The best classifier.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>"
   ]
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   "source": [
    "In this notebook we try to practice all the classification algorithms that we learned in this course.\n",
    "\n",
    "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
    "\n",
    "Lets first load required libraries:"
   ]
  },
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   "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"
   ]
  },
  {
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   "source": [
    "### About dataset"
   ]
  },
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   "source": [
    "This dataset is about past loans. The __Loan_train.csv__ data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
    "\n",
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
    "| Principal      | Basic principal loan amount at the                                                    |\n",
    "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "| Effective_date | When the loan got originated and took effects                                         |\n",
    "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
    "| Age            | Age of applicant                                                                      |\n",
    "| Education      | Education of applicant                                                                |\n",
    "| Gender         | The gender of applicant                                                               |"
   ]
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   },
   "source": [
    "Lets download the dataset"
   ]
  },
  {
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     "text": [
      "--2019-05-08 02:31:41--  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)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|: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",
      "2019-05-08 02:31:41 (1.06 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"
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "### Load Data From CSV File  "
   ]
  },
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   "execution_count": 3,
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
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       "      <td>9/8/2016</td>\n",
       "      <td>9/22/2016</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
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       "    <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",
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      "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": {},
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   "source": [
    "df.shape"
   ]
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   },
   "source": [
    "### Convert to date time object "
   ]
  },
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       "      <td>2016-10-07</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
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       "      <td>college</td>\n",
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       "    </tr>\n",
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       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>8</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>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>39</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>900</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>27</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>40</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>29</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>39</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>34</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>31</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>33</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>33</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>37</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>37</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>367</td>\n",
       "      <td>367</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>28</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>368</td>\n",
       "      <td>368</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>24</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>371</td>\n",
       "      <td>371</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>18</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>372</td>\n",
       "      <td>372</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>373</td>\n",
       "      <td>373</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>40</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>376</td>\n",
       "      <td>376</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>377</td>\n",
       "      <td>377</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>33</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>32</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>35</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>382</td>\n",
       "      <td>382</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>383</td>\n",
       "      <td>383</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>46</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>387</td>\n",
       "      <td>387</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>36</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>388</td>\n",
       "      <td>388</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>38</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>390</td>\n",
       "      <td>390</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>391</td>\n",
       "      <td>391</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>35</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>393</td>\n",
       "      <td>393</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>394</td>\n",
       "      <td>394</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>395</td>\n",
       "      <td>395</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>397</td>\n",
       "      <td>397</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-09-26</td>\n",
       "      <td>39</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 10 columns</p>\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",
       "5             7             7     PAIDOFF       1000     30     2016-09-09   \n",
       "6             8             8     PAIDOFF       1000     30     2016-09-09   \n",
       "7             9             9     PAIDOFF        800     15     2016-09-10   \n",
       "8            10            10     PAIDOFF        300      7     2016-09-10   \n",
       "9            11            11     PAIDOFF       1000     15     2016-09-10   \n",
       "10           12            12     PAIDOFF       1000     30     2016-09-10   \n",
       "11           13            13     PAIDOFF        900      7     2016-09-10   \n",
       "12           14            14     PAIDOFF       1000      7     2016-09-10   \n",
       "13           15            15     PAIDOFF        800     15     2016-09-10   \n",
       "14           16            16     PAIDOFF       1000     30     2016-09-10   \n",
       "15           17            17     PAIDOFF       1000     15     2016-09-10   \n",
       "16           18            18     PAIDOFF       1000     30     2016-09-10   \n",
       "17           19            19     PAIDOFF        800     30     2016-09-10   \n",
       "18           20            20     PAIDOFF       1000     30     2016-09-10   \n",
       "19           22            22     PAIDOFF       1000     30     2016-09-10   \n",
       "20           23            23     PAIDOFF       1000     15     2016-09-10   \n",
       "21           25            25     PAIDOFF       1000     30     2016-09-10   \n",
       "22           26            26     PAIDOFF        800     15     2016-09-10   \n",
       "23           27            27     PAIDOFF       1000     15     2016-09-10   \n",
       "24           28            28     PAIDOFF       1000     30     2016-09-11   \n",
       "25           29            29     PAIDOFF       1000     30     2016-09-11   \n",
       "26           30            30     PAIDOFF        800     15     2016-09-11   \n",
       "27           31            31     PAIDOFF       1000     30     2016-09-11   \n",
       "28           32            32     PAIDOFF       1000     30     2016-09-11   \n",
       "29           33            33     PAIDOFF       1000     30     2016-09-11   \n",
       "..          ...           ...         ...        ...    ...            ...   \n",
       "316         367           367  COLLECTION        800     15     2016-09-11   \n",
       "317         368           368  COLLECTION       1000     30     2016-09-11   \n",
       "318         371           371  COLLECTION       1000     30     2016-09-11   \n",
       "319         372           372  COLLECTION        800     15     2016-09-11   \n",
       "320         373           373  COLLECTION       1000     15     2016-09-11   \n",
       "321         374           374  COLLECTION       1000     30     2016-09-11   \n",
       "322         375           375  COLLECTION        800     15     2016-09-11   \n",
       "323         376           376  COLLECTION       1000     15     2016-09-11   \n",
       "324         377           377  COLLECTION       1000     30     2016-09-11   \n",
       "325         378           378  COLLECTION       1000     30     2016-09-11   \n",
       "326         379           379  COLLECTION       1000     30     2016-09-11   \n",
       "327         380           380  COLLECTION       1000     30     2016-09-11   \n",
       "328         381           381  COLLECTION        800     15     2016-09-11   \n",
       "329         382           382  COLLECTION       1000     15     2016-09-11   \n",
       "330         383           383  COLLECTION       1000     30     2016-09-11   \n",
       "331         384           384  COLLECTION       1000     30     2016-09-11   \n",
       "332         385           385  COLLECTION       1000     30     2016-09-11   \n",
       "333         386           386  COLLECTION        800     15     2016-09-11   \n",
       "334         387           387  COLLECTION       1000     30     2016-09-11   \n",
       "335         388           388  COLLECTION       1000     15     2016-09-11   \n",
       "336         389           389  COLLECTION       1000     15     2016-09-11   \n",
       "337         390           390  COLLECTION       1000     15     2016-09-11   \n",
       "338         391           391  COLLECTION        800     15     2016-09-11   \n",
       "339         392           392  COLLECTION       1000     30     2016-09-11   \n",
       "340         393           393  COLLECTION       1000     30     2016-09-11   \n",
       "341         394           394  COLLECTION        800     15     2016-09-11   \n",
       "342         395           395  COLLECTION       1000     30     2016-09-11   \n",
       "343         397           397  COLLECTION        800     15     2016-09-12   \n",
       "344         398           398  COLLECTION       1000     30     2016-09-12   \n",
       "345         399           399  COLLECTION       1000     30     2016-09-12   \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  \n",
       "5   2016-10-08   36               college    male  \n",
       "6   2016-10-08   28               college    male  \n",
       "7   2016-09-24   26               college    male  \n",
       "8   2016-09-16   29               college    male  \n",
       "9   2016-10-09   39  High School or Below    male  \n",
       "10  2016-10-09   26               college    male  \n",
       "11  2016-09-16   26               college  female  \n",
       "12  2016-09-16   27  High School or Below    male  \n",
       "13  2016-09-24   26               college    male  \n",
       "14  2016-10-09   40  High School or Below    male  \n",
       "15  2016-09-24   32  High School or Below    male  \n",
       "16  2016-10-09   32  High School or Below    male  \n",
       "17  2016-10-09   26               college    male  \n",
       "18  2016-10-09   26               college    male  \n",
       "19  2016-10-09   25  High School or Below    male  \n",
       "20  2016-09-24   26               college    male  \n",
       "21  2016-10-09   29  High School or Below    male  \n",
       "22  2016-09-24   39              Bechalor    male  \n",
       "23  2016-09-24   34              Bechalor    male  \n",
       "24  2016-10-10   31               college    male  \n",
       "25  2016-10-10   33               college    male  \n",
       "26  2016-09-25   33  High School or Below    male  \n",
       "27  2016-10-10   37               college    male  \n",
       "28  2016-10-10   27               college    male  \n",
       "29  2016-10-10   37               college    male  \n",
       "..         ...  ...                   ...     ...  \n",
       "316 2016-09-25   28              Bechalor    male  \n",
       "317 2016-10-10   24               college    male  \n",
       "318 2016-10-10   18               college    male  \n",
       "319 2016-09-25   25  High School or Below    male  \n",
       "320 2016-09-25   40  High School or Below    male  \n",
       "321 2016-10-10   29               college    male  \n",
       "322 2016-09-25   26  High School or Below  female  \n",
       "323 2016-09-25   30               college    male  \n",
       "324 2016-10-10   33               college    male  \n",
       "325 2016-10-10   30               college    male  \n",
       "326 2016-10-10   32               college    male  \n",
       "327 2016-10-10   25  High School or Below    male  \n",
       "328 2016-09-25   35  High School or Below    male  \n",
       "329 2016-09-25   30              Bechalor    male  \n",
       "330 2016-10-10   26  High School or Below    male  \n",
       "331 2016-10-10   29  High School or Below    male  \n",
       "332 2016-11-09   26  High School or Below    male  \n",
       "333 2016-09-25   46  High School or Below    male  \n",
       "334 2016-10-10   36  High School or Below    male  \n",
       "335 2016-09-25   38              Bechalor    male  \n",
       "336 2016-10-25   32  High School or Below    male  \n",
       "337 2016-09-25   30               college    male  \n",
       "338 2016-09-25   35  High School or Below    male  \n",
       "339 2016-10-10   29               college  female  \n",
       "340 2016-11-09   26               college    male  \n",
       "341 2016-09-25   32  High School or Below    male  \n",
       "342 2016-10-10   25  High School or Below    male  \n",
       "343 2016-09-26   39               college    male  \n",
       "344 2016-11-10   28               college    male  \n",
       "345 2016-10-11   26               college    male  \n",
       "\n",
       "[346 rows x 10 columns]"
      ]
     },
     "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(500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Data visualization and pre-processing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let’s see how many of each class is in our data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "260 people have paid off the loan on time while 86 have gone into collection \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot some columns to underestand data better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata: done\n",
      "Solving environment: - \n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - defaults/linux-64::anaconda==5.3.1=py37_0\n",
      "  - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
      "  - defaults/linux-64::bkcharts==0.2=py37_0\n",
      "  - defaults/linux-64::blaze==0.11.3=py37_0\n",
      "  - defaults/linux-64::bokeh==0.13.0=py37_0\n",
      "  - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
      "  - defaults/linux-64::dask==0.19.1=py37_0\n",
      "  - defaults/linux-64::datashape==0.5.4=py37_1\n",
      "  - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
      "  - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
      "  - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
      "  - defaults/linux-64::odo==0.5.1=py37_0\n",
      "  - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
      "  - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
      "  - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
      "  - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
      "  - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
      "  - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
      "done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs:\n",
      "    - seaborn\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    ca-certificates-2019.1.23  |                0         126 KB  anaconda\n",
      "    certifi-2019.3.9           |           py36_0         155 KB  anaconda\n",
      "    conda-4.6.14               |           py36_0         2.1 MB  anaconda\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         2.4 MB\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  openssl            conda-forge::openssl-1.1.1b-h14c3975_1 --> anaconda::openssl-1.1.1-h7b6447c_0\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  ca-certificates    conda-forge::ca-certificates-2019.3.9~ --> anaconda::ca-certificates-2019.1.23-0\n",
      "  certifi                                       conda-forge --> anaconda\n",
      "  conda                                         conda-forge --> anaconda\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "conda-4.6.14         | 2.1 MB    | ##################################### | 100% \n",
      "ca-certificates-2019 | 126 KB    | ##################################### | 100% \n",
      "certifi-2019.3.9     | 155 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "# notice: installing seaborn might takes a few minutes\n",
    "!conda install -c anaconda seaborn -y"
   ]
  },
  {
   "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": [
    "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": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pre-processing:  Feature selection/extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Lets look at the day of the week people get the loan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": [
    "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()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Convert Categorical features to numerical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets look at gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "86 % of female pay there loans while only 73 % of males pay there loan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets convert male to 0 and female to 1:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>7</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>36</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>8</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>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>39</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>900</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>27</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>40</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>29</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>39</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>34</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>31</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>33</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>33</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>37</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>37</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>367</td>\n",
       "      <td>367</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>28</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>368</td>\n",
       "      <td>368</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>24</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>371</td>\n",
       "      <td>371</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>18</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>372</td>\n",
       "      <td>372</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>373</td>\n",
       "      <td>373</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>40</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>376</td>\n",
       "      <td>376</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>377</td>\n",
       "      <td>377</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>33</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>32</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>35</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>382</td>\n",
       "      <td>382</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>383</td>\n",
       "      <td>383</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>46</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>387</td>\n",
       "      <td>387</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>36</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>388</td>\n",
       "      <td>388</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>38</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>390</td>\n",
       "      <td>390</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>391</td>\n",
       "      <td>391</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>35</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>393</td>\n",
       "      <td>393</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>394</td>\n",
       "      <td>394</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>395</td>\n",
       "      <td>395</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>397</td>\n",
       "      <td>397</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-09-26</td>\n",
       "      <td>39</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 12 columns</p>\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",
       "5             7             7     PAIDOFF       1000     30     2016-09-09   \n",
       "6             8             8     PAIDOFF       1000     30     2016-09-09   \n",
       "7             9             9     PAIDOFF        800     15     2016-09-10   \n",
       "8            10            10     PAIDOFF        300      7     2016-09-10   \n",
       "9            11            11     PAIDOFF       1000     15     2016-09-10   \n",
       "10           12            12     PAIDOFF       1000     30     2016-09-10   \n",
       "11           13            13     PAIDOFF        900      7     2016-09-10   \n",
       "12           14            14     PAIDOFF       1000      7     2016-09-10   \n",
       "13           15            15     PAIDOFF        800     15     2016-09-10   \n",
       "14           16            16     PAIDOFF       1000     30     2016-09-10   \n",
       "15           17            17     PAIDOFF       1000     15     2016-09-10   \n",
       "16           18            18     PAIDOFF       1000     30     2016-09-10   \n",
       "17           19            19     PAIDOFF        800     30     2016-09-10   \n",
       "18           20            20     PAIDOFF       1000     30     2016-09-10   \n",
       "19           22            22     PAIDOFF       1000     30     2016-09-10   \n",
       "20           23            23     PAIDOFF       1000     15     2016-09-10   \n",
       "21           25            25     PAIDOFF       1000     30     2016-09-10   \n",
       "22           26            26     PAIDOFF        800     15     2016-09-10   \n",
       "23           27            27     PAIDOFF       1000     15     2016-09-10   \n",
       "24           28            28     PAIDOFF       1000     30     2016-09-11   \n",
       "25           29            29     PAIDOFF       1000     30     2016-09-11   \n",
       "26           30            30     PAIDOFF        800     15     2016-09-11   \n",
       "27           31            31     PAIDOFF       1000     30     2016-09-11   \n",
       "28           32            32     PAIDOFF       1000     30     2016-09-11   \n",
       "29           33            33     PAIDOFF       1000     30     2016-09-11   \n",
       "..          ...           ...         ...        ...    ...            ...   \n",
       "316         367           367  COLLECTION        800     15     2016-09-11   \n",
       "317         368           368  COLLECTION       1000     30     2016-09-11   \n",
       "318         371           371  COLLECTION       1000     30     2016-09-11   \n",
       "319         372           372  COLLECTION        800     15     2016-09-11   \n",
       "320         373           373  COLLECTION       1000     15     2016-09-11   \n",
       "321         374           374  COLLECTION       1000     30     2016-09-11   \n",
       "322         375           375  COLLECTION        800     15     2016-09-11   \n",
       "323         376           376  COLLECTION       1000     15     2016-09-11   \n",
       "324         377           377  COLLECTION       1000     30     2016-09-11   \n",
       "325         378           378  COLLECTION       1000     30     2016-09-11   \n",
       "326         379           379  COLLECTION       1000     30     2016-09-11   \n",
       "327         380           380  COLLECTION       1000     30     2016-09-11   \n",
       "328         381           381  COLLECTION        800     15     2016-09-11   \n",
       "329         382           382  COLLECTION       1000     15     2016-09-11   \n",
       "330         383           383  COLLECTION       1000     30     2016-09-11   \n",
       "331         384           384  COLLECTION       1000     30     2016-09-11   \n",
       "332         385           385  COLLECTION       1000     30     2016-09-11   \n",
       "333         386           386  COLLECTION        800     15     2016-09-11   \n",
       "334         387           387  COLLECTION       1000     30     2016-09-11   \n",
       "335         388           388  COLLECTION       1000     15     2016-09-11   \n",
       "336         389           389  COLLECTION       1000     15     2016-09-11   \n",
       "337         390           390  COLLECTION       1000     15     2016-09-11   \n",
       "338         391           391  COLLECTION        800     15     2016-09-11   \n",
       "339         392           392  COLLECTION       1000     30     2016-09-11   \n",
       "340         393           393  COLLECTION       1000     30     2016-09-11   \n",
       "341         394           394  COLLECTION        800     15     2016-09-11   \n",
       "342         395           395  COLLECTION       1000     30     2016-09-11   \n",
       "343         397           397  COLLECTION        800     15     2016-09-12   \n",
       "344         398           398  COLLECTION       1000     30     2016-09-12   \n",
       "345         399           399  COLLECTION       1000     30     2016-09-12   \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  \n",
       "5   2016-10-08   36               college       0          4        1  \n",
       "6   2016-10-08   28               college       0          4        1  \n",
       "7   2016-09-24   26               college       0          5        1  \n",
       "8   2016-09-16   29               college       0          5        1  \n",
       "9   2016-10-09   39  High School or Below       0          5        1  \n",
       "10  2016-10-09   26               college       0          5        1  \n",
       "11  2016-09-16   26               college       1          5        1  \n",
       "12  2016-09-16   27  High School or Below       0          5        1  \n",
       "13  2016-09-24   26               college       0          5        1  \n",
       "14  2016-10-09   40  High School or Below       0          5        1  \n",
       "15  2016-09-24   32  High School or Below       0          5        1  \n",
       "16  2016-10-09   32  High School or Below       0          5        1  \n",
       "17  2016-10-09   26               college       0          5        1  \n",
       "18  2016-10-09   26               college       0          5        1  \n",
       "19  2016-10-09   25  High School or Below       0          5        1  \n",
       "20  2016-09-24   26               college       0          5        1  \n",
       "21  2016-10-09   29  High School or Below       0          5        1  \n",
       "22  2016-09-24   39              Bechalor       0          5        1  \n",
       "23  2016-09-24   34              Bechalor       0          5        1  \n",
       "24  2016-10-10   31               college       0          6        1  \n",
       "25  2016-10-10   33               college       0          6        1  \n",
       "26  2016-09-25   33  High School or Below       0          6        1  \n",
       "27  2016-10-10   37               college       0          6        1  \n",
       "28  2016-10-10   27               college       0          6        1  \n",
       "29  2016-10-10   37               college       0          6        1  \n",
       "..         ...  ...                   ...     ...        ...      ...  \n",
       "316 2016-09-25   28              Bechalor       0          6        1  \n",
       "317 2016-10-10   24               college       0          6        1  \n",
       "318 2016-10-10   18               college       0          6        1  \n",
       "319 2016-09-25   25  High School or Below       0          6        1  \n",
       "320 2016-09-25   40  High School or Below       0          6        1  \n",
       "321 2016-10-10   29               college       0          6        1  \n",
       "322 2016-09-25   26  High School or Below       1          6        1  \n",
       "323 2016-09-25   30               college       0          6        1  \n",
       "324 2016-10-10   33               college       0          6        1  \n",
       "325 2016-10-10   30               college       0          6        1  \n",
       "326 2016-10-10   32               college       0          6        1  \n",
       "327 2016-10-10   25  High School or Below       0          6        1  \n",
       "328 2016-09-25   35  High School or Below       0          6        1  \n",
       "329 2016-09-25   30              Bechalor       0          6        1  \n",
       "330 2016-10-10   26  High School or Below       0          6        1  \n",
       "331 2016-10-10   29  High School or Below       0          6        1  \n",
       "332 2016-11-09   26  High School or Below       0          6        1  \n",
       "333 2016-09-25   46  High School or Below       0          6        1  \n",
       "334 2016-10-10   36  High School or Below       0          6        1  \n",
       "335 2016-09-25   38              Bechalor       0          6        1  \n",
       "336 2016-10-25   32  High School or Below       0          6        1  \n",
       "337 2016-09-25   30               college       0          6        1  \n",
       "338 2016-09-25   35  High School or Below       0          6        1  \n",
       "339 2016-10-10   29               college       1          6        1  \n",
       "340 2016-11-09   26               college       0          6        1  \n",
       "341 2016-09-25   32  High School or Below       0          6        1  \n",
       "342 2016-10-10   25  High School or Below       0          6        1  \n",
       "343 2016-09-26   39               college       0          0        0  \n",
       "344 2016-11-10   28               college       0          0        0  \n",
       "345 2016-10-11   26               college       0          0        0  \n",
       "\n",
       "[346 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## One Hot Encoding  \n",
    "#### How about education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Feature befor One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "   Principal  terms  age  Gender             education\n",
       "0       1000     30   45       0  High School or Below\n",
       "1       1000     30   33       1              Bechalor\n",
       "2       1000     15   27       0               college\n",
       "3       1000     30   28       1               college\n",
       "4       1000     30   29       0               college"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Principal','terms','age','Gender','education']].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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      "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": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets define feature sets, X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
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       "      <td>1</td>\n",
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       "    <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",
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       "      <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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our lables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/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/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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
    "You should use the following algorithm:\n",
    "- K Nearest Neighbor(KNN)\n",
    "- Decision Tree\n",
    "- Support Vector Machine\n",
    "- Logistic Regression\n",
    "\n",
    "\n",
    "\n",
    "__ Notice:__ \n",
    "- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
    "- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
    "- You should include the code of the algorithm in the following cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbor(KNN)\n",
    "Notice: You should find the best k to build the model with the best accuracy.  \n",
    "**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458, -0.95911111,  0.67333883, ..., -0.38170062,\n",
       "        -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.81902922, ..., -0.38170062,\n",
       "         1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.01006414, ..., -0.38170062,\n",
       "        -0.87997669,  1.14984679],\n",
       "       ...,\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, ..., -0.38170062,\n",
       "        -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.81902922, ..., -0.38170062,\n",
       "        -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.15575453, ..., -0.38170062,\n",
       "         1.13639374, -0.86968108]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67142857, 0.65714286, 0.71428571, 0.68571429, 0.75714286,\n",
       "       0.71428571, 0.78571429, 0.75714286, 0.75714286])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc #print accuracy array for eack k\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.7857142857142857 with k= 7\n"
     ]
    }
   ],
   "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 Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>7</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>36</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>8</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>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>39</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>900</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-16</td>\n",
       "      <td>27</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>40</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-10-09</td>\n",
       "      <td>29</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>39</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-10</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>34</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>31</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>33</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>33</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>37</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>37</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>367</td>\n",
       "      <td>367</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>28</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>368</td>\n",
       "      <td>368</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>24</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>371</td>\n",
       "      <td>371</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>18</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>372</td>\n",
       "      <td>372</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>373</td>\n",
       "      <td>373</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>40</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>376</td>\n",
       "      <td>376</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>377</td>\n",
       "      <td>377</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>33</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>378</td>\n",
       "      <td>378</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>379</td>\n",
       "      <td>379</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>32</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>380</td>\n",
       "      <td>380</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>381</td>\n",
       "      <td>381</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>35</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>382</td>\n",
       "      <td>382</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>383</td>\n",
       "      <td>383</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>385</td>\n",
       "      <td>385</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>26</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>46</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>387</td>\n",
       "      <td>387</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>36</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>388</td>\n",
       "      <td>388</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>38</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>389</td>\n",
       "      <td>389</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>390</td>\n",
       "      <td>390</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>30</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>391</td>\n",
       "      <td>391</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>35</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>392</td>\n",
       "      <td>392</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>393</td>\n",
       "      <td>393</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-11-09</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>394</td>\n",
       "      <td>394</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-09-25</td>\n",
       "      <td>32</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>395</td>\n",
       "      <td>395</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-11</td>\n",
       "      <td>2016-10-10</td>\n",
       "      <td>25</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>397</td>\n",
       "      <td>397</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-09-26</td>\n",
       "      <td>39</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>399</td>\n",
       "      <td>399</td>\n",
       "      <td>COLLECTION</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-12</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 12 columns</p>\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",
       "5             7             7     PAIDOFF       1000     30     2016-09-09   \n",
       "6             8             8     PAIDOFF       1000     30     2016-09-09   \n",
       "7             9             9     PAIDOFF        800     15     2016-09-10   \n",
       "8            10            10     PAIDOFF        300      7     2016-09-10   \n",
       "9            11            11     PAIDOFF       1000     15     2016-09-10   \n",
       "10           12            12     PAIDOFF       1000     30     2016-09-10   \n",
       "11           13            13     PAIDOFF        900      7     2016-09-10   \n",
       "12           14            14     PAIDOFF       1000      7     2016-09-10   \n",
       "13           15            15     PAIDOFF        800     15     2016-09-10   \n",
       "14           16            16     PAIDOFF       1000     30     2016-09-10   \n",
       "15           17            17     PAIDOFF       1000     15     2016-09-10   \n",
       "16           18            18     PAIDOFF       1000     30     2016-09-10   \n",
       "17           19            19     PAIDOFF        800     30     2016-09-10   \n",
       "18           20            20     PAIDOFF       1000     30     2016-09-10   \n",
       "19           22            22     PAIDOFF       1000     30     2016-09-10   \n",
       "20           23            23     PAIDOFF       1000     15     2016-09-10   \n",
       "21           25            25     PAIDOFF       1000     30     2016-09-10   \n",
       "22           26            26     PAIDOFF        800     15     2016-09-10   \n",
       "23           27            27     PAIDOFF       1000     15     2016-09-10   \n",
       "24           28            28     PAIDOFF       1000     30     2016-09-11   \n",
       "25           29            29     PAIDOFF       1000     30     2016-09-11   \n",
       "26           30            30     PAIDOFF        800     15     2016-09-11   \n",
       "27           31            31     PAIDOFF       1000     30     2016-09-11   \n",
       "28           32            32     PAIDOFF       1000     30     2016-09-11   \n",
       "29           33            33     PAIDOFF       1000     30     2016-09-11   \n",
       "..          ...           ...         ...        ...    ...            ...   \n",
       "316         367           367  COLLECTION        800     15     2016-09-11   \n",
       "317         368           368  COLLECTION       1000     30     2016-09-11   \n",
       "318         371           371  COLLECTION       1000     30     2016-09-11   \n",
       "319         372           372  COLLECTION        800     15     2016-09-11   \n",
       "320         373           373  COLLECTION       1000     15     2016-09-11   \n",
       "321         374           374  COLLECTION       1000     30     2016-09-11   \n",
       "322         375           375  COLLECTION        800     15     2016-09-11   \n",
       "323         376           376  COLLECTION       1000     15     2016-09-11   \n",
       "324         377           377  COLLECTION       1000     30     2016-09-11   \n",
       "325         378           378  COLLECTION       1000     30     2016-09-11   \n",
       "326         379           379  COLLECTION       1000     30     2016-09-11   \n",
       "327         380           380  COLLECTION       1000     30     2016-09-11   \n",
       "328         381           381  COLLECTION        800     15     2016-09-11   \n",
       "329         382           382  COLLECTION       1000     15     2016-09-11   \n",
       "330         383           383  COLLECTION       1000     30     2016-09-11   \n",
       "331         384           384  COLLECTION       1000     30     2016-09-11   \n",
       "332         385           385  COLLECTION       1000     30     2016-09-11   \n",
       "333         386           386  COLLECTION        800     15     2016-09-11   \n",
       "334         387           387  COLLECTION       1000     30     2016-09-11   \n",
       "335         388           388  COLLECTION       1000     15     2016-09-11   \n",
       "336         389           389  COLLECTION       1000     15     2016-09-11   \n",
       "337         390           390  COLLECTION       1000     15     2016-09-11   \n",
       "338         391           391  COLLECTION        800     15     2016-09-11   \n",
       "339         392           392  COLLECTION       1000     30     2016-09-11   \n",
       "340         393           393  COLLECTION       1000     30     2016-09-11   \n",
       "341         394           394  COLLECTION        800     15     2016-09-11   \n",
       "342         395           395  COLLECTION       1000     30     2016-09-11   \n",
       "343         397           397  COLLECTION        800     15     2016-09-12   \n",
       "344         398           398  COLLECTION       1000     30     2016-09-12   \n",
       "345         399           399  COLLECTION       1000     30     2016-09-12   \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  \n",
       "5   2016-10-08   36               college       0          4        1  \n",
       "6   2016-10-08   28               college       0          4        1  \n",
       "7   2016-09-24   26               college       0          5        1  \n",
       "8   2016-09-16   29               college       0          5        1  \n",
       "9   2016-10-09   39  High School or Below       0          5        1  \n",
       "10  2016-10-09   26               college       0          5        1  \n",
       "11  2016-09-16   26               college       1          5        1  \n",
       "12  2016-09-16   27  High School or Below       0          5        1  \n",
       "13  2016-09-24   26               college       0          5        1  \n",
       "14  2016-10-09   40  High School or Below       0          5        1  \n",
       "15  2016-09-24   32  High School or Below       0          5        1  \n",
       "16  2016-10-09   32  High School or Below       0          5        1  \n",
       "17  2016-10-09   26               college       0          5        1  \n",
       "18  2016-10-09   26               college       0          5        1  \n",
       "19  2016-10-09   25  High School or Below       0          5        1  \n",
       "20  2016-09-24   26               college       0          5        1  \n",
       "21  2016-10-09   29  High School or Below       0          5        1  \n",
       "22  2016-09-24   39              Bechalor       0          5        1  \n",
       "23  2016-09-24   34              Bechalor       0          5        1  \n",
       "24  2016-10-10   31               college       0          6        1  \n",
       "25  2016-10-10   33               college       0          6        1  \n",
       "26  2016-09-25   33  High School or Below       0          6        1  \n",
       "27  2016-10-10   37               college       0          6        1  \n",
       "28  2016-10-10   27               college       0          6        1  \n",
       "29  2016-10-10   37               college       0          6        1  \n",
       "..         ...  ...                   ...     ...        ...      ...  \n",
       "316 2016-09-25   28              Bechalor       0          6        1  \n",
       "317 2016-10-10   24               college       0          6        1  \n",
       "318 2016-10-10   18               college       0          6        1  \n",
       "319 2016-09-25   25  High School or Below       0          6        1  \n",
       "320 2016-09-25   40  High School or Below       0          6        1  \n",
       "321 2016-10-10   29               college       0          6        1  \n",
       "322 2016-09-25   26  High School or Below       1          6        1  \n",
       "323 2016-09-25   30               college       0          6        1  \n",
       "324 2016-10-10   33               college       0          6        1  \n",
       "325 2016-10-10   30               college       0          6        1  \n",
       "326 2016-10-10   32               college       0          6        1  \n",
       "327 2016-10-10   25  High School or Below       0          6        1  \n",
       "328 2016-09-25   35  High School or Below       0          6        1  \n",
       "329 2016-09-25   30              Bechalor       0          6        1  \n",
       "330 2016-10-10   26  High School or Below       0          6        1  \n",
       "331 2016-10-10   29  High School or Below       0          6        1  \n",
       "332 2016-11-09   26  High School or Below       0          6        1  \n",
       "333 2016-09-25   46  High School or Below       0          6        1  \n",
       "334 2016-10-10   36  High School or Below       0          6        1  \n",
       "335 2016-09-25   38              Bechalor       0          6        1  \n",
       "336 2016-10-25   32  High School or Below       0          6        1  \n",
       "337 2016-09-25   30               college       0          6        1  \n",
       "338 2016-09-25   35  High School or Below       0          6        1  \n",
       "339 2016-10-10   29               college       1          6        1  \n",
       "340 2016-11-09   26               college       0          6        1  \n",
       "341 2016-09-25   32  High School or Below       0          6        1  \n",
       "342 2016-10-10   25  High School or Below       0          6        1  \n",
       "343 2016-09-26   39               college       0          0        0  \n",
       "344 2016-11-10   28               college       0          0        0  \n",
       "345 2016-10-11   26               college       0          0        0  \n",
       "\n",
       "[346 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "drugTree # it shows the default parameters\n",
    "drugTree.fit(X_train,y_train)\n",
    "predTree = drugTree.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata: done\n",
      "Solving environment: / \n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - defaults/linux-64::anaconda==5.3.1=py37_0\n",
      "  - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
      "  - defaults/linux-64::bkcharts==0.2=py37_0\n",
      "  - defaults/linux-64::blaze==0.11.3=py37_0\n",
      "  - defaults/linux-64::bokeh==0.13.0=py37_0\n",
      "  - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
      "  - defaults/linux-64::dask==0.19.1=py37_0\n",
      "  - defaults/linux-64::datashape==0.5.4=py37_1\n",
      "  - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
      "  - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
      "  - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
      "  - defaults/linux-64::odo==0.5.1=py37_0\n",
      "  - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
      "  - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
      "  - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
      "  - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
      "  - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
      "  - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
      "done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs:\n",
      "    - pydotplus\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    certifi-2019.3.9           |           py36_0         149 KB  conda-forge\n",
      "    conda-4.6.14               |           py36_0         2.1 MB  conda-forge\n",
      "    pydotplus-2.0.2            |             py_2          23 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:         2.3 MB\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "  ca-certificates     anaconda::ca-certificates-2019.1.23-0 --> conda-forge::ca-certificates-2019.3.9-hecc5488_0\n",
      "  pydotplus          anaconda/linux-64::pydotplus-2.0.2-py~ --> conda-forge/noarch::pydotplus-2.0.2-py_2\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  certifi                                          anaconda --> conda-forge\n",
      "  conda                                            anaconda --> conda-forge\n",
      "  openssl                anaconda::openssl-1.1.1-h7b6447c_0 --> conda-forge::openssl-1.1.1b-h14c3975_1\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "pydotplus-2.0.2      | 23 KB     | ##################################### | 100% \n",
      "conda-4.6.14         | 2.1 MB    | ##################################### | 100% \n",
      "certifi-2019.3.9     | 149 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "#INSTALLATIONS TO VIEW THE DECISION TREE\n",
    "!conda install -c conda-forge pydotplus -y\n",
    " \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata: done\n",
      "Solving environment: | \n",
      "The environment is inconsistent, please check the package plan carefully\n",
      "The following packages are causing the inconsistency:\n",
      "\n",
      "  - defaults/linux-64::anaconda==5.3.1=py37_0\n",
      "  - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
      "  - defaults/linux-64::bkcharts==0.2=py37_0\n",
      "  - defaults/linux-64::blaze==0.11.3=py37_0\n",
      "  - defaults/linux-64::bokeh==0.13.0=py37_0\n",
      "  - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
      "  - defaults/linux-64::dask==0.19.1=py37_0\n",
      "  - defaults/linux-64::datashape==0.5.4=py37_1\n",
      "  - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
      "  - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
      "  - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
      "  - defaults/linux-64::odo==0.5.1=py37_0\n",
      "  - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
      "  - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
      "  - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
      "  - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
      "  - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
      "  - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
      "done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /home/jupyterlab/conda\n",
      "\n",
      "  added / updated specs:\n",
      "    - python-graphviz\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    python-graphviz-0.10.1     |             py_0          17 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:          17 KB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  python-graphviz    conda-forge/noarch::python-graphviz-0.10.1-py_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "python-graphviz-0.10 | 17 KB     | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n"
     ]
    }
   ],
   "source": [
    "!conda install -c conda-forge python-graphviz -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5115141198>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "%matplotlib inline \n",
    "dot_data = StringIO()\n",
    "filename = \"drugtree.png\"\n",
    "featureNames = df.columns[3: 11]\n",
    "targetNames = df[\"loan_status\"].unique().tolist()\n",
    "out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), 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",
    "plt.figure(figsize=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "clf.fit(X_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhatsvm = clf.predict(X_test)\n",
    "yhatsvm [0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  COLLECTION       0.42      0.33      0.37        15\n",
      "     PAIDOFF       0.83      0.87      0.85        55\n",
      "\n",
      "   micro avg       0.76      0.76      0.76        70\n",
      "   macro avg       0.62      0.60      0.61        70\n",
      "weighted avg       0.74      0.76      0.75        70\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[ 5 10]\n",
      " [ 7 48]]\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": [
    "\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\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')\n",
    "    \n",
    "    # Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=df[\"loan_status\"].unique().tolist(),normalize= False,  title='Confusion matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
    "LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n",
       "       'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n",
       "       'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhatl = LR.predict(X_test)\n",
    "yhatl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_prob = LR.predict_proba(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download and load the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-05-08 02:47:26--  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)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|: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",
      "2019-05-08 02:47:26 (52.2 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": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Test set for evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>1</td>\n",
       "      <td>1</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>50</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>9/15/2016</td>\n",
       "      <td>35</td>\n",
       "      <td>Master or Above</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>9/11/2016</td>\n",
       "      <td>9/25/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</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           1             1     PAIDOFF       1000     30       9/8/2016   \n",
       "1           5             5     PAIDOFF        300      7       9/9/2016   \n",
       "2          21            21     PAIDOFF       1000     30      9/10/2016   \n",
       "3          24            24     PAIDOFF       1000     30      9/10/2016   \n",
       "4          35            35     PAIDOFF        800     15      9/11/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   50              Bechalor  female  \n",
       "1  9/15/2016   35       Master or Above    male  \n",
       "2  10/9/2016   43  High School or Below  female  \n",
       "3  10/9/2016   26               college    male  \n",
       "4  9/25/2016   29              Bechalor    male  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0         int64\n",
      "Unnamed: 0.1       int64\n",
      "loan_status       object\n",
      "Principal          int64\n",
      "terms              int64\n",
      "effective_date    object\n",
      "due_date          object\n",
      "age                int64\n",
      "education         object\n",
      "Gender            object\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/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/lib/python3.6/site-packages/ipykernel_launcher.py:13: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "#test_df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)\n",
    "print(test_df.dtypes)\n",
    "import numpy as np\n",
    "test_df['Gender'].replace(to_replace=['male','female'], value=[np.int64(0),np.int64(1)],inplace=True)\n",
    "\n",
    "test_df.groupby(['education'])['loan_status'].value_counts(normalize=True)\n",
    "Feature = test_df[['Principal','terms','age','Gender']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(test_df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n",
    "X2 = Feature\n",
    "y2 = test_df['loan_status'].values\n",
    "X2= preprocessing.StandardScaler().fit(X2).transform(X2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.6142857142857143\n"
     ]
    }
   ],
   "source": [
    "predTree = drugTree.predict(X_test)\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predTree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  COLLECTION       0.42      0.33      0.37        15\n",
      "     PAIDOFF       0.83      0.87      0.85        55\n",
      "\n",
      "   micro avg       0.76      0.76      0.76        70\n",
      "   macro avg       0.62      0.60      0.61        70\n",
      "weighted avg       0.74      0.76      0.75        70\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#for knn\n",
    "from sklearn.metrics import classification_report\n",
    "cr = classification_report(y_test, yhat)\n",
    "print(cr)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM ACCY:               precision    recall  f1-score   support\n",
      "\n",
      "  COLLECTION       0.36      0.27      0.31        15\n",
      "     PAIDOFF       0.81      0.87      0.84        55\n",
      "\n",
      "   micro avg       0.74      0.74      0.74        70\n",
      "   macro avg       0.59      0.57      0.57        70\n",
      "weighted avg       0.72      0.74      0.73        70\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# for SVM\n",
    "classification_report(y_test,yhatsvm )\n",
    "print('SVM ACCY:', classification_report(y_test, yhatsvm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logit               precision    recall  f1-score   support\n",
      "\n",
      "  COLLECTION       0.18      0.13      0.15        15\n",
      "     PAIDOFF       0.78      0.84      0.81        55\n",
      "\n",
      "   micro avg       0.69      0.69      0.69        70\n",
      "   macro avg       0.48      0.48      0.48        70\n",
      "weighted avg       0.65      0.69      0.67        70\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#for logit\n",
    "classification_report(y_test, yhatl)\n",
    "print('Logit', classification_report(y_test, yhatl))"
   ]
  }
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