Loan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"loan_data_set.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() #look at few top rows using the fiunction head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # looks at the summary of the data (numerical columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    233\n",
       "Urban        202\n",
       "Rural        179\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Property_Area'].value_counts() # we can look at the frequency distribution to understand whether they make sense or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efdaaa64a90>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df.boxplot(column='ApplicantIncome', by = 'Education') #to understand the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efdaa9f8b10>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['LoanAmount'].hist(bins=50) #there are some extreme values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mavis beacon #tableau #other data science tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7efdaa8eb710>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df['ApplicantIncome'].hist(bins=50) #has some extreme values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency Table for Credit History:\n",
      "0.0     89\n",
      "1.0    475\n",
      "Name: Credit_History, dtype: int64\n",
      "'nProbability of getting loan for each Credit History class:\n",
      "                Loan_Status\n",
      "Credit_History             \n",
      "0.0                0.078652\n",
      "1.0                0.795789\n"
     ]
    }
   ],
   "source": [
    "temp1 = df['Credit_History'].value_counts(ascending=True)\n",
    "temp2 = df.pivot_table(values='Loan_Status', index=['Credit_History'],aggfunc=lambda x: x.map({'Y':1, 'N':0}).mean())\n",
    "print('Frequency Table for Credit History:')\n",
    "print(temp1)\n",
    "\n",
    "print('\\'nProbability of getting loan for each Credit History class:')\n",
    "print(temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAMfklEQVR4nO3bb4hld33H8ffHbFNpGk0xI8juaiLdVLehkHRIU4QaMS2bFHafiOxCaC0hi9bYB0ohxZJKfFSlFYRt7dKGqGDi6oM6yEpAG0kJbsyExOhu2DJd02aINKumPgnmD/32wb3a6+xs5szdOzfTL+8XDNxzzm/u/ebOvvfcOTmbqkJST695tQeQtHUMXGrMwKXGDFxqzMClxgxcamzDwJPcneTZJN87z/Ek+XSSlSRPJLl29mNKmsaQM/g9wL5XOH4TsGf8dRj4+wsfS9IsbBh4VT0I/PgVlhwAPlcjJ4DLkrxpVgNKmt6OGTzHTuDpie3V8b4frF2Y5DCjszyXXHLJb7/tbW+bwctLvT366KM/rKqFab53FoFnnX3r3v9aVUeBowCLi4u1vLw8g5eXekvyH9N+7yyuoq8Cuye2dwHPzOB5JV2gWQS+BPzR+Gr69cBPquqcj+eS5m/Dj+hJ7gVuAC5Psgr8FfBLAFX1GeA4cDOwAjwP/MlWDStpczYMvKoObXC8gA/ObCJJM+OdbFJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmODAk+yL8npJCtJ7ljn+JuTPJDksSRPJLl59qNK2qwNA09yEXAEuAnYCxxKsnfNsr8EjlXVNcBB4O9mPaikzRtyBr8OWKmqM1X1InAfcGDNmgJeN378euCZ2Y0oaVpDAt8JPD2xvTreN+ljwC1JVoHjwIfWe6Ikh5MsJ1k+e/bsFONK2owhgWedfbVm+xBwT1XtAm4GPp/knOeuqqNVtVhViwsLC5ufVtKmDAl8Fdg9sb2Lcz+C3wocA6iqbwGvBS6fxYCSpjck8EeAPUmuTHIxo4toS2vW/CfwboAkb2cUuJ/BpVfZhoFX1cvA7cD9wJOMrpafTHJXkv3jZR8BbkvyHeBe4H1VtfZjvKQ52zFkUVUdZ3TxbHLfnROPTwHvmO1oki6Ud7JJjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNTYoMCT7EtyOslKkjvOs+a9SU4lOZnkC7MdU9I0dmy0IMlFwBHg94FV4JEkS1V1amLNHuAvgHdU1XNJ3rhVA0sabsgZ/DpgparOVNWLwH3AgTVrbgOOVNVzAFX17GzHlDSNIYHvBJ6e2F4d75t0FXBVkoeSnEiyb70nSnI4yXKS5bNnz043saTBhgSedfbVmu0dwB7gBuAQ8I9JLjvnm6qOVtViVS0uLCxsdlZJmzQk8FVg98T2LuCZddZ8papeqqrvA6cZBS/pVTQk8EeAPUmuTHIxcBBYWrPmn4F3ASS5nNFH9jOzHFTS5m0YeFW9DNwO3A88CRyrqpNJ7kqyf7zsfuBHSU4BDwB/XlU/2qqhJQ2TqrW/Ts/H4uJiLS8vvyqvLf1/kuTRqlqc5nu9k01qzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGpsUOBJ9iU5nWQlyR2vsO49SSrJ4uxGlDStDQNPchFwBLgJ2AscSrJ3nXWXAn8GPDzrISVNZ8gZ/DpgparOVNWLwH3AgXXWfRz4BPDTGc4n6QIMCXwn8PTE9up4388luQbYXVVffaUnSnI4yXKS5bNnz256WEmbMyTwrLOvfn4weQ3wKeAjGz1RVR2tqsWqWlxYWBg+paSpDAl8Fdg9sb0LeGZi+1LgauCbSZ4CrgeWvNAmvfqGBP4IsCfJlUkuBg4CSz87WFU/qarLq+qKqroCOAHsr6rlLZlY0mAbBl5VLwO3A/cDTwLHqupkkruS7N/qASVNb8eQRVV1HDi+Zt+d51l7w4WPJWkWvJNNaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGBgWeZF+S00lWktyxzvEPJzmV5Ikk30jyltmPKmmzNgw8yUXAEeAmYC9wKMneNcseAxar6reALwOfmPWgkjZvyBn8OmClqs5U1YvAfcCByQVV9UBVPT/ePAHsmu2YkqYxJPCdwNMT26vjfedzK/C19Q4kOZxkOcny2bNnh08paSpDAs86+2rdhcktwCLwyfWOV9XRqlqsqsWFhYXhU0qayo4Ba1aB3RPbu4Bn1i5KciPwUeCdVfXCbMaTdCGGnMEfAfYkuTLJxcBBYGlyQZJrgH8A9lfVs7MfU9I0Ngy8ql4GbgfuB54EjlXVySR3Jdk/XvZJ4FeBLyV5PMnSeZ5O0hwN+YhOVR0Hjq/Zd+fE4xtnPJekGfBONqkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqTEDlxozcKkxA5caM3CpMQOXGjNwqbFBgSfZl+R0kpUkd6xz/JeTfHF8/OEkV8x6UEmbt2HgSS4CjgA3AXuBQ0n2rll2K/BcVf068Cngr2c9qKTNG3IGvw5YqaozVfUicB9wYM2aA8Bnx4+/DLw7SWY3pqRp7BiwZifw9MT2KvA751tTVS8n+QnwBuCHk4uSHAYOjzdfSPK9aYaeo8tZ89+wzWz3+WD7z7jd5wP4jWm/cUjg652Ja4o1VNVR4ChAkuWqWhzw+q+a7T7jdp8Ptv+M230+GM047fcO+Yi+Cuye2N4FPHO+NUl2AK8HfjztUJJmY0jgjwB7klyZ5GLgILC0Zs0S8Mfjx+8B/qWqzjmDS5qvDT+ij3+nvh24H7gIuLuqTia5C1iuqiXgn4DPJ1lhdOY+OOC1j17A3POy3Wfc7vPB9p9xu88HFzBjPNFKfXknm9SYgUuNbXng2/021wHzfTjJqSRPJPlGkrfMc74hM06se0+SSjL3/+0zZMYk7x2/lyeTfGE7zZfkzUkeSPLY+Gd985znuzvJs+e7NyQjnx7P/0SSawc9cVVt2Reji3L/DrwVuBj4DrB3zZo/BT4zfnwQ+OJWzjTFfO8CfmX8+APznG/ojON1lwIPAieAxe02I7AHeAz4tfH2G7fZfEeBD4wf7wWemvN7+HvAtcD3znP8ZuBrjO45uR54eMjzbvUZfLvf5rrhfFX1QFU9P948weg+gHka8h4CfBz4BPDTeQ43NmTG24AjVfUcQFU9u83mK+B148ev59x7PbZUVT3IK987cgD4XI2cAC5L8qaNnnerA1/vNted51tTVS8DP7vNdR6GzDfpVkZ/i87ThjMmuQbYXVVfnedgE4a8j1cBVyV5KMmJJPvmNt2w+T4G3JJkFTgOfGg+ow222T+rwLBbVS/EzG5z3SKDXzvJLcAi8M4tnWidl15n389nTPIaRv+C733zGmgdQ97HHYw+pt/A6FPQvya5uqr+e4tng2HzHQLuqaq/SfK7jO7ruLqq/mfrxxtkqk62+gy+3W9zHTIfSW4EPgrsr6oX5jTbz2w046XA1cA3kzzF6PezpTlfaBv6c/5KVb1UVd8HTjMKfrvMdytwDKCqvgW8ltE/RNkuBv1ZPccWXzjYAZwBruT/Lm785po1H+QXL7Idm+OFjSHzXcPoAs2eeV502cyMa9Z/k/lfZBvyPu4DPjt+fDmjj5tv2EbzfQ143/jx28fxZM7v4xWc/yLbH/KLF9m+Peg55zD0zcC/jSP56HjfXYzOhjD6m/JLwArwbeCtc35TN5rv68B/AY+Pv5bmOd+QGdesnXvgA9/HAH8LnAK+CxzcZvPtBR4ax/848Adznu9e4AfAS4zO1rcC7wfeP/H+HRnP/92hP2NvVZUa8042qTEDlxozcKkxA5caM3CpMQOXGjNwqbH/BUQx4kP1ZuNnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax1 = fig.add_subplot(121)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA WRANGLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: sum(x.isnull()), axis=0) #number of missing values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LoanAmount'].fillna(df['LoanAmount'].mean(), inplace=True)\n",
    "#filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            0\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: sum(x.isnull()), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean(), inplace=True)\n",
    "df['Credit_History'].fillna(df['Credit_History'].mean(), inplace=True) #filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ApplicantIncome      5403.459283\n",
      "CoapplicantIncome    1621.245798\n",
      "LoanAmount            146.412162\n",
      "Loan_Amount_Term      342.000000\n",
      "Credit_History          0.842199\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.mean()) #basic math operations ex. df.median, cumulative summation, etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_ID               object\n",
      "Gender                object\n",
      "Married               object\n",
      "Dependents            object\n",
      "Education             object\n",
      "Self_Employed         object\n",
      "ApplicantIncome        int64\n",
      "CoapplicantIncome    float64\n",
      "LoanAmount           float64\n",
      "Loan_Amount_Term     float64\n",
      "Credit_History       float64\n",
      "Property_Area         object\n",
      "Loan_Status           object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Predictive Model in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import models from scikit learn module:\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
      "0  LP001002   Male      No          0      Graduate            No   \n",
      "1  LP001003   Male     Yes          1      Graduate            No   \n",
      "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
      "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
      "4  LP001008   Male      No          0      Graduate            No   \n",
      "\n",
      "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
      "0             5849                0.0  146.412162             360.0   \n",
      "1             4583             1508.0  128.000000             360.0   \n",
      "2             3000                0.0   66.000000             360.0   \n",
      "3             2583             2358.0  120.000000             360.0   \n",
      "4             6000                0.0  141.000000             360.0   \n",
      "\n",
      "   Credit_History Property_Area Loan_Status  \n",
      "0             1.0         Urban           Y  \n",
      "1             1.0         Rural           N  \n",
      "2             1.0         Urban           Y  \n",
      "3             1.0         Urban           Y  \n",
      "4             1.0         Urban           Y  \n"
     ]
    }
   ],
   "source": [
    "# Extracting only the independent variables\n",
    "print(df.head())\n",
    "X = df.iloc[:, [8,10]].values #credit history and loan amount\n",
    "\n",
    "#Extracting the dependent variables\n",
    "y = df.iloc[:, 12].values #loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset into the Training set and Test set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Logistic Regression to training dataset\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'N', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'Y', 'N', 'N', 'Y',\n",
       "       'Y', 'Y', 'Y', 'Y', 'N', 'Y'], dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14, 19],\n",
       "       [ 2, 88]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8292682926829268"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bar</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     3\n",
       "1  bar     4\n",
       "2  bar     5"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'bar', 'bar'], 'rval': [3, 4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     3\n",
       "1  bar     2     4\n",
       "2  bar     2     5"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.846687</td>\n",
       "      <td>0.279688</td>\n",
       "      <td>2.024776</td>\n",
       "      <td>0.538802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.836137</td>\n",
       "      <td>-2.371811</td>\n",
       "      <td>0.192645</td>\n",
       "      <td>-2.140590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.385492</td>\n",
       "      <td>-0.124966</td>\n",
       "      <td>-0.492677</td>\n",
       "      <td>2.150685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.125812</td>\n",
       "      <td>-0.823051</td>\n",
       "      <td>-0.136224</td>\n",
       "      <td>1.332348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.312281</td>\n",
       "      <td>0.027399</td>\n",
       "      <td>1.637404</td>\n",
       "      <td>0.548803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -1.846687  0.279688  2.024776  0.538802\n",
       "1  1.836137 -2.371811  0.192645 -2.140590\n",
       "2 -0.385492 -0.124966 -0.492677  2.150685\n",
       "3 -0.125812 -0.823051 -0.136224  1.332348\n",
       "4 -1.312281  0.027399  1.637404  0.548803"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one = pd.DataFrame(np.random.randn(5, 4))\n",
    "one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show whether or not new customers will be granted a loan or not"
   ]
  }
 ],
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