Machine_Learning_Loan_Data_KNN_LogisticRegression_DecisionTrees_SVM_Hassan_Bhatti.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning With Python IBM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this project, I applied four machine learning algorithms to a loan data set and the accuracy is checked by using the test data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step # 01: Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "#matplotlib inline\n",
    "import itertools\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import scipy.optimize as opt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step # 02: Loading the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is about past loans acquired on the basis of different features. The Loan_train.csv data set includes details of 346 customers whose loan are paid or in collection. The data set contains different features which are briefly mentioned below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Field | Description   |\n",
    "|------|------|\n",
    "|   Loan_status  | Whether a loan is paid off or is in collection|\n",
    "|   Principal  | Basic principal loan amount at the time of loan approval|\n",
    "|   Terms  | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "|   Effective_date\t  | When the loan got originated and took effects |\n",
    "|   Due_date  | As it’s a 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 the Applicant |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2020-11-09 19:34:05--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23101 (23K) [text/csv]\n",
      "Saving to: 'loan_train.csv'\n",
      "\n",
      "     0K .......... .......... ..                              100%  119K=0.2s\n",
      "\n",
      "2020-11-09 19:34:07 (119 KB/s) - 'loan_train.csv' saved [23101/23101]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "     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",
       "341         394           394  COLLECTION        800     15      9/11/2016   \n",
       "342         395           395  COLLECTION       1000     30      9/11/2016   \n",
       "343         397           397  COLLECTION        800     15      9/12/2016   \n",
       "344         398           398  COLLECTION       1000     30      9/12/2016   \n",
       "345         399           399  COLLECTION       1000     30      9/12/2016   \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  \n",
       "..          ...  ...                   ...     ...  \n",
       "341   9/25/2016   32  High School or Below    male  \n",
       "342  10/10/2016   25  High School or Below    male  \n",
       "343   9/26/2016   39               college    male  \n",
       "344  11/10/2016   28               college    male  \n",
       "345  10/11/2016   26               college    male  \n",
       "\n",
       "[346 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Unnamed: 0.1', 'loan_status', 'Principal', 'terms',\n",
       "       'effective_date', 'due_date', 'age', 'education', 'Gender'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
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       "      <th>count</th>\n",
       "      <td>346.000000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>346.000000</td>\n",
       "      <td>346.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>202.167630</td>\n",
       "      <td>202.167630</td>\n",
       "      <td>943.641618</td>\n",
       "      <td>22.653179</td>\n",
       "      <td>30.939306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.459715</td>\n",
       "      <td>115.459715</td>\n",
       "      <td>109.425530</td>\n",
       "      <td>7.991006</td>\n",
       "      <td>6.039418</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>107.250000</td>\n",
       "      <td>107.250000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>204.500000</td>\n",
       "      <td>204.500000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>298.750000</td>\n",
       "      <td>298.750000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>399.000000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0  Unnamed: 0.1    Principal       terms         age\n",
       "count  346.000000    346.000000   346.000000  346.000000  346.000000\n",
       "mean   202.167630    202.167630   943.641618   22.653179   30.939306\n",
       "std    115.459715    115.459715   109.425530    7.991006    6.039418\n",
       "min      0.000000      0.000000   300.000000    7.000000   18.000000\n",
       "25%    107.250000    107.250000   900.000000   15.000000   27.000000\n",
       "50%    204.500000    204.500000  1000.000000   30.000000   30.000000\n",
       "75%    298.750000    298.750000  1000.000000   30.000000   35.000000\n",
       "max    399.000000    399.000000  1000.000000   30.000000   51.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 346 entries, 0 to 345\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Unnamed: 0      346 non-null    int64 \n",
      " 1   Unnamed: 0.1    346 non-null    int64 \n",
      " 2   loan_status     346 non-null    object\n",
      " 3   Principal       346 non-null    int64 \n",
      " 4   terms           346 non-null    int64 \n",
      " 5   effective_date  346 non-null    object\n",
      " 6   due_date        346 non-null    object\n",
      " 7   age             346 non-null    int64 \n",
      " 8   education       346 non-null    object\n",
      " 9   Gender          346 non-null    object\n",
      "dtypes: int64(5), object(5)\n",
      "memory usage: 27.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting the data to date time object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <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": 8,
     "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(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step #03: Data Visualization and pre processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       260\n",
       "COLLECTION     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_paid = df['loan_status'].value_counts()\n",
    "loan_paid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of the 346 total applicant, 260 have paid their loan, while 86 have gone into collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "loan_paid_total = df[df['loan_status']=='PAIDOFF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total applicants who paid their loan =  75.14450867052022 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Total applicants who paid their loan = \", (len(loan_paid_total)/len(df))*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection_total = df[df['loan_status']=='COLLECTION']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total applicants with loan in collection =  24.85549132947977 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Total applicants with loan in collection = \", (len(collection_total)/len(df))*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the correlation between the data set features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b5dbbbfd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see the similar features in our data set by using pairplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:369: UserWarning: Default bandwidth for data is 0; skipping density estimation.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x15b5e3c4700>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 998.875x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue = 'loan_status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "bins_width = 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_width, ec = \"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "bins_width = 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_width, ec = \"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
    "bins_width = 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_width, ec = \"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that people, who get the loan at the end of the week don't pay it off. We can set threshold values to less than day 4 by using Feature Binarization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b5e7de5e0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (40,15))\n",
    "sns.barplot(x = 'loan_status', y = 'terms', data = df, palette='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "86 % of female pay their loans while only 73 % of males pay their loan. As the Gender is a categorical feature, we have to convert it into numerical as the machine learning model requires binary features which will correspond to the categorical features.\n",
    "\n",
    "Lets convert male to 0 and female to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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": 22,
     "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": {},
   "source": [
    "Let's see how the education has an effect on the load status."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the Master or Above feature has 50% value for loan_status paid off or collection, we will be dropping this feature as it will overburden our model calculation and can affect the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>Master or Above</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Bechalor  High School or Below  Master or Above  college\n",
       "0         0                     1                0        0\n",
       "1         1                     0                0        0\n",
       "2         0                     0                0        1\n",
       "3         0                     0                0        1\n",
       "4         0                     0                0        1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "education_dummies = pd.get_dummies(df['education'])\n",
    "education_dummies[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One hot encoding technique is used to convert categorical features to binary and concatenating them to the original Data Frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, education_dummies], axis = 1)\n",
    "df.drop(['Master or Above'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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",
       "      <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>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",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</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  Bechalor  \\\n",
       "0 2016-10-07   45  High School or Below       0          3        0         0   \n",
       "1 2016-10-07   33              Bechalor       1          3        0         1   \n",
       "2 2016-09-22   27               college       0          3        0         0   \n",
       "3 2016-10-08   28               college       1          4        1         0   \n",
       "4 2016-10-08   29               college       0          4        1         0   \n",
       "\n",
       "   High School or Below  college  \n",
       "0                     1        0  \n",
       "1                     0        0  \n",
       "2                     0        1  \n",
       "3                     0        1  \n",
       "4                     0        1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step #04: Selecting the features for the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Principal', 'terms', 'age', 'Gender', 'weekend', 'Bechalor', 'High School or Below', 'college']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using normalization to get the data values between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "my_scaler = StandardScaler()\n",
    "X = my_scaler.fit_transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the training and testing data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set :  (276, 8) (276,)\n",
      "Testing set :  (70, 8) (70,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set : \", X_train.shape, y_train.shape)\n",
    "print(\"Testing set : \", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification Models:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using the training data set, accurate models are built for the following machine learning algorithms. The test data set is used to report the accuracy of the model. Following algorithms are used:\n",
    "\n",
    "- K Nearest Neighbor(KNN)\n",
    "- Decision Tree\n",
    "- Support Vector Machines\n",
    "- Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "k = 5\n",
    "my_KNN_classifier = KNeighborsClassifier(n_neighbors=k)\n",
    "my_KNN_classifier.fit(X_train, y_train)\n",
    "my_KNN_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_KNN = my_KNN_classifier.predict(X_test)\n",
    "y_predict_KNN[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.8152173913043478\n",
      "Test set Accuracy:  0.7571428571428571\n"
     ]
    }
   ],
   "source": [
    "print(\"Train set Accuracy: \", accuracy_score(y_train, my_KNN_classifier.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", accuracy_score(y_test, y_predict_KNN))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K in KNN, is the number of nearest neighbors to examine. It is supposed to be specified by the User. So, how can we choose right value for K? The general solution is to reserve a part of your data for testing the accuracy of the model. Then chose k =1, use the training part for modeling, and calculate the accuracy of prediction using all samples in your test set. Repeat this process, increasing the k, and see which k is the best for your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67142857, 0.65714286, 0.71428571, 0.68571429, 0.75714286,\n",
       "       0.71428571, 0.78571429, 0.75714286, 0.75714286])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "\n",
    "for n in range(1,Ks):\n",
    "    my_KNN_classifier = KNeighborsClassifier(n_neighbors=n)\n",
    "    my_KNN_classifier.fit(X_train, y_train)\n",
    "    y_predict_KNN = my_KNN_classifier.predict(X_test)\n",
    "    mean_acc[n-1] = accuracy_score(y_test, y_predict_KNN)\n",
    "    \n",
    "    std_acc[n-1] = np.std(y_predict_KNN == y_test)/np.sqrt(y_predict_KNN.shape[0])\n",
    "    \n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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=0.2)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.7857142857142857 with k= 7\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b5ef5f550>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm = confusion_matrix(y_test, y_predict_KNN)\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jaccard_KNN_score = jaccard_score(y_test, y_predict_KNN, average = None)\n",
    "jaccard_KNN_score = jaccard_KNN_score[1]\n",
    "jaccard_KNN_score = float(\"{:.2f}\".format(jaccard_KNN_score))\n",
    "jaccard_KNN_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "my_decision_tree = DecisionTreeClassifier(criterion='entropy', max_depth=4)\n",
    "X_train_DTC, X_test_DTC, y_train_DTC, y_test_DTC = train_test_split(X,y,test_size = 0.3, random_state = 3)\n",
    "my_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_decision_tree.fit(X_train_DTC, y_train_DTC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict_DTC = my_decision_tree.predict(X_test_DTC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Truth :  ['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF']\n",
      "Model Prediction :  ['PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF']\n"
     ]
    }
   ],
   "source": [
    "print(\"Actual Truth : \",y_test_DTC[0:5])\n",
    "print(\"Model Prediction : \",y_predict_DTC[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Decision Tree 0.6538461538461539\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of the Decision Tree\", accuracy_score(y_test_DTC, y_predict_DTC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from six import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x15b5fbf7610>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "filename = \"loantree.png\"\n",
    "featureNames = df.columns[0:8]\n",
    "targetNames = df[\"loan_status\"].unique().tolist()\n",
    "out=tree.export_graphviz(my_decision_tree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train_DTC), filled=True,  special_characters=True,rotate=False)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png(filename)\n",
    "img = mpimg.imread(filename)\n",
    "plt.figure(figsize=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b5fbdc340>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm = confusion_matrix(y_test_DTC, y_predict_DTC)\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "DTC_jaccard_score = jaccard_score(y_test_DTC, y_predict_DTC, average=None)\n",
    "DTC_jaccard_score = DTC_jaccard_score[1]\n",
    "DTC_jaccard_score = float(\"{:.2f}\".format(DTC_jaccard_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTC_jaccard_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276, 8) (276,)\n",
      "Test set: (70, 8) (70,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SVM algorithm offers a choice of kernel functions for performing its processing. Basically, mapping data into a higher dimensional space is called kernelling. The mathematical function used for the transformation is known as the kernel function, and can be of different types, such as:\n",
    "\n",
    "1. Linear\n",
    "2. Polynomial\n",
    "3. Radial basis function (RBF)\n",
    "4. Sigmoid\n",
    "Each of these functions has its characteristics, its pros and cons, and its equation, but as there's no easy way of knowing which function performs best with any given dataset, we usually choose different functions in turn and compare the results. Let's just use the default, RBF (Radial Basis Function) for this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "my_SVM = SVC(kernel = 'rbf')\n",
    "my_SVM.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_svm = my_SVM.predict(X_test)\n",
    "y_predict_svm[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set accuracy :  0.782608695652174\n",
      "Testing set accuracy :  0.7428571428571429\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set accuracy : \", accuracy_score(y_train, my_SVM.predict(X_train)))\n",
    "print(\"Testing set accuracy : \", accuracy_score(y_test, my_SVM.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  COLLECTION       0.36      0.27      0.31        15\n",
      "     PAIDOFF       0.81      0.87      0.84        55\n",
      "\n",
      "    accuracy                           0.74        70\n",
      "   macro avg       0.59      0.57      0.57        70\n",
      "weighted avg       0.72      0.74      0.73        70\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_predict_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7272727272727273"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "jaccard_score_SVM = jaccard_score(y_test, y_predict_svm, average = None)\n",
    "jaccard_score_SVM = jaccard_score_SVM[1]\n",
    "jaccard_score_SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15b5fc29520>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cm = confusion_matrix(y_test, y_predict_svm)\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7275882012724117"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_predict_svm, average='weighted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: It is possible to change the kernel and rebuild the model to possibly increase the efficiency and accuracy of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276, 8) (276,)\n",
      "Test set: (70, 8) (70,)\n"
     ]
    }
   ],
   "source": [
    "X_train_LR, X_test_LR, y_train_LR, y_test_LR = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train_LR.shape,  y_train_LR.shape)\n",
    "print ('Test set:', X_test_LR.shape,  y_test_LR.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets build our model using LogisticRegression from Scikit-learn package. This function implements logistic regression and can use different numerical optimizers to find parameters, including ‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’ solvers. You can find extensive information about the pros and cons of these optimizers if you search it in internet.\n",
    "\n",
    "The version of Logistic Regression in Scikit-learn, support regularization. Regularization is a technique used to solve the overfitting problem in machine learning models. C parameter indicates inverse of regularization strength which must be a positive float. Smaller values specify stronger regularization. Now lets fit our model with train set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=0.01, solver='liblinear')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train_LR, y_train_LR)\n",
    "print(LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__predict_proba__ returns estimates for all classes, ordered by the label of classes. So, the first column is the probability of class 1, __P(Y=1|X)__, and second column is probability of class 0, __P(Y=0|X)__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_LR = LR.predict(X_test_LR)\n",
    "y_predict_LR[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.5034238  0.4965762 ]\n",
      " [0.45206111 0.54793889]\n",
      " [0.30814132 0.69185868]\n",
      " [0.34259428 0.65740572]\n",
      " [0.32025894 0.67974106]\n",
      " [0.31680537 0.68319463]\n",
      " [0.48830185 0.51169815]\n",
      " [0.47823073 0.52176927]\n",
      " [0.34259428 0.65740572]\n",
      " [0.4934056  0.5065944 ]\n",
      " [0.33806706 0.66193294]\n",
      " [0.49662231 0.50337769]\n",
      " [0.24891907 0.75108093]\n",
      " [0.3419095  0.6580905 ]\n",
      " [0.43751789 0.56248211]\n",
      " [0.25760497 0.74239503]\n",
      " [0.52357188 0.47642812]\n",
      " [0.30450278 0.69549722]\n",
      " [0.50166363 0.49833637]\n",
      " [0.3195971  0.6804029 ]\n",
      " [0.44276988 0.55723012]\n",
      " [0.49410185 0.50589815]\n",
      " [0.51350333 0.48649667]\n",
      " [0.47203498 0.52796502]\n",
      " [0.40944694 0.59055306]\n",
      " [0.50846442 0.49153558]\n",
      " [0.51098415 0.48901585]\n",
      " [0.37457647 0.62542353]\n",
      " [0.50418423 0.49581577]\n",
      " [0.25299635 0.74700365]\n",
      " [0.46824113 0.53175887]\n",
      " [0.46024688 0.53975312]\n",
      " [0.46206917 0.53793083]\n",
      " [0.48402425 0.51597575]\n",
      " [0.38818191 0.61181809]\n",
      " [0.45821326 0.54178674]\n",
      " [0.50166363 0.49833637]\n",
      " [0.28973585 0.71026415]\n",
      " [0.4569882  0.5430118 ]\n",
      " [0.45494718 0.54505282]\n",
      " [0.50670462 0.49329538]\n",
      " [0.32179362 0.67820638]\n",
      " [0.45245776 0.54754224]\n",
      " [0.50846442 0.49153558]\n",
      " [0.30664231 0.69335769]\n",
      " [0.49515584 0.50484416]\n",
      " [0.47075244 0.52924756]\n",
      " [0.49662231 0.50337769]\n",
      " [0.45571125 0.54428875]\n",
      " [0.45567623 0.54432377]\n",
      " [0.27794059 0.72205941]\n",
      " [0.46744865 0.53255135]\n",
      " [0.30501081 0.69498919]\n",
      " [0.48906194 0.51093806]\n",
      " [0.28058426 0.71941574]\n",
      " [0.24921106 0.75078894]\n",
      " [0.31522806 0.68477194]\n",
      " [0.43036995 0.56963005]\n",
      " [0.46824113 0.53175887]\n",
      " [0.33513632 0.66486368]\n",
      " [0.41925226 0.58074774]\n",
      " [0.33133167 0.66866833]\n",
      " [0.45821326 0.54178674]\n",
      " [0.52608635 0.47391365]\n",
      " [0.32399805 0.67600195]\n",
      " [0.49410185 0.50589815]\n",
      " [0.33133167 0.66866833]\n",
      " [0.41737926 0.58262074]\n",
      " [0.44996108 0.55003892]\n",
      " [0.32399805 0.67600195]]\n"
     ]
    }
   ],
   "source": [
    "y_predict_LR_prob = LR.predict_proba(X_test_LR)\n",
    "print(y_predict_LR_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try __Jaccard index__ for accuracy evaluation. we can define jaccard as the size of the intersection divided by the size of the union of two label sets. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is __1.0__; otherwise it is __0.0__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jaccard_score_LR = jaccard_score(y_test_LR, y_predict_LR, average = None)\n",
    "jaccard_score_LR = jaccard_score_LR[1]\n",
    "jaccard_score_LR = float(\"{:.2f}\".format(jaccard_score_LR))\n",
    "jaccard_score_LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test_LR,y_predict_LR))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the count of each section, we can calculate precision and recall of each label:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - __Precision__ is a measure of the accuracy provided that a class label has been predicted. It is defined by: precision = TP / (TP + FP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - __Recall__ is true positive rate. It is defined as: Recall =  TP / (TP + FN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__F1 score__: Now we are in the position to calculate the F1 scores for each label based on the precision and recall of that label.\n",
    "\n",
    "The F1 score is the harmonic average of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. It is a good way to show that a classifer has a good value for both recall and precision.\n",
    "\n",
    "And finally, we can tell the average accuracy for this classifier is the average of the F1-score for both labels, which is 0.67 in our case."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating the model using the Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "--2020-11-09 19:34:20--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3642 (3.6K) [text/csv]\n",
      "Saving to: 'loan_test.csv'\n",
      "\n",
      "     0K ...                                                   100% 9.53M=0s\n",
      "\n",
      "2020-11-09 19:34:21 (9.53 MB/s) - 'loan_test.csv' saved [3642/3642]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "test_Feature = test_df[['Principal','terms','age','Gender','weekend']]\n",
    "test_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\n",
    "test_Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "test_X = StandardScaler().fit(test_Feature).transform(test_Feature)\n",
    "test_X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y = test_df['loan_status'].values\n",
    "test_y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN - jaccard accuracy =  0.6862745098039216\n",
      "KNN - f1 score accuracy =  0.67\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbor (KNN)\n",
    "test_yhat= my_KNN_classifier.predict(test_X)\n",
    "\n",
    "# Calculating the Jaccard Score for KNN\n",
    "KNN_Jaccard = jaccard_score(test_y, test_yhat, average=None)\n",
    "KNN_Jaccard = KNN_Jaccard[1]\n",
    "KNN_jaccard = float(\"{:.2f}\".format(KNN_Jaccard))\n",
    "print(\"KNN - jaccard accuracy = \" , KNN_Jaccard)\n",
    "\n",
    "# f1_score\n",
    "KNN_f1_score = f1_score(test_y, test_yhat, average='weighted') \n",
    "KNN_f1_score = float(\"{:.2f}\".format(KNN_f1_score))\n",
    "print(\"KNN - f1 score accuracy = \" , KNN_f1_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree - jaccard accuracy =  0.73\n",
      "Decision Tree - f1 score accuracy =  0.78\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree\n",
    "test_yhat = my_decision_tree.predict(test_X)\n",
    "\n",
    "# jaccard\n",
    "DTree_Jaccard = jaccard_score(test_y, test_yhat, average=None)\n",
    "DTree_Jaccard = DTree_Jaccard[1]\n",
    "DTree_Jaccard = float(\"{:.2f}\".format(DTree_Jaccard))\n",
    "print(\"Decision Tree - jaccard accuracy = \" , DTree_Jaccard)\n",
    "\n",
    "# f1_score\n",
    "DTree_f1_score = f1_score(test_y, test_yhat, average='weighted')\n",
    "DTree_f1_score = float(\"{:.2f}\".format(DTree_f1_score))\n",
    "print(\"Decision Tree - f1 score accuracy = \" , DTree_f1_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine - jaccard accuracy =  0.78\n",
      "Support Vector Machine - f1 score accuracy =  0.76\n"
     ]
    }
   ],
   "source": [
    "# Support Vector Machine\n",
    "test_yhat = my_SVM.predict(test_X)\n",
    "\n",
    "# jaccard\n",
    "SVM_Jaccard = jaccard_score(test_y, test_yhat, average=None)\n",
    "SVM_Jaccard = SVM_Jaccard[1]\n",
    "SVM_Jaccard = float(\"{:.2f}\".format(SVM_Jaccard))\n",
    "print(\"Support Vector Machine - jaccard accuracy = \" , SVM_Jaccard)\n",
    "\n",
    "# f1_score\n",
    "SVM_f1_score = f1_score(test_y, test_yhat, average='weighted') \n",
    "SVM_f1_score = float(\"{:.2f}\".format(SVM_f1_score))\n",
    "print(\"Support Vector Machine - f1 score accuracy = \" , SVM_f1_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression - jaccard accuracy =  0.74\n",
      "Logistic Regression - f1 score accuracy =  0.66\n",
      "Logistic Regression - log loss =  0.57\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "test_yhat = LR.predict(test_X)\n",
    "test_yhat_prob = LR.predict_proba(test_X)\n",
    "\n",
    "# jaccard\n",
    "LGR_Jaccard = jaccard_score(test_y, test_yhat, average=None)\n",
    "LGR_Jaccard = LGR_Jaccard[1]\n",
    "LGR_Jaccard = float(\"{:.2f}\".format(LGR_Jaccard))\n",
    "print(\"Logistic Regression - jaccard accuracy = \" , LGR_Jaccard)\n",
    "\n",
    "# f1_score\n",
    "LGR_f1_score = f1_score(test_y, test_yhat, average='weighted')\n",
    "LGR_f1_score = float(\"{:.2f}\".format(LGR_f1_score))\n",
    "print(\"Logistic Regression - f1 score accuracy = \" , LGR_f1_score)\n",
    "\n",
    "LGR_log_loss = log_loss(test_y, test_yhat_prob)\n",
    "LGR_log_loss = float(\"{:.2f}\".format(LGR_log_loss))\n",
    "print(\"Logistic Regression - log loss = \" , LGR_log_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.67</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.76</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoisticRegression</th>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Jaccard  F1-score LogLoss\n",
       "Algorithm                                    \n",
       "KNN                0.686275      0.67      NA\n",
       "Decision Tree      0.730000      0.78      NA\n",
       "SVM                0.780000      0.76      NA\n",
       "LoisticRegression  0.740000      0.66    0.57"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the Accuracy Report\n",
    "#col_names = ['Algorithm', 'Jaccard', 'F1-score', 'LogLoss']\n",
    "algorithm_list = ['KNN', 'Decision Tree', 'SVM', 'LoisticRegression']\n",
    "Jaccard_list = [KNN_Jaccard, DTree_Jaccard, SVM_Jaccard, LGR_Jaccard]\n",
    "F1_score_list = [KNN_f1_score, DTree_f1_score, SVM_f1_score, LGR_f1_score]\n",
    "LogLoss_list = ['NA', 'NA', 'NA', LGR_log_loss]\n",
    "\n",
    "df = pd.DataFrame(list(zip(algorithm_list, Jaccard_list, F1_score_list, LogLoss_list)),\n",
    "              columns=['Algorithm','Jaccard', 'F1-score', 'LogLoss'])\n",
    "\n",
    "df.set_index('Algorithm', inplace = True)\n",
    "df"
   ]
  }
 ],
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