09. Machine Learning with Python/Final Project/Machine Learning with Python - The Best Classifier.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["<center>\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n","</center>\n","\n","<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>\n"]},{"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 have learned in this course.\n","\n","We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n","\n","Let's first load required libraries:\n"]},{"cell_type":"code","execution_count":1,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[],"source":["import itertools\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from matplotlib.ticker import NullFormatter\n","import pandas as pd\n","import numpy as np\n","import matplotlib.ticker as ticker\n","from sklearn import preprocessing\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### About dataset\n"]},{"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’s one-time payoff schedule, each loan has one single due date                |\n","| Age            | Age of applicant                                                                      |\n","| Education      | Education of applicant                                                                |\n","| Gender         | The gender of applicant                                                               |\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Load Data From CSV File\n"]},{"cell_type":"code","execution_count":2,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Unnamed: 0.1</th>\n","      <th>loan_status</th>\n","      <th>Principal</th>\n","      <th>terms</th>\n","      <th>effective_date</th>\n","      <th>due_date</th>\n","      <th>age</th>\n","      <th>education</th>\n","      <th>Gender</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>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  \\\n","0           0             0     PAIDOFF       1000     30       9/8/2016   \n","1           2             2     PAIDOFF       1000     30       9/8/2016   \n","2           3             3     PAIDOFF       1000     15       9/8/2016   \n","3           4             4     PAIDOFF       1000     30       9/9/2016   \n","4           6             6     PAIDOFF       1000     30       9/9/2016   \n","\n","    due_date  age             education  Gender  \n","0  10/7/2016   45  High School or Below    male  \n","1  10/7/2016   33              Bechalor  female  \n","2  9/22/2016   27               college    male  \n","3  10/8/2016   28               college  female  \n","4  10/8/2016   29               college    male  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv')\n","df.head()"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/plain":["(346, 10)"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Convert to date time object\n"]},{"cell_type":"code","execution_count":4,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Unnamed: 0.1</th>\n","      <th>loan_status</th>\n","      <th>Principal</th>\n","      <th>terms</th>\n","      <th>effective_date</th>\n","      <th>due_date</th>\n","      <th>age</th>\n","      <th>education</th>\n","      <th>Gender</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>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  \\\n","0           0             0     PAIDOFF       1000     30     2016-09-08   \n","1           2             2     PAIDOFF       1000     30     2016-09-08   \n","2           3             3     PAIDOFF       1000     15     2016-09-08   \n","3           4             4     PAIDOFF       1000     30     2016-09-09   \n","4           6             6     PAIDOFF       1000     30     2016-09-09   \n","\n","    due_date  age             education  Gender  \n","0 2016-10-07   45  High School or Below    male  \n","1 2016-10-07   33              Bechalor  female  \n","2 2016-09-22   27               college    male  \n","3 2016-10-08   28               college  female  \n","4 2016-10-08   29               college    male  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df['due_date'] = pd.to_datetime(df['due_date'])\n","df['effective_date'] = pd.to_datetime(df['effective_date'])\n","df.head()"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["# Data visualization and pre-processing\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["Let’s see how many of each class is in our data set\n"]},{"cell_type":"code","execution_count":5,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["PAIDOFF       260\n","COLLECTION     86\n","Name: loan_status, dtype: int64"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df['loan_status'].value_counts()"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["260 people have paid off the loan on time while 86 have gone into collection\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's plot some columns to underestand data better:\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x216 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["import seaborn as sns\n","\n","bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n","g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n","g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n","\n","g.axes[-1].legend()\n","plt.show()"]},{"cell_type":"code","execution_count":7,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x216 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["bins = np.linspace(df.age.min(), df.age.max(), 10)\n","g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n","g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n","\n","g.axes[-1].legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["# Pre-processing:  Feature selection/extraction\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Let's look at the day of the week people get the loan\n"]},{"cell_type":"code","execution_count":8,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x216 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["df['dayofweek'] = df['effective_date'].dt.dayofweek\n","bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n","g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n","g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n","g.axes[-1].legend()\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["We see that people who get the loan at the end of the week don't pay it off, so let's use Feature binarization to set a threshold value less than day 4\n"]},{"cell_type":"code","execution_count":9,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Unnamed: 0.1</th>\n","      <th>loan_status</th>\n","      <th>Principal</th>\n","      <th>terms</th>\n","      <th>effective_date</th>\n","      <th>due_date</th>\n","      <th>age</th>\n","      <th>education</th>\n","      <th>Gender</th>\n","      <th>dayofweek</th>\n","      <th>weekend</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>2016-09-08</td>\n","      <td>2016-10-07</td>\n","      <td>45</td>\n","      <td>High School or Below</td>\n","      <td>male</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>2016-09-08</td>\n","      <td>2016-10-07</td>\n","      <td>33</td>\n","      <td>Bechalor</td>\n","      <td>female</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>15</td>\n","      <td>2016-09-08</td>\n","      <td>2016-09-22</td>\n","      <td>27</td>\n","      <td>college</td>\n","      <td>male</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>2016-09-09</td>\n","      <td>2016-10-08</td>\n","      <td>28</td>\n","      <td>college</td>\n","      <td>female</td>\n","      <td>4</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>2016-09-09</td>\n","      <td>2016-10-08</td>\n","      <td>29</td>\n","      <td>college</td>\n","      <td>male</td>\n","      <td>4</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n","0           0             0     PAIDOFF       1000     30     2016-09-08   \n","1           2             2     PAIDOFF       1000     30     2016-09-08   \n","2           3             3     PAIDOFF       1000     15     2016-09-08   \n","3           4             4     PAIDOFF       1000     30     2016-09-09   \n","4           6             6     PAIDOFF       1000     30     2016-09-09   \n","\n","    due_date  age             education  Gender  dayofweek  weekend  \n","0 2016-10-07   45  High School or Below    male          3        0  \n","1 2016-10-07   33              Bechalor  female          3        0  \n","2 2016-09-22   27               college    male          3        0  \n","3 2016-10-08   28               college  female          4        1  \n","4 2016-10-08   29               college    male          4        1  "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n","df.head()"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["## Convert Categorical features to numerical values\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["Let's look at gender:\n"]},{"cell_type":"code","execution_count":10,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["Gender  loan_status\n","female  PAIDOFF        0.865385\n","        COLLECTION     0.134615\n","male    PAIDOFF        0.731293\n","        COLLECTION     0.268707\n","Name: loan_status, dtype: float64"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["86 % of female pay there loans while only 73 % of males pay there loan\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["Let's convert male to 0 and female to 1:\n"]},{"cell_type":"code","execution_count":11,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Unnamed: 0.1</th>\n","      <th>loan_status</th>\n","      <th>Principal</th>\n","      <th>terms</th>\n","      <th>effective_date</th>\n","      <th>due_date</th>\n","      <th>age</th>\n","      <th>education</th>\n","      <th>Gender</th>\n","      <th>dayofweek</th>\n","      <th>weekend</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>2016-09-08</td>\n","      <td>2016-10-07</td>\n","      <td>45</td>\n","      <td>High School or Below</td>\n","      <td>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  \\\n","0           0             0     PAIDOFF       1000     30     2016-09-08   \n","1           2             2     PAIDOFF       1000     30     2016-09-08   \n","2           3             3     PAIDOFF       1000     15     2016-09-08   \n","3           4             4     PAIDOFF       1000     30     2016-09-09   \n","4           6             6     PAIDOFF       1000     30     2016-09-09   \n","\n","    due_date  age             education  Gender  dayofweek  weekend  \n","0 2016-10-07   45  High School or Below       0          3        0  \n","1 2016-10-07   33              Bechalor       1          3        0  \n","2 2016-09-22   27               college       0          3        0  \n","3 2016-10-08   28               college       1          4        1  \n","4 2016-10-08   29               college       0          4        1  "]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n","df.head()"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["## One Hot Encoding\n","\n","#### How about education?\n"]},{"cell_type":"code","execution_count":12,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["education             loan_status\n","Bechalor              PAIDOFF        0.750000\n","                      COLLECTION     0.250000\n","High School or Below  PAIDOFF        0.741722\n","                      COLLECTION     0.258278\n","Master or Above       COLLECTION     0.500000\n","                      PAIDOFF        0.500000\n","college               PAIDOFF        0.765101\n","                      COLLECTION     0.234899\n","Name: loan_status, dtype: float64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df.groupby(['education'])['loan_status'].value_counts(normalize=True)"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["#### Features before One Hot Encoding\n"]},{"cell_type":"code","execution_count":13,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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\n","0       1000     30   45       0  High School or Below\n","1       1000     30   33       1              Bechalor\n","2       1000     15   27       0               college\n","3       1000     30   28       1               college\n","4       1000     30   29       0               college"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df[['Principal','terms','age','Gender','education']].head()"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame\n"]},{"cell_type":"code","execution_count":14,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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  \\\n","0       1000     30   45       0        0         0                     1   \n","1       1000     30   33       1        0         1                     0   \n","2       1000     15   27       0        0         0                     0   \n","3       1000     30   28       1        1         0                     0   \n","4       1000     30   29       0        1         0                     0   \n","\n","   college  \n","0        0  \n","1        0  \n","2        1  \n","3        1  \n","4        1  "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["Feature = df[['Principal','terms','age','Gender','weekend']]\n","Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n","Feature.drop(['Master or Above'], axis = 1,inplace=True)\n","Feature.head()\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Feature Selection\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["Let's define feature sets, X:\n"]},{"cell_type":"code","execution_count":15,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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  \\\n","0       1000     30   45       0        0         0                     1   \n","1       1000     30   33       1        0         1                     0   \n","2       1000     15   27       0        0         0                     0   \n","3       1000     30   28       1        1         0                     0   \n","4       1000     30   29       0        1         0                     0   \n","\n","   college  \n","0        0  \n","1        0  \n","2        1  \n","3        1  \n","4        1  "]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["X = Feature\n","X[0:5]"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["What are our lables?\n"]},{"cell_type":"code","execution_count":16,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n","      dtype=object)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["y = df['loan_status'].values\n","y[0:5]"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["## Normalize Data\n"]},{"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)\n"]},{"cell_type":"code","execution_count":17,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/plain":["array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n","        -0.38170062,  1.13639374, -0.86968108],\n","       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n","         2.61985426, -0.87997669, -0.86968108],\n","       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n","        -0.38170062, -0.87997669,  1.14984679],\n","       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n","        -0.38170062, -0.87997669,  1.14984679],\n","       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n","        -0.38170062, -0.87997669,  1.14984679]])"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["X= preprocessing.StandardScaler().fit(X).transform(X)\n","X[0:5]"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["# Classification\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n","You should use the following algorithm:\n","\n","*   K Nearest Neighbor(KNN)\n","*   Decision Tree\n","*   Support Vector Machine\n","*   Logistic Regression\n","\n","\\__ Notice:\\__\n","\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.\n"]},{"cell_type":"markdown","metadata":{},"source":["# K Nearest Neighbor(KNN)\n","\n","Notice: You should find the best k to build the model with the best accuracy.\\\n","**warning:** You should not use the **loan_test.csv** for finding the best k, however, you can split your train_loan.csv into train and test to find the best **k**.\n"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[],"source":["#Import Libraries\n","from sklearn.model_selection import train_test_split\n","from sklearn.neighbors import KNeighborsClassifier"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Train set: (276, 8) (276,)\n","Test set: (70, 8) (70,)\n"]}],"source":["#Split data set into train and test\n","X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n","print ('Train set:', X_train.shape,  y_train.shape)\n","print ('Test set:', X_test.shape,  y_test.shape)"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.67142857, 0.65714286, 0.71428571, 0.68571429, 0.75714286,\n","       0.71428571, 0.78571429, 0.75714286, 0.75714286])"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["#Determine K value through Accuracy Evaluation:\n","from sklearn import metrics\n","Ks = 10\n","mean_acc = np.zeros((Ks-1))\n","std_acc = np.zeros((Ks-1))\n","\n","for n in range(1,Ks):\n","    \n","    #Train Model and Predict  \n","    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n","    yhat=neigh.predict(X_test)\n","    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n","\n","    \n","    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n","\n","mean_acc"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/plain":["array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n","      dtype=object)"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["#quick check that predicted values are as expected (either PAIDOFF or COLLECTION):\n","yhat = neigh.predict(X_test)\n","yhat[0:5]"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["The best accuracy was with 0.7857142857142857 with k= 7\n"]}],"source":["# Plot the model accuracy for a different number of neighbors\n","plt.plot(range(1,Ks),mean_acc,'g')\n","plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n","plt.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n","plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n","plt.ylabel('Accuracy ')\n","plt.xlabel('Number of Neighbors (K)')\n","plt.tight_layout()\n","plt.show()\n","\n","print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"data":{"text/plain":["KNeighborsClassifier(n_neighbors=7)"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["#Build the model this time using the k value that produced the highest accuracy \n","knn = KNeighborsClassifier(n_neighbors = mean_acc.argmax()+1)\n","#Fit the model with the training set\n","knn.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[],"source":["#Make some predictions\n","knn_yhat = knn.predict(X_test)"]},{"cell_type":"markdown","metadata":{},"source":["# Decision Tree"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["#Comment these out when installed\n","#!conda install -c conda-forge pydotplus -y\n","#!conda install -c conda-forge python-graphviz -y"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[],"source":["#Import Libraries\n","from sklearn.tree import DecisionTreeClassifier\n","from  io import StringIO\n","import pydotplus\n","import matplotlib.image as mpimg\n","from sklearn import tree"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["For depth = 1  the accuracy score is 0.7857142857142857 \n","For depth = 2  the accuracy score is 0.7857142857142857 \n","For depth = 3  the accuracy score is 0.6142857142857143 \n","For depth = 4  the accuracy score is 0.6142857142857143 \n","For depth = 5  the accuracy score is 0.6428571428571429 \n","For depth = 6  the accuracy score is 0.7714285714285715 \n","For depth = 7  the accuracy score is 0.7571428571428571 \n","For depth = 8  the accuracy score is 0.7571428571428571 \n","For depth = 9  the accuracy score is 0.6571428571428571 \n"]}],"source":["#We must find the optimum depth to choose:\n","ds = 10\n","mean_acc = np.zeros((ds-1))\n","std_acc = np.zeros((ds-1))\n","\n","for d in range(1,ds):\n","    \n","    #Train Model and Predict  \n","    dt = DecisionTreeClassifier(criterion = 'entropy', max_depth = d).fit(X_train, y_train)\n","    \n","    #Predict the response for the test dataset\n","    yhat=dt.predict(X_test)\n","    \n","    #Calculate the accuracy score\n","    mean_acc[d-1] = metrics.accuracy_score(y_test, yhat)\n","    std_acc[d-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n","    \n","    print(\"For depth = {}  the accuracy score is {} \".format(d, mean_acc[d-1]))\n"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["The best accuracy was with 0.7857142857142857 with d= 2\n"]}],"source":["# Plot the model accuracy for different depths\n","plt.plot(range(1,ds),mean_acc,'g')\n","plt.fill_between(range(1,ds),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n","plt.fill_between(range(1,ds),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n","plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n","plt.ylabel('Accuracy ')\n","plt.xlabel('Depth (d)')\n","plt.tight_layout()\n","plt.show()\n","\n","print( \"The best accuracy was with\", mean_acc.max(), \"with d=\", mean_acc.argmax()+2) #best is with d=1, but use 2 so +2 instead of +1 in print statement"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[],"source":["#The greatest accuracy was with depth = 1, but depth = 2 makes more sense to use here as it was also equal"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/plain":["DecisionTreeClassifier(criterion='entropy', max_depth=2)"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["#Build the model this time using the d value that produced the highest accuracy \n","loanTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=2)\n","\n","#fit the data with the training set\n","loanTree.fit(X_train, y_train)"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[],"source":["#make some predictions:\n","predTree = loanTree.predict(X_test)"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.image.AxesImage at 0x23dcc9f39c8>"]},"execution_count":36,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 7200x14400 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["dot_data = StringIO()\n","filename = \"loantree.png\"\n","featureNames = df.columns[3:11]\n","out=tree.export_graphviz(loanTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True,  special_characters=True,rotate=False)  \n","graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n","graph.write_png(filename)\n","img = mpimg.imread(filename)\n","plt.figure(figsize=(100, 200))\n","plt.imshow(img,interpolation='nearest')"]},{"cell_type":"markdown","metadata":{},"source":["# Support Vector Machine\n"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"data":{"text/plain":["SVC()"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["#Import Libraries\n","from sklearn import svm\n","from sklearn.metrics import classification_report, confusion_matrix\n","import itertools\n","\n","#Use the Radial Basis Function (the default)\n","svm_model = svm.SVC(kernel='rbf')\n","#Fit the model with the training set\n","svm_model.fit(X_train, y_train) "]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"data":{"text/plain":["array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n","      dtype=object)"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["#Make some predictions\n","yhat = svm_model.predict(X_test)\n","yhat [0:5]"]},{"cell_type":"markdown","metadata":{},"source":["# Logistic Regression\n"]},{"cell_type":"code","execution_count":39,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import log_loss"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Solver: lbfgs, logloss: 0.4920179847937498\n","Solver: saga, logloss: 0.49201948568367027\n","Solver: liblinear, logloss: 0.5772287609479654\n","Solver: newton-cg, logloss: 0.492017801467927\n","Solver: sag, logloss: 0.4920289144344473\n"]}],"source":["#choose which function to use\n","for k in ('lbfgs', 'saga', 'liblinear', 'newton-cg', 'sag'):\n","    LR_model = LogisticRegression(C = 0.01, solver = k).fit(X_train, y_train)\n","    LR_yhat = LR_model.predict(X_test)\n","    y_prob = LR_model.predict_proba(X_test)\n","    print('Solver: {}, logloss: {}'.format(k, log_loss(y_test, y_prob)))"]},{"cell_type":"code","execution_count":41,"metadata":{},"outputs":[],"source":["#logloss is highest when solver is liblinear"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[{"data":{"text/plain":["LogisticRegression(C=0.01, solver='liblinear')"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["#Train and fit the model with the training set\n","LR_model = LogisticRegression(solver = 'liblinear', C=0.01).fit(X_train,y_train)\n","LR_model"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[{"data":{"text/plain":["array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n","      dtype=object)"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["#Make some predictions\n","yhat = LR_model.predict(X_test)\n","yhat[0:5]"]},{"cell_type":"markdown","metadata":{},"source":["# Model Evaluation using Test set\n"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[],"source":["from sklearn.metrics import jaccard_score\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import log_loss\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Load Test set for evaluation\n"]},{"cell_type":"code","execution_count":45,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Unnamed: 0.1</th>\n","      <th>loan_status</th>\n","      <th>Principal</th>\n","      <th>terms</th>\n","      <th>effective_date</th>\n","      <th>due_date</th>\n","      <th>age</th>\n","      <th>education</th>\n","      <th>Gender</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>9/8/2016</td>\n","      <td>10/7/2016</td>\n","      <td>50</td>\n","      <td>Bechalor</td>\n","      <td>female</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>PAIDOFF</td>\n","      <td>300</td>\n","      <td>7</td>\n","      <td>9/9/2016</td>\n","      <td>9/15/2016</td>\n","      <td>35</td>\n","      <td>Master or Above</td>\n","      <td>male</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>21</td>\n","      <td>21</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>9/10/2016</td>\n","      <td>10/9/2016</td>\n","      <td>43</td>\n","      <td>High School or Below</td>\n","      <td>female</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>24</td>\n","      <td>24</td>\n","      <td>PAIDOFF</td>\n","      <td>1000</td>\n","      <td>30</td>\n","      <td>9/10/2016</td>\n","      <td>10/9/2016</td>\n","      <td>26</td>\n","      <td>college</td>\n","      <td>male</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>PAIDOFF</td>\n","      <td>800</td>\n","      <td>15</td>\n","      <td>9/11/2016</td>\n","      <td>9/25/2016</td>\n","      <td>29</td>\n","      <td>Bechalor</td>\n","      <td>male</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n","0           1             1     PAIDOFF       1000     30       9/8/2016   \n","1           5             5     PAIDOFF        300      7       9/9/2016   \n","2          21            21     PAIDOFF       1000     30      9/10/2016   \n","3          24            24     PAIDOFF       1000     30      9/10/2016   \n","4          35            35     PAIDOFF        800     15      9/11/2016   \n","\n","    due_date  age             education  Gender  \n","0  10/7/2016   50              Bechalor  female  \n","1  9/15/2016   35       Master or Above    male  \n","2  10/9/2016   43  High School or Below  female  \n","3  10/9/2016   26               college    male  \n","4  9/25/2016   29              Bechalor    male  "]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["test_df = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv')\n","test_df.head()"]},{"cell_type":"code","execution_count":46,"metadata":{},"outputs":[],"source":["#convert date types to date time objects\n","test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n","test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n","test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n","\n","#set a threshold less than day 4\n","test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n","\n","#Convert Categorical features to numerical values\n","test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n","\n","#one hot encoding for education \n","test_feature = test_df[['Principal','terms','age','Gender','weekend']]\n","test_feature = pd.concat([test_feature,pd.get_dummies(test_df['education'])], axis=1)\n","test_feature.drop(['Master or Above'], axis = 1,inplace=True)\n","\n","# Testing feature\n","X_loan_test = test_feature\n","\n","# Normalizing Test Data\n","X_loan_test = preprocessing.StandardScaler().fit(X_loan_test).transform(X_loan_test)\n","\n","# Target result\n","y_loan_test = test_df['loan_status'].values"]},{"cell_type":"code","execution_count":47,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>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  \\\n","0           1             1     PAIDOFF       1000     30     2016-09-08   \n","1           5             5     PAIDOFF        300      7     2016-09-09   \n","2          21            21     PAIDOFF       1000     30     2016-09-10   \n","3          24            24     PAIDOFF       1000     30     2016-09-10   \n","4          35            35     PAIDOFF        800     15     2016-09-11   \n","\n","    due_date  age             education  Gender  dayofweek  weekend  \n","0 2016-10-07   50              Bechalor       1          3        0  \n","1 2016-09-15   35       Master or Above       0          4        1  \n","2 2016-10-09   43  High School or Below       1          5        1  \n","3 2016-10-09   26               college       0          5        1  \n","4 2016-09-25   29              Bechalor       0          6        1  "]},"execution_count":47,"metadata":{},"output_type":"execute_result"}],"source":["test_df.head()"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[],"source":["#METRICS\n","\n","# KNN\n","knn_yhat = knn.predict(X_loan_test)\n","#Jaccard Score:\n","knn_js = round(jaccard_score(y_loan_test, knn_yhat, pos_label = \"PAIDOFF\"), 2)\n","#F1 Score:\n","knn_f1 = round(f1_score(y_loan_test, knn_yhat, average = 'weighted'), 2)\n","\n","# Decision Tree\n","loanTree_yhat = loanTree.predict(X_loan_test)\n","#Jaccard Score:\n","loanTree_js = round(jaccard_score(y_loan_test, loanTree_yhat, pos_label = \"PAIDOFF\"), 2)\n","#F1 Score:\n","loanTree_f1 = round(f1_score(y_loan_test, loanTree_yhat, average = 'weighted'), 2)\n","\n","# Support Vector Machine\n","svm_model_yhat = svm_model.predict(X_loan_test)\n","#Jaccard Score:\n","svm_model_js = round(jaccard_score(y_loan_test, svm_model_yhat, pos_label = \"PAIDOFF\"), 2)\n","#F1 Score:\n","svm_model_f1 = round(f1_score(y_loan_test, svm_model_yhat, average = 'weighted'), 2)\n","\n","# Logistic Regression\n","LR_model_yhat = LR_model.predict(X_loan_test)\n","#Jaccard Score:\n","LR_model_js = round(jaccard_score(y_loan_test, LR_model_yhat, pos_label = \"PAIDOFF\"), 2)\n","#F1 Score:\n","LR_model_f1 = round(f1_score(y_loan_test, LR_model_yhat, average = 'weighted'), 2)\n","#LogLoss:\n","LR_model_logloss = round(log_loss(y_test, LR_model.predict_proba(X_test)),2)"]},{"cell_type":"code","execution_count":50,"metadata":{},"outputs":[],"source":["Jaccard_scores = [knn_js, loanTree_js, svm_model_js, LR_model_js]\n","F1_scores = [knn_f1, loanTree_f1, svm_model_f1, LR_model_f1]\n","LogLoss_scores = ['NA', 'NA', 'NA', LR_model_logloss]\n","\n","\n","all_values = [Jaccard_scores, F1_scores, LogLoss_scores]"]},{"cell_type":"code","execution_count":51,"metadata":{},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Jaccard</th>\n","      <th>F1-score</th>\n","      <th>Logloss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>KNN</th>\n","      <td>0.65</td>\n","      <td>0.63</td>\n","      <td>NA</td>\n","    </tr>\n","    <tr>\n","      <th>Decision Tree</th>\n","      <td>0.74</td>\n","      <td>0.63</td>\n","      <td>NA</td>\n","    </tr>\n","    <tr>\n","      <th>SVM</th>\n","      <td>0.78</td>\n","      <td>0.76</td>\n","      <td>NA</td>\n","    </tr>\n","    <tr>\n","      <th>Logistic Regression</th>\n","      <td>0.74</td>\n","      <td>0.66</td>\n","      <td>0.58</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                    Jaccard F1-score Logloss\n","KNN                    0.65     0.63      NA\n","Decision Tree          0.74     0.63      NA\n","SVM                    0.78     0.76      NA\n","Logistic Regression    0.74     0.66    0.58"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":["algorithms = ['KNN', 'Decision Tree', 'SVM', 'Logistic Regression']\n","metrics = ['Jaccard', 'F1-score', 'Logloss']\n","\n","accuracy_df = pd.DataFrame(all_values, index = metrics, columns = algorithms)\n","accuracy_df.transpose()"]}],"metadata":{"kernelspec":{"display_name":"Python 3.8","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"}},"nbformat":4,"nbformat_minor":2}