ML_Final_Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-10-27 23:51:28--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23101 (23K) [text/csv]\n",
      "Saving to: ‘loan_train.csv’\n",
      "\n",
      "loan_train.csv      100%[===================>]  22.56K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2020-10-27 23:51:28 (1.21 MB/s) - ‘loan_train.csv’ saved [23101/23101]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>9/22/2016</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30       9/8/2016   \n",
       "1           2             2     PAIDOFF       1000     30       9/8/2016   \n",
       "2           3             3     PAIDOFF       1000     15       9/8/2016   \n",
       "3           4             4     PAIDOFF       1000     30       9/9/2016   \n",
       "4           6             6     PAIDOFF       1000     30       9/9/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   45  High School or Below    male  \n",
       "1  10/7/2016   33              Bechalor  female  \n",
       "2  9/22/2016   27               college    male  \n",
       "3  10/8/2016   28               college  female  \n",
       "4  10/8/2016   29               college    male  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0 2016-10-07   45  High School or Below    male  \n",
       "1 2016-10-07   33              Bechalor  female  \n",
       "2 2016-09-22   27               college    male  \n",
       "3 2016-10-08   28               college  female  \n",
       "4 2016-10-08   29               college    male  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       260\n",
       "COLLECTION     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()\n",
    "#df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAADQCAYAAABStPXYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZtklEQVR4nO3de3hU9b3v8fdHSI0I1htqJIVExQsIO2p6rFVbxMtDvYHbe9GCx25OrTeOpW61tj27nsdS8fHS7a3WqrQVlFpvpacqUtiKFStiFBGLbk0xFRSwrVJBQb/nj1lJAwQySdZkFjOf1/PMMzNr1vqt7wr58p3fbya/nyICMzOzrNmq2AGYmZm1xQXKzMwyyQXKzMwyyQXKzMwyyQXKzMwyyQXKzMwyyQUqZZJ2lTRF0huSnpf0jKSTUmp7mKTpabTVHSTNllRf7Dis+EopLyT1lfSspBckHV7A86wqVNtbCheoFEkS8BDwZETsEREHAWcA1UWKp2cxzmvWWgnmxZHAqxFxQEQ8lUZM1jYXqHQNBz6OiNuaN0TEnyPiPwEk9ZA0SdJzkl6S9L+S7cOS3sb9kl6VdE+S1EgakWybA/xrc7uStpV0Z9LWC5JGJtvHSvqVpN8Aj3flYiTdLelWSbOSd75fTs65SNLdrfa7VdI8SQsl/ccm2jomedc8P4mvd1disy1KyeSFpDrgGuBYSQ2SttnU77akRklXJ6/Nk3SgpMck/bekbyT79JY0Mzl2QXO8bZz3261+Pm3mWEmKCN9SugEXAddv5vVxwJXJ462BeUAtMAz4O7l3lFsBzwCHAZXAW8BAQMA0YHpy/NXAWcnj7YHFwLbAWKAJ2HETMTwFNLRxO6qNfe8G7k3OPRJ4HxiSxPg8UJfst2Ny3wOYDQxNns8G6oGdgSeBbZPt/w58r9j/Xr51z60E82IscFPyeJO/20AjcF7y+HrgJaAP0Bd4N9neE9iuVVuvA0qer0rujwFuT651K2A68KVi/7t2x81DQAUk6WZyCfVxRHye3C/aUEmnJLt8llySfQz8MSKakuMagBpgFfBmRLyWbP8luWQmaetESROS55VA/+TxjIh4r62YIqKjY+a/iYiQtAB4JyIWJLEsTGJsAE6TNI5cslUBg8glY7MvJNueTt4Af4bcfzZWhkokL5q197v9SHK/AOgdER8AH0haI2l74B/A1ZK+BHwK9AN2BZa1auOY5PZC8rw3uZ/Pk52MeYvhApWuhcDJzU8i4nxJO5N7Rwi5d0AXRsRjrQ+SNAz4qNWmT/jnv82mJksUcHJE/GmDtg4m90vf9kHSU+TexW1oQkQ80cb25rg+3SDGT4GekmqBCcDnI+KvydBfZRuxzoiIMzcVl5W0UsyL1ufb3O/2ZvMHGE2uR3VQRKyV1Ejb+fPDiPjJZuIoSf4MKl2/ByolnddqW69Wjx8DzpNUASBpb0nbbqa9V4FaSXsmz1snwWPAha3G5A/IJ8CIODwi6tq4bS4JN2c7con/d0m7Al9pY5+5wKGS9kpi7SVp706ez7Y8pZwXXf3d/iy54b61ko4ABrSxz2PA/2z12VY/Sbt04BxbLBeoFEVuwHgU8GVJb0r6IzCZ3Lg0wB3AK8B8SS8DP2EzvdiIWENu6OK3yYfBf2718lVABfBS0tZVKV9OXiLiRXJDDwuBO4Gn29hnOblx+6mSXiKX1Pt2Y5hWRKWcFyn8bt8D1EuaR6439Wob53gcmAI8kwy130/bvb2S0/xhnJmZWaa4B2VmZpnkAmVmZpnkAmVmZpnkAmVmZpnUrQVqxIgRQe7vF3zzrRxuneI88a0Mb23q1gK1YsWK7jyd2RbJeWKW4yE+MzPLJBcoMzPLJBcoMzPLJE8Wa2Ylb+3atTQ1NbFmzZpih1LWKisrqa6upqKiIq/9XaDMrOQ1NTXRp08fampqSOaRtW4WEaxcuZKmpiZqa2vzOsZDfGZW8tasWcNOO+3k4lREkthpp5061It1gbKyMqCqCkmp3AZUVRX7cqwDXJyKr6P/Bh7is7KyZNkymnavTqWt6rebUmnHzNrmHpSZlZ00e9L59qZ79OhBXV0d+++/P6eeeioffvghAOvWrWPnnXfm8ssvX2//YcOGMW9ebtHhmpoahgwZwpAhQxg0aBBXXnklH330zwV6Fy5cyPDhw9l7770ZOHAgV111Fc1LKd1999307duXuro66urq+NrXvgbA2LFjqa2tbdn+4x//OJWfbZry6kFJ+t/A18lNSbEAOIfcipj3ATVAI3BaRPy1IFGamaUozZ405Neb3mabbWhoaABg9OjR3HbbbVxyySU8/vjj7LPPPkybNo2rr756k8Ngs2bNYuedd2bVqlWMGzeOcePGMXnyZFavXs2JJ57IrbfeyjHHHMOHH37IySefzC233ML5558PwOmnn85NN920UZuTJk3ilFNO6fyFF1i7PShJ/YCLgPqI2B/oAZwBXAbMjIiBwMzkuZmZtePwww/n9ddfB2Dq1KlcfPHF9O/fn7lz57Z7bO/evbntttt46KGHeO+995gyZQqHHnooxxxzDAC9evXipptuYuLEiQW9hu6Q7xBfT2AbST3J9ZzeBkaSW7aZ5H5U6tGZmZWYdevW8bvf/Y4hQ4awevVqZs6cyfHHH8+ZZ57J1KlT82pju+22o7a2ltdee42FCxdy0EEHrff6nnvuyapVq3j//fcBuO+++1qG8u66666W/b797W+3bF+wYEF6F5mSdgtURPwFuBZYAiwF/h4RjwO7RsTSZJ+lwC5tHS9pnKR5kuYtX748vcjNSojzpPStXr2auro66uvr6d+/P+eeey7Tp0/niCOOoFevXpx88sk8+OCDfPLJJ3m11/wZU0Rscliwefvpp59OQ0MDDQ0NnHPOOS2vT5o0qWX7kCFDuniF6Wv3MyhJO5DrLdUCfwN+JemsfE8QEbcDtwPU19dvclp1s3LmPCl9rT+DajZ16lSefvppampqAFi5ciWzZs3iqKOO2mxbH3zwAY2Njey9994MHjyYJ598cr3X33jjDXr37k2fPn3SvIRul88Q31HAmxGxPCLWAg8AXwTekVQFkNy/W7gwzcxKy/vvv8+cOXNYsmQJjY2NNDY2cvPNN7c7zLdq1Sq++c1vMmrUKHbYYQdGjx7NnDlzeOKJJ4BcT+2iiy7i0ksv7Y7LKKh8vsW3BPiCpF7AauBIYB7wD2AMMDG5f7hQQZqZpan/brul+nds/XfbrcPHPPDAAwwfPpytt966ZdvIkSO59NJL1/sKebMjjjiCiODTTz/lpJNO4rvf/S6Q65k9/PDDXHjhhZx//vl88sknnH322VxwwQWdv6CMUPM45mZ3kv4DOB1YB7xA7ivnvYFpQH9yRezUiHhvc+3U19dH8/f6zYpBUqp/qNtO/nRq6gLnSfoWLVrEfvvtV+wwjE3+W7SZK3n9HVREfB/4/gabPyLXmzIzM0udZ5IwM7NMcoEyM7NMcoEyM7NMcoEyM7NMcoEyM7NMcoEys7Kze3X/VJfb2L26f7vnXLZsGWeccQZ77rkngwYN4thjj2Xx4sXtLpXR1t8z1dTUsGLFivW2bbisRl1dHa+88goAixcv5thjj2WvvfZiv/3247TTTltvfr7evXuzzz77tCzHMXv2bI4//viWth966CGGDh3Kvvvuy5AhQ3jooYdaXhs7diz9+vVr+dutFStWtMyM0VVesNDMys7Sv7zFwd97NLX2nv3BiM2+HhGcdNJJjBkzhnvvvReAhoYG3nnnHcaOHbvZpTI6oq1lNdasWcNxxx3HddddxwknnADklu7o27dvy9RLw4YN49prr6W+vh6A2bNntxz/4osvMmHCBGbMmEFtbS1vvvkmRx99NHvssQdDhw4Fcmtd3XnnnZx33nkdjnlz3IMyMyuwWbNmUVFRwTe+8Y2WbXV1dSxevLjgS2VMmTKFQw45pKU4QW5Wiv333z+v46+99lquuOIKamtrAaitreXyyy9n0qRJLfuMHz+e66+/nnXr1qUWN7hAmZkV3Msvv7zRkhhAXktldETrYbu6ujpWr169yXPnq60Y6+vrWbhwYcvz/v37c9hhh/GLX/yi0+dpi4f4zMyKJJ+lMjpiUyvndkVbMba17YorruDEE0/kuOOOS+3c7kGZmRXY4MGDef7559vcvuG8i2kvlbGpc3fk+A1jnD9/PoMGDVpv21577UVdXR3Tpk3r9Lk25AJlZlZgw4cP56OPPuKnP/1py7bnnnuOgQMHFnypjK9+9av84Q9/4Le//W3LtkcffTTvFXQnTJjAD3/4QxobGwFobGzk6quv5lvf+tZG+37nO9/h2muvTSVu8BCfmZWhqn6fa/ebdx1tb3Mk8eCDDzJ+/HgmTpxIZWUlNTU13HDDDe0ulXH33Xev97XuuXPnAjB06FC22irXxzjttNMYOnQo9913H3PmzGnZ95ZbbuGLX/wi06dPZ/z48YwfP56KigqGDh3KjTfemNe11dXV8aMf/YgTTjiBtWvXUlFRwTXXXENdXd1G+w4ePJgDDzyQ+fPn59V2e/JabiMtXkbAis3LbZQnL7eRHR1ZbsNDfGZmlkmZK1ADqqpS++vuAVVVxb4cMzPrpMx9BrVk2bJUh2DMzGDzX+m27tHRj5Qy14MyM0tbZWUlK1eu7PB/kJaeiGDlypVUVlbmfUzmelBmZmmrrq6mqamJ5cuXFzuUslZZWUl1df4jZC5QZlbyKioqWuaSsy2Hh/jMzCyTXKDMzCyTXKDMzCyTXKDMzCyTXKDMzCyT8ipQkraXdL+kVyUtknSIpB0lzZD0WnK/Q6GDNTOz8pFvD+pG4NGI2Bf4F2ARcBkwMyIGAjOT52ZmZqlot0BJ2g74EvAzgIj4OCL+BowEJie7TQZGFSZEMzMrR/n0oPYAlgN3SXpB0h2StgV2jYilAMn9Lm0dLGmcpHmS5vmvuM3a5jwx21g+BaoncCBwa0QcAPyDDgznRcTtEVEfEfV9+/btZJhmpc15YraxfApUE9AUEc8mz+8nV7DekVQFkNy/W5gQzcysHLVboCJiGfCWpH2STUcCrwCPAGOSbWOAhwsSoZmZlaV8J4u9ELhH0meAN4BzyBW3aZLOBZYApxYmRLP0qEdFauuEqUdFKu2YWdvyKlAR0QDUt/HSkalGY1Zg8claDv7eo6m09ewPRqTSjpm1zTNJmJlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJrlAmZlZJuVdoCT1kPSCpOnJ8x0lzZD0WnK/Q+HCNDOzctORHtTFwKJWzy8DZkbEQGBm8tzMzCwVeRUoSdXAccAdrTaPBCYnjycDo1KNzMzMylq+PagbgEuBT1tt2zUilgIk97u0daCkcZLmSZq3fPnyrsRqVrKcJ2Yba7dASToeeDcinu/MCSLi9oioj4j6vn37dqYJs5LnPDHbWM889jkUOFHSsUAlsJ2kXwLvSKqKiKWSqoB3CxmomZmVl3Z7UBFxeURUR0QNcAbw+4g4C3gEGJPsNgZ4uGBRmplZ2enK30FNBI6W9BpwdPLczMwsFfkM8bWIiNnA7OTxSuDI9EMyMzPzTBJmZpZRLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBFMKCqCkmp3AZUVRX7cszMCqJD60FZOpYsW0bT7tWptFX9dlMq7ZiZZY17UGZmlkkuUGZmlkkuUGZmlkkuUGZmlkkuUGZmlkkuUGZmlkkuUGZmlkkuUGZmlkkuUGZmlkntFihJn5M0S9IiSQslXZxs31HSDEmvJfc7FD5cMzMrF/n0oNYB34qI/YAvAOdLGgRcBsyMiIHAzOS5mZlZKtotUBGxNCLmJ48/ABYB/YCRwORkt8nAqALFaGZmZahDn0FJqgEOAJ4Fdo2IpZArYsAumzhmnKR5kuYtX768i+GalSbnidnG8i5QknoDvwbGR8T7+R4XEbdHRH1E1Pft27czMZqVPOeJ2cbyKlCSKsgVp3si4oFk8zuSqpLXq4B3CxOimZmVo3y+xSfgZ8CiiLiu1UuPAGOSx2OAh9MPz8zMylU+CxYeCpwNLJDUkGy7ApgITJN0LrAEOLUgEZqZWVlqt0BFxBxAm3j5yHTDMTOzYhtQVcWSZctSaav/brvx56VLO3Wsl3w3M7P1LFm2jKbdq1Npq/rtpk4f66mOLPMGVFUhKZVbqUjzZzKgqqrYl2PWJvegLPOy8m4uS/wzsXLgHpSZmWVSSfegtobUhnW68kGfdY16VPhdvlkZKukC9RF4GKQExCdrOfh7j6bS1rM/GJFKO2ZWeB7iMzOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTCrpqY7MzKzj0pz/Uj0qOn2sC5SZma0nK/NfeojPrMw1z/rvxQ8ta9yDMitznvXfsso9KDMzyyQXKCuI3av7pzZsZGblyUN8VhBL//JWJj5kNbMtV+YKVFa+3mhmxTWgqooly5al0lb/3Xbjz0uXptKWdZ/MFaisfL1xS9H8Daw0OIktS5YsW+Yvb5S5LhUoSSOAG4EewB0RMTGVqCxv/gaWmZWqTn9JQlIP4GbgK8Ag4ExJg9IKzMwsLVn9W68BVVWpxdWrR8+S+2JSV3pQ/wN4PSLeAJB0LzASeCWNwMzM0pLVkYa0hzGzeI1doYjo3IHSKcCIiPh68vxs4OCIuGCD/cYB45Kn+wB/aqfpnYEVnQpqy+FrLA3tXeOKiMjrg1DnSZt8jaUhn2tsM1e60oNqqx+4UbWLiNuB2/NuVJoXEfVdiCvzfI2lIc1rdJ5szNdYGrpyjV35Q90m4HOtnlcDb3ehPTMzsxZdKVDPAQMl1Ur6DHAG8Eg6YZmZWbnr9BBfRKyTdAHwGLmvmd8ZEQtTiCnvYY4tmK+xNBTzGv3zLQ2+xs3o9JckzMzMCsmTxZqZWSa5QJmZWSZlpkBJGiHpT5Jel3RZseNJm6TPSZolaZGkhZIuLnZMhSKph6QXJE0vdiyFIGl7SfdLejX59zykG89d0nkC5ZMrpZ4n0PVcycRnUMm0SYuBo8l9ff054MyIKJlZKSRVAVURMV9SH+B5YFQpXWMzSZcA9cB2EXF8seNJm6TJwFMRcUfyDdZeEfG3bjhvyecJlE+ulHqeQNdzJSs9qJZpkyLiY6B52qSSERFLI2J+8vgDYBHQr7hRpU9SNXAccEexYykESdsBXwJ+BhARH3dHcUqUfJ5AeeRKqecJpJMrWSlQ/YC3Wj1vosR+IVuTVAMcADxb5FAK4QbgUuDTIsdRKHsAy4G7kuGZOyRt203nLqs8gZLOlRso7TyBFHIlKwUqr2mTSoGk3sCvgfER8X6x40mTpOOBdyPi+WLHUkA9gQOBWyPiAOAfQHd9FlQ2eQKlmytlkieQQq5kpUCVxbRJkirIJdw9EfFAseMpgEOBEyU1kht+Gi7pl8UNKXVNQFNENL+jv59cEnbXuUs+T6Dkc6Uc8gRSyJWsFKiSnzZJuUVWfgYsiojrih1PIUTE5RFRHRE15P4Nfx8RZxU5rFRFxDLgLUn7JJuOpPuWmCn5PIHSz5VyyBNIJ1cyseR7AadNypJDgbOBBZIakm1XRMT/K15I1kkXAvckReIN4JzuOGmZ5Ak4V0pJl3IlE18zNzMz21BWhvjMzMzW4wJlZmaZ5AJlZmaZ5AJlZmaZ5AJlZmaZ5AKVIZL+j6QJKba3r6SGZJqRPdNqt1X7jZJ2Trtds81xnpQPF6jSNgp4OCIOiIj/LnYwZhk1CudJJrlAFZmk7yTr+zwB7JNs+zdJz0l6UdKvJfWS1EfSm8kUMEjaLnlnViGpTtJcSS9JelDSDpKOBcYDX0/W1rlF0onJsQ9KujN5fK6k/5s8PkvSH5N3kz9JlndA0jGSnpE0X9KvkjnSWl/DNpIelfRv3fVzs/LiPClPLlBFJOkgclOdHAD8K/D55KUHIuLzEfEv5JYaODdZdmA2uSn6SY77dUSsBX4O/HtEDAUWAN9P/ur+NuD6iDgCeBI4PDm2HzAoeXwY8JSk/YDTgUMjog74BBidDE1cCRwVEQcC84BLWl1Gb+A3wJSI+Gk6Pxmzf3KelC8XqOI6HHgwIj5MZmtunldtf0lPSVoAjAYGJ9vv4J9ThZxDbhr7zwLbR8R/Jdsnk1uDZUNPAYdLGkRuPqx3lFsY7hDgD+TmyToIeC6ZXuZIctPlf4Fckj6dbB8DDGjV7sPAXRHx887/GMw2y3lSpjIxF1+Za2uuqbvJrSD6oqSxwDCAiHhaUo2kLwM9IuLlJPHaP0nEXyTtAIwg9y5xR+A0YFVEfCBJwOSIuLz1cZJOAGZExJmbaPpp4CuSpoTnzbLCcZ6UIfegiutJ4KRkbLoPcEKyvQ+wNBlHH73BMT8HpgJ3AUTE34G/Smoeljgb+C/a9gy58fYnyb1TnJDcA8wETpG0C4CkHSUNAOYCh0raK9neS9Lerdr8HrASuKWD126WL+dJmXKBKqJkWev7gAZya980J8F3ya0gOgN4dYPD7gF2IJd8zcYAkyS9BNQBP9jEKZ8CekbE68B8cu8On0pieYXcGPrjSTszgKqIWA6MBaYm2+cC+27Q7nigUtI1+V25Wf6cJ+XLs5lvYSSdAoyMiLOLHYtZVjlPSoM/g9qCSPpP4CvAscWOxSyrnCelwz0oMzPLJH8GZWZmmeQCZWZmmeQCZWZmmeQCZWZmmeQCZWZmmfT/AcKH/fljK6RSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
    "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below    male          3        0  \n",
       "1 2016-10-07   33              Bechalor  female          3        0  \n",
       "2 2016-09-22   27               college    male          3        0  \n",
       "3 2016-10-08   28               college  female          4        1  \n",
       "4 2016-10-08   29               college    male          4        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  loan_status\n",
       "female  PAIDOFF        0.865385\n",
       "        COLLECTION     0.134615\n",
       "male    PAIDOFF        0.731293\n",
       "        COLLECTION     0.268707\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below       0          3        0  \n",
       "1 2016-10-07   33              Bechalor       1          3        0  \n",
       "2 2016-09-22   27               college       0          3        0  \n",
       "3 2016-10-08   28               college       1          4        1  \n",
       "4 2016-10-08   29               college       0          4        1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education             loan_status\n",
       "Bechalor              PAIDOFF        0.750000\n",
       "                      COLLECTION     0.250000\n",
       "High School or Below  PAIDOFF        0.741722\n",
       "                      COLLECTION     0.258278\n",
       "Master or Above       COLLECTION     0.500000\n",
       "                      PAIDOFF        0.500000\n",
       "college               PAIDOFF        0.765101\n",
       "                      COLLECTION     0.234899\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "# remove Master or Above attribute from education object\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Principal', 'terms', 'age', 'Gender', 'weekend', 'Bechalor',\n",
       "       'High School or Below', 'college'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/preprocessing/data.py:625: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:1: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         PAIDOFF\n",
       "1         PAIDOFF\n",
       "2         PAIDOFF\n",
       "3         PAIDOFF\n",
       "4         PAIDOFF\n",
       "          ...    \n",
       "341    COLLECTION\n",
       "342    COLLECTION\n",
       "343    COLLECTION\n",
       "344    COLLECTION\n",
       "345    COLLECTION\n",
       "Name: loan_status, Length: 346, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = df['loan_status']\n",
    "y = target\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 classid=\"knn\">K-Nearest neighbor (KNN)</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Train set: (242, 8) (242,)\n",
      "KNN Test set: (104, 8) (104,)\n"
     ]
    }
   ],
   "source": [
    "# Import K-Nearest Neighbor (KNN) library\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "# spliting the dataset into Train/Test sets mutually exclusive, which will provide a more accurate evaluation on out-of-sample accuracy\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# starting number of k-neighbor\n",
    "k = 1 \n",
    "\n",
    "# KNN features\n",
    "KNN_X = X\n",
    "# KNN target\n",
    "KNN_y = y\n",
    "\n",
    "\n",
    "KNN_X_train, KNN_X_test, KNN_y_train, KNN_y_test = train_test_split( KNN_X, KNN_y, test_size=0.3, random_state=4)\n",
    "\n",
    "print ('KNN Train set:', KNN_X_train.shape,  KNN_y_train.shape)\n",
    "print ('KNN Test set:', KNN_X_test.shape,  KNN_y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Model and Predict  \n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(KNN_X_train,KNN_y_train)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_yhat = neigh.predict(KNN_X_test)\n",
    "KNN_yhat[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.897\n",
      "Test set Accuracy:  0.712\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", round(metrics.accuracy_score(KNN_y_train, neigh.predict(KNN_X_train)),3))\n",
    "print(\"Test set Accuracy: \", round(metrics.accuracy_score(KNN_y_test, KNN_yhat),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.71153846, 0.625     , 0.72115385, 0.72115385, 0.73076923,\n",
       "       0.71153846, 0.72115385, 0.72115385, 0.75      , 0.68269231,\n",
       "       0.69230769, 0.71153846, 0.71153846, 0.72115385, 0.71153846,\n",
       "       0.71153846, 0.72115385, 0.69230769, 0.70192308])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 20\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "\n",
    "for n in range(1,Ks):    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(KNN_X_train, KNN_y_train)\n",
    "    KNN_yhat=neigh.predict(KNN_X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(KNN_y_test, KNN_yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(KNN_yhat==KNN_y_test)/np.sqrt(KNN_yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABKfklEQVR4nO3dd5xTZdbA8d9JnUYHAUUFCwoyMHQFCyggdtFVUaxY17q61t3Xurp2167runbF3tYuArpW6sCAgCCCImWGztRMkuf9Ixk2DJPkJpM+5/v5sM4k9948yWZy8rRzxBiDUkoplWls6W6AUkop1RQNUEoppTKSBiillFIZSQOUUkqpjKQBSimlVEbSAKWUUiojOZJ5cREZCzwE2IGnjTF3Nbq/DfASsFuwLfcZY561cm5TOnbsaLp3757Q56CUUiq5Zs2atc4Y06nx7ZKsfVAiYgd+AkYDK4EZwKnGmB9DjvkL0MYYc52IdAIWA10AX7RzmzJo0CAzc+bMZDwdpZRSSSIis4wxgxrfnswhviHAUmPMMmOMB3gVOK7RMQZoJSICFAEbAK/Fc5VSSuWwZAaoXYDfQn5fGbwt1KNAL2AVUAZcYYzxWzxXKaVUDktmgJImbms8nng4UArsDJQAj4pIa4vnBh5E5AIRmSkiMysqKuJvrVJKqYySzEUSK4FdQ37vRqCnFOoc4C4TmAhbKiK/APtaPBcAY8xTwFMQmINKTNOVUgrq6+tZuXIltbW16W5KTsjLy6Nbt244nU5LxyczQM0A9haRHsDvwHjgtEbH/AocBvxXRDoD+wDLgE0WzlVKqaRauXIlrVq1onv37gSmylW8jDGsX7+elStX0qNHD0vnJC1AGWO8InIp8CmBpeLPGGMWiMhFwfufBP4GPCciZQSG9a4zxqwDaOrcZLVVKaWaUltbq8EpQUSEDh06EMtUTFL3QRljPgI+anTbkyE/rwLGWD1XKaVSTYNT4sT6WmomCaWUUhlJA5RSSmW4d955BxFh0aJF6W5KSmmAUipJajy+dDdB5YhJkyZx4IEH8uqrryb1cXy+zHrPaoBSKgmqPV621tWnuxkqB1RWVvLNN9/w73//e7sA5fP5uPrqqykuLqZv37488sgjAMyYMYNhw4bRr18/hgwZwtatW3nuuee49NJLt5179NFHM23aNACKioq46aabGDp0KN999x233XYbgwcPpk+fPlxwwQU0pMNbunQpo0aNol+/fgwYMICff/6ZM844g/fee2/bdSdMmMD777+fsOee1EUSSrVEXp+fylovBvB4/bgc+j0wF/zpkz9RuqY0odcs6VLCg2MfjHjMu+++y9ixY+nZsyft27dn9uzZDBgwgKeeeopffvmFOXPm4HA42LBhAx6Ph1NOOYXXXnuNwYMHs2XLFvLz8yNev6qqij59+nDbbbcB0Lt3b2666SYAzjjjDD744AOOOeYYJkyYwPXXX8+4ceOora3F7/dz3nnn8Y9//IPjjjuOzZs38+233/L8888n5LUB7UEplXBbgsEJoM6bWUMmKvtMmjSJ8ePHAzB+/HgmTZoEwOTJk7noootwOAL9jPbt27N48WK6du3K4MGDAWjduvW2+8Ox2+2ceOKJ236fOnUqQ4cOpbi4mClTprBgwQK2bt3K77//zrhx44DAhtuCggIOOeQQli5dSnl5OZMmTeLEE0+M+nix0B6UUglUVeel3uff9nud10+rNLZHJU60nk4yrF+/nilTpjB//nxEBJ/Ph4hwzz33YIzZYdl2U7cBOBwO/P7/vS9DM2Pk5eVht9u33X7xxRczc+ZMdt11V2655RZqa2uJVPXijDPO4OWXX+bVV1/lmWeeae5T3o72oJRKEK/PT1Wdd7vbfH6Dz68ZuFR83nzzTc4880xWrFjB8uXL+e233+jRowdff/01Y8aM4cknn8TrDbznNmzYwL777suqVauYMWMGAFu3bsXr9dK9e3dKS0vx+/389ttvTJ8+vcnHawhcHTt2pLKykjfffBMI9MS6devGu+++C0BdXR3V1dUAnH322Tz44IMA7Lfffgl9/hqglEoAYwyba+qbzGisw3wqXpMmTdo2rNbgxBNP5JVXXuG8885jt912o2/fvvTr149XXnkFl8vFa6+9xmWXXUa/fv0YPXo0tbW1DB8+nB49elBcXMzVV1/NgAEDmny8tm3bcv7551NcXMzxxx+/bagQ4MUXX+Thhx+mb9++DBs2jDVr1gDQuXNnevXqxTnnnJPw55+0goXpoAULVbpU1nl36D01cDtstC1wpbhFKhEWLlxIr1690t2MjFZdXU1xcTGzZ8+mTZs2UY9v6jVNR8FCpVoEj3fHob3G9+fSF0GlGkyePJl9992Xyy67zFJwipUuklCqGYwxbKmNvN/JEFgskee0p6ZRSqXIqFGj+PXXX5N2fe1BKdUMW+u8lhZBeEJW9imlrNEApVSc6rw+y+mM6uo1QCkVKw1QSsXBGMOWmvDzTo35jcGrvSilYqIBSqk4bKn14o9x4UOdVwOUUrHQAKVUjGrrfdTWx763yaMBKuut3VKb0H+J8t1333H++edHPObJJ5+kuLiYkpISDjzwQH788ceYH2fTpk08/vjjYe8/++yzt23uTQQNUErFwO+PvmovHI9Pl5ur+E2bNo2zzz67yfs++eQTxo4dG/H80047jbKyMkpLS7n22mu56qqrYm5DtACVaBqglIrB1lovzYkxOsynkuGLL75g1KhREY9p3br1tp+rqqq25ex75513GDVqFMYYVq9eTc+ePVmzZg0LFixgyJAhlJSU0LdvX5YsWcL111/Pzz//TElJCddccw3GGC699FJ69+7NUUcdRXl5eUKfl+6DUsqi2noftc1MW6T7oVSirVu3DqfTaWmj7GOPPcYDDzyAx+NhypQpAIwbN4633nqLxx57jE8++YRbb72VLl26cMcdd3DFFVcwYcIEPB4PPp+Pu+66i/nz51NaWgrA22+/zeLFiykrK2Pt2rX07t2biRMnJuy5aYBSlhljqKn3Ue81tM53NJk1OVf5mjG0FyqQl8/Z/AapFmPo0KHU1dVRWVnJhg0bKCkpAeDuu+/m8MMP57PPPmPMmDGWrnXJJZdwySWX8Morr3D77bdvq930yCOP0KdPH/bff39OPfVUAA444ADuuOMOVq5cyQknnMDee++9w/W++uorTj31VOx2OzvvvDOHHnpoYp50kA7xqaiMMVTVeVlX6WFrrZdar49N1fUtaj5lS019s4b2GhjDduU4lIrmhx9+oLS0lKeffppjjz2W0tJSSktLOfzwwwH4+OOPt80/nXPOOZSUlHDkkUdGvOb48eO3ZSYH+P3337HZbKxdu3ZbWY7TTjuN999/n/z8fA4//PBtPa7GkvlFVQOUCqshMFVU1lFZt/2yao/Pz8YWEqSqPd6EZoLQeSiVKMYY5s2bt61X9eyzz1JaWspHH320w7FLlizZ9vOHH364rUfk9Xo555xzeOWVV+jVqxcPPPAAAMuWLWOPPfbg8ssv59hjj2XevHm0atWKrVu3brvOwQcfzKuvvorP52P16tVMnTo1oc9Ph/jUDvx+Q3W9j2pP5AUB9cEg1Tbfic2Wm8N9DeXbE8nj9YM7oZdUKdK5dV66m7CdWbNm0b9/f0u9mEcffZTJkyfjdDpp167dtuG9v//97xx00EEcdNBBlJSUMHjwYI466ijeffddXnrpJZxOJ126dOGmm26iffv2DB8+nD59+nDEEUdwzz33MGXKFIqLi+nZsyeHHHJIQp+flttQ21gNTI05bEK7AldOBqkNVZ6kDMl1KnLn5OuVazK93Mbtt9/OXnvtta0kfDaIpdyG9qAUfr+hyuOlxuNrsuBeNF6/YWO1J+eCVOPy7YlU5/WT79LVfKp5/u///i/dTUgqnYNqwRpWpq2rrKM6zuDUoCFI+XOkvHlT5dsTSbNKKBWd9qBaIF+wx1TbzKDUmNdv2FDtoX2W96QilW9PlDqfLjfPFsaYFrWlIplinVLSHlQL4vMHPnjXV9bFPZxn5TE2VHss1UjKVFUeH94kt98Y7UVlg7y8PNavX98iVqsmmzGG9evXk5dnfaGJ9qAy3Oaa+oSVaUj2h24DX8iclD0FPana+kBdplizi4eTqtepzuvD5dDviIlgjKHa48PrMxS47TjtiXldu3XrxsqVK6moqEjI9eJljMEAgpDNnbm8vDy6detm+XgNUBmuzutLyAbRVPP5DRuqPLQvTF6Qqq33UWmxom0mqvP6aZXuRmS5hsBUFbLytNbrw+2wUeh2NDtQOZ1OevTokYCWxicwF/q/FFsuu412ha60tSfVNEBlMJ/fZGVwauA3yQlSNcEPpGwNTA18foPPb1LSy8w10bZE1Hn91Hk9uOyBQJVtPdX64CKdxpu6PT4/1R4vBa6W8dHdMp5llsqFlDgNQapdgRNHM77NGmOorffnRGAKVef1tZgPm0SIda+ex+fHU509gSpcYApVWevF7bC3iC82+peRwXIhQEEgSG2sro8rSDUkqK2qS9wcUybxeP0UtJwRm7g1d69eQ6By2m0Uuu24HZm1B83jDQQmKym1DIG56fYtYKhPA1QG8/py5wPZb/63BN1KkMr1wNTA4/XrMuYImhuYGqv3+dlU7cdp92VEoKrz+qiu88Wc67Ghp1Xozu2P8Nx+dlmu3p8bPagGxsCG4Oq+cJPXTU165zJD4Nt9uj8oM02y9uo1aAhUDlvgQz7VNbrqvIEvX80ZJamq8+J22Jo1dJ7pNEBlKK/Pn5Mf0MawbQl6aJBqaYEpVJ1XA1SDZAemxrzBvYENvZFkB6raeh/VnuYFpgYG2FLrzemhPg1QGSpVe3HSoSFItc134bBJXAlqc0ldvR8yK0l2yvn8hso6L3X1qQlMjTUEqso6L0VJCFS19T6q6rwJ/7vO9aG+3HxWccqkuYBcWSARjjGwqdoDQosNTA38xuD1+bNqqMbr81NZl7gvFYmst9UcvpBAZU/QZ4HPmKSuPK2q8+Jy2BK2OTmTaIAKkUlzAfU5tEAiHLPtf1SdN3sClNfnZ0O1J6e/WPj8hvT05WJnCFR8bl/oypgv2ImSHX8RKZJJQSFR6Y1UdsiWvHz1LSA4ZSOv31Dl8aW7GQmX1AAlImNFZLGILBWR65u4/xoRKQ3+my8iPhFpH7xvuYiUBe9LSRXCTAkKXp8/S767qUSp9/kzPiFpoIKyBqdMlcz6ZemStAAlInbgMeAIoDdwqoj0Dj3GGHOvMabEGFMC3AB8aYzZEHLIyOD9O1RaTAZjMmPuJ5cXSKimGYiYPSDdNDhlh8019Rn/RScWyexBDQGWGmOWGWM8wKvAcRGOPxWYlMT2WJIJASpTJoxV/Krrq3l0xv1srdti+ZxMDVAerwanbNGwGjJXJDNA7QL8FvL7yuBtOxCRAmAs8FbIzQb4TERmicgF4R5ERC4QkZkiMjMRKfHrven/K8ylDBIt1T3f3cbtX/8fby6y/p2rzpt5cwger59NGpyySrXHlzVzmtEkM0A1tZwk3Nv8GOCbRsN7w40xAwgMEV4iIgc3daIx5iljzCBjzKBOnTo1r8VkRu8lU+bCVHxmr5nBU7MfAWDa8smWz8uUIeYG24JTuhuiYralNjeG+pIZoFYCu4b83g1YFebY8TQa3jPGrAr+txx4h8CQYdL5k7xnIRpdIJHdPD4PV332RzoXduGEfU/h69+m4fF5LJ+fKcN8Gpyym89v2JoDQ33JDFAzgL1FpIeIuAgEofcbHyQibYBDgPdCbisUkVYNPwNjgPlJbOt20vktVhdIZLdHZtzHovULuPvQhzlm7xOoqq9kxqrvLZ+fCUMzdV6fBqccUOPxJXXYOFACJ7nD0knbqGuM8YrIpcCngB14xhizQEQuCt7/ZPDQccBnxpiqkNM7A+8EN505gFeMMZ8kq62N1fv8KU8e2SAThhhVfBat+5EHf7iLcfuczJg9j2Jr3RYcNgfTVnzO8F2bHKHeQb3Pj99vsKWp1k+d18fm6noNTjliS42XjkW2hG7gDa00YDBJ/ayUXBinbDBo0CAzc2b8W6Y2Vnnw+Pw47ba0JWDcUOXJqHkIZY3P7+OY10ayfNMyvjprDh0LAvOhx78+iqr6Kj6f8J3la7XOc5LvSv0XJA1OuSnPaadNvrPZ12lI6Fzt+V8JHBHYqVXzE0mKyKymthNpJokmeNO4aVIXSGSnf5c+zuw1M7h95H3bghPAiN1HU1ZeSkXVWsvXSscwX229BqdcVVvva9ZQnDGGqjovFZV1VNZ5U1qfTQNUEwzpSXukCySy04pNv3DnN7cwqscRjNvnlO3uG9l9NABf/jrF8vXqfKldbl5b72NLjQanXLalth5/jPPbjQNTOr6za4AKIx3DbJmUC1BZY4zh6smXYLfZufuwh3YY6y/eqYQO+Z2YtvzzGK6Zul6UBqeWwRjYWmttVZ8/uNk3nYGpgWYzDyMdm2VzrYJuSzBpwQv897ep3H3ow+zSatcd7reJjUN2P5Rpv36B3/ixibXvhHVeHy5Hcr8/1tb72FxTn9THUJmj1uvDXW8Lu6jB7zcZV5tNe1BhpGM1nWaQyC5rKldxy1fXsf8uB3JG33PDHjdi99Gsqy5nfvlcy9dO9n4oDU4tU1NDfQ09pnWVdVSlucfUmAaoMNKxYVcXSGQPYww3TPkTHm8d949+PGLPaMTuhwEwbYX1rBI+f/LefxqcWi5jAkEKAoFpa239/wJTmtvWFA1QEaRyHqpeF0hklQ+WvMPHP/+Hqw/4P/Zst3fEY3cq7MJ+nfoydYX1eShIfG6+hklvDU4tW10wS8i6yjqqPZldllEDVASpHObT4b3ssbF2AzdMvZK+O/XnooFXWDpnxO6jmLHqOyo9Wy0/TqIWSgT2r3hZV+nJqUzXKn513uz4QqwBKoJUBg1dIJE9bv7yOjbWrOeB0U/gsFlbZzRy99F4/V6++e1Ly4/j8TZvP17oMuGttandv6JUImiAiiCVG3a1B5Udpi7/nNd/fIlLB/+ZPjv1s3ze4J0PIN9RwNQY5qEM8fXiM2H/ilKJoMvMI2jYsOtyJDcvmjFGF0hkgUrPVq6efAl7tevJlUNviOlct8PNgbuOiGk/FASGYtwOa2mPGlLRVGXQMmGlmkN7UFGkYqGE12+yYjy4pbvzm5tZtXUlD4x+gjxH7PnHRnQfxfLNy/hl08+Wz6mrj/7+y6SNlUolkgaoKFISoLJoeG/ZxqU8P/dfMdU4ygUzVn3HM6VPck7JhQzZZVhc1xi5eyDt0dQYelH+CL3rbftXqjJv/4pSiaABKopUrOTLhgUSv25ewZWfXcRBz5dw3ZTL+eNHZ1LvaxnLlWu9tVz1+R/ZuVU3/jL8triv06PtnuzWuntM+6Fgx027mbyxUqlE0jmoKIwJbJq0J7E+T30GFKkLZ3Xl7zz4wz28Mv9ZbGLj3JI/0qGgE3d+czOXfXouj419FrstPbWzUuWh6XezZMNiXhn3HkWuVnFfR0QY0X00by2chMfnwWW3VtLF4/VT6A4EpiqPl5oM37uiVKJogLKg3udP2oewSXOJ+XAqqtbyyIz7eH7ev/AbP6f1OYc/Db2WrkW7AOCwOfjbf/+Ky+7mwTH/tJxjLtssqJjHIzPu46ReEzi0+5hmX2/k7qN4Yd6/mLHq+5iKGG6trdfApFocDVAWeJJYYTfTFkhsqFnP4zP/wb9Ln8Djq+Pk3qdz5dAb2K3N7tsdd8mgq/D4PNz97a24bC7uGfVIzgUpr9/LlZ/9kbZ57bn1kLsTcs0Ddx0Rc5VdA1R7UluCQ6lMoAHKgmQOwWVK9dwtdZt5ctbDPDXnEao8lZyw7yn8ef+/ske7vcKec+XQ66nz1vLg9Ltx2p38feQ/ElpaOt3+Ofth5pXP5qmjXqJ9foeEXLOVuzWDug5l2orJ/PXAvyXkmkrlKg1QFnj9BmNMUj58010DqspTydOlj/PEzAfZVLeRo/Y6nmsOuJF9O/a2dP51w26mzlfHE7MexGV3c8vBd+VEkFq2cSn3fvs3jtjzGI7Z+4SEXntk9zHc+c3NVFStpVNh54ReW6lcogHKomRt2E3XBt0abw3Pz32KR2bcz/qaCkb3OJJrh91I8U4lMV1HRLjpoL9T7/Pwz9kP47a7uWH4rVkdpPzGz58nX4zL4ebOQx9M+HMZsfso7vzmZr78dQp/6HVqQq+tVC7RAGVRvc+f8AJy6VggUeet4+X5z/LQ9LtZW7WGQ3Y7jGuH3cjArkPjvqaI8LcR9+HxeXh4xr247G6uPuCvCWx1ar1U9gzfrfwvD4x+gi5FOyf8+qFVdjVAKRWeBiiLkjFXlMoFEvW+el7/8SUe+OFOft/6G/vvMpwnjnyBYd0OSsj1RYS7DnsIj9/Dfd/fjtvu4rIh1yTk2qm0autKbvvvXzhw1xGcut9ZSXmMhiq7U1dMjqnKrlItjQYoi5KxYTda0Pvil085/8MJ+PzNL5HgMz68fi/9uwzigdFPcPBuhyZ86MomNu4f9Tj1Pg93fHMTLoebCwdcntDHaKyiai0Pz7iXVxe8iMdX1+zref1eXHYX9496PKnDlCN2H83bi15jfvlc+nbun7THaSl+rCjjvu9vZ8mGxZxbcjGn9Tnb8j6zTOf1e3lr4as8OvN+jtn7BK4ddmO6m5QyGqAsMiYwX+SwJ+7bbrQFEl8s/wRjDOf3vzQhjzd0l+GM6jE2qR+8dpudhw4PpEK6+cvrcNpcTCy5KOGP03g5/HH7/IGdi7ol5Noju49h97Y9EnKtcEKr7GqAit+SDYu577vbee+nN2ntbkOPtnty/ZQreHTm/Vw19C+c3HuC5ZIomcZv/Lz/05vc+93t/LxxCW3d7Xhw+l2M6nE4A7oOSXfzUkJSVU4iFQYNGmRmzpwZ9/kbqzwRe0qt85zkuxK3H2p9ZR3eCHNQx7w6ErvNzrsnx5YaJxPU++o5/8PT+OTnD7hv1GOcXjwxIdfdXLuJf85+JKbl8JnqsJeG0trdhndO+izdTck6yzct4/7v/85biyaR7yjg/P6XcNHAK2jjbsu0FZO5+9tbKV07iz3a7sWf9/8Lx+9zctZkPDHG8PHP73PPt39j0foF7NthP64ddiMH7jqCES8MpJW7DZ9P+C4jeogisFOr2BMn73gdmWWMGdT4dh38jkEic+YZYyIGJ5/fx/yKefTpZL3mUCZx2p3888iXOKzHWK6ZfCmvLXipWder8lTy0PR7GPJMLx744e+M2H0UU8+YyWNHPJuVwQniq7Lb0q3c8itXf34JBz7fj//89BYXDricHyb+yPXDb6FtXjtEhJHdR/Pxqf/l+WPfIN9ZwCWfTGTki4P5YMk7+E1m7DtsijGGyb98wuGvHMjE/4yn3u/hySOfZ8oZ0zlyr+No7W7D3Yc9wuL1P/Lw9HvT3dyU0AAVg0Ru2I02vPfzxiXUeKsp3il7h3/cDjf/PnoSB+02kis/v5B3F78e8zVqvDU8MetBBj/Tizu/uZkhOw/j8wnf8fTRr1jeq5WpRnYfE3OV3ZZqbeVq/jL1SoY9V8zrC1/irL7n88PEH7n54DvpWNBph+NFhMP3PJrPJ3zHU0e9hMHPeR+cxpiXh/HZso9SVojUqq9/ncYxr43k9HfHsal2Aw+N+Rdfnjmb4/c5ebtFNKP3OIIT9j2Fh6bfzcJ1C9LY4tTQABWDhg27iblW5GBXVj4HgL6dSxLyeOmS58jjuWPfYOguw7nk44l8sOQdS+fVeet4pvRJ9n+mN7d+dQPFnfrx0fgvefH4t2Leq5Wphux8AAXOwpiq7LY066oruPWrGxj6TG9emPc0p/Q+g2/PLuOOkQ/Quahr1PNtYuPYnicy7YxZPDr2GSo9WznzvRM56tVD+HLFF2kPVD/8/g0nvHE4f3jrCFZtXck9hz3C12fP5ZT9Tg87d3bbIffSyt2Gqz6/CJ8/t1NgaYCKUaJW80XrQZVVzCXPnsfe7fdNyOOlU4GzgJeOe5sBXQZz0Udn8tnPH4Y9tt5Xz8tlzzLsuWL+MvVKerTdk3dO+pzXTvwg5yaGXXYXw7sdEnOV3ZZgU+1G7vrmFoY804t/zn6YY3qeyNdnzeXeUY/SrfVuMV/PbrPzh16n8t+zSrl/1OOsrVrNKW8fzbg3xvDdyq+T8Awim7NmJqe+fSzHvT6KJRsWc/uI+/j2nPmc2fe8qHNLHQs6cceI+5izZiZPlz6WohanhwaoGCUqNVG0JeZl5aX06tQna1cgNVboKuLlce/Sp1M/zvvwtB2K9vn8Pt748RUOer6EP0++mM6FXXjthA9456TPOaDbgWlqdfLFU2U3l22t28ID39/JkGd68eD0uxnd4wi+PHM2j4x9mu5t92j29Z12JxOKz+Hbs+fz95H/4JdNSxn3xmhOeetoZq+enoBnENmCinmc9d5JHDHpIOaunc2NB93BDxN/5Lz+l8RUpfn4fU5mdI8jueubW1ix6Zcktji9NEDFKBHzUNEySBhjKCsvpbhTSbMfK5O0drfh1RP+Q8/2vTjn/ZP5+tdp+I2f9xa/yYgXB3HZp+dS5GrFC8e9xYfjv+SQ3Q/L6pRJVsRTZTcXVdVX8eiM+xnyTC/u+e42hnc7mCmnT+efR73I3u33SfjjuR1uJpZcxPcTf+SWg++irGIuR756CGe8eyJl5aUJf7yf1i/igg9P57CXhvLd7//lumE3M/3chVwy6CoKnAUxX09EuPuwh7DbHPx58sVpH6pMFl1mHjRz1Uw+XTKV8/pfFvG4RCyr9Hj9bKwOXzJ9xaZfGPpsb+497FHO6Htusx4rE62vWceJb4xlxeZf6N52Dxaum0/P9r24dtiNHLnXcS0qs4IxhqHP9GbfjvvxwnFvprs5lq3Y9AufLfuQRORC2Vy3mRfmPU1F9VpGdh/DtQfcSP8uO6w4TqqGpMmPz/wHm+s2cdRexzF0l+EJufa8tXN4e/Fr5DsKuGDApVw44HLa5rVLyLVfmPc0135xGQ+MfoLT+pydkGvGItnLzHNj/CgBPv/5c/5v2l84sdcE2uW1D3tcIjbsRl0gUVEKkDOLARrrkN+RN078kD+8dSS13hoeG/tMVu1TSaR4q+ym00/rFzHujTGsr6lI2DWHdTuYfx/9CkN2GZawa8ai0FXEFUOu5ey+F2zbZ/fh0vcScu18Rz4XDricSwZd1eSKw+Y4vXgi7yx+nVu+up5Du49JSu7IdNIAFbR/t/2BwORltMqp9T6DoxmfpfXeKAskyktx2Bzs23G/+B8kw3Uq7MyU06djE1vOD+NFE0+V3XRZtnEpJ711BHaxM+X06ezSatdmX9MmNlq5Wyegdc3XJq8t1w67kT8NvY6a+uqEXDPPkY/b4U7ItRprSC926IuDuX7KFTx7zOs59fekASpo0M6DEITZq2dEDVAen5984o9Q0Tb8zisvpWf7XjFNmmajlthjako8VXbTYcXm5fzhzbF4/T7ePulT9unQK91NShqX3ZUVvVmAPdrtxTXDbuRv//0r/1nyNsf2PDHdTUqYljPYH0Urdyv27dib2WtmRD22OZnNrSyQmLd2Ts4O76kdBars7s+0DN4PtXLLr5z45uHUeGt448QPczo4ZaMLB1xO350G8JepV7GhZn26m5MwGqBCDOo6lDlrZkRdEePzG/xx1nGKtkx9TdUq1tdUaIDKAc4Y5ilHdh9NWXkpFVVrk9ii+Kyu/J0/vHkEW+o289oJH9C7U3G6m6Qacdgc/GPME2yq3cDNX16X7uYkjAaoEAO7DmZj7QZLe1LizcsXPYPEXAD6aoDKak67jTb5TsvHj9h9FADTVnyRrCbFpbxqDSe9eSTraip4ddz7mnk9g+3XqS+XDb6aNxa+zJTluZGAWANUiEHBTAXWhvni7EFZWCAhCPt16hvX9VVmaJXnwG4TbBYnrLdV2V2ROfuh1lVXcNJbR7Gq8ndePv6dnMvkkYv+NOR69m6/D9dMvjQnkhBHDVAicrRIy9iYsk+HXhQ6i5i1+oeox8a7YTdaz6usfA57ttubQldRXNdX6Zfvsm8b3nM5rP3pNFTZnbbii4zIuL2xdgOnvH00KzYt44Xj3krYniCVXG6HmwdGP8GqrSv5+zc3pbs5zWblr2c8sERE7hGRmGZGRWSsiCwWkaUicn0T918jIqXBf/NFxCci7a2cmwx2m52SLgOTtlAi2gIJCPSgdP4pe9lEaOX+3+JYt8UABYEqu+trKpgfHOZNly11mxn/9jEs2bCI5457gwN3PSSt7VGxGbzzAZxb8keeLf0nP/z+TVIfyxhDTX1N0q4f9a/HGHM60B/4GXhWRL4TkQtEpFWk80TEDjwGHAH0Bk4Vke3qIxhj7jXGlBhjSoAbgC+NMRusnJssA7oMZkHFPGq8kV90Q+xBKtqw4Pqadfy+daUGqCzWKs+x3T4UVwwLJUKr7KZLpWcrp71zHD9WlPH00ZO2zY2p7HLD8FvZpfWu/Pnzi6n11ib8+sYYpq2YzBGTDubqz65O+PUbWPrrMcZsAd4CXgW6AuOA2SISKS/QEGCpMWaZMcYTPPe4CMefCkyK89yEGdh1CF6/l/kW8nF5Y5yHihbQ5m9bIKET0dnIZbeR59x+b5fNJjhs1uahdirsQp9O/ZiapnmoqvoqTn/3BOasmck/j3qRMXscmZZ2qOYrdBVx72GPsnTjT/zjhzsTeu3vVn7NuDdGM/7tY6ioWsvQbkMTev1QVuagjhGRd4ApgBMYYow5AugHRAqduwC/hfy+MnhbU49RAIwlEARjOjfRBnQZDFhbKBFr6Y1oAW1esAZUn52ys4puSyZA6zCr9qzOQ0H6quzWeGs4+72TmL7qWx474lmO3Csl3wdVEo3sPpqTe5/OozPuT8iw8azVP3DyW0cx7o3RLN+0jDsPfZBvzynjzH5nJqC1TbPyl3MS8A9jTN/gkFw5gDGmGpgY4bymvjaG+4Q+BvjGGLMh1nODw40zRWRmRUXzc4PtVNiFXVrtyuzViZ+Hir5AopRdW++esESSKnUK3IFVe02JKUB1H53yKrt13jom/mc8X/82jYfG/Ivj9zkpZY+tkuvWQ+6mXX4Hrvz8Irx+b1zXKCsv5fR3T+CoV0ewoKKMWw+5m+8mLuCcfhcmLYVTAyt/OTcD2wqliEi+iHQHMMZE2rSxEghN1NUNWBXm2PH8b3gvpnONMU8ZYwYZYwZ16pSYRIwDugxm1protWFi2bDr91tbIKH7n7KP3SYUusKnbXLZbU1+42pKqqvsenweLvjwdKYu/4z7Rz/OSb1PS8njqtRol9eeO0f+g7LyUp6Y9WBM5y5a9yPn/udURr98ADNXfc9fht/G9Ik/cuGAy8l35CenwY1YCVBvAKFf/X3B26KZAewtIj1ExEUgCL3f+CARaQMcArwX67nJMrDrEFZu+dXSrn6rw3zRek9b67bwy6afKdb5p6zTeGFEYyJiOatEQ5XdqSnYaOn1e/njR2fx6bIPuPPQB9NSrkEl39F7j+PIvY7l/u/u4OeNS6Ie//PGJVz88dmMfHEQX/76BX/e/69MP3chlw+5JuXbX6z81TiCCxUACP4cNYuiMcYLXAp8CiwEXjfGLBCRi0TkopBDxwGfGWOqop1r5QklQizzUFaH+aLNP82vmAfkbomNXJXnsOO2kNo+tmG+UazY/EtSq+z6/D4u++RcPlz6Lrcdcg/n9LswaY+l0ktEuHPkg7gcbv78+cVh99n9unkFf/rsQg5+vj8fL/0Plw7+M9MnLuSaA/6P1u42KW51gJW/mgoRObbhFxE5Dlhn5eLGmI+MMT2NMXsaY+4I3vakMebJkGOeM8aMt3JuqhR37o/D5mCWhRLQVlfyRTuuLLhAQof4sodIoPdkRSwBKtlVdv3Gz1Wf/5F3Fr/OXw/8GxcMiFykU2W/zkVdueXgu/n+9695cd6/t7tv1daVXPvF5Qx/rph3Fr3Gef0vZvrEH/nrgX+jfX6HNLU4wMpf10XAyyLyKIHFC78ByVu2kQHyHfn07tg3oT0oKyU2Ohd2oVNhZ0vXU+lX5HZgs7iE3Gm3IRIoeBlNj7Z7slvr7kxbMZmJJRdFPyEGxhiu++JyXvvxRa454EYuG5y8PSwqs5y635m8s+g1/vb1Xxm1x1hcNhcPz7iXF+Y9jd/4mVA8kSuGXEPXopQsmLYkaoAyxvwM7C8iRQRKxGd/gicLBnQdzJsLX8Hn90WsW9SwYTfSHIOVBRLzy+fq/FMWcdptFLhiK6fmstuos5AiK1lVdj0+Dzd/eS0vlv2bK4Zcy1VDb0jIdVV2EBHuG/UYI14cxPi3j2Hllt/w+Oo4uffpXDn0BnZrs3u6m7gDS+MOInIUcDFwpYjcJCLZn+QpigFdBlPp2cpPGxZFPTZaLypa76m6vpqfNizU+acsYnVoL1Qsw3yHdh9NVX0lM1Z9H/PjNOb1e5m04AWGP9eXZ+f+k4sGXsH1w27Jqcqryprd2/bg/w66naUbfuLIvY7lv2eV8o8xT2ZkcAILPSgReRIoAEYCTwN/IGTZea4aGMzcPGfNDHpFKb1e7zURl41Em39atG4+fuPX+acsURCSDDYWsaQ9Gt7tkGZX2fX5fbz30xvc990dLNu0lL47DeCuQx/i0O5jNDi1YOeW/JFTep9OkStitrqMYOUvZpgx5kxgozHmVuAAtt+jlJP2aLsXbd3tLG3YjbbUPHoGiVJAM0hkA5sIRe7Ye08ADrvNcvmN5lTZNcbw4ZJ3OfSlIVz88Tm4HXk8d+zrfHra1xzW43ANTiorghNYC1ANmQarRWRnoB7okbwmZQYRoX+XQZY27PpN5A270QJYWXkp7fLa063VbjG3U6VWtD1P0cS0mi/GKrvGGD5f9jFjXhnGuR+cis/v5ckjX+CL039g7J7HaGBSWcfKX8t/RKQtcC8wG1jO9lkfctaArkNYvP5HqjyVUY8NF4T8foM/ytKtsvK5FO9Uoh8gGc7t2DEZbDzXsMpqlV1jDF+tmMJRrx7CGe+dwJa6zTx8+NNMO3MWx+9zEraWUc5N5aCI79xgocIvjDGbjDFvAbsD+xpjcn6RBAQWSviNn9K1s6MeG26hRLQFEh6fh0Xr52fNAgm7TZr9IZ2NBGiVZ72EezixzENZqbL7/cqvOeHNwzn57aNYW7Wa+0Y9xtdnzeXk3hNw2OIbilQqU0R8Bxtj/CJyP4F5J4wxdUBdKhqWCfp3GQTA7NU/RJ2oDlfrKdr800/rF+LxeTI+QDXMveQHc87ZBKo9vjS3KnUKIySDjUVD+Q2vhRyOjavshvaEZq+ZwT3f3sa0FZPZqaALd4x8gNP7TEx68k6lUsnK17nPROREaYHjT+3zO7BH270sbdj1+vyYJobyoi1Bb1ggkakBSiSwIbVjkWtbcIJAb6Kl9KTsNqEgQjLYWMVWfmP7Krvzy+dy5nt/4MhJBzOvvJSbD76T7ycu4NySP2pwUjnHyhjAVUAh4BWRWgKjHcYY0zqpLcsQA7oM5qtfp2KMiThHZACv3+C0b39MtCq68ytKKXQW0aPtnolobsIIkO+yU+gKny2hTb4TY4ylzafZrHWeM6Hzg4El6tZ6nw1Vdl8s+zebZmzkP0vepo27LdcPu4Xz+l+cNauxlIqHlUwSLfovoH/Xwby5aBK/b/2Nbq0jr7JrnFHC0gKJtaUU79Qvoyay85x2iiwOabXJd7Kpuj7m4o3ZIs9pj6nHY4XbESi/YSWLY0OV3RfL/k2hs4grh97ARQMup01e24S2SalMZGWjbpOTL8aYrxLfnMwzsEtgw+7sNTOiB6hGG3ajLZDw+X3Mr5jH6cXnNLudieB22Ch0O2LahCoitC1wsrG6PuYCjplOBFrFuecp8nUFh91m+fX664F/Y/aaGZzd7wI65HdMeHuUylRW/vquCfk5DxgCzAIOTUqLMkzvTsW47W5mr5nBsT1PjHhs415EtOG9nzcuocZbnfYcfE67jSK3I+6egojQNt/JhmpP1JyD2aSV22k5GWysXA7rAWpk99GM7D46Ke1QKpNZGeI7JvR3EdkVuCdpLcowLruL4p1KmG2h9IbfBJLCNgyNeaNu0A2U2ChOUwYJuy2wMi8Rix1sNqFdgYsNVZ6ow5rZwGm3bbcoJNFcdhtV0Q9TqkWL5yvzSqBPohuSyQZ2HcK8tXOo99VHPTb0W3G0HlRZxVzy7Hns3X7fZrcxFg11jDoWuRO6Es9uE9oVOMmF9Z7xJIONhcthvQy8Ui1V1AAlIo+IyMPBf48C/wXmJr9pmaN/l8HU+mpZuG5+1GMbApS1DBKl9OrUJ2UbKoXAfp5ORe6YS0VY5bDbaFfgyuogFW8y2FglevGFUrnGyqfUzJCfvcAkY8w3SWpPRhoQXCgxa810+naOPF/U0GuKtqrNGENZeSnH73NyxOMKXHYctuZ/kIkEhpWSNacSymm30TbfxaZqj6WVas1lE6HQbUcS1CfJc6YmcLgc1upDKWucdhtOu1Dj8aXkfZdKAuS57NTm4HOLxEqAehOoNcb4AETELiIFxpjq5DYtc+zaejc6FXRm9uoZnNPvwojHNmzYjZYp4NfNy9lSt5niTpHnnwpciclgkGouh402BU42V9cn7Q+qYRNxvtOelXkMY0l7pMJrPJda6HJQ6fFSkyOZTkK3fdhFqKzzprtJKWPlL+QLID/k93wg9hoAWUxEGNBlMHMsZJQIVNg1URdIzNu2QKIkwuOSlcGpgdthp3V+8/PXNdZ4qDIbgxPEVn5D7cgmQus85w5zqTZb4PYOhS7yHNmb7cTtsNG+0EWbfOe2z4FCtwNHFn8mxMpKgMozxmxL5x38uSB5TcpMA7oOZunGn9hUuzHqsfU+v4UMEnNx2BzsG6EYojMBQ3vplue0J3TBQb7LTsciN0Xu7A1MobQXFbtw6bcac9gDvfj2ha6sep2dwXnctgWuJudCE5G0OFtY+X+tSkQGNPwiIgOBmuQ1KTM1bNids2ZmlCOhzuuPukBiXnkp+3ToTZ4jL+wxDnv2fwBDYJiysJkbXvMcdjoUumidl7y9SemgCyWsEwJzsh0L3RTG8AXFabfRrtBF2wJnRvc+7DahTX4woEZ4X7gcyd0CkUmsfGr8CXhDRFYFf+8KnJK0FmWofp0HIAiz18yIumky2gZMYwzz1s5h9B5HRDwuEYsjMkWR24ExJuYM6C67jaK82LJbZBMNUNbEkn4rHLfDjrvITm29j6213ozZr9e4UoAVRS4HtfU+MuQpJI2VjbozRGRfYB8CX2IWGWOibwjKMa3crenZoRezLGzYjWZN1SrW11TQJ8oCiVzpQTVolefEb6C2PnqQctiEojwH7iyeQ7DCbhPsNsmpDByJ5HYEspw4EvgFJc9px+2wUVPvo7LOm7YPeZHAgo4CV+yLfBrm2TbX5PZHsZV9UJcAhcaY+caYMqBIRC5OftMyz8CuQyhdM7PJshqxKAuWTugbaYEEZPRwRLza5DsjVpW1SWCYo0ORO+eDUwPtRe0odB4mkcGpgYhQ4AostCl0O1K6aTreocrG8pz2rJpbi4eVZ3e+MWZTwy/GmI3A+UlrUQYb0GUwG2rXs3zzsmZdp6y8FEHYr1PfsMfYbZITiwCa0ibfucMf1v+yW7haTJ2pBrn+IRMLq/MwiSLB4bWORe6UzOvkOe10KHLTKkFzqa3yUhtcU83KO8AWWqxQROxsl7O75RjQZTBAs4f5ysrnsFf7nhS6isIek4xvjZmiIQO6wyY5s2S8OTRAhV8ynrLHT/LSdLfDRodGS8YTwWG3UZCEjPuZwsoz+xR4XUSeJLDN5yLgk6S2Kk2ifTbu06E3Bc5CZq+Zzh96nRr345SVlzJ0l+ERj2lc+DDXiASSywI5tSovHjab4Iyh/EYmsIkkdPuA22HLiC8ngaXpNgp89oTNC9pEktobLHQFFn7k4jymlXfYdcAFwB8JDJ9+BvwrmY1KF4c9cuoZu81OSeeBzF4dfcNuOOtr1vH71pXRF0jk0Aq+cFp6YAoVS/mNdBOBdgXOnO7lB9ImpbsV1kjwy8Km6txbMBH1HWaM8RtjnjTG/MEYcyKwAHgk+U1LPSuLEgZ0GcyCinnUemvjeoz52xZIRM7pl+s9KLW9bPn/W4B2SVq4oOLndthzcu7W0rtMREpE5G4RWQ78DViU1FaliZW5gAFdB1Pvr2d+eWlcj9GQ4qhPhBpQNsndBRKqaS575pffEKBNgTNn96Rlu1ZuR1ZXEWhK2HeaiPQUkZtEZCHwKIE6UGKMGWmMyckelM0mUXOjNSyUmG0hL19TyspL2a11d9rmtQt7TLZ8m1aJIyIZ/8HfOt/ZYpb+ZyObTWjlzq00SJH+IhYBhwHHGGMODAal3EgPHEG0XlSXop3ZpVW3uFfylZWXRq2gq8MnLVMm74dqk+/MySGkXJOfolpmqRLpmZwIrAGmisi/ROQwyPhRiGazkr2hf5fBcfWgttZt4ZdNP1McZf4pFzfoqugyNUC1ynNocMoirXNob1TYvwhjzDvGmFOAfYFpwJVAZxF5QkTGpKh9KWclQA3sOoTftqygomptTNeeXzEPiFxiA8ipb0DKOqfdlnFzCIVuR9KqL6vkcNhzJ5mslVV8VcaYl40xRwPdgFLg+mQ3LF0sLZSIcx6qLLhAImKKoyyvAaWax23PnA+WfFcgQavKPkVuR07UGovpq7oxZoMx5p/GmEOT1aB0E5GoAaJ4p/7YxR5zgJpXXkqXwq50Kuwc9phcqAGl4pcpw3x5TjutW1DdoVwjIrTOz/4vF5nx15Bhog2xFTgL2K9T35g37JaVl9InyvBermUwV7HJhADldthok4RKyCq13A57UisKB5LeJjcIpv+vIQNZWebdv8tg5qydic9vbWFjdX01SzYs0vknFZHdwlaHZHLZNTjlkqIkLZjIdwWS3iZ7CFg/DZtgJUgM6DKYSs9WlmxYbOmai9bNx2/8EeefQOefVPp6UU67jbYFTt0knkPswbpqiRJa2ToVn1UaoJrQkGU7koFdAyXgrc5DzQtmnojUgxK0B6WIWC8rWew2oW2+BqdcVOByNHvristuo32hizYpzsGY1EcSkbEislhElopIkyv/RGSEiJSKyAIR+TLk9uUiUha8b2Yy29lEm6J+O9ij3V60cbdltsUNu2XlpbTP68AurXYNe4z2nhSkvvyGLZhZXpP35q7WcQ7bOmyB0jjtCl1p+fKctAHEYN2ox4DRBNIkzRCR940xP4Yc0xZ4HBhrjPlVRHZqdJmRxph1yWpjJE6HDa8n/PySTWz07zLIcg+qrHwufXbqF/EbqmaQUBBIWeOwCd4UlE+widC+0KVfjnKcM7g3qibCZ1oouy1QyDHdG7ST+Yk4BFhqjFlmjPEArwLHNTrmNOBtY8yvAMaY8iS2JyZWlnsP6DKEResXUOWpjHicx+dh0fr5FhZI6IeECkhNNdlA2QwNTi1DKwt7oxoqW3cozIzK1sn8K9gF+C3k95XB20L1BNqJyDQRmSUiZ4bcZ4DPgrdfEO5BROQCEZkpIjMrKioS1ngrwWJA18H4jZ/StbMjHvfT+oV4fJ6oAaol1IBS1iQ7QAnQNl/LZrQkEqHIZKZWtk7mu7OpZ9h4zMIBDASOAg4HbhSRnsH7hhtjBgBHAJeIyMFNPYgx5iljzCBjzKBOnTolqOmB4bZo/xf17zIIgNlrIs9DNSyQ0BpQyqpklt9oKJuRCXuuVGrlOe3bLcIRAkvGOwaXjGdKYGqQzHfoSiB0RUA3YFUTx3wSTKe0DvgK6AdgjFkV/G858A6BIcOUijYp2CG/Iz3a7smcKPNQZeVzKHK1onvbPcIeozWgVKhklt/QshktW6s8J0IgWHUoctM6z5mxC2SSGaBmAHuLSA8RcQHjgfcbHfMecJCIOESkABgKLBSRQhFpBSAihcAYYH4S29okK1kdBnQZzKzV0zEm/IT2/PK59OnUF5uEf7m196QaS3QPxx5ckZUJcwsqfew2oWORmzb5mT//mLQAZYzxApcCnwILgdeNMQtE5CIRuSh4zELgE2AeMB142hgzH+gMfC0ic4O3f2iM+SRZbQ3HyjfY/l0Gs7ZqDasqVzZ5v8/vY37FvOjzTzoXoBpJVA/KJkLrPCcdi9zac1IAGdtjaiypeSqMMR8BHzW67clGv98L3NvotmUEh/rSycoHxLYNu6tnNLnH6eeNS6jxVmsNKBUzlyNQfiNC5zwikUBW63ynXYePVVbSr+0R2G0StT7Pfp364ra7w+6HaiixoTn4VDzi2bSbqSuylIpV9udjTzKX3Uad1x/hfhd9duoXNqNEWcVc8ux57N1+n7DX0BpQKhyXI/L7L5QAeS47RS5H1gzhKBWJfm2Pwsrc0MAuQ5hXPod6X/0O95WVl9KrUx8ctvDfBbQGlArHag8qz5H5K7KUipV+MkZhdcNujbeGResXbHe7MYay8tLo80+6gk+F4bDbIu7+D03iqb1wlWs0QEVhpXfTv0tgocSsRsN8v25ezpa6zVFLbOj8k4qkqeXmTruNdgWutCXxVCoV9J0dhc1CAbndWu9Oh/xOOyyUmGdxgYSu4FORhO78t9uENvlO2he6NBOEynm6SMICl91GrTd8FmARYWDXITtklJhfMReHzcG+HfYLfy66B0pF5goO8xW5HeS7dB+Tajn0k9ECqxkllmxYzKbajdtum1deyj4deuN2uMOep/MGKhqbTehY5NLgpFocDVAWWC0BD1C6dhYQWCAxb+0czSChEkL3MqmWSD8dLbCykq+ky0AEYfbqwDDfmqpVrK+p0BpQSikVJw1QFlgpAd/a3Ya92+/LrGDpjbK1pQBRV/BpDSillGqafjpaZGmYr+tg5qyeEdj/VDEXQejdsTjKdbUHpZRSTdEAZZGVQDKwyxA21K5nxeZfKCufw17te1LoKgp7fCDXnwYopZRqigYoi6z2oABmrf4hmEGiJOLxuv9JKaXC0wBlkcMmUUtw79OhN/mOAib/8gm/b12pK/iUUqoZ9BPSIhGJGlAcNgclXQby4dJ3ASjuVBLleO1BKaVUOBqgYmB1w67H5wGgz06Ray5qDjWllApPPyFjYKX0QcOG3d1ad6dtXruwx2kNKKWUikwDVAysDMk1lICPukFX9z8ppVREmiw2Bg67DREwJvwxXYp25pTeZ3DkXsdGuZb2npRSKhINUDFy2mx4fJFLcD90+FPRr6PzT0opFZF+SsYoUT0fXcGnlFKRaYCKUSJ6PloDSimlotNPyRglIkDp6j2llIpOA1SM7BZKwEejvSellIpOPynj0NwM5JrBXCmlotMAFYfm9oC0BpRSSkWnn5Rx0B6UUkolnwaoODQnC4TWgFJKKWs0QMXBZoteAj4cTXGklFLW6KdlnOINNHYd3lNKKUs0QMUp3owSmkFCKaWs0QAVp3g37GoOPqWUskY/LeMUz0o8rQGllFLWaYCKk4jEPFynCySUUso6/cRshlg37GoNKKWUsk4DVDNYKQEfSueflFLKOv3EbIZYe0S6gk8ppazTANUMTrsNqyFHa0AppVRs9BOzmawGHV29p5RSsUlqgBKRsSKyWESWisj1YY4ZISKlIrJARL6M5dxMYHWYT3tPSikVG0eyLiwiduAxYDSwEpghIu8bY34MOaYt8Dgw1hjzq4jsZPXcTOGy26jBF/U4zWCulFKxSebX+iHAUmPMMmOMB3gVOK7RMacBbxtjfgUwxpTHcG5GsLrwQWtAKaVUbJL5qbkL8FvI7yuDt4XqCbQTkWkiMktEzozh3IzgsNuwUj1De1BKKRWbpA3xQZML3EwTjz8QOAzIB74Tke8tnht4EJELgAsAdtttt7gb2xxOmw2Pzx/2fq0BpZRSsUtmD2olsGvI792AVU0c84kxpsoYsw74Cuhn8VwAjDFPGWMGGWMGderUKWGNj0W0hRKa4kgppWKXzE/OGcDeItJDRFzAeOD9Rse8BxwkIg4RKQCGAgstnpsxomWI0BpQSikVu6QN8RljvCJyKfApYAeeMcYsEJGLgvc/aYxZKCKfAPMAP/C0MWY+QFPnJqutzRUtQGkGCaWUip0Y0+TUTlYaNGiQmTlzZloeu2JrHf4wr2XHIrdu1FVKqTBEZJYxZlDj23VyJEHCrdLTGlBKKRUfDVAJEm6YTxdIKKVUfPTTM0HCreTTGlBKKRUfDVAJEq6npDWglFIqPvrpmSA2mzQ516Qr+JRSKj4aoBKocS9Ka0AppVT89NMzgZyO7XtLunpPKaXipwEqgRpnLNfek1JKxU8/QRPIaZftstxqBnOllIqfBqgEEtl+oYTWgFJKqfjpJ2iCOR3/e0m1B6WUUvHTAJVgDSv5tAaUUko1jwaoBGvIHKEpjpRSqnn0UzTBnHZbcP+T9p6UUqo5NEAlgcNu0z1QSinVTBqgksBpF83Bp5RSzaSfokngcmgPSimlmksDVBK4HfZ0N0EppbKeBiillFIZSQOUUkqpjKQBSimlVEbSAKWUUiojaYBSSimVkTRAKaWUykgaoJRSSmUkDVBKKaUykgYopZRSGUkDlFJKqYwkxph0tyFhRKQCWJHudsSgI7Au3Y2IQ7a2G7K37dru1MvWtmdju3c3xnRqfGNOBahsIyIzjTGD0t2OWGVruyF7267tTr1sbXu2trspOsSnlFIqI2mAUkoplZE0QKXXU+luQJyytd2QvW3XdqdetrY9W9u9A52DUkoplZG0B6WUUiojaYBKMhHZVUSmishCEVkgIlc0ccwIEdksIqXBfzelo62NichyESkLtmlmE/eLiDwsIktFZJ6IDEhHOxsTkX1CXstSEdkiIn9qdExGvOYi8oyIlIvI/JDb2ovI5yKyJPjfdmHOHSsii4Ov//Wpa3XYdt8rIouC74V3RKRtmHMjvq+SLUzbbxGR30PeD0eGOTfTXvPXQtq8XERKw5yb1tc8bsYY/ZfEf0BXYEDw51bAT0DvRseMAD5Id1ubaPtyoGOE+48EPgYE2B/4Id1tbqKNdmANgX0WGfeaAwcDA4D5IbfdA1wf/Pl64O4wz+tnYA/ABcxt/L5KQ7vHAI7gz3c31W4r76s0tf0W4GoL76WMes0b3X8/cFMmvubx/tMeVJIZY1YbY2YHf94KLAR2SW+rEuY44AUT8D3QVkS6prtRjRwG/GyMycgN3MaYr4ANjW4+Dng++PPzwPFNnDoEWGqMWWaM8QCvBs9LiababYz5zBjjDf76PdAtVe2JRZjX3IqMe80biIgAJwOTUtWeVNAAlUIi0h3oD/zQxN0HiMhcEflYRPZLbcvCMsBnIjJLRC5o4v5dgN9Cfl9J5gXf8YT/o83E1xygszFmNQS+4AA7NXFMpr/2Ewn0rpsS7X2VLpcGhyefCTOsmsmv+UHAWmPMkjD3Z+prHpEGqBQRkSLgLeBPxpgtje6eTWAIqh/wCPBuipsXznBjzADgCOASETm40f3SxDkZsyxURFzAscAbTdydqa+5VRn72ovIXwEv8HKYQ6K9r9LhCWBPoARYTWC4rLGMfc2BU4nce8rE1zwqDVApICJOAsHpZWPM243vN8ZsMcZUBn/+CHCKSMcUN3MHxphVwf+WA+8QGOIItRLYNeT3bsCq1LTOkiOA2caYtY3vyNTXPGhtw1Bp8L/lTRyTka+9iJwFHA1MMMHJj8YsvK9Szhiz1hjjM8b4gX+FaVOmvuYO4ATgtXDHZOJrboUGqCQLjg3/G1hojHkgzDFdgschIkMI/P+yPnWtbLJNhSLSquFnAhPg8xsd9j5wZnA13/7A5oahqQwR9ltlJr7mId4Hzgr+fBbwXhPHzAD2FpEewZ7i+OB5aSMiY4HrgGONMdVhjrHyvkq5RnOn42i6TRn3mgeNAhYZY1Y2dWemvuaWpHuVRq7/Aw4kMAwwDygN/jsSuAi4KHjMpcACAquCvgeGZUC79wi2Z26wbX8N3h7abgEeI7CyqQwYlO52h7S/gEDAaRNyW8a95gQC6GqgnsA39HOBDsAXwJLgf9sHj90Z+Cjk3CMJrAr9ueH/nzS3eymBOZqG9/mTjdsd7n2VAW1/Mfgenkcg6HTNhtc8ePtzDe/rkGMz6jWP959mklBKKZWRdIhPKaVURtIApZRSKiNpgFJKKZWRNEAppZTKSBqglFJKZSQNUEoppTKSBiiVc0TEiMj9Ib9fLSK3JOjaz4nIHxJxrSiPc5IESrRMbXR79+DzuyzktkdF5Owo10tVu98UkT2CPy9vyM4hIgNF5BcR6S8iR4vIrclui8p+GqBULqoDTsig1EUAiIg9hsPPBS42xoxs4r5y4IpgNoOkC6bSsXLcfoDdGLOs0e19gTeBU4wxc4APgWNFpCDhjVU5RQOUykVe4CngysZ3NO5JiEhl8L8jRORLEXldRH4SkbtEZIKITA8Wetsz5DKjROS/weOODp5vl0DBvhnBjNgXhlx3qoi8QiBTQeP2nBq8/nwRuTt4200EMpA8KSL3NvH8KghkmDir8R0icn6wDXNF5K1GQaCpdueJyLPBNswRkZHB288WkTdE5D8EsmB3FZGvJFDwbr6IHNREuyawY1qmXgQS8Z5hjJkOYALZAaYRyNmnVFgaoFSuegyYICJtYjinH3AFUAycAfQ0xgwBngYuCzmuO3AIcBSBIJJHoMez2RgzGBgMnC8iPYLHDyGQXqZ36IOJyM4ECvsdSiCL9mAROd4Ycxswk0DC1WvCtPUu4M9N9MreNsYMNoEs7QuD7YrU7ksAjDHFBHIXPh+8HeAA4CxjzKHAacCnxpiS4OtU2kSbhgOzGt32HnCpMebrRrfPJFAiQqmwNECpnGQCJU1eAC6P4bQZJlBgso5ArrXPgreXEfhwb/C6McZvArV3lgH7EkjAeaYESm7/QCCf3t7B46cbY35p4vEGA9OMMRUmUOjvZQJVU608v1+A6QQCR6g+wV5SGYEeTWidq6bafSCBPHQYYxYBK4CeweM/N8Y0FMibAZwTnMsrNoHim411JdC7CzUZOK+JQFpOIF+cUmFpgFK57EECPYjCkNu8BN/3wWzmofM4dSE/+0N+9wOh8zCNE1gaAolzLzPGlAT/9TDGNAS4qjDta6q+UCz+TiB7eOjf8XMEeizFwK1AXsh94dodzrZ2m0A114OB34EXReTMJo6vafR4EEjKC/B4o9vzgscrFZYGKJWzgt/+X2f7Ya7lwMDgz8cBzjgufZKI2ILzUnsAi4FPgT9KoPYXItIzWNogkh+AQ0SkY7CHcSrwpdVGBHs8P7L9XE4rYHWwHRMstPurhuNEpCewW/D27YjI7kC5MeZfBMrHDGiiSQuBvRrd5g8+r31E5LaQ23uSLSUfVNpogFK57n4gdDXfvwgEhenAUML3biJZTCCQfEygzEEtgXmqH4HZIjIf+Cfb97p2YAK1s24AphIohTDbGNNU7adI7iBQOK/BjQQC3+fAIgvtfhywB4cEXwPODg5xNjYCKBWROcCJwENNHPNh8LjtBK93HIGVe5cEbx4ZPF6psLTchlIqIUQkn0CwHW6M8UU4rjPwijHmsJQ1TmUlDVBKqYQRkcMJVI/+NcIxg4F6Y0xpyhqmspIGKKWUUhlJ56CUUkplJA1QSimlMpIGKKWUUhlJA5RSSqmMpAFKKaVURvp/fo3VMtlsoe8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Nabors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best KNN accuracy 0.75 with k= 9\n"
     ]
    }
   ],
   "source": [
    "print( \"The best KNN accuracy\", round(mean_acc.max(),3), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 classid=\"decision_tree\">Decision Tree</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X-trainset shape : (242, 8)\n",
      "y-trainset shape : (242,)\n",
      "X-testset shape : (104, 8)\n",
      "y-testset shape : (104,)\n"
     ]
    }
   ],
   "source": [
    "DT_X = X\n",
    "DT_y = y\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# split 70/30 (train/test) \n",
    "DT_X_trainset, DT_X_testset, DT_y_trainset, DT_y_testset = train_test_split(DT_X, DT_y, test_size=0.3, random_state=3)          \n",
    "# check the shape of train/test shape variables\n",
    "print('X-trainset shape :', DT_X_trainset.shape)\n",
    "print('y-trainset shape :', DT_y_trainset.shape)\n",
    "\n",
    "print('X-testset shape :', DT_X_testset.shape)\n",
    "print('y-testset shape :', DT_y_testset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT_X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    PAIDOFF\n",
       "1    PAIDOFF\n",
       "2    PAIDOFF\n",
       "3    PAIDOFF\n",
       "4    PAIDOFF\n",
       "Name: loan_status, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DT_y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "loanTree.fit(DT_X_trainset, DT_y_trainset)\n",
    "loanTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n",
      "73        PAIDOFF\n",
      "24        PAIDOFF\n",
      "282    COLLECTION\n",
      "295    COLLECTION\n",
      "163       PAIDOFF\n",
      "Name: loan_status, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#store prediction dataset into new variable predTree\n",
    "predTree = loanTree.predict(DT_X_testset)\n",
    "\n",
    "#compare the prediction to actual value\n",
    "print (predTree [0:5])\n",
    "print (DT_y_testset [0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy score:  0.654\n"
     ]
    }
   ],
   "source": [
    "#evaluation\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# check accurency of hte model\n",
    "print(\"DecisionTrees's Accuracy score: \", round(metrics.accuracy_score(DT_y_testset, predTree),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualization\n",
    "from sklearn.externals.six import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f408b853a20>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "\n",
    "filename = \"loanTree.png\"\n",
    "featureNames = df.columns[0:8]\n",
    "targetNames = df[\"loan_status\"].unique().tolist()\n",
    "\n",
    "out=tree.export_graphviz(loanTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(DT_y_trainset), filled=True,  special_characters=True,rotate=False)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png(filename)\n",
    "img = mpimg.imread(filename)\n",
    "\n",
    "plt.figure(figsize=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 classid=\"suport_vector_machine\">Super Vector Machine (SVM)</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM features atributes\n",
    "SVM_X = X\n",
    "# SVM target object\n",
    "SVM_y = y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert target characteristic value into numerical value (integers)\n",
    "SVM_y.replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=True)\n",
    "SVM_y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    260\n",
       "1     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM_y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 260 PAIDOFF and 86 COLLECTION values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (242, 8) (242,)\n",
      "Test set: (104, 8) (104,)\n"
     ]
    }
   ],
   "source": [
    "# 70/30 - Test/Train split \n",
    "SVM_X_train, SVM_X_test, SVM_y_train, SVM_y_test = train_test_split( SVM_X, SVM_y, test_size=0.3, random_state=4)\n",
    "print ('Train set:', SVM_X_train.shape,  SVM_y_train.shape)\n",
    "print ('Test set:', SVM_X_test.shape,  SVM_y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# modeling\n",
    "from sklearn import svm\n",
    "\n",
    "# the differnt kernels impact accuracy - 'linear' 'poly' 'sigmoid' 'rbf'\n",
    "k ='rbf'\n",
    "\n",
    "clf = svm.SVC(kernel=k ,gamma='auto', class_weight='balanced')\n",
    "clf.fit(SVM_X_train, SVM_y_train) \n",
    "\n",
    "# predict the class  \n",
    "SVM_yhat = clf.predict(SVM_X_test)\n",
    "SVM_yhat [0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy:  0.625\n"
     ]
    }
   ],
   "source": [
    "# test accuracy\n",
    "print(\"SVM accuracy: \", metrics.accuracy_score(SVM_y_test, SVM_yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluation\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "   \n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.53      0.68        77\n",
      "           1       0.40      0.89      0.55        27\n",
      "\n",
      "   micro avg       0.62      0.62      0.62       104\n",
      "   macro avg       0.67      0.71      0.61       104\n",
      "weighted avg       0.79      0.62      0.64       104\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[41 36]\n",
      " [ 3 24]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(SVM_y_test, SVM_yhat, labels=[0,1])\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(SVM_y_test, SVM_yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM_Jaccard score: 0.6250\n",
      "SVM_Avg F1-score: 0.6450\n",
      "SVM_log Loss score: 12.9523\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "print(\"SVM_Jaccard score: %.4f\" % jaccard_similarity_score(SVM_y_test, SVM_yhat))\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "print(\"SVM_Avg F1-score: %.4f\" % f1_score(SVM_y_test, SVM_yhat, average='weighted'))\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "print(\"SVM_log Loss score: %.4f\" % log_loss(SVM_y_test, SVM_yhat))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 classid=\"logistic_regression\">Logistic Regression</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "LR_X = X\n",
    "# LR_X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.astype('int')\n",
    "LR_y = np.asarray(y)\n",
    "# LR_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.52,  0.92,  2.33, -0.42, -1.21, -0.38,  1.14, -0.87],\n",
       "       [ 0.52,  0.92,  0.34,  2.38, -1.21,  2.62, -0.88, -0.87],\n",
       "       [ 0.52, -0.96, -0.65, -0.42, -1.21, -0.38, -0.88,  1.15],\n",
       "       [ 0.52,  0.92, -0.49,  2.38,  0.83, -0.38, -0.88,  1.15],\n",
       "       [ 0.52,  0.92, -0.32, -0.42,  0.83, -0.38, -0.88,  1.15]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# experiment with diferent solvers to see how they impact accuracy - 'newton-cg' 'lbfgs' 'saga' 'liblinear'\n",
    "s = 'saga'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (242, 8) (242,)\n",
      "Test set: (104, 8) (104,)\n"
     ]
    }
   ],
   "source": [
    "# train/test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "LR_X_train, LR_X_test, LR_y_train, LR_y_test = train_test_split( LR_X, LR_y, test_size=0.3, random_state=4)\n",
    "print ('Train set:', LR_X_train.shape,  LR_y_train.shape)\n",
    "print ('Test set:', LR_X_test.shape,  LR_y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modeling (Logistic Regression with Scikit-learn)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "LR = LogisticRegression(C=0.01, solver=s ).fit(LR_X_train,LR_y_train)\n",
    "\n",
    "LR_yhat = LR.predict(LR_X_test)\n",
    "LR_yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.69, 0.31],\n",
       "       [0.72, 0.28],\n",
       "       [0.83, 0.17],\n",
       "       [0.8 , 0.2 ],\n",
       "       [0.82, 0.18]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR_yhat_prob = LR.predict_proba(LR_X_test)\n",
    "LR_yhat_prob[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression Accuracy using ' saga 'solver :  0.74\n",
      "Jaccard evaluation: 0.74\n",
      "LR_Avg F1-score: 0.8508\n",
      "Log-loss evaluation: 0.523\n"
     ]
    }
   ],
   "source": [
    "# test accuracy\n",
    "print(\"Linear Regression Accuracy using '\",s,\"'solver : \", round(metrics.accuracy_score(LR_y_test, LR_yhat),3))\n",
    "\n",
    "from sklearn.metrics import jaccard_similarity_score\n",
    "print(\"Jaccard evaluation:\",round(jaccard_similarity_score(LR_y_test, LR_yhat),3))\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "print(\"LR_Avg F1-score: %.4f\" % f1_score(LR_y_test, LR_yhat, average='weighted', labels=np.unique(LR_yhat)))\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "print(\"Log-loss evaluation:\",round(log_loss(LR_y_test, LR_yhat_prob),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 class='model_evaluation'>Model Evaluation using Test set</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the various metrics\n",
    "from sklearn.metrics import jaccard_similarity_score,f1_score, log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First download and load the test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-10-27 23:51:46--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3642 (3.6K) [text/csv]\n",
      "Saving to: ‘loan_test.csv’\n",
      "\n",
      "loan_test.csv       100%[===================>]   3.56K  --.-KB/s    in 0s      \n",
      "\n",
      "2020-10-27 23:51:46 (11.2 MB/s) - ‘loan_test.csv’ saved [3642/3642]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Test for evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.52,  0.92,  2.33, -0.42, -1.21, -0.38,  1.14, -0.87],\n",
       "       [ 0.52,  0.92,  0.34,  2.38, -1.21,  2.62, -0.88, -0.87],\n",
       "       [ 0.52, -0.96, -0.65, -0.42, -1.21, -0.38, -0.88,  1.15],\n",
       "       [ 0.52,  0.92, -0.49,  2.38,  0.83, -0.38, -0.88,  1.15],\n",
       "       [ 0.52,  0.92, -0.32, -0.42,  0.83, -0.38, -0.88,  1.15]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "\n",
    "X_test = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = test_df['loan_status'].values\n",
    "y_test[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jaccard Index : intersection and Unions 0 to 1, Closer to 1 is better"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN Scores: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN_Jaccard score: 0.7019\n",
      "KNN_Avg F1-score: 0.6737\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN_Jaccard score: %.4f\" % jaccard_similarity_score(KNN_y_test, KNN_yhat))\n",
    "print(\"KNN_Avg F1-score: %.4f\" % f1_score(KNN_y_test, KNN_yhat, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DT_Jaccard score: 0.6538\n",
      "DT_Avg F1-score: 0.6667\n"
     ]
    }
   ],
   "source": [
    "print(\"DT_Jaccard score: %.4f\" % jaccard_similarity_score(DT_y_testset, predTree))\n",
    "print(\"DT_Avg F1-score: %.4f\" % f1_score(DT_y_testset, predTree, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM_Jaccard score: 0.6250\n",
      "SVM_Avg F1-score: 0.6450\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM_Jaccard score: %.4f\" % jaccard_similarity_score(SVM_y_test, SVM_yhat))\n",
    "print(\"SVM_Avg F1-score: %.4f\" % f1_score(SVM_y_test, SVM_yhat, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression Scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM_Jaccard score: 0.6250\n",
      "SVM_Avg F1-score: 0.6450\n",
      "SVM_log Loss score: 0.5226\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM_Jaccard score: %.4f\" % jaccard_similarity_score(SVM_y_test, SVM_yhat))\n",
    "print(\"SVM_Avg F1-score: %.4f\" % f1_score(SVM_y_test, SVM_yhat, average='weighted'))\n",
    "print(\"SVM_log Loss score: %.4f\" % log_loss(LR_y_test, LR_yhat_prob))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}