ML IBM.ipynb

{
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    "cells": [
        {
            "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>", 
            "cell_type": "markdown", 
            "metadata": {
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        }, 
        {
            "source": "In this notebook we try to practice all the classification algorithms that we learned in this course.\n\nWe 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\nLets first load required libraries:", 
            "cell_type": "markdown", 
            "metadata": {
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                "new_sheet": false, 
                "run_control": {
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                }
            }
        }, 
        {
            "source": "import itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import NullFormatter\nimport pandas as pd\nimport numpy as np\nimport matplotlib.ticker as ticker\nfrom sklearn import preprocessing\n%matplotlib inline", 
            "cell_type": "code", 
            "metadata": {
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                "run_control": {
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            }, 
            "outputs": [], 
            "execution_count": 1
        }, 
        {
            "source": "### About dataset", 
            "cell_type": "markdown", 
            "metadata": {
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                "run_control": {
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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\u2019s 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                                                               |", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
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                }
            }
        }, 
        {
            "source": "Lets download the dataset", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
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                }
            }
        }, 
        {
            "source": "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv", 
            "cell_type": "code", 
            "metadata": {
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                "new_sheet": false, 
                "run_control": {
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            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "--2019-05-30 17:22:20--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 23101 (23K) [text/csv]\nSaving to: \u2018loan_train.csv\u2019\n\n100%[======================================>] 23,101      --.-K/s   in 0.07s   \n\n2019-05-30 17:22:20 (302 KB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
                }
            ], 
            "execution_count": 2
        }, 
        {
            "source": "### Load Data From CSV File  ", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df = pd.read_csv('loan_train.csv')\ndf.head()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30       9/8/2016   \n1           2             2     PAIDOFF       1000     30       9/8/2016   \n2           3             3     PAIDOFF       1000     15       9/8/2016   \n3           4             4     PAIDOFF       1000     30       9/9/2016   \n4           6             6     PAIDOFF       1000     30       9/9/2016   \n\n    due_date  age             education  Gender  \n0  10/7/2016   45  High School or Below    male  \n1  10/7/2016   33              Bechalor  female  \n2  9/22/2016   27               college    male  \n3  10/8/2016   28               college  female  \n4  10/8/2016   29               college    male  ", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>9/8/2016</td>\n      <td>9/22/2016</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 3, 
                    "metadata": {}
                }
            ], 
            "execution_count": 3
        }, 
        {
            "source": "df.shape", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "(346, 10)"
                    }, 
                    "execution_count": 4, 
                    "metadata": {}
                }
            ], 
            "execution_count": 4
        }, 
        {
            "source": "### Convert to date time object ", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  \n0 2016-10-07   45  High School or Below    male  \n1 2016-10-07   33              Bechalor  female  \n2 2016-09-22   27               college    male  \n3 2016-10-08   28               college  female  \n4 2016-10-08   29               college    male  ", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 5, 
                    "metadata": {}
                }
            ], 
            "execution_count": 5
        }, 
        {
            "source": "# Data visualization and pre-processing\n\n", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "Let\u2019s see how many of each class is in our data set ", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df['loan_status'].value_counts()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "PAIDOFF       260\nCOLLECTION     86\nName: loan_status, dtype: int64"
                    }, 
                    "execution_count": 6, 
                    "metadata": {}
                }
            ], 
            "execution_count": 6
        }, 
        {
            "source": "260 people have paid off the loan on time while 86 have gone into collection \n", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "Lets plot some columns to underestand data better:", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/DSX-Python35\n\n  added / updated specs: \n    - seaborn\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    ca-certificates-2019.1.23  |                0         126 KB  anaconda\n    seaborn-0.9.0              |           py35_0         378 KB  anaconda\n    certifi-2018.8.24          |           py35_1         139 KB  anaconda\n    openssl-1.0.2r             |       h7b6447c_0         3.2 MB  anaconda\n    ------------------------------------------------------------\n                                           Total:         3.8 MB\n\nThe following packages will be UPDATED:\n\n    ca-certificates: 2019.1.23-0          --> 2019.1.23-0       anaconda\n    certifi:         2018.8.24-py35_1     --> 2018.8.24-py35_1  anaconda\n    openssl:         1.0.2r-h7b6447c_0    --> 1.0.2r-h7b6447c_0 anaconda\n    seaborn:         0.8.0-py35h15a2772_0 --> 0.9.0-py35_0      anaconda\n\n\nDownloading and Extracting Packages\nca-certificates-2019 | 126 KB    | ##################################### | 100% \nseaborn-0.9.0        | 378 KB    | ##################################### | 100% \ncertifi-2018.8.24    | 139 KB    | ##################################### | 100% \nopenssl-1.0.2r       | 3.2 MB    | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"
                }
            ], 
            "execution_count": 7
        }, 
        {
            "source": "import seaborn as sns\n\nbins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd286b001d0>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 8
        }, 
        {
            "source": "bins = np.linspace(df.age.min(), df.age.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'age', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd242d36b00>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 9
        }, 
        {
            "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": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df['dayofweek'] = df['effective_date'].dt.dayofweek\nbins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\ng.axes[-1].legend()\nplt.show()\n", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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lLZJ0p6Sdgb0jYiVA8nOvpjaWdI6k+ZLm+1vdZk1znphtLp8C1REYDNwWEYOAv9OC7ryImBwR1RFR3b17920M06y0OU/MNpdPgaoD6iLi+eTxdHIF611J5QDJz/cKE6KZmbVHzRaoiFgFvCWpX7JoOPAK8FtgbLJsLPBIQSI0M7N2Kd/BYi8EpkraEXgd+Da54vagpLOBFcBphQnRrHXUoSyV+cHUoSyFaMwsX3kVqIioAaqbeGp4uuGYpS8+W8/hVzza6nae/8nIFKIxs3x5JAkzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8skFygzM8ukvAuUpA6SFkmamTyulPS8pD9LekDSjoUL08zM2puWnEF9D1jW6PE1wA0R0Rf4C3B2moGZmVn7lleBklQBHAfcmTwWMAyYnqxyDzC6EAGamVn7lO8Z1I3ARODz5PGewPsRsSF5XAf0aGpDSedImi9p/urVq1sVrFmpcp6Yba7ZAiXpeOC9iFjQeHETq0ZT20fE5Iiojojq7t27b2OYZqXNeWK2uY55rHMUcKKkUUAnYBdyZ1S7SeqYnEVVAO8ULkwzM2tvmj2DiojLIqIiInoDY4AnI+IMYDZwarLaWOCRgkVpZmbtTmu+B3UJcLGk18h9JnVXOiGZmZnl18XXICLmAHOS+68Dh6UfkpmZmUeSMDOzjHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKBMjOzTHKB2o56lZcjKZVbr/LyYh+OmVlBtWg+KGudFatWUbdvRSptVbxTl0o7ZmZZ5TMoMzPLJBcoMzPLJBcoMzPLJBcoMzPLJBcoMzPLJBcoMzPLJBcoMzPLJBcoMzPLJBcoMzPLpGYLlKQvSJotaZmkpZK+lyzfQ9IsSX9Ofu5e+HDNzKy9yOcMagPw/Yg4EPgScL6kAcClwBMR0Rd4InlsZmaWimYLVESsjIiFyf2/AcuAHsBJwD3JavcAowsVpJmZtT8t+gxKUm9gEPA8sHdErIRcEQP22sI250iaL2n+6tWrWxetWYlynphtLu8CJakL8GtgfER8kO92ETE5Iqojorp79+7bEqNZyXOemG0urwIlqYxccZoaEb9JFr8rqTx5vhx4rzAhmplZe5TPVXwC7gKWRcT1jZ76LTA2uT8WeCT98MzMrL3KZ8LCo4AzgSWSapJllwNXAw9KOhtYAZxWmBDNzKw9arZARcQ8QFt4eni64ZiZWTH0Ki9nxapVqbTVc599eHPlyla34ynfzcyMFatWUbdvRSptVbxTl0o7HurIMqlXeTmSUrmVorR+P73Ky4t9KGZb5DMoy6QsvpvLkrR+P6X4u7HS4TMoMzPLpJI9g/onSK17J60P/Cx/6lDmd/dm7VzJFqhPwF1EbVh8tp7Dr3g0lbae/8nIVNoxs+3LXXxmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJLlBmZpZJJTvUkZmZ5S/N8S/VoSyVdlygzMwsk+NfuovPrB2rH/Xfkx9aFvkMyqwd86j/lmU+gzIzs0xygbLU7FvRM7XuIjMzd/FZala+/VbmPmQ1s7YrUwUqi5c5mtn216u8nBWrVrW6nZ777MObK1emEJEVQ6YKVBYvc8yq+quv0uAktqxZsWpVKhdv+MKNtq1VBUrSSOAmoANwZ0RcnUpU1ixffWVmpW6bL5KQ1AG4BfgaMAD4hqQBaQVmZtZaWf2eV6/y8lRi6tyhY0lfmNSaM6jDgNci4nUASfcDJwGvpBGYmVlrZbWnIc0uzCweX1oUEdu2oXQqMDIivpM8PhM4PCIu2GS9c4Bzkof9gD9tpdluwJptCqht8PG1bfkc35qIaPEHoC3Mk3xjact8fG1bc8eXV5605gyqqXPCzapdREwGJufVoDQ/IqpbEVOm+fjatkIeX0vypNCxZIGPr21L6/ha80XdOuALjR5XAO+0LhwzM7Oc1hSoF4G+kiol7QiMAX6bTlhmZtbebXMXX0RskHQB8Bi5y8zvjoilrYwn7y6ONsrH17Zl6fiyFEsh+PjatlSOb5svkjAzMyskDxZrZmaZ5AJlZmaZlJkCJWmkpD9Jek3SpcWOJ02SviBptqRlkpZK+l6xY0qbpA6SFkmaWexYCkHSbpKmS3o1+TseUaQ4nCdtXCnnStp5konPoJJhk5YDXyV3+fqLwDcioiRGpZBUDpRHxEJJXYEFwOhSOT4ASRcD1cAuEXF8seNJm6R7gLkRcWdy1WrniHh/O8fgPCkBpZwraedJVs6gGoZNiohPgfphk0pCRKyMiIXJ/b8By4AexY0qPZIqgOOAO4sdSyFI2gU4BrgLICI+3d7FKeE8aeNKOVcKkSdZKVA9gLcaPa6jxF6Y9ST1BgYBzxc3klTdCEwEPi92IAWyH7Aa+GXSNXOnpJ2LEIfzpO0r5VxJPU+yUqDyGjaprZPUBfg1MD4iPih2PGmQdDzwXkQsKHYsBdQRGAzcFhGDgL8Dxfj8x3nShrWDXEk9T7JSoEp+2CRJZeSSbmpE/KbY8aToKOBESbXkupyGSbq3uCGlrg6oi4j6d/PTySViMeJwnrRdpZ4rqedJVgpUSQ+bpNxkK3cByyLi+mLHk6aIuCwiKiKiN7m/25MR8a0ih5WqiFgFvCWpX7JoOMWZVsZ50oaVeq4UIk8yMeV7gYZNypKjgDOBJZJqkmWXR8R/FjEma5kLgalJYXgd+Pb2DsB5Ym1AqnmSicvMzczMNpWVLj4zM7ONuECZmVkmuUCZmVkmuUCZmVkmuUCZmVkmuUBlgKQfS5qQYnv9JdUkw430SavdRu3PkVSddrtmW+M8aX9coErTaOCRiBgUEf9V7GDMMsp5knEuUEUi6QfJvD6PA/2SZf8q6UVJL0n6taTOkrpKeiMZAgZJu0iqlVQmqUrSc5IWS3pY0u6SRgHjge8kc+tMlHRRsu0Nkp5M7g+vH2ZF0ghJz0paKOmhZCw0JB0q6Y+SFkh6LJkOofEx7CDpHkn/e7v94qxdcZ60by5QRSDpUHJDnQwC/gX4YvLUbyLiixHxz+SmGjg7mXZgDrkh+km2+3VErAf+HbgkIgYCS4AfJd+6vx24ISKOBZ4ChiTbVgNdkiQ+GpgrqRvwQ+ArETEYmA9cnKzzc+DUiDgUuBv4P40OoyMwFVgeET9M8ddjBjhPLCNDHbVDQ4CHI+IjAEn146kdnLzL2g3oQm5IG8jNHTMRmEFu6JB/lbQrsFtE/DFZ5x7goSb2tQA4VLkJ4D4BFpJLwCHARcCXgAHA07mh0NgReJbcu9WDgVnJ8g7Aykbt3gE8GBGNk9EsTc6Tds4FqniaGmNqCrkZRF+SNA4YChART0vqLenLQIeIeDlJvOZ3ErFeudGTvw08AywGjgX6kHv32QeYFRHfaLydpEOApRGxpSmbnwGOlfR/I2JdPrGYbQPnSTvmLr7ieAo4WdJOyTu2E5LlXYGVSbfBGZts8+/ANOCXABHxV+Avkuq7Jc4E/kjTngImJD/nAucCNZEbiPE54ChJ+wMk/fkHAH8Cuks6IlleJumgRm3eBfwn8JAkv9GxQnCetHMuUEWQTGv9AFBDbu6buclT/4vcDKKzgFc32WwqsDu55Ks3FrhO0mKgCvjJFnY5FygHno2Id4F19fuMiNXAOGBa0s5zQP9kSvFTgWskvZTEeuQmx3E9ua6QX0nya8lS5Twxj2beRkg6FTgpIs4sdixmWeU8KS0+5WwDJP0c+BowqtixmGWV86T0+AzKzMwyyf2hZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSf8feZ3K8s9z83MAAAAASUVORK5CYII=\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd242bdf710>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 10
        }, 
        {
            "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": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ndf.head()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   45  High School or Below    male          3        0  \n1 2016-10-07   33              Bechalor  female          3        0  \n2 2016-09-22   27               college    male          3        0  \n3 2016-10-08   28               college  female          4        1  \n4 2016-10-08   29               college    male          4        1  ", 
                        "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>"
                    }, 
                    "execution_count": 11, 
                    "metadata": {}
                }
            ], 
            "execution_count": 11
        }, 
        {
            "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": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "Gender  loan_status\nfemale  PAIDOFF        0.865385\n        COLLECTION     0.134615\nmale    PAIDOFF        0.731293\n        COLLECTION     0.268707\nName: loan_status, dtype: float64"
                    }, 
                    "execution_count": 12, 
                    "metadata": {}
                }
            ], 
            "execution_count": 12
        }, 
        {
            "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": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   45  High School or Below       0          3        0  \n1 2016-10-07   33              Bechalor       1          3        0  \n2 2016-09-22   27               college       0          3        0  \n3 2016-10-08   28               college       1          4        1  \n4 2016-10-08   29               college       0          4        1  ", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n      <th>dayofweek</th>\n      <th>weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 13, 
                    "metadata": {}
                }
            ], 
            "execution_count": 13
        }, 
        {
            "source": "## One Hot Encoding  \n#### How about education?", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "education             loan_status\nBechalor              PAIDOFF        0.750000\n                      COLLECTION     0.250000\nHigh School or Below  PAIDOFF        0.741722\n                      COLLECTION     0.258278\nMaster or Above       COLLECTION     0.500000\n                      PAIDOFF        0.500000\ncollege               PAIDOFF        0.765101\n                      COLLECTION     0.234899\nName: loan_status, dtype: float64"
                    }, 
                    "execution_count": 14, 
                    "metadata": {}
                }
            ], 
            "execution_count": 14
        }, 
        {
            "source": "#### Feature befor One Hot Encoding", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "df[['Principal','terms','age','Gender','education']].head()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Principal  terms  age  Gender             education\n0       1000     30   45       0  High School or Below\n1       1000     30   33       1              Bechalor\n2       1000     15   27       0               college\n3       1000     30   28       1               college\n4       1000     30   29       0               college", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>education</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>High School or Below</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>Bechalor</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>college</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>college</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>college</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 15, 
                    "metadata": {}
                }
            ], 
            "execution_count": 15
        }, 
        {
            "source": "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame ", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "Feature = df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\n", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n0       1000     30   45       0        0         0                     1   \n1       1000     30   33       1        0         1                     0   \n2       1000     15   27       0        0         0                     0   \n3       1000     30   28       1        1         0                     0   \n4       1000     30   29       0        1         0                     0   \n\n   college  \n0        0  \n1        0  \n2        1  \n3        1  \n4        1  ", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 16, 
                    "metadata": {}
                }
            ], 
            "execution_count": 16
        }, 
        {
            "source": "### Feature selection", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "Lets defind feature sets, X:", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "X = Feature\nX[0:5]", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n0       1000     30   45       0        0         0                     1   \n1       1000     30   33       1        0         1                     0   \n2       1000     15   27       0        0         0                     0   \n3       1000     30   28       1        1         0                     0   \n4       1000     30   29       0        1         0                     0   \n\n   college  \n0        0  \n1        0  \n2        1  \n3        1  \n4        1  ", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 17, 
                    "metadata": {}
                }
            ], 
            "execution_count": 17
        }, 
        {
            "source": "What are our lables?", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "y = df['loan_status'].values\ny[0:5]", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
                    }, 
                    "execution_count": 18, 
                    "metadata": {}
                }
            ], 
            "execution_count": 18
        }, 
        {
            "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": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "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": {}
                }
            ], 
            "execution_count": 19
        }, 
        {
            "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\nYou 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": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "# K Nearest Neighbor(KNN)\nNotice: 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": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
                }
            ], 
            "execution_count": 20
        }, 
        {
            "source": "from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn import metrics\n# finding the best k number\nKs = 10\nmean_acc = np.zeros((Ks-1))\nstd_acc = np.zeros((Ks-1))\nConfustionMx = [];\nfor n in range(1,Ks):\n    \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\nmean_acc\n# plotting \nplt.plot(range(1,Ks),mean_acc,'g')\nplt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\nplt.legend(('Accuracy ', '+/- 3xstd'))\nplt.ylabel('Accuracy ')\nplt.xlabel('Number of Naighbors (K)')\nplt.tight_layout()\nplt.show()\nprint( \"The best accuracy was with\", mean_acc.max(), \"with k =\", mean_acc.argmax()+1)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd242d29278>"
                    }, 
                    "metadata": {}
                }, 
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The best accuracy was with 0.785714285714 with k = 7\n"
                }
            ], 
            "execution_count": 21
        }, 
        {
            "source": "k = mean_acc.argmax()+1\n\n#Train Model and Predict  \nneigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\nneigh\nyhat = neigh.predict(X_test)\nyhat[0:5]\n# evaluation\nfrom sklearn import metrics\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))\n\nfrom sklearn.metrics import f1_score\nprint (\"the f1 score is: \", f1_score(y_test, yhat, average='weighted')) \n\nfrom sklearn.metrics import jaccard_similarity_score\nprint (\"Jaccard_score is:\", jaccard_similarity_score(y_test, yhat))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train set Accuracy:  0.807971014493\nTest set Accuracy:  0.785714285714\nthe f1 score is:  0.776654024442\nJaccard_score is: 0.785714285714\n"
                }
            ], 
            "execution_count": 22
        }, 
        {
            "source": "# Decision Tree", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn.tree import DecisionTreeClassifier\n# Setting up the Decision Tree\n#split train/test\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split (X, y, test_size =0.20, random_state=13)\nprint ('Train_set:', X_train.shape, y_train.shape)\nprint ('Test set:' , X_test.shape, y_test.shape)\n\n#Modelling\nloantree= DecisionTreeClassifier (criterion = \"entropy\", max_depth=5)\nloantree # it shows the default parameters\nloantree.fit (X_train, y_train)\n\n#prediction\npredtree = loantree.predict (X_test)\nprint (predtree [0:5])\nprint (y_test [0:5])", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train_set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n['COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION']\n"
                }
            ], 
            "execution_count": 23
        }, 
        {
            "source": "\n\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\nprint (\"Decision tree accuracy: \", metrics.accuracy_score (y_test, predtree))\n\n", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Decision tree accuracy:  0.814285714286\n"
                }
            ], 
            "execution_count": 24
        }, 
        {
            "source": "from sklearn.externals.six import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\ndot_data = StringIO()\nfilename = \"loantree.png\"\nfeatureNames = df.columns[0:8]\ntargetNames = df[\"loan_status\"].unique().tolist()\nout=tree.export_graphviz(loantree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True,  special_characters=True,rotate=False)  \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(10, 20))\nplt.imshow(img,interpolation='nearest')\nfrom sklearn.metrics import f1_score\nprint (\"the f1 score is: \", f1_score(y_test, yhat, average='weighted')) \n\nfrom sklearn.metrics import jaccard_similarity_score\nprint (\"Jaccard_score is:\", jaccard_similarity_score(y_test, yhat))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "error", 
                    "evalue": "No module named 'pydotplus'", 
                    "traceback": [
                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 
                        "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)", 
                        "\u001b[0;32m<ipython-input-26-be7fe698d8d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexternals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 
                        "\u001b[0;31mImportError\u001b[0m: No module named 'pydotplus'"
                    ], 
                    "ename": "ImportError"
                }
            ], 
            "execution_count": 26
        }, 
        {
            "source": "# Support Vector Machine", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "ax = df[df['loan_status'] == 'PAIDOFF'][0:50].plot(kind='scatter', x='age', y='Gender', color='DarkBlue', label='PAIDOFF');\ndf[df['loan_status'] == 'COLLECTION'][0:50].plot(kind='scatter', x='age', y='Gender', color='Yellow', label='COLLECTION', ax=ax);\nplt.show()\n# train and test set determination\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)\n#modeling and prediction\nfrom sklearn import svm\nclf = svm.SVC(kernel='rbf', gamma = 15, C=1)\nclf.fit(X_train, y_train) \n\nyhat = clf.predict(X_test)\nyhat [0:5]", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd27c5a21d0>"
                    }, 
                    "metadata": {}
                }, 
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
                }, 
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
                    }, 
                    "execution_count": 27, 
                    "metadata": {}
                }
            ], 
            "execution_count": 27
        }, 
        {
            "source": "# classification_report and confusion_matrix\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport itertools\n\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n        if normalize:\n            cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n            print(\"Normalized confusion matrix\")\n        else:\n            print('Confusion matrix, without normalization')\n\n        print(cm)\n\n        plt.imshow(cm, interpolation='nearest', cmap=cmap)\n        plt.title(title)\n        plt.colorbar()\n        tick_marks = np.arange(len(classes))\n        plt.xticks(tick_marks, classes, rotation=45)\n        plt.yticks(tick_marks, classes)\n\n        fmt = '.2f' if normalize else 'd'\n        thresh = cm.max() / 2.\n        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n            plt.text(j, i, format(cm[i, j], fmt),\n                     horizontalalignment=\"center\",\n                     color=\"white\" if cm[i, j] > thresh else \"black\")\n\n        plt.tight_layout()\n        plt.ylabel('True label')\n        plt.xlabel('Predicted label')", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 28
        }, 
        {
            "source": "# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhat))\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False,  title='Confusion matrix')\n\nfrom sklearn.metrics import f1_score\nprint (\"the f1 score is: \", f1_score(y_test, yhat, average='weighted')) \n\nfrom sklearn.metrics import jaccard_similarity_score\nprint (\"Jaccard_score is:\", jaccard_similarity_score(y_test, yhat))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "             precision    recall  f1-score   support\n\n COLLECTION       0.50      0.27      0.35        15\n    PAIDOFF       0.82      0.93      0.87        55\n\navg / total       0.75      0.79      0.76        70\n\nConfusion matrix, without normalization\n[[51  4]\n [11  4]]\nthe f1 score is:  0.759515846472\nJaccard_score is: 0.785714285714\n"
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd23f889160>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 29
        }, 
        {
            "source": "# Logistic Regression", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "\n\n# train and test set determination\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=6)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)\n\n", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Train set: (242, 8) (242,)\nTest set: (104, 8) (104,)\n"
                }
            ], 
            "execution_count": 30
        }, 
        {
            "source": "# modeilng and prediction\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nLR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\nprint (LR)\n\nyhat = LR.predict(X_test)\nprint (yhat)\n\nyhat_prob = LR.predict_proba(X_test)\nyhat_prob", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n          verbose=0, warm_start=False)\n['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF'\n 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF'\n 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF'\n 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION']\n"
                }, 
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array([[ 0.49,  0.51],\n       [ 0.46,  0.54],\n       [ 0.52,  0.48],\n       [ 0.51,  0.49],\n       [ 0.5 ,  0.5 ],\n       [ 0.46,  0.54],\n       [ 0.43,  0.57],\n       [ 0.4 ,  0.6 ],\n       [ 0.3 ,  0.7 ],\n       [ 0.38,  0.62],\n       [ 0.49,  0.51],\n       [ 0.49,  0.51],\n       [ 0.38,  0.62],\n       [ 0.48,  0.52],\n       [ 0.49,  0.51],\n       [ 0.3 ,  0.7 ],\n       [ 0.28,  0.72],\n       [ 0.39,  0.61],\n       [ 0.43,  0.57],\n       [ 0.38,  0.62],\n       [ 0.41,  0.59],\n       [ 0.38,  0.62],\n       [ 0.49,  0.51],\n       [ 0.46,  0.54],\n       [ 0.36,  0.64],\n       [ 0.38,  0.62],\n       [ 0.49,  0.51],\n       [ 0.49,  0.51],\n       [ 0.3 ,  0.7 ],\n       [ 0.34,  0.66],\n       [ 0.35,  0.65],\n       [ 0.52,  0.48],\n       [ 0.42,  0.58],\n       [ 0.49,  0.51],\n       [ 0.42,  0.58],\n       [ 0.42,  0.58],\n       [ 0.44,  0.56],\n       [ 0.47,  0.53],\n       [ 0.39,  0.61],\n       [ 0.51,  0.49],\n       [ 0.47,  0.53],\n       [ 0.49,  0.51],\n       [ 0.43,  0.57],\n       [ 0.5 ,  0.5 ],\n       [ 0.37,  0.63],\n       [ 0.46,  0.54],\n       [ 0.5 ,  0.5 ],\n       [ 0.38,  0.62],\n       [ 0.27,  0.73],\n       [ 0.33,  0.67],\n       [ 0.48,  0.52],\n       [ 0.3 ,  0.7 ],\n       [ 0.36,  0.64],\n       [ 0.34,  0.66],\n       [ 0.33,  0.67],\n       [ 0.46,  0.54],\n       [ 0.34,  0.66],\n       [ 0.49,  0.51],\n       [ 0.51,  0.49],\n       [ 0.37,  0.63],\n       [ 0.45,  0.55],\n       [ 0.5 ,  0.5 ],\n       [ 0.44,  0.56],\n       [ 0.46,  0.54],\n       [ 0.34,  0.66],\n       [ 0.36,  0.64],\n       [ 0.51,  0.49],\n       [ 0.36,  0.64],\n       [ 0.38,  0.62],\n       [ 0.42,  0.58],\n       [ 0.31,  0.69],\n       [ 0.39,  0.61],\n       [ 0.3 ,  0.7 ],\n       [ 0.28,  0.72],\n       [ 0.29,  0.71],\n       [ 0.33,  0.67],\n       [ 0.51,  0.49],\n       [ 0.45,  0.55],\n       [ 0.52,  0.48],\n       [ 0.44,  0.56],\n       [ 0.5 ,  0.5 ],\n       [ 0.51,  0.49],\n       [ 0.29,  0.71],\n       [ 0.5 ,  0.5 ],\n       [ 0.31,  0.69],\n       [ 0.44,  0.56],\n       [ 0.45,  0.55],\n       [ 0.37,  0.63],\n       [ 0.33,  0.67],\n       [ 0.5 ,  0.5 ],\n       [ 0.39,  0.61],\n       [ 0.4 ,  0.6 ],\n       [ 0.39,  0.61],\n       [ 0.39,  0.61],\n       [ 0.39,  0.61],\n       [ 0.36,  0.64],\n       [ 0.3 ,  0.7 ],\n       [ 0.51,  0.49],\n       [ 0.42,  0.58],\n       [ 0.33,  0.67],\n       [ 0.44,  0.56],\n       [ 0.38,  0.62],\n       [ 0.51,  0.49],\n       [ 0.51,  0.49]])"
                    }, 
                    "execution_count": 31, 
                    "metadata": {}
                }
            ], 
            "execution_count": 31
        }, 
        {
            "source": "# evaluation\nfrom sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat)\n# classification_report and confusion_matrix\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport itertools\ndef 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    \nprint(confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION']))\n\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False,  title='Confusion matrix')\n\nprint (classification_report(y_test, yhat))\n\nfrom sklearn.metrics import log_loss\nprint (\"the log_loss is: \", log_loss(y_test, yhat_prob))\n\nfrom sklearn.metrics import f1_score\nprint (\"the f1 score is: \", f1_score(y_test, yhat, average='weighted')) \n\nfrom sklearn.metrics import jaccard_similarity_score\nprint (\"Jaccard_score is:\", jaccard_similarity_score(y_test, yhat))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "[[73  7]\n [15  9]]\nConfusion matrix, without normalization\n[[73  7]\n [15  9]]\n             precision    recall  f1-score   support\n\n COLLECTION       0.56      0.38      0.45        24\n    PAIDOFF       0.83      0.91      0.87        80\n\navg / total       0.77      0.79      0.77       104\n\nthe log_loss is:  0.571790637942\nthe f1 score is:  0.772344322344\nJaccard_score is: 0.788461538462\n"
                }, 
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<matplotlib.figure.Figure at 0x7fd240d5d358>"
                    }, 
                    "metadata": {}
                }
            ], 
            "execution_count": 32
        }, 
        {
            "source": "# Model Evaluation using Test set", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 33
        }, 
        {
            "source": "First, download and load the test set:", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "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": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "--2019-05-30 17:33:31--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 3642 (3.6K) [text/csv]\nSaving to: \u2018loan_test.csv\u2019\n\n100%[======================================>] 3,642       --.-K/s   in 0s      \n\n2019-05-30 17:33:31 (574 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n"
                }
            ], 
            "execution_count": 34
        }, 
        {
            "source": "### Load Test set for evaluation ", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()", 
            "cell_type": "code", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           1             1     PAIDOFF       1000     30       9/8/2016   \n1           5             5     PAIDOFF        300      7       9/9/2016   \n2          21            21     PAIDOFF       1000     30      9/10/2016   \n3          24            24     PAIDOFF       1000     30      9/10/2016   \n4          35            35     PAIDOFF        800     15      9/11/2016   \n\n    due_date  age             education  Gender  \n0  10/7/2016   50              Bechalor  female  \n1  9/15/2016   35       Master or Above    male  \n2  10/9/2016   43  High School or Below  female  \n3  10/9/2016   26               college    male  \n4  9/25/2016   29              Bechalor    male  ", 
                        "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>"
                    }, 
                    "execution_count": 35, 
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            "execution_count": 35
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        {
            "source": "# Report\nYou should be able to report the accuracy of the built model using different evaluation metrics:", 
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        {
            "source": "| Algorithm          | Jaccard | F1-score | LogLoss |\n|--------------------|---------|----------|---------|\n| KNN                | ?       | ?        | NA      |\n| Decision Tree      | ?       | ?        | NA      |\n| SVM                | ?       | ?        | NA      |\n| LogisticRegression | ?       | ?        | ?       |", 
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        {
            "source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems \u2013 by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n\n<h3>Thanks for completing this lesson!</h3>\n\n<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients\u2019 ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n\n<hr>\n\n<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>", 
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