Final Project.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": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "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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                "new_sheet": false, 
                "run_control": {
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                }
            }, 
            "outputs": [], 
            "execution_count": 2
        }, 
        {
            "source": "### About dataset", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "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": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "Lets download the dataset", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "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": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
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                }
            }, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "--2019-07-11 00:43:34--  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 0.002s  \n\n2019-07-11 00:43:34 (12.8 MB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
                }
            ], 
            "execution_count": 3
        }, 
        {
            "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": {
                "scrolled": true, 
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>9/8/2016</td>\n      <td>9/22/2016</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\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  "
                    }, 
                    "execution_count": 4, 
                    "metadata": {}
                }
            ], 
            "execution_count": 4
        }, 
        {
            "source": "df.shape", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "(346, 10)"
                    }, 
                    "execution_count": 5, 
                    "metadata": {}
                }
            ], 
            "execution_count": 5
        }, 
        {
            "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/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\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  "
                    }, 
                    "execution_count": 6, 
                    "metadata": {}
                }
            ], 
            "execution_count": 6
        }, 
        {
            "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": 7, 
                    "metadata": {}
                }
            ], 
            "execution_count": 7
        }, 
        {
            "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/Python36\n\n  added / updated specs: \n    - seaborn\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    ca-certificates-2019.5.15  |                0         133 KB  anaconda\n    openssl-1.1.1              |       h7b6447c_0         5.0 MB  anaconda\n    certifi-2019.6.16          |           py36_0         154 KB  anaconda\n    seaborn-0.9.0              |           py36_0         379 KB  anaconda\n    ------------------------------------------------------------\n                                           Total:         5.7 MB\n\nThe following packages will be UPDATED:\n\n    ca-certificates: 2019.5.15-0       --> 2019.5.15-0      anaconda\n    certifi:         2019.6.16-py36_0  --> 2019.6.16-py36_0 anaconda\n    openssl:         1.1.1c-h7b6447c_1 --> 1.1.1-h7b6447c_0 anaconda\n    seaborn:         0.9.0-py36_0      --> 0.9.0-py36_0     anaconda\n\n\nDownloading and Extracting Packages\nca-certificates-2019 | 133 KB    | ##################################### | 100% \nopenssl-1.1.1        | 5.0 MB    | ##################################### | 100% \ncertifi-2019.6.16    | 154 KB    | ##################################### | 100% \nseaborn-0.9.0        | 379 KB    | ##################################### | 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": "<Figure size 432x216 with 2 Axes>"
                    }, 
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ], 
            "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": "<Figure size 432x216 with 2 Axes>"
                    }, 
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ], 
            "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": "<Figure size 432x216 with 2 Axes>"
                    }, 
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ], 
            "execution_count": 9
        }, 
        {
            "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/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  \\\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  "
                    }, 
                    "execution_count": 16, 
                    "metadata": {}
                }
            ], 
            "execution_count": 16
        }, 
        {
            "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": 17, 
                    "metadata": {}
                }
            ], 
            "execution_count": 17
        }, 
        {
            "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/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n      <th>dayofweek</th>\n      <th>weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\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  "
                    }, 
                    "execution_count": 18, 
                    "metadata": {}
                }
            ], 
            "execution_count": 18
        }, 
        {
            "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": 19, 
                    "metadata": {}
                }
            ], 
            "execution_count": 19
        }, 
        {
            "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/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>", 
                        "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"
                    }, 
                    "execution_count": 20, 
                    "metadata": {}
                }
            ], 
            "execution_count": 20
        }, 
        {
            "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/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\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  "
                    }, 
                    "execution_count": 21, 
                    "metadata": {}
                }
            ], 
            "execution_count": 21
        }, 
        {
            "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/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\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  "
                    }, 
                    "execution_count": 36, 
                    "metadata": {}
                }
            ], 
            "execution_count": 36
        }, 
        {
            "source": "What are our lables?", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }, 
        {
            "source": "y = pd.get_dummies(df['loan_status'])['PAIDOFF'].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([1, 1, 1, 1, 1], dtype=uint8)"
                    }, 
                    "execution_count": 45, 
                    "metadata": {}
                }
            ], 
            "execution_count": 45
        }, 
        {
            "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": [], 
            "execution_count": null
        }, 
        {
            "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.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 48
        }, 
        {
            "source": "mean_acc=np.zeros(50)\nstd_acc = np.zeros(50)\nfor n in range(1,51):\n    knnmodel=KNeighborsClassifier(n_neighbors=n).fit(X_train,y_train)\n    y_pred=knnmodel.predict(X_test)\n    mean_acc[n-1]=metrics.accuracy_score(y_test,y_pred)\n    std_acc[n-1]=np.std(y_pred==y_test)/np.sqrt(y_pred.shape[0])\n    \nplt.plot(range(1,51),mean_acc,'g')\nplt.fill_between(range(1,51),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\nplt.legend(('Accuracy ', '+/- 3xstd'))\nplt.ylabel('Accuracy ')\nplt.xlabel('Number of Nabors (K)')\nplt.tight_layout()\nplt.show()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "display_data", 
                    "data": {
                        "image/png": 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\n", 
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    }, 
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ], 
            "execution_count": 49
        }, 
        {
            "source": "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) ", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The best accuracy was with 0.7857142857142857 with k= 37\n"
                }
            ], 
            "execution_count": 50
        }, 
        {
            "source": "# Decision Tree", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn.tree import DecisionTreeClassifier", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 51
        }, 
        {
            "source": "dtmodel = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\ndtmodel.fit(X_train,y_train)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best')"
                    }, 
                    "execution_count": 79, 
                    "metadata": {}
                }
            ], 
            "execution_count": 79
        }, 
        {
            "source": "y_pred=dtmodel.predict(X_test)\nTreeAccuracy=metrics.accuracy_score(y_test,y_pred)\nTreeAccuracy", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "0.6142857142857143"
                    }, 
                    "execution_count": 80, 
                    "metadata": {}
                }
            ], 
            "execution_count": 80
        }, 
        {
            "source": "# Support Vector Machine", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn import svm\nsvmmodel=svm.SVC(kernel='rbf')\nsvmmodel.fit(X_train,y_train)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stderr", 
                    "text": "/opt/conda/envs/Python36/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"
                }, 
                {
                    "output_type": "execute_result", 
                    "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": 68, 
                    "metadata": {}
                }
            ], 
            "execution_count": 68
        }, 
        {
            "source": "y_pred=svmmodel.predict(X_test)\ny_pred", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array([1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1], dtype=uint8)"
                    }, 
                    "execution_count": 75, 
                    "metadata": {}
                }
            ], 
            "execution_count": 75
        }, 
        {
            "source": "metrics.accuracy_score(y_test,y_pred)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "0.7571428571428571"
                    }, 
                    "execution_count": 76, 
                    "metadata": {}
                }
            ], 
            "execution_count": 76
        }, 
        {
            "source": "# Logistic Regression", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nlrmodel=LogisticRegression(C=0.01,solver='liblinear').fit(X_train,y_train)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 81
        }, 
        {
            "source": "y_pred=lrmodel.predict(X_test)\nprint(y_pred)\nprint(LR.predict_proba(X_test))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n[[0.32295451 0.67704549]\n [0.25028458 0.74971542]\n [0.18058968 0.81941032]\n [0.31842814 0.68157186]\n [0.23877924 0.76122076]\n [0.20537939 0.79462061]\n [0.27303975 0.72696025]\n [0.24257638 0.75742362]\n [0.31842814 0.68157186]\n [0.29873276 0.70126724]\n [0.30139145 0.69860855]\n [0.31870506 0.68129494]\n [0.29486638 0.70513362]\n [0.29712705 0.70287295]\n [0.13826387 0.86173613]\n [0.16477552 0.83522448]\n [0.3961522  0.6038478 ]\n [0.18361846 0.81638154]\n [0.33624938 0.66375062]\n [0.22107799 0.77892201]\n [0.28403971 0.71596029]\n [0.31011812 0.68988188]\n [0.3587312  0.6412688 ]\n [0.36668443 0.63331557]\n [0.21036655 0.78963345]\n [0.34061581 0.65938419]\n [0.34961925 0.65038075]\n [0.1467569  0.8532431 ]\n [0.34519783 0.65480217]\n [0.1398959  0.8601041 ]\n [0.22240276 0.77759724]\n [0.34397153 0.65602847]\n [0.28135638 0.71864362]\n [0.27709473 0.72290527]\n [0.21009772 0.78990228]\n [0.19606528 0.80393472]\n [0.33624938 0.66375062]\n [0.14255195 0.85744805]\n [0.31313687 0.68686313]\n [0.21412393 0.78587607]\n [0.35425731 0.64574269]\n [0.22801433 0.77198567]\n [0.16927732 0.83072268]\n [0.34061581 0.65938419]\n [0.18966569 0.81033431]\n [0.34010247 0.65989753]\n [0.22936843 0.77063157]\n [0.31870506 0.68129494]\n [0.18986182 0.81013818]\n [0.18629367 0.81370633]\n [0.24092811 0.75907189]\n [0.20179757 0.79820243]\n [0.2389319  0.7610681 ]\n [0.29333468 0.70666532]\n [0.26315166 0.73684834]\n [0.10211024 0.89788976]\n [0.20766284 0.79233716]\n [0.24532558 0.75467442]\n [0.22240276 0.77759724]\n [0.27278662 0.72721338]\n [0.1154932  0.8845068 ]\n [0.27683928 0.72316072]\n [0.19606528 0.80393472]\n [0.40572037 0.59427963]\n [0.23510262 0.76489738]\n [0.31011812 0.68988188]\n [0.27683928 0.72316072]\n [0.20154559 0.79845441]\n [0.16374879 0.83625121]\n [0.23510262 0.76489738]]\n"
                }
            ], 
            "execution_count": 59
        }, 
        {
            "source": "metrics.accuracy_score(y_test,y_pred)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "0.6142857142857143"
                    }, 
                    "execution_count": 82, 
                    "metadata": {}
                }
            ], 
            "execution_count": 82
        }, 
        {
            "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": 136
        }, 
        {
            "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": "--2019-07-11 02:32:38--  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\n2019-07-11 02:32:38 (638 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n"
                }
            ], 
            "execution_count": 62
        }, 
        {
            "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/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  \\\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  "
                    }, 
                    "execution_count": 120, 
                    "metadata": {}
                }
            ], 
            "execution_count": 120
        }, 
        {
            "source": "test_df['effective_date']=pd.to_datetime(test_df['effective_date'])\ntest_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\nFeature_test = test_df[['Principal','terms','age','Gender','weekend']]\nFeature_test = pd.concat([Feature_test,pd.get_dummies(test_df['education'])], axis=1)\nFeature_test.drop(['Master or Above'], axis = 1,inplace=True)\nFeature_test.head()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>50</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>1</th>\n      <td>300</td>\n      <td>7</td>\n      <td>35</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>43</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>26</td>\n      <td>0</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>800</td>\n      <td>15</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n0       1000     30   50       1        0         1                     0   \n1        300      7   35       0        1         0                     0   \n2       1000     30   43       1        1         0                     1   \n3       1000     30   26       0        1         0                     0   \n4        800     15   29       0        1         1                     0   \n\n   college  \n0        0  \n1        0  \n2        0  \n3        1  \n4        0  "
                    }, 
                    "execution_count": 131, 
                    "metadata": {}
                }
            ], 
            "execution_count": 131
        }, 
        {
            "source": "X_testset=Feature_test\ny_testset=pd.get_dummies(test_df['loan_status'])['PAIDOFF'].values\ny_testset", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=uint8)"
                    }, 
                    "execution_count": 143, 
                    "metadata": {}
                }
            ], 
            "execution_count": 143
        }, 
        {
            "source": "y_pred_knn=knnmodel.predict(X_testset)\ny_pred_dt=dtmodel.predict(X_testset)\ny_pred_svm=svmmodel.predict(X_testset)\ny_pred_lr=lrmodel.predict(X_testset)\ny_pred_lr_proba=lrmodel.predict_proba(X_testset)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 145
        }, 
        {
            "source": "print(f1_score(y_testset,y_pred_knn))\nprint(f1_score(y_testset,y_pred_dt))\nprint(f1_score(y_testset,y_pred_svm))\nprint(f1_score(y_testset,y_pred_lr))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "0.851063829787234\n0.8157894736842106\n0.8602150537634409\n0.851063829787234\n"
                }
            ], 
            "execution_count": 151
        }, 
        {
            "source": "print(jaccard_similarity_score(y_testset,y_pred_knn))\nprint(jaccard_similarity_score(y_testset,y_pred_dt))\nprint(jaccard_similarity_score(y_testset,y_pred_svm))\nprint(jaccard_similarity_score(y_testset,y_pred_lr))", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "0.7407407407407407\n0.7407407407407407\n0.7592592592592593\n0.7407407407407407\n"
                }
            ], 
            "execution_count": 152
        }, 
        {
            "source": "LR_log_loss=log_loss(y_testset,y_pred_lr_proba)\nLR_log_loss", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "0.5446922340322017"
                    }, 
                    "execution_count": 154, 
                    "metadata": {}
                }
            ], 
            "execution_count": 154
        }, 
        {
            "source": "# Report\nYou should be able to report the accuracy of the built model using different evaluation metrics:", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "| Algorithm          | Jaccard | F1-score | LogLoss |\n|--------------------|---------|----------|---------|\n| KNN                | 0.741   | 0.851    | NA      |\n| Decision Tree      | 0.741   | 0.816    | NA      |\n| SVM                | 0.759   | 0.860    | NA      |\n| LogisticRegression | 0.741   | 0.851    | 0.545   |", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "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>", 
            "cell_type": "markdown", 
            "metadata": {
                "button": false, 
                "new_sheet": false, 
                "run_control": {
                    "read_only": false
                }
            }
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.6.8", 
            "name": "python", 
            "pygments_lexer": "ipython3", 
            "file_extension": ".py", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
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    "nbformat": 4
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