14_S6_Final_Assignement_ML_Loan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In this notebook we try to practice all the classification algorithms that we have learned in this course.\n",
    "\n",
    "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
    "\n",
    "Let's first load required libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### About dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "This dataset is about past loans. The **Loan_train.csv** data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
    "\n",
    "| Field          | Description                                                                           |\n",
    "| -------------- | ------------------------------------------------------------------------------------- |\n",
    "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
    "| Principal      | Basic principal loan amount at the                                                    |\n",
    "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "| Effective_date | When the loan got originated and took effects                                         |\n",
    "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
    "| Age            | Age of applicant                                                                      |\n",
    "| Education      | Education of applicant                                                                |\n",
    "| Gender         | The gender of applicant                                                               |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's download the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-01-27 13:36:51--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv\n",
      "Résolution de cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)… 169.63.118.104\n",
      "Connexion à 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… connecté.\n",
      "requête HTTP transmise, en attente de la réponse… 200 OK\n",
      "Taille : 23101 (23K) [text/csv]\n",
      "Sauvegarde en : « loan_train.csv »\n",
      "\n",
      "loan_train.csv      100%[===================>]  22,56K  --.-KB/s    ds 0,004s  \n",
      "\n",
      "2022-01-27 13:36:52 (6,24 MB/s) — « loan_train.csv » sauvegardé [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": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>9/22/2016</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30       9/8/2016   \n",
       "1           2             2     PAIDOFF       1000     30       9/8/2016   \n",
       "2           3             3     PAIDOFF       1000     15       9/8/2016   \n",
       "3           4             4     PAIDOFF       1000     30       9/9/2016   \n",
       "4           6             6     PAIDOFF       1000     30       9/9/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   45  High School or Below    male  \n",
       "1  10/7/2016   33              Bechalor  female  \n",
       "2  9/22/2016   27               college    male  \n",
       "3  10/8/2016   28               college  female  \n",
       "4  10/8/2016   29               college    male  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Convert to date time object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0 2016-10-07   45  High School or Below    male  \n",
       "1 2016-10-07   33              Bechalor  female  \n",
       "2 2016-09-22   27               college    male  \n",
       "3 2016-10-08   28               college  female  \n",
       "4 2016-10-08   29               college    male  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Data visualization and pre-processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let’s see how many of each class is in our data set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       260\n",
       "COLLECTION     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "260 people have paid off the loan on time while 86 have gone into collection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot some columns to underestand data better:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving 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": 8,
   "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",
    "\n",
    "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pre-processing:  Feature selection/extraction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Let's look at the day of the week people get the loan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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\n",
    "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We see that people who get the loan at the end of the week don't pay it off, so let's use Feature binarization to set a threshold value less than day 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below    male          3        0  \n",
       "1 2016-10-07   33              Bechalor  female          3        0  \n",
       "2 2016-09-22   27               college    male          3        0  \n",
       "3 2016-10-08   28               college  female          4        1  \n",
       "4 2016-10-08   29               college    male          4        1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Convert Categorical features to numerical values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's look at gender:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  loan_status\n",
       "female  PAIDOFF        0.865385\n",
       "        COLLECTION     0.134615\n",
       "male    PAIDOFF        0.731293\n",
       "        COLLECTION     0.268707\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "86 % of female pay there loans while only 73 % of males pay there loan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's convert male to 0 and female to 1:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below       0          3        0  \n",
       "1 2016-10-07   33              Bechalor       1          3        0  \n",
       "2 2016-09-22   27               college       0          3        0  \n",
       "3 2016-10-08   28               college       1          4        1  \n",
       "4 2016-10-08   29               college       0          4        1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## One Hot Encoding\n",
    "\n",
    "#### How about education?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education             loan_status\n",
       "Bechalor              PAIDOFF        0.750000\n",
       "                      COLLECTION     0.250000\n",
       "High School or Below  PAIDOFF        0.741722\n",
       "                      COLLECTION     0.258278\n",
       "Master or Above       COLLECTION     0.500000\n",
       "                      PAIDOFF        0.500000\n",
       "college               PAIDOFF        0.765101\n",
       "                      COLLECTION     0.234899\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Features before One Hot Encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>High School or Below</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Bechalor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender             education\n",
       "0       1000     30   45       0  High School or Below\n",
       "1       1000     30   33       1              Bechalor\n",
       "2       1000     15   27       0               college\n",
       "3       1000     30   28       1               college\n",
       "4       1000     30   29       0               college"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Principal','terms','age','Gender','education']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature Selection\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's define feature sets, X:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our lables?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
    "You should use the following algorithm:\n",
    "\n",
    "*   K Nearest Neighbor(KNN)\n",
    "*   Decision Tree\n",
    "*   Support Vector Machine\n",
    "*   Logistic Regression\n",
    "\n",
    "\\__ Notice:\\__\n",
    "\n",
    "*   You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
    "*   You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
    "*   You should include the code of the algorithm in the following cells.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276, 8) (276,)\n",
      "Test set: (70, 8) (70,)\n"
     ]
    }
   ],
   "source": [
    "# Train Test split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbor(KNN)\n",
    "\n",
    "Notice: You should find the best k to build the model with the best accuracy.\\\n",
    "**warning:** You should not use the **loan_test.csv** for finding the best k, however, you can split your train_loan.csv into train and test to find the best **k**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import librairy\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.7898550724637681\n",
      "Test set Accuracy:  0.7\n"
     ]
    }
   ],
   "source": [
    "# Find the best k\n",
    "Ks = 12\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc\n",
    "\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.7857142857142857 with k= 7\n"
     ]
    }
   ],
   "source": [
    "# Plot model accuracy for Different number of Neighbors\n",
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.fill_between(range(1,Ks),mean_acc - 3 * std_acc,mean_acc + 3 * std_acc, alpha=0.10,color=\"green\")\n",
    "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Extract the best accuracy from the plot\n",
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.8079710144927537\n",
      "Test set Accuracy:  0.7\n"
     ]
    }
   ],
   "source": [
    "# Verify by training model with k = 7 \n",
    "k = 7\n",
    "\n",
    "# Train model\n",
    "neigh_knn = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "\n",
    "# Predict Test set\n",
    "yhat_knn = neigh.predict(X_test)\n",
    "\n",
    "# Accuracy evaluation\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh_knn.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "help(clf.tree_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import librairy\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn import metrics\n",
    "\n",
    "from  io import StringIO\n",
    "import pydotplus\n",
    "\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1 : Find the best accuracy with cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ressource : https://towardsdatascience.com/how-to-find-decision-tree-depth-via-cross-validation-2bf143f0f3d6\n",
    "# \"How To Find Decision Tree Depth via Cross-Validation\"\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# function for fitting trees of various depths on the training data using cross-validation\n",
    "def run_cross_validation_on_trees(X, y, tree_depths, cv=9, scoring='accuracy'):\n",
    "    cv_scores_list = []\n",
    "    cv_scores_std = []\n",
    "    cv_scores_mean = []\n",
    "    accuracy_scores = []\n",
    "    for depth in tree_depths:\n",
    "        tree_model = DecisionTreeClassifier(max_depth=depth)\n",
    "        cv_scores = cross_val_score(tree_model, X, y, cv=cv, scoring=scoring)\n",
    "        cv_scores_list.append(cv_scores)\n",
    "        cv_scores_mean.append(cv_scores.mean())\n",
    "        cv_scores_std.append(cv_scores.std())\n",
    "        accuracy_scores.append(tree_model.fit(X, y).score(X, y))\n",
    "    cv_scores_mean = np.array(cv_scores_mean)\n",
    "    cv_scores_std = np.array(cv_scores_std)\n",
    "    accuracy_scores = np.array(accuracy_scores)\n",
    "    return cv_scores_mean, cv_scores_std, accuracy_scores\n",
    "  \n",
    "# function for plotting cross-validation results\n",
    "def plot_cross_validation_on_trees(depths, cv_scores_mean, cv_scores_std, accuracy_scores, title):\n",
    "    fig, ax = plt.subplots(1,1, figsize=(15,5))\n",
    "    ax.plot(depths, cv_scores_mean, '-o', label='mean cross-validation accuracy', alpha=0.9)\n",
    "    ax.fill_between(depths, cv_scores_mean-2*cv_scores_std, cv_scores_mean+2*cv_scores_std, alpha=0.2)\n",
    "    ylim = plt.ylim()\n",
    "    ax.plot(depths, accuracy_scores, '-*', label='train accuracy', alpha=0.9)\n",
    "    ax.set_title(title, fontsize=16)\n",
    "    ax.set_xlabel('Tree depth', fontsize=14)\n",
    "    ax.set_ylabel('Accuracy', fontsize=14)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_xticks(depths)\n",
    "    ax.legend()\n",
    "\n",
    "# fitting trees of depth 1 to 24\n",
    "sm_tree_depths = range(1,25)\n",
    "sm_cv_scores_mean, sm_cv_scores_std, sm_accuracy_scores = run_cross_validation_on_trees(X_train, y_train, sm_tree_depths)\n",
    "\n",
    "# plotting accuracy\n",
    "plot_cross_validation_on_trees(sm_tree_depths, sm_cv_scores_mean, sm_cv_scores_std, sm_accuracy_scores, \n",
    "                               'Accuracy per decision tree depth on training data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The depth-1 tree achieves the best mean cross-validation accuracy 74.27718 +/- 0.9149% on training dataset\n"
     ]
    }
   ],
   "source": [
    "idx_max = sm_cv_scores_mean.argmax()\n",
    "sm_best_tree_depth = sm_tree_depths[idx_max]\n",
    "sm_best_tree_cv_score = sm_cv_scores_mean[idx_max]\n",
    "sm_best_tree_cv_score_std = sm_cv_scores_std[idx_max]\n",
    "print('The depth-{} tree achieves the best mean cross-validation accuracy {} +/- {}% on training dataset'.format(\n",
    "      sm_best_tree_depth, round(sm_best_tree_cv_score*100,5), round(sm_best_tree_cv_score_std*100, 5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single tree depth for Accuracy:  1\n",
      "Accuracy, Training Set:  74.27536 %\n",
      "Accuracy, Test Set:  78.57143 %\n"
     ]
    }
   ],
   "source": [
    "# function for training and evaluating a tree\n",
    "def run_single_tree(X_train, y_train, X_test, y_test, depth):\n",
    "    model = DecisionTreeClassifier(max_depth=depth).fit(X_train, y_train)\n",
    "    accuracy_train = model.score(X_train, y_train)\n",
    "    accuracy_test = model.score(X_test, y_test)\n",
    "    print('Single tree depth for Accuracy: ', depth)\n",
    "    print('Accuracy, Training Set: ', round(accuracy_train*100,5), '%')\n",
    "    print('Accuracy, Test Set: ', round(accuracy_test*100,5), '%')\n",
    "    return accuracy_train, accuracy_test\n",
    "  \n",
    "\n",
    "# train and evaluate a 5-depth tree\n",
    "sm_best_tree_accuracy_train, sm_best_tree_accuracy_test = run_single_tree(X_train, y_train, \n",
    "                                                                          X_test, y_test, \n",
    "                                                                          sm_best_tree_depth)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Evaluation matrices</th>\n",
       "      <th>d = 1</th>\n",
       "      <th>d = 2</th>\n",
       "      <th>d = 3</th>\n",
       "      <th>d = 4</th>\n",
       "      <th>d = 5</th>\n",
       "      <th>d = 6</th>\n",
       "      <th>d = 7</th>\n",
       "      <th>d = 8</th>\n",
       "      <th>d = 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jaccard</th>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.716418</td>\n",
       "      <td>0.596774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1_score</th>\n",
       "      <td>0.691429</td>\n",
       "      <td>0.691429</td>\n",
       "      <td>0.644599</td>\n",
       "      <td>0.644599</td>\n",
       "      <td>0.658892</td>\n",
       "      <td>0.723810</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.707329</td>\n",
       "      <td>0.670925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Evaluation matrices     d = 1     d = 2     d = 3     d = 4     d = 5  \\\n",
       "Jaccard              0.785714  0.785714  0.571429  0.571429  0.580645   \n",
       "F1_score             0.691429  0.691429  0.644599  0.644599  0.658892   \n",
       "\n",
       "Evaluation matrices     d = 6     d = 7     d = 8     d = 9  \n",
       "Jaccard              0.764706  0.750000  0.716418  0.596774  \n",
       "F1_score             0.723810  0.714286  0.707329  0.670925  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the best depth for decision tree\n",
    "depth_range = range(1, 10)\n",
    "jaccard_score_opti = []\n",
    "f1_score_opti = []\n",
    "\n",
    "for d in depth_range:\n",
    "    modelTree = DecisionTreeClassifier(criterion=\"gini\", max_depth = d)\n",
    "    modelTree.fit(X_train, y_train)\n",
    "    predTree = modelTree.predict(X_test)\n",
    "    jaccard_score_opti.append(jaccard_score(y_test, predTree, average=None)[1])\n",
    "    f1_score_opti.append(f1_score(y_test,predTree, average='weighted'))\n",
    "    \n",
    "# Generate a df to show the best value of depth for decision tree\n",
    "result_tree_opti = pd.DataFrame([jaccard_score_opti, f1_score_opti], index = ['Jaccard', 'F1_score'], columns = \n",
    "                                                                            ['d = 1', 'd = 2', 'd = 3', 'd = 4',\n",
    "                                                                            'd = 5', 'd = 6', 'd = 7', 'd = 8',\n",
    "                                                                            'd = 9'] )\n",
    "result_tree_opti.columns.name = 'Evaluation matrices'\n",
    "result_tree_opti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.7714285714285715\n"
     ]
    }
   ],
   "source": [
    "# Train model and fit data in with max_depth = 6\n",
    "modelTree = DecisionTreeClassifier(criterion=\"gini\", max_depth = 6)\n",
    "modelTree.fit(X_train, y_train)\n",
    "\n",
    "# Predict the test set\n",
    "predTree = modelTree.predict(X_test)\n",
    "\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, predTree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max depth is: 6\n",
      "Node count are: 43\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the data\n",
    "dot_data = StringIO()\n",
    "filename = \"modeltree.png\"\n",
    "featureNames = Feature.columns\n",
    "targetNames = df[\"loan_status\"].unique().tolist()\n",
    "out=tree.export_graphviz(modelTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True,  special_characters=True,rotate=False)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png(filename)\n",
    "img = mpimg.imread(filename)\n",
    "plt.figure(figsize=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')\n",
    "\n",
    "print(\"Max depth is: {}\".format(modelTree.tree_.max_depth))\n",
    "print(\"Node count are: {}\".format(modelTree.tree_.node_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import librairy\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg F1-score: 0.6914\n",
      "Jaccard score : [0.   0.79]\n"
     ]
    }
   ],
   "source": [
    "# Modeling and Predict\n",
    "clf = svm.SVC(kernel='linear')\n",
    "clf.fit(X_train, y_train) \n",
    "yhat_SVM = clf.predict(X_test)\n",
    "\n",
    "# Evaluation\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat_SVM, average='weighted'))\n",
    "#print(\"Jaccard score for Paidoff : %.4f\" % jaccard_score(y_test, yhat_SVM,pos_label='PAIDOFF'))\n",
    "print(\"Jaccard score : {}\".format(jaccard_score(y_test, yhat_SVM, average=None)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  COLLECTION       0.00      0.00      0.00        15\n",
      "     PAIDOFF       0.79      1.00      0.88        55\n",
      "\n",
      "    accuracy                           0.79        70\n",
      "   macro avg       0.39      0.50      0.44        70\n",
      "weighted avg       0.62      0.79      0.69        70\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[ 0 15]\n",
      " [ 0 55]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jennyferwan/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/jennyferwan/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/jennyferwan/opt/anaconda3/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\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": [
    "# Visualise the data\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools\n",
    "\n",
    "# Confusion matrix and classification report\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "# Compute confusion matrix\n",
    "cnf_matrix = confusion_matrix(y_test, yhat_SVM)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat_SVM))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix, classes=['COLLECTION','PAIDOFF'],normalize= False,  title='Confusion matrix for SVM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaccard score for Paidoff : 0.6765\n",
      "Jaccard score : [0.08 0.68]\n"
     ]
    }
   ],
   "source": [
    "# Modeling and Predict\n",
    "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
    "yhat_LR = LR.predict(X_test)\n",
    "yhat_prob = LR.predict_proba(X_test)\n",
    "\n",
    "# Evaluation\n",
    "print(\"Jaccard score for Paidoff : %.4f\" % jaccard_score(y_test, yhat_LR,pos_label='PAIDOFF'))\n",
    "print(\"Jaccard score : {}\".format(jaccard_score(y_test, yhat_LR, average=None)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              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\n",
      "weighted avg       0.65      0.69      0.67        70\n",
      "\n",
      "Confusion matrix, without normalization\n",
      "[[ 2 13]\n",
      " [ 9 46]]\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": [
    "# Confusion matrix and classification report\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    \n",
    "# Compute confusion matrix\n",
    "cnf_matrix1 = confusion_matrix(y_test, yhat_LR)\n",
    "np.set_printoptions(precision=2)\n",
    "\n",
    "print (classification_report(y_test, yhat_LR))\n",
    "\n",
    "# Plot non-normalized confusion matrix\n",
    "plt.figure()\n",
    "plot_confusion_matrix(cnf_matrix1, classes=['COLLECTION','PAIDOFF'],normalize= False,  title='Confusion matrix for LR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library\n",
    "import itertools\n",
    "from  io import StringIO\n",
    "import pydotplus\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download and load the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-01-27 14:15:27--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
      "Résolution de s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)… 67.228.254.196\n",
      "Connexion à s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443… connecté.\n",
      "requête HTTP transmise, en attente de la réponse… 200 OK\n",
      "Taille : 3642 (3,6K) [text/csv]\n",
      "Sauvegarde en : « loan_test.csv »\n",
      "\n",
      "loan_test.csv       100%[===================>]   3,56K  --.-KB/s    ds 0s      \n",
      "\n",
      "2022-01-27 14:15:28 (868 MB/s) — « loan_test.csv » sauvegardé [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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>50</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>9/15/2016</td>\n",
       "      <td>35</td>\n",
       "      <td>Master or Above</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>9/11/2016</td>\n",
       "      <td>9/25/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           1             1     PAIDOFF       1000     30       9/8/2016   \n",
       "1           5             5     PAIDOFF        300      7       9/9/2016   \n",
       "2          21            21     PAIDOFF       1000     30      9/10/2016   \n",
       "3          24            24     PAIDOFF       1000     30      9/10/2016   \n",
       "4          35            35     PAIDOFF        800     15      9/11/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   50              Bechalor  female  \n",
       "1  9/15/2016   35       Master or Above    male  \n",
       "2  10/9/2016   43  High School or Below  female  \n",
       "3  10/9/2016   26               college    male  \n",
       "4  9/25/2016   29              Bechalor    male  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Datetime and insert weekend on the dataframe\n",
    "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
    "test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
    "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
    "test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "\n",
    "# Convert gender into numerical data\n",
    "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "\n",
    "# Convert categorical variables to numerical \n",
    "test_Feature = test_df[['Principal','terms','age','Gender','weekend']]\n",
    "test_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\n",
    "test_Feature.drop(['Master or Above'], axis = 1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   50       1        0         1                     0   \n",
       "1        300      7   35       0        1         0                     0   \n",
       "2       1000     30   43       1        1         0                     1   \n",
       "3       1000     30   26       0        1         0                     0   \n",
       "4        800     15   29       0        1         1                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        1  \n",
       "4        0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine target\n",
    "y2 = test_df['loan_status'].values\n",
    "\n",
    "# Determine Features\n",
    "X2 = test_Feature\n",
    "X2[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of y2 is (54,)\n",
      "Shape of X2 is (54, 8)\n"
     ]
    }
   ],
   "source": [
    "# Verify if shape are the same \n",
    "print(\"Shape of y2 is {}\".format(y2.shape))\n",
    "print(\"Shape of X2 is {}\".format(X2.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.49362588,  0.92844966,  3.05981865,  1.97714211, -1.30384048,\n",
       "         2.39791576, -0.79772404, -0.86135677],\n",
       "       [-3.56269116, -1.70427745,  0.53336288, -0.50578054,  0.76696499,\n",
       "        -0.41702883, -0.79772404, -0.86135677],\n",
       "       [ 0.49362588,  0.92844966,  1.88080596,  1.97714211,  0.76696499,\n",
       "        -0.41702883,  1.25356634, -0.86135677],\n",
       "       [ 0.49362588,  0.92844966, -0.98251057, -0.50578054,  0.76696499,\n",
       "        -0.41702883, -0.79772404,  1.16095912],\n",
       "       [-0.66532184, -0.78854628, -0.47721942, -0.50578054,  0.76696499,\n",
       "         2.39791576, -0.79772404, -0.86135677]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize data\n",
    "X2 = preprocessing.StandardScaler().fit(X2).transform(X2)\n",
    "X2[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (43, 8) (43,)\n",
      "Test set: (11, 8) (11,)\n"
     ]
    }
   ],
   "source": [
    "# Train test split\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X2_train.shape,  y2_train.shape)\n",
    "print ('Test set:', X2_test.shape,  y2_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### K Nearest Neighbor (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modeling and Predict\n",
    "k = 7\n",
    "neigh2 = KNeighborsClassifier(n_neighbors = k).fit(X2_train,y2_train)\n",
    "yhat_knn2 = neigh2.predict(X2_test)\n",
    "\n",
    "# Evaluation for KNN\n",
    "jc_knn = jaccard_score(y2_test, yhat_knn2, average=None)\n",
    "f1_knn = f1_score(y2_test, yhat_knn2, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modeling and Predict\n",
    "modelTree2 = DecisionTreeClassifier(criterion=\"gini\", max_depth = 6)\n",
    "modelTree2.fit(X2_train, y2_train)\n",
    "predTree2 = modelTree2.predict(X2_test)\n",
    "\n",
    "# Evaluation for Decision Tree\n",
    "jc_tree = jaccard_score(y2_test, predTree2, average=None)\n",
    "f1_tree = f1_score(y2_test, predTree2, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modeling and Predict\n",
    "clf2 = svm.SVC(kernel='rbf')\n",
    "clf2.fit(X2_train, y2_train) \n",
    "yhat_SVM2 = clf2.predict(X2_test)\n",
    "\n",
    "# Evaluation for SVM\n",
    "jc_SVM = jaccard_score(y2_test, yhat_SVM2, average=None)\n",
    "f1_SVM = f1_score(y2_test, yhat_SVM2, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression (LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modeling and Predict\n",
    "LR2 = LogisticRegression(C=0.1, solver='liblinear').fit(X2_train,y2_train)\n",
    "yhat_LR2 = LR2.predict(X2_test)\n",
    "yhat_LR_prob2 = LR2.predict_proba(X2_test)\n",
    "\n",
    "# Evaluation\n",
    "jc_LR = jaccard_score(y2_test, yhat_LR2, average=None)\n",
    "f1_LR = f1_score(y2_test, yhat_LR2, average='weighted')\n",
    "ll_LR = log_loss(y2_test, yhat_LR_prob2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Report Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "jc_total = [jc_knn[1], jc_tree[1], jc_SVM[1], jc_LR[1]]\n",
    "f1_total = [f1_knn, f1_tree, f1_SVM, f1_LR]\n",
    "ll_total = ['NA', 'NA', 'NA', ll_LR]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Algorithm</th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.641711</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.865801</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.865801</td>\n",
       "      <td>0.492448</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Algorithm             Jaccard  F1-score   LogLoss\n",
       "KNN                  0.818182  0.818182        NA\n",
       "Decision Tree        0.545455  0.641711        NA\n",
       "SVM                  0.909091  0.865801        NA\n",
       "Logistic Regression  0.909091  0.865801  0.492448"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = pd.DataFrame(jc_total, index=['KNN','Decision Tree','SVM','Logistic Regression'])\n",
    "report.columns = ['Jaccard']\n",
    "report.insert(loc=1, column='F1-score', value=f1_total)\n",
    "report.insert(loc=2, column='LogLoss', value=ll_total)\n",
    "report.columns.name = 'Algorithm'\n",
    "report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report\n",
    "\n",
    "You should be able to report the accuracy of the built model using different evaluation metrics:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm          | Jaccard | F1-score | LogLoss |\n",
    "| ------------------ | ------- | -------- | ------- |\n",
    "| KNN                | ?       | ?        | NA      |\n",
    "| Decision Tree      | ?       | ?        | NA      |\n",
    "| SVM                | ?       | ?        | NA      |\n",
    "| LogisticRegression | ?       | ?        | ?       |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">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’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                                                             |\n",
    "| ----------------- | ------- | ------------- | ------------------------------------------------------------------------------ |\n",
    "| 2020-10-27        | 2.1     | Lakshmi Holla | Made changes in import statement due to updates in version of  sklearn library |\n",
    "| 2020-08-27        | 2.0     | Malika Singla | Added lab to GitLab                                                            |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
    "\n",
    "<p>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}