HM_Classification_Algorithms_for_loan_Applications.ipynb

{"nbformat_minor": 2, "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": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"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}}}, {"execution_count": 1, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "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"}, {"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}}}, {"execution_count": 2, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "--2019-05-16 02:19:24--  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\n2019-05-16 02:19:25 (12.7 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"}, {"execution_count": 3, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/DSX-Python35\n\n  added / updated specs: \n    - pydotplus\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    pydotplus-2.0.2            |             py_2          23 KB  conda-forge\n    openssl-1.0.2r             |       h14c3975_0         3.1 MB  conda-forge\n    ca-certificates-2019.3.9   |       hecc5488_0         146 KB  conda-forge\n    certifi-2018.8.24          |        py35_1001         139 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:         3.4 MB\n\nThe following NEW packages will be INSTALLED:\n\n    pydotplus:       2.0.2-py_2        conda-forge\n\nThe following packages will be UPDATED:\n\n    ca-certificates: 2019.1.23-0                   --> 2019.3.9-hecc5488_0 conda-forge\n    certifi:         2018.8.24-py35_1              --> 2018.8.24-py35_1001 conda-forge\n    openssl:         1.0.2p-h14c3975_0             --> 1.0.2r-h14c3975_0   conda-forge\n\n\nDownloading and Extracting Packages\npydotplus-2.0.2      | 23 KB     | ##################################### | 100% \nopenssl-1.0.2r       | 3.1 MB    | ##################################### | 100% \nca-certificates-2019 | 146 KB    | ##################################### | 100% \ncertifi-2018.8.24    | 139 KB    | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"}], "source": "!conda install -c conda-forge pydotplus -y"}, {"execution_count": 4, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/DSX-Python35\n\n  added / updated specs: \n    - python-graphviz\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    python-graphviz-0.10.1     |             py_0          17 KB  conda-forge\n\nThe following NEW packages will be INSTALLED:\n\n    python-graphviz: 0.10.1-py_0 conda-forge\n\n\nDownloading and Extracting Packages\npython-graphviz-0.10 | 17 KB     | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"}], "source": "!conda install -c conda-forge python-graphviz -y"}, {"source": "### Load Data From CSV File  ", "cell_type": "markdown", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"execution_count": 57, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 57, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "df = pd.read_csv('loan_train.csv')\ndf.head()"}, {"execution_count": 6, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 6, "metadata": {}, "data": {"text/plain": "(346, 10)"}, "output_type": "execute_result"}], "source": "df.shape"}, {"source": "### Convert to date time object ", "cell_type": "markdown", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"execution_count": 7, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 7, "metadata": {}, "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  "}, "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()"}, {"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}}}, {"execution_count": 8, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 8, "metadata": {}, "data": {"text/plain": "PAIDOFF       260\nCOLLECTION     86\nName: loan_status, dtype: int64"}, "output_type": "execute_result"}], "source": "df['loan_status'].value_counts()"}, {"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": {}}, {"execution_count": 9, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/DSX-Python35\n\n  added / updated specs: \n    - seaborn\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    openssl-1.0.2r             |       h7b6447c_0         3.2 MB  anaconda\n    seaborn-0.9.0              |           py35_0         378 KB  anaconda\n    certifi-2018.8.24          |           py35_1         139 KB  anaconda\n    ca-certificates-2019.1.23  |                0         126 KB  anaconda\n    ------------------------------------------------------------\n                                           Total:         3.8 MB\n\nThe following packages will be UPDATED:\n\n    openssl:         1.0.2r-h14c3975_0    conda-forge --> 1.0.2r-h7b6447c_0 anaconda\n    seaborn:         0.8.0-py35h15a2772_0             --> 0.9.0-py35_0      anaconda\n\nThe following packages will be DOWNGRADED:\n\n    ca-certificates: 2019.3.9-hecc5488_0  conda-forge --> 2019.1.23-0       anaconda\n    certifi:         2018.8.24-py35_1001  conda-forge --> 2018.8.24-py35_1  anaconda\n\n\nDownloading and Extracting Packages\nopenssl-1.0.2r       | 3.2 MB    | ##################################### | 100% \nseaborn-0.9.0        | 378 KB    | ##################################### | 100% \ncertifi-2018.8.24    | 139 KB    | ##################################### | 100% \nca-certificates-2019 | 126 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"}, {"execution_count": 10, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "display_data", "data": {"image/png": 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aVFjnWqcTlKSJwOeAsZG+VYmIjcDGdH6RpN8DBwG+BcnMukVVVVXLOHLWs3Wqi0/SeOAq4LSIWJ9TXi2pVzp/ADAUeL0YgZqZWWVp8wpK0kxgNNBfUhNwPcldezsB89LxmZ5J79g7AfiWpM3AFuDiiPhD3orNzMy2o80EFRHn5im+p5Vtfwb8rNCgzMzMPBafmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllkhOUmZllUrsSlKTpkt6TtDSnrJ+keZJ+l/7cIy2XpB9IWiFpiaRRXRW8mZmVr/ZeQc0Axm9TdjXwWEQMBR5LlwFOInmS7lBgEjC18DDNzKzStCtBRcSTwLZPxj0d+FE6/yPgjJzyf4/EM8DukgYUI1gzM6schXwGtXdErARIf+6Vlg8E3s7Zrikt24qkSZIWSlq4Zs2aAsIwKz9uH2Zdc5OE8pTFJwoipkVEfUTUV1dXd0EYZj2X24dZYQlqdXPXXfrzvbS8CdgvZ7tBwLsFHMfMzCpQIQnqYWBiOj8ReCin/IL0br7PAH9s7go0MzNrrx3bs5GkmcBooL+kJuB64CbgQUkXAW8BZ6eb/wI4GVgBrAf+scgxm5lZBWhXgoqIc1tZNTbPtgFcWkhQZmZmHknCzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyyQnKzMwyqV2jmecj6WDggZyiA4DrgN2B/wk0P6f62oj4RacjNDOzitTpBBURrwF1AJJ6Ae8As0me/3RbREwpSoRmZlaRitXFNxb4fUS8WaT6zMyswhUrQU0AZuYsXyZpiaTpkvbIt4OkSZIWSlq4Zs2afJuYVSy3D7MiJChJfwOcBvw0LZoKDCHp/lsJfCfffhExLSLqI6K+urq60DDMyorbh1lxrqBOAhZHxGqAiFgdEVsi4mPgbuDIIhzDzMwqTDES1LnkdO9JGpCz7kxgaRGOYWZmFabTd/EBSNoFOBH4ck7xLZLqgAAat1lnZmbWLgUlqIhYD+y5Tdn5BUVkZmaGR5IwM7OMcoIyM7NMcoIyM7NMcoIyM7NMcoIyM7NMcoIyM7NMKug2c7OeRL2qGPRuU9HqMrOu5QRlFSO2bOKo6x4pSl3Pfmt8Ueoxs9a5i8/MzDLJCcrMzDLJCcrMzDLJCcrMzDLJCcrMzDLJCcrMzDKp4NvMJTUCfwK2AJsjol5SP+ABoIbkmVDnRMT7hR7LzMwqR7GuoD4bEXURUZ8uXw08FhFDgcfSZasw+w8YgKSCp/0HDGj7YGZWdrrqi7qnA6PT+R8B84GruuhYllFvrVpF076DCq6nWKM/mFnPUowrqAB+JWmRpElp2d4RsRIg/bnXtjtJmiRpoaSFa9asKUIYZuXD7cOsOAnq2IgYBZwEXCrphPbsFBHTIqI+Iuqrq6uLEIZZ+XD7MCtCgoqId9Of7wGzgSOB1ZIGAKQ/3yv0OGZmVlkKSlCSdpXUt3keGAcsBR4GJqabTQQeKuQ4ZmZWeQq9SWJvYLak5rrui4hHJD0PPCjpIuAt4OwCj2NmZhWmoAQVEa8Dn85Tvg4YW0jdZmZW2TyShJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZZITlJmZZfIJ2F31RF0zM+tBsvgEbF9BmZlZJnU6QUnaT9ITkl6RtEzSP6Xl35T0jqSGdDq5eOGamVmlKKSLbzPw9YhYnD60cJGkeem62yJiSuHhmZlZpep0goqIlcDKdP5Pkl4BBhYrMDMzq2xF+QxKUg0wEng2LbpM0hJJ0yXt0co+kyQtlLRwzZo1xQjDrGy4fZgVIUFJ6gP8DJgcER8AU4EhQB3JFdZ38u0XEdMioj4i6qurqwsNw6ysuH2YFZigJFWRJKd7I+I/ACJidURsiYiPgbuBIwsP08zMKk0hd/EJuAd4JSK+m1Oe+y2tM4GlnQ/PzMwqVSF38R0LnA+8JKkhLbsWOFdSHRBAI/DlgiI0M7OKVMhdfAsA5Vn1i86HY2ZmlvBIEmZmlkkei8+6jHpVFWVcLvWqKkI0ZtbTOEFZl4ktmzjqukcKrufZb40vQjRm1tO4i8/MzDLJCcrMzDLJCcrMzDLJCcrMzDLJCcrMrJtl8fHqWeS7+MzMulkWH6+eRb6CMjOzTHKCMjOzTHIXn5mZZXLkFycoMzPL5Mgv7uIzM7NM6rIEJWm8pNckrZB0daH1+bZMM7PK0iVdfJJ6AXcAJwJNwPOSHo6Ilztbp2/LNDOrLF31GdSRwIqIeB1A0v3A6UCnE1TW7D9gAG+tWlVwPYP32Yc3V64sQkTlTcr3bEzLIreNthXrhoQdelWVddtQRBS/UunzwPiI+B/p8vnAURFxWc42k4BJ6eLBwGtFD6T9+gNrS3j8Qjj20mgr9rUR0elPizPUPsr5d5Rl5Rx7u9tGV11B5UvpW2XCiJgGTOui43eIpIURUV/qODrDsZdGV8eelfbh31FpOPZEV90k0QTsl7M8CHi3i45lZmZlqKsS1PPAUEm1kv4GmAA83EXHMjOzMtQlXXwRsVnSZcCjQC9gekQs64pjFUnJu1IK4NhLoyfH3hE9+XU69tIoWuxdcpOEmZlZoTyShJmZZZITlJmZZVLFJChJvSS9IGluulwr6VlJv5P0QHozB5J2SpdXpOtrShz37pJmSXpV0iuSjpbUT9K8NPZ5kvZIt5WkH6SxL5E0qsSx/7OkZZKWSpopqXdWz7uk6ZLek7Q0p6zD51nSxHT730ma2J2vobPcNkoSu9tGO1RMggL+CXglZ/lm4LaIGAq8D1yUll8EvB8RBwK3pduV0veBRyJiGPBpktdwNfBYGvtj6TLAScDQdJoETO3+cBOSBgKXA/URcRjJzTITyO55nwFs++XBDp1nSf2A64GjSEZTub654Wac20Y3ctvoQNuIiLKfSL6H9RgwBphL8kXitcCO6fqjgUfT+UeBo9P5HdPtVKK4PwW8se3xSUYVGJDODwBeS+fvAs7Nt10JYh8IvA30S8/jXOAfsnzegRpgaWfPM3AucFdO+VbbZXFy23DbaGfMJWkblXIF9T3gSuDjdHlP4L8iYnO63ETyRwN//eMhXf/HdPtSOABYA/zftAvm3yTtCuwdESvTGFcCe6Xbt8Seyn1d3Soi3gGmAG8BK0nO4yJ6xnlv1tHznJnz3wFuG93MbWOr8u0q+wQl6XPAexGxKLc4z6bRjnXdbUdgFDA1IkYCf+avl9L5ZCb29PL9dKAW2BfYleTyf1tZPO9taS3WnvQa3DbcNrpCUdtG2Sco4FjgNEmNwP0kXRnfA3aX1PxF5dyhmFqGaUrX7wb8oTsDztEENEXEs+nyLJJGuVrSAID053s522dliKm/B96IiDURsQn4D+AYesZ5b9bR85yl898ebhul4bbRzvNf9gkqIq6JiEERUUPyQeTjEXEe8ATw+XSzicBD6fzD6TLp+scj7TTtbhGxCnhb0sFp0ViSR5bkxrht7Bekd9J8Bvhj82V4CbwFfEbSLpLEX2PP/HnP0dHz/CgwTtIe6bvkcWlZJrltuG0UoHvaRik+JCzVBIwG5qbzBwDPASuAnwI7peW90+UV6foDShxzHbAQWALMAfYg6X9+DPhd+rNfuq1IHhT5e+AlkruEShn7vwCvAkuBHwM7ZfW8AzNJPg/YRPJu76LOnGfgS+lrWAH8Y6n/5jvw+t02ujd2t412HNtDHZmZWSaVfRefmZn1TE5QZmaWSU5QZmaWSU5QZmaWSU5QZmaWSU5QGSZpi6SGdMTjn0rapZXtfiFp907Uv6+kWQXE1yipf2f3N+sst43K4NvMM0zShxHRJ52/F1gUEd/NWS+S3+HHrdXRxfE1knzPYW0pjm+Vy22jMvgKqud4CjhQUo2SZ9/cCSwG9mt+t5az7m4lz5r5laSdASQdKOnXkl6UtFjSkHT7pen6CyU9JOkRSa9Jur75wJLmSFqU1jmpJK/erHVuG2XKCaoHSMffOonkm9kABwP/HhEjI+LNbTYfCtwREYcC/wWclZbfm5Z/mmTcr3zDvBwJnEfyDf2zJdWn5V+KiCOAeuBySaUeSdkMcNsod05Q2bazpAaS4VzeAu5Jy9+MiGda2eeNiGhI5xcBNZL6AgMjYjZARGyIiPV59p0XEesi4iOSASyPS8svl/Qi8AzJgI9DC35lZoVx26gAO7a9iZXQRxFRl1uQdK3z5+3sszFnfguwM/mHus9n2w8kQ9JoktGXj46I9ZLmk4wNZlZKbhsVwFdQFSAiPgCaJJ0BIGmnVu56OlFSv7Rv/gzgaZKh/d9PG+Aw4DPdFrhZF3PbyDYnqMpxPkl3xBLgN8A+ebZZQDKycgPws4hYCDwC7JjudwNJV4ZZOXHbyCjfZm5AcqcSyW2xl5U6FrMscdsoHV9BmZlZJvkKyszMMslXUGZmlklOUGZmlklOUGZmlklOUGZmlklOUGZmlkn/H+LDZoiBEQ8dAAAAAElFTkSuQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9d33702710>"}, "metadata": {}}], "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()"}, {"execution_count": 11, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9d2acb4b70>"}, "metadata": {}}], "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()"}, {"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}}}, {"execution_count": 12, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9d2ab59b70>"}, "metadata": {}}], "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"}, {"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}}}, {"execution_count": 13, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 13, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ndf.head()"}, {"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}}}, {"execution_count": 14, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 14, "metadata": {}, "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"}, "output_type": "execute_result"}], "source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"}, {"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}}}, {"execution_count": 15, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 15, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()"}, {"source": "## One Hot Encoding  \n#### How about education?", "cell_type": "markdown", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"execution_count": 16, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 16, "metadata": {}, "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"}, "output_type": "execute_result"}], "source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"}, {"source": "#### Feature befor One Hot Encoding", "cell_type": "markdown", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"execution_count": 17, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 17, "metadata": {}, "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"}, "output_type": "execute_result"}], "source": "df[['Principal','terms','age','Gender','education']].head()"}, {"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}}}, {"execution_count": 134, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 134, "metadata": {}, "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  "}, "output_type": "execute_result"}], "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"}, {"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}}}, {"execution_count": 19, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 19, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "X = Feature\nX[0:5]"}, {"source": "What are our lables?", "cell_type": "markdown", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"execution_count": 66, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 66, "metadata": {}, "data": {"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"}, "output_type": "execute_result"}], "source": "y = df['loan_status'].values\ny[0:5]"}, {"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}}}, {"execution_count": 21, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 21, "metadata": {}, "data": {"text/plain": "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n        -0.38170062,  1.13639374, -0.86968108],\n       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n         2.61985426, -0.87997669, -0.86968108],\n       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n        -0.38170062, -0.87997669,  1.14984679],\n       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n        -0.38170062, -0.87997669,  1.14984679],\n       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n        -0.38170062, -0.87997669,  1.14984679]])"}, "output_type": "execute_result"}], "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"}, {"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": {}}, {"execution_count": 22, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"}], "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)"}, {"execution_count": 23, "cell_type": "code", "metadata": {}, "outputs": [], "source": "#import the Library\nfrom sklearn.neighbors import KNeighborsClassifier"}, {"execution_count": 24, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 24, "metadata": {}, "data": {"text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n           metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n           weights='uniform')"}, "output_type": "execute_result"}], "source": "# Let us start with K=2 for KNN \nk = 7\n#Train Model and Predict  \nneighbor = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\nneighbor"}, {"execution_count": 25, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 25, "metadata": {}, "data": {"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"}, "output_type": "execute_result"}], "source": "yhat = neighbor.predict(X_test)\nyhat[0:5]"}, {"execution_count": 26, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Train set Accuracy:  0.807971014493\nTest set Accuracy:  0.785714285714\n"}], "source": "from sklearn import metrics\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neighbor.predict(X_train)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"}, {"execution_count": 27, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 27, "metadata": {}, "data": {"text/plain": "array([ 0.67142857,  0.65714286,  0.71428571,  0.68571429,  0.75714286,\n        0.71428571,  0.78571429,  0.75714286,  0.75714286,  0.67142857,\n        0.7       ,  0.72857143,  0.7       ,  0.7       ,  0.68571429,\n        0.72857143,  0.72857143])"}, "output_type": "execute_result"}], "source": "Ks = 18\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_train,y_train)\n    yhat=neigh.predict(X_test)\n    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n\n    \n    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n\nmean_acc"}, {"execution_count": 36, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f67d01ccac8>"}, "metadata": {}}], "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()"}, {"source": "# Decision Tree", "cell_type": "markdown", "metadata": {}}, {"execution_count": 28, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 28, "metadata": {}, "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')"}, "output_type": "execute_result"}], "source": "from sklearn.tree import DecisionTreeClassifier\nPaidTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\nPaidTree "}, {"execution_count": 29, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 29, "metadata": {}, "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')"}, "output_type": "execute_result"}], "source": "PaidTree.fit(X_train,y_train)"}, {"execution_count": 30, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "['COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n"}], "source": "predTree = PaidTree.predict(X_test)\nprint (predTree [0:5])\nprint (y_test [0:5])"}, {"execution_count": 31, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "DecisionTrees's Accuracy:  0.614285714286\n"}], "source": "from sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predTree))"}, {"execution_count": 32, "cell_type": "code", "metadata": {}, "outputs": [], "source": "from sklearn.externals.six import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline "}, {"execution_count": 33, "cell_type": "code", "metadata": {"scrolled": true}, "outputs": [{"execution_count": 33, "metadata": {}, "data": {"text/plain": "<matplotlib.image.AxesImage at 0x7f9d28bf86d8>"}, "output_type": "execute_result"}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f9d28cc2588>"}, "metadata": {}}], "source": "dot_data = StringIO()\nfilename = \"paidtree.png\"\nfeatureNames = Feature.columns[0:8]\ntargetNames = df[\"loan_status\"].unique().tolist()\nout=tree.export_graphviz(PaidTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True,  special_characters=True,rotate=False)  \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(100, 200))\nplt.imshow(img,interpolation='nearest')"}, {"source": "# Support Vector Machine", "cell_type": "markdown", "metadata": {}}, {"execution_count": 60, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 60, "metadata": {}, "data": {"text/plain": "Principal               int64\nterms                   int64\nage                     int64\nGender                  int64\nweekend                 int64\nBechalor                int64\nHigh School or Below    int64\ncollege                 int64\ndtype: object"}, "output_type": "execute_result"}], "source": "Feature['Bechalor'] = Feature['Bechalor'].astype('int')\nFeature['High School or Below'] = Feature['High School or Below'].astype('int')\nFeature['college'] = Feature['college'].astype('int')\nFeature.dtypes"}, {"execution_count": 85, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 85, "metadata": {}, "data": {"text/plain": "dtype('int64')"}, "output_type": "execute_result"}], "source": "y_df = df\ny_df['loan_status'] = y_df['loan_status'].astype('int')\ny_1=y_df['loan_status']\ny_1.dtypes"}, {"execution_count": 81, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 81, "metadata": {}, "data": {"text/plain": "array([[1000,   30,   45,    0,    0,    0,    1,    0],\n       [1000,   30,   33,    1,    0,    1,    0,    0],\n       [1000,   15,   27,    0,    0,    0,    0,    1],\n       [1000,   30,   28,    1,    1,    0,    0,    1],\n       [1000,   30,   29,    0,    1,    0,    0,    1]])"}, "output_type": "execute_result"}], "source": "X = np.asarray(Feature)\nX[0:5]"}, {"execution_count": 86, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 86, "metadata": {}, "data": {"text/plain": "array([0, 0, 0, 0, 0])"}, "output_type": "execute_result"}], "source": "Y = np.asarray(y_1)\nY[0:5]"}, {"execution_count": 87, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"}], "source": "X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)\n"}, {"execution_count": 88, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 88, "metadata": {}, "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', kernel='rbf',\n  max_iter=-1, probability=False, random_state=None, shrinking=True,\n  tol=0.001, verbose=False)"}, "output_type": "execute_result"}], "source": "from sklearn import svm\nclf = svm.SVC(kernel='rbf')\nclf.fit(X_train, y_train) "}, {"execution_count": 89, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 89, "metadata": {}, "data": {"text/plain": "array([0, 0, 0, 1, 0])"}, "output_type": "execute_result"}], "source": "yhat = clf.predict(X_test)\nyhat [0:5]"}, {"execution_count": 90, "cell_type": "code", "metadata": {}, "outputs": [], "source": "from sklearn.metrics import classification_report, confusion_matrix\nimport itertools"}, {"execution_count": 91, "cell_type": "code", "metadata": {}, "outputs": [], "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')"}, {"execution_count": 92, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "             precision    recall  f1-score   support\n\n          0       0.79      0.95      0.86        55\n          1       0.25      0.07      0.11        15\n\navg / total       0.67      0.76      0.70        70\n\nConfusion matrix, without normalization\n[[52  3]\n [14  1]]\n"}, {"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9d28c878d0>"}, "metadata": {}}], "source": "# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhat, labels=[0,1])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhat))\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF(0)','COLLECTION(1)'],normalize= False,  title='Confusion matrix')"}, {"execution_count": 93, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 93, "metadata": {}, "data": {"text/plain": "0.69788106630211888"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import f1_score\nf1_score(y_test, yhat, average='weighted') "}, {"execution_count": 94, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 94, "metadata": {}, "data": {"text/plain": "0.75714285714285712"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat)"}, {"source": "# Logistic Regression", "cell_type": "markdown", "metadata": {}}, {"execution_count": 95, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stderr", "text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n  warnings.warn(msg, DataConversionWarning)\n"}, {"execution_count": 95, "metadata": {}, "data": {"text/plain": "array([[ 0.52,  0.92,  2.33, -0.42, -1.21, -0.38,  1.14, -0.87],\n       [ 0.52,  0.92,  0.34,  2.38, -1.21,  2.62, -0.88, -0.87],\n       [ 0.52, -0.96, -0.65, -0.42, -1.21, -0.38, -0.88,  1.15],\n       [ 0.52,  0.92, -0.49,  2.38,  0.83, -0.38, -0.88,  1.15],\n       [ 0.52,  0.92, -0.32, -0.42,  0.83, -0.38, -0.88,  1.15]])"}, "output_type": "execute_result"}], "source": "from sklearn import preprocessing\nX = preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"}, {"execution_count": 98, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"}], "source": "X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)"}, {"execution_count": 111, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 111, "metadata": {}, "data": {"text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n          verbose=0, warm_start=False)"}, "output_type": "execute_result"}], "source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nLR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\nLR"}, {"execution_count": 112, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 112, "metadata": {}, "data": {"text/plain": "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n       0])"}, "output_type": "execute_result"}], "source": "yhat = LR.predict(X_test)\nyhat"}, {"execution_count": 105, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 105, "metadata": {}, "data": {"text/plain": "array([[ 0.65,  0.35],\n       [ 0.71,  0.29],\n       [ 0.82,  0.18],\n       [ 0.8 ,  0.2 ],\n       [ 0.82,  0.18],\n       [ 0.82,  0.18],\n       [ 0.67,  0.33],\n       [ 0.68,  0.32],\n       [ 0.8 ,  0.2 ],\n       [ 0.67,  0.33],\n       [ 0.8 ,  0.2 ],\n       [ 0.67,  0.33],\n       [ 0.86,  0.14],\n       [ 0.79,  0.21],\n       [ 0.72,  0.28],\n       [ 0.86,  0.14],\n       [ 0.63,  0.37],\n       [ 0.83,  0.17],\n       [ 0.66,  0.34],\n       [ 0.81,  0.19],\n       [ 0.72,  0.28],\n       [ 0.67,  0.33],\n       [ 0.64,  0.36],\n       [ 0.69,  0.31],\n       [ 0.75,  0.25],\n       [ 0.65,  0.35],\n       [ 0.65,  0.35],\n       [ 0.77,  0.23],\n       [ 0.66,  0.34],\n       [ 0.87,  0.13],\n       [ 0.69,  0.31],\n       [ 0.71,  0.29],\n       [ 0.7 ,  0.3 ],\n       [ 0.68,  0.32],\n       [ 0.76,  0.24],\n       [ 0.7 ,  0.3 ],\n       [ 0.66,  0.34],\n       [ 0.84,  0.16],\n       [ 0.7 ,  0.3 ],\n       [ 0.7 ,  0.3 ],\n       [ 0.66,  0.34],\n       [ 0.81,  0.19],\n       [ 0.7 ,  0.3 ],\n       [ 0.65,  0.35],\n       [ 0.83,  0.17],\n       [ 0.66,  0.34],\n       [ 0.69,  0.31],\n       [ 0.67,  0.33],\n       [ 0.71,  0.29],\n       [ 0.71,  0.29],\n       [ 0.84,  0.16],\n       [ 0.69,  0.31],\n       [ 0.82,  0.18],\n       [ 0.67,  0.33],\n       [ 0.83,  0.17],\n       [ 0.87,  0.13],\n       [ 0.82,  0.18],\n       [ 0.73,  0.27],\n       [ 0.69,  0.31],\n       [ 0.8 ,  0.2 ],\n       [ 0.74,  0.26],\n       [ 0.81,  0.19],\n       [ 0.7 ,  0.3 ],\n       [ 0.63,  0.37],\n       [ 0.81,  0.19],\n       [ 0.67,  0.33],\n       [ 0.81,  0.19],\n       [ 0.75,  0.25],\n       [ 0.71,  0.29],\n       [ 0.81,  0.19]])"}, "output_type": "execute_result"}], "source": "yhat_prob = LR.predict_proba(X_test)\nyhat_prob"}, {"execution_count": 113, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 113, "metadata": {}, "data": {"text/plain": "0.68571428571428572"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat)"}, {"execution_count": 114, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "[[ 2 13]\n [ 9 46]]\n"}], "source": "from sklearn.metrics import classification_report, confusion_matrix\nimport itertools\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\nprint(confusion_matrix(y_test, yhat, labels=[1,0]))"}, {"execution_count": 108, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Confusion matrix, without normalization\n[[ 0 15]\n [ 0 55]]\n"}, {"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9d24815cf8>"}, "metadata": {}}], "source": "cnf_matrix = confusion_matrix(y_test, yhat, labels=[1,0])\nnp.set_printoptions(precision=2)\n\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF(0)','COLLECTION(1)'],normalize= False,  title='Confusion matrix')"}, {"execution_count": 115, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "             precision    recall  f1-score   support\n\n          0       0.78      0.84      0.81        55\n          1       0.18      0.13      0.15        15\n\navg / total       0.65      0.69      0.67        70\n\n"}], "source": "print (classification_report(y_test, yhat))"}, {"execution_count": 116, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 116, "metadata": {}, "data": {"text/plain": "0.49202682262287284"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import log_loss\nlog_loss(y_test, yhat_prob)"}, {"source": "# Model Evaluation using Test set", "cell_type": "markdown", "metadata": {}}, {"execution_count": null, "cell_type": "code", "metadata": {}, "outputs": [], "source": ""}, {"execution_count": null, "cell_type": "code", "metadata": {}, "outputs": [], "source": ""}, {"execution_count": 120, "cell_type": "code", "metadata": {}, "outputs": [], "source": "from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss"}, {"source": "First, download and load the test set:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 121, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "--2019-05-16 03:56:56--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 3642 (3.6K) [text/csv]\nSaving to: \u2018loan_test.csv\u2019\n\n100%[======================================>] 3,642       --.-K/s   in 0s      \n\n2019-05-16 03:56:56 (691 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"}, {"source": "### Load Test set for evaluation ", "cell_type": "markdown", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}}, {"execution_count": 123, "cell_type": "code", "metadata": {"button": false, "new_sheet": false, "run_control": {"read_only": false}}, "outputs": [{"execution_count": 123, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()"}, {"source": "# KNN evaluation using TEST SET", "cell_type": "markdown", "metadata": {}}, {"execution_count": 125, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 125, "metadata": {}, "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>2016-09-08</td>\n      <td>2016-10-07</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>2016-09-09</td>\n      <td>2016-09-15</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>2016-09-10</td>\n      <td>2016-10-09</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>2016-09-10</td>\n      <td>2016-10-09</td>\n      <td>26</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35</td>\n      <td>35</td>\n      <td>PAIDOFF</td>\n      <td>800</td>\n      <td>15</td>\n      <td>2016-09-11</td>\n      <td>2016-09-25</td>\n      <td>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     2016-09-08   \n1           5             5     PAIDOFF        300      7     2016-09-09   \n2          21            21     PAIDOFF       1000     30     2016-09-10   \n3          24            24     PAIDOFF       1000     30     2016-09-10   \n4          35            35     PAIDOFF        800     15     2016-09-11   \n\n    due_date  age             education  Gender  \n0 2016-10-07   50              Bechalor  female  \n1 2016-09-15   35       Master or Above    male  \n2 2016-10-09   43  High School or Below  female  \n3 2016-10-09   26               college    male  \n4 2016-09-25   29              Bechalor    male  "}, "output_type": "execute_result"}], "source": "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\ntest_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\ntest_df.head()"}, {"execution_count": 127, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 127, "metadata": {}, "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>1</td>\n      <td>1</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>50</td>\n      <td>Bechalor</td>\n      <td>female</td>\n      <td>3</td>\n      <td>0</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>2016-09-09</td>\n      <td>2016-09-15</td>\n      <td>35</td>\n      <td>Master or Above</td>\n      <td>male</td>\n      <td>4</td>\n      <td>1</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>2016-09-10</td>\n      <td>2016-10-09</td>\n      <td>43</td>\n      <td>High School or Below</td>\n      <td>female</td>\n      <td>5</td>\n      <td>1</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>2016-09-10</td>\n      <td>2016-10-09</td>\n      <td>26</td>\n      <td>college</td>\n      <td>male</td>\n      <td>5</td>\n      <td>1</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>2016-09-11</td>\n      <td>2016-09-25</td>\n      <td>29</td>\n      <td>Bechalor</td>\n      <td>male</td>\n      <td>6</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           1             1     PAIDOFF       1000     30     2016-09-08   \n1           5             5     PAIDOFF        300      7     2016-09-09   \n2          21            21     PAIDOFF       1000     30     2016-09-10   \n3          24            24     PAIDOFF       1000     30     2016-09-10   \n4          35            35     PAIDOFF        800     15     2016-09-11   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   50              Bechalor  female          3        0  \n1 2016-09-15   35       Master or Above    male          4        1  \n2 2016-10-09   43  High School or Below  female          5        1  \n3 2016-10-09   26               college    male          5        1  \n4 2016-09-25   29              Bechalor    male          6        1  "}, "output_type": "execute_result"}], "source": "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ntest_df.head()"}, {"execution_count": 124, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 124, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)\ntest_df.head()"}, {"execution_count": 128, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 128, "metadata": {}, "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>1</td>\n      <td>1</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>50</td>\n      <td>Bechalor</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</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>2016-09-09</td>\n      <td>2016-09-15</td>\n      <td>35</td>\n      <td>Master or Above</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</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>2016-09-10</td>\n      <td>2016-10-09</td>\n      <td>43</td>\n      <td>High School or Below</td>\n      <td>1</td>\n      <td>5</td>\n      <td>1</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>2016-09-10</td>\n      <td>2016-10-09</td>\n      <td>26</td>\n      <td>college</td>\n      <td>0</td>\n      <td>5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35</td>\n      <td>35</td>\n      <td>PAIDOFF</td>\n      <td>800</td>\n      <td>15</td>\n      <td>2016-09-11</td>\n      <td>2016-09-25</td>\n      <td>29</td>\n      <td>Bechalor</td>\n      <td>0</td>\n      <td>6</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           1             1     PAIDOFF       1000     30     2016-09-08   \n1           5             5     PAIDOFF        300      7     2016-09-09   \n2          21            21     PAIDOFF       1000     30     2016-09-10   \n3          24            24     PAIDOFF       1000     30     2016-09-10   \n4          35            35     PAIDOFF        800     15     2016-09-11   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   50              Bechalor       1          3        0  \n1 2016-09-15   35       Master or Above       0          4        1  \n2 2016-10-09   43  High School or Below       1          5        1  \n3 2016-10-09   26               college       0          5        1  \n4 2016-09-25   29              Bechalor       0          6        1  "}, "output_type": "execute_result"}], "source": "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ntest_df.head()"}, {"execution_count": 129, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 129, "metadata": {}, "data": {"text/plain": "education             loan_status\nBechalor              0              0.750000\n                      1              0.250000\nHigh School or Below  0              0.741722\n                      1              0.258278\nMaster or Above       0              0.500000\n                      1              0.500000\ncollege               0              0.765101\n                      1              0.234899\nName: loan_status, dtype: float64"}, "output_type": "execute_result"}], "source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"}, {"execution_count": 131, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 131, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "test_Feature = test_df[['Principal','terms','age','Gender','weekend']]\ntest_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\ntest_Feature.drop(['Master or Above'], axis = 1,inplace=True)\ntest_Feature.head()"}, {"execution_count": 135, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 135, "metadata": {}, "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  "}, "output_type": "execute_result"}], "source": "X = test_Feature\nX[0:5]"}, {"execution_count": 136, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 136, "metadata": {}, "data": {"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"}, "output_type": "execute_result"}], "source": "y = test_df['loan_status'].values\ny[0:5]"}, {"execution_count": 137, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 137, "metadata": {}, "data": {"text/plain": "array([[ 0.49,  0.93,  3.06,  1.98, -1.3 ,  2.4 , -0.8 , -0.86],\n       [-3.56, -1.7 ,  0.53, -0.51,  0.77, -0.42, -0.8 , -0.86],\n       [ 0.49,  0.93,  1.88,  1.98,  0.77, -0.42,  1.25, -0.86],\n       [ 0.49,  0.93, -0.98, -0.51,  0.77, -0.42, -0.8 ,  1.16],\n       [-0.67, -0.79, -0.48, -0.51,  0.77,  2.4 , -0.8 , -0.86]])"}, "output_type": "execute_result"}], "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"}, {"execution_count": 139, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Test set: (54, 8) (54,)\n"}], "source": "X_test = X\ny_test = y\nprint ('Test set:', X_test.shape,  y_test.shape)"}, {"execution_count": 140, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 140, "metadata": {}, "data": {"text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n           metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n           weights='uniform')"}, "output_type": "execute_result"}], "source": "# Let us start with K=2 for KNN \nk = 7\n#Train Model and Predict  \nneighbor = KNeighborsClassifier(n_neighbors = k).fit(X_test,y_test)\nneighbor"}, {"execution_count": 141, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 141, "metadata": {}, "data": {"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"}, "output_type": "execute_result"}], "source": "yhat = neighbor.predict(X_test)\nyhat[0:5]"}, {"execution_count": 142, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Test set Accuracy:  0.87037037037\n"}], "source": "from sklearn import metrics\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"}, {"execution_count": 159, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Avg F1-score: 0.8823\nJaccard score: 0.8889\n"}], "source": "clf2 = KNeighborsClassifier()\nclf2.fit(X_test, y_test) \nyhat2 = clf2.predict(X_test)\nprint(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\nprint(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))"}, {"source": "# DECISION Tree evaluation using TEST SET\n", "cell_type": "markdown", "metadata": {}}, {"execution_count": 155, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 155, "metadata": {}, "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')"}, "output_type": "execute_result"}], "source": "from sklearn.tree import DecisionTreeClassifier\nPaidTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n\n\nPaidTree.fit(X_test,y_test)"}, {"execution_count": 156, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF']\n['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n"}], "source": "predTree = PaidTree.predict(X_test)\nprint (predTree [0:5])\nprint (y_test [0:5])"}, {"execution_count": 157, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "DecisionTrees's Accuracy:  0.833333333333\n"}], "source": "from sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predTree))"}, {"execution_count": 160, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Avg F1-score: 0.8352\nJaccard score: 0.8333\n"}], "source": "clf2 = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\nclf2.fit(X_test, y_test) \nyhat2 = clf2.predict(X_test)\nprint(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\nprint(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))"}, {"source": "# SVM evaluation using TEST SET", "cell_type": "markdown", "metadata": {}}, {"execution_count": 162, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 162, "metadata": {}, "data": {"text/plain": "Principal               int64\nterms                   int64\nage                     int64\nGender                  int64\nweekend                 int64\nBechalor                int64\nHigh School or Below    int64\ncollege                 int64\ndtype: object"}, "output_type": "execute_result"}], "source": "test_Feature['Bechalor'] = test_Feature['Bechalor'].astype('int')\ntest_Feature['High School or Below'] = test_Feature['High School or Below'].astype('int')\ntest_Feature['college'] = test_Feature['college'].astype('int')\ntest_Feature.dtypes"}, {"execution_count": 164, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 164, "metadata": {}, "data": {"text/plain": "dtype('int64')"}, "output_type": "execute_result"}], "source": "y_df = test_df\ny_df['loan_status'].replace(to_replace=['PAIDOFF','COLLECTION'], value=[0,1],inplace=True)\ny_df['loan_status'] = y_df['loan_status'].astype('int')\ny_1=y_df['loan_status']\ny_1.dtypes"}, {"execution_count": 167, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 167, "metadata": {}, "data": {"text/plain": "array([[1000,   30,   50,    1,    0,    1,    0,    0],\n       [ 300,    7,   35,    0,    1,    0,    0,    0],\n       [1000,   30,   43,    1,    1,    0,    1,    0],\n       [1000,   30,   26,    0,    1,    0,    0,    1],\n       [ 800,   15,   29,    0,    1,    1,    0,    0]])"}, "output_type": "execute_result"}], "source": "X = np.asarray(test_Feature)\nX[0:5]\n"}, {"execution_count": 168, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 168, "metadata": {}, "data": {"text/plain": "array([0, 0, 0, 0, 0])"}, "output_type": "execute_result"}], "source": "Y = np.asarray(y_1)\nY[0:5]"}, {"execution_count": 170, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 170, "metadata": {}, "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', kernel='rbf',\n  max_iter=-1, probability=False, random_state=None, shrinking=True,\n  tol=0.001, verbose=False)"}, "output_type": "execute_result"}], "source": "from sklearn import svm\nclf3 = svm.SVC(kernel='rbf')\nclf3.fit(X, Y) "}, {"execution_count": 178, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 178, "metadata": {}, "data": {"text/plain": "array([0, 0, 0, 0, 0])"}, "output_type": "execute_result"}], "source": "yhat = clf.predict(X)\nyhat [0:5]"}, {"execution_count": 179, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 179, "metadata": {}, "data": {"text/plain": "0.67176423735563517"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import f1_score\nf1_score(Y, yhat, average='weighted') "}, {"execution_count": 180, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 180, "metadata": {}, "data": {"text/plain": "0.7592592592592593"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(Y, yhat)"}, {"source": "# LOGISTIC REGR evaluation using TEST SET", "cell_type": "markdown", "metadata": {}}, {"execution_count": 181, "cell_type": "code", "metadata": {}, "outputs": [{"output_type": "stream", "name": "stderr", "text": "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n  warnings.warn(msg, DataConversionWarning)\n"}, {"execution_count": 181, "metadata": {}, "data": {"text/plain": "array([[ 0.49,  0.93,  3.06,  1.98, -1.3 ,  2.4 , -0.8 , -0.86],\n       [-3.56, -1.7 ,  0.53, -0.51,  0.77, -0.42, -0.8 , -0.86],\n       [ 0.49,  0.93,  1.88,  1.98,  0.77, -0.42,  1.25, -0.86],\n       [ 0.49,  0.93, -0.98, -0.51,  0.77, -0.42, -0.8 ,  1.16],\n       [-0.67, -0.79, -0.48, -0.51,  0.77,  2.4 , -0.8 , -0.86]])"}, "output_type": "execute_result"}], "source": "from sklearn import preprocessing\nX = preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"}, {"execution_count": 182, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 182, "metadata": {}, "data": {"text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n          verbose=0, warm_start=False)"}, "output_type": "execute_result"}], "source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nLR = LogisticRegression(C=0.01, solver='liblinear').fit(X,Y)\nLR"}, {"execution_count": 183, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 183, "metadata": {}, "data": {"text/plain": "array([0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n       1, 1, 0, 1, 0, 1, 1, 0])"}, "output_type": "execute_result"}], "source": "yhat = LR.predict(X_test)\nyhat"}, {"execution_count": 184, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 184, "metadata": {}, "data": {"text/plain": "0.7823361823361823"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import f1_score\nf1_score(Y, yhat, average='weighted') "}, {"execution_count": 185, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 185, "metadata": {}, "data": {"text/plain": "0.77777777777777779"}, "output_type": "execute_result"}], "source": "from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(Y, yhat)"}, {"execution_count": 186, "cell_type": "code", "metadata": {}, "outputs": [{"execution_count": 186, "metadata": {}, "data": {"text/plain": "0.63550951720182147"}, "output_type": "execute_result"}], "source": "yhat_prob = LR.predict_proba(X)\nfrom sklearn.metrics import log_loss\nlog_loss(Y, yhat_prob)"}, {"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                | 0.8823  | 0.8889   | NA      |\n| Decision Tree      | 0.8352  | 0.8333   | NA      |\n| SVM                | 0.7592  | 0.6717   | NA      |\n| LogisticRegression | 0.7823  | 0.7777   | 0.6355  |", "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 3.5", "name": "python3", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "3.5.5", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython3", "codemirror_mode": {"version": 3, "name": "ipython"}}}, "nbformat": 4}