UmbreLu · September 5, 2022 17:17
diff --git a/ML0101EN-Proj-Loan-py-v1.ipynb b/ML0101EN-Proj-Loan-py-v1.ipynb
 {"cells":[{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n","    </a>\n","</p>\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":["Let's download the dataset\n"]},{"cell_type":"code","execution_count":2,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[],"source":["#!wget -O loan_train.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv"]},{"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":3,"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: 0.1</th>\n","      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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":3,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('loan_train.csv')\n","df.head()"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/plain":["(346, 10)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Convert to date time object\n"]},{"cell_type":"code","execution_count":5,"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: 0.1</th>\n","      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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":5,"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":6,"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":6,"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":7,"metadata":{},"outputs":[],"source":["# notice: installing seaborn might takes a few minutes\n","#!conda install -c anaconda seaborn -y"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"image/png":"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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, 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loan\n"]},{"cell_type":"code","execution_count":10,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[{"data":{"image/png":"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x=\"562.255859\"/>\n     </g>\n    </g>\n   </g>\n  </g>\n </g>\n <defs>\n  <clipPath id=\"p96c1390b8d\">\n   <rect x=\"26.925\" y=\"20.798438\" width=\"188.2375\" height=\"149.4\"/>\n  </clipPath>\n  <clipPath id=\"p6a5cd35941\">\n   <rect x=\"229.4625\" y=\"20.798438\" width=\"188.2375\" height=\"149.4\"/>\n  </clipPath>\n </defs>\n</svg>\n","text/plain":["<Figure size 432x216 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["df['dayofweek'] = df['effective_date'].dt.dayofweek\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":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: 0.1</th>\n","      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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":11,"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":12,"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":12,"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":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>Unnamed: 0.1</th>\n","      <th>Unnamed: 0</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: 0.1  Unnamed: 0 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":13,"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":14,"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":14,"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":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>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":15,"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 convert categorical varables to binary variables and append them to the feature Data Frame\n"]},{"cell_type":"code","execution_count":16,"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":16,"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":17,"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":17,"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":18,"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":18,"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":19,"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":19,"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":20,"metadata":{},"outputs":[],"source":["from sklearn.neighbors import KNeighborsClassifier\n","from sklearn.model_selection import train_test_split\n","from sklearn import metrics"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Train set: (276, 8) (276,)\n","Test 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)\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":22,"metadata":{},"outputs":[{"data":{"text/plain":["array([0.64285714, 0.58571429, 0.74285714, 0.7       , 0.74285714,\n","       0.71428571, 0.8       , 0.75714286, 0.74285714, 0.68571429,\n","       0.71428571, 0.71428571, 0.68571429, 0.7       , 0.68571429,\n","       0.72857143, 0.71428571, 0.71428571, 0.71428571])"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["Ks = 20\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":23,"metadata":{},"outputs":[{"data":{"image/png":"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Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["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()"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["The best accuracy was 0.8 with k = 7\n"]},{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"],"text/plain":["KNeighborsClassifier(n_neighbors=7)"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["print( \"The best accuracy was\", mean_acc.max(), \"with k =\", mean_acc.argmax()+1)\n","k = 7\n","knn_model = KNeighborsClassifier(n_neighbors = k).fit(X,y)\n","knn_model"]},{"cell_type":"markdown","metadata":{},"source":["# Decision Tree\n"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[],"source":["from sklearn.tree import DecisionTreeClassifier\n","import sklearn.tree as tree"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre></div></div></div></div></div>"],"text/plain":["DecisionTreeClassifier(criterion='entropy', max_depth=4)"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["loanTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n","loanTree.fit(X,y)"]},{"cell_type":"markdown","metadata":{},"source":["# Support Vector Machine\n"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"],"text/plain":["SVC()"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn import svm\n","svm_model = svm.SVC(kernel='rbf')\n","svm_model.fit(X, y)\n","svm_model"]},{"cell_type":"markdown","metadata":{},"source":["# Logistic Regression\n"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.01, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.01, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["LogisticRegression(C=0.01, solver='liblinear')"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["from sklearn.linear_model import LogisticRegression\n","lr_model = LogisticRegression(C=0.01, solver='liblinear').fit(X,y)\n","lr_model"]},{"cell_type":"markdown","metadata":{},"source":["# Model Evaluation using Test set\n"]},{"cell_type":"code","execution_count":29,"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":{},"source":["First, download and load the test set:\n"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[],"source":["#!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["### Load Test set for evaluation\n"]},{"cell_type":"code","execution_count":31,"metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"outputs":[],"source":["test_df = pd.read_csv('loan_test.csv')"]},{"cell_type":"markdown","metadata":{},"source":["### task: Repeate preprocessing operations with the test set"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["test_df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n","test_df['due_date'] = pd.to_datetime(df['due_date'])\n","test_df['effective_date'] = pd.to_datetime(df['effective_date'])\n","test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\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","X_test = preprocessing.StandardScaler().fit(test_Feature).transform(test_Feature)\n","y_test = test_df['loan_status'].values"]},{"cell_type":"markdown","metadata":{},"source":["#### evaluation: getting the models predictions for the test set"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[],"source":["knn_prediction = knn_model.predict(X_test)\n","tree_prediction = loanTree.predict(X_test)\n","svm_prediction = svm_model.predict(X_test)\n","lr_prediction = lr_model.predict(X_test)\n","predictions = [knn_prediction, tree_prediction, svm_prediction, lr_prediction]"]},{"cell_type":"markdown","metadata":{},"source":["#### evaluation: Jaccard"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6346153846153846\n","0.7450980392156863\n","0.7222222222222222\n","0.7407407407407407\n"]}],"source":["for pred in predictions:\n","    print(jaccard_score(pred, y_test, pos_label='PAIDOFF'))"]},{"cell_type":"markdown","metadata":{},"source":["#### evaluation: F1-score"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.776470588235294\n","0.8539325842696629\n","0.8387096774193549\n","0.851063829787234\n"]}],"source":["for pred in predictions:\n","    print(f1_score(pred, y_test, pos_label='PAIDOFF'))"]},{"cell_type":"markdown","metadata":{},"source":["#### evaluation: LogLoss"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.5932154076674484\n"]}],"source":["pred_prob = lr_model.predict_proba(X_test)\n","print(log_loss(y_test, pred_prob))"]},{"cell_type":"markdown","metadata":{},"source":["# Report\n","\n","You should be able to report the accuracy of the built model using different evaluation metrics:\n"]},{"cell_type":"markdown","metadata":{},"source":["| Algorithm          | Jaccard | F1-score | LogLoss |\n","| ------------------ | ------- | -------- | ------- |\n","| KNN                | ?       | ?        | NA      |\n","| Decision Tree      | ?       | ?        | NA      |\n","| SVM                | ?       | ?        | NA      |\n","| LogisticRegression | ?       | ?        | ?       |\n"]},{"cell_type":"markdown","metadata":{"button":false,"new_sheet":false,"run_control":{"read_only":false}},"source":["<h2>Want to learn more?</h2>\n","\n","IBM 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 – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n","\n","Also, 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?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n","\n","<h3>Thanks for completing this lesson!</h3>\n","\n","<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">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’ 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","## Change Log\n","\n","| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                                                             |\n","| ----------------- | ------- | ------------- | ------------------------------------------------------------------------------ |\n","| 2020-10-27        | 2.1     | Lakshmi Holla | Made changes in import statement due to updates in version of  sklearn library |\n","| 2020-08-27        | 2.0     | Malika Singla | Added lab to GitLab                                                            |\n","\n","<hr>\n","\n","## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n","\n","<p>\n"]}],"metadata":{"kernelspec":{"display_name":"pandas_kernel","language":"python","name":"pandas_kernel"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.10"}},"nbformat":4,"nbformat_minor":2}