Capstone_Intro_Notebook.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "collapsed": true
            },
            "source": "\n<h1 align=\"center\"><font size=\"5\">Classification with Loan Dataset</font></h1>"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "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": "markdown",
            "metadata": {},
            "source": "### About dataset"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\n# 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": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2020-07-26 04:42:36--  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)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|: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\n2020-07-26 04:42:36 (14.2 MB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
                }
            ],
            "source": "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>9/8/2016</td>\n      <td>9/22/2016</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\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": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df = pd.read_csv('loan_train.csv')\ndf.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(346, 10)"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.shape"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Convert to date time object "
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\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": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Data visualization and pre-processing\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Let\u2019s see how many of each class is in our data set "
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "PAIDOFF       260\nCOLLECTION     86\nName: loan_status, dtype: int64"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['loan_status'].value_counts()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "260 people have paid off the loan on time while 86 have gone into collection \n\nLets plot some columns to underestand data better:"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "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-2020.6.24  |                0         133 KB  anaconda\n    openssl-1.1.1g             |       h7b6447c_0         3.8 MB  anaconda\n    seaborn-0.10.1             |             py_0         160 KB  anaconda\n    certifi-2020.6.20          |           py36_0         160 KB  anaconda\n    ------------------------------------------------------------\n                                           Total:         4.2 MB\n\nThe following packages will be UPDATED:\n\n    ca-certificates: 2020.6.24-0        --> 2020.6.24-0       anaconda\n    certifi:         2020.6.20-py36_0   --> 2020.6.20-py36_0  anaconda\n    openssl:         1.1.1g-h7b6447c_0  --> 1.1.1g-h7b6447c_0 anaconda\n    seaborn:         0.9.0-pyh91ea838_1 --> 0.10.1-py_0       anaconda\n\n\nDownloading and Extracting Packages\nca-certificates-2020 | 133 KB    | ##################################### | 100% \nopenssl-1.1.1g       | 3.8 MB    | ##################################### | 100% \nseaborn-0.10.1       | 160 KB    | ##################################### | 100% \ncertifi-2020.6.20    | 160 KB    | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"
                }
            ],
            "source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "import seaborn as sns\n\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",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "bins = np.linspace(df.age.min(), df.age.max(), 10)\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": "markdown",
            "metadata": {},
            "source": "# Pre-processing:  Feature selection/extraction"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Now look at the day of the week people get the loan "
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "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": "markdown",
            "metadata": {},
            "source": "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Convert Categorical features to numerical values"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "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": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "86 % of female pay there loans while only 73 % of males pay there loan"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Lets convert male to 0 and female to 1:"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>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": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## One Hot Encoding  \n#### How about education?"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "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": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>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": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df[['Principal','terms','age','Gender','education']].head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>Master or Above</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      <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      <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>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>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>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   Master or Above  college  \n0                0        0  \n1                0        0  \n2                0        1  \n3                0        1  \n4                0        1  "
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "Feature = df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n#Feature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Feature selection\n\nLets defind feature sets, X:"
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>Master or Above</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      <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      <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>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>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>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   Master or Above  college  \n0                0        0  \n1                0        0  \n2                0        1  \n3                0        1  \n4                0        1  "
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X = Feature\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "What are our lables?"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "y = df['loan_status'].values\ny[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Normalize Data \n\nData Standardization give data zero mean and unit variance (technically should be done after train test split )"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:1: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n  if __name__ == '__main__':\n"
                },
                {
                    "data": {
                        "text/plain": "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n        -0.38170062,  1.13639374, -0.07624929, -0.86968108],\n       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n         2.61985426, -0.87997669, -0.07624929, -0.86968108],\n       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n        -0.38170062, -0.87997669, -0.07624929,  1.14984679],\n       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n        -0.38170062, -0.87997669, -0.07624929,  1.14984679],\n       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n        -0.38170062, -0.87997669, -0.07624929,  1.14984679]])"
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Classification \n\nNow, 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": {},
            "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": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn import metrics"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (276, 9) (276,)\nTest set: (70, 9) (70,)\nTrain set Accuracy:  0.7644927536231884\nTest set Accuracy:  0.7\nThe best accuracy was with 0.8 with k= 7\n"
                }
            ],
            "source": "X_trainKNN, X_testKNN, y_trainKNN, y_testKNN = train_test_split(X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_trainKNN.shape,  y_trainKNN.shape)\nprint ('Test set:', X_testKNN.shape,  y_testKNN.shape)\n\nk = 15\nneigh = KNeighborsClassifier(n_neighbors = k).fit(X_trainKNN, y_trainKNN)\nneigh\n\nyhatKNN = neigh.predict(X_testKNN)\nyhatKNN[0:5]\n\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_trainKNN, neigh.predict(X_trainKNN)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_testKNN, yhatKNN))\n\n#Iterative Approach to select the best fit\nKs = 15\nmean_acc = np.zeros((Ks-1))\nstd_acc = np.zeros((Ks-1))\nConfustionMx = [];\nfor n in range(1,Ks):\n    \n    #Train Model and Predict  \n    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_trainKNN, y_trainKNN)\n    yhatKNN=neigh.predict(X_testKNN)\n    mean_acc[n-1] = metrics.accuracy_score(y_testKNN, yhatKNN)\n\n    \n    std_acc[n-1]=np.std(yhatKNN==y_testKNN)/np.sqrt(yhatKNN.shape[0])\n\nmean_acc\n\nprint( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.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 Nabors (K)')\nplt.tight_layout()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Decision Tree"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/Python36\n\n  added / updated specs: \n    - pydotplus\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    pydotplus-2.0.2            |     pyhd1c1de3_3          23 KB  conda-forge\n    ca-certificates-2020.6.20  |       hecda079_0         145 KB  conda-forge\n    certifi-2020.6.20          |   py36h9f0ad1d_0         151 KB  conda-forge\n    python_abi-3.6             |          1_cp36m           4 KB  conda-forge\n    openssl-1.1.1g             |       h516909a_0         2.1 MB  conda-forge\n    ------------------------------------------------------------\n                                           Total:         2.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n    pydotplus:       2.0.2-pyhd1c1de3_3 conda-forge\n    python_abi:      3.6-1_cp36m        conda-forge\n\nThe following packages will be UPDATED:\n\n    certifi:         2020.6.20-py36_0   anaconda    --> 2020.6.20-py36h9f0ad1d_0 conda-forge\n    openssl:         1.1.1g-h7b6447c_0  anaconda    --> 1.1.1g-h516909a_0        conda-forge\n\nThe following packages will be DOWNGRADED:\n\n    ca-certificates: 2020.6.24-0        anaconda    --> 2020.6.20-hecda079_0     conda-forge\n\n\nDownloading and Extracting Packages\npydotplus-2.0.2      | 23 KB     | ##################################### | 100% \nca-certificates-2020 | 145 KB    | ##################################### | 100% \ncertifi-2020.6.20    | 151 KB    | ##################################### | 100% \npython_abi-3.6       | 4 KB      | ##################################### | 100% \nopenssl-1.1.1g       | 2.1 MB    | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"
                }
            ],
            "source": "!conda install -c conda-forge pydotplus -y\n#!conda install -c conda-forge python-graphviz -y"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.tree import DecisionTreeClassifier\n\nfrom sklearn.externals.six import StringIO \nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "DecisionTrees's Accuracy:  0.6826923076923077\n"
                }
            ],
            "source": "X_trainDT, X_testDT, y_trainDT, y_testDT = train_test_split(X, y, test_size=0.3, random_state=3)\nloanTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 5)\nloanTree.fit(X_trainDT, y_trainDT)\npredTree = loanTree.predict(X_testDT)\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testDT, predTree))"
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.image.AxesImage at 0x7f61f0195080>"
                    },
                    "execution_count": 31,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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                        "text/plain": "<Figure size 14400x28800 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "dot_data = StringIO()\nfilename = \"loantree.png\"\nfeatureNames = Feature.columns[0:9]\ntargetNames = df['loan_status'].unique().tolist()\nout=tree.export_graphviz(loanTree, feature_names=featureNames, out_file=dot_data, class_names = np.unique(y_trainDT), 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=(200, 400))\nplt.imshow(img,interpolation='nearest')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Support Vector Machine"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn import svm\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import jaccard_similarity_score"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "              precision    recall  f1-score   support\n\n  COLLECTION       0.36      0.27      0.31        15\n     PAIDOFF       0.81      0.87      0.84        55\n\n   micro avg       0.74      0.74      0.74        70\n   macro avg       0.59      0.57      0.57        70\nweighted avg       0.72      0.74      0.73        70\n\nConfusion matrix, without normalization\n[[48  7]\n [11  4]]\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "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"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "X_trainSVM, X_testSVM, y_trainSVM, y_testSVM = train_test_split(X, y, test_size=0.2, random_state=4)\nclf = svm.SVC(kernel='rbf')\nclf.fit(X_trainSVM, y_trainSVM) \nyhatSVM = clf.predict(X_testSVM)\nyhatSVM [0:5]\n\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  \n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_testSVM, yhatSVM, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_testSVM, yhatSVM))\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False,  title='Confusion matrix')"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "PAIDOFF       55\nCOLLECTION    15\nName: LoanStatus, dtype: int64"
                    },
                    "execution_count": 34,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "#test1 = pd.DataFrame(yhatSVM)\n#test1.columns=['LoanStatus']\n#test1['LoanStatus'].value_counts()\ntest2 = pd.DataFrame(y_testSVM)\ntest2.columns=['LoanStatus']\ntest2['LoanStatus'].value_counts()"
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "F1 score with linear SVM: 0.7276\nJaccard Index:  0.7428571428571429\n"
                }
            ],
            "source": "f1_score(y_testSVM, yhatSVM, average='weighted') \njaccard_similarity_score(y_testSVM, yhatSVM)\nprint(\"F1 score with linear SVM: %.4f\" % f1_score(y_testSVM, yhatSVM, average='weighted') )\nprint('Jaccard Index: ', jaccard_similarity_score(y_testSVM, yhatSVM))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Logistic Regression"
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport itertools"
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [],
            "source": "X_trainLR, X_testLR, y_trainLR, y_testLR = train_test_split( X, y, test_size=0.2, random_state=4)\n\nLR = LogisticRegression(C=0.01, solver='liblinear').fit(X_trainLR,y_trainLR)\nyhatLR = LR.predict(X_testLR)\nyhatLR_prob = LR.predict_proba(X_testLR)"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "[[46  9]\n [13  2]]\nConfusion matrix, without normalization\n[[46  9]\n [13  2]]\n"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "def plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\nprint(confusion_matrix(y_testLR, yhatLR, labels=['PAIDOFF','COLLECTION']))\n\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_testLR, yhatLR, 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')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Model Evaluation using Test set"
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss"
        },
        {
            "cell_type": "code",
            "execution_count": 40,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2020-07-26 05:13:55--  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)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|: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\n2020-07-26 05:13:55 (200 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n"
                }
            ],
            "source": "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Load Test set for evaluation "
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(54, 10)"
                    },
                    "execution_count": 41,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.shape\n"
        },
        {
            "cell_type": "code",
            "execution_count": 42,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n  return self.partial_fit(X, y)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:17: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n"
                }
            ],
            "source": "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\ntest_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n\ntest_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n\ntest_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n\ntest_Feature = test_df[['Principal','terms','age','Gender','weekend']]\ntest_Feature = pd.concat([test_Feature, pd.get_dummies(test_df['education'])], axis=1)\n#test_Feature.drop(['Master or Above'], axis = 1,inplace=True)\n\ntest_X = test_Feature\n\ntest_y = test_df['loan_status'].values\n\ntest_X= preprocessing.StandardScaler().fit(test_X).transform(test_X)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "0.6851851851851852 0.6453810131971051\n"
                }
            ],
            "source": "best_k = 7    \nneigh_best_k = KNeighborsClassifier(n_neighbors = best_k).fit(X_trainKNN, y_trainKNN)\nyhat_test_KNN=neigh_best_k.predict(test_X)\n\nKNN_JAC = jaccard_similarity_score(test_y, yhat_test_KNN)\nKNN_F1 = f1_score(test_y, yhat_test_KNN, average='weighted') \n\nprint(KNN_JAC, KNN_F1)"
        },
        {
            "cell_type": "code",
            "execution_count": 44,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "0.7777777777777778 0.7777777777777778\n"
                }
            ],
            "source": "#loanTree.fit(X_trainDT, y_trainDT)\nTest_predTree = loanTree.predict(test_X)\n\nDT_JAC = jaccard_similarity_score(test_y, Test_predTree)\nDT_F1 = f1_score(test_y, Test_predTree, average='weighted') \n\nprint(DT_JAC, DT_F1)"
        },
        {
            "cell_type": "code",
            "execution_count": 45,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "0.7962962962962963 0.7583503077293734\n"
                }
            ],
            "source": "#clf.fit(X_trainSVM, y_trainSVM) \nyhat_test_SVM = clf.predict(test_X)\n\nSVM_JAC = jaccard_similarity_score(test_y, yhat_test_SVM)\nSVM_F1 = f1_score(test_y, yhat_test_SVM, average='weighted') \n\nprint(SVM_JAC, SVM_F1)"
        },
        {
            "cell_type": "code",
            "execution_count": 46,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "0.7407407407407407 0.6604267310789049 0.56657760217051\n"
                }
            ],
            "source": "#LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_trainLR,y_trainLR)\nyhat_test_LR = LR.predict(test_X)\nyhat_test_LR_prob = LR.predict_proba(test_X)\n\nLR_JAC = jaccard_similarity_score(test_y, yhat_test_LR)\nLR_F1 = f1_score(test_y, yhat_test_LR, average='weighted') \n\nLR_LL = log_loss(test_y, yhat_test_LR_prob)\n\nprint (LR_JAC, LR_F1, LR_LL)"
        },
        {
            "cell_type": "code",
            "execution_count": 47,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Algorithm</th>\n      <th>Jaccard</th>\n      <th>F1-score</th>\n      <th>LogLoss</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>KNN</td>\n      <td>0.685185</td>\n      <td>0.645381</td>\n      <td>NA</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Decision Tree</td>\n      <td>0.777778</td>\n      <td>0.777778</td>\n      <td>NA</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>SVM</td>\n      <td>0.796296</td>\n      <td>0.758350</td>\n      <td>NA</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Logistic Regression</td>\n      <td>0.740741</td>\n      <td>0.660427</td>\n      <td>0.566578</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "             Algorithm   Jaccard  F1-score   LogLoss\n0                  KNN  0.685185  0.645381        NA\n1        Decision Tree  0.777778  0.777778        NA\n2                  SVM  0.796296  0.758350        NA\n3  Logistic Regression  0.740741  0.660427  0.566578"
                    },
                    "execution_count": 47,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "Report = {'Algorithm':['KNN', 'Decision Tree', 'SVM', 'Logistic Regression'], 'Jaccard': [KNN_JAC, DT_JAC, SVM_JAC, LR_JAC], 'F1-score':[KNN_F1, DT_F1, SVM_F1, LR_F1], 'LogLoss': ['NA', 'NA', 'NA', LR_LL]}\nReport = pd.DataFrame(Report)\nReport"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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                | ?       | ?        | NA      |\n| Decision Tree      | ?       | ?        | NA      |\n| SVM                | ?       | ?        | NA      |\n| LogisticRegression | ?       | ?        | ?       |"
        },
        {
            "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": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        }
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