
    "    print(confusion_matrix(y_train, y_train_pred))\n",
    "\n",
    "    print(\"Classification report train\")\n",
    "    print(classification_report(y_train, y_train_pred))\n",
    "\n",
    "    # Figure test\n",
    "    conf_mat = pd.crosstab(np.ravel(y_test), np.ravel(y_pred),\n",
    "                           colnames=[\"Predicted\"], rownames=[\"Actual\"])\n",
    "    sns.heatmap(conf_mat, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
    "\n",
    "    plt.title('Confusion Matrix: Test Data')\n",
    "    plt.savefig(\n",
    "        f\"plots/conf-matrix-test-{filename}.png\", dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "\n",
    "    print(\"Confusion matrix test\")\n",
    "    print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "    print(\"Classification report test\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "    # Figure train\n",
    "    conf_mat = pd.crosstab(np.ravel(y_train), np.ravel(y_train_pred),\n",
    "                           colnames=[\"Predicted\"], rownames=[\"Actual\"])\n",
    "    sns.heatmap(conf_mat, annot=True, cmap=\"Blues\", fmt=\"g\")\n",
    "\n",
    "    plt.title('Confusion Matrix: Training Data')\n",
    "    plt.savefig(\n",
    "        f\"plots/conf-matrix-train-{filename}.png\", dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upscaling of the minority class default = 1 and downscaling the majority class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll import the resampling module from Scikit-Learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll create a new DataFrame with an up-sampled minority class and a down-sampled majority class. \n",
    "\n",
    "First, we'll separate observations from each class into different DataFrames, then we'll resample the minority and majority class with replacement. Finally, we'll combine the both sampled classes into a new DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35006\n",
       "1     6286\n",
       "Name: default, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1    20000\n",
       " Name: default, dtype: int64,\n",
       " 0    20000\n",
       " Name: default, dtype: int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate majority and minority classes\n",
    "df_majority = df[df.default==0]\n",
    "df_minority = df[df.default==1]\n",
    "\n",
    "target_sample_size = 20000\n",
    "\n",
    "# Upsample minority class\n",
    "df_minority_upsampled = resample(df_minority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=target_sample_size,    # to match majority class\n",
    "                                 random_state=random_state) # reproducible results\n",
    "\n",
    "# Upsample minority class\n",
    "df_majority_downsampled = resample(df_majority, \n",
    "                                 replace=True,     # sample with replacement\n",
    "                                 n_samples=target_sample_size,    # to match majority class\n",
    "                                 random_state=random_state) # reproducible results\n",
    "\n",
    "df_minority_upsampled.default.value_counts(),df_majority_downsampled.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    20000\n",
       "0    20000\n",
       "Name: default, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine majority class with upsampled minority class\n",
    "df_upsampled = pd.concat([df_majority_downsampled, df_minority_upsampled])\n",
    " \n",
    "# Display new class counts\n",
    "df_upsampled.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>term_is_36</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>purpose</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>dti</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec_bankruptcies</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16848</th>\n",
       "      <td>21000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>medical</td>\n",
       "      <td>IL</td>\n",
       "      <td>0.0863</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14467</th>\n",
       "      <td>24000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2211</td>\n",
       "      <td>G</td>\n",
       "      <td>G1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>home_improvement</td>\n",
       "      <td>CA</td>\n",
       "      <td>0.1463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>9500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0890</td>\n",
       "      <td>A</td>\n",
       "      <td>A5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>TX</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19346</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1037</td>\n",
       "      <td>B</td>\n",
       "      <td>B3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38004.0</td>\n",
       "      <td>other</td>\n",
       "      <td>NJ</td>\n",
       "      <td>0.1544</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19852</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>small_business</td>\n",
       "      <td>MN</td>\n",
       "      <td>0.0948</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  term_is_36  int_rate grade sub_grade  emp_length  \\\n",
       "16848    21000.0           0    0.1268     C        C1         3.0   \n",
       "14467    24000.0           0    0.2211     G        G1         6.0   \n",
       "97        9500.0           1    0.0890     A        A5         7.0   \n",
       "19346    10000.0           1    0.1037     B        B3         5.0   \n",
       "19852    10000.0           1    0.1268     C        C1         0.0   \n",
       "\n",
       "       annual_inc           purpose addr_state     dti  inq_last_6mths  \\\n",
       "16848     96000.0           medical         IL  0.0863             4.0   \n",
       "14467     98000.0  home_improvement         CA  0.1463             0.0   \n",
       "97        50000.0       credit_card         TX  0.0818             0.0   \n",
       "19346     38004.0             other         NJ  0.1544             2.0   \n",
       "19852     80000.0    small_business         MN  0.0948             1.0   \n",
       "\n",
       "       open_acc  pub_rec_bankruptcies  default  \n",
       "16848      11.0                     0        0  \n",
       "14467       8.0                     0        0  \n",
       "97          8.0                     0        0  \n",
       "19346      12.0                     0        0  \n",
       "19852       5.0                     0        0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_upsampled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000, 14)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_upsampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['loan_amnt', 'term_is_36', 'int_rate', 'grade', 'sub_grade',\n",
       "       'emp_length', 'annual_inc', 'purpose', 'addr_state', 'dti',\n",
       "       'inq_last_6mths', 'open_acc', 'pub_rec_bankruptcies', 'default'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_upsampled.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_feats = ['grade','sub_grade','purpose','addr_state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_upsampled.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.get_dummies(df, columns=cat_feats, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>term_is_36</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>open_acc</th>\n",
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       "      <th>default</th>\n",
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       "      <th>sub_grade_B3</th>\n",
       "      <th>sub_grade_B4</th>\n",
       "      <th>sub_grade_B5</th>\n",
       "      <th>...</th>\n",
       "      <th>addr_state_MS</th>\n",
       "      <th>addr_state_MT</th>\n",
       "      <th>addr_state_NC</th>\n",
       "      <th>addr_state_NE</th>\n",
       "      <th>addr_state_NH</th>\n",
       "      <th>addr_state_NJ</th>\n",
       "      <th>addr_state_NM</th>\n",
       "      <th>addr_state_NV</th>\n",
       "      <th>addr_state_NY</th>\n",
       "      <th>addr_state_OH</th>\n",
       "      <th>addr_state_OK</th>\n",
       "      <th>addr_state_OR</th>\n",
       "      <th>addr_state_PA</th>\n",
       "      <th>addr_state_RI</th>\n",
       "      <th>addr_state_SC</th>\n",
       "      <th>addr_state_SD</th>\n",
       "      <th>addr_state_TN</th>\n",
       "      <th>addr_state_TX</th>\n",
       "      <th>addr_state_UT</th>\n",
       "      <th>addr_state_VA</th>\n",
       "      <th>addr_state_VT</th>\n",
       "      <th>addr_state_WA</th>\n",
       "      <th>addr_state_WI</th>\n",
       "      <th>addr_state_WV</th>\n",
       "      <th>addr_state_WY</th>\n",
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       "      <th>16848</th>\n",
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       "      <td>96000.0</td>\n",
       "      <td>0.0863</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14467</th>\n",
       "      <td>24000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2211</td>\n",
       "      <td>6.0</td>\n",
       "      <td>98000.0</td>\n",
       "      <td>0.1463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>9500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0890</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>0.0818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19346</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1037</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38004.0</td>\n",
       "      <td>0.1544</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19852</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>0.0948</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_amnt  term_is_36  int_rate  emp_length  annual_inc     dti  \\\n",
       "16848    21000.0           0    0.1268         3.0     96000.0  0.0863   \n",
       "14467    24000.0           0    0.2211         6.0     98000.0  0.1463   \n",
       "97        9500.0           1    0.0890         7.0     50000.0  0.0818   \n",
       "19346    10000.0           1    0.1037         5.0     38004.0  0.1544   \n",
       "19852    10000.0           1    0.1268         0.0     80000.0  0.0948   \n",
       "\n",
       "       inq_last_6mths  open_acc  pub_rec_bankruptcies  default  grade_B  \\\n",
       "16848             4.0      11.0                     0        0        0   \n",
       "14467             0.0       8.0                     0        0        0   \n",
       "97                0.0       8.0                     0        0        0   \n",
       "19346             2.0      12.0                     0        0        1   \n",
       "19852             1.0       5.0                     0        0        0   \n",
       "\n",
       "       grade_C  grade_D  grade_E  grade_F  grade_G  sub_grade_A2  \\\n",
       "16848        1        0        0        0        0             0   \n",
       "14467        0        0        0        0        1             0   \n",
       "97           0        0        0        0        0             0   \n",
       "19346        0        0        0        0        0             0   \n",
       "19852        1        0        0        0        0             0   \n",
       "\n",
       "       sub_grade_A3  sub_grade_A4  sub_grade_A5  sub_grade_B1  sub_grade_B2  \\\n",
       "16848             0             0             0             0             0   \n",
       "14467             0             0             0             0             0   \n",
       "97                0             0             1             0             0   \n",
       "19346             0             0             0             0             0   \n",
       "19852             0             0             0             0             0   \n",
       "\n",
       "       sub_grade_B3  sub_grade_B4  sub_grade_B5  ...  addr_state_MS  \\\n",
       "16848             0             0             0  ...              0   \n",
       "14467             0             0             0  ...              0   \n",
       "97                0             0             0  ...              0   \n",
       "19346             1             0             0  ...              0   \n",
       "19852             0             0             0  ...              0   \n",
       "\n",
       "       addr_state_MT  addr_state_NC  addr_state_NE  addr_state_NH  \\\n",
       "16848              0              0              0              0   \n",
       "14467              0              0              0              0   \n",
       "97                 0              0              0              0   \n",
       "19346              0              0              0              0   \n",
       "19852              0              0              0              0   \n",
       "\n",
       "       addr_state_NJ  addr_state_NM  addr_state_NV  addr_state_NY  \\\n",
       "16848              0              0              0              0   \n",
       "14467              0              0              0              0   \n",
       "97                 0              0              0              0   \n",
       "19346              1              0              0              0   \n",
       "19852              0              0              0              0   \n",
       "\n",
       "       addr_state_OH  addr_state_OK  addr_state_OR  addr_state_PA  \\\n",
       "16848              0              0              0              0   \n",
       "14467              0              0              0              0   \n",
       "97                 0              0              0              0   \n",
       "19346              0              0              0              0   \n",
       "19852              0              0              0              0   \n",
       "\n",
       "       addr_state_RI  addr_state_SC  addr_state_SD  addr_state_TN  \\\n",
       "16848              0              0              0              0   \n",
       "14467              0              0              0              0   \n",
       "97                 0              0              0              0   \n",
       "19346              0              0              0              0   \n",
       "19852              0              0              0              0   \n",
       "\n",
       "       addr_state_TX  addr_state_UT  addr_state_VA  addr_state_VT  \\\n",
       "16848              0              0              0              0   \n",
       "14467              0              0              0              0   \n",
       "97                 1              0              0              0   \n",
       "19346              0              0              0              0   \n",
       "19852              0              0              0              0   \n",
       "\n",
       "       addr_state_WA  addr_state_WI  addr_state_WV  addr_state_WY  \n",
       "16848              0              0              0              0  \n",
       "14467              0              0              0              0  \n",
       "97                 0              0              0              0  \n",
       "19346              0              0              0              0  \n",
       "19852              0              0              0              0  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000, 112)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df_final.drop('default', axis=1)\n",
    "y = df_final['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=random_state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jonasbechthold/opt/anaconda3/envs/nf/lib/python3.6/site-packages/pandas/core/frame.py:3391: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n",
      "/Users/jonasbechthold/opt/anaconda3/envs/nf/lib/python3.6/site-packages/ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Users/jonasbechthold/opt/anaconda3/envs/nf/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/Users/jonasbechthold/opt/anaconda3/envs/nf/lib/python3.6/site-packages/pandas/core/frame.py:3391: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[k1] = value[k2]\n",
      "/Users/jonasbechthold/opt/anaconda3/envs/nf/lib/python3.6/site-packages/ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/jonasbechthold/opt/anaconda3/envs/nf/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaling_list = [\"dti\", \"int_rate\", \"loan_amnt\",\"annual_inc\"]\n",
    "scaling_min_max = [\"annual_inc\"]\n",
    "\n",
    "def logarithmize(value):\n",
    "    logvalue = np.log(value)\n",
    "    return logvalue\n",
    "\n",
    "# scaling train\n",
    "X_train[scaling_min_max] = X_train[scaling_min_max].apply(lambda x: logarithmize(x))\n",
    "X_train[scaling_list] = scaler.fit_transform(X_train[scaling_list])\n",
    "\n",
    "# scaling test with same scaling parameters\n",
    "X_test[scaling_min_max] = X_test[scaling_min_max].apply(lambda x: logarithmize(x))\n",
    "X_test[scaling_list] = scaler.transform(X_test[scaling_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "X_train.annual_inc.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "X_train.dti.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "X_train.int_rate.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "X_train.loan_amnt.hist(bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = rnc.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = rnc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14014\n",
      "           1       1.00      1.00      1.00     13986\n",
      "\n",
      "    accuracy                           1.00     28000\n",
      "   macro avg       1.00      1.00      1.00     28000\n",
      "weighted avg       1.00      1.00      1.00     28000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[14013     1]\n",
      " [    0 13986]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_train, pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.88      0.90      5986\n",
      "           1       0.89      0.93      0.91      6014\n",
      "\n",
      "    accuracy                           0.91     12000\n",
      "   macro avg       0.91      0.91      0.91     12000\n",
      "weighted avg       0.91      0.91      0.91     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5292  694]\n",
      " [ 426 5588]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, pred_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest with Stacking - Future Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "#clf = StackingClassifier(RandomForestClassifier(random_state=random_state))\n",
    "\n",
    "#clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Optimization through RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=-1)]: Done 250 out of 250 | elapsed: 36.7min finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Hyperparameter grid\n",
    "param_grid = {\n",
    "    'n_estimators': np.linspace(10, 500,50).astype(int),\n",
    "    'max_depth': [None] + list(np.linspace(3, 100).astype(int)),\n",
    "    'max_features': ['auto', 'sqrt', None],\n",
    "    'bootstrap': [True, False]\n",
    "}\n",
    "\n",
    "# Estimator for use in random search\n",
    "estimator = RandomForestClassifier(random_state=random_state)\n",
    "\n",
    "# Create the random search model\n",
    "rnc = RandomizedSearchCV(estimator, param_grid, n_jobs = -1, \n",
    "                        scoring = 'f1', cv = 5, \n",
    "                        n_iter = 50, verbose = -1)\n",
    "\n",
    "# Fit \n",
    "rnc.fit(X_train, y_train)\n",
    "best_model = rnc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 240, 'max_features': 'sqrt', 'max_depth': 88, 'bootstrap': False}\n",
      "0.8919803157797664\n"
     ]
    }
   ],
   "source": [
    "print(rnc.best_params_)\n",
    "print(rnc.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 270, 'max_features': 'auto', 'max_depth': 88, 'bootstrap': False}\n",
      "0.8913923346742287\n"
     ]
    }
   ],
   "source": [
    "train_rf_predictions = best_model.predict(X_train)\n",
    "train_rf_probs = best_model.predict_proba(X_train)[:, 1]\n",
    "\n",
    "rf_predictions = best_model.predict(X_test)\n",
    "rf_probs = best_model.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_pred_train = best_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_pred_test = rnc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     13946\n",
      "           1       1.00      1.00      1.00     14054\n",
      "\n",
      "    accuracy                           1.00     28000\n",
      "   macro avg       1.00      1.00      1.00     28000\n",
      "weighted avg       1.00      1.00      1.00     28000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, best_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[13946     0]\n",
      " [    0 14054]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_train, best_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.80      0.86      6054\n",
      "           1       0.82      0.94      0.87      5946\n",
      "\n",
      "    accuracy                           0.87     12000\n",
      "   macro avg       0.87      0.87      0.87     12000\n",
      "weighted avg       0.87      0.87      0.87     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, best_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4835 1219]\n",
      " [ 376 5570]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, best_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of nodes 8082\n",
      "Average maximum depth 45\n"
     ]
    }
   ],
   "source": [
    "n_nodes = []\n",
    "max_depths = []\n",
    "\n",
    "for ind_tree in best_model.estimators_:\n",
    "    n_nodes.append(ind_tree.tree_.node_count)\n",
    "    max_depths.append(ind_tree.tree_.max_depth)\n",
    "    \n",
    "print(f'Average number of nodes {int(np.mean(n_nodes))}')\n",
    "print(f'Average maximum depth {int(np.mean(max_depths))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest with optimized parameter - Score Recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using optimization with RandomizedSearchCV and using recall as scor, the best prameter of the optimization are the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'n_estimators': 160, 'max_features': None, 'max_depth': 68, 'bootstrap': False}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     13946\n",
      "           1       1.00      1.00      1.00     14054\n",
      "\n",
      "    accuracy                           1.00     28000\n",
      "   macro avg       1.00      1.00      1.00     28000\n",
      "weighted avg       1.00      1.00      1.00     28000\n",
      "\n",
      "[[13946     0]\n",
      " [    0 14054]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.80      0.86      6054\n",
      "           1       0.82      0.94      0.88      5946\n",
      "\n",
      "    accuracy                           0.87     12000\n",
      "   macro avg       0.88      0.87      0.87     12000\n",
      "weighted avg       0.88      0.87      0.87     12000\n",
      "\n",
      "[[4854 1200]\n",
      " [ 376 5570]]\n"
     ]
    }
   ],
   "source": [
    "rnc_opt = RandomForestClassifier(n_estimators=160, max_features=None,max_depth=68,bootstrap=False)\n",
    "rnc_opt.fit(X_train,y_train)\n",
    "\n",
    "opt_pred_train = rnc_opt.predict(X_train)\n",
    "opt_pred_test = rnc_opt.predict(X_test)\n",
    "\n",
    "print(classification_report(y_train, opt_pred_train))\n",
    "\n",
    "print(confusion_matrix(y_train,opt_pred_train))\n",
    "\n",
    "print(classification_report(y_test, opt_pred_test))\n",
    "\n",
    "print(confusion_matrix(y_test, opt_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix train\n",
      "[[13946     0]\n",
      " [    0 14054]]\n",
      "Classification report train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     13946\n",
      "           1       1.00      1.00      1.00     14054\n",
      "\n",
      "    accuracy                           1.00     28000\n",
      "   macro avg       1.00      1.00      1.00     28000\n",
      "weighted avg       1.00      1.00      1.00     28000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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xN1m9q+xyiIuLw8HBQa+IJCQksHXrVr19ReElPZLnZGFhwaJFi+jXrx/du3enY8eO1K1bl7i4OH788UciIyPx8/Ojb9++QOaw1ZQpUxg1ahTdunWje/fuAGzevJm7d++yYMGCFzIU1KFDB6ZNm8aAAQPo1KkTMTExbNq0Se9fy+7u7nh6ehIaGsqtW7dwcXHh1q1brF27lpo1a+b4WJRx48Zx/PhxAgMDCQwMpFy5cuzdu5djx47Rt2/ffLtBsE2bNixatIiSJUvmOBleqlQp3N3d2bJlCzY2Njg5OXHx4kXCwsJ0P9fExERKlSqlG9f//vvvURSFLl26GJ3Ld999R0REBGPGjNHd8T548GAWLlxIWFgYPXr0ADIXBWTlboqPPvqIoKAgunXrxrvvvkvp0qUJDw/nzJkzjB07VnctHTt2ZP369Tx+/BgPDw/i4uJYu3YtZcuW1S16yO66cxqWhcylvVnP1UpPT+f+/fvs27ePM2fO8N577+ktV2/RogW7du1i6tSp1K1bl+vXr7Np0yYeP34MZP7sAUqXLo1arebAgQNUqlQJX19fWrRowddff83IkSNp1qwZ9+7dY/PmzbqFJVn7isJLCokJXF1d2b59O6tWreKnn35i165dKIqCi4sLM2bMoGvXrnqri9q2bcuKFSv46quv+PLLLylRogTu7u58/vnneHp6vpCcevfurXtsy7Rp06hduzaLFi1ixYoVutUvKpWKL7/8kkWLFnHw4EE2btxIqVKl8PX1ZeTIkQZzHVmqVavGpk2bmD9/Pt9++y1PnjyhVq1afP7557rCmB+yCknz5s2xtLTMMe6LL75g5syZfPfdd6SkpFC5cmUGDRpErVq1GDFiBMeOHaNt27bUqlWLwMBAtmzZwtmzZ2nUqJFRedy9e5dZs2bxyiuv0K9fP137oEGD2LFjB7Nnz6ZFixY4OjoyY8YMXe6m8PDwYMOGDSxcuJCVK1eSlpaGk5MTs2bN0iuA06ZNo2rVqoSHhxMeHo6VlRWNGzdm9OjRun/pZ3fd/3ze19P27t3L3r17gcx/CJUvXx4nJydCQ0MNHo3yySefULJkSQ4cOMD27dupUKECnTt3xsfHh3fffZdjx47h6uqKlZUVo0ePZvny5UyfPp1q1aoxYsQI0tPT2bVrFwcPHqR8+fI0adKEfv360b59e44dO5ZjT1QUDirlWbOoQgghRC5kjkQIIYRJpJAIIYQwiRQSIYQQJpFCIoQQwiRSSIQQQpikyC7/te6xsqBTEIXMqYW9CjoFUUi5VCiZe9AzWHkMNzr28elFJp2rMCqyhUQIIf41quI9uCOFRAghTJXNaw2KEykkQghhKumRCCGEMIn0SIQQQphEbfjE7OJECokQQphKhraEEEKYRIa2hBBCmER6JEIIIUwiPRIhhBAmkR6JEEIIk8iqLSGEECaRHokQQgiTqGWORAghhCmkRyKEEMIksmpLCCGESWSyXQghhElkaEsIIYRJ8mloKy0tjfr165OcnKzXXrJkSU6fPg1AREQEoaGhXLp0iTJlyhAQEEC/fv304s+ePUtwcDDnzp3D2tqarl27MmLECMzMzHQx0dHRzJo1ixMnTqDRaPDz82P8+PHY2NjkmqcUEiGEMFU+9UiioqJITk5m9uzZ1KhRQ9euVmee79SpUwwZMoR27doxcuRITp48SXBwMIqi0L9/fwBiYmLo06cPHh4ezJ8/n8uXLxMaGopWq2Xq1KkAPHr0iKCgIMqVK8fs2bOJjY0lJCSE27dvs2TJklzzlEIihBCmyqceyV9//YVaraZt27ZYWVkZbF+wYAGurq6EhIQA0KJFC9LS0li8eDGBgYGYm5uzdOlSbG1t+eqrrzA3N8fLywtLS0umT5/O4MGDcXR0ZN26dcTHx7Nt2zbs7e0BcHR0ZNCgQZw5cwZ3d/dn5lm8B/aEEOJFUKmN/+RBZGQk1apVy7aIJCcnc+LECXx9ffXa27ZtS3x8PKdOnQLg6NGjeHt7Y25urovx8/MjPT2diIgIXUyDBg10RQSgWbNmWFtbc/jw4VzzlEIihBCmUmuM/+TB+fPnMTc3p3///nh4eNCgQQOmTp2KVqvl2rVrpKam4uTkpLdP9erVgcxhscePH3Pr1i2DGAcHB2xsbIiKigLgypUrBjEajYYqVaroYp5FhraEEMJUeehpxMfHEx8fb9BuZ2eHnZ2dXttff/2FVqulR48eDBkyhHPnzrFw4UKioqIYM2YMgMFkuLW1NQBarZaEhIRsY7LitFotAAkJCbnGPIsUEiGEMFUe5khWr17NokWLDNqHDx/OiBEj9NpCQ0MpVaoULi4uADRo0IAyZcowfvx4jh49+vepsz+3Wq1GUZQcYxRF0U3aGxuTEykkQghhqjz0SIKCgujSpYtB+9O9EYCGDRsatLVs2VLv+9M9hqzvtra2ul5Gdr2KpKQkbG1tgcweS3YxiYmJVK5cOYcr+R8pJEIIYao89EiyG8LKTmxsLAcOHODNN9+katWquvYnT54AUKZMGTQaDVevXtXbL+u7k5MT1tbWODo6EhMTY3BsrVarmxdxcnIyiElPT+f69eu0bds211xlsl0IIUyVD6u2VCoVU6dOZe3atXrtu3btQqPR0KRJEzw9PdmzZ49uCAtg9+7d2Nra4ubmBkDTpk05ePAgKSkpejEajUbX42natCnHjx8nLi5OFxMREUFSUhJNmjTJNVfpkQghhIlURswj5JWDgwP+/v6sWbMGGxsbPD09OXnyJIsXL8bf35/q1aszdOhQ+vbty+jRo+nSpQunT59m+fLljB07VrdkeMCAAYSHhzNo0CCCgoKIjo5m3rx59OzZk0qVKgHQu3dv1q5dS58+fRg2bBhxcXGEhITQokUL6tevn/v1K/8sZUWIdY+VBZ2CKGROLexV0CmIQsqlQkmT9rfubvzvm8TNfY2OTU1NZdWqVXz33XfcuHEDR0dHevbsyYABA3ST4Hv37mXBggVERUXh6OiIv7+/wSNSTpw4QXBwMJGRkdjb29O5c2eDR6RcuHCBGTNmcPr0aaytrWnTpg0TJkww6hEpUkhEsSGFROTE5EKSh983iWHGF5KXhQxtCSGEiXJagltcSCERQggTSSERQghhEmNu2ivKpJAIIYSpineHRAqJEEKYSoa2hBBCmEQKiRBCCJNIIRFCCGESKSRCCCFMolJLIRFCCGEC6ZEIIYQwiRQSIYQQpinedUQKiRBCmEp6JEIIIUwihUQIIYRJ5Flb4qXgVs2eI7M7ErLl/5gR9ruuvV7NMkzzf4NGzuVJz1CI+PM2H675jYs34/X2PzyzA56vlDM47tZj0QTMPZinc4rCZVHwZ9y8cZUZXyzTaz/1689s+mYZly5EolarcHGti3//YdSu87pe3O1bN1jx5TzO/X4CgAaNm9Nv2BhKlXZ4rrhiqXh3SKSQvAw0ahVLhjXHvIRGr/3VSnb8+Ek7HienMWvzGQA+6FiHvdPa8+a4bdx++FgX61K5NN//GsP2Y9F6x7h6LzFP5xSFy57wrewJ34pbvTf02s/9foJPJwynWo1aBA4YRnp6Oru2beLDkQOYtXAFzq9lvs87/lEcH40aSFpqKt169yE9PZ2t364m+spF5ixeq3uDnrFxxZUMbYlCb1yX13mtammD9mHt62BrZUbbqbs4E/0AgEPnbnFkVkdGdKjDf9Zk/suxenkbbK3MCP/tKt8euWLSOUXhkJ6eTtiaZWxYtSTb7V8vnEPZ8o7MWfwNFpaZ7+72btuBYe91Zc3Xi5g2bzEA2zet5f69uyxcsYmqNWoC4PyaG1PHDuXAjzto27FrnuKKq+JeSIr3wN5LoE41eyZ2c9f1OP7Jqbwt9+Kf6IoIwKnL97kf/4Q61ex1ba9VKQ3AX9fjTD6nKHgpycmMHvgu61cupqVve8qUK6+3XZsQT/TlCzTz9tUVEQB7hzK4ub/BX3/878/1yIHd1K33hq44ANTzfJPK1Wpw5MDuPMcVVyqVyuhPUVTgheTGjRtERETw448/smfPHn755Rdu375d0GkVChq1isXvN+Pg2Zt8e+SywfZLt+NxsDGnrJ2Frs3expzS1uZ6w1quVTOLyvkbjwAoaZFzRzS3c4qCl5KSQlJiIhM+ns3oD6eh0egPP1qVtOa/a7fSqYe/wb7xj+LQaDL//LUJ8dy+eZ1azq4GcbVerc2l83/mKa44U6lVRn+KogIb2tqzZw9ffPEFV65cQVEUvW0qlYrq1aszatQo/Pz8CijDgjemc11qVbSjV/B+SmgMa37o9rO89UZVVo1syaTVv6IAMwIbkJKWwX93/e8vt2u10sQnpTArqCHdmjhha2XGldvxfLrhFJt/jsrTOUXBK2ltzZJ129GUyP6vr0ajoVKV6gbtUZcvEHnudzwaNAYg9t5dAMqUNVyEYV+mLEmJWhK1CUbHWdvYPvc1veyKak/DWAVSSLZt28akSZNo164dI0aMoHr16lhbW6MoComJicTExLB7925Gjx5NamoqHTt2LIg0C9RrVUozuXs9xiw/xs0HSVQrZ2MQc/1+IiFb/o95/d/k+NzOAKSlZ+A/96DecNdrVeyxK2lOKWtzBi76idIlzXm/vSurR7fErISaDT9dNvqcouCp1WrI43LTx0lJzJ8xBYDu/n3/bstcaGFhaWkQb2GR2fbkyWOj46SQFF8FUkiWLl3Ku+++y8cff5ztdldXV9q1a8cnn3zCkiVLil0hUatVLB7WjF/+usOq/RdyjJvSy4NJ3evx0x+3WLnvAhq1igG+tVkzpiW95xzkh5PXAFix7zwatYqlu//S7Rv2cxS/ze3M54GebIzInIA35pzi5ZP85DHTPxxF1KULdPfvh1s9TwAyskYCnvFLUK1SGx1XnEkhKQA3btygTZs2uca1bt2arVu3/gsZFS6jO7lRt7oDbabsooxt5vyHvbU5kDm/UcbWggxFYVQnN05eukf7z3aTkZH5lz3s6BWOzOrIl0OaUHtoGClpGSzfe97gHE9S0tnw02X+09OD16qUxq9+lVzP+UCbzFOjkKKQ0yYkMG3yB0Se/Z02b71N4MDhum1WJUsCmZP3T0tOfvJ3jLXRccVa8a4jBVNIqlatSkREBE2bNn1m3KFDh6hQocK/lFXh0aZeZSzMNByZZdgTG/12XUa/XZfmk3ZgaV6CsKNRuiICkJausPHIFT4PbIBL5VKcjXmY43nuPcr8JWBtWcKoc772fhhX72lfwBWKf0Pcwwd8PO59oi6dp23Hbrw/9j96/3IuV74iAA9j7xvs++D+PaxtbLG0sjI6rjiTHkkBGDJkCOPHj+fu3bv4+vri5OSEjY0NKpUKrVarmyPZuXMnn376aUGkWKAmf/ObrjeQpXwpK1aM9GL94UusP3yJlNR0IHOV1dOy2tQqFRUdSvL9R75893OUwXJe58qlAIi5qzXqnHfiHiNeDklJiboi8nYPf/oPH2cQY2Nri2PFyly+EGmw7cql87zi4pqnuOJMXURXYxmrQApJhw4d0Gg0hIaGEh4eblDNFUWhSpUqzJgxgy5duhREigXq9yuxBm1ZE99RdxI4ePYWarWKmw8S8W/5Cv/9IZLkvwuLhZmGd71e4V78E/649pC0dIVSJc3p29qFL8P/JOFxKgCVy1gT0PIVDp+7xZ24x9kWiafPKV4eS0JnEnXpPB279862iGRp4tWa78PWcz0miirVnQD4/cQxblyNpss77+U5rriSHkkBadeuHe3atePatWtcuXIFrVaLoijY2tri5OREtWrVCiq1l0JGhsKY5cdYN9abn2Z2YPWBi2jUKt7zfhWXSqUYsPAn0tIzh7zGLD/GxgmtOTC9PSv3X8DG0owh7WqTlqEwetkvBXwl4kW7Fn2Fg3vCsbaxoeYrLhzcE24Q4+3bHoCu7wZxYPdOPhozmM49A0lJSWHrt6t4xeU1vH3a6+KNjSuuinkdKfhHpFStWpWqVasWdBovpR2/XqXjtD1M6u7OJ+/WB+D3qFi6ztzL3t9v6OJ2/naVnrP3M77r60zzf4PHKekc+fM2H687yYWbjwoqfZFPzp05CUCiVssXs7JfGZlVSEqVdmDmguUsXzSH9Sv/i4WFJY2aedN36CjMzP831GlsXHFV3HskKuXpuwGLCOseKws6BVHInFrYq6BTEIWUS4WSJu1fe5Lxj4n5a1bb5z7P8OHDOX/+PHv37tW1RUREEBoayqVLlyhTpgwBAQH069dPb7+zZ88SHBzMuXPnsLa2pmvXrowYMULvYZvR0dHMmjWLEydOoNFo8PPzY/z48djY5H4/WfFe/C2EEC+AWq0y+vO8tm/frldAAE6dOsWQIUOoWbMmCxcupGPHjgQHB7N8+XJdTExMDH369MHCwoL58+fTr18/Vq5cycyZM3Uxjx49IigoiPv37zN79mzGjh3Lrl27GDt2rFG5FfjQlhBCvOzye9XWnTt3+Pzzzw1uh1iwYAGurq6EhIQA0KJFC9LS0li8eDGBgYGYm5uzdOlSbG1t+eqrrzA3N8fLywtLS0umT5/O4MGDcXR0ZN26dcTHx7Nt2zbs7TOfzefo6MigQYM4c+YM7u7uz8xPeiRCCGEilcr4z/P46KOPaNq0KY0bN9a1JScnc+LECXx9ffVi27ZtS3x8PKdOnQLg6NGjeHt7Y/6PuSw/Pz/S09OJiIjQxTRo0EBXRACaNWuGtbU1hw8fzjU/KSRCCGGi/HyMfFhYGH/88QdTpkzRa7927Rqpqak4OTnptVevnvnAzqioKB4/fsytW7cMYhwcHLCxsSEqKvOhrVeuXDGI0Wg0VKlSRRfzLDK0JYQQJspLgYiPjyc+Pt6g3c7ODjs7O722GzduMHPmTGbOnImDg/4rjRMSEgAMJsOtrTMfV6PVanOMyYrTarW6Y+UW8yxSSIQQwkR56WisXr2aRYsWGbQPHz6cESNG6L4risKHH36Il5cXbdsarvTKWnCbUxFTq9XPjFEUJfNJ0rpryD0mJ1JIhBDCRHmZbA8KCsr2iR1P90bWrVvH+fPn2bFjB2lpacD/ikdaWhq2tpmP7X+6x5D13dbWVtfLyK5XkZSUpDuGjY1NtjGJiYlUrlw512uSQiKEECbKy9BWdkNY2dm9ezcPHz6kWbNmBtvq1KnDJ598gkaj4erVq3rbsr47OTlhbW2No6MjMTExejGxsbFotVrdvIiTk5NBTHp6OtevX8+2N/Q0mWwXQggT5ceqrU8//ZTNmzfrfby9valQoQKbN2/Gz88PT09P9uzZo/eW2d27d2Nra4ubmxsATZs25eDBg6SkpOjFaDQaGjZsqIs5fvw4cXFxupiIiAiSkpJo0qRJrrlKj0QIIUyUH49IqVmzpkFb6dKlMTc3p27dugAMHTqUvn37Mnr0aLp06cLp06dZvnw5Y8eOxervR/sPGDCA8PBwBg0aRFBQENHR0cybN4+ePXtSqVIlAHr37s3atWvp06cPw4YNIy4ujpCQEFq0aEH9+vVzzVV6JEIIYaL8vo8kJ40bN2bhwoVcvnyZYcOGsWPHDiZMmMDAgQN1MbVq1WLFihUkJSXxwQcfsHLlSvr27ct//vMfXYyDgwPffPMNpUuXZty4cYSGhuLn50doaKhx1y/P2hLFhTxrS+TE1GdtNZqZ+017WY5P9jLpXIWRDG0JIYSJ5MVWQgghTFLMnyIvhUQIIUxV3N9HIoVECCFMVMzriBQSIYQwlfRIhBBCmEQKiRBCCJPIqi0hhBAmKeYdEikkQghhKhnaEkIIYZJiXkekkAghhKnUxbySSCERQggTyWS7EEIIkxTzOpJzIWndunWeD6ZSqdi3b59JCQkhxMtGJttzkPXCEyGEEM9WzOtIzoVkzZo1/2YeQgjx0lJRvCvJC31D4p9//vkiDyeEEC8Ftcr4T1Fk9GR7amoqS5cuZc+ePSQlJZGRkaHblp6eTmJiIlqtlsjIyHxJVAghCqvivmrL6B7J/PnzWbhwIY8ePcLKyoobN25QsWJFSpQowe3bt0lNTdV7B7AQQhQXapXK6E9RZHQh+fHHH2nYsCEHDhzg66+/BmDq1Kns3r2bJUuWkJaWhpmZWb4lKoQQhZVKZfynKDK6kNy5cwdfX1/UajWOjo6UKVOG06dPA+Dl5UWXLl3YtGlTviUqhBCFlUqlMvpTFBldSCwtLfV6HNWqVePChQu676+//jrXrl17sdkJIcRLQHokRnrttdf46aefdN9r1qyp65FAZo+lqFZbIYR4Fo1KZfSnKDK6kPj7+7N//3569+6NVqulffv2/Pnnn0yePJmvv/6aVatWUbdu3fzMVQghCqXiPrRl9PJfPz8/pk2bxsqVK7GysqJJkyYMHDhQN/FeqVIlJk+enG+JCiFEYVXMV/+iUhRFMeUAN2/e5NGjR9SqVQtzc/MXlZfJrHusLOgURCFzamGvgk5BFFIuFUqatH/A2jNGx64NcDfpXIWRyU//rVSpkjyXSwhRrBXRESujGV1IjH0a8P79+587GSGEeBkV1bkPYxldSLLrdWRkZHD//n1iYmKoUaMGTZs2faHJCSHEy0BTzCdJjC4kz3oa8Llz5xgwYAANGzZ8IUkJIcTLJL/KiKIorF69mg0bNnDr1i1q1KjBwIED6dixoy4mIiKC0NBQLl26RJkyZQgICKBfv356xzl79izBwcGcO3cOa2trunbtyogRI/TuDYyOjmbWrFmcOHECjUaDn58f48ePx8bGJtc8X8jTf93c3AgICODLL798EYcTQoiXSn49a2vJkiUEBwfTuXNnlixZQtOmTRk3bhy7du0C4NSpUwwZMoSaNWuycOFCOnbsSHBwMMuXL9cdIyYmhj59+mBhYcH8+fPp168fK1euZObMmbqYR48eERQUxP3795k9ezZjx45l165djB071qg8X9irdsuWLUt0dPSLOpwQQrw08mOKJDU1lRUrVvDuu+8ydOhQABo3bsy5c+dYu3Ytb731FgsWLMDV1ZWQkBAAWrRoQVpaGosXLyYwMBBzc3OWLl2Kra0tX331Febm5nh5eWFpacn06dMZPHgwjo6OrFu3jvj4eLZt24a9vT0Ajo6ODBo0iDNnzuDu/uyVZi+kR3Lv3j02bNggq7eEEMVSftyQqNFoWLNmDYMGDdJrNzMzIzk5meTkZE6cOIGvr6/e9rZt2xIfH8+pU6cAOHr0KN7e3nq3Z/j5+ZGenk5ERIQupkGDBroiAtCsWTOsra05fPhwrrmavGorJSWFBw8ekJ6ezscff2zs4YQQosjIS48kPj6e+Ph4g3Y7Ozvs7Ox039VqNS4uLkDmXElsbCxbtmzh559/5rPPPuPatWukpqbi5OSkd5zq1asDEBUVhbu7O7du3TKIcXBwwMbGhqioKACuXLlCp06d9GI0Gg1VqlTRxTyLSau2sk7WqFEjOnToQMuWLY09nBBCFBl5WbW1evVqFi1aZNA+fPhwRowYke0+e/bs4YMPPgCgZcuWdOrUSfcSwacnw62trQHQarUkJCRkG5MVp9VqAUhISMg15lleyKqtwih2Q9+CTkEUMvYNhhd0CqKQenza8Bd7XuRlyCooKIguXboYtP+zN/I0V1dX1q5dy/nz5/niiy8YNGgQo0aNeua51Wo1WQ8uyS5GURTU6v/NbhgTkxOjC8l7773H0KFDady4cbbbDxw4wNy5cwkPDzf2kEIIUSTkZbL56SEsY1StWpWqVavSoEEDbGxsmDhxoq5IPN1jyPpua2ur62Vk16tISkrC1tYWyOyxZBeTmJhI5cqVc80vx0Ly+PFjHj58qPv+66+/4uPjoxt/+6eMjAx++uknrl+/nusJhRCiqMmPO9vj4uI4dOgQjRs3xtHRUdfu6uoKwPXr19FoNFy9elVvv6zvTk5OWFtb4+joSExMjF5MbGwsWq1WN3fi5ORkEJOens7169dp27Ztrrk+s5B07txZN8amUqmYMWMGM2bMyDZeURS5s10IUSzlx43tGRkZTJo0iffff183PwKZK6wA6tati6enJ3v27CEoKEhXzHbv3o2trS1ubm4ANG3alIMHDzJhwgTdyq3du3ej0Wh0N5E3bdqUFStWEBcXR+nSpYHMGx2TkpJo0qRJrrnmWEgcHBwICQnh7NmzKIrCl19+iY+Pj24VwT+p1WocHBxo3769MT8fIYQoUvLjESkODg707t2bpUuXYmlpSd26dTl58iRLliyhR48e1KxZk6FDh9K3b19Gjx5Nly5dOH36NMuXL2fs2LFYWVkBMGDAAMLDwxk0aBBBQUFER0czb948evbsqVtE1bt3b9auXUufPn0YNmwYcXFxhISE0KJFC+rXr59rrkY/Rn7y5Mm88847ud6YUlg8SSvoDERhI5PtIiemTraP33ne6NiQDob/GM9Jamoqq1atYvPmzdy8eZMKFSrQo0cPBgwYoJsE37t3LwsWLCAqKgpHR0f8/f0NHpFy4sQJgoODiYyMxN7ens6dOxs8IuXChQvMmDGD06dPY21tTZs2bZgwYYJRj0jJ0/tIbt68yfr16xk4cCClSpUC4Ouvv+bBgwcMGDCAMmXKGHuofCeFRDxNConIiamFZEK48YUkuL3xheRlYfRigwsXLtClSxdWrlzJrVu3dO2PHj1i3bp1dO7cmWvXruVLkkIIUZjl17O2XhZGF5K5c+dibW1NeHg4tWvX1rWPGzeO8PBwzMzMmDNnTr4kKYQQhZk6D5+iyOjr+v333wkKCqJGjRoG26pWrUpAQAC//fbbi8xNCCFeCiqV8Z+iyOgbEhVFITk5+Znbnzx58kKSEkKIl0lxf7GV0T0Sd3d3Nm7cmO3DxhITEwkLC3tpVnQJIcSLpFYZ/ymKjO6RDB8+nICAADp06EDHjh2pXr06KpWKq1evEh4ezt27d/VelCKEEMVFUZ1EN5bRhcTd3Z2VK1cye/ZsVqxYwT9XDdeuXZtZs2bh4eGRL0kKIURhVszrSN7ekOjp6UlYWBgPHjzgxo0bZGRkULFiRQC+//57PvvsM3bu3JkviQohRGFVVIesjPVcr9p1cHDA1taW/fv389VXX3H06FHS0tLQaDQvOj8hhCj0VBTvSpLnQnLu3Dm2bt3Kzp07iY+PR1EUypYtS7du3ejVq1d+5CiEEIVaiaJ6g4iRjCoksbGxbN++na1bt3Lp0iUURdE9aXLEiBEMHjyYEiWeq3MjhBAvvfx4jPzLJMff/mlpaRw4cIAtW7YQERFBWloa5ubmeHl56Z4C3L17d2rXri1FRAhRrMkcSQ6aN29OXFwcNjY2+Pj44OPjg5eXl+59wDdu3PjXkhRCiMKsmHdIci4kDx8+pGTJknTs2JFGjRrRoEEDXRERQgjxP3IfSQ5WrVrFzp072blzJxs2bEClUlGvXj18fX3x8fH5N3MUQohCTSOT7dl78803efPNN5k6dSqHDx9mx44dHD58mFOnTjF79mxq1KiBSqUiKSnp38xXCCEKHXUxX/6bpxdbabVadu/ezY4dO/jtt99IT09HrVbTqFEjunfvjo+Pj+6dwAVNXmwlniYvthI5MfXFVl/9HG107PtNaph0rsIoT8utbGxs6NatG926dePevXuEh4ezY8cOfvnlF44dO4adnR3Hjx/Pr1yFEKJQklVbz6lcuXL06dOHPn36EBMTw/fffy+PRxFCFEvFfbL9hUwRVa9enREjRrB79+4XcTghhHipyIuthBBCmKS4v9hKCokQQpiomK/+lUIihBCmkmdtCSGEMEnxLiNSSIQQwmTFfdWWFBIhhDBR8S4jUkiEEMJkalm1JYQQwhSyaksIIYRJivuqreJeSIUQwmSqPHzyIiMjgw0bNtCxY0c8PDxo06YNM2fORKvV6mIiIiLo1q0b7u7utGrVihUrVhgc5+zZswQGBuLh4UGzZs2YN28eqampejHR0dEMGTIET09PGjVqxMcff6x3nmeRHokQQpgov3oky5YtY/78+fTv35/GjRsTFRXFggULuHTpEsuXL+fUqVMMGTKEdu3aMXLkSE6ePElwcDCKotC/f38AYmJi6NOnDx4eHsyfP5/Lly8TGhqKVqtl6tSpADx69IigoCDKlSvH7NmziY2NJSQkhNu3b7NkyZJc85RCIoQQJtLkQyFRFIVly5bRq1cvxo4dC0CTJk2wt7dn9OjRREZGsmDBAlxdXQkJCQGgRYsWpKWlsXjxYgIDAzE3N2fp0qXY2try1VdfYW5ujpeXF5aWlkyfPp3Bgwfj6OjIunXriI+PZ9u2bdjb2wPg6OjIoEGDOHPmDO7u7s/MVYa2hBDCRPkxtJWYmEinTp3o0KGDXnvNmjUBuHjxIidOnMDX11dve9u2bYmPj+fUqVMAHD16FG9vb713Rfn5+ZGenk5ERIQupkGDBroiAtCsWTOsra05fPhwrrlKj0QIIUyUlw5JfHw88fHxBu12dnbY2dnpvtvY2PDRRx8ZxO3btw8AV1dXUlNTcXJy0ttevXp1AKKionB3d+fWrVsGMQ4ODtjY2BAVFQXAlStX6NSpk16MRqOhSpUquphnkUIihBAmysurdlevXs2iRYZvZBw+fDgjRox45r5nzpxh6dKltGnThoSEBCCz4PyTtbU1kPlG25xisuKyJtMTEhJyjXkWKSRCCGGivPRIgoKC6NKli0H7P3sj2Tl58iRDhgyhSpUqTJ8+XddTyGmiX61Wk/Um9exiFEVBrf7f7IYxMTmRQiKEECZS5aFH8vQQljF27drFpEmTqFGjBsuWLcPe3p779+8DGPQYsr7b2trqehnZ9SqSkpKwtbUFMnss2cUkJiZSuXLlXPOTyXYhhDCRRqUy+pNXK1euZMyYMdSrV49169ZRvnx5AKpVq4ZGo+Hq1at68VnfnZycsLa2xtHRkZiYGL2Y2NhYtFqtbu7EycnJICY9PZ3r168bzK9kRwqJEEKYKL9etRsWFsasWbNo164dy5Yt0/UgACwsLPD09GTPnj26ISyA3bt3Y2tri5ubGwBNmzbl4MGDpKSk6MVoNBoaNmyoizl+/DhxcXG6mIiICJKSkmjSpEnu16/8M4Mi5ElaQWeQP44f+4WvFi3gwvm/sLaxwdfXj+EfjKKktTU3blznLd/Wz9x/2cpvaNCwEQAPHjxg4fx5HDp0gOQnT3jNtQ4jR4/ldfd6/8KV/PvsGwwv6BReiCNrxuHpVsOgfeu+0/Qev9yomGoVHTi/67Nnnsd3wBccOXkRgOqVyjB7TBeae74KwA9HzjFp3lbuPzTuzufC7vFpw8nvvNgTec/oWN/XyhkVFxsbS+vWrXFwcCA4OJgSJfRnIqpVq8b58+fp27cvfn5+dOnShdOnT7N48WLGjh3LwIEDAbh8+TJdunShfv36BAUFER0dzbx58+jWrRuffPIJkPm74K233qJChQoMGzaMuLg4QkJCcHd35+uvv841VykkL5Ffjx9j8IC+vOZah05vd+H27VusX/sNr7nWYeU363jy5AkH9u812C/5STKzZkzDwaEMYVu2Y1eqFImJWvzf6cG9u3cJeK8PdnZ2fLt+HXfv3mHtt2G8+qpzAVxh/ioqheRuxBwOHj/P1v2/67Vfu/WAo6cvGxVT0tKcTq0MbzKzsjBj3sQe3HuQQMNeM4lLeIxDKWt+Xj8Bc7MSfLn+IBqNhtFBrYm5+YDmASGkpqXn16X+a0wtJHsj7xsd6/NaWaPitm3bxsSJE3PcHhwczNtvv83evXtZsGABUVFRODo64u/vT79+/fRiT5w4QXBwMJGRkdjb29O5c2dGjBiBmZmZLubChQvMmDGD06dPY21tTZs2bZgwYUK2q7meJpPtL5F5c4KpULEiK1avxdLSEoCKFSsyY/pn/Hz0CM2ae9Gh49sG+wXP/Jy0tDRmBs/BrlQpAFYs+5roqCiWr1rDG54NAGjr9xbt/dqwasUyPp8Z/O9dmDBa9UplsLW2ZMeh/+PbXb89d0zSk5Rst4WM64ZZCQ19/7OauITHAHwQ0IrK5Uvj2XMG56PuAPDbuWh2LR5BQMdGrNz68wu6updXfjxFvnPnznTu3DnXOB8fH3x8fJ4Z4+npyaZNm54Z4+zszKpVq/KQ4f/IHMlLIjk5GXt7e7p176krIgBvNMgc47xw/ny2+128cJ4N69fSqXMX6r/hCWQu6ft++1aat2ipKyIAZcuVY8y4CdSv75mPVyJM4VqrIgDno26bFJOdOq9UYug7XqzZcUzXswHo0bY+P528qCsiAAePn+d81G16tH0jT+coqtQqldGfokh6JC8JCwsL/rt0uUH7+b8iAahYsVK2+y38IhQLC0uGfzBK13bjxnXu3rlDn34DgMzC8jgpiZLW1vR61//FJy9eGNdaFQD46+9f6iUtzUl6kpLnmOx8Mrwjj5NT+fTLnbq20rZW1Kxajq37fjeI//2v6/g1q/Nc11HU5GX5b1EkPZKX1M2bN9i+dQuzZ3zOK68606qNYdf2wvm/OHzoID16vUO5cuV17Vf/Xubn4FCGeXNm0+xNTxo3rE8HPx8OHTzwr12DyDvXWpWI1z5m9tiu3I2YQ+wv8/jj+4/1egbGxDzN7dVKdPCqy7LNEdy+/7/Hd1QqXxqAm/fiDPa5ff8RpWytsLOxNNhW3KhVxn+KIumRvIQexcXRzqcVAJZWVkz68CMsLCwM4jZt3IBGo+Fd/wC99oSEzF8UXy38ghIlSjBh0n9Qa9SsXrmc0R8M479Ll/Nm49yX/Il/32u1KmJnY0VpWysGTPmGUrYlGda7Jd/M6kuJEmo2hP9mVMzTBvZoTlpaOv/99pBeu611ZpHIrkfz+Enm+yysrSyI1z558Rf7EinuPZICKyR37tzJPegfHB0d8ymTl49KpWL2nFBSU1PYsHYNgwf0ZXbIPHza+ulinjx5QviO7/HybkWlSvp3pmatJ09IiOf78N26CXivlq3o4OfDgtC5UkgKqRVbjqJRq1my6SddW9juk5wM+5AZo7qw8YcTRsVkZPxvsaalhRnvvtWA8MNnuXrrod75ssb0n7W2MyMj4wVd3curiE59GK3ACknr1q1JTzd+2WBkZGQ+ZvNysStVCr92bwHg4+tHt84dmBM8S6+Q/PbrcZKSkvD19TPY38qqJACt2vjqighkPrrBy7sVO7ZvJSkxkZJ/P/xNFB7LNkcYtD1JTmV9+G98NOQtXqtZ0aiYPy7d1G3zavAqttaWbNl32mC/hKTMnoaVhZnBNivLzLaExOTnvp6iopjXkYIrJGFhYQwePJiUlBTGjh1rcLONMI6lpSUtvFqyfu0aHj58gL29AwBHfjqMmZkZzb1aGuxT/u/enYODg8E2BwcHFEUh6e/Jd/FyuPfg76e8ljQc4swtxq9pHZJTUvnhyB8G+1z7u4dSoWwpg20Vy5XiYXySURP5RV1+vNjqZVJgv71fe+01Vq1aRY8ePbh37x7vv/9+QaXyUoi6cpn3Bw+kT7/+BiurEhMTUalUei+u+f30Keq41c32ZqJXXnkVc3NzLl++ZLDt5o3rWFhYYJ9NkREFq1K5Uuz473A27znJzKU/6m1zccr8x8G1Ww84ufk/z4yJvqF/89yb9Wpy8o+rJCQaznM80j4m6vp96r1WxWCbu0sVTv151aC9WCredaRgV23VrFmTMWPGsGzZMh48eFCQqRR6VatVJ0GbQNimb0n9xzNzbt68wf69e3jDswHW1plFIzU1lSuXL1H7Nddsj1WyZElaerfip0OHuHTpoq79+vVrHDp4gJatWqPRaPL3gkSe3bz3iFI2lvTr0lQ3CQ5QxbE0AR0bcejX80bF3IlN0LWXKKHmtZoVOHP+eo7n3bb/d1o1rI1zjf/NU3o3csHFqQJhu0++4Kt8Oany8L+iqMDHk9555x1effXVgk6j0CtRogSTPvyI/0yaQL8+gbTv0Im4uId8u2EdKpWKSf+Zoou9fesWqampVKxYMcfjjRo7nt9++5UBfd/DP+A9zMzMWLf2GywsLflg5Jh/45LEcxg9K4xNoYM4uGoMK7f8jI21BUN6eZGWnsGoWZuMjslStYIDFuZmXLuV8z/k5q3eR+8ODflhyQi+WHMAC/MSjOnThpN/Xs12BVhxVMxHtgq+kPzzCZTi2Tp0fBszMzNWLl/GnOCZWFmVpNGbbzJ85Ghq1Pjfo56znuBp/Yxn5FSuXIW16zcxf14Iq1cuR1EUPN7wZMzYCVSpWjW/L0U8px2H/o8eo5cwoV9bpo98m8fJqRw5cZEpC7/nQvQdo2OylCmVOQ8Wn82wVpb7D7X49J9P8LhuTBnanqQnKew4+H98OH8bKalF8KF2z6GY1xF5aKMoPorKQxvFi2fqQxt/i3pkdGwDJ8OFCy+7Au+RCCHEy66oPkPLWFJIhBDCRMW7jEghEUII0xXzSiKFRAghTFRUl/UaSwqJEEKYqJhPkUghEUIIU0khEUIIYRIZ2hJCCGES6ZEIIYQwSTGvI1JIhBDCZMW8kkghEUIIE8kciRBCCJOoi3cdkUIihBAmk0IihBDCFDK0JYQQwiSy/FcIIYRJinkdkUIihBAmK+aVRF3QCQghxMtOrVIZ/XlekZGR1KlTh9u3b+u1R0RE0K1bN9zd3WnVqhUrVqww2Pfs2bMEBgbi4eFBs2bNmDdvHqmpqXox0dHRDBkyBE9PTxo1asTHH3+MVqs1KjfpkQghhInyu0Ny5coVBg8eTFqa/jvET506xZAhQ2jXrh0jR47k5MmTBAcHoygK/fv3ByAmJoY+ffrg4eHB/PnzuXz5MqGhoWi1WqZOnQrAo0ePCAoKoly5csyePZvY2FhCQkK4ffs2S5YsyTU/KSRCCGGqfKokaWlpbNy4kblz52JmZmawfcGCBbi6uhISEgJAixYtSEtLY/HixQQGBmJubs7SpUuxtbXlq6++wtzcHC8vLywtLZk+fTqDBw/G0dGRdevWER8fz7Zt27C3twfA0dGRQYMGcebMGdzd3Z+ZpwxtCSGEiVR5+F9enDx5kjlz5tCvXz/GjRunty05OZkTJ07g6+ur1962bVvi4+M5deoUAEePHsXb2xtzc3NdjJ+fH+np6UREROhiGjRooCsiAM2aNcPa2prDhw/nmqf0SIQQwkR5mfqIj48nPj7eoN3Ozg47Ozu9tlq1arFv3z7KlCnDli1b9LZdu3aN1NRUnJyc9NqrV68OQFRUFO7u7ty6dcsgxsHBARsbG6KiooDMobNOnTrpxWg0GqpUqaKLeRYpJEIIYaK8FJLVq1ezaNEig/bhw4czYsQIvbayZcvmeJyEhAQAbGxs9Nqtra0B0Gq1OcZkxWVNpickJOQa8yxSSIQQwkR5GbIKCgqiS5cuBu1P90ZyoyhK5rlzqGJqtfqZMYqioFb/b3bDmJicSCERQggT5aVHkt0Q1vOwtbUFMOgxZH23tbXV9TKy61UkJSXpjmFjY5NtTGJiIpUrV841F5lsF0IIE6ny8HlRqlWrhkaj4erVq3rtWd+dnJywtrbG0dGRmJgYvZjY2Fi0Wq1u7sTJyckgJj09nevXrxvMr2RHCokQQphIpTL+86JYWFjg6enJnj17dENYALt378bW1hY3NzcAmjZtysGDB0lJSdGL0Wg0NGzYUBdz/Phx4uLidDEREREkJSXRpEmTXHORQiKEECYriD4JDB06lFOnTjF69GgOHz7M/PnzWb58OYMHD8bKygqAAQMGcO/ePQYNGsTBgwdZuXIlM2fOpGfPnlSqVAmA3r17Y25uTp8+fdi7dy9hYWGMHz+eFi1aUL9+/dyvXvlnKStCnqTlHiOKF/sGwws6BVFIPT5tuIoqL27GpeQe9LdKpc1zD8rGli1bmDx5MocPH6ZChQq69r1797JgwQKioqJwdHTE39+ffv366e174sQJgoODiYyMxN7ens6dOzNixAi9mxwvXLjAjBkzOH36NNbW1rRp04YJEyZku5rraVJIRLEhhUTkxNRCcuuR8YWkYqnnKySFmazaEkIIE8mLrYQQQpimeNcRKSRCCGGqYl5HpJAIIYSp5FW7QgghTJLTY0qKCykkQghhouJdRqSQCCGEyYp5h0QKiRBCmEqW/wohhDCJ9EiEEEKYRAqJEEIIk8jQlhBCCJNIj0QIIYRJinkdkUIihBAmK+aVRAqJEEKYSOZIhBBCmERdvOuIFBIhhDCZFBIhhBCmKO5DW0X2VbtCCCH+HeqCTkAIIcTLTQqJEEIIk0ghEUIIYRIpJEIIIUwihUQIIYRJpJAIIYQwiRQSIYQQJpFCIoQQwiRSSIQQQphECkkRtnPnTtq3b8/rr79Ou3bt2LZtW0GnJAqRyMhI6tSpw+3btws6FfGSk0JSRP3www+MGzeOpk2b8uWXX9KwYUMmTpzIjz/+WNCpiULgypUrDB48mLS0tIJORRQB8qytIsrHxwc3NzdCQ0N1baNGjeL8+fP88MMPBZiZKEhpaWls3LiRuXPnYmZmRlxcHIcPH6ZChQoFnZp4iUmPpAi6du0aV69exdfXV6+9bdu2XLlyhWvXrhVQZqKgnTx5kjlz5tCvXz/GjRtX0OmIIkIKSRF05coVAJycnPTaq1evDkBUVNS/npMoHGrVqsW+ffsYPnw4Go2moNMRRYS8j6QISkhIAMDGxkav3draGgCtVvuv5yQKh7JlyxZ0CqIIkh5JEZQ17aVSqbJtV6vlj10I8eLIb5QiyNbWFjDseSQmJuptF0KIF0EKSRGUNTdy9epVvfaYmBi97UII8SJIISmCqlevTpUqVQzuGdmzZw81atSgUqVKBZSZEKIoksn2ImrYsGFMnjyZUqVK0bJlSw4cOMAPP/ygd1+JEEK8CFJIiqiuXbuSkpLCihUrCAsLo2rVqsyePZu33nqroFMTQhQxcme7EEIIk8gciRBCCJNIIRFCCGESKSRCCCFMIoVECCGESaSQCCGEMIkUEiGEECaRQiLyzaRJk3BxcdH7vPbaa9SvX58ePXqwdevWfM+hVatWBAYG6r4HBgbSqlWrPB9Hq9Xy4MGDF5ZX1s9GiKJAbkgU+W7y5MnY29sDmU8g1mq1fP/990yaNImHDx/Sr1+/fy2XIUOG8Pjx4zztc+7cOYYOHcqcOXNo1KhRPmUmxMtLConId23atKFKlSp6bd27d+ett97iyy+/JCAgAHNz838ll6ZNm+Z5nwsXLnD37t18yEaIokGGtkSBsLS0pFWrVmi1Wi5evFjQ6QghTCA9ElFgsl68lZ6eTqtWrWjSpAkZGRns2LEDe3t7tm3bhoODA6dPn2bBggX8/vvvAHh4eDBq1Chef/11vePt2rWLJUuWEBUVRbVq1fjoo48MzhkYGMiNGzc4cOCAru3y5cssWLCAY8eOkZaWhqurKyNHjsTT05OFCxeyaNEiAN577z0qV66s2/f27dvMmzePn376icTERGrVqkW/fv3o1KmT3jnPnTvHvHnzOH36NDY2NgwcOPCF/QyFKAykkIgCkZGRwa+//oq5uTm1atUCIDw8HCcnJ/7zn/9w//59HBwcOHr0KIMHD6Z27dqMHDmSlJQUtmzZgr+/PytXrsTT0xOALVu2MHnyZDw8PBg/fjwxMTEMGTKEjIwMKleunGMe0dHR9OzZkxIlShAQEICDgwPffvstffv2Zd26dfj4+HDv3j02btzIkCFDqFu3LgB37tyhR48eKIpCYGAgpUqVYv/+/YwfP567d+8yYMAAAC5evEhgYCB2dna8//77pKam8uWXX5Kenp7PP2Eh/kWKEPlk4sSJirOzs/LHH38osbGxSmxsrHL37l3l9OnTysiRIxVnZ2dlxowZiqIoire3t1K7dm0lJiZGt396errSunVr5Z133lHS0tJ07YmJiYqPj4/y9ttvK4qiKGlpaUrjxo2Vbt26KSkpKbq47777TnF2dlYCAgJ0bQEBAYq3t7fu+8iRI5XXX39diY6O1rU9ePBAeeONN5QPPvhA7zjHjh3Tu7aGDRsqd+7c0bvmMWPGKG5ubsr9+/cVRVGUESNGKPXq1VNu3rypi7l06ZLi5uamODs75/2HKkQhJD0Ske+6dOli0GZubk5gYCBjx47VtVWrVo1q1arpvv/5559cu3aNd999l0ePHunt7+3tzapVq7h9+zZ3794lNjaW4cOHY2Zmpot5++23mTVrVo55ZWRkcPjwYby8vKhevbqu3d7envXr1+tWmmW33759+2jUqBElSpTQWxbs6+vLzp07OXr0KB06dODIkSN4eXlRsWJFXUytWrVo1qyZ3vCaEC8zKSQi34WEhFC2bFkA1Go1dnZ21KpVCwsLC724MmXK6H3PelVwcHAwwcHB2R771q1b3L59G0CvCAFoNBq9AvG0uLg4kpKSso1xdnbOcb+HDx+SkJDAvn372LdvX455ZR3/6bwAatasKYVEFBlSSES+q1+/vsHy3+xoNBq97xkZGQCMHDmSevXqZbtPzZo1uXPnDgDJyckG27OOkZ2seQq1Om+LF7P2a9u2Le+88062MVWrVtX9/7zmJcTLRgqJKLSyJslLlixJkyZN9Lb93//9H48ePcLS0lL3Szs6OlovRlEUbty4wauvvprt8e3t7bG0tCQmJsZg2/Lly7l//z4TJ0402Obg4ICVlRVpaWkGed28eZM///wTKysr7O3tsbGxMcgL4Pr16zletxAvG7mPRBRabm5ulCtXjjVr1pCYmKhr12q1jBo1ismTJ6PRaHB1daVy5cps2LBB76718PBwHj58mOPxS5QoQdOmTTl8+DC3bt3StT969Ijly5frhtayeixZvYgSJUrQokULDh8+zF9//aV3zFmzZjFs2DAePnyISqXCx8eHI0eOcOHCBV3M9evXOXTo0PP/YIQoZKRHIgotMzMzpkyZwqhRo+jatSvdu3fHwsKCsLAwbt68yZw5cyhRIvM/4SlTpjBs2DB69epFt27duHPnDuvWraN06dLPPMfYsWPp0aMHPXr0wN/fHxsbGzZt2kRSUhKjRo0CMnsgABs2bOD+/ft07NiRcePGcfz4cfz9/fH396dSpUocOnSIgwcP0qtXL10vaOTIkRw6dIjAwED69OmDRqNhzZo1WFtbk5KSkm8/OyH+TfLOdpFvJk2axNatW9m/f3+ucyStWrWicuXKrFmzxmDbL7/8wn//+1/Onj2LWq3m1VdfZfDgwXh7e+vFHTlyhIULF3L+/HkcHR354IMPWLduHSVKlNAdN7sbEi9evMi8efP49ddfUavVvP7664wdOxZXV1cAUlNTGT9+PAcPHsTCwoIjR45gYWFBTEwMCxYs4OjRoyQlJVG1alV69OhBYGCg3nxPVFQUwcHBuvtmevToAcCSJUs4f/788/1whShEpJAIIYQwicyRCCGEMIkUEiGEECaRQiKEEMIkUkiEEEKYRAqJEEIIk0ghEUIIYRIpJEIIIUwihUQIIYRJpJAIIYQwiRQSIYQQJvl/8QjZDdQ8ftEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix test\n",
      "[[4854 1200]\n",
      " [ 376 5570]]\n",
      "Classification report test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.80      0.86      6054\n",
      "           1       0.82      0.94      0.88      5946\n",
      "\n",
      "    accuracy                           0.87     12000\n",
      "   macro avg       0.88      0.87      0.87     12000\n",
      "weighted avg       0.88      0.87      0.87     12000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores(y_train, y_test, opt_pred_train, opt_pred_test, filename='random_forest_recall_focus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could improve the recall for default = 1 to 94 % before the optimization it was 93 percent. \n",
    "It would be really usefull to do further optimization on both the recall and also the f1 score. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest with optimized parameter - Score F1 - Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best prameter of the optimization with scocring on F1 are the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'n_estimators': 270, 'max_features': 'auto', 'max_depth': 88, 'bootstrap': False}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomForestClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-2dd9dc9985e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrnc_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m270\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m88\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbootstrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mrnc_opt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mopt_pred_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnc_opt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mopt_pred_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnc_opt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "rnc_opt = RandomForestClassifier(n_estimators=270, max_features='auto',max_depth=88,bootstrap=False)\n",
    "rnc_opt.fit(X_train,y_train)\n",
    "\n",
    "opt_pred_train = rnc_opt.predict(X_train)\n",
    "opt_pred_test = rnc_opt.predict(X_test)\n",
    "\n",
    "print(classification_report(y_train, opt_pred_train))\n",
    "\n",
    "print(confusion_matrix(y_train,opt_pred_train))\n",
    "\n",
    "print(classification_report(y_test, opt_pred_test))\n",
    "\n",
    "print(confusion_matrix(y_test, opt_pred_test))\n",
    "\n",
    "scores(y_train, y_test, opt_pred_train, opt_pred_test, filename='random_forest_f1_focus')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that we could improve also the recall for the fully paid loans, which is good for the lending club platform. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBClassifier()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_train = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3994 2060]\n",
      " [1836 4110]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.66      0.67      6054\n",
      "           1       0.67      0.69      0.68      5946\n",
      "\n",
      "    accuracy                           0.68     12000\n",
      "   macro avg       0.68      0.68      0.68     12000\n",
      "weighted avg       0.68      0.68      0.68     12000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9291 4655]\n",
      " [4373 9681]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.67      0.67     13946\n",
      "           1       0.68      0.69      0.68     14054\n",
      "\n",
      "    accuracy                           0.68     28000\n",
      "   macro avg       0.68      0.68      0.68     28000\n",
      "weighted avg       0.68      0.68      0.68     28000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost Optimization through Randomized SearchCV - Future Work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed: 20.2min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 37.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.8738027006433452\n",
      "Best params: \n",
      "learning_rate: 0.18373847549667507\n",
      "max_depth: 9\n",
      "n_estimators: 927\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, cross_validate, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "clf_xgb = XGBClassifier()\n",
    "\n",
    "param_dist = {'n_estimators': stats.randint(150, 1000),\n",
    "              'learning_rate': stats.uniform(0.01, 0.6),\n",
    "              'max_depth': [3, 4, 5, 6, 7, 8, 9, 10],\n",
    "             }\n",
    "\n",
    "clf = RandomizedSearchCV(clf_xgb, \n",
    "                         param_distributions = param_dist,\n",
    "                         cv = 5,  \n",
    "                         n_iter = 20,\n",
    "                         scoring = 'f1', \n",
    "                         error_score = 0, \n",
    "                         verbose = -1, \n",
    "                         n_jobs = -1)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "best_score = clf.best_score_\n",
    "best_params = clf.best_params_\n",
    "print(\"Best score: {}\".format(best_score))\n",
    "print(\"Best params: \")\n",
    "for param_name in sorted(best_params.keys()):\n",
    "    print('%s: %r' % (param_name, best_params[param_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost with optimized parameter - Score F1 - Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best prameter of the optimization on the F1 score are the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'learning_rate': 0.18373847549667507, 'max_depth': 9, 'n_estimators': 927}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14014\n",
      "           1       1.00      1.00      1.00     13986\n",
      "\n",
      "    accuracy                           1.00     28000\n",
      "   macro avg       1.00      1.00      1.00     28000\n",
      "weighted avg       1.00      1.00      1.00     28000\n",
      "\n",
      "[[14013     1]\n",
      " [    0 13986]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.86      0.89      5986\n",
      "           1       0.87      0.93      0.90      6014\n",
      "\n",
      "    accuracy                           0.90     12000\n",
      "   macro avg       0.90      0.90      0.90     12000\n",
      "weighted avg       0.90      0.90      0.90     12000\n",
      "\n",
      "[[5144  842]\n",
      " [ 408 5606]]\n"
     ]
    }
   ],
   "source": [
    "xgb_opt = XGBClassifier(learning_rate=0.18373847549667507, max_depth=9, n_estimators=927)\n",
    "xgb_opt.fit(X_train,y_train)\n",
    "\n",
    "xgb_opt_pred_train = xgb_opt.predict(X_train)\n",
    "xgb_opt_pred_test = xgb_opt.predict(X_test)\n",
    "\n",
    "print(classification_report(y_train, xgb_opt_pred_train))\n",
    "\n",
    "print(confusion_matrix(y_train,xgb_opt_pred_train))\n",
    "\n",
    "print(classification_report(y_test, xgb_opt_pred_test))\n",
    "\n",
    "print(confusion_matrix(y_test, xgb_opt_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix train\n",
      "[[14013     1]\n",
      " [    0 13986]]\n",
      "Classification report train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     14014\n",
      "           1       1.00      1.00      1.00     13986\n",
      "\n",
      "    accuracy                           1.00     28000\n",
      "   macro avg       1.00      1.00      1.00     28000\n",
      "weighted avg       1.00      1.00      1.00     28000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix test\n",
      "[[5144  842]\n",
      " [ 408 5606]]\n",
      "Classification report test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.86      0.89      5986\n",
      "           1       0.87      0.93      0.90      6014\n",
      "\n",
      "    accuracy                           0.90     12000\n",
      "   macro avg       0.90      0.90      0.90     12000\n",
      "weighted avg       0.90      0.90      0.90     12000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores(y_train, y_test, xgb_opt_pred_train, xgb_opt_pred_test, filename='xgboost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3936 2118]\n",
      " [1962 3984]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.65      0.66      6054\n",
      "           1       0.65      0.67      0.66      5946\n",
      "\n",
      "    accuracy                           0.66     12000\n",
      "   macro avg       0.66      0.66      0.66     12000\n",
      "weighted avg       0.66      0.66      0.66     12000\n",
      "\n",
      "[[9114 4832]\n",
      " [4665 9389]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.65      0.66     13946\n",
      "           1       0.66      0.67      0.66     14054\n",
      "\n",
      "    accuracy                           0.66     28000\n",
      "   macro avg       0.66      0.66      0.66     28000\n",
      "weighted avg       0.66      0.66      0.66     28000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "# fit model no training data\n",
    "ada = AdaBoostClassifier(random_state = random_state)\n",
    "ada.fit(X_train, y_train)\n",
    "\n",
    "y_pred = ada.predict(X_test)\n",
    "y_pred_train = ada.predict(X_train)\n",
    "\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "print(confusion_matrix(y_train, y_pred_train))\n",
    "\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost Optimization with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  6.8min finished\n"
     ]
    }
   ],
   "source": [
    "ada = AdaBoostClassifier(random_state=random_state)\n",
    "parameters = {'n_estimators':[50, 100, 200, 500, 1000, 1500, 2000], \n",
    "              'learning_rate':[0.05, 0.1, 0.15, 0.2, 0.25, 0.5, 0.75, 1.0]}\n",
    "\n",
    "ada_obj = RandomizedSearchCV(ada, \n",
    "                            param_distributions=parameters, \n",
    "                            scoring = 'f1', \n",
    "                            cv = 5,\n",
    "                            n_iter = 10,\n",
    "                            random_state=random_state,\n",
    "                            verbose=-1,\n",
    "                            n_jobs = -1)\n",
    "\n",
    "ada_fit = ada_obj.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 50, 'learning_rate': 0.05}\n",
      "0.6679904914134471\n"
     ]
    }
   ],
   "source": [
    "print(ada_fit.best_params_)\n",
    "print(ada_fit.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost with optimized parameter - Score F1 - Future Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best prameter of the optimization on the f1 are the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'n_estimators': 50, 'learning_rate': 0.05}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.55      0.60     13946\n",
      "           1       0.62      0.72      0.67     14054\n",
      "\n",
      "    accuracy                           0.64     28000\n",
      "   macro avg       0.64      0.64      0.64     28000\n",
      "weighted avg       0.64      0.64      0.64     28000\n",
      "\n",
      "[[ 7693  6253]\n",
      " [ 3879 10175]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.55      0.60      6054\n",
      "           1       0.61      0.73      0.66      5946\n",
      "\n",
      "    accuracy                           0.64     12000\n",
      "   macro avg       0.64      0.64      0.63     12000\n",
      "weighted avg       0.64      0.64      0.63     12000\n",
      "\n",
      "[[3306 2748]\n",
      " [1632 4314]]\n"
     ]
    }
   ],
   "source": [
    "ada_opt = AdaBoostClassifier(random_state=random_state,n_estimators=50,learning_rate=0.05)\n",
    "\n",
    "ada_opt.fit(X_train,y_train)\n",
    "\n",
    "ada_opt_pred_train = ada_opt.predict(X_train)\n",
    "ada_opt_pred_test = ada_opt.predict(X_test)\n",
    "\n",
    "print(classification_report(y_train, ada_opt_pred_train))\n",
    "\n",
    "print(confusion_matrix(y_train, ada_opt_pred_train))\n",
    "\n",
    "print(classification_report(y_test, ada_opt_pred_test))\n",
    "\n",
    "print(confusion_matrix(y_test, ada_opt_pred_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix train\n",
      "[[ 7693  6253]\n",
      " [ 3879 10175]]\n",
      "Classification report train\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.55      0.60     13946\n",
      "           1       0.62      0.72      0.67     14054\n",
      "\n",
      "    accuracy                           0.64     28000\n",
      "   macro avg       0.64      0.64      0.64     28000\n",
      "weighted avg       0.64      0.64      0.64     28000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix test\n",
      "[[3306 2748]\n",
      " [1632 4314]]\n",
      "Classification report test\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.55      0.60      6054\n",
      "           1       0.61      0.73      0.66      5946\n",
      "\n",
      "    accuracy                           0.64     12000\n",
      "   macro avg       0.64      0.64      0.63     12000\n",
      "weighted avg       0.64      0.64      0.63     12000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores(y_train, y_test, ada_opt_pred_train, ada_opt_pred_test, filename='adaboost')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:nf] *",
   "language": "python",
   "name": "conda-env-nf-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "424.391px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}