IBMProjectMachineLearning.ipynb

{
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            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {
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            },
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {
                "button": false,
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                "run_control": {
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            },
            "cell_type": "code",
            "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",
            "execution_count": 2,
            "outputs": []
        },
        {
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                "button": false,
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            },
            "cell_type": "markdown",
            "source": "### About dataset\n"
        },
        {
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            },
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
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            },
            "cell_type": "markdown",
            "source": "Let's download the dataset\n"
        },
        {
            "metadata": {
                "button": false,
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            },
            "cell_type": "code",
            "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",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "--2022-01-26 15:43:02--  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\n2022-01-26 15:43:03 (94.2 MB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
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            },
            "cell_type": "markdown",
            "source": "### Load Data From CSV File\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
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            },
            "cell_type": "code",
            "source": "df = pd.read_csv('loan_train.csv')\ndf.head()",
            "execution_count": 4,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 4,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.shape",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 5,
                    "data": {
                        "text/plain": "(346, 10)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
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            },
            "cell_type": "markdown",
            "source": "### Convert to date time object\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()",
            "execution_count": 6,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 6,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
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                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "# Data visualization and pre-processing\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "Let\u2019s see how many of each class is in our data set\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df['loan_status'].value_counts()",
            "execution_count": 7,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 7,
                    "data": {
                        "text/plain": "PAIDOFF       260\nCOLLECTION     86\nName: loan_status, dtype: int64"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "260 people have paid off the loan on time while 86 have gone into collection\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Let's plot some columns to underestand data better:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y",
            "execution_count": 8,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Collecting package metadata (current_repodata.json): done\nSolving environment: done\n\n# All requested packages already installed.\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "import seaborn as sns\n\nbins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()",
            "execution_count": 9,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x216 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "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()",
            "execution_count": 10,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x216 with 2 Axes>",
                        "image/png": "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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "# Pre-processing:  Feature selection/extraction\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "### Let's look at the day of the week people get the loan\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "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",
            "execution_count": 11,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x216 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ndf.head()",
            "execution_count": 12,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 12,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "## Convert Categorical features to numerical values\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "Let's look at gender:\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)",
            "execution_count": 13,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 13,
                    "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"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "86 % of female pay there loans while only 73 % of males pay there loan\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "Let's convert male to 0 and female to 1:\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()",
            "execution_count": 14,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 14,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "## One Hot Encoding\n\n#### How about education?\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)",
            "execution_count": 15,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 15,
                    "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"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "#### Features before One Hot Encoding\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "df[['Principal','terms','age','Gender','education']].head()",
            "execution_count": 16,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 16,
                    "data": {
                        "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",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "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",
            "execution_count": 17,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 17,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "### Feature Selection\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "Let's define feature sets, X:\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "X = Feature\nX[0:5]",
            "execution_count": 18,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 18,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "What are our lables?\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "y = df['loan_status'].values\ny[0:5]",
            "execution_count": 19,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 19,
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "## Normalize Data\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "Data Standardization give data zero mean and unit variance (technically should be done after train test split)\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]",
            "execution_count": 20,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 20,
                    "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]])"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "# Classification\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "\nfrom 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\n",
            "execution_count": 23,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn import metrics\n\n\n# Training\n\nKs = 12\nmean_accuracy = np.zeros((Ks-1))\nstd_accuracy = 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    yhatPred=neigh.predict(X_test)\n    mean_accuracy[n-1] = metrics.accuracy_score(y_test, yhatPred)\n\n    \n    std_accuracy[n-1]=np.std(yhatPred==y_test)/np.sqrt(yhatPred.shape[0])\n\nmean_accuracy",
            "execution_count": 24,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 24,
                    "data": {
                        "text/plain": "array([0.67142857, 0.65714286, 0.71428571, 0.68571429, 0.75714286,\n       0.71428571, 0.78571429, 0.75714286, 0.75714286, 0.67142857,\n       0.7       ])"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Predicting\nyhatPred = neigh.predict(X_test)\nyhatPred[0:5]",
            "execution_count": 25,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 25,
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# plot accuracy\nplt.plot(range(1,Ks),mean_accuracy,'g')\nplt.fill_between(range(1,Ks),mean_accuracy - 1 * std_accuracy,mean_accuracy + 1 * std_accuracy, alpha=0.10)\nplt.legend(('Accuracy ', '+/- 3xstd'))\nplt.ylabel('Accuracy ')\nplt.xlabel('Number of Neighbors (K)')\nplt.tight_layout()\nplt.show()\nprint( \"The best accuracy was with\", mean_accuracy.max(), \"with k=\", mean_accuracy.argmax()+1) ",
            "execution_count": 26,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                },
                {
                    "output_type": "stream",
                    "text": "The best accuracy was with 0.7857142857142857 with k= 7\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# Decision Tree\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "from sklearn.model_selection import train_test_split\n\nX_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)",
            "execution_count": 27,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "from sklearn.tree import DecisionTreeClassifier\n\n# Modeling\n\ndecisionT = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\ndecisionT # it shows the default parameters\n\n\ndecisionT.fit(X_trainset,y_trainset)",
            "execution_count": 28,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 28,
                    "data": {
                        "text/plain": "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Prediction\npredictTree = decisionT.predict(X_testset)\n\nprint (predictTree [0:5])\nprint (y_testset [0:5])",
            "execution_count": 29,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF']\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Evaluation\n\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predictTree))",
            "execution_count": 30,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "DecisionTrees's Accuracy:  0.6538461538461539\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "!conda install -c conda-forge python-graphviz -y",
            "execution_count": 31,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Collecting package metadata (current_repodata.json): done\nSolving environment: failed with initial frozen solve. Retrying with flexible solve.\nSolving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\nCollecting package metadata (repodata.json): done\nSolving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/Python-3.8-main\n\n  added / updated specs:\n    - python-graphviz\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    ca-certificates-2021.10.8  |       ha878542_0         139 KB  conda-forge\n    expat-2.2.9                |       he1b5a44_2         191 KB  conda-forge\n    gettext-0.19.8.1           |    h0b5b191_1005         3.6 MB  conda-forge\n    graphviz-2.40.1            |       h0dab3d1_0         6.8 MB  conda-forge\n    ibm-wsrt-py38main-keep-0.0.0|                0           2 KB\n    ibm-wsrt-py38main-main-custom|                0           2 KB\n    jpeg-9d                    |       h36c2ea0_0         264 KB  conda-forge\n    libtool-2.4.6              |    h58526e2_1007         497 KB  conda-forge\n    openssl-1.1.1m             |       h7f8727e_0         2.5 MB\n    pango-1.40.14              |       he752989_2         527 KB  conda-forge\n    python-graphviz-0.16       |     pyhd3deb0d_1          20 KB  conda-forge\n    xorg-kbproto-1.0.7         |    h7f98852_1002          27 KB  conda-forge\n    xorg-libice-1.0.10         |       h7f98852_0          58 KB  conda-forge\n    xorg-libsm-1.2.2           |       h470a237_5          24 KB  conda-forge\n    xorg-libx11-1.7.2          |       h7f98852_0         941 KB  conda-forge\n    xorg-libxext-1.3.4         |       h7f98852_1          54 KB  conda-forge\n    xorg-libxpm-3.5.13         |       h7f98852_0          63 KB  conda-forge\n    xorg-libxrender-0.9.10     |    h7f98852_1003          32 KB  conda-forge\n    xorg-libxt-1.2.1           |       h7f98852_2         375 KB  conda-forge\n    xorg-renderproto-0.11.1    |    h7f98852_1002           9 KB  conda-forge\n    xorg-xextproto-7.3.0       |    h7f98852_1002          28 KB  conda-forge\n    xorg-xproto-7.0.31         |    h7f98852_1007          73 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:        16.2 MB\n\nThe following NEW packages will be INSTALLED:\n\n  expat              conda-forge/linux-64::expat-2.2.9-he1b5a44_2\n  gettext            conda-forge/linux-64::gettext-0.19.8.1-h0b5b191_1005\n  graphviz           conda-forge/linux-64::graphviz-2.40.1-h0dab3d1_0\n  ibm-wsrt-py38main~ opt/ibm/custom-channels/meta-wscloud/noarch::ibm-wsrt-py38main-keep-0.0.0-0\n  libtool            conda-forge/linux-64::libtool-2.4.6-h58526e2_1007\n  pango              conda-forge/linux-64::pango-1.40.14-he752989_2\n  python-graphviz    conda-forge/noarch::python-graphviz-0.16-pyhd3deb0d_1\n  xorg-kbproto       conda-forge/linux-64::xorg-kbproto-1.0.7-h7f98852_1002\n  xorg-libice        conda-forge/linux-64::xorg-libice-1.0.10-h7f98852_0\n  xorg-libsm         conda-forge/linux-64::xorg-libsm-1.2.2-h470a237_5\n  xorg-libx11        conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n  xorg-libxext       conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n  xorg-libxpm        conda-forge/linux-64::xorg-libxpm-3.5.13-h7f98852_0\n  xorg-libxrender    conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n  xorg-libxt         conda-forge/linux-64::xorg-libxt-1.2.1-h7f98852_2\n  xorg-renderproto   conda-forge/linux-64::xorg-renderproto-0.11.1-h7f98852_1002\n  xorg-xextproto     conda-forge/linux-64::xorg-xextproto-7.3.0-h7f98852_1002\n  xorg-xproto        conda-forge/linux-64::xorg-xproto-7.0.31-h7f98852_1007\n\nThe following packages will be UPDATED:\n\n  jpeg                        pkgs/main::jpeg-9b-h024ee3a_2 --> conda-forge::jpeg-9d-h36c2ea0_0\n  openssl                                 1.1.1l-h7f8727e_0 --> 1.1.1m-h7f8727e_0\n\nThe following packages will be SUPERSEDED by a higher-priority channel:\n\n  ca-certificates    pkgs/main::ca-certificates-2021.10.26~ --> conda-forge::ca-certificates-2021.10.8-ha878542_0\n\nThe following packages will be DOWNGRADED:\n\n  ibm-wsrt-py38main~                                0.0.0-0 --> custom-0\n\n\n\nDownloading and Extracting Packages\nibm-wsrt-py38main-ma | 2 KB      | ##################################### | 100% \nxorg-libxpm-3.5.13   | 63 KB     | ##################################### | 100% \nxorg-libxt-1.2.1     | 375 KB    | ##################################### | 100% \nxorg-libxext-1.3.4   | 54 KB     | ##################################### | 100% \nibm-wsrt-py38main-ke | 2 KB      | ##################################### | 100% \nxorg-xextproto-7.3.0 | 28 KB     | ##################################### | 100% \nxorg-kbproto-1.0.7   | 27 KB     | ##################################### | 100% \npython-graphviz-0.16 | 20 KB     | ##################################### | 100% \nxorg-libx11-1.7.2    | 941 KB    | ##################################### | 100% \nopenssl-1.1.1m       | 2.5 MB    | ##################################### | 100% \nxorg-libxrender-0.9. | 32 KB     | ##################################### | 100% \nlibtool-2.4.6        | 497 KB    | ##################################### | 100% \nxorg-libsm-1.2.2     | 24 KB     | ##################################### | 100% \njpeg-9d              | 264 KB    | ##################################### | 100% \nexpat-2.2.9          | 191 KB    | ##################################### | 100% \nca-certificates-2021 | 139 KB    | ##################################### | 100% \nxorg-libice-1.0.10   | 58 KB     | ##################################### | 100% \ngettext-0.19.8.1     | 3.6 MB    | ##################################### | 100% \npango-1.40.14        | 527 KB    | ##################################### | 100% \nxorg-xproto-7.0.31   | 73 KB     | ##################################### | 100% \ngraphviz-2.40.1      | 6.8 MB    | ##################################### | 100% \nxorg-renderproto-0.1 | 9 KB      | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Visualization\n!conda install -c conda-forge pydotplus -y\n\n\nfrom  io import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline ",
            "execution_count": 34,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Collecting package metadata (current_repodata.json): done\nSolving environment: done\n\n# All requested packages already installed.\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "from  io import StringIO\n\n# from sklearn.externals.six import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline \n\n\ndot_data = StringIO()\nfilename = \"decisionT.png\"\nfeatureNames = df.columns[3: 11]\ntargetNames = df[\"loan_status\"].unique().tolist()\nout=tree.export_graphviz(decisionT,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True,  special_characters=True,rotate=False)  \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(100, 200))\nplt.imshow(img,interpolation='nearest')",
            "execution_count": 38,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 38,
                    "data": {
                        "text/plain": "<matplotlib.image.AxesImage at 0x7fa0628e2b50>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 7200x14400 with 1 Axes>",
                        "image/png": 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                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# Support Vector Machine\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Modeling (SVM with Scikit-learn)\nfrom sklearn import svm\nsvm_loan = svm.SVC(kernel='rbf')\nsvm_loan.fit(X_train, y_train) \n\n\nyhat = svm_loan.predict(X_test)\nyhat [0:5]",
            "execution_count": 64,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 64,
                    "data": {
                        "text/plain": "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Evaluation\n\nfrom sklearn.metrics import classification_report, confusion_matrix\nimport itertools\n\ndef plot_confusion_matrix_loan(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')",
            "execution_count": 80,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhatPred)\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhatPred))\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix_loan(cnf_matrix, classes=df[\"loan_status\"].unique().tolist(),normalize= False,  title='Confusion matrix')",
            "execution_count": 65,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "              precision    recall  f1-score   support\n\n  COLLECTION       0.29      0.27      0.28        15\n     PAIDOFF       0.80      0.82      0.81        55\n\n    accuracy                           0.70        70\n   macro avg       0.54      0.54      0.54        70\nweighted avg       0.69      0.70      0.70        70\n\nConfusion matrix, without normalization\n[[ 4 11]\n [10 45]]\n",
                    "name": "stdout"
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# Logistic Regression\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Modeling (Logistic Regression with Scikit-learn)\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nLogicReg = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\nLogicReg\n\n",
            "execution_count": 43,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 43,
                    "data": {
                        "text/plain": "LogisticRegression(C=0.01, solver='liblinear')"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# predict\n\nyhat_LR = LogicReg.predict(X_test)\nyhat_LR\n\nyhat_prob_LR = LogicReg.predict_proba(X_test)\nyhat_prob_LR[0:5]",
            "execution_count": 57,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 57,
                    "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]])"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# Model Evaluation using Test set\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "from sklearn.metrics import jaccard_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss\n",
            "execution_count": 46,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "First, download and load the test set:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv",
            "execution_count": 47,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "--2022-01-26 16:47:08--  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\n2022-01-26 16:47:09 (44.7 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "source": "### Load Test set for evaluation\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "code",
            "source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()",
            "execution_count": 51,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 51,
                    "data": {
                        "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  ",
                        "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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "import numpy as np\ntest_df['Gender'].replace(to_replace=['male','female'], value=[np.int64(0),np.int64(1)],inplace=True)\n\ntest_df.groupby(['education'])['loan_status'].value_counts(normalize=True)\nFeature = test_df[['Principal','terms','age','Gender']]\nFeature = pd.concat([Feature,pd.get_dummies(test_df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\nX2 = Feature\ny2 = test_df['loan_status'].values\nevaluaion= preprocessing.StandardScaler().fit(X2).transform(X2)\n\n\n\n\nprint(\"Evaluation Accuracy:\", evaluaion[0:5])\n",
            "execution_count": 56,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Evaluation Accuracy: [[ 0.49  0.93  3.06  1.98  2.4  -0.8  -0.86]\n [-3.56 -1.7   0.53 -0.51 -0.42 -0.8  -0.86]\n [ 0.49  0.93  1.88  1.98 -0.42  1.25 -0.86]\n [ 0.49  0.93 -0.98 -0.51 -0.42 -0.8   1.16]\n [-0.67 -0.79 -0.48 -0.51  2.4  -0.8  -0.86]]\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# Report\n\nYou should be able to report the accuracy of the built model using different evaluation metrics:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "predictTree = decisionT.predict(X_test)\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predictTree))",
            "execution_count": 58,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "DecisionTrees's Accuracy:  0.7285714285714285\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#for knn\nfrom sklearn.metrics import classification_report\nclassificationRep = classification_report(y_test, yhatPred)\nprint(classificationRep)",
            "execution_count": 66,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "              precision    recall  f1-score   support\n\n  COLLECTION       0.29      0.27      0.28        15\n     PAIDOFF       0.80      0.82      0.81        55\n\n    accuracy                           0.70        70\n   macro avg       0.54      0.54      0.54        70\nweighted avg       0.69      0.70      0.70        70\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# for SVM\nclassification_report(y_test,yhat )\nprint('SVM Accuracy:', classification_report(y_test, yhat))",
            "execution_count": 67,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "SVM Accuracy:               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\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#for logit\nclassification_report(y_test, yhat_LR)\nprint('Logit', classification_report(y_test, yhat_LR))",
            "execution_count": 68,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Logit               precision    recall  f1-score   support\n\n  COLLECTION       0.18      0.13      0.15        15\n     PAIDOFF       0.78      0.84      0.81        55\n\n    accuracy                           0.69        70\n   macro avg       0.48      0.48      0.48        70\nweighted avg       0.65      0.69      0.67        70\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "| Algorithm          | Jaccard | F1-score | LogLoss |\n| ------------------ | ------- | -------- | ------- |\n| KNN                | ?       | ?        | NA      |\n| Decision Tree      | ?       | ?        | NA      |\n| SVM                | ?       | ?        | NA      |\n| LogisticRegression | ?       | ?        | ?       |\n"
        },
        {
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "cell_type": "markdown",
            "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"
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