Cousera-Python-Machine-Learning.ipynb

{
    "cells": [
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            "metadata": {
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            },
            "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\nWe load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n\nLet's first load required libraries:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [],
            "source": "import itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import NullFormatter\nimport pandas as pd\nimport numpy as np\nimport matplotlib.ticker as ticker\nfrom sklearn import preprocessing\n%matplotlib inline"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
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                }
            },
            "source": "### About dataset\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
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                "new_sheet": false,
                "run_control": {
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            },
            "source": "This dataset is about past loans. The **Loan_train.csv** data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n\n| Field          | Description                                                                           |\n| -------------- | ------------------------------------------------------------------------------------- |\n| Loan_status    | Whether a loan is paid off on in collection                                           |\n| Principal      | Basic principal loan amount at the                                                    |\n| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n| Effective_date | When the loan got originated and took effects                                         |\n| Due_date       | Since it\u2019s one-time payoff schedule, each loan has one single due date                |\n| Age            | Age of applicant                                                                      |\n| Education      | Education of applicant                                                                |\n| Gender         | The gender of applicant                                                               |\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": 3,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
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            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2021-10-01 20:55:56--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv\nResolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\nConnecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 23101 (23K) [text/csv]\nSaving to: \u2018loan_train.csv\u2019\n\nloan_train.csv      100%[===================>]  22.56K  --.-KB/s    in 0s      \n\n2021-10-01 20:55:57 (96.5 MB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
                }
            ],
            "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": 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</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>9/8/2016</td>\n      <td>9/22/2016</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30       9/8/2016   \n1           2             2     PAIDOFF       1000     30       9/8/2016   \n2           3             3     PAIDOFF       1000     15       9/8/2016   \n3           4             4     PAIDOFF       1000     30       9/9/2016   \n4           6             6     PAIDOFF       1000     30       9/9/2016   \n\n    due_date  age             education  Gender  \n0  10/7/2016   45  High School or Below    male  \n1  10/7/2016   33              Bechalor  female  \n2  9/22/2016   27               college    male  \n3  10/8/2016   28               college  female  \n4  10/8/2016   29               college    male  "
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df = pd.read_csv('loan_train.csv')\ndf.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(346, 10)"
                    },
                    "execution_count": 6,
                    "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": 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>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  \n0 2016-10-07   45  High School or Below    male  \n1 2016-10-07   33              Bechalor  female  \n2 2016-09-22   27               college    male  \n3 2016-10-08   28               college  female  \n4 2016-10-08   29               college    male  "
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "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\u2019s see how many of each class is in our data set\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "PAIDOFF       260\nCOLLECTION     86\nName: loan_status, dtype: int64"
                    },
                    "execution_count": 7,
                    "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": 8,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Collecting package metadata (current_repodata.json): done\nSolving environment: done\n\n# All requested packages already installed.\n\n"
                }
            ],
            "source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "import seaborn as sns\n\nbins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "bins = np.linspace(df.age.min(), df.age.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'age', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "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": 15,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "df['dayofweek'] = df['effective_date'].dt.dayofweek\nbins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\ng.axes[-1].legend()\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "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": 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>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n      <th>dayofweek</th>\n      <th>weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   45  High School or Below    male          3        0  \n1 2016-10-07   33              Bechalor  female          3        0  \n2 2016-09-22   27               college    male          3        0  \n3 2016-10-08   28               college  female          4        1  \n4 2016-10-08   29               college    male          4        1  "
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ndf.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": 17,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "Gender  loan_status\nfemale  PAIDOFF        0.865385\n        COLLECTION     0.134615\nmale    PAIDOFF        0.731293\n        COLLECTION     0.268707\nName: loan_status, dtype: float64"
                    },
                    "execution_count": 17,
                    "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": 18,
            "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  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   45  High School or Below       0          3        0  \n1 2016-10-07   33              Bechalor       1          3        0  \n2 2016-09-22   27               college       0          3        0  \n3 2016-10-08   28               college       1          4        1  \n4 2016-10-08   29               college       0          4        1  "
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.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": 19,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "education             loan_status\nBechalor              PAIDOFF        0.750000\n                      COLLECTION     0.250000\nHigh School or Below  PAIDOFF        0.741722\n                      COLLECTION     0.258278\nMaster or Above       COLLECTION     0.500000\n                      PAIDOFF        0.500000\ncollege               PAIDOFF        0.765101\n                      COLLECTION     0.234899\nName: loan_status, dtype: float64"
                    },
                    "execution_count": 19,
                    "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": 20,
            "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\n0       1000     30   45       0  High School or Below\n1       1000     30   33       1              Bechalor\n2       1000     15   27       0               college\n3       1000     30   28       1               college\n4       1000     30   29       0               college"
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "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": 21,
            "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  \\\n0       1000     30   45       0        0         0                     1   \n1       1000     30   33       1        0         1                     0   \n2       1000     15   27       0        0         0                     0   \n3       1000     30   28       1        1         0                     0   \n4       1000     30   29       0        1         0                     0   \n\n   college  \n0        0  \n1        0  \n2        1  \n3        1  \n4        1  "
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "Feature = df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\n"
        },
        {
            "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": 22,
            "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  \\\n0       1000     30   45       0        0         0                     1   \n1       1000     30   33       1        0         1                     0   \n2       1000     15   27       0        0         0                     0   \n3       1000     30   28       1        1         0                     0   \n4       1000     30   29       0        1         0                     0   \n\n   college  \n0        0  \n1        0  \n2        1  \n3        1  \n4        1  "
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X = Feature\nX[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": 23,
            "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": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "y = df['loan_status'].values\ny[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": 24,
            "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": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[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\nYou 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\nNotice: You should find the best k to build the model with the best accuracy.\\\n**warning:** You should not use the **loan_test.csv** for finding the best k, however, you can split your train_loan.csv into train and test to find the best **k**.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
                }
            ],
            "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set Accuracy:  0.8333333333333334\nTest set Accuracy:  0.7142857142857143\n"
                }
            ],
            "source": "from sklearn.neighbors import KNeighborsClassifier\nk = 3\n#Train Model and Predict  \nneigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\nneigh\nyhat = neigh.predict(X_test)\nyhat[0:5]\nfrom sklearn import metrics\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "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": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# calculate the accuracy of KNN for different values of k.\n\nKs = 10\nmean_acc = np.zeros((Ks-1))\nstd_acc = np.zeros((Ks-1))\n\nfor n in range(1,Ks):\n    \n    #Train Model and Predict  \n    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n    yhat=neigh.predict(X_test)\n    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n\n    \n    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n\nmean_acc"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot the model accuracy for a different number of neighbors.\n\nplt.plot(range(1,Ks),mean_acc,'g')\nplt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\nplt.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\nplt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\nplt.ylabel('Accuracy ')\nplt.xlabel('Number of Neighbors (K)')\nplt.tight_layout()\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "The best accuracy was with 0.7857142857142857 with k= 7\n"
                }
            ],
            "source": "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Decision Tree\n"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [],
            "source": "import numpy as np \nimport pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\n"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Shape of X training set (242, 8) &  Size of Y training set (242,)\nShape of X training set (104, 8) &  Size of Y training set (104,)\n"
                }
            ],
            "source": "from sklearn.model_selection import train_test_split\nX_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)\nprint('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\nprint('Shape of X training set {}'.format(X_testset.shape),'&',' Size of Y training set {}'.format(y_testset.shape))"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
                    },
                    "execution_count": 32,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "LoanTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\nLoanTree # it shows the default parameters"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION'\n 'COLLECTION' 'PAIDOFF']\n['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION'\n 'PAIDOFF' 'COLLECTION' 'PAIDOFF']\n"
                }
            ],
            "source": "LoanTree.fit(X_trainset,y_trainset)\npredTree = LoanTree.predict(X_testset)\nprint (predTree [0:9])\nprint (y_testset [0:9])\n"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "DecisionTrees's Accuracy:  0.6538461538461539\n"
                }
            ],
            "source": "from sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Requirement already satisfied: graphviz in /opt/conda/envs/Python-3.8-main/lib/python3.8/site-packages (0.16)\nCollecting pydotplus\n  Downloading pydotplus-2.0.2.tar.gz (278 kB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 278 kB 22.5 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: pyparsing>=2.0.1 in /opt/conda/envs/Python-3.8-main/lib/python3.8/site-packages (from pydotplus) (2.4.7)\nBuilding wheels for collected packages: pydotplus\n  Building wheel for pydotplus (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pydotplus: filename=pydotplus-2.0.2-py3-none-any.whl size=24567 sha256=a836e12a481489dfb2a664cd7926117516840bf8d3e8bbc2ab2e378ad36811fe\n  Stored in directory: /tmp/wsuser/.cache/pip/wheels/fe/cd/78/a7e873cc049759194f8271f780640cf96b35e5a48bef0e2f36\nSuccessfully built pydotplus\nInstalling collected packages: pydotplus\nSuccessfully installed pydotplus-2.0.2\n"
                }
            ],
            "source": "#!conda config --set channel_priority flexible\n#!conda install -c conda-forge pydotplus -y\n#!conda install -c conda-forge python-graphviz -y\n!pip install graphviz\n!pip install pydotplus"
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.image.AxesImage at 0x7fbf028cad60>"
                    },
                    "execution_count": 37,
                    "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": "from  io import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline \ndot_data = StringIO()\nfilename = \"loantree.png\"\nfeatureNames = df.columns[0:8]\nout=tree.export_graphviz(LoanTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_trainset), filled=True,  special_characters=True,rotate=False)  \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(100, 200))\nplt.imshow(img,interpolation='nearest')\n"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Support Vector Machine\n"
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
                }
            ],
            "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)\n"
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn import svm\nclf = svm.SVC(kernel='rbf')\nclf.fit(X_train, y_train) \n\nyhat = clf.predict(X_test)\nyhat [0:5]"
        },
        {
            "cell_type": "code",
            "execution_count": 42,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "              precision    recall  f1-score   support\n\n  COLLECTION       0.36      0.27      0.31        15\n     PAIDOFF       0.81      0.87      0.84        55\n\n    accuracy                           0.74        70\n   macro avg       0.59      0.57      0.57        70\nweighted avg       0.72      0.74      0.73        70\n\nConfusion matrix, without normalization\n[[48  7]\n [11  4]]\n"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.metrics import classification_report, confusion_matrix\nimport itertools\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhat))\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF(2)','COLLECTION(4)'],normalize= False,  title='Confusion matrix')"
        },
        {
            "cell_type": "code",
            "execution_count": 96,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.7275882012724117"
                    },
                    "execution_count": 96,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.metrics import f1_score\nf1_score(y_test, yhat, average='weighted') \n"
        },
        {
            "cell_type": "code",
            "execution_count": 97,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.7272727272727273"
                    },
                    "execution_count": 97,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.metrics import jaccard_score\njaccard_score(y_test, yhat,pos_label = \"PAIDOFF\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Logistic Regression\n"
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "LogisticRegression(C=0.01, solver='liblinear')"
                    },
                    "execution_count": 66,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nLR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\nLR"
        },
        {
            "cell_type": "code",
            "execution_count": 67,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
                }
            ],
            "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)\n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 45,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n       'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n       'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n       'PAIDOFF', 'PAIDOFF'], dtype=object)"
                    },
                    "execution_count": 45,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "yhat = LR.predict(X_test)\nyhat"
        },
        {
            "cell_type": "code",
            "execution_count": 99,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[0.5 , 0.5 ],\n       [0.45, 0.55],\n       [0.31, 0.69],\n       [0.34, 0.66],\n       [0.32, 0.68],\n       [0.32, 0.68],\n       [0.49, 0.51],\n       [0.48, 0.52],\n       [0.34, 0.66],\n       [0.49, 0.51],\n       [0.34, 0.66],\n       [0.5 , 0.5 ],\n       [0.25, 0.75],\n       [0.34, 0.66],\n       [0.44, 0.56],\n       [0.26, 0.74],\n       [0.52, 0.48],\n       [0.3 , 0.7 ],\n       [0.5 , 0.5 ],\n       [0.32, 0.68],\n       [0.44, 0.56],\n       [0.49, 0.51],\n       [0.51, 0.49],\n       [0.47, 0.53],\n       [0.41, 0.59],\n       [0.51, 0.49],\n       [0.51, 0.49],\n       [0.37, 0.63],\n       [0.5 , 0.5 ],\n       [0.25, 0.75],\n       [0.47, 0.53],\n       [0.46, 0.54],\n       [0.46, 0.54],\n       [0.48, 0.52],\n       [0.39, 0.61],\n       [0.46, 0.54],\n       [0.5 , 0.5 ],\n       [0.29, 0.71],\n       [0.46, 0.54],\n       [0.45, 0.55],\n       [0.51, 0.49],\n       [0.32, 0.68],\n       [0.45, 0.55],\n       [0.51, 0.49],\n       [0.31, 0.69],\n       [0.5 , 0.5 ],\n       [0.47, 0.53],\n       [0.5 , 0.5 ],\n       [0.46, 0.54],\n       [0.46, 0.54],\n       [0.28, 0.72],\n       [0.47, 0.53],\n       [0.31, 0.69],\n       [0.49, 0.51],\n       [0.28, 0.72],\n       [0.25, 0.75],\n       [0.32, 0.68],\n       [0.43, 0.57],\n       [0.47, 0.53],\n       [0.34, 0.66],\n       [0.42, 0.58],\n       [0.33, 0.67],\n       [0.46, 0.54],\n       [0.53, 0.47],\n       [0.32, 0.68],\n       [0.49, 0.51],\n       [0.33, 0.67],\n       [0.42, 0.58],\n       [0.45, 0.55],\n       [0.32, 0.68]])"
                    },
                    "execution_count": 99,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "yhat_prob = LR.predict_proba(X_test)\nyhat_prob"
        },
        {
            "cell_type": "code",
            "execution_count": 100,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.7272727272727273"
                    },
                    "execution_count": 100,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.metrics import jaccard_score\njaccard_score(y_test, yhat,pos_label = \"PAIDOFF\")"
        },
        {
            "cell_type": "code",
            "execution_count": 101,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.7275882012724117"
                    },
                    "execution_count": 101,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "from sklearn.metrics import f1_score\nf1_score(y_test, yhat, average='weighted')\n"
        },
        {
            "cell_type": "code",
            "execution_count": 102,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "LR LogLoss: 0.58\n"
                }
            ],
            "source": "from sklearn.metrics import log_loss\nprint(\"LR LogLoss: %.2f\" % log_loss(y_test, yhat_prob))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Model Evaluation using Test set\n"
        },
        {
            "cell_type": "code",
            "execution_count": 105,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.metrics import jaccard_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, download and load the test set:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 106,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2021-10-01 21:52:11--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 3642 (3.6K) [text/csv]\nSaving to: \u2018loan_test.csv\u2019\n\nloan_test.csv       100%[===================>]   3.56K  --.-KB/s    in 0s      \n\n2021-10-01 21:52:11 (47.6 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n"
                }
            ],
            "source": "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Load Test set for evaluation\n"
        },
        {
            "cell_type": "code",
            "execution_count": 107,
            "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  \\\n0           1             1     PAIDOFF       1000     30       9/8/2016   \n1           5             5     PAIDOFF        300      7       9/9/2016   \n2          21            21     PAIDOFF       1000     30      9/10/2016   \n3          24            24     PAIDOFF       1000     30      9/10/2016   \n4          35            35     PAIDOFF        800     15      9/11/2016   \n\n    due_date  age             education  Gender  \n0  10/7/2016   50              Bechalor  female  \n1  9/15/2016   35       Master or Above    male  \n2  10/9/2016   43  High School or Below  female  \n3  10/9/2016   26               college    male  \n4  9/25/2016   29              Bechalor    male  "
                    },
                    "execution_count": 107,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 111,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[ 0.49,  0.93,  3.06,  1.98, -1.3 ,  2.4 , -0.8 , -0.86],\n       [-3.56, -1.7 ,  0.53, -0.51,  0.77, -0.42, -0.8 , -0.86],\n       [ 0.49,  0.93,  1.88,  1.98,  0.77, -0.42,  1.25, -0.86],\n       [ 0.49,  0.93, -0.98, -0.51,  0.77, -0.42, -0.8 ,  1.16],\n       [-0.67, -0.79, -0.48, -0.51,  0.77,  2.4 , -0.8 , -0.86]])"
                    },
                    "execution_count": 111,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "## Preprocessing\ntest_df['due_date'] = pd.to_datetime(test_df['due_date'])\ntest_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\ntest_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ntest_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ntest_Feature = test_df[['Principal','terms','age','Gender','weekend']]\ntest_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\ntest_Feature.drop(['Master or Above'], axis = 1,inplace=True)\ntest_X = preprocessing.StandardScaler().fit(test_Feature).transform(test_Feature)\ntest_X[0:5]"
        },
        {
            "cell_type": "code",
            "execution_count": 116,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "execution_count": 116,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_y = test_df['loan_status'].values\ntest_y[0:5]"
        },
        {
            "cell_type": "code",
            "execution_count": 117,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "KNN Jaccard index: 0.69\nKNN F1-score: 0.67\n"
                }
            ],
            "source": "knn_yhat = neigh.predict(test_X)\nprint(\"KNN Jaccard index: %.2f\" % jaccard_score(test_y, knn_yhat,pos_label = \"PAIDOFF\" ))\nprint(\"KNN F1-score: %.2f\" % f1_score(test_y, knn_yhat, average='weighted') )"
        },
        {
            "cell_type": "code",
            "execution_count": 118,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "DT Jaccard index: 0.73\nDT F1-score: 0.78\n"
                }
            ],
            "source": "DT_yhat = LoanTree.predict(test_X)\nprint(\"DT Jaccard index: %.2f\" % jaccard_score(test_y, DT_yhat, pos_label = \"PAIDOFF\"))\nprint(\"DT F1-score: %.2f\" % f1_score(test_y, DT_yhat, average='weighted') )"
        },
        {
            "cell_type": "code",
            "execution_count": 119,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "SVM Jaccard index: 0.78\nSVM F1-score: 0.76\n"
                }
            ],
            "source": "SVM_yhat = clf.predict(test_X)\nprint(\"SVM Jaccard index: %.2f\" % jaccard_score(test_y, SVM_yhat, pos_label = \"PAIDOFF\"))\nprint(\"SVM F1-score: %.2f\" % f1_score(test_y, SVM_yhat, average='weighted') )"
        },
        {
            "cell_type": "code",
            "execution_count": 123,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "LR Jaccard index: 0.74\nLR F1-score: 0.66\nLR LogLoss: 0.57\n"
                }
            ],
            "source": "LR_yhat = LR.predict(test_X)\nLR_yhat_prob = LR.predict_proba(test_X)\nprint(\"LR Jaccard index: %.2f\" % jaccard_score(test_y, LR_yhat, pos_label = \"PAIDOFF\"))\nprint(\"LR F1-score: %.2f\" % f1_score(test_y, LR_yhat, average='weighted') )\nprint(\"LR LogLoss: %.2f\" % log_loss(test_y, LR_yhat_prob))"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Report\n\nYou 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                | 0.69    |0.67      | NA      |\n| Decision Tree      | 0.73    |0.78      | NA      |\n| SVM                | 0.78    |0.76      | NA      |\n| LogisticRegression | 0.74    |0.66      | 0.57    |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems \u2013 by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">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\u2019 ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n\n<hr>\n\n## 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\"> \u00a9 IBM Corporation 2020. All rights reserved. <h3/>\n\n<p>\n"
        }
    ],
    "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": "3.8.11"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}