Machine Learning/Projects/Credit Card Approval/Credit Card Approval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52aa284e",
   "metadata": {},
   "source": [
    "# Credit Card Approval Prediction using ML\n",
    "### Jessica Campbell\n",
    "\n",
    "We will predict whether credit card applications will be approved or denied using machine learning models. This is a type of classification task (i.e., the application is either accepted or denied), so we will evaluate logistic regression, KNN, and Random Forest models.\n",
    "\n",
    "## Import Packages\n",
    "Let's start by importing the packages that we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f9b36c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# math and visualization\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# machine learning\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "# set seaborn style\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# render Matplotlib figures\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5960c3ba",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e021c71",
   "metadata": {},
   "source": [
    "The credit card application data was obtained from the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/dataset/27/credit+approval), which was anonymized for confidentiality. This dataset contains 690 rows with mixed attributes and missing values.\n",
    "\n",
    "The attributes are as follows:\n",
    "\n",
    "A1:\ta, b<br>\n",
    "A2:\tcontinuous<br>\n",
    "A3:\tcontinuous<br>\n",
    "A4:\tu, y, l, t<br>\n",
    "A5:\tg, p, gg<br>\n",
    "A6:\tc, d, cc, i, j, k, m, r, q, w, x, e, aa, ff<br>\n",
    "A7:\tv, h, bb, j, n, z, dd, ff, o<br>\n",
    "A8:\tcontinuous<br>\n",
    "A9:\tt, f<br>\n",
    "A10: t, f<br>\n",
    "A11: continuous<br>\n",
    "A12: t, f<br>\n",
    "A13: g, p, s<br>\n",
    "A14: continuous<br>\n",
    "A15: continuous<br>\n",
    "A16: +,- (class)\n",
    "\n",
    "**Add column names to the dataframe, which we will read in as `credit_df`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3fa2428d",
   "metadata": {},
   "outputs": [],
   "source": [
    "header_names = ['A1','A2','A3','A4','A5','A6','A7','A8','A9','A10','A11','A12','A13','A14','A15','A16']\n",
    "credit_df = pd.read_csv('./data/crx.data',names=header_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1ec0a10",
   "metadata": {},
   "source": [
    "**Check the header, info, and summary statistics.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19c5003c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>A1</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
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       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
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       "      <td>00043</td>\n",
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       "      <td>+</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
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      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14  A15 A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t    1   f   g  00202    0   +\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t    6   f   g  00043  560   +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f    0   f   g  00280  824   +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t    5   t   g  00100    3   +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f    0   f   s  00120    0   +"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "512e0709",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A1      690 non-null    object \n",
      " 1   A2      690 non-null    object \n",
      " 2   A3      690 non-null    float64\n",
      " 3   A4      690 non-null    object \n",
      " 4   A5      690 non-null    object \n",
      " 5   A6      690 non-null    object \n",
      " 6   A7      690 non-null    object \n",
      " 7   A8      690 non-null    float64\n",
      " 8   A9      690 non-null    object \n",
      " 9   A10     690 non-null    object \n",
      " 10  A11     690 non-null    int64  \n",
      " 11  A12     690 non-null    object \n",
      " 12  A13     690 non-null    object \n",
      " 13  A14     690 non-null    object \n",
      " 14  A15     690 non-null    int64  \n",
      " 15  A16     690 non-null    object \n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 86.4+ KB\n"
     ]
    }
   ],
   "source": [
    "credit_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff08025b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A11</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.00000</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.758725</td>\n",
       "      <td>2.223406</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>1017.385507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.978163</td>\n",
       "      <td>3.346513</td>\n",
       "      <td>4.86294</td>\n",
       "      <td>5210.102598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.207500</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>395.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>67.00000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A3          A8        A11            A15\n",
       "count  690.000000  690.000000  690.00000     690.000000\n",
       "mean     4.758725    2.223406    2.40000    1017.385507\n",
       "std      4.978163    3.346513    4.86294    5210.102598\n",
       "min      0.000000    0.000000    0.00000       0.000000\n",
       "25%      1.000000    0.165000    0.00000       0.000000\n",
       "50%      2.750000    1.000000    0.00000       5.000000\n",
       "75%      7.207500    2.625000    3.00000     395.500000\n",
       "max     28.000000   28.500000   67.00000  100000.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# limited to numerical columns by default\n",
    "credit_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63b8ed1c",
   "metadata": {},
   "source": [
    "Notice that the A15 column has a range of values that are several orders of magnitude larger than the other numerical columns. In this case, *feature scaling* will be used later to normalize the numerical columns to a consistent distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b575de4f",
   "metadata": {},
   "source": [
    "## Data Cleaning and Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93cbaec8",
   "metadata": {},
   "source": [
    "**Inspect the unique values for each column in the data frame.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "53c4ec3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1: ['b' 'a' '?']\n",
      "A2: ['30.83' '58.67' '24.50' '27.83' '20.17' '32.08' '33.17' '22.92' '54.42'\n",
      " '42.50' '22.08' '29.92' '38.25' '48.08' '45.83' '36.67' '28.25' '23.25'\n",
      " '21.83' '19.17' '25.00' '47.75' '27.42' '41.17' '15.83' '47.00' '56.58'\n",
      " '57.42' '42.08' '29.25' '42.00' '49.50' '36.75' '22.58' '27.25' '23.00'\n",
      " '27.75' '54.58' '34.17' '28.92' '29.67' '39.58' '56.42' '54.33' '41.00'\n",
      " '31.92' '41.50' '23.92' '25.75' '26.00' '37.42' '34.92' '34.25' '23.33'\n",
      " '23.17' '44.33' '35.17' '43.25' '56.75' '31.67' '23.42' '20.42' '26.67'\n",
      " '36.00' '25.50' '19.42' '32.33' '34.83' '38.58' '44.25' '44.83' '20.67'\n",
      " '34.08' '21.67' '21.50' '49.58' '27.67' '39.83' '?' '37.17' '25.67'\n",
      " '34.00' '49.00' '62.50' '31.42' '52.33' '28.75' '28.58' '22.50' '28.50'\n",
      " '37.50' '35.25' '18.67' '54.83' '40.92' '19.75' '29.17' '24.58' '33.75'\n",
      " '25.42' '37.75' '52.50' '57.83' '20.75' '39.92' '24.75' '44.17' '23.50'\n",
      " '47.67' '22.75' '34.42' '28.42' '67.75' '47.42' '36.25' '32.67' '48.58'\n",
      " '33.58' '18.83' '26.92' '31.25' '56.50' '43.00' '22.33' '32.83' '40.33'\n",
      " '30.50' '52.83' '46.67' '58.33' '37.33' '23.08' '32.75' '68.67' '28.00'\n",
      " '44.00' '25.08' '32.00' '60.58' '40.83' '19.33' '41.33' '56.00' '49.83'\n",
      " '22.67' '27.00' '26.08' '18.42' '21.25' '57.08' '22.42' '48.75' '40.00'\n",
      " '40.58' '28.67' '33.08' '21.33' '41.75' '34.50' '48.17' '27.58' '24.08'\n",
      " '24.83' '36.33' '35.42' '71.58' '39.50' '39.33' '24.33' '60.08' '55.92'\n",
      " '53.92' '18.92' '50.08' '65.42' '17.58' '18.08' '19.67' '25.17' '33.50'\n",
      " '58.42' '26.17' '42.83' '38.17' '20.50' '48.25' '28.33' '18.75' '18.50'\n",
      " '45.00' '40.25' '41.42' '17.83' '18.17' '20.00' '52.17' '50.75' '17.08'\n",
      " '18.33' '59.67' '18.00' '37.58' '30.67' '18.58' '16.25' '21.17' '17.67'\n",
      " '16.50' '29.50' '21.75' '18.25' '35.75' '16.08' '69.17' '32.92' '16.33'\n",
      " '22.17' '57.58' '15.92' '31.75' '19.00' '17.50' '33.67' '30.17' '33.25'\n",
      " '25.25' '34.75' '47.33' '39.08' '42.75' '38.92' '62.75' '32.25' '26.75'\n",
      " '63.33' '30.75' '16.00' '19.50' '32.42' '30.25' '26.83' '16.92' '24.42'\n",
      " '39.42' '23.58' '21.42' '33.00' '26.33' '26.25' '28.17' '20.83' '43.17'\n",
      " '56.83' '15.17' '29.83' '31.00' '51.92' '69.50' '19.58' '22.25' '38.42'\n",
      " '26.58' '35.00' '29.42' '49.17' '51.83' '58.58' '53.33' '27.17' '25.92'\n",
      " '30.58' '17.25' '27.33' '36.50' '29.75' '52.42' '36.17' '34.58' '21.92'\n",
      " '36.58' '31.08' '30.42' '21.08' '17.42' '39.17' '26.50' '17.33' '23.75'\n",
      " '34.67' '74.83' '45.33' '47.25' '24.17' '39.25' '39.00' '64.08' '31.33'\n",
      " '21.00' '13.75' '46.00' '20.25' '60.92' '30.00' '22.83' '45.17' '41.58'\n",
      " '55.75' '25.33' '31.83' '33.92' '24.92' '80.25' '30.08' '48.33' '76.75'\n",
      " '51.33' '41.92' '29.58' '32.17' '51.42' '42.17' '43.08' '59.50' '65.17'\n",
      " '20.33' '48.50' '28.08' '73.42' '51.58' '38.67' '46.08' '20.08' '42.25'\n",
      " '16.17' '47.83' '22.00' '38.33' '25.58' '21.58' '36.08' '38.75' '35.58'\n",
      " '31.58' '15.75' '17.92' '30.33' '47.17' '25.83' '50.25' '36.42']\n",
      "A3: [ 0.     4.46   0.5    1.54   5.625  4.     1.04  11.585  4.915  0.83\n",
      "  1.835  6.     6.04  10.5    4.415  0.875  5.875  0.25   8.585 11.25\n",
      "  1.     8.    14.5    6.5    0.585 13.    18.5    8.5   14.79   9.79\n",
      "  7.585  5.125 10.75   1.5    1.585 11.75   9.415  9.17  15.     1.415\n",
      " 13.915 28.     6.75   2.04   0.665  2.5    3.    11.625  4.5   12.25\n",
      " 16.165  0.79   0.835  4.25   0.375 25.125  7.5    5.     7.     5.29\n",
      "  1.165  9.75  19.     3.5    0.625  2.21  12.75  15.5    1.375  3.54\n",
      " 11.     1.75  16.5   12.     2.25   0.75  12.5    1.25   1.125  7.04\n",
      " 10.335  6.21   6.665  9.     5.5    0.54   2.75   9.5   13.5    3.75\n",
      " 16.     0.29   1.665  7.54   0.46  10.    11.5    3.04   2.     0.08\n",
      "  1.71   3.25   2.54  13.585  8.665  9.25   8.17   2.335 19.5    5.665\n",
      "  4.625  0.205  0.96   4.04   5.04   3.165  7.625 10.04  10.25   2.125\n",
      "  9.335  6.625  2.71   9.625 12.54   9.54   8.46  13.75  21.    10.125\n",
      " 25.085  0.21  21.5   11.125 11.045  1.335  0.085  1.21   0.165  5.71\n",
      "  5.415 12.625  0.58   0.415  2.415  0.335  3.125 12.125  2.875 13.665\n",
      " 26.335 10.29   1.29  22.     0.125  1.085  4.085  4.71   6.165  4.585\n",
      " 11.46  14.585  0.17   1.625  2.085  5.085  8.125  2.835  1.79   0.705\n",
      "  2.165  2.29  18.125  3.085 11.665  4.125  1.08  13.335 11.835  4.79\n",
      "  9.96   7.08  25.21   0.67   3.79  22.29   3.335  0.42   1.46   0.04\n",
      " 12.33  12.335  0.915 14.    17.75  20.     5.25   4.165 10.915  4.75\n",
      " 10.415  7.835  0.71   2.46   9.585  3.625  2.665  5.835 12.835 10.665\n",
      "  7.25  10.21   3.29  10.085  3.375]\n",
      "A4: ['u' 'y' '?' 'l']\n",
      "A5: ['g' 'p' '?' 'gg']\n",
      "A6: ['w' 'q' 'm' 'r' 'cc' 'k' 'c' 'd' 'x' 'i' 'e' 'aa' 'ff' 'j' '?']\n",
      "A7: ['v' 'h' 'bb' 'ff' 'j' 'z' '?' 'o' 'dd' 'n']\n",
      "A8: [ 1.25   3.04   1.5    3.75   1.71   2.5    6.5    0.04   3.96   3.165\n",
      "  2.165  4.335  1.     5.     0.25   0.96   3.17   0.665  0.75   0.835\n",
      "  7.875  3.085  0.5    5.165 15.     7.     5.04   7.96   7.585  0.415\n",
      "  2.     1.835 14.415  4.5    5.335  8.625 28.5    2.625  0.125  6.04\n",
      "  3.5    0.165  0.875  1.75   0.     7.415  0.085  5.75   6.     3.\n",
      "  1.585  4.29   1.54   1.46   1.625 12.5   13.5   10.75   0.375  0.585\n",
      "  0.455  4.     8.5    9.46   2.25  10.     0.795  1.375  1.29  11.5\n",
      "  6.29  14.     0.335  1.21   7.375  7.5    3.25  13.     5.5    4.25\n",
      "  0.625  5.085  2.75   2.375  8.     1.085  2.54   4.165  1.665 11.\n",
      "  9.     1.335  1.415  1.96   2.585  5.125 15.5    0.71   5.665 18.\n",
      "  5.25   8.665  2.29  20.     2.46  13.875  2.085  4.58   2.71   2.04\n",
      "  0.29   4.75   0.46   0.21   0.54   3.335  2.335  1.165  2.415  2.79\n",
      "  4.625  1.04   6.75   1.875 16.    12.75   5.375  2.125 17.5    3.125\n",
      "  0.79   8.29 ]\n",
      "A9: ['t' 'f']\n",
      "A10: ['t' 'f']\n",
      "A11: [ 1  6  0  5  7 10  3 17  2  9  8 15 11 12 40 23  4 20 67 14 16 13 19]\n",
      "A12: ['f' 't']\n",
      "A13: ['g' 's' 'p']\n",
      "A14: ['00202' '00043' '00280' '00100' '00120' '00360' '00164' '00080' '00180'\n",
      " '00052' '00128' '00260' '00000' '00320' '00396' '00096' '00200' '00300'\n",
      " '00145' '00500' '00168' '00434' '00583' '00030' '00240' '00070' '00455'\n",
      " '00311' '00216' '00491' '00400' '00239' '00160' '00711' '00250' '00520'\n",
      " '00515' '00420' '?' '00980' '00443' '00140' '00094' '00368' '00288'\n",
      " '00928' '00188' '00112' '00171' '00268' '00167' '00075' '00152' '00176'\n",
      " '00329' '00212' '00410' '00274' '00375' '00408' '00350' '00204' '00040'\n",
      " '00181' '00399' '00440' '00093' '00060' '00395' '00393' '00021' '00029'\n",
      " '00102' '00431' '00370' '00024' '00020' '00129' '00510' '00195' '00144'\n",
      " '00380' '00049' '00050' '00381' '00150' '00117' '00056' '00211' '00230'\n",
      " '00156' '00022' '00228' '00519' '00253' '00487' '00220' '00088' '00073'\n",
      " '00121' '00470' '00136' '00132' '00292' '00154' '00272' '00340' '00108'\n",
      " '00720' '00450' '00232' '00170' '01160' '00411' '00460' '00348' '00480'\n",
      " '00640' '00372' '00276' '00221' '00352' '00141' '00178' '00600' '00550'\n",
      " '02000' '00225' '00210' '00110' '00356' '00045' '00062' '00092' '00174'\n",
      " '00017' '00086' '00454' '00254' '00028' '00263' '00333' '00312' '00290'\n",
      " '00371' '00099' '00252' '00760' '00560' '00130' '00523' '00680' '00163'\n",
      " '00208' '00383' '00330' '00422' '00840' '00432' '00032' '00186' '00303'\n",
      " '00349' '00224' '00369' '00076' '00231' '00309' '00416' '00465' '00256']\n",
      "A15: [     0    560    824      3  31285   1349    314   1442    200   2690\n",
      "    245   1208   1260     11  10000   5000   4000     35    713    551\n",
      "    500    300    221   2283    100     15    284   1236   5800    730\n",
      "    400  50000    456  15108   2954      2     20     27    225      1\n",
      "     38      5    130    147    210  11202   1332     50    258    567\n",
      "   1000   2510    809    610    150  51100    367    600    247    375\n",
      "    278    827   2072    582   2300   3065   2200      6   1602   2184\n",
      "   3376   2000   7544  10561    837  11177    639   2028   1065    540\n",
      "    158  15000   3000   3257   1655   1430      7    790    396    678\n",
      "   1187   6590    168   1270   1210    742   8851   7059   1704    857\n",
      "   6700   2503   9800    196     14  26726  18027     99    444   1200\n",
      "   2010     13    120     32    722     40    484    204     98   5552\n",
      "    105   2803    126      4     21    173     10     25     42 100000\n",
      "    113      8     44   2732    179     16   1062    251    228     67\n",
      "     12    122   4208   1300    112   1110   1004    286   4500   1212\n",
      "    195     87     17    184    140     18    146     22     55     70\n",
      "     60   1058    769   5200     19    316    350   3552    687   1950\n",
      "     53     41     33     80    351   2100    475    892   4607   2206\n",
      "   5860     28   1391   2279    591    960    690    234    800    990\n",
      "   2197     90    340    347    327   4071    109   1249    134   1344\n",
      "    321    948   2079   2384    458   5298    162   1583     58     59\n",
      "   1400   1465   8000   4700   1097   3290  13212   5777   5124     23\n",
      "   4159    918    768    283    108      9     68    587    141    501\n",
      "    160    390    154    117    246    237    364    537    394    750]\n",
      "A16: ['+' '-']\n"
     ]
    }
   ],
   "source": [
    "for col in credit_df.columns:\n",
    "    print('{}: {}'.format(col,credit_df[col].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3418655c",
   "metadata": {},
   "source": [
    "Note that missing values are entered as '?' so let's replace these with the more common convension of NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b2040a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_df.replace('?',np.nan,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7a08e4",
   "metadata": {},
   "source": [
    "Let's check that the replacement worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7c7bdb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A1: ['b' 'a' nan]\n",
      "A2: ['30.83' '58.67' '24.50' '27.83' '20.17' '32.08' '33.17' '22.92' '54.42'\n",
      " '42.50' '22.08' '29.92' '38.25' '48.08' '45.83' '36.67' '28.25' '23.25'\n",
      " '21.83' '19.17' '25.00' '47.75' '27.42' '41.17' '15.83' '47.00' '56.58'\n",
      " '57.42' '42.08' '29.25' '42.00' '49.50' '36.75' '22.58' '27.25' '23.00'\n",
      " '27.75' '54.58' '34.17' '28.92' '29.67' '39.58' '56.42' '54.33' '41.00'\n",
      " '31.92' '41.50' '23.92' '25.75' '26.00' '37.42' '34.92' '34.25' '23.33'\n",
      " '23.17' '44.33' '35.17' '43.25' '56.75' '31.67' '23.42' '20.42' '26.67'\n",
      " '36.00' '25.50' '19.42' '32.33' '34.83' '38.58' '44.25' '44.83' '20.67'\n",
      " '34.08' '21.67' '21.50' '49.58' '27.67' '39.83' nan '37.17' '25.67'\n",
      " '34.00' '49.00' '62.50' '31.42' '52.33' '28.75' '28.58' '22.50' '28.50'\n",
      " '37.50' '35.25' '18.67' '54.83' '40.92' '19.75' '29.17' '24.58' '33.75'\n",
      " '25.42' '37.75' '52.50' '57.83' '20.75' '39.92' '24.75' '44.17' '23.50'\n",
      " '47.67' '22.75' '34.42' '28.42' '67.75' '47.42' '36.25' '32.67' '48.58'\n",
      " '33.58' '18.83' '26.92' '31.25' '56.50' '43.00' '22.33' '32.83' '40.33'\n",
      " '30.50' '52.83' '46.67' '58.33' '37.33' '23.08' '32.75' '68.67' '28.00'\n",
      " '44.00' '25.08' '32.00' '60.58' '40.83' '19.33' '41.33' '56.00' '49.83'\n",
      " '22.67' '27.00' '26.08' '18.42' '21.25' '57.08' '22.42' '48.75' '40.00'\n",
      " '40.58' '28.67' '33.08' '21.33' '41.75' '34.50' '48.17' '27.58' '24.08'\n",
      " '24.83' '36.33' '35.42' '71.58' '39.50' '39.33' '24.33' '60.08' '55.92'\n",
      " '53.92' '18.92' '50.08' '65.42' '17.58' '18.08' '19.67' '25.17' '33.50'\n",
      " '58.42' '26.17' '42.83' '38.17' '20.50' '48.25' '28.33' '18.75' '18.50'\n",
      " '45.00' '40.25' '41.42' '17.83' '18.17' '20.00' '52.17' '50.75' '17.08'\n",
      " '18.33' '59.67' '18.00' '37.58' '30.67' '18.58' '16.25' '21.17' '17.67'\n",
      " '16.50' '29.50' '21.75' '18.25' '35.75' '16.08' '69.17' '32.92' '16.33'\n",
      " '22.17' '57.58' '15.92' '31.75' '19.00' '17.50' '33.67' '30.17' '33.25'\n",
      " '25.25' '34.75' '47.33' '39.08' '42.75' '38.92' '62.75' '32.25' '26.75'\n",
      " '63.33' '30.75' '16.00' '19.50' '32.42' '30.25' '26.83' '16.92' '24.42'\n",
      " '39.42' '23.58' '21.42' '33.00' '26.33' '26.25' '28.17' '20.83' '43.17'\n",
      " '56.83' '15.17' '29.83' '31.00' '51.92' '69.50' '19.58' '22.25' '38.42'\n",
      " '26.58' '35.00' '29.42' '49.17' '51.83' '58.58' '53.33' '27.17' '25.92'\n",
      " '30.58' '17.25' '27.33' '36.50' '29.75' '52.42' '36.17' '34.58' '21.92'\n",
      " '36.58' '31.08' '30.42' '21.08' '17.42' '39.17' '26.50' '17.33' '23.75'\n",
      " '34.67' '74.83' '45.33' '47.25' '24.17' '39.25' '39.00' '64.08' '31.33'\n",
      " '21.00' '13.75' '46.00' '20.25' '60.92' '30.00' '22.83' '45.17' '41.58'\n",
      " '55.75' '25.33' '31.83' '33.92' '24.92' '80.25' '30.08' '48.33' '76.75'\n",
      " '51.33' '41.92' '29.58' '32.17' '51.42' '42.17' '43.08' '59.50' '65.17'\n",
      " '20.33' '48.50' '28.08' '73.42' '51.58' '38.67' '46.08' '20.08' '42.25'\n",
      " '16.17' '47.83' '22.00' '38.33' '25.58' '21.58' '36.08' '38.75' '35.58'\n",
      " '31.58' '15.75' '17.92' '30.33' '47.17' '25.83' '50.25' '36.42']\n",
      "A3: [ 0.     4.46   0.5    1.54   5.625  4.     1.04  11.585  4.915  0.83\n",
      "  1.835  6.     6.04  10.5    4.415  0.875  5.875  0.25   8.585 11.25\n",
      "  1.     8.    14.5    6.5    0.585 13.    18.5    8.5   14.79   9.79\n",
      "  7.585  5.125 10.75   1.5    1.585 11.75   9.415  9.17  15.     1.415\n",
      " 13.915 28.     6.75   2.04   0.665  2.5    3.    11.625  4.5   12.25\n",
      " 16.165  0.79   0.835  4.25   0.375 25.125  7.5    5.     7.     5.29\n",
      "  1.165  9.75  19.     3.5    0.625  2.21  12.75  15.5    1.375  3.54\n",
      " 11.     1.75  16.5   12.     2.25   0.75  12.5    1.25   1.125  7.04\n",
      " 10.335  6.21   6.665  9.     5.5    0.54   2.75   9.5   13.5    3.75\n",
      " 16.     0.29   1.665  7.54   0.46  10.    11.5    3.04   2.     0.08\n",
      "  1.71   3.25   2.54  13.585  8.665  9.25   8.17   2.335 19.5    5.665\n",
      "  4.625  0.205  0.96   4.04   5.04   3.165  7.625 10.04  10.25   2.125\n",
      "  9.335  6.625  2.71   9.625 12.54   9.54   8.46  13.75  21.    10.125\n",
      " 25.085  0.21  21.5   11.125 11.045  1.335  0.085  1.21   0.165  5.71\n",
      "  5.415 12.625  0.58   0.415  2.415  0.335  3.125 12.125  2.875 13.665\n",
      " 26.335 10.29   1.29  22.     0.125  1.085  4.085  4.71   6.165  4.585\n",
      " 11.46  14.585  0.17   1.625  2.085  5.085  8.125  2.835  1.79   0.705\n",
      "  2.165  2.29  18.125  3.085 11.665  4.125  1.08  13.335 11.835  4.79\n",
      "  9.96   7.08  25.21   0.67   3.79  22.29   3.335  0.42   1.46   0.04\n",
      " 12.33  12.335  0.915 14.    17.75  20.     5.25   4.165 10.915  4.75\n",
      " 10.415  7.835  0.71   2.46   9.585  3.625  2.665  5.835 12.835 10.665\n",
      "  7.25  10.21   3.29  10.085  3.375]\n",
      "A4: ['u' 'y' nan 'l']\n",
      "A5: ['g' 'p' nan 'gg']\n",
      "A6: ['w' 'q' 'm' 'r' 'cc' 'k' 'c' 'd' 'x' 'i' 'e' 'aa' 'ff' 'j' nan]\n",
      "A7: ['v' 'h' 'bb' 'ff' 'j' 'z' nan 'o' 'dd' 'n']\n",
      "A8: [ 1.25   3.04   1.5    3.75   1.71   2.5    6.5    0.04   3.96   3.165\n",
      "  2.165  4.335  1.     5.     0.25   0.96   3.17   0.665  0.75   0.835\n",
      "  7.875  3.085  0.5    5.165 15.     7.     5.04   7.96   7.585  0.415\n",
      "  2.     1.835 14.415  4.5    5.335  8.625 28.5    2.625  0.125  6.04\n",
      "  3.5    0.165  0.875  1.75   0.     7.415  0.085  5.75   6.     3.\n",
      "  1.585  4.29   1.54   1.46   1.625 12.5   13.5   10.75   0.375  0.585\n",
      "  0.455  4.     8.5    9.46   2.25  10.     0.795  1.375  1.29  11.5\n",
      "  6.29  14.     0.335  1.21   7.375  7.5    3.25  13.     5.5    4.25\n",
      "  0.625  5.085  2.75   2.375  8.     1.085  2.54   4.165  1.665 11.\n",
      "  9.     1.335  1.415  1.96   2.585  5.125 15.5    0.71   5.665 18.\n",
      "  5.25   8.665  2.29  20.     2.46  13.875  2.085  4.58   2.71   2.04\n",
      "  0.29   4.75   0.46   0.21   0.54   3.335  2.335  1.165  2.415  2.79\n",
      "  4.625  1.04   6.75   1.875 16.    12.75   5.375  2.125 17.5    3.125\n",
      "  0.79   8.29 ]\n",
      "A9: ['t' 'f']\n",
      "A10: ['t' 'f']\n",
      "A11: [ 1  6  0  5  7 10  3 17  2  9  8 15 11 12 40 23  4 20 67 14 16 13 19]\n",
      "A12: ['f' 't']\n",
      "A13: ['g' 's' 'p']\n",
      "A14: ['00202' '00043' '00280' '00100' '00120' '00360' '00164' '00080' '00180'\n",
      " '00052' '00128' '00260' '00000' '00320' '00396' '00096' '00200' '00300'\n",
      " '00145' '00500' '00168' '00434' '00583' '00030' '00240' '00070' '00455'\n",
      " '00311' '00216' '00491' '00400' '00239' '00160' '00711' '00250' '00520'\n",
      " '00515' '00420' nan '00980' '00443' '00140' '00094' '00368' '00288'\n",
      " '00928' '00188' '00112' '00171' '00268' '00167' '00075' '00152' '00176'\n",
      " '00329' '00212' '00410' '00274' '00375' '00408' '00350' '00204' '00040'\n",
      " '00181' '00399' '00440' '00093' '00060' '00395' '00393' '00021' '00029'\n",
      " '00102' '00431' '00370' '00024' '00020' '00129' '00510' '00195' '00144'\n",
      " '00380' '00049' '00050' '00381' '00150' '00117' '00056' '00211' '00230'\n",
      " '00156' '00022' '00228' '00519' '00253' '00487' '00220' '00088' '00073'\n",
      " '00121' '00470' '00136' '00132' '00292' '00154' '00272' '00340' '00108'\n",
      " '00720' '00450' '00232' '00170' '01160' '00411' '00460' '00348' '00480'\n",
      " '00640' '00372' '00276' '00221' '00352' '00141' '00178' '00600' '00550'\n",
      " '02000' '00225' '00210' '00110' '00356' '00045' '00062' '00092' '00174'\n",
      " '00017' '00086' '00454' '00254' '00028' '00263' '00333' '00312' '00290'\n",
      " '00371' '00099' '00252' '00760' '00560' '00130' '00523' '00680' '00163'\n",
      " '00208' '00383' '00330' '00422' '00840' '00432' '00032' '00186' '00303'\n",
      " '00349' '00224' '00369' '00076' '00231' '00309' '00416' '00465' '00256']\n",
      "A15: [     0    560    824      3  31285   1349    314   1442    200   2690\n",
      "    245   1208   1260     11  10000   5000   4000     35    713    551\n",
      "    500    300    221   2283    100     15    284   1236   5800    730\n",
      "    400  50000    456  15108   2954      2     20     27    225      1\n",
      "     38      5    130    147    210  11202   1332     50    258    567\n",
      "   1000   2510    809    610    150  51100    367    600    247    375\n",
      "    278    827   2072    582   2300   3065   2200      6   1602   2184\n",
      "   3376   2000   7544  10561    837  11177    639   2028   1065    540\n",
      "    158  15000   3000   3257   1655   1430      7    790    396    678\n",
      "   1187   6590    168   1270   1210    742   8851   7059   1704    857\n",
      "   6700   2503   9800    196     14  26726  18027     99    444   1200\n",
      "   2010     13    120     32    722     40    484    204     98   5552\n",
      "    105   2803    126      4     21    173     10     25     42 100000\n",
      "    113      8     44   2732    179     16   1062    251    228     67\n",
      "     12    122   4208   1300    112   1110   1004    286   4500   1212\n",
      "    195     87     17    184    140     18    146     22     55     70\n",
      "     60   1058    769   5200     19    316    350   3552    687   1950\n",
      "     53     41     33     80    351   2100    475    892   4607   2206\n",
      "   5860     28   1391   2279    591    960    690    234    800    990\n",
      "   2197     90    340    347    327   4071    109   1249    134   1344\n",
      "    321    948   2079   2384    458   5298    162   1583     58     59\n",
      "   1400   1465   8000   4700   1097   3290  13212   5777   5124     23\n",
      "   4159    918    768    283    108      9     68    587    141    501\n",
      "    160    390    154    117    246    237    364    537    394    750]\n",
      "A16: ['+' '-']\n"
     ]
    }
   ],
   "source": [
    "for col in credit_df.columns:\n",
    "    print('{}: {}'.format(col,credit_df[col].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c18087",
   "metadata": {},
   "source": [
    "**Inspect the distribution of missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d48dc75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     12\n",
       "A2     12\n",
       "A3      0\n",
       "A4      6\n",
       "A5      6\n",
       "A6      9\n",
       "A7      9\n",
       "A8      0\n",
       "A9      0\n",
       "A10     0\n",
       "A11     0\n",
       "A12     0\n",
       "A13     0\n",
       "A14    13\n",
       "A15     0\n",
       "A16     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d88146c",
   "metadata": {},
   "source": [
    "The fraction of missing values is quite low, so we will use *imputation* to replace them. All of the missing values belong to a categorical columns, so we will replace with the last valid value using panda's `ffill` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "541e38a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# columns with missing values\n",
    "columns = ['A1','A2','A4','A5','A6','A7','A14']\n",
    "\n",
    "for col in columns:\n",
    "    credit_df[col] = credit_df[col].ffill()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac9dc94",
   "metadata": {},
   "source": [
    "Check that the replacement worked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7c2db4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1     0\n",
       "A2     0\n",
       "A3     0\n",
       "A4     0\n",
       "A5     0\n",
       "A6     0\n",
       "A7     0\n",
       "A8     0\n",
       "A9     0\n",
       "A10    0\n",
       "A11    0\n",
       "A12    0\n",
       "A13    0\n",
       "A14    0\n",
       "A15    0\n",
       "A16    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf9a26e",
   "metadata": {},
   "source": [
    "**Column conversions.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648fe55e",
   "metadata": {},
   "source": [
    "Change object columns to numeric values where appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffdc8f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_df['A2'] = credit_df.A2.astype(float)\n",
    "credit_df['A3'] = credit_df.A3.astype(float)\n",
    "credit_df['A14'] = credit_df.A14.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd238df8",
   "metadata": {},
   "source": [
    "Replace integer columns with float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d5eb598e",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_df['A11'] = credit_df.A11.astype(float)\n",
    "credit_df['A15'] = credit_df.A15.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a58eca9",
   "metadata": {},
   "source": [
    "Verify columns by checking the summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0cb469dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "      <th>A16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5.0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1     A2     A3 A4 A5 A6 A7    A8 A9 A10  A11 A12 A13    A14    A15 A16\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t   t  1.0   f   g  202.0    0.0   +\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t   t  6.0   f   g   43.0  560.0   +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t   f  0.0   f   g  280.0  824.0   +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t   t  5.0   t   g  100.0    3.0   +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t   f  0.0   f   s  120.0    0.0   +"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3dc63493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A1      690 non-null    object \n",
      " 1   A2      690 non-null    float64\n",
      " 2   A3      690 non-null    float64\n",
      " 3   A4      690 non-null    object \n",
      " 4   A5      690 non-null    object \n",
      " 5   A6      690 non-null    object \n",
      " 6   A7      690 non-null    object \n",
      " 7   A8      690 non-null    float64\n",
      " 8   A9      690 non-null    object \n",
      " 9   A10     690 non-null    object \n",
      " 10  A11     690 non-null    float64\n",
      " 11  A12     690 non-null    object \n",
      " 12  A13     690 non-null    object \n",
      " 13  A14     690 non-null    float64\n",
      " 14  A15     690 non-null    float64\n",
      " 15  A16     690 non-null    object \n",
      "dtypes: float64(6), object(10)\n",
      "memory usage: 86.4+ KB\n"
     ]
    }
   ],
   "source": [
    "credit_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40085cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A8</th>\n",
       "      <th>A11</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.00000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.483058</td>\n",
       "      <td>4.758725</td>\n",
       "      <td>2.223406</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>183.792754</td>\n",
       "      <td>1017.385507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.912872</td>\n",
       "      <td>4.978163</td>\n",
       "      <td>3.346513</td>\n",
       "      <td>4.86294</td>\n",
       "      <td>173.567750</td>\n",
       "      <td>5210.102598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.375000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.750000</td>\n",
       "      <td>7.207500</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>395.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>67.00000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A2          A3          A8        A11          A14  \\\n",
       "count  690.000000  690.000000  690.000000  690.00000   690.000000   \n",
       "mean    31.483058    4.758725    2.223406    2.40000   183.792754   \n",
       "std     11.912872    4.978163    3.346513    4.86294   173.567750   \n",
       "min     13.750000    0.000000    0.000000    0.00000     0.000000   \n",
       "25%     22.580000    1.000000    0.165000    0.00000    73.000000   \n",
       "50%     28.375000    2.750000    1.000000    0.00000   160.000000   \n",
       "75%     37.750000    7.207500    2.625000    3.00000   280.000000   \n",
       "max     80.250000   28.000000   28.500000   67.00000  2000.000000   \n",
       "\n",
       "                 A15  \n",
       "count     690.000000  \n",
       "mean     1017.385507  \n",
       "std      5210.102598  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         5.000000  \n",
       "75%       395.500000  \n",
       "max    100000.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "820434ac",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76240194",
   "metadata": {},
   "source": [
    "**Data Splitting**\n",
    "\n",
    "We will split the dataframe into the features (denoted by `X`) and target variable (denoted by `y`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "10276728",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = credit_df.drop(['A16'],axis=1)\n",
    "y = credit_df['A16']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bce4b3",
   "metadata": {},
   "source": [
    "**Variable Encoding**\n",
    "\n",
    "We will assume that the classification variables are ordinal in that they contain a natural order that reflects the likelihood of whether their credit card application will be approved or not (e.g., credit score). To convert the categorical variables to numeric values, we will use scikit-learn's `OrdinalEncoder` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d964d3b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A4</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>b</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>e</td>\n",
       "      <td>h</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>a</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>a</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>aa</td>\n",
       "      <td>v</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>b</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>h</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>690 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1 A4 A5  A6  A7 A9 A10 A12 A13\n",
       "0    b  u  g   w   v  t   t   f   g\n",
       "1    a  u  g   q   h  t   t   f   g\n",
       "2    a  u  g   q   h  t   f   f   g\n",
       "3    b  u  g   w   v  t   t   t   g\n",
       "4    b  u  g   w   v  t   f   f   s\n",
       "..  .. .. ..  ..  .. ..  ..  ..  ..\n",
       "685  b  y  p   e   h  f   f   f   g\n",
       "686  a  u  g   c   v  f   t   t   g\n",
       "687  a  y  p  ff  ff  f   t   t   g\n",
       "688  b  u  g  aa   v  f   f   f   g\n",
       "689  b  u  g   c   h  f   f   t   g\n",
       "\n",
       "[690 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_columns = ['A1','A4','A5','A6','A7','A9','A10','A12','A13']\n",
    "X[categorical_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "81bf1b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc                    = OrdinalEncoder()\n",
    "X[categorical_columns] = enc.fit_transform(X[categorical_columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dca8797d",
   "metadata": {},
   "source": [
    "Verify the results of the ordinal encoder by checking the header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ce97c230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1     A2     A3   A4   A5    A6   A7    A8   A9  A10  A11  A12  A13  \\\n",
       "0  1.0  30.83  0.000  1.0  0.0  12.0  7.0  1.25  1.0  1.0  1.0  0.0  0.0   \n",
       "1  0.0  58.67  4.460  1.0  0.0  10.0  3.0  3.04  1.0  1.0  6.0  0.0  0.0   \n",
       "2  0.0  24.50  0.500  1.0  0.0  10.0  3.0  1.50  1.0  0.0  0.0  0.0  0.0   \n",
       "3  1.0  27.83  1.540  1.0  0.0  12.0  7.0  3.75  1.0  1.0  5.0  1.0  0.0   \n",
       "4  1.0  20.17  5.625  1.0  0.0  12.0  7.0  1.71  1.0  0.0  0.0  0.0  2.0   \n",
       "\n",
       "     A14    A15  \n",
       "0  202.0    0.0  \n",
       "1   43.0  560.0  \n",
       "2  280.0  824.0  \n",
       "3  100.0    3.0  \n",
       "4  120.0    0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f157e3",
   "metadata": {},
   "source": [
    "Concatenate the encoded features and target variable data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "40038cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "credit_df_new = pd.concat([X,y],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63c704e",
   "metadata": {},
   "source": [
    "**Removing Outliers**\n",
    "\n",
    "Before we can perform feature centering and scaling, we need to identify and remove outliers. The concept of 'outliers' does not apply to categorical data because there is no distance metric that applies to them. We will therefore search for outliers only in the numeric features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d1bad815",
   "metadata": {},
   "outputs": [],
   "source": [
    "ignore_columns = categorical_columns.copy()\n",
    "ignore_columns.append('A16')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b9e1f0",
   "metadata": {},
   "source": [
    "There are several ways to identify outliers, many of which rely on an assumption about the distribution of data. Given the heavy-tailed distributions seen here, we will use the standard approach of identifying outliers as those that are beyond 3 standard deviations from the mean. Let's start by visualizing the histograms and using vertical dashed lines to represent 3 standard deviations from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c46c962f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BSzW36rTjsiOogksQUs2N7NnLyJ692THUhgUs1dwNP3yOG374XHYMtWEBS1ISC1iSkljAkpTEApakJJ6GJtXcJ5Yemx1BFSxgqebOtoB7lksQUs3tGn6LXcNvZcdQGxZwD5noY+kbjca0jtPsdtMDm7jpgU3ZMdSGSxA9ZKKPsx8a2s28efN/br8fZy/1J2fAkpTEApakJBawJCVxDViquVXh7Sh7VVcLOCKeAHa2Nl8A/gi4BdgPPA1cXkrZ181MUt0tP8kbsveqrhVwRMwFKKWsHLPvTuDqUsoDEfFd4ELgjm5lkmaD7bub5ykePX9OchKN180Z8BnAvIi4r/VzrwKWAQ+2vn43cA4WsDSjVj/8PABXnndachKN180CHgK+AdwMnEqzcA8ppexvfX0XsHCibzIyMsLg4OCUf3ij0WBoaPcUj5o/jWM6d9y+ffsqvj5/Wn8nGYaHh/sma5V+G8O217cDvCNzv42hnX4aQ9VFVN0s4A3AxlbhboiIbTRnwAcsAHZM9E0GBgYqBzORdhcxdOKYTh1XdSEGVL/AvWZwcLBvslbptzEc89NnAWg0/mEG3G9jaKcOY+jmaWiXAN8EiIgTgCOB+yJiZevr5wMPdzGPJKXq5gz4e8AtEfEIzbMeLgFeB1ZHxBxgEFjTxTySlKprBVxKGQU+1+ZLn+pWBmk2OvdDi7IjqIIXYkg195ETj8qOoApeiizV3Nadw2zdOZwdQ21YwFLN3fqjzdz6o83ZMdSGBSxJSSxgSUpiAUtSEgtYkpJ4GppUc79++vHZEVTBAlZXjI42P3R0KtfuHzhG786HTpjwHldKYgGrKw584vPBbig0np/2PDO2bBsCYPEx85KTaDzXgKWau+3xLdz2+JbsGGrDApakJBawJCWxgCUpiQWsnjU62t3jpG7zLAj1rANnTkyVZ0+802fP/EB2BFWwgKWaW3rcguwIquAShFRzG1/dxcZXd2XHUBsWsFRzt//k77n9J3+fHUNtWMCaEt/gkmaOa8CaEt8Yk2aOM+AamM6s1JmslM8ZcA1MZ1bqjFTKZwFLNXfRLy/OjqAKFrBUc96Gsne5BizV3DMv7+SZl3dmx1AbzoClmrvrb18B/GSMXuQMWJKSWMCSlMQClqQkFvAs5YUYUj7fhJulvKR49rj4Y0uyI6iCBSzV3KKFc7MjqIJLEFLNPfniDp58cUd2DLXhDFiquXuf2QrAR048KjeIfo4zYElKYgFLUhILWJKSWMCSlMQ34aSau2zFydkRVMEClmru6PlzsiOogksQUs2te2E7617Ynh1DbVjAqp3p3ueirvfHWFteZW15NTuG2nAJQrXjfS7UL5wBS1ISC1hqmewSRKPRmNZx0nguQUgtk126GBrazbx589/edulC02UBSzX3uytPyY6gChawVHML5h6eHUEVXAOWau7Rja/z6MbXs2OoDWfAUs090irfs5cem5xE4zkDlqQkFrAkJbGAJSmJBSxJSXwTTnqXRkebF3F0+pjpuuLTp3bnB2nKLGDpXZrOzX+me/XcdIp74LBDvVy6R1nAUh+ZTtkPLXyVP/5jWHXacVM6brqz9G7O7vudBSzV3PCR21m3eeoF7G09O8834SQpSfoMOCLeA9wEnAGMAJeWUjbmppI6y1/Tf95U/04ajQYjIzAw0Pmf1SnpBQz8M2BuKeVjEfFR4JvAhbmRpM7q9q/3e/e+c3v8PY17wVT/ToaGdvPjH8+f1t/jvfdO/RiY+eLuhQL+BHAPQCnlf0fELyXnkWrn0EPfWW7j72ncTp3XcntlffuQ/fv3z+x3nKKIuBm4vZRyd2t7C3ByKWVPu+evX7/+NeCnXYwoSe/W68uWLTtv/M5emAG/ASwYs/2eqvIFWLZs2fs6H0mSOq8XzoJ4FPg1gNYa8P/NjSNJ3dELM+A7gF+NiMeAQ4DfSs4jSV2RvgYsSbNVLyxBSNKsZAFLUhILWJKS9MKbcB1Tl8ucI+IJYGdr84VSSt+8URkRZwHXlVJWRsRS4BZgP/A0cHkpZV9mvskYN4Yzgb8Gnmt9+U9KKX+Zl+7gIuJw4PvAEmAAuBb4O/rodagYw0v00etQpdYFTA0uc46IuQCllJXJUaYsIr4KfAHY3dp1PXB1KeWBiPguzdfijqx8k9FmDGcC15dSvpmXako+D2wrpXwhIo4BngCepL9eh3Zj+A/01+vQVt2XIN5xmTPQj5c5nwHMi4j7IuJ/tf4h6RebgM+M2V4GPNh6fDfw6a4nmrp2Y/inEfFQRHwvIhZUHNcr/gq4Zsz2HvrvdagaQz+9Dm3VvYCP5B9+dQfYGxH9NusfAr4BnAv8NvDf+mUMpZTbgbfG7DqklHLgvMddwMLup5qaNmNYB/xBKeWTwPPAH6YEm6RSypullF2tgloDXE2fvQ4VY+ir16FK3Qt4Spc596gNwJ+VUvaXUjYA24DjkzNN19h1xgXAjqQc78YdpZT1Bx4D/zgzzGRExInAWuAHpZQ/pw9fhzZj6LvXoZ26F3AdLnO+hObaNRFxAs1Z/SupiabviYhY2Xp8PvBwYpbpujcilrce/wqw/mBPzhYR7wfuA64spXy/tbuvXoeKMfTV61ClL36VfRfqcJnz94BbIuIRmu9aX9KHs/gDfh9YHRFzgEGav072m98Bvh0Ro8BW4IvJeSZyFfBe4JqIOLCO+hXgxj56HdqN4d8AN/TR69CWlyJLUpK6L0FIUs+ygCUpiQUsSUksYElKYgFLUpK6n4YmHVREXAlcAZxUShmOiF8E/gvN0xafAn6vlLL3IN9CmjZnwJrt/iXwF8C/aG3/R+CqUsrZwDzggqxgqj8LWLNW62qwTcB3gctbuz9bSnmodZHCIuD/JcXTLGABaza7FLi5lFKAkYg4q5SyNyJ+AXgGOBYoqQlVa14Jp1kpIt5Lc/b7f2jenOYDwJOllC+Mec6lwIpSysU5KVV3zoA1W30e+F4p5ZxSynnAWcA5EXF3RJzaes4u3nnnMGlGeRaEZqtLaX7SBQCllKGIuJ3mR93c0rrJy1DreVJHuAQhSUlcgpCkJBawJCWxgCUpiQUsSUksYElKYgFLUhILWJKS/H+FXXEdYEtn4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in credit_df_new.columns:\n",
    "    if column not in ignore_columns:\n",
    "        sns.displot(data=credit_df_new,x=column,kde=False,color='blue')\n",
    "        plt.axvline(np.mean(credit_df_new[column])+3.*np.std(credit_df_new[column]),linestyle='dashed',alpha=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f6a5cca",
   "metadata": {},
   "source": [
    "Let's define a function that we can use to clip data that is beyond N times the standard deviation from the mean, called `fclip()`. For the `A15` feature where there are a significant number of outliers, we will be more aggressive and use N=1. Otherwise, we will continue to use N=3. In light of the significant heavy-tailed distributions shown here, we will perform two rounds of clipping where the mean and standard deviation are re-evaluated after the first round of clipping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9dd07330",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fclip(df,columns):\n",
    "    df_clipped = df.copy()\n",
    "    \n",
    "    for column in columns:\n",
    "        \n",
    "        # threshold for the number of standard deviations from the mean\n",
    "        if column in ['A15']:\n",
    "            sigma = 1\n",
    "        else:\n",
    "            sigma = 3\n",
    "        \n",
    "        # first round\n",
    "        val = np.mean(df_clipped[column])+sigma*np.std(df_clipped[column])\n",
    "        df_clipped.query('{}<@val'.format(column),inplace=True)\n",
    "        \n",
    "        # second round\n",
    "        val = np.mean(df_clipped[column])+sigma*np.std(df_clipped[column])\n",
    "        df_clipped.query('{}<@val'.format(column),inplace=True)\n",
    "        \n",
    "    return df_clipped\n",
    "\n",
    "credit_df_clipped = fclip(credit_df_new,credit_df_new.drop(ignore_columns,axis=1).columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9578c3",
   "metadata": {},
   "source": [
    "Inspect the clipped data using histograms to ensure that outliers were removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "32fc954c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2bdte+PiT5WvAwE+ALSuOzzhZ+AJs27btp9svSZLaNwpLEF8FXgQQEc8DvlFbjiR1YxRmwDcBL4iIfwKmgNcW1yNJnShfA5akSTUKSxCSNJEMYEkqYgBLUpFR+BBubEXEJuAG4AJgBngn8E3gRmAZuAvYm5lLRSWetoh4ErAfCOBRBh+eTjEGvR0XEecAtwMvAI4xXr3dATzYHH4PeBdj0l9EXAO8BJhmcJuD2xjR3pwBt+sVwP2ZuR3YDXwAuB7Y15ybAvYU1jeM3wDIzIuAP2HQ17j0dvyH54eBh5tT49TbWQCZubP59VrGpL+I2AlcCFwE7ADOY4R7M4Db9RnguhXHx4BtDH4iAxwELum6qPWQmZ8HXtccPh34IWPSW+M9wIeAHzTH49Tbs4HNEXFLRHypuf5+XPq7jMFegpuAvwe+wAj3ZgC3KDMfyszDEbEFOADsA6Yy8/i1f4eBrWUFDikzj0XEx4C/ZtDfWPQWEa8B7svMm1ecHoveGvMMfsBcBrwe+FvGp7+nMLifzMv4/97OGNXeDOCWRcR5wBzwicz8FLBy7WkL8EBFXeslM18N/DyD9eCfWvHWRu7tCgabg74MPAf4OHDOivc3cm8A9wCfzMzlzLwHuB84d8X7G7m/+4GbM/NoZiawwGMDd6R6M4BbFBHnArcAV2fmDc3pO5p1KhisCx+qqG1YEfHK5sMOGMyoloCvj0NvmXlxZu7IzJ3AncCrgIPj0FvjCprbvkbE04CzgVvGpL+vAC+MiKmmt1ng1lHtzZ1wLYqI9wGXA3evOP1G4P0MPqHtA1dl5qMF5Q0lImaBjwJPBTYBf86gn/1s8N5WambBr2fwA2YseouIaQZXBZzP4MqAq4EfMT79/SWwi8EE81oGV3mMZG8GsCQVcQlCkooYwJJUxACWpCIGsCQVMYAlqYg349FEi4irgTcBz8jMhYh4DoOdfY8Ci8CrMvOHdRVqnDkD1qT7XeDTwMub4/cBb2g2YXyOwTWyUisMYE2sZnfUdxjcdGdvc/rlmXln8/pMBltZpVYYwJpkVwIfae4ZsBgRz83M/wSIiAuBPwDeW1mgxps74TSRIuLJDGa/X2ewzfhngTsz85URcTnwduClmfndwjI15gxgTaSIeANwfma+tTnezOCeAW9hcJ/jPZn548ISNQFcgtCkuhL4xPGDzJxn8KHbxxjcsvBzEfHliHhHUX2aAM6AJamIM2BJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpyP8CZBX4xAb3/9cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/Wy1vBe6pbu8Gzm84jyQV09gURES8FXgsM++OiGur1esyc666fQjYstQ4MzMzdLvdFf/8TqfD1NThFT9usMkhjvX0eLOzs0Mad5j5Jp+1v6enp1f1O2hSGzJCO3K2ISOMds5OpzNwfZNzwG8H5iLifOB04KPAc/vu3wwcWGqQiYmJBZ/MUjZtmlzV4+oe69h4U1OHhzbuMPPN39/dbnfVv4OmtCEjtCNnGzJCe3L2a2wKIjO3Z+a5mbkDeAi4DNgdETuqTS4C9jaVR5JKa/xTEPO8B9gVERuALnB74TyS1JgiBVwdBR9zbokMklSaJ2JIUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsCQVYgFLUiEWsFSD5X6X3nIvn+h3861Npa+GJq1Jy/1evuVeA9rv5lubPAKWpEIsYEkqxALWogbNPR7P1744lyk9zTlgLWrQXObxfHedc5nS0zwClqRCLGBJKsQClqRCLGBJKsQClqRCLGBJKsQClqRCLGBJKsQClqRCLGBJKsQClqRCLGA1apgX4xmnC/sM+7mO074bZY1djCciTgJuBU4DJoAbgP8J3AbMAfuAqzJztqlMat5yL1S+HON0YZ9h7jcYr303ypo8Ar4UeDwztwEXAb8H3ATsrNatAy5uMI8kFdXk5Sj/DLi9b/kosBW4p1reDVwA3LnYIDMzM3S73RX/8E6nw9TU4RU/brDJIY719Hizs7NDGneY+Z491vHlHG62hV4L09PTq3qdDMtyX2/L35fDf80td/+U3pfLNco5F7qGdmMFnJlPAETEZnpFvBP4YGbOVZscArYsNc7ExMSqLwi+2mvY1j3WsfGO5zq7g8YblvljHW/OYWZb6LXQ7XaP68Lxw7Cc57mSfTns19xy988o7MvlaEvOfo2+CRcRLwD2AH+cmX8K9M/3bgYONJlHkkpqrIAj4nnAp4FrMvPWavWDEbGjun0RsLepPJJUWpNzwNcBpwDvjYj3Vut+Abg5IjYAXZ45RyxJa1qTc8C/QK9w5zu3qQySNEo8EUOSCrGAJakQC1iSCrGAJakQC1iqjNMFalbyXJc6uWGc9tuwNfkxNGmkjdOFglbyXJc6W2/Un+so8whYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtYkgqxgCWpEAtY0nEZ9rUgxunaEl4LQtJxGeY1NGC8ri3hEbBaa7EjpbZ9PbnGk0fAaq3FjryWuoLXION05KXR4BGwJBViAUtSIRawpJGy2k9BDJr3H/VPVDgHLGmkrPZTFYPm/Ud9Xt8jYEkqxAKWtGaN+kkiTkFIWrNG/SQRj4AlqZDiR8ARcQLw+8ArgBng8sx8pGwqSarfKBwBvwHYmJn/Cvj3wG+WjSNJzRiFAn4V8JcAmfk3wI+WjSNJzVg3NzdXNEBE3ALckZm7q+WvAT+YmUcHbf/AAw88Bvx9gxEl6Xj9w9atW18zf2XxOWDg28DmvuUTFipfgK1bt35//ZEkqX6jMAVxH/BagIg4C/jbsnEkqRmjcAR8J/DqiPgCsA54W+E8ktSI4nPAkjSuRmEKQpLGkgUsSYVYwJJUyCi8CTc0S53WHBE/AfwKcBS4NTN3Fcp5EnArcBowAdyQmZ/su/+XgHcAj1WrrszMLJDzQeBgtfh3mfm2vvtGZV++FXhrtbgROB04NTMPVPcX3ZcRcSbwgczcEREvAW4D5oB9wFWZOdu3bbHT8uflPB34XeCpKsdlmfnNedsv+NpoKOMrgU8BX6nu/oPM/K9927biEgdrqoDpO625+kjbbwIXw3dL77eAfwkcBu6LiE9l5jcK5LwUeDwz3xIR3wc8CHyy7/5X0nvRP1AgGwARsREgM3cMuG9k9mVm3kav1IiI/0zvH4MDfZsU25cRcTXwFnr7COAmYGdmfi4iPkTvtXln30PewAKv34Zz/g7wzsx8KCKuBK4Bfqlv+wVfGw1mfCVwU2YudOmCN1BgX67UWpuCWOy05g7wSGZ+KzOPAPcC25qPCMCfAe/tW55/4slW4NqIuDcirm0u1jO8AtgUEZ+OiM9WL+JjRmlfAhARPwr8cGZ+eN5dJfflo8Al87LcU93eDZw/b/tSp+XPz/mmzHyour0emJ63/WKvjaYybgVeFxGfj4iPRMTmedu34hIHa62AT+bp/xYBPBUR6xe47xCwpalg/TLzicw8VL1obgd2ztvk48DPAT8GvCoiXt90RmAK+CBwYZXlT0ZxX/a5DviPA9YX25eZeQfwnb5V6zLz2Oc+B+2zxV6/tZmfMzO/DhARZwM/T+9/O/0We200khG4H/jlzNwOfBV437yHFNmXK7XWCnix05rn37cZONBQrmeJiBcAe4A/zsw/7Vu/DvjtzPyH6ujyz4F/USDiw8DHMnMuMx8GHgd+oLpv1PblPwF+KDP3zFs/KvvymNm+24P22YpOy69TRLwR+BDwusx8bN7di702mnJn37TSnTz79zoy+3Ixa62AFzutuQu8NCK+NyI2ANuBv24+IkTE84BPA9dk5q3z7j4Z2BcR31MVyI8BJeaC3051adCIeH6V6+vVfSOzLyvbgb8asH5U9uUxD0bEjur2RcDeefePxGn5EXEpvSPfHZn51QGbLPbaaMrdEXFGdfvHefbvdST25VJG7pD8OD3rtOaIeDPwPZn54eod8bvp/cNza2b+n0I5rwNOAd4bEcfmgncBk1XO6+gdHc8An8nMvyiQ8SPAbRFxL7137d8O/FREjNq+BAh6/w3tLTzzdz4K+/KY9wC7qn+0uvSmn4iIj9Kbhip+Wn5EnAjcDHwN+EREANyTme/ry/ms10aBo8t/C/xeRBwBvgH8bJV/ZPblcngqsiQVstamICSpNSxgSSrEApakQixgSSrEApakQtbax9CkFYmIa4B3Ay/OzOmIeBnwYXofXfof9K6J8FTBiFrDPALWuPtpeqcrv6la/jXgusw8B9gE/GSpYFr7LGCNreqstEfpnXJ7VbX6X2fm56uTJU4FvrnAw6XjZgFrnF0O3FJdH3gmIs7MzKci4kXAl4DnAI1fh1njwzPhNJYi4hR6R79fpHeRnH8KPJSZb+nb5nJgW2b+TJmUWus8Ata4uhT4SGZekJmvAc4ELoiI3RHx0mqbQzzzCmbSUPkpCI2ry+l9wwIAmTkVEXcA/5vehWaO0Lvu7eWF8mkMOAUhSYU4BSFJhVjAklSIBSxJhVjAklSIBSxJhVjAklSIBSxJhfx/zLnp6VzzNXEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in credit_df_clipped.columns:\n",
    "    if column not in ignore_columns:\n",
    "        sns.displot(data=credit_df_clipped,x=column,kde=False,color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e034ac2",
   "metadata": {},
   "source": [
    "## Training and Testing Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff63b42a",
   "metadata": {},
   "source": [
    "**Split the data into features and target variables.**\n",
    "\n",
    "Let's separate the feature data (`X`) from the target variable (`y`) using standard nomenclature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f32e3ae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = credit_df_clipped.drop('A16',axis=1)\n",
    "y = credit_df_clipped['A16']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8055ce5c",
   "metadata": {},
   "source": [
    "Verify the columns in `X` and `y` by inspecting their headers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "530db64a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1     A2     A3   A4   A5    A6   A7    A8   A9  A10  A11  A12  A13  \\\n",
       "0  1.0  30.83  0.000  1.0  0.0  12.0  7.0  1.25  1.0  1.0  1.0  0.0  0.0   \n",
       "1  0.0  58.67  4.460  1.0  0.0  10.0  3.0  3.04  1.0  1.0  6.0  0.0  0.0   \n",
       "2  0.0  24.50  0.500  1.0  0.0  10.0  3.0  1.50  1.0  0.0  0.0  0.0  0.0   \n",
       "3  1.0  27.83  1.540  1.0  0.0  12.0  7.0  3.75  1.0  1.0  5.0  1.0  0.0   \n",
       "4  1.0  20.17  5.625  1.0  0.0  12.0  7.0  1.71  1.0  0.0  0.0  0.0  2.0   \n",
       "\n",
       "     A14    A15  \n",
       "0  202.0    0.0  \n",
       "1   43.0  560.0  \n",
       "2  280.0  824.0  \n",
       "3  100.0    3.0  \n",
       "4  120.0    0.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ac1717d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    +\n",
       "1    +\n",
       "2    +\n",
       "3    +\n",
       "4    +\n",
       "Name: A16, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2272b491",
   "metadata": {},
   "source": [
    "**Split the data into the training and testing sets.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d856a7e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54be9188",
   "metadata": {},
   "source": [
    "**Standardization**\n",
    "\n",
    "With the data being split into training and testing data sets, we will standardize the training set by removing the mean and normalizing the variance from each numerical feature such that the mean is zero and the variance is one. Similar to removing outliers, standardization only applies to numerical features so we will use `ColumnTransformer()`, which allows us to specify the numerical features of `X_train` that we want to be standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "36c42f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "ct = ColumnTransformer([('ctname', StandardScaler(), X.drop(categorical_columns,axis=1).columns)], remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bf3a54b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = ct.fit_transform(X_train)\n",
    "X_test_scaled  = ct.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2133de30",
   "metadata": {},
   "source": [
    "Visualize the standardized data using histograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0f2cff87",
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xOxPIBEBm3gY8s8xQU3MFrJoLGpqvzHwqM/dHxAZgJ72t30WNzNcamaDZ19bBiPgivZ//nX1DTb+2VsoFY56vI7IIMvPGzHxN/z96/6GLp7LYAOxdcrMDwF9m5oHM3A98k96+8rqsdmqNpWPL5Z1opohYB3wqMx+vfjv6GvC6CWRaS1Nztaqm5ysiXgF8C7g5M2/pG2psvlbK1PRcAWTme4BXA3MRcWy1uPHX1nK56pivko4RLJ7K4gHgDOC+JeOvBm6NiJPpFeTpwBdrzvNO4G+XObVGBzgxIn4OeAr4DeCTNWYZJNNxwO6ImKW3v/TN9A7+Na2puVpLY/MVES8D7gH+IDO/sWS4kflaI1OTc3Uu8IuZ+TF6vww+S+/gLDT42loj19jnq6QiuB74YkTcDzwNnA3PHX3fk5l3RMTfAN+ht6n/pcz89xrzPO/UGhFxNvCzmfn5Ktfd9EppR2b+V41ZBs10Bb3f6LrANzLzHyaQaVlTMFeD5Gpqvq4AjgeujojF/fJzwLENztdamZqaq78DvhAR3wbWA5cAZ0VE06+ttXKNdb48xYQkFe6IPEYgSRqcRSBJhbMIJKlwFoEkFc4ikKTCWQTSCxARH4mI/46ImSXLz46If2kqlzQMi0B6YX6X3tf937W4ICJ+ld6ZIdc1lEkaikUgjag6K+TDwOeAC6tlLwE+Tu8LQNJhwSKQRvd+4IbqPPDd6u8S3Ah8iP//mxbS1PObxdIIIuJ4elsD/0rvPDAvB34M/BK9PxYyA/wKvdMSXNJQTGkgJZ1rSBqnc4AbM/OPACLixcAPgddk5mMRsQm41RLQ4cBdQ9Jo3g/cvHglMw8AtwHnN5ZIGpG7hiSpcG4RSFLhLAJJKpxFIEmFswgkqXAWgSQVziKQpMJZBJJUuP8DslaxYiOaf3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X5Yxfa81bc2gt3QlcDNwVEefX+ndl5osj7NM4+gLwYER8BZgEbs7MzlKNvWWDJBXGpR5JKozBL0mFMfglqTAGvyQVxuCXpML4c05pQPWFMTcDr8/MTkS8BfgS8G91k7/IzM+Nqn/SoAx+aXC/DvwN8GvAIeAtwIHM9EZ0WlcMfmkA9X2ZngHuBf6a+eCfmd8Ve5if9d+cmadH1UdpUK7xS4O5CbgvMxPo1vfR/wbwe5l5BfAs8Puj7KA0KK/clfqIiIuZn+1/k/l7oLwOeAr4ncw8Vbd5I/BnmfnOEXVTGpgzfqm/a4H7M/PKzHw3cBlwJXA8It5Wt3kncHxUHZSWwzV+qb+bgOvOb2RmOyIeAv4d+GREvAT8J///IAxprLnUI0mFcalHkgpj8EtSYQx+SSqMwS9JhTH4JakwBr8kFcbgl6TC/C9HBQcYiN62NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8UluFSUfKIJBGFBE7GDw871bg8uHiM4HXRcQ/MAiJh1opTjoKBoE0uouB3ZmZQH/4+wrbgB9m5ruA7wJXt1ifdEQMAmkEEXEK8H7gyoj4KrAV+DDwLDD/ozL34uPGtQ44WCyN5jzgs5n5hwAR8Wrg28BeBgFxB/B24N9bq1A6Ql4RSKO5mMHJHoDMPAjcDTwCXDB8uOD7gD9rpzzpyPnQOUkqzisCSSrOIJCk4gwCSSrOIJCk4gwCSSrOIJCk4gwCSSru/wHBN0ja3B7m9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WFoEkFWcRSFJxFoEkFWcRSFJxVU4flRZVRNwGfAH4QGZOzth+P5CZ+VD/+o3AZ+ktDXJ3Zo7SchUqwhGBNJw/Br4JfBIgIs6LiL30Viylv+0C4PP0Fq+7GvhyRHRayCqdkkUgDaj/rdeX6S0jsLW/+VeAvwAem3HTDwPPZuZUZr4FvAR8qLmk0vxYBNLgbgAe7q9DNBURl2TmK5n5Tyfc7sTFDpf1AmqqyyKQBhAR59Bbt+qmiPgevQ/2z81y8xMXOxy5BdRUg5PF0mCuA76emX8GEBG/DLwSEeedZKXJ54B7+qt/doBxer+4JS0rjgikwdzAjHmAzHwHeBy48cQb9ldbfYDe6plPA1+ceYaRtFy46JwkFeeIQJKKswgkqTiLQJKKswgkqTiLQJKKswgkqTiLQJKK+18urEn77QCBOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OiHj36FNKK2MRSMP5JeBPgV8EiIhjI+JB+lezZLDsh4BHgJ9qJaG0QhaBtEqDK1c+T/9icNsGi98E/DZw74JN3wRcSv/yG9LYsgik1bsEuGtwjZrZiDglM1/IzL9fuFFm/mNm9tqJKK2cRSCtQkQcA7yH/nVqPg9sAj7YbirpjbEIpNW5ALg7M8/KzHcDpwBnRcTYXrlSWo5FIK3OJSw4D5CZe4H76J8LkDrJi85JUnGOCCSpOItAkoqzCCSpOItAkoqzCCSpOItAkoqzCCSpuP8FtZdJGNEBe50AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0mMz8LPBW4L0R8b7xVykdGm8WS4fmQuBLmflbABHxRuD5iDguM78z98CICOBG4MP0phTuAiv2v8Jp9XJEIB2aS/n/9wH2AvcBl80/sH/p6J/pzQ75GPB4Zj7UUJ3SsjnpnCQV54hAkoozCCSpOINAkoozCCSpOINAkoozCCSpOINAkor7XzsXbgrNGLP4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for i in np.arange(X_train_scaled.shape[1]):\n",
    "    column = X.columns[i]\n",
    "    fig = plt.figure()\n",
    "    plt.hist(X_train_scaled[:,i])\n",
    "    plt.xlabel(column)\n",
    "    plt.ylabel('N')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcdcff9",
   "metadata": {},
   "source": [
    "## Logistic Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef06a02a",
   "metadata": {},
   "source": [
    "**Train the logistic regression model using the standardized training set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef995d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logm = LogisticRegression()\n",
    "logm.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617a5152",
   "metadata": {},
   "source": [
    "**Predict values of the logistic regression model using the testing set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "346b3614",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predictions = logm.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec1b5cb2",
   "metadata": {},
   "source": [
    "**Check the confusion matrix and classification report for the logistic regression model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "65e22517",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[44  7]\n",
      " [15 92]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           +       0.75      0.86      0.80        51\n",
      "           -       0.93      0.86      0.89       107\n",
      "\n",
      "    accuracy                           0.86       158\n",
      "   macro avg       0.84      0.86      0.85       158\n",
      "weighted avg       0.87      0.86      0.86       158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test,y_predictions))\n",
    "print('\\nClassification report:')\n",
    "print(classification_report(y_test,y_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8208fb7",
   "metadata": {},
   "source": [
    "## KNN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e09aa87e",
   "metadata": {},
   "source": [
    "**Train the KNN model using the standardized training set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ffa6b706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train_scaled,y_train)\n",
    "#knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b66da9",
   "metadata": {},
   "source": [
    "**Predict values of the KNN model using the testing set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "706caccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = knn.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03cd715d",
   "metadata": {},
   "source": [
    "**Check the confusion matrix and classification report for the KNN model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "21149734",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[35 16]\n",
      " [18 89]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           +       0.66      0.69      0.67        51\n",
      "           -       0.85      0.83      0.84       107\n",
      "\n",
      "    accuracy                           0.78       158\n",
      "   macro avg       0.75      0.76      0.76       158\n",
      "weighted avg       0.79      0.78      0.79       158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print('\\nClassification report:')\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f706d750",
   "metadata": {},
   "source": [
    "**Choosing a K Value**\n",
    "\n",
    "We will use the elbow method to choose the value of K that optimizes our model. We will create a for loop that trains KNN models with a range of k values and analyze the error rate of our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "85308218",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rate = []\n",
    "\n",
    "for k in range(1,40):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled,y_train)\n",
    "    #knn.fit(X_train,y_train)\n",
    "    y_predict = knn.predict(X_test_scaled)\n",
    "    error_rate.append(np.mean(y_predict!=y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c257056a",
   "metadata": {},
   "source": [
    "Plot the error rate vs. K for all KNN models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7455bcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40),error_rate,ls='--',color='blue',marker='o',markerfacecolor='red',markersize=10)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.title('Error Rate vs. K Value')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5395b0e",
   "metadata": {},
   "source": [
    "Looking at the plot above, we can see that the value of k=10 is a resonable value to choose that optimizes the error rate of the KNN model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b67c63",
   "metadata": {},
   "source": [
    "**Re-train the KNN model using the optimized K value.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8b206c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "knn.fit(X_train_scaled,y_train)\n",
    "#knn.fit(X_train,y_train)\n",
    "y_predict = knn.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd9728a6",
   "metadata": {},
   "source": [
    "**Check the confusion matrix and classification report for the optimized KNN model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4a6194fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[30 21]\n",
      " [ 8 99]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           +       0.79      0.59      0.67        51\n",
      "           -       0.82      0.93      0.87       107\n",
      "\n",
      "    accuracy                           0.82       158\n",
      "   macro avg       0.81      0.76      0.77       158\n",
      "weighted avg       0.81      0.82      0.81       158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print('\\nClassification report:')\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e06f1f",
   "metadata": {},
   "source": [
    "## Random Forest Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a653f54",
   "metadata": {},
   "source": [
    "**Train the random forest model using the standardized training set.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ec36342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=1)\n",
    "rf.fit(X_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b37c7d",
   "metadata": {},
   "source": [
    "**Predict values using the testing data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "764b600a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = rf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e688176d",
   "metadata": {},
   "source": [
    "**Check the confusion matrix and classification report for the random forest model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2932a597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[43  8]\n",
      " [16 91]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           +       0.73      0.84      0.78        51\n",
      "           -       0.92      0.85      0.88       107\n",
      "\n",
      "    accuracy                           0.85       158\n",
      "   macro avg       0.82      0.85      0.83       158\n",
      "weighted avg       0.86      0.85      0.85       158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print('\\nClassification report:')\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c64532f",
   "metadata": {},
   "source": [
    "**Choosing n_values.**\n",
    "\n",
    "We will create a for loop that trains random forest models with a range of n_values and analyze the accuracy score of our models to find an optimal value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6cf1965c",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_values        = np.arange(1,100)\n",
    "accuracy_scores = []\n",
    "\n",
    "for n in n_values:\n",
    "    rf = RandomForestClassifier(n_estimators=n)\n",
    "    rf.fit(X_train_scaled,y_train)\n",
    "    y_predictions_rf = rf.predict(X_test_scaled)\n",
    "    accuracy_scores.append(accuracy_score(y_test,y_predictions_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d242c634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8UK8Xj8vJSJ+f6Sq1WIurCWZXYkX1GgDDwdbVphQGiAN+V9HnSs2HYgz3Wm9s6y/im6sjy2az+Vq0rbpIYHJ2hf27AmVX+h4aDXJ6fpVYsrXNi2PJNHPhONkszC53Zt3dAyfn8sXn7ax5+uLjsyTTWbrczvybezXHcwHORtZz39EZPLnt0ONbVNtkj+K6csjaVfC6XfQHPC0JcAtbstSqc8xms0yeWSl7zaglf2LYwFXTk7Mhdvd0sSdYnGU7NBrkzHK04Qu27DYbz8xFWv58225tyZCZppkB3l/y6ZMFt38C+ETJ7Qng3nasR4ozZO0Mauzu8eMlAZnD4eBdt47zX/7+BFNnQ0yMNPakt1te9Po89HldLK525gUJ9u/XDjraMfh7Phynx+sm0GU9ba1eZJVfCO2zx9Kz0XzA2IIrQdczZF5Wo+6izxXKZrPFGbKChpJ2G4yNPn48ZRUTTy9GuX6sf8P31ajSomTbodEg6UyWk+fC3DjeunUUXvwwvbTG5bu7W3bfrXLk6DR7gl4isVTbrjDOZrPcd3SaG8b76fG66vZ922iGzM72vf7gXiZnV7YsC2OP4nI517fdh3pa00Kn8GeYnF3h9ipbi+dDcRZWE00FZPVODIvXEeLgaF9ZaUH+opizIV7+kt1178c++Upnsjx9Prwlz/ut0nn5b2mLUDRF0O8m6Pe0dctyZmkNpwNG+svT/HffuI8ul5P7js40fH92y4ser5ter7Oj2170BQozZK0PyObC8fyWKFhbf9UyQ+s9jYpfXHu9bnweZ0vWZ2cBh3u9BDwO634rvCEvryVJprP5tdvZ0832Iit8E2imW/hGhWJJXlhYKwtyYT3wbfUbeOHP1YlXpp4PxfiWOcc7bx5juI3zF5+cWeHkuTD3Hh7j0Ggf5rkwySpX9mWzWU6ctf7+mw0Qvzl1nsXVBPfkHmcrGv7a662UzW5FgDs5G2K0z8dwr7dmhix/ErevduF/qbEBf93nXzyV5pm5cJWTmeaeOzNLa/nA9WJryKyA7BKQSGWIJtO5LUt3Wzv1Ty9FGenz46nQzHCgu4vXHdzD/Y/NEE81lmpez5C5CfpcHbllGU+liSUzBH1uBgIePC5H2zJkuwsCsrEBP+dD8Ypp+8nZEPv6/fQHiuuq7EvqW7G+uXAct9PBQKBr/X4rvCHbj2UHq/4uF7t7ujY9aaDwTWArtiynqtSPgXUsgj53y98gCoPOTmym+/lHZ8hk4d7D41WPfyscOTqN1+3krhtGOTQaJJHOcGouUvFr7dFWu7q7uBBJkMk0Pt3jyNFp9gZ9vOLqIQ7tCzKzFGVlrb1Z+XORVH4UV6GhntY8TydnVzi0r49Do8Gaf5+TsyEcDhrevbCNDQTqPpefOR8hmc5WDMgGu7sY7fM1/NyZXoxyYG8vvT73RVdHpoDsEmDXjAX9HoI+DyvRZNtGEE0vruVrhCq599ZxltaSfHNqrqH7C9sZMp+bPq+zIwMye2xSn99jBSY93rZkyOZLM2S5TFOl2qLJ2eq1IMO9vpasbz4cZ3ePF2fubHW4t3KGxH6s4aJgMsDM8iYzZLkA5aqh7i0ZI3W8StYRrED30Ghf3dqmZs0srtHldlrb0x2WIbO2EWd46RWDXL67u21TKqKJNF9+fJY3XzdC0OcpyKhUfgO336Rfdc0Q6UyWxQZfM86txPj20/P8+C1juJyO9cc52943/VML1u/s2n2lGTLrebqZ1+q1RIpnL6xyaDTIodE+Ts1Xr7uanF3hil3d9DR5MdL4oJ/z4VjNk+xqJRS2g6N9HG/wuTO9tMb4QICDI7UDzJ1IAdklwN6itIv605ksa4n2FENOL62VFfQXuuMluxnp8/HZRxor7s9nyLweeju0hqww4AValoEqZQVk61vB9pWnpW/Uq/EUz11YrfriN9zCDFnhZfBDPZUHItvbLoVXVI4P1j+rrmd6Mcruni6u2dO7JRmyydkVhnq9Rceg0KHRICfPhVs6PWF6aY2xfj/7dwU6LkP2yPNLPHdhlXtzw6bblSH7+uQ5wvFU/nGu2N2N3+Oq+gZuj7a642qrHqnRNdnZvnsOW211tqrh7+nFRNEoLttwr5d4KpM/Kd2IqbNhslkrECqsc6yktOFxo8YHAmSzcKbGCcPkbIger5v9g5XfGw6NBnn2wiprido/ayZjXcgzPujn0GgfU2etPmgXCwVklwC7M3/Q58kHDe240jKWTHM+FC8r6C/kcjr48VvG+M4z8w1dNRYpzJD5XCyvJTpuwHio4PcLMNTra/lVlmuJFJF4qigAyl+tWPJGPXU2lHsRrvziOtTrbcn6SjN2w8EqW5bh4i1LgPEBP7PL0U29mM4sW8G/3fm/ma2pjThRpaDfdmhfkHgqw+n51l0cPr0YZWwwwPhAYEvq5Jpx5Og0PV43b75uL2BlSCPxVN031WZ99pFpLhsM8CNXWP30XE4HEyPVh1Lbo63sE5ZG6tqy2SxHjk5z25WD7N9lXTixu8fLnqC37VmY04vxolFctlbUo57IZ6aCNWu1VtaSzCxFq57E1VLtxLDQ5GyIiZHefDa91KHRINmsFUDWciHX/mV8MMCh0SCxZIZn5ytvXe9ECsguAXbNWNDvzgcN7RifZAdYtbYsAe65ZZxsFr7waP3i/nAucOz2ugh6naQy2U2dMbZDPuAtzJC1OFNQadtvT68Pj8tRlh3KF/Tvq7Zl6SUUS236kvG5cDzfRsO+35Vosux+58JxAl2uoq2Q8cEAqUyWsysbz5JNL0YZHwwwPuAnkcpwoQ1ZSVssmeaZuUjtgKwNhf0zS2uMD/gZHwxwIZIg2qbMdrPCsST/8ORZ7rphJH/VbzsuaHlxYY0fPLvAPbeMFb2ZWwO4QxWDcDvTYz9XGnku/vC5RV5YWMtn4Qofp9GttI06vZiomJlqxRXRx8+EGAh4GOnzMT7oz9VdlQeY9rZsM1dY2uzX+2onDOlMlqmzoZrBnn0hwYk6zx37tW58IJB/fbuYti0VkF0CSrcsrc+1Pqixz5DGq6SlbZftCnD7lbs4cnSmblYjHE/R5XbidbvyI1k67UpLO+Dt81tvTMO9XhbXElWvAtuI0sJ4AKfTwb5+f9nVd5OzKwx2d7G3SkPLVszJS6UzLKzGGeop2LLM3W9pYDRfsrUJVhE8sOFty3Qmy+xylPEB//pVm23MID19Pkw6k8037qzkyt3deN3Olr1BROIpltaSjA8G8r+vTsmS/cOTZ4km00UBTD4AamFg/Llj0zgc8OOHi6dzHBoNEomneLEkOzwXjjEXjnNotK+pgObI0Wl6vW7edO1I0eevHQ1yej7StkB4LhxjKZquGKw0E1BWM3l2hUO5VhNWnWPluiu7Me1GArJh+8SwynP5+YVV1hLpmvc92uejP+Cp+9yxH2N80M9VQz1WT7qLqLBfAdkloHjL0l30uVayt87sN49a7r11jBcX1/jhc7Xnl0ViKXpzmZU+Xy4ga/NVT81aKduy9JLNwkKkdYFjPkNW0lRxfDBQ1pTR7pVVbZSQXQO1mRYFi6sJslkYKgj6qt3vXDhWlNmD4l5kG3F2JUoqk81tWW4uuGvEehuR6gGZ2+VkYiTYsjeIwufTVgSdzThydJqrh3uKeq7Zf5utypClM1k+d2yGV149xEhf8WtKtcL+wnYvgS43PV533fWEY0m++tRZ7rpxFH9X8bbhwdE+Mlmre3872Ou9tmKGLNfTcIPP02Q6w9PnirO6h0b7OHk2VFbnODm7wt6gj1093tK7qcuVOzGs9rd5vEqH/kIOh4NrG2gzYj8n9vUH8LicTOztvagyZO0anSQdpLDoPJrbTmpHL7LppTW6XE72VCl6LvSma0f4z1+a5MjR6aqNCsHKEvT4rD/ToNc6f2hFhiwSTzG9uNb0Jd6VhEq2LAvPbPf2NTZ2JZvN8uiLy9x8WX/FQGre7tLfU5ppCvD12XP5jxOpDE+fD/Mzd1xR9bEazZCdmA1x5VB3WW0LFHTpr5AhK73f+XAcY29xwfJovx+HY+Pd+meW1s+UG+lrls1meWx6mZvGK/9+C03OrvDCQvF9fePEeXp97rrb8YdGg3z5iVm++tTZos/7PS5eec1QUePPeuyfZ3wgkO/rtxVXk9Zzai7Moy8u8+tvnij6Xdp/C/Mb2GKbXlzjqZKtwecurDK7EuPX33Kw7Ouv2duD2+lgcnaFt1y/ntUqHW3VyAU2f//EWWLJTNl2JaxnjCZnQ9xUZVTT9565sOGaXHsUVKUty6DPTZfbWfN5+sT0MgdHgxXbDD1zPkIinSnqK3Zo1KpzfPbCatFFBNUaHjeq0omh7cRsiC6Xk6v39FS8vXBt/+fB50mmMxV/HrCe97t7vPnA+eBoH1996izZbLbi83ouFONohbFMe/t8NUdvbRcFZJeAUNTa9vN5XPksTjuK+mcWo+wb8Fct3Czk87h446G9fH3yXM2vi8RS+dqjYG7LcrEFAdl//8cpPvvINA9+8DVVr5prVCiWzP9+Yf1qQmurpLEi2S8/McsvfeZx7nv/7UXDwG2FPb8KjQ/6WVxNsBpP0e118/T5MMl07a219YCx+htnKJbkx/7ke/yH1xn83J1Xld2e79JfUkNmr7V07a+4unjgdJfbyUjQ19DIlUoKgxWfx8VQr7dm49RvmfO851OP8Bf/z2Fee3BP1a8Lx5Lc84kfVLwK+VXXDNUN5g5fPsDf/vBFfv5vHy277dM/81Je1cTg7cISgIGAB5/H2RFXWv7jU+dwOODtN+8r+vxAoAu309F05jWbzfKeTz1Ssa/YUK+X1x4cLvu81+3i6j29+VYkttLRVkO9XubrZMi+ceIcV+zu5oax8ufM2ICfPn/1rbQfnF7gp/7yhzXvv57L+z30+srnSzocDoZ7K1+5DNZz4O6PPcivvMHg5+98Sdntk7PldWF2lur4mZV8QBZNpDk9H+FN142U3UejxgYC/NNs5dfyh55dYGKkt2qQZTuY6y/3zPlI1as9p5eK2yodGg3ydw+/mLvysrxU5gP3PcF3n7lQ9nmX08GxD7+2rE/jdlNAdgmwx/qA1WDV/lyrzSytNbRdaRsfDBCKpWoOTQ7HCwIyXy5DtsleZNFEmi89Zs3Fu//RM/zbV5UHHM0IlQzsHaoSmNTymYetNiBPTC9XDMjsOqzSYHcsv/UXxdi7fuVZrbPdXT1enI7aGbLpxTWS6SyPT1ce+jtXIWM32N2Fw2HNrbTFkmnCsVRZDZm99o1uwU0vRXE4rEybdV/Vt0wAHpteBuCzR6drBmRfefIsa4k0H//Jm/NzBW31smNgTaO4fqyfVHq9NnIlmuTeP/0Bz19YbSogm1lao7vLxUDA6m83NhDoiG79T52x+lXtLsnWOp0OdldpfVLLoy8uc2ouwq+98QCvOVAcfA31evG6yzO0YP2Nf8ucK8qOlGZ6hnvrXyX54uIaxp7eisG2w+Hg4EiwarH5kaPT9PrcfOZ9t+F2bqwCKHT+haq31ert9uTMCtksHHlkmp971VVl65+cDRHocnHFrvVxW1cNrdc5vuNm63Mnz4XI1LgquxFjA34WCk4Mbea5ME/MrPAbby3PcpYqvCimVkB20/hAwfesZzBLA7KZpTW+d+oCP/PyK3jXrevZz8enl/i1zz/FidkQL2tgVNNWUkB2CbAGi1uH2u1y0uN1t62o/41NjN0Y6LbOTpbXEkU9qgpFYilGc9s13R4nLqdj0wHZ1ybPEo6n2N3TxWePTvO+V15ZN/NRSyiaIuhbfyrt7rF+rkZrP15YWOUHzy4A1a8YKu35ZRvPF8evYeztZXJ2he4uF5fvqj7z0OV0MNhd+43T3hqrup5Q+UUGbpeTXd3FW0SVWl7Yxgb9/OD0QtU11DKzuMZI0JcP5McHAjxWJXiE9au3Hjg5l6tpq/z39tlHprlmTw9vvHbvhv4mHA4HV5UEctlsdkPZLfsqUnsd43WCzq1ibd/1V7ytWuuTWu47Ok2gy8W/vn1/U01JD40G+dyxGebCcfYEffnRVoVbj/VavNgD6ksDwdLH+auHXijbSgvlas/sMUsbNbVUOeAEa/3PXajcRsXOgD2/sMbDzy3yI1cWl36cmLVmBheexLldTg6U1DlWG7PWDDsYsk8MbfcdncbjcnD3jaN178PuLzc5G+KeCren0hlml2Pcdf36idGBvUGcDuv5/cZr9xZ9/eePnQHgZ+64vKg35q6eLuApJjswIFNR/yUgFE3m65vAqk1odYZsNZ5icTXRVIZsMJcurlWkHynIkDkcDgYCnk03h/3sI9NcvivAr77hAM/Or/Loi9XfyBuxUvL79bpd9Ac8DV+u/rljMzgdVqfuagXhpXMsbes9gKw36skKL8KV1OuqbhfbVxsdMx+J0+f3lNWXDfcWTynIN4WtGEwGOBeq3eG7+vqiRS+y44N+ZpdjVZuyTs6GuH6sj3TGyopW8vT5MI9PL3Pv4fFNBeil7OxWs8FUacbZGlGzvQHZ8lqCM8vV+1VVaw5czWo8xd8/MctbrhtpukN8aZuRSqOthnt9rCbSrFZplWMPqK91Zfi1+/pIpDKcLul39eXHZ4mnKteetUq16Rdg/U1fudvqrH+kZD5wJpOtOq3j0GiQE7OhfD/HydkQfX4P+/obf+0uVXhiaEukMnzhsTO8dmJPQxcL1Osvdy4UI53JFh0rf5eLq4Z6yk4cM5ks9x2b5uVX7S5rVL67x8veoK8jr85UQHYJCBVsWYJVfN7qGrLC/jCNGghYa6pVE1ZY1G99TxfLm8iQvbCwykPPLnLP4XHecv0IgS4XRx5pfNh5JVYGsrgGpNExMvkrya4Z4jXGMKfmKl9iX6l1BMCu7i78HhfTi1Zj1KkKQ4orsQYX196ytFUaHTMXqrye0iLqWhmy8UGrw/fs8gaKwJfWGCvYQhwfCJDOZDm7Un5fi6sJzq7EeOv1IxzeP8CRo9MVmwvfd3Qat9PB22/aV3bbZo0P+JsqyM9ms0wvrpUFnaFYqi3lBo2qtyXebIbsq0+dZTWR5t5bmw9qJkasTIzdsqHSaKt6F7A08rqV3xY7U/ymf9/RaQ7s7eW6JodxN2Oo18vyWrLspCWbtQKum/cPcNcNo3z1qbP5no0ALyyusVql1cSh0SChWCq//W0Hbps5CSk9MQR44KQ1qL2ZY3vtvur95fItL0qOVaVWHj94doGZpWjVx64313O7KCC7BIRiqaKAIej3tPwqy/X+ME0EZN12hqxGQBZL0eNdX/tAoGtTRf12NuodN++j2+vmrdeP8JUnZ6ueQTeiNOCF2me2hb77zDxnV2Lce3i86iX2+Z5fFbbZHA4H44N+ZpbWeH5hNfciXP8NwspkVA+EZpai+T5mlc5Y5yOVM3blGTK7oW352iudVTcinkpzLhQremEu3DIpVThH797D45yukBVNpDJ84dHGz+abNT7YXIZsaS3JaiJd9HzabKuQVqi3vTXU62NhNd7w+Kj7js5w5e5uDu9v/oq3Xp+Hy3cF8muqNNqq2oUmtsK+VtVcOdSDz1PcX+7kuRBPzKy0PJtayl7/hZIWOnPhOBciCQ6NBrn38BjRZJqvPLl+ZW+t2ZGFmcVkOsPJc+FNbVfC+olh4fPvyNEZ9gZ9vPLqxusm7f5yL1R4TcgHzyXH6tp9fZwLxYr6Hx45Ok3Q5+b1VepFD7W5v9xGKSC7BFhbautZJnvAeCvN5M80m9iyrBOQxVNpEulM/kIEgIFuz4ZryAqzUXZfo3sPj7OaSPMPJW0KmrESLc+QNdqt/76jMwx2d/HaiT1FBaqF8j2/KgRAYBfHR9czBFU69BcaDnq5EElUbcw7vbTGdWN97A36KnYqnwvHqmbILkTi+fudC8VxOtaPddG6awRRtcwux8hmi4P/fKPZCsFKYRBRLSv6wMk5FlYT3HvrWNn3t8L4QIBwE9mtSs+nfBZiG1tfHJ9dYaSver+qfA++Bk6anp2P8PDzVrZ6o0HNoX19HM8FH5VGW9lXAVd7Ltq/5339tce9HdhbXE5w5JEZqzaqDdnUQtUyfIUB143j/Vyzp4cjR6cLbg/hcTkqtpo4sLcXl9PB8TMhTs9HSKQyXLvJLJ+1Le/Pn1ydW4nxLXOOd96yr6lWL7WmXcwsruF0UNaT7mDJ6+bKWpJ/PH6Ou2/aV7Flj/U91snvVJv6y22UArKLXDabLcvg9Pk9hGOtLeqfXozi97gqvvFW05/bsqzWV8weLF5YWzLY3bXhxrB2NupdBTUft+wf4Mqhbu472tiw81LZbJZQLFUU8AL5y9Vrzd1cXE3wTyfOcfeN++hyO6teYr+eZar8Jjg+4GdmcY3J2RXrRXi4t+LXFRrq8ZLOZFmsENxa22VRxgcCubq2UNntc6HqGbJUJpsPmufDcXb3eCu+KO8NVh79VI/9JlpYXzXa78fpoGIbjeNnVtjX76c/0FU1K3rf0Wn2BL1Nnc03Y6zJbKAddBVuWXZCt/56/aqa6S5/37EZXE4H77x540HNodEgM0tR5sIxnpmLlLV7sa8CrpYNtgbUe8sawlZ6nBNnrbqrRCrD/Y/N8LqDe5p6vduIfLPlkgsT7O3TiRHr6tB7D4/z2IvLPHPemgU5ORvi6uHeileo+jwurhrqZnJ2ZVMd+ktZWWDr7zY/qP2W5rair95j95crD5TsrH3pFfmHRoqDuC8/cYZEndq+aie/200B2UVuLZEmlcmWbFm6W79lmesP08yZrtftorvLVbVIPz9YvCAg6w90sbS6sQHjdjbqRyfW09j2i9kjzy+VFe02Yi2RJp3Jlm1ZDvV6SaQyNa9m/eJjZ0ims/msTLVL7OfrBWSDAcLxFD84vcA1e3qrthApZF/VWumNc2E1QTSZZmzAz8HRvrLUfjieIp7KVMmQFXfrnwvHyqYL2FxOB6P9/g1dfQjFGTKPy8lIn79io9nSzElpVvR8KMa/mHO88+Yx3HV6JW3U+pZqgwFZhe2ZPr+HXq972wr7o4k0z85HOFhjS7zRcUWpdIbPH5vh1cZQ1SusG2FnVL742BnSmWxZYGH3RqtVQ9ZIO5Nr9/URjqWYXozyzanzLK0luaeNxfy2fIYvUpohC3H5rkC+f9ndN+3D7XTk6yNPVCnot9ld8SdnQ/g9Lq7YXbtpayPsE8NsNst9R6f5kSsGuXx39au9K7H7y1UKlKy60fJMZl/Aw9iAP/89R47OcHAkWDPrZ5/81pududUUkF3kCrv02/r8HsLxFOk6cySbMb241lRBv22gu3qRvp3FKyzqHwx0bWjAeGk2qtA7brbS6vcdbb64f6VgTmih/FZDpPIbUzab5cjRaa4f6+PA3vUXzmv3BZk6Fy6ag5nv+VVjyxKsvkSNnunW6pU2U9CQ9NBosKyubX3QefkbaekW0XwkXjZdoND4QKDpbv3TS2t4XI6yWZ1jA/6ygGc1nuK5hdWiWprSrGj+bL6Nb7DjA81tN04vrtEfKG4Y6nA4GBts/vfVKlMN9KtqNEP27afnmQvHN/07t9fy2Uemcx8XvwnX6402vdTY69Z6RmWFzx6dbro2aqN25Xr7lY5/mjy7UtSBf3ePlx+dGOYLj57hzHI0X19WzcHRIHPhON9+eo4DI71NbStWY58YfuPEeZ6vMKi9UdZVoCtlJ9121r7694Q4MRviqTMr3Hu4dulBrbme20kB2UXOztAUZchyL/LhFl1paffyaaag3zYQ6Kq4bQbrGbJeb2ENWa53WZOtL+xs1LsqXHUz3Ovj1cYwn390puFiZFulgBcKAp4qXcKfOrPCyXPhshetQ6Pll9hX6vlVqLhzdWO1ILXeOPNd8Af9FVP7tTJ2pUXU1tZm9QzI+KC/6W7904trjPb7y95ErLYQxcHK1NkQ2ZIgojQret/RGV56xSBXNHk234y+gIden7vh7dnppcpvPuMVgs6t0ki/KrtZbL35kUeOTrO7p6tm/69G7O7xsifo5fT8atXRVtWu/LT7WjWSIbtmjxW0/PPUHN95ep4fv2WsJUFMPVZvv66igHJlLcn0YrTsOLzr1nEWVhP88TdPARQFbKXs14nT86st2a6E9RPD//mNp+nxunnzBjv/Xzsa5EIkUfQzx1NpzoerH6tDo308d2GVTz74HF0uZ0O1fYdGg5wsOfndbgrILnKlg69hPXhoVWH/SjRJJJ5qqgeZbaC7q24NWWGWIN8qo4nCfjsbdcNYX9lMRdu9h8eYD8f5ljnf8P0C+R5d5W0vcluCVeboHTk6jdft5K4bihsmVrrE3u75Va1jeWGdUfMZsvIM3nS+RivAvv7yurZaGbvCIuR0JsvCaqJqIGk/xsJqgrVE4xnPmWrByqCf8+HivmaTVS50eMdNVlb0Q194iucurLa1l5StmU77M1W20sYHraBzI1v2m3VidoX+QO1+VT6Piz6/p+b8yAuRON+cmuPtN+2rO06nEXZwcXCkcuuGauOH7L5WpX2qKvF5XFw93MMXHrOyqT9+S3su/qhkd09xQGm3oSk9+Xrl1UMM93o5cmwah4Oac3oLe7VtpqltIfv1/+S5MHfdUD6ovVF2IFl4MZF9IU+1Y2W/7n3+0Rlef2hPQyORKp38bjcFZBe59cHX61kmO3hoVbf+SgXIjRoMeKoW6edryHzlGbJmBozb2aha2yOvPjDM7h5v0ZVKjQjlgsaythfB6pmCWDLNlx6f5U3X7i0L5CpdYl+tgN7W5/cQ9LnrvggXCnS56fG6K65vejHKQMBDj9ddkNpff3Gcr9HKIn+/4RiLqwnSmWzVGjIoLFRvfBuu2oiu8QGrr9mZgvuanF1hsLurbHtzOOjj1cYQDz+3mDub31t6dy03PtBYvVwmky1rfGsbG/ATTaYbuoqx1eyC/np1oqWtT0rd/+gZUplsy4Jg+824arPaXm/Fua3V+lpVc3A0SDbLhmqjNmM46Ctaf7VecG6Xk3feMkY2C5fv6q7ZaLfP78kH/K3KkBXukNTbMqxlYiSIw1GclV+fXVs9QwaQzdLw35X9cx8/0znblhqd1KG++8w8nzs2w/96142b6nNTqcbJHvPTquaw1frDNMIu0q8kXKGof727f+NvSJ87NoPX7eRtNcZ3eFxO3nnzPv78u8/y6o9+q+i2LpeTP/qJmypm1yoFvGBts3rdzoqZgq8dP0c4lqrYtLDSJfbzkcpNWAuNDwaIJtNFc+TqGS5p4mqzsjPFWbdP/2B9dMx8OE6X21n2M9uGchmJSvMuK60b4PkLq/lhx7WsJVJciCQqbo+vN6eM5udQ1goi7j08zj9PzXHXDSMEutr/Ujg+GOC7z1womr1YyXwkTiKVqfjms16LtlY2S/LkuRC/85UTfOKnbqk4rNqWzWZ5318fqzjM+123jvP+CrNdk+kMJ8+G+Tcvv7zq/dqs41+9qP9zx2a46bJ+rm7geDfCfjO+tkq7F6s3WoJUOlN00Uazr1vXjvbxhUfPVCx7aKehHi+ncldPgvU3vSfoLTv+YP1Nf/xbpxsKsq4d7ePscqyh510j7BPDvX0+bhzv3/D99HjdXL6ru+g1cP1YVQ6erd9HF163i5c3OA5p/eR3ZUsznrUoIOtQ35ya40uPz/Kbdx3a1KXV+RqnwrYXgdZuWa7XHG0gQ9bdRTheecD4+pZlcad+qN3dv9TDzy1y25W7yrJYpd7z8itYWE2QSK3XFKQyGb761Dm+f/pCxYCsWlG/w+GwuuFXmKN35Og044N+brtiV9ltYAVAX358lkwmi9PpYC4c45bLajfO/OXXXtP0RRq7e73MV8hkzCxFOThSvKVhp/YP7LWKgYd6vFWDCrsHWz6TViNDNrE3SI/Xzdcnz/P6Q/WzVHYmrVKGrLQtRCKV4enzYf5/d1xZ8b5ec2CYn7/zKn7ipZfVfdxWGM9lty5Eam/j2s+nSleUFQadN5X8Tfz5d57jwVMLTM6GuO3Kyn9bAMtrSb5x4jzXj/UVzTw9eS7Enzxwip++/fKy7aZTcxES6UxjUyB6vRyrMo5sZS2JeT7Mr7zBqHs/jXrVNUP821ddWXVofGFvtD0FmVK7r9VogyOD7rphlLlwfMO1URs1HLROnOxA3uqsXzkbeMXubv7zWw9WnTVa6GdfeSUve8nuqr26NuLX3zJRNH91ow6OBnliejn/8fRiFI/LUXT8CjkcDn79LRP0+7saru2zRjV1VmG/ArIOZb+ZzSytbS4gi5YHNXZg0qrWFzNL0dzZUe2Ap5J8kX40UbYFFokncTsdeAsCtV6fu6kB47FkmmfmIvzoRP3i4b19Pj56zw1Fn8tmsxz8z1+vuqVmB7yVtgcqzfWbXlzj+6cX+A+vu6bqvMlDo3387Q9fZHppjcsGA8yH43VbA7yuyptRLcO93ooz4M4sRXn9ofX7szMPk2dCHNgbzK2nekBh32+tLv02f5eLu24Y4YuPzfJbbztYM7MDBQ1TKwQre+y+ZrmtqKfPh0mmy1sh2NwuJ7/6xgM1H6+Vxgo67dcKyPJXuVbZsrTvo1A4N+i68Pvr3f/P3/mSooHMPzi9wE/8+UN8bfIsb7+pOGPQzADqodyWZaVMoF3/tNlGpIX8XS4+9KaJqrcXXsBSFJAtRRnp8zdcxzbU6+WDb9q6vxfbcK+XZDrL8loSn8fFqbkIb6hx8vIzd1zR0P3efNkAN9c50WvWu25tzcnNodEg//DkWVbWkvQFPMwsrbGvwoU8hUr/Zht9nC89tn7yu91UQ9ah7JT/Zrtyr0ST9HjdRan6Vhf1T1ep6WnEQL45bPlaIjFrjmXhi7rTaQ0Yb7Q57NPnw7n+RBt7AyjtQF2q0u/XNtzrK7u6676jVsFtrRR5PgCaDRGOp4glMzW3/TZquNdXlsE7H46RSGeKgoErdvfg97jyb8pz4VjN9ZRmyOptt957eJxoMs0/FIx+qWa9XrH8783ldLCv35/f3qg3d3GrFWa3aslnyCr8jN1eN4PdXWWvC1958izRZLro+6vef5Wtuh+5YpDLBgMVZ7tOzq403K9quNdHPJWp2JpmO47JcJULWKaX1ti3wdetrVTYouZkvvVI++ZndoLSjv3TVWoqW/E44Xiq6ebU7aKArEPZb2ab/UMJxZL5mjFbd5cLl9PRuhqyDfYgg/WasEpbkOF4qmLmqVbdWalmzuyrGa/R+ykUTZVtV9pKB3jbo5tecfVQzW0S+xL7ydmV9Z5fNTJSGzXU62U1kS7qWF8p4HE5HRwY6c2/ONbPkPmseXQLViuCelsiN473c/VwD59t4IKK6cU1fB5n1YBwfDCQb6MxObtCd5eraFtuOzXarX86l0Gr9nur1PriyNFprh7uYaTPV/c1Yz3gK37OOp0O7j08xg+eXeDFheL7mJwNNdyvqlbLl1r1T+1SbfxQrb5WnSTfrT8ca8nr2U5Q2m5nZrGxBr6bfZztpoCsQ9lv5Jvtyh2KJst6ZDkcDoI+d0uuslzvQbbBDJm9ZVlhC9IaLF4ekA02MWB8cnaFXq97Uy+8YwUdqEuFYsmi7eBCQz1eVqLJfBuGB09dYHYlVvcKJPsS+8nZ0HqWqS0ZsvI3qmpbgnbjxXgqzdJakqGe6tuQ9hvg5GyobnYMqDj6pRorG1u9RmVsYL1b/+RsiImRYEdsRYCV3drV3VW3j5gVKFR/Po0NBopeF545H+axF5e59/A44wMBZupk1aeX1gj63BVPJN55yxgOB3zu2HpwnMlkmZoNlY0lqqZaRgqoWf/ULpUCxHp9rTpJYUA5ORuiz+/Z8I7ETrG7x8veoI/J2RVW4ykWVhNtyZDZJ7+V5vVuBwVkHWg1nmItN6pms125VyoEZGAVobdiy3I+HCeeymyooB8KivQrBWTxVMVgZ6Dbw3KDW5aTsyEmRjf3pjw+YHWgrvT7qjRY3Fbatf7I0Wn6A56G6r0OjgY5fiaUf1NrV4YMirv12xmy0l5Tdmr/0ReW667HfkN++ny4ZruOQm+/2Rr9ct+x2tMSrB5kNYKVgQCLqwnCsSRTZ0MtrVVqhbHB8ua1paZLrnItNT4Q4MxyND/A/cjRadxOB2+/eR9jg/Ubx04vVm/iPNLn55VXD3HfsZn8RSIvLq4RjqcazspUG+gdS6Zb2oi0UV631Rut8O/8zFLUGlC/IzJk68/TydmVqv3WLjZ2J/0zy+Wj0lql8OS3Eygg60D2C4fTsflBwqFYqmKxfdDvacmW5XSNAuRG1BowHq6SIavV3b9QOpPl5Nnwpt8A7LPoSsXSlTKQtsKAZ2k1wT9NnufuG/dVbfBa6NBoHxci8XzNTa2M1EZVeuOcXlpjT7B8u8z+HX7LnMutp3YNGUAync3PtqxnffTLTM3O2dOLdYKV3G0PnlpgNZEuaoDZCSqNdyqUSmc4uxKr+XwaH/STTGc5H46RTGf4wqNn+NEJq4/e+ECAs6FY0ZXCpWbqjAt6163jnF2J8b1TF4DCbf/Gglv7b7U0IDt5Llxx3uRWGO71lmSC2/cm32rdXjeBLhdnl6OcPLf517Od4tBokNPzEZ45b7VnqXUitrnH6VNAJtXZLxwH9gaZWVo/E96IUJUMTtDnaclVlvaby0ZT6D6PNWC8UpF+JJ6ip0IwaXf3r9et/LkLEaLJdMNbLdWMFfR+KhWuEvBCQbf+cJwvPX6GRDrTdNPCb5nzNXt+bUZhbYqtWj2gndq3Jxk0kiEr/Xc99x4e50IkwQMn5yrevhJNEorVnghhv2h/7bh1gUCnvXnZ2a1qLUrOrtjd42tnAcHKdD1wco6F1US+N9bYgJ9sFmaXK2fhGikx+NGJYQYCnnyT5MnZFdxOB9fsbWwAddDvpsvtLAvI7G2h7ShIL+2NNr3J162tNtzr5aFnF0mkMmVTJy5WB0f7yGThGyfOARtrPN6IQ6NBLkTiFVsUbTUFZB3IfuG4Zf8AiVSm5hiSeqwMTvmbeau2LKsVCDejWpF+tQyZPWA8UmfAuN2BebMvYOtXx5UHZDW3LAsyZEeOznDdvr6GMzb215m5bb92bFH0+z24nY6yzEGlNyk7tW/marxqtbIYCHThzm0RNxOQveoaa/TLfVWK+9e7dVf/W7P/Dr95cg6Py8HVw61petkq+exWlRf/eg0wYT3onF5c48gj0wz3evODrmv9rUJjJQZet4u7b9rHNybPs7SaYHI2xEuGexrK7IJVE1ip5ct21j+VNkGu19eq0wz3+vLPvYv9CkubfTL1zZNz+D0udvdsvP1TI4/TCVkyBWQdyC4+vXl/P7Dxwv50Jks4XvkqwKDfnR/7sxnTi1F293g3PLcMrOawlWvIKhfM99dolVFocnaFLreTq4YaO7Ovps+fGwxdUvuTSmeIxFNVs1eD3V04HPBtc44TZ0NNjRMJ+jzs32W9aTZSGL8RTqcj31UfrG7sZ1eq1xfZbwQOB+yq8eLodDryV9E1s3Z79Mu/mPMVz1Zr9SCz7e7pwu9xEY6luGZPb1mz4e02XiPbCuQL8msFnfsG/DgccOzFJf7FnOOdt4zl267Yv5tqvcgazQy969ZxEukMX3z8TG7aQXNBgHWFcfExPLGN9U/DQV++NxrkWl7U6WvVSeznkc/j5MotHNu0ncYGrDm64VxWvF1/NwfzAdn2F/Z31quVANboFI/LwXW5guSNtr4IV+jSbwv6WpQhqzIEuRkD3V1lW5bJdIZYMlM5Q9Zd/UKAQpOzIQ7s7W3JAOPxgUCFZpxWQFstQ+Z2OdnV7eWfp+bocjt52w37mnpM+8ytmSxTs4YKamvOLsfI1Ch0ttczGOiq+zu1tzRrZdIqueeWMdKZLJ9/9EzZbbUaptrsvnHApreq2yHf+qJGwOR0wEh/9d+b1+1iT6+Pzx21Bl0XboPvDfpwOx1VA75G5zce2Bvk+rE+/uK7z3EhEq86lqia0pqtVDqzrfVPQz3eot5oM3VqETuNHZAd2Bus2PPwYmTP0YX21vr1+jxcvivQETMtL40ju8PMhazRNPnO3htsDmu3tahUdB70e0ikMsRyzSQ3qtoQ5GYMBDxlW5arFeZY2vobmGeZzWbzcwxbYXzQX/YmWmksVSn7hfSNh/bmR1Y1ys5KtCtDBrlB0CU978aqBNj277KR9QxtIEMG1ny5Wy8f4L6j02U1gtOLa/R63XXr6ewX706stbGzW9UK+xvtHj824CeRzvDSywe5oiBj4nI6GO0v/1tdv//GSwzuOTyev8Kt2QxZYeYV4PT8KvFtrH+yTxDs3YdqW/Odyn4edVpNZLvZP2+7j9Wh0b78FInt1JbRSYZhOIGPATcAceC9pmmeKrj9J4EPAGngk6Zpfjz3+Q8BbwO6gI+ZpvmX7Vhfp7OHSfs8LoZ6vRvOkFWbswjrQVooltzwLLN0JsvscpS3Xr+52W4DFWrI7OxTT4UtSztDVqs57JnlKCvRJAdblCUZHwjw7afni8bB1Ap4bcO9XqbOsqGBxAfzGbL21bkM9Xp5PDczLr8lWOXN+mATAdl6hqz5YPLew+P8yuee5GPfOl30/cdeXGKsgTl59ot3J7552dmtaq0vphcbm3oxPhjg6AtLFQfUjw9WnyzRTInB224Y5Xe/coJ4KsPESHO1eMO9PpbXrB58Xrcrvx20XfVP9gnCXDjGSJ+vbX2t2mU4H5B1Xta3neyft93tSQ6OBvmHp87WrAneCu2aZXk34DNN83bDMG4Dfh/4sYLbPwocAiLACcMwPoMVvL0MeDkQAP5jm9bW8eZCsfyLxfiAf8Pjk9YzOJWL+sEq+t/oG/7k7AqpTJYrN1mjZQ8YT6Yz+cyAXbDfW6WoH2oPGLcLNK9t0Zvy2ICfWNK6wML+fdUKeG3G3l5ml6PcXmPYczU3jPXT43VXHGreKkO91ptTKp1hejGKy+lgpK/y30Ovz8MNY30caGA9B/YG2Rv05ev9mvHm60b4r1+d4v/7ull22ztvrl+Hd/1YP/2BWSZGOi8gAzvbWh4wxZJpzHNh3nrDaN37uHZfH9995gJvvq58puH4QIB/nqp8pWozY876/B7ecfM+jp8J1Z0xWsoO2i9EEuzr9zM5G9rW+qfCFi87qeWF7cDeIB6Xg5de0drZk53ulv0DdLmdXDfW3kD0ZVftwuNyEEumL8qA7A7gawCmaT5kGMbhktufBPqAFOAAssAbgKeA+4Eg8CttWlvHmw/HuSk39HV8MMCxF5Y2dD92W4uKW5a5IG1lE9367zs6g9ft3NBg60L5eZZr6wPG7YCsUoas1+fG6aBmc9jJMys4HdYLWSvkr15bjObXmA94a2yhffCNB/jA66sPEq9lsLuLox9+bdFw9VYb7vWSzcLCaoLppTVG+nw1a1Tue//LGiqE/te37edfvXR8Q4W43V433/7VV7NS4fhWCxYLvfPmfbz1+pENZ37bbWwgwA+fXSj7/NeOnyMcT3HXDfUzzj/z8sv5yR+5rOLPOD4Y4EIkTjSRLsuETS+tceN442/qv/Nj17KRrjv5K4xDMfb1+zl+ZmVb65+GClrQdHfZmeCds2V53VgfT/3WGzr2b7pdxgcDPPmbr2/7z33TZQNM/pc3bvtFQO0KyIJA4YZs2jAMt2ma9rv/ceAYsAp8wTTNZcMwdgP7gbcCVwBfNgzjgGmaVV8O4vE4U1NTLVt0LBZr6f1tRCqTZXE1gTMeYmpqCn96jdnlKMcnTzR9RdDJZ60s0fyZF2C5+FAvzltXQB1/+lkCa82fKcZTGe5/dJqXXRZg9vlTzDZ9D+vWlq3Gf8eeMrl8wMp+nZixXjQXzs4wlbIaVBYen16vk2fPnGdqqnIDzIeePsdY0MPzp5/exMrWJZesbNzDx08RWLMygmbu9zs38wLZpXY9ldorvrIKwMNPnuTpMwvs8jo29BzYqufOM+fb/hBt50uvci4U48njJ/C41p/T/+fbs+ztcdMXn2Nqan7D9+9Ys55P3z52nP391vMpFotxfPIEs0tRXrbP2/ZjFblg1Wo9NnUabyTA8ZllXnVF97a9vmazWTxOByefn+XCvPVcjV04w9TquW1ZT6FOeN+R6rby+LTrXSQEFO5rOO1gzDCM64G3YAVdEeBvDMO4B1gATpqmmQBMwzBiwBBQOfcOeL1eJiYmWrboqamplt7fRpxbiZHlOQ5eOcbExH5uirzIZ55apm/k8qZT7N+dPw1c4ObrJsqK47t2R+Crs/QP7WViormr/wC+9PgZIonn+dnXHGLiJbub/v5CS54L8O05BvaOMZHb2nsmMQuc49oDV/OSYSsAKjw+Q8E5sl3dVY/Xi/fPctuVQy07nvsTKfjyDJnAABMTLwHgO3PW7/eW6yborrC1uhPEupfgX87TvXuEhdgF7jR2b+h31gnPnZ3iptVp/u7JZfpG9rM/N/j8xYU1njj3LB943TUcOnj1pu5/LbAE352ja2AvEwes7PXU1BS9e/eTzj7HjVePMzFx2aZ/jloGVmLwD2fo6huiZ88Qq8nnuOPQ5W1/3Fr29J0j3dVDsqsLv2eZ22461BEjiPTc6WytPj7Hjh2relu78nMPAm8GyNWQPVVw2woQBaKmaaaxAq4B4HvAGw3DcBiGMQp0YwVpl5TSYdL5rtwbKOwPRVO4nA66KxTw2lcGbrRb/5Gj04wP+rltA7VRpfJXTRbUhEVyRf3VBncPBDxVr7JciMQ5F4q1tAA20OVmd09XUbF0KJbE5XQQ2EQPtu1m1/pML0aZC8d3VKHzTjVe0Gnf9rlj0zgc1nDvTd9/lVFfjba8aIVdPVYPvvnc/EXY/oss7BYv9oUTnRCMiRRq12n9/cDrDMP4PlaN2HsMw3g30GOa5p8ZhvGnwPcMw0gAp4FPmaaZMAzjlcDDWIHiL+QCtkvK+jBpq+ZhvLD1xVXN3ddKNEnQ5674wmPXPW2kF9n04hoPnlrgP7xuY7VRpSr1FYvErXVVansB1pWZLyxUDlLXZ++19g1g30CgKDC2r8jZyS/sdkBmX2m52Z5yUt96LzLrbymdyfK5YzO88uohRvs3//sf6vHi8zjLrrTcynFBHpeTwUAXc+E46UwWl9PR1otTGjHc6+W5C6u4nM4dVdAvl462BGSmaWaA95d8+mTB7Z8APlHh+361HevZSezePfYb5Ui/D6djgxmyWPXB1163C5/HuaFu/Z87NtOys3lY77xfWKQfiaVwOKiafRrs7uKxXBBRqtlhyI0aH/Dz1Jn10shQNFXxCtadxOt20R/w5C8c2YrsyaVupM+Hq6B564OnLjC7EuPX33KwJfdvNccNlF2dPbO4hsNBS4K+RtgZqXMrUa4e7tn2gvThXh8/fG6RdDrLSy+/tK5WlJ1BjWE7jL1lac/t8ricjPRV7ytUS72eKhsZMG6fzd/xkt3sa9ELu8/jItDlKmpjEY5bcyyrZZ/6A10sr1UeMD45u2KN3dhAy4VaxgcDzBYMhq4V8O4kQz1eXrTnRCpz0HZul5PRfl9+S/GzR6cZCHh47cHhlj3G2EB5a42ZpSgjQd+WXUlmBWQxjs+GGp7h2u71LK8lCcdT+juXjqSArMPMhWP0BzxFg3zHBvxVZ9PVEooma3aR38iA8e+fvsCZ5eiGGp3WUtocNhJLVexBZhvs9pBMVx4w3soO/YXGBwIk01nO5eYsbncTwVaxezR1uZ352kVpr/Hc9vfSaoJvTJ7n7pv2NTy8u9H7L6shW1pjbAsDkeFeH8/MRZgPxzuioWlhk+Gd1KVfLh0KyDrMfDhe1t18fDCwwS3L6oOvwepPZvfSatSRozP0Bzyb7j1WarC7q6hIPxJPVexBZhvIXwhQvP5IPMVzF1bb8gaQr/3JZZPqBbw7Rf4Ckn5/S2oCpb6xXMPnLz1+hkQ6wz23tPYEZ3zQz0o0WfT8nl7c2nFBQ71e1hJWGfB2F/RD8YQJXbwinUgBWYeZC8fLOuePDwQ4H4o3PXeyXgan2QzZ8lqCr0+e4+4bW3s2D1Yd2WJhDVluy7KagSrzLKfOtqegHwqbw1oB2Uo0dVFsWdoXkGxl9uRSNz5gNW/964de4Lp9fS3f0lu/ktP6W02ks5wPx7a0RrDwxLITtiwLX1e1ZSmdSAFZh5kLxctmBdpXvtmDfhtVL4MT9Lnz8xgb8aXHZ0mkMtxzuDXF/IUGu4u3LMOxFD011j5Q4cpMsDr0Q3tmvo32+3KDoa3jYNWQ7eyiflh/49xJnct3OjsgOD2/WnEe5WaNlbTWmF9Nkc1ubSBib4VfNhjoiEyyvZ6gz31RlBrIxUcBWQfJZrO5WYnlW5ZQ3leollgyTTyVqZnBaXbL8sjRaa7dF2xLsDMQKN+yrF1DVnnA+ORsiN09XewJtr4Wyut2sTfoY3ppjVgyTSKV6Yg3ms2yTwCUNdg69kmW1+3kbQ3Mrtzo/dsD48+Fref5Vgbd9lb4tfu2PzsGsKvb6o2mv3PpVDv/9P4iEoqmSKQyZRmy0tqlQguROD/z6aP8zo8d4vqx/vX7ilWfY2nr81tXWWYy2bq1Q1NnQ0zOhvjtHzvU6I/TlIFAF+HY+oDxSKz2lqU9YPw3vzzJR76W76jC0mqS267a1bbeYOMDAWYWo/mrUy+GM237702FzlvH3jp847V72/I31Of30Ot150/izkesTPjWZsisLcJOKOgH6+rWXd1d+juXjqWArIPMR6yr90oDsj29PrpczoqF/Z87NsMT08v8xXef449+4qb85+2tyFp9soI+D5ksrCZS9NbJ9Bx9fhGA1xxo3aX5hQa7iweM1yvq7wt4+JU3GBWD1Hfc3PotVdvYgJ8fPLvQUMC7UxzeP8gHXndN246tlBvq9fLht0zw+oN723L/DoeDscFA/vlxLpLE43KwJ1h/OHurXL4rwAffdIB33Nz8aLZ2+a23HVJBv3QsBWQdZC5U3BTW5nQ62Dfgt7r1F8hmsxw5Og3A1ybPsbKWzPfeWmkgg2PfthJN1g3IJmdD9Ac8Les9VsquCVteS7K721u3qB/gF179kraspZaxwQDnHj/DhYi1VXoxZMi63E5+8Uc3Nz9RmuNwOHjvK65s62OMDfh5YcEaHn8+kmK0349rC6+idTgcvP9VTY4XabO3Xt/67WGRVlENWQeZj1gBWelVllC50eOjLy5zen6Vn759P4lUhi8/cSZ/WyMZHLsgvZHCfru3V7u2Au2rJhdXE0QStedYbqfxAT/ZLJzMXc250zv1y8VrPNetP5vNcj6S0hQGkQ6ngKyD2Bmy4QoF6eOD5Y0ejzwyTaDLxa+88QAHR4J8Npctg/Wh4bWvsswNGK9T2J9MZzDPhdtaCzJQMGDcHixeL0O2HewanOO58UwXw5alXJzGB/1Ek2kWVxOciyQ1p1Skwykg6yBz4Rhet7Pi1YVjA34WVxOs5jrTr8ZTfOXJWd56/Qg9XjfvunWc42dCTM5abR8aKToP+ou3N6s5NRchkc60tblj/qrJtWS++36tGrLtYhcE2/MyL4YtS7k42Rkx83yYlVhGtVMiHU4BWQeZD8cZDnorbgvmGz3mti2/+tRZVhNp7j1s9TD6sRtH6XI5ue/oDEB+aHitPll2MFFvnuX6sO72BWT2gPGltQThDs6QjfT5cTsdPHM+DNTOQIpsp7FcRuyh0wvWx7q6UKSjKSDrIHPheNVZgutd4q1ty/uOznDl7m5u2T8AWMO2X39oD/c/doZYMs1KNInP46zZUd/OkNnBWzXHz6zg97i4YndP0z9TowoHjNsZsk6sIXM5HYz2+0llsvg9ri0b1CzSLPsk7vu5gEz9t0Q6m95NOsh8hbFJtvGCXmTPzkd4+PlF7jk8XpRNu/fwOCvRJN84cb6hOYu9XjcOR/0tyxOzISZGett+hZbdHNauIat35ed2sWtxLoYu/XLx6va6Gezu4vHpZQAV9Yt0OAVkHWQuXD42yTbY3UWgy8XMUpT7js3gcjp4Z0l/n5e/ZDf7+v0cOTqdG+tTO6BxOh30eN01tywzmSwnzoa2pLnjQLfHKuqPW+vpxC1LWH9j03aldLrxASub63U52N3Ttd3LEZEaFJB1CHubsXRsks3hcOT7Cn3+2AyvNobynbBtLqeDd94yxvdOXeDk2XBDBed2t/5qXlxcIxJPtbV+zDYQ6GJxLbleQ9aBW5awXoujgn7pdHYh/3CPu20ta0SkNRSQdYgLkeotL2zjAwG+/fQ8c+E49xyuPJD4nlvGyGbh2QurDfXICvpqz7NcL+jfggxZoIvltfUasu6uzgzI7FoctbyQTmcX9u/t6cznkoisU0DWIebClbv0FxofDJDKZNnd01V1zM34YICXv2QX0FgGx8qQVS/qn5xdwe10cM3e9hX02wa7u6yi/liK7i7XlnYVb4addVCGTDqdvb2+t0d/qyKdTgFZh5gPV+/Sb7O3yt5x8xgeV/VDZ7fCaCSDE/S7axb1T86GuHpPb82rNVvFHjC+tJbs2O1KKCjq7+A1isB6NndPr/5WRTqdArIO0UiG7MbxfgJdLt51a+XtStsbDu1lX7+fq4frZ7X2Bn28uLiWbzhbKJvNMjm7siX1Y2AV9QPMLK11bEE/wFCPl8sGA1yzt3e7lyJS08TeXgJdLozdWzdUXEQ2pnPf9S4x86EYDgfs6q5+JdThywd56rfeUHcrz+dx8Z1ffXVDW3533TDKp3/wAv/w1Nl8Zs02F45zIZLYuoAsNz5penGNoWDnvoE4HA6+/St3qkhaOt5w0MeJ334jU1NT270UEalDGbIOMR+Js6u7C3eNrUig4bqqRr/ulv0DXLm7m/sK5mDa7DFMW1HQD+vjk86GYhXHR3USBWMiItJKCsg6xFwozlCN+rF2cTgc3HN4nEeeX+LZ+UjRbZNnrCssJ0a2ZmvOHp+UzXZuDzIREZF2UEDWIeYj8ao9yNrtnTfvw+V0cCQ3B9M2ORvi8l2BLeuYP1iwXdvJRf0iIiKtpoCsQ1gZsu0JyIaDPl5tDPH5R2dIpTP5z0+eXeHQvq3ZroT1GjJQhkxERC4tCsg6QCaT5cI2ZsjAapUxH47z7afnAVhZSzK9GN2ygn6wLkbwe6z2Gp04WFxERKRdFJB1gKW1BKlMdtsyZACvPjDM7p4uPvuIVdw/eXZrC/pt9ralMmQiInIpUUDWAeYaaArbbh6Xk3fcPMYDJ+eYD8c5kR+ZtHUZMljvRaYaMhERuZQoIOsA+S79NeZYboV7D4+RymT54mNnmJwNsSfoZXfP1q7JriNThkxERC4letfrAPku/Vsc/JR6yXAvN1/Wz2ePTuN0bP12JawHZKohExGRS4kyZB1gvoGxSVvlXbeOc2ouwtPnI1y7xduVUFhDpmHIIiJy6VBA1gHmwjG6u1x0d8A23VuuH81f6XhwGzJkdnNYbVmKiMilRO96W+zf/d1j/MvJuaLPRZNpxgcD27SiYj1eN2+5foTPHZvZ8oJ+WJ/lGfTrT1NERC4detfbYg89u8C+AT8vu2p30edvv2rXNq2o3Adefw03jvdvS5B41w2jOBwO9vX7t/yxRUREtktbAjLDMJzAx4AbgDjwXtM0TxXc/pPAB4A08EnTND9ecNswcAx4nWmaJ9uxvu0Uiad42w2jfPitB7d7KVWN9Pn5qdv2b8tj9we6tu2xRUREtku7asjuBnymad4OfBD4/ZLbPwq8Fng58AHDMAYADMPwAH8KRNu0rm2VzmRZS6TVY0tERESKtCsguwP4GoBpmg8Bh0tufxLoA3yAA8jmPv9R4BPAbJvWta0i8RSggnUREREp1q7IIAisFHycNgzDbZpmKvfxcaxtyVXgC6ZpLhuG8W+AedM0v24YxocaeZB4PM7U1FTLFh2LxVp6f6XmItaPH1maZ2oq3rbHuVi1+/jIxunYdDYdn86lY9PZtvL4tCsgCwG9BR877WDMMIzrgbcAVwAR4G8Mw7gH+BkgaxjGa4Ebgb8yDONtpmmeq/YgXq+XiYmJli16amqqpfdXynkuDLzI1ZdfxsTESNse52LV7uMjG6dj09l0fDqXjk1na/XxOXbsWNXb2hWQPQjcBRwxDOM24KmC21awasSipmmmDcOYAwZM03yl/QWGYXwLeH+tYGwnisSTgOY0ioiISLF2RQb3A68zDOP7WDVi7zEM491Aj2maf2YYxp8C3zMMIwGcBj7VpnV0lHBMNWQiIiJSri2RgWmaGeD9JZ8+WXD7J7CK96t9/53tWNd2s4v6NadRRERECml00haKKEMmIiIiFSgg20L5thfKkImIiEgBBWRbyK4h6+5SQCYiIiLrFJC1QDab5e+fmCWVztT8ukg8RXeXC5fTsUUrExERkZ1AAVkLHD8T4hf/7jG++8yFml8XiaW0XSkiIiJlFJC1QDhm9RdbXE3U/LpIPKWCfhERESmjgKwFosk0ACvRZM2vC8dT9Pg8W7EkERER2UEUkLVALGnVjoVitQOySCxJUFuWIiIiUkIBWQs0nCGLactSREREyikgawE7IAtFUzW/TjVkIiIiUokCshaIJXIBWd0tS11lKSIiIuXqRgeGYfQCvwaMAP8APGma5ql2L2wnaWTLMpPJEkmk6FWGTEREREo0kiH7JPAscA1wDvjLtq5oB1rfsqwekK0l02SzGpskIiIi5RoJyHaZpvlJIGma5vcBtZkvEcsFZPZopErWB4ur7YWIiIgUa6iGzDCMA7n/jwHptq5oB4o1sGUZiVu3KUMmIiIipRqJDv4d8H+ACeBzwM+3dUU7UDRX1B+Jp0ilM7hd5XGunT1TDZmIiIiUaiQ6eKNpmre3fSU7mF1DBlbgNdDdVfY1kXhuy1IZMhERESnRyJblmw3DcLV9JTtYNNepH6q3vlivIVNAJiIiIsUaiQ6GgFnDMJ4DskDWNM2XtXdZO4vdhwyqN4cNxxWQiYiISGWNRAdvbfsqdrhYKk2P100knqpa2G9nyHq1ZSkiIiIlGtmyTAMfBb4K/C/U9qJMNJFmT9AL1NiyzGXIupUhExERkRKNBGR/Dvw18HLg06gxbJloMs1wrw+o3voiEk/h8zjxVLgCU0RERC5tjaRrfKZpfjn37y8ahvEf2rmgnSiWLMiQVQnIwrGUmsKKiIhIRY2ka9yGYVwHkPt/tr1L2nmiiTS7ery4nI6aW5aqHxMREZFKGm0M+0nDMEaAWeB97V3SzpLNZokm0wS6XAR97hpF/UldYSkiIiIVNZIhOwG8zzTNMeC/ApPtXdLOkkxnyWTB53HR5/dUbXsRiacUkImIiEhFjQRkfwv8SO7f12AV9kuO3aXf73ER9HuqZsjCMW1ZioiISGWNBGT7TNP8BIBpmv8DGGnvknYWe7C4z+Mi6PPUrCHT2CQRERGppKEeDIZhXJP7/1WAxigVsAeL+7ucuS3LGkX92rIUERGRChqJEH4JOGIYxh7gDPD+9i5pZynesnSzUqGGLJvNEokpQyYiIiKV1Y0QTNN8GLjRMIwBIGWaZrj9y9o5ooVblv7KW5axZIZUJqs+ZCIiIlJR1S1LwzBuNgzjMcMwPIZhvB04CRw1DOOurVte54sVZsh8HhKpTP5ztnDcCtKUIRMREZFKatWQ/R7w06ZpJnP/fjNwK/DBrVjYTpEPyLqsDBmUd+vPDxZXDZmIiIhUUCsgc5qm+aRhGKNAt2max0zTDAGZLVrbjhBNWL8Ouw8ZlA8YtweLqw+ZiIiIVFIrQrCDtTcC/wxgGIYX6K13p4ZhOIGPATcAceC9pmmeKrj9J4EPAGngk6ZpftwwDA/wSeBywAv8bsEMzY5VVNSf25Is7UVmZ8i0ZSkiIiKV1MqQ/bNhGA8CvwX8ca7lxd8Dn23gfu/GGkp+O9YW5++X3P5R4LXAy4EP5C4Y+ClgwTTNVwBvAv53Ez/Htiks6s9nyEqutAwrQyYiIiI1VA3ITNP8CPBe4CbTNB/Pffrjpmn+twbu9w7ga7n7eQg4XHL7k0Af4AMcWAPL7wN+o+BrKs8g6jCxRIUastItS7uGTBkyERERqaBmhGCa5lTBv08Dpxu83yCwUvBx2jAMt2madpB1HDgGrAJfME1z2f5CwzB6gc8BH673IPF4nKmpqXpf1rBYLNb0/U3PLgHw/KmnCcWt4Mx8bpopbyj/NadesH4Vsy8+x9qc+upu1EaOj2wNHZvOpuPTuXRsOttWHp92pWxCFNeaOe1gzDCM64G3AFcAEeBvDMO4xzTN+wzDGAfuBz5mmub/rfcgXq+XiYmJli16amqq6fvreeEkXa4Vrj10kHgqDUdeJNC3i4mJq/Nf88C5U8ACN183gdetgGyjNnJ8ZGvo2HQ2HZ/OpWPT2Vp9fI4dO1b1trqjk3LF9s16EKtNBoZh3AY8VXDbChAFoqZppoE5YCA3CeCfgF8zTfOTG3jMbRFNpvF6rF+j1+3C53GWFfWHYym6XE4FYyIiIlJRIxmyY4ZhPAD8hWmaxxu83/uB1xmG8X2sGrH3GIbxbqDHNM0/MwzjT4HvGYaRwNoG/RTw/wEDwG8YhmHXkr3JNM1oEz/Plosl0/g964GWNc+yuPwtEk/qCksRERGpqpEo4Uas1he/aRjGEPA3wGdM04xU+wbTNDOUz7w8WXD7J4BPlNz+S7n/dpRoIo2/az0gC/rKxydFYildYSkiIiJV1d2yzAVX/4jVI2wB+EXg64ZhvK/Na9sRoiUZsqDfU96HLK6ATERERKprpIbsfwAm8HbgI6Zp3gC8Avi5Nq9tR4glM/hKtyxj5TVk2rIUERGRauoGZMAzWL3I3gc8Bvms2dvbubCdoixD5nNXzJAFFZCJiIhIFY0EZA7gd3P//gfDMP41gGmaz7drUTtJLJnG51n/NQYrFvVry1JERESqayQgez/wody/3wL8fPuWs/OUFvX3+T2EY0kymWz+cxFtWYqIiEgNjQRkadM0YwCmaSaxxhxJTjSZLqohC/o8ZLIQSaxnycLxFD3ejbRzExERkUtBI2mbLxmG8V3gYeBm4MvtXdLOUqkPGUAomiTo8xBPpUmkMppjKSIiIlU10vbid7FaXTwM/LJpmv+97avaQWLJTEnbCyvwsgv7V3PzLVVDJiIiItU00vbiJcCbAAO4O9dlX4BsNltxyxLIF/ZHYtb/FZCJiIhINY3UkP1V7v93YA0E39W+5ewsyXSWdCZb3Knf3rLM9SILx63/q6hfREREqmkkIFszTfO/ATOmaf4bYE97l7RzRJPWdmRpY1hY37K0M2S9ypCJiIhIFQ31ITMMYy/QYxhGNzDY5jXtGLFcQFY6Ogmson6wuvSDMmQiIiJSXSMB2X8B7sYaKv4c1lxLwepBBuDvWv819nrdOBwQygVikbhqyERERKS2RqKEl5qm+dHcv4fbuZidJpYqz5A5nQ56vO71DFlcGTIRERGprZEM2ZsNw3DV/7JLj50h83qKfz19fk8+IFuvIVNjWBEREamskbTNEDBrGMZzWF36s6Zpvqy9y9oZohVqyMBqfZEv6o8ncTkdRfMuRURERAo1EpC9te2r2KEqFfVDLkMWW8+Q9XjdOByOLV+fiIiI7AyNBGQ/XeFzv93qhexE0UQGoKgPGVjd+p+/sAbYcyxVPyYiIiLVNRIpnM/934E1y1J7bzkNbVnGUppjKSIiIjXVjRRM0ywalWQYhtpe5MQqNIaFki1LZchERESkjrqRgmEY1xR8OAJc1r7l7CzrAVlx0jDo97CWSJNMZ4jEU+zq7tqO5YmIiMgO0Ujq5k+xrq50AFHgP7Z1RTuI3faiUoYMrG79kViK/bu6t3xtIiIisnM0Ug/2JuADpmm+Gvgz4J/bu6SdI5pM43E58LhKM2RWnBuKpVTULyIiInU1EpD9DfAjuX9fA3y6fcvZWaLJdFl2DKyifrAGjKuoX0REROppJCDbZ5rmJwBM0/wfWHVkglVDVnqFJaxvWS6tJogm08qQiYiISE0NtbCwC/sNw7gK0BilnFgyU9aDDKyifoAzy1FAg8VFRESktkYihV8GjhiGMQzMAu9v64p2kGgijc9dPUM2awdk2rIUERGRGhrJkD0OvMc0zVHgd4En2rqiHSSaTOOrlCHzFQdkvcqQiYiISA2NBGR/i4r6K4om0/grDA33eZx4XI71LUtlyERERKQGFfVvQrWifofDQZ/fw+xyDFANmYiIiNTWbFH/S1BRf140ka5Y1A/WtuW5kBWQqe2FiIiI1NJsUX8U+FQ7F7STxFKV+5CBdaVlOpMFoMfr2cpliYiIyA5TN0NmmuYPgfdhdejvBva0e1E7RTSRqRmQ2VRDJiIiIrVUjRQMw+gCfgL4BSAOBIErTNOMbtHaOl61GjKAYC4IczggUOVrRERERKB2hux54HrgJ03TfAUwq2BsXTabzV1lWTnYsnuR9XS5cTodW7k0ERER2WFq7aX9IfBu4HLDMP4CaDiqMAzDCXwMuAEru/Ze0zRPFdz+k8AHgDTwSdM0P17vezpNMp0lnclWL+q3AzJtV4qIiEgdVTNkpml+xDTNG4A/wgrMbjUM4yOGYVzbwP3eDfhM07wd+CDw+yW3fxR4LfBy4AOGYQw08D0dJZpMA1SvIcs1h1XLCxEREamnkaL+b5um+a+Bq4AZ4K8buN87gK/lvv8h4HDJ7U8CfYAPK/OWbeB7Oko8F5DV3bJUhkxERETqaDhaME1zGfjj3H/1BIGVgo/ThmG4TdNM5T4+DhwDVoEvmKa5bBhGve8pE4/HmZqaavRHqCsWizV8f7OhJACL8+eYmlotuz28GAHAkWrtGi9lzRwf2Vo6Np1Nx6dz6dh0tq08Pu1K34SA3oKPnXZgZRjG9cBbgCuACPA3hmHcU+t7qvF6vUxMTLRs0VNTUw3fn+NcCJjmqv3jTEyUDy+44J6Hb88xsqu/pWu8lDVzfGRr6dh0Nh2fzqVj09lafXyOHTtW9baGOvVvwIPAmwEMw7gNeKrgthWsBrNR0zTTwBwwUOd7Ok40kashq9GpH1RDJiIiIvW1K1q4H3idYRjfx6oRe49hGO8GekzT/DPDMP4U+J5hGAngNFb3/1Tp97RpbS0RrVNDpqssRUREpFFtiRZM08wA7y/59MmC2z8BfKLCt5Z+T8eKNVrUrwyZiIiI1NGuLcuLXiyZAaq3vejze7jTGOKlVwxu5bJERERkB1L6ZoPsGrJqGTKX08Gn3vPSrVySiIiI7FDKkG1QvjFsl36FIiIisjmKJjaoXg2ZiIiISKMUkG1Qvu2FAjIRERHZJAVkGxRNpvG4HHhc+hWKiIjI5iia2KBYMoPPreyYiIiIbJ4Csg2KJtNVu/SLiIiINEMB2QbFkmkV9IuIiEhLKCDboGhCAZmIiIi0hgKyDdKWpYiIiLSKArINiibT+D369YmIiMjmKaLYoHgyrR5kIiIi0hIKyDYoqqJ+ERERaREFZBukgExERERaRQHZBkUTGRX1i4iISEsoINsg9SETERGRVlFAtkHashQREZFWUUC2Acl0hnQmi09tL0RERKQFFFFsQDSZBlDbCxEREWkJBWQbEEtYAZlfRf0iIiLSAgrINsDOkKmGTERERFpBAdkGKCATERGRVlJAtgHR3Jal+pCJiIhIKygg24BYMgOAz62ATERERDZPAdkGxJIq6hcREZHWUUC2AaohExERkVZSQLYBdg2ZAjIRERFpBQVkG5BvDNulX5+IiIhsniKKDYhpy1JERERaSAHZBsQ0OklERERaSAHZBkSTadxOBx6Xfn0iIiKyeYooNiCayGi7UkRERFpGAdkGRJNpdekXERGRlnG3404Nw3ACHwNuAOLAe03TPJW7bS/wmYIvvxH4IPCXwKeBy4E08LOmaZ5sx/o2K5ZMK0MmIiIiLdOuDNndgM80zduxgq3ft28wTfOcaZp3mqZ5J/Ah4FHgz4E3A27TNF8G/Dbwe21a26ZFE2l8HiUXRUREpDXaFVXcAXwNwDTNh4DDpV9gGIYD+GPg50zTTANPA+5cdi0IJNu0tk2LpZQhExERkdZpy5YlVkC1UvBx2jAMt2maqYLP3QVMmqZp5j6OYG1XngR2A2+t9yDxeJypqanWrBiIxWIN3d/CchigpY8t9TV6fGTr6dh0Nh2fzqVj09m28vi0KyALAb0FHztLgjGAnwL+sODjfw983TTNDxmGMQ48YBjGdaZpxqo9iNfrZWJiomWLnpqaauj+nN9cYKC7q6WPLfU1enxk6+nYdDYdn86lY9PZWn18jh07VvW2dm1ZPohVE4ZhGLcBT1X4mluA7xd8vMR6Vm0R8AAduS8YVVG/iIiItFC7MmT3A68zDOP7gAN4j2EY7wZ6TNP8M8MwhoCwaZrZgu/5A+CThmF8F+gC/pNpmqttWt+mKCATERGRVmpLQGaaZgZ4f8mnTxbcPo/V7qLweyLAve1YT6tFExm8CshERESkRdS7YQPiypCJiIhICykg24BoMo2/S786ERERaQ1FFU1KpjOkMlllyERERKRlFJA1KZpMA+BTQCYiIiItooCsSbGEFZD5NVxcREREWkQBWZPyGTK3AjIRERFpDQVkTYolM4AyZCIiItI6CsiaZGfIVNQvIiIiraKArEnRhIr6RUREpLUUkDUpllRRv4iIiLSWArImactSREREWk0BWZPWtyz1qxMREZHWUFTRpJVoEoBen2ebVyIiIiIXCwVkTZqPxPG4HAwEFJCJiIhIaygga9JcKM5QjxeHw7HdSxEREZGLhAKyJs2FYwz1erd7GSIiInIRUUDWpPlwnKFe33YvQ0RERC4iCsiaNB+OMxxUhkxERERaRwFZE5LpDItrCYZ6FJCJiIhI6ygga8JCJEE2izJkIiIi0lIKyJowH44DKEMmIiIiLaWArAlz4RgAw0EV9YuIiEjrKCBrwlwuQzasthciIiLSQgrImmBvWe7WlqWIiIi0kAKyJsyFYwwEPHS59WsTERGR1lFk0QSrKayyYyIiItJaCsiaMBeOM6wu/SIiItJiCsiaMBeKq6BfREREWk4BWYOy2SzzEW1ZioiISOspIGtQKJoikcooIBMREZGWU0DWoPmI1RRWAZmIiIi0mgKyBs2F7KawKuoXERGR1lJA1iC7S78yZCIiItJqCsgaZHfpHw4qIBMREZHWUkDWoLlwDJ/HSa/Xvd1LERERkYtMW6ILwzCcwMeAG4A48F7TNE/lbtsLfKbgy28EPmia5icMw/gQ8DagC/iYaZp/2Y71bYTdpd/hcGz3UkREROQi0650z92AzzTN2w3DuA34feDHAEzTPAfcCWAYxu3A7wF/bhjGncDLgJcDAeA/tmltG6Iu/SIiItIu7dqyvAP4GoBpmg8Bh0u/wDAMB/DHwM+ZppkG3gA8BdwP/D3wlTatbUPmw3GGelQ/JiIiIq3XrgxZEFgp+DhtGIbbNM1UwefuAiZN0zRzH+8G9gNvBa4AvmwYxgHTNLPVHiQejzM1NdWyRcdisar3d3Z5jQMDzpY+njSn1vGR7aVj09l0fDqXjk1n28rj066ALAT0FnzsLAnGAH4K+MOCjxeAk6ZpJgDTMIwYMATMVXsQr9fLxMREi5YMU1NTFe8vlkwTSTyLsX8vExNXt+zxpDnVjo9sPx2bzqbj07l0bDpbq4/PsWPHqt7Wri3LB4E3A+RqyJ6q8DW3AN8v+Ph7wBsNw3AYhjEKdGMFadvuQkQ9yERERKR92hWQ3Q/EDMP4PvAHwL83DOPdhmG8D8AwjCEgXLgdaZrmV4DHgIexash+IVdbtu3sprAq6hcREZF2aMuWpWmaGeD9JZ8+WXD7PFa7i9Lv+9V2rGez5tWlX0RERNpIjWEbsJ4hU0AmIiIiraeArAHzoRgOBwx2d233UkREROQipICsAfOROLu6vbhd+nWJiIhI6ynCaMBcKK7tShEREWkbBWQNmI/EVdAvIiIibaOArAHKkImIiEg7KSCrI5PJckEZMhEREWkjBWR1LK0lSGWyypCJiIhI2yggqyPfgyyoLv0iIiLSHgrI6lCXfhEREWk3BWR1qEu/iIiItJsCsjqUIRMREZF2U0BWx1w4Ro/XTaCrLXPYRURERBSQ1TMXVssLERERaS8FZHXMKyATERGRNlNAVsd8WF36RUREpL0UkNWhDJmIiIi0mwKyGtYSKSLxFMO9agorIiIi7aOArIa5kFpeiIiISPspIKthPqKmsCIiItJ+CshqsDNkw0EFZCIiItI+CshqmA/HABjqUUAmIiIi7aOArIb5SBy308FAoGu7lyIiIiIXMc0DquE1B4bp9rpxOh3bvRQRERG5iCkgq+GW/YPcsn9wu5chIiIiFzltWYqIiIhsMwVkIiIiIttMAZmIiIjINlNAJiIiIrLNFJCJiIiIbDMFZCIiIiLbTAGZiIiIyDZTQCYiIiKyzRSQiYiIiGwzBWQiIiIi26wto5MMw3ACHwNuAOLAe03TPJW7bS/wmYIvvxH4oGman8jdPgwcA15nmubJdqxPREREpJO0a5bl3YDPNM3bDcO4Dfh94McATNM8B9wJYBjG7cDvAX+e+9gD/CkQbdO6RERERDpOu7Ys7wC+BmCa5kPA4dIvMAzDAfwx8HOmaaZzn/4o8Algtk3rEhEREek47cqQBYGVgo/ThmG4TdNMFXzuLmDSNE0TwDCMfwPMm6b5dcMwPtTIg6ytrV04duzYC61aNMCxY8daeXfSYjo+nUvHprPp+HQuHZvO1uLjs7/aDe0KyEJAb8HHzpJgDOCngD8s+PhngKxhGK/Fqiv7K8Mw3pbb4qzolltuGWrRekVERES2TbsCsgexMmBHcjVkT1X4mluA79sfmKb5SvvfhmF8C3h/rWBMRERE5GLRroDsfuB1hmF8H3AA7zEM491Aj2maf2YYxhAQNk0z26bHFxEREdkxHNmsYiIRERGR7aTGsCIiIiLbTAGZiIiIyDZrVw3ZjlJrsoBsvVyD4E8ClwNe4HeBE8CngCxwHPgF0zQz27REoXiqBpBCx6cj5NoGvQ3ownpd+zY6Nh0h99r2aazXtjTws+i5s+0Mw/gR4COmad5pGMZLqHA8DMP4WeDfYh2v3zVN8yutXocyZJa7yU0WAD6INVlAts9PAQumab4CeBPwv4H/CXw49zkHuckPsj0qTNXQ8ekAhmHcCbwMeDnwKmAcHZtO8mbAbZrmy4DfxppUo+OzjQzD+FXgLwBf7lNlxyM38vHfYT2v3gD8N8MwvK1eiwIyS93JArKl7gN+o+DjFFablG/nPv5H4LVbvSgpUjpVQ8enM7wBq83Q/cDfA19Bx6aTPA24c7syQSCJjs92Ow28o+DjSsfjpcCDpmnGTdNcAU4B17d6IQrILBUnC2zXYi51pmlGTNMMG4bRC3wO+DDgKGiTEgb6tm2Bl7jCqRoFn9bx6Qy7sU4o7wHeD/wtVmNuHZvOEMHarjyJNcP5j9BzZ1uZpvl5rMDYVul4lMYIbTlOCsgsjUwWkC1kGMY48C/AX5um+X+BwpqKXmB5O9YlgDVV43W5Bs43An8FDBfcruOzfRaAr5ummciNpYtR/MahY7O9/j3W8bkGq2b501i1fjYdn+1X6b2mNEZoy3FSQGZ5EGtvnxqTBWSLGIaxB/gn4NdM0/xk7tOP5epjwKor++52rE2sqRqmab7KNM07gceB/wf4Rx2fjvA94I2GYTgMwxgFuoFv6th0jCXWMy2LgAe9tnWaSsfjYeAVhmH4DMPoAyawCv5bSttylrLJAtu8nkvdfwIGgN8wDMOuJfsl4I8Mw+gCprC2MqVzfAD4cx2f7WWa5lcMw3gl1huIE/gF4Dl0bDrFHwCfNAzju1iZsf8EHEXHp5OUvZaZppk2DOOPsIIzJ/DrpmnGWv3A6tQvIiIiss20ZSkiIiKyzRSQiYiIiGwzBWQiIiIi20wBmYiIiMg2U0AmIiIiss0UkImIiIhsMwVkInJJMwxj0DCMd+f+/UHDMF66ifvyGYbx3tatTkQuFQrIRORSdz3wNgDTNP+7aZoPb+K+9gIKyESkaWoMKyI7Rm6w+ZuBAHAV8BHTND9V5Wt/EXg3kAU+Y5rmHxmG8Q7g17CGCT+PNfbp61hzBT8MvAz4DFZgdRfgB0aAPwR+DLgW+I+maX7JMIz/F3gH1vibldy//wR4F/BRrMHRf4M1mNgNfNg0zQcMwzgOPA3ETdP8iRb9akRkh1OGTER2mj7TNN+KldX6YKUvMAzjIFZgdEfuv7sNwzCAnwD+wDTNO7DmpQaB3wMeME3zz0ruptc0zTcDHwF+Divgeh/wHsMwnMAu4LWmab4CKyi7NXdfJ0zT/G2sAO8bpmm+ErgH+Mvc9/UAv6NgTEQKKSATkZ3m8dz/pwFfla+5FtgPfBN4ACt4egnwH4BXGobxbaxsWKbG4zyW+/8yMGWaZhZrOLTPNM0MkAD+zjCMvwTGsIKyQhPAdwBM0zwDhICh3G1mvR9SRC4tCshEZKdppM7CBCaBV5umeSfwKeAprAzXb5mm+SrAAbwdKyir9FpY9XEMw7geuNs0zXcBv5j7fkfJfU0Br8h9/T5gAFjI3VYrEBSRS5ACMhG56Jim+QRWdux7hmEcBa4GzgAPA98wDOMBrDqxrwCngesMw/jlJh7iFLCau+9vAGeBUWAO6DIM4yPAfwVeYxjGd4AvAu8zTTPVgh9PRC5CKuoXERER2Wbu7V6AiMhGGYbxPqwrKUt9yDTNH2z1ekRENkoZMhEREZFtphoyERERkW2mgExERERkmykgExEREdlmCshEREREtpkCMhEREZFt9v8HtZkVPG+I1ucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "plt.plot(n_values,accuracy_scores)\n",
    "plt.xlabel('n_estimator')\n",
    "plt.ylabel('Accuracy Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3657c8b9",
   "metadata": {},
   "source": [
    "Looking at the plot above, we can see that the value of n_estimator=19 is a resonable value to choose that optimizes the accuracy score of the random forest model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f897c452",
   "metadata": {},
   "source": [
    "**Retrain with Optimized n_values**\n",
    "\n",
    "We will re-train the random forest model with the optimal value of n_estimator that we found above using the same training data as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d2468433",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=10)\n",
    "rf.fit(X_train_scaled,y_train)\n",
    "y_predict = rf.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70dfb2b",
   "metadata": {},
   "source": [
    "**Check confusion matrix and classification report for the optimized random forest model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "afbf33c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[45  6]\n",
      " [13 94]]\n",
      "\n",
      "Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           +       0.78      0.88      0.83        51\n",
      "           -       0.94      0.88      0.91       107\n",
      "\n",
      "    accuracy                           0.88       158\n",
      "   macro avg       0.86      0.88      0.87       158\n",
      "weighted avg       0.89      0.88      0.88       158\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Confusion matrix:')\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print('\\nClassification report:')\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79bc457",
   "metadata": {},
   "source": [
    "## Model Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffd4541",
   "metadata": {},
   "source": [
    "As we can see from comparing the three models below, the optimized random forest model out-performed the logistic regression model and the optimised KNN model."
   ]
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    "| Model | Precision | Recall | f1-score\n",
    "| --- | --- | --- | --- |\n",
    "| Logistic regression | 0.87 | 0.86 | 0.86 |\n",
    "| KNN | 0.81 | 0.82 | 0.81 |\n",
    "| Random forest | 0.89 | 0.88 | 0.88 |"
   ]
  }
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