loanpredict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np                 #mathematical \n",
    "import seaborn as sns             #data visualization\n",
    "import matplotlib.pyplot as plt   #plotting graphs\n",
    "%matplotlib inline\n",
    "import warnings                  #import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(\"train_u6lujuX_CVtuZ9i.csv\")\n",
    "test=pd.read_csv(\"test_Y3wMUE5_7gLdaTN.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_original=train.copy()\n",
    "test_original=test.copy()\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1dbb6e66a0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j7kBjcFT83+QjGXcHg/r1QQaNKugX+leqX9qp2gZsG9HnH9OSTFXV5Lj7kA7l/83xGNXUzQywbt7+WmDviD5LkrSIUQX9t4ANSc5M8hLgMmDniD5LkrSIkUzdVNWBJO8F/h1YBXyqqu4fxWdpQU6J6cXK/5tjkKpaepQk6ajlk7GS1DmDXpI6Z9AfxZLcmmT9uPuQ9OJm0B/dbgK+muQvkhw37mYkvTj5y9ijXJKXAx8CLgQ+Azx78FhVXTeuviS9eIzqyVgdOf8H/Bw4HjiBeUEvjVOSDy1yuKrqL49YM8c4g/4oluRC4DrmHkY7u6qeGnNL0nw/X6D2q8CfAKcABv0R4tTNUSzJfwLv8WE0vdglOQG4EtgC7AA+XlX7x9vVscOglzQySU4G3g+8HdgO/F1VPTbero49Tt1IGokkfw38EXN/9uC3q+rJMbd0zPKKXtJIJHkWeBo4wC//mfIw98vYV46lsWOQQS9JnfOBKUnqnEEvSZ0z6CWpcwa9JHXu/wHMa/UWjxzV5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(221)\n",
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title= 'Gender')\n",
    "plt.subplot(222)\n",
    "train['Married'].value_counts(normalize=True).plot.bar(title= 'Married')\n",
    "plt.subplot(223)\n",
    "train['Self_Employed'].value_counts(normalize=True).plot.bar(title= 'Self_Employed')\n",
    "plt.subplot(224)\n",
    "train['Credit_History'].value_counts(normalize=True).plot.bar(title= 'Credit_History')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(131)\n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6), title= 'Dependents')\n",
    "\n",
    "plt.subplot(132)\n",
    "train['Education'].value_counts(normalize=True).plot.bar(title= 'Education')\n",
    "\n",
    "plt.subplot(133)\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title= 'Property_Area')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['ApplicantIncome']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'')"
      ]
     },
     "execution_count": 12,
     "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": [
    "train.boxplot(column='ApplicantIncome', by = 'Education')\n",
    "plt.suptitle(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkiRJ1WLAHaW2rl4yGfAMbuk2dvQ4TFmSJEmSqsWAO0oHOnoAmNnywjO4LQ31AD6HK0mSJElVZMAdpQOd3QCHrYML0NntTMqSJEmSVC0VBdyIOD8iHo6IbRFxxSD7myPim8X+OyNiYdm+TxTlD0fEeSOdMyLWRcR9EfHziLgtImYc2SWOr+cP9eC+EHCb7cGVJEmSpKobMeBGRD3wJeAdwFJgZUQsHVBtFbAnM08HPg9cXRy7FLgQOBM4H/hyRNSPcM7/kJmvyszfBp4EPnKE1ziuDg1RHrAOLuBauJIkSZJURZX04J4DbMvMRzOzC7gFuGBAnQuAm4rt24C3RkQU5bdkZmdmPgZsK8435Dkz83mA4vhpwKSamvhAZyngDjZE2R5cSZIkSaqeSgLufGB72esdRdmgdTKzB9gHzB3m2GHPGRH/A/gN8Erg2sEaFRGXRsSWiNiya9euCi5jfOzvKD2D+6JJphodoixJkiRJ1VZJwI1Bygb2qg5VZ7TlpY3MDwInAVuB9w3WqMy8PjPPzsyz582bN1iVl8T+Yohy+TJBTQ11BNDpEGVJkiRJqppKAu4O4OSy1wuAp4aqExENwDHA7mGOHfGcmdkLfBP4wwraOGEODVEuC7h1ETQ11NmDK0mSJElVVEnAvRtYHBGLIqKJ0qRRGwbU2QBcXGy/B/h+ZmZRfmExy/IiYDFw11DnjJLT4dAzuP8S+MWRXeL42t/RQ2tTPfV1L+6Ebmmsp8NlgiRJkiSpakYMuMUztR8B7qA0ZPjWzHwwIq6KiD8oqq0D5kbENuDjwBXFsQ8CtwIPAX8PfDgze4c6J6WhyzdFxP3A/cCJwFXjdrXj4EBHz4smmOrX0mgPriRp4kXEyRGxKSK2RsSDEfHRovzYiPhuRDxSfJ9TlEdEfKFYpu/nEfE7Zee6uKj/SERcXFb+moi4vzjmC8WH0JKOwOrVq2lpaSEiaGlpYfXq1dVuklQTDk9qg8jMjcDGAWWfLNvuAN47xLFrgbUVnrMPeGMlbaqWA509Lxqe3K+loZ6OHgOuJGnC9QD/MTN/GhEzgXsi4rvAvwG+l5mfK9abvwK4nNISfYuLr3OBrwDnRsSxwKeAsynNi3FPRGzIzD1FnUuBn1D63X0+8J0JvEappqxevZrrrruOq6++mssuu4zrrruOyy+/HIBrrx10flVJFapkiLLKPN/R/aIZlPu1NNbT6RBlSdIEy8ydmfnTYns/pZFR83nxEn43Ae8uti8Avp4lPwFmR8SJwHnAdzNzdxFqvwucX+yblZk/Lh4/+nrZuSSNwQ033MDVV1/Nxz/+caZPn87HP/5xrr76am644YZqN02a8gy4o3Sgs4eZgwxRbnaIsiSpyiJiIXAWcCdwQmbuhFIIBo4vqo12Cb/5xfbA8sHevypL+ElTTWdnJ5dddtmLyi677DI6Ozur1CKpdhhwR+lAx+ABt6Wh3oArSaqaiJgB/A3wscx8friqg5SNeWm/FxVWaQk/aappbm7muuuue1HZddddR3Nzc5VaJNUOA+4o7e8Y4hncxjo6XAdXklQFEdFIKdx+IzP/d1H8dDG8mOL7M0X5aJfw21FsDyyXNEYf+tCHuPzyy7nmmmtob2/nmmuu4fLLL+dDH/pQtZsmTXkG3FEqTTI1+DO4vX1Jd68hV5I0cYoZjdcBWzPzmrJd5Uv4XQx8u6z8A8Vsyq8D9hVDmO8A3h4Rc4oZl98O3FHs2x8Rryve6wNl55I0Btdeey2XXXYZV155Ja2trVx55ZVcdtllTjAljYOKZlFWSV9fDvMMbj0AHd29NNb7uYEkacK8EXg/cH9E3FuUXQl8Drg1IlYBT/LCagcbgXcC24B24IMAmbk7Iv6C0lr1AFdl5u5i+98BXwOmUZo92RmUpSN07bXXGmill4ABdxQOdPUADPEMbinUdnb3MbNlQpslSTqKZeZmBn9OFuCtg9RP4MNDnOtG4MZByrcAy46gmZIkTQi7GkfhQEcp4A7+DG7Rg+tauJIkSZJUFQbcUTjQ2d+DO/gzuAAdroUrSZIkSVVhwB2F/R3dAMwYbIhyY+lWulSQJEmSJFWHAXcU9ncM9wxuqQe30yHKkiRJklQVBtxROBRwh3sG1yHKkiRJklQVBtxR6H8Gd7Ahys0OUZYkSZKkqjLgjsKBjqEnmaqLoKmhzoArSZIkSVViwB2F/R3dRMD0YjjyQC0NdXT0OERZkiRJkqrBgDsK+zt7mNHUQF1dDLq/ubHeHlxJkiRJqhID7igc6OgZdAblfi0NdXQ6yZQkSZIkVYUBdxT2d/QMOsFUv5bGejpcJkiSJEmSqsKAOwoHOnuYMcgSQf1aHKIsSZIkSVVjwB2FA509tA4bcOtcB1eSJEmSqsSAOwrtXT20Ng33DG49nQ5RliRJkqSqMOCOQntXL9ObB18iCEqzKHf3Jr19OYGtkiRJkiSBAXdUDnb1Mr1p6IDb0li6nT6HK0mSJEkTb+jxtjpMW1cP04cbotxYCr8d3b3DPqt7851PDrnvonNPGXsDJUmSJOkoZg9uhXr7ko7uvuF7cBuKgNvjRFOSJEmSNNEMuBU6WAw7Hi7gNjtEWZIkSZKqxoBbofauHoCKhih3GnAlSZIkacIZcCt0sGvkHtyWhv4eXIcoS5IkSdJEM+BWqK2zgoDbP8mUa+FKkiRJ0oQz4FboYPfIQ5SnNdUTQFtnzwS1SpIkSZLUz4BboUp6cOsimNHSwP4OA64kSZIkTTQDboXai2dwpw0TcAFmGnAlSZIkqSoMuBXqH6LcOswQZYCZzY0839E9EU2SJEmSJJUx4FaokiHKYA+uJEmSJFWLAbdCh5YJah6hB7elkbbOHnr7ciKaJUmSJEkqGHAr1NZV6pWd1jhyD27iTMqSJEmSNNEMuBU62NVLc0Md9XUxbL1ZLaUeXocpS5IkSdLEqijgRsT5EfFwRGyLiCsG2d8cEd8s9t8ZEQvL9n2iKH84Is4b6ZwR8Y2i/IGIuDEiGo/sEsdHe1cvrSMMT4bSEGWA/RVONOVQZkmSJEkaHyMG3IioB74EvANYCqyMiKUDqq0C9mTm6cDngauLY5cCFwJnAucDX46I+hHO+Q3glcBvAdOAPzmiKxwnbV09Iw5PhtIQZRi5B7erp4/1dz3JZ7+z9dDzvZIkSZKksaukB/ccYFtmPpqZXcAtwAUD6lwA3FRs3wa8NSKiKL8lMzsz8zFgW3G+Ic+ZmRuzANwFLDiySxwfB7t6aW0eOeDOKALu851D9+Dubuviuh/+ivt/vY/2rl6272kft3ZKkiRJ0tGqkoA7H9he9npHUTZonczsAfYBc4c5dsRzFkOT3w/8/WCNiohLI2JLRGzZtWtXBZdxZNq6epk2whq4AA11dUxvqh+yB/eJ59r48g+2sfdgFxedcwp1AU88Z8CVJEmSpCNVScAdbFalgQ+ODlVntOXlvgz8U2b+aLBGZeb1mXl2Zp49b968waqMq4NdPUyvYIgywKyWxiED7t8/8Bvau3r5t797GsvmH8PLjmnhyd1t49lUSZIkSToqVRJwdwAnl71eADw1VJ2IaACOAXYPc+yw54yITwHzgI9XchETob3CIcpQeg53qEmm7tuxl2NbmzhhVgsApxzbyvY9B51sSpIkSZKOUCUB925gcUQsiogmSpNGbRhQZwNwcbH9HuD7xTO0G4ALi1mWFwGLKT1XO+Q5I+JPgPOAlZnZd2SXN37aKxyiDP0Bd/Ae3Pu272PBnGmHXp967HS6evp4+vmOcWmnJEmSJB2tRgy4xTO1HwHuALYCt2bmgxFxVUT8QVFtHTA3IrZR6nW9ojj2QeBW4CFKz9J+ODN7hzpnca7rgBOAH0fEvRHxyXG61iPS3tVDa1OlPbiNHOjooW9Ar+wz+zv49d6DLJgz/VDZKXNL20/u9jlcSZIkSToSFXVJZuZGYOOAsk+WbXcA7x3i2LXA2krOWZRX1k06wdo7e5lWccBtoDeTPe1dzJ3RfKj859v3AXByWQ/u7GmNzGpp4Mnd7bzu5XPHt9GSJEmSdBSpZIjyUS8zae/uZfooenABntnf+aLy+3bspb4uOPGYFwJuRHDKsdN54jknmpIkSZKkI2HArUBXbx+9fcn0Sp/BbS7VGxhw792+lzNOmElTw4tv+ylzW9nT3s3zQ0xMJUmSJEkamQG3Au2dvQCj6MEtAm7ZxFGZyc937OPVJx9zWP1Tjy2ew3U9XEmSJEkaMwNuBdq7SwG3teJZlA8fovzEc+3sO9jNqxbMPqz+ibNbaKgLJ5qSJEk6Sqxfv55ly5ZRX1/PsmXLWL9+fbWbJNUEA24F2jtLS/5UOslUU0MdzQ117CoLuPft2AvAq04+POA21NUxf840A64kSdJRYP369axZs4Zrr72Wjo4Orr32WtasWWPIlcaBAbcC7V2jG6IMpV7cZ/a/MET53u17mdZYz+LjZwxa/5Q50/n13oP09E6apX8lSZL0Eli7di0XXXQRq1evpqWlhdWrV3PRRRexdu1hC49IGqVJuSTPZPNCwK38ds1saeCZ58t6cLfvZdn8WTTUD/6ZwvGzWujtS7bvOcii41qPrMGSJEmatB566CHa2tq48cYbWb58OZs3b+aSSy7hiSeeqHbTpCnPHtwKtHeVhiiPpgd3VkvDoWdwu3v7eOCp5wd9/rbfvJml9XIf3XXgCFoqSZKkya6pqYnVq1ezYsUKGhsbWbFiBatXr6apqanaTZOmPANuBfp7cFubRz9EOTP58a+eo6unj98e5PnbfvNmlALurwy4kiRJNa2rq4svfvGLbNq0ie7ubjZt2sQXv/hFurq6qt00acpziHIF+ntwp41yiHJHdx+PP9fOf77tPk6dO523vPL4IetPa6qntbmBXz3TdsTtlSRJ0uS1dOlS3v3ud7N69Wq2bt3KkiVLuOiii/jWt75V7aZJU549uBU49Axu4+h6cAFW3XQ3+w52c92/fg0zmocPyPNmNPPos/bgSpIk1bI1a9Zw8803v2gW5Ztvvpk1a9ZUu2nSlGcPbgUOBdxRDVEu3dpHd7XxX9/7KpacOGvEY+bNbOJXu+zBlSSNTkTcCLwLeCYzlxVlxwLfBBYCjwN/lJl7IiKA/w68E2gH/k1m/rQ45mLg/y1O+5eZeVNR/hrga8A0YCPw0czMCbk4qQatXLkS4EU9uGvXrj1ULmns7MGtQHtXD/V1QdMQMyAPZs700iQBF517Cn/4mgUVHTNvRjO727rY3ebzF5KkUfkacP6AsiuA72XmYuB7xWuAdwCLi69Lga/AoUD8KeBc4BzgUxExpzjmK0Xd/uMGvpekUVq5ciUPPPAAvb29PPDAA4ZbaZwYcCvQ3tXL9KZ6Sh96V+bY1iZuX72cq/7gzIqPcSZlSdJYZOY/AbsHFF8A3FRs3wS8u6z861nyE2B2RJwInAd8NzN3Z+Ye4LvA+cW+WZn546LX9utl55IkaVIx4FagvbN3VEsE9Vs2/5gh170dzHEz+gOuw5QlSUfshMzcCVB875/pcD6wvazejqJsuPIdg5QfJiIujYgtEbFl165d43IRkiSNhgG3Au3dvUwfxQzKYzWntYmm+jqXCpIkvZQGG46UYyg/vDDz+sw8OzPPnjdv3hE0UZKksTHgVuBgV8+YenBHqy6CRce1GnAlSePh6WJ4McX3Z4ryHcDJZfUWAE+NUL5gkHJJkiYdA24F2sY4RHksXj6v1SHKkqTxsAG4uNi+GPh2WfkHouR1wL5iCPMdwNsjYk4xudTbgTuKffsj4nXFDMwfKDuXpDFav349y5Yto76+nmXLlrF+/fpqN0mqCS4TVIH27l5mT2sc9XE33/nkqI85bd4M/uGhp+nq6aOpwc8fJEkji4j1wJuB4yJiB6XZkD8H3BoRq4AngfcW1TdSWiJoG6Vlgj4IkJm7I+IvgLuLeldlZv/EVf+OF5YJ+k7xJWmM1q9fz5o1a1i3bh3Lly9n8+bNrFq1CsDZlKUjZMCtQHtnDycd0zIh7/Xyea309iVP7m7j9ONnTsh7SpKmtswc6i/itw5SN4EPD3GeG4EbBynfAiw7kjZKesHatWtZt24dK1asAGDFihWsW7eO1atXG3ClI2QXYQXau3qZNkFDlE+bNwMmAFHrAAAey0lEQVSAXzlMWZIkqSZt3bqV5cuXv6hs+fLlbN26tUotkmqHAbcCB7t7aZ2AWZSh1IMLONGUJElSjVqyZAmf/vSnX/QM7qc//WmWLFlS7aZJU54BtwJtnRMzizLAzJZGjp/ZzK+esQdXkiSpFq1YsYLPfvazPPvss/T19fHss8/y2c9+9tCQZUljZ8AdQW9f0tnTN2FDlKE0TPnRZ+3BlSRJqkXf+ta3mDlzJtOmTaOuro5p06Yxc+ZMvvWtb1W7adKUZ8AdQXtXD8CEDVEGOP34GTzy9AH6+nLC3lOSJEkTY8eOHbzpTW9i586d9PX1sXPnTt70pjexY8eOajdNmvIMuCM42NULMKE9uEtOnMWBzh527Dk4Ye8pSZKkiXP77bfzmc98hra2Nj7zmc9w++23V7tJUk0w4I6gvQi4rc0TF3CXnjQLgId2Pj9h7ylJkqSJ09rayllnnUVjYyNnnXUWra2t1W6SVBNcB3cEbcUQ5WmNE3erXnHCTOqiFHDPX/ayCXtfSZIkTYzM5LzzzqO7u5vGxkaam5ur3SSpJhhwR9A/RHmiZlGG0nDoRce1stUeXEmSpJrT0NBAe3s7fX19AHR3d9Pb20tDg3+aS0fKIcojaKvCEGUoPYdrwJUkSapNfX19vOENb+Cpp57iDW94w6GwK+nIGHBHcLAKQ5Sh9Bzujj0H2Xewe0LfV5IkSS+tnp4eli5dyj333MNJJ53EPffcw9KlS+np6al206Qpz4A7gmpMMgWlHlyAX9iLK0mSVHMWLlw47GtJY2PAHUFbFZYJAjjzRGdSliRJqlUbN27kkksuYe/evVxyySVs3Lix2k2SaoJPso+gf4jy9KaJvVXzZjYzt7XJ53AlSZJqTESQmfzVX/0VX/nKV6irqztULunI2IM7grbOoge3cWJ7cCOCpSfNsgdXkiSpxmQmzc3NhyaW6uvro7m5mcyscsukqc+AO4KD3b20NNZRXzfxn6gtOXEWv3z6AD29zqonSZJUKyKCSy65hMw89HXJJZfYgyuNAwPuCNq7emid4OHJ/ZaeOIuunj4efbatKu8vSZKk8ZeZ3HDDDVxzzTW0t7dzzTXXcMMNN9iDK42DigJuRJwfEQ9HxLaIuGKQ/c0R8c1i/50RsbBs3yeK8ocj4ryRzhkRHynKMiKOO7LLO3Ltnb0TPsFUv/6ZlB96ymHKkiRJteLMM8/kXe96F1deeSWtra1ceeWVvOtd7+LMM8+sdtOkKW/EgBsR9cCXgHcAS4GVEbF0QLVVwJ7MPB34PHB1cexS4ELgTOB84MsRUT/COf8v8DbgiSO8tnFxoLN6Pbgvn9dKU0OdE01JkiTVkDVr1nDffffxne98h66uLr7zne9w3333sWbNmmo3TZryKklu5wDbMvNRgIi4BbgAeKiszgXAnxfbtwFfjNJDBBcAt2RmJ/BYRGwrzsdQ58zMnxVlR3Jd42Z3WxfHtjZV5b0b6+s444QZPGgPriRJUs1YuXIlAKtXr2br1q0sWbKEtWvXHiqXNHaVBNz5wPay1zuAc4eqk5k9EbEPmFuU/2TAsfOL7ZHOOayIuBS4FOCUU04ZzaGjsrutiyUnzXrJzj+S15wyh1u37KCzp5fmhuoMlZYkSdLgxqNT5sEHH+Siiy7ioosuGvWxPrcrvVglz+AO9n/twP+Thqoz2vKKZeb1mXl2Zp49b9680Rw6Ks+1dTG3Sj24AMsXz+Ngdy8/fWJv1dogSZKkwZXPhDyWr1Mvv/2Ijpf0YpUE3B3AyWWvFwBPDVUnIhqAY4DdwxxbyTmrrru3j30Hu6s2RBngdS8/lvq6YPO2XVVrgyRJkiRNBZUMUb4bWBwRi4BfU5o0auD4iQ3AxcCPgfcA38/MjIgNwM0RcQ1wErAYuItSD+5I56y6Pe1dABPag3vznU8eVrZgzjS+9bOn+M/nvXLC2iFJkiRJU82IPbiZ2QN8BLgD2ArcmpkPRsRVEfEHRbV1wNxiEqmPA1cUxz4I3EppQqq/Bz6cmb1DnRMgIv40InZQ6tX9eUR8dfwud3R2t5UC7rGtzdVqAgCnHz+Dp/YeZE/RHkmSJEnS4Spa/yYzNwIbB5R9smy7A3jvEMeuBdZWcs6i/AvAFypp10tt94H+gFu9IcoAi+fN4Htbn+Gff/Ucv//bJ1a1LZIkSZI0WVXyDO5R67mix3TujOoG3PlzptPSWMePHvE5XEmSJEkaigF3GC8MUa5uwK2vC15+3Ax+9MizzpYnSZIkSUMw4A7juQOdRMCc6dUNuFB6DvfXew/y2LNt1W6KJEmSJE1KBtxhPNfWxZzpTdTXHfkC3kdq8fEzANi87dkqt0SSJEmSJicD7jB2t3VVfXhyv2Nbmzj52Gl8b+sz1W6KJEmSJE1KBtxhPDeJAm5E8O5Xz+dHj+zi13sPVrs5kiRJkjTpGHCHsbuti7mTJOAC/NHZJ5PArXdvr3ZTJEmSJGnSMeAOYzINUQY4+djp/IvF87h1y3Z6+5xNWZIkSZLKGXCH0NuX7GmfXD24ACtfezI793Xww1/6LK4kSZIklTPgDmFvexeZ1V8Dd6C3LT2B42Y0s/4uhylLkiRJUjkD7hB2t3UBMHdGc5Vb8mKN9XW85zUL+P4vnuHp5zuq3RxJkiRJmjQMuEN49kARcCdZDy7Aha89md6+ZP1dT1a7KZIkSZI0aRhwh9Dfg3vsjMkXcBce18rblpzAV3/0GLv2d1a7OZIkSZI0KRhwh7C7rRQcJ9szuP0+8c5X0tHdyzXf/WW1myJJkiRJk0JDtRswWT1X9ODOmT45A+5p82bw/tefyk3//DgfeP2pLDlxVrWbJEmSNCW96tP/wL6D3VV7/4VX/N2Ev+cx0xq571Nvn/D3lV5qBtwh7G7r4phpjTTWT95O7o++dTF/+7Nf85d/9xD/c9W5RES1myRJkjTl7DvYzeOf+/1qN2NCVSNUSxNh8qa3KnuubfKtgTvQ7OlNfOyti/m/257jHx56utrNkSRJkqSqsgd3CLsPdE26529vvvPwWZPr6+p42awW1vzt/bzm1DkcN8mWNZIkSZKkiWIP7hB2t02+gDuY+rrgj84+mec7erj8tp+TmdVukiRJkiRVhT24Q3iurZPfOXVOtZtRkZcd08LvLTmBv7t/Jx/75r2cu2jui/ZfdO4pVWqZJEmSJE0cA+4g+vqSPe3dk/4Z3HKvP20uv3x6Pxvv38miua0cP6ul2k2SJEmaEmYuuYLfuumKajdjQs1cAnB0Taylo4MBdxD7DnbT25dTYohyv7oI/vA1C/jC9x7h5rue5N+/+XSaGhyBLkmSNJL9Wz/nLMpSjTABDaJ/Ddy5M6ZOwAWY1dLI+157Mrv2d/Kte3/t87iSJEmSjioG3EHsLgLuVOrB7bf4+Jm8dcnx3Lt9L3c+trvazZEkSZKkCWPAHcTutk5gagZcgDe/4njOOGEGf3f/Tp54rq3azZEk1YCIOD8iHo6IbRFxdD2sKEmaMgy4gzg0RLl1aq4pWxfBH73mZGZPa+SmHz/O1p3PV7tJkqQpLCLqgS8B7wCWAisjYml1WyVJ0uEMuIPYfaAUcOe0Nla5JWM3vbmBS5Yvoqm+jvevu8ueXEnSkTgH2JaZj2ZmF3ALcEGV2yRJ0mGcRXkQv3m+g5ktDTQ31Fe7KUdkzvQmPvjGRdz048f51+vu5JZLX8/82dOq3SxJ0tQzH9he9noHcO7AShFxKXApwCmnuAa7ppaxzir8xNXvGueWjM6pl98+puOOmTZ1O3Kk4RhwB/HjXz3HWafMqXYzxsUJs1r42gfP4f1fvZP3fuWf+Z9/ci4vnzej2s2SJE0tMUjZYVP1Z+b1wPUAZ599tlP5a8o4oiWCPuc/dWkycYjyAE8+186jz7bx5jPmVbsp4+ahp57n4jcsZN/Bbv7ltZv5/+54mJvvfJKb73yy2k2TJE0NO4CTy14vAJ6qUlskSRqSAXeAH/7yGQDe/IraCbgAJ82exr/93dNoqK/jhh89ymPP+kyuJKlidwOLI2JRRDQBFwIbqtwmSZIOY8Ad4Ie/3MXJx05j0XGt1W7KuDtuZjP/9ndfzsyWRr72z4/x8G/2V7tJkqQpIDN7gI8AdwBbgVsz88HqtkqSpMMZcMt09vTyz796jjefcTwRgz1uNPXNnt7Epb/7cubNaOavf/I4/+c+R5hJkkaWmRsz84zMPC0z11a7PZIkDcaAW+bux/bQ3tXLm2ro+dvBzGhu4E/+xcs55djprF7/M9b87f20dfZUu1mSJEmSdEScRbnMD3/5DE31dbz+tLnVbspLrqWxng++cRG/3nuQG370KP/0yC7+/F+eyRtOO45pTVN7eSRJkiRJRycDbpkfPLyL1y6aQ2vz0XFbGuvruPKdS/i9pSfwn/7Xfay6aQsNdcGZJ83ilLmtPLrrAD29SW9f0tPXR09f0tJYz+xpjbzpjHm84mUzefUpszl+Zku1L0WSJEmSKgu4EXE+8N+BeuCrmfm5Afubga8DrwGeA96XmY8X+z4BrAJ6gT/NzDuGO2dELAJuAY4Ffgq8PzO7juwyR/brvQd55JkD/NHZJ49cuca8duGx3PGx32XzI8/y0yf3sOWJPdy/Yy/tXb001AcNdXXU1wXTGus42N3L1n0dbHliz6HjZ09v5OQ50zl5zjQuWb6IZfOPoaXRXmBJkiRJE2vEgBsR9cCXgN+jtA7e3RGxITMfKqu2CtiTmadHxIXA1cD7ImIppaUEzgROAv4xIs4ojhnqnFcDn8/MWyLiuuLcXxmPix3ODx/eBdTe8kCVamms521LT+BtS084VDbcOrldPX3s3HeQ7bvb2b7nINv3tHP/r/ex8YHf0NRQx6sWHMNrFx7Lby+YzW8vOIYTj2k5oom7MpPOnj4a60thW4dr6+zhuQNdtDbXM3t6k/dJkiRJR51KenDPAbZl5qMAEXELcAFQHnAvAP682L4N+GKU0swFwC2Z2Qk8FhHbivMx2DkjYivwFuCios5NxXlf8oA7d0YTv/9bJ3L68TNe6reaVIYLscNpaqjj1LmtnDr3heWU9nd0s+i4Vu5+fDd3Pb6H6//pUXr6EoBjW5s4fd4MTju+lYVzWzlhVgvzZjYze3ojQSmIHezuZdf+Dp5+vpOn9h7k8efaeOK5drbvbqejp4/eviSAaU31tDY3MGd6I2847TgWzp3OibOnHTrntMZ6GuuDpoY6GuvqqKuBoNfT20dHTx+7D3Sx60AHt93za57Z38Ezz3eya38new920dHd96Jj+u/5GS+bwenzZjB/znROPKZ0j5ob6misr6OpoY6GuqjZWcMnm96+pLu3j67ePnp6k7bOHva2d7OnvYu9B7vZ197F3vbuQ//f1EUwvamemS0NzGxpLL6XtmcV31sa66r23y8zySy2+19DWdkL+ynK+8sS6Oju5UBHD/s7etjf2c2Bjh4OdJZeH+jsoa2zh+lN9cyaVrr2WS2NpWuf9sI9aGmsp72rl/auHjq6+2pyiTdJklS5SgLufGB72esdwLlD1cnMnojYB8wtyn8y4Nj5xfZg55wL7C3W2xtY/yV13pkv47wzXzYRb1WzZrY08uyBLhYdN4NFx82g+6z5/GZfBzv2HmTn3oMkyR0PPs3utpFHnDfV13HK3OksnNvKrJZGWhrraWmso6cvD/3hu6eti/+1ZTttXb3DnquxGGY9VqU/2ccuj+DwpBRu+4Y4x7GtTRw/s5lFx7VyzLRGWpsb+K35s9jd3s2u/R088vQBvn3vU+zvGHqW7IjS89j1htyXTG/msP8dj0RdlIIwlP5bAqUPjQ5tc/i+stfloRNK/+YYpGxgeJ0I9XVB7yhu2szmBu7/9HkvYYskSdJkV0nAHeyv3oF/cQxVZ6jywdLGcPUPb1TEpcClxcsDEfHwYPUmwHHAs1V671py2H18pEoNmUqeGLzYf5Pjw/s4fibsXsZV43aqU8ftTEepe+6559mIGOLHlKQy/r6RKlPR7+ZKAu4OoHzmpQXAU0PU2RERDcAxwO4Rjh2s/FlgdkQ0FL24g70XAJl5PXB9Be1/SUXElsw8u9rtmOq8j+PHezk+vI/jx3t5dMrMo3NSC2mU/Bkpja9Kxm3eDSyOiEUR0URp0qgNA+psAC4utt8DfD8zsyi/MCKai9mRFwN3DXXO4phNxTkozvntsV+eJEmSJOloMWIPbvFM7UeAOygt6XNjZj4YEVcBWzJzA7AO+OtiEqndlAIrRb1bKU1I1QN8ODN7AQY7Z/GWlwO3RMRfAj8rzi1JkiRJ0rAiJ3LGkBoUEZcWw6V1BLyP48d7OT68j+PHeylJQ/NnpDS+DLiSJEmSpJow9rVTJEmSJEmaRAy4kiRJkqSaYMAdo4g4PyIejohtEXFFtdszWUTEjRHxTEQ8UFZ2bER8NyIeKb7PKcojIr5Q3MOfR8TvlB1zcVH/kYi4uKz8NRFxf3HMFyJisLWTp7yIODkiNkXE1oh4MCI+WpR7L0cpIloi4q6IuK+4l58uyhdFxJ3FfflmMaM7xazv3yzuy50RsbDsXJ8oyh+OiPPKyo+anwcRUR8RP4uI24vX3kdJVRcRL4uIWyLiVxHxUERsjIgzJuB9/zwi/lOxfVVEvG2cz/+xiJhe9vrxiDhuPN9DqjUG3DGIiHrgS8A7gKXAyohYWt1WTRpfA84fUHYF8L3MXAx8r3gNpfu3uPi6FPgKlEIc8CngXOAc4FP9Qa6oc2nZcQPfq1b0AP8xM5cArwM+XPwb816OXifwlsx8FfBq4PyIeB1wNfD54l7uAVYV9VcBezLzdODzRT2K+38hcCale/XlIuwdbT8PPgpsLXvtfZRUVcUHtH8L/CAzT8vMpcCVwAkT2Y7M/GRm/uM4n/ZjwPQRa0k6xIA7NucA2zLz0czsAm4BLqhymyaFzPwnSktFlbsAuKnYvgl4d1n517PkJ8DsiDgROA/4bmbuzsw9wHcphZITgVmZ+eNizeSvl52rpmTmzsz8abG9n1KgmI/3ctSKe3KgeNlYfCXwFuC2onzgvey/x7cBby3+eLoAuCUzOzPzMWAbpZ8FR83Pg4hYAPw+8NXideB9lFR9K4DuzLyuvyAz7wU2R8R/iYgHihFL7wOIiBkR8b2I+GlRfkFRvjAifhERNxWjoW7r7z0tek6vLkYE3RURpw9sRER8LSLeU2y/NiL+uRg9dFdEzCzO/6PifX8aEW8o6r45In5QvN8vIuIbUfKnwEnApojYNOC9FkZplNcNURqd9A8RMa3Yd3pE/GPx3j+NiNOK8w12L94cET+MiFsj4pcR8bmI+OOizfdHxGlFvXkR8TcRcXfx9cbx/o8ojRcD7tjMB7aXvd5RlGlwJ2TmTigFN+D4onyo+zhc+Y5BymtalIZ2ngXcifdyTIoewnuBZyiF/F8BezOzp6hSfv2H7lmxfx8wl9Hf41r034A/A/qK13PxPkqqvmXAPYOU/z+URu68Cngb8F+KD3g7gH+Vmb9DKRz/1+IDOIBXANdn5m8DzwP/vux8z2fmOcAXKf08HFSUHtX4JvDRYvTQ24CDlH4H/V7xvu8DvlB22FmUemuXAi8H3piZXwCeAlZk5opB3mox8KXMPBPYC/xhUf6NovxVwBuAncPcC4qyjwK/BbwfOKO4zq8Cq4s6/53SaJ3XFu/z1aGuX6o2A+7YDPasoustjd5Q93G05TUrImYAfwN8LDOfH67qIGXey0Jm9mbmq4EFlHoKlwxWrfjuvRxERLwLeCYzy/+IHO7avY+Sqm05sL74HfA08EPgtZR+3nwmIn4O/COlD9P6hzNvz8z/W2z/z+Ic/daXfX/9MO/7CmBnZt4NkJnPFx/0NQI3RMT9wP+iFGb73ZWZOzKzD7gXWFjB9T1W9FRDKeAvjIiZwPzM/NvivTsys32YewFwdzFyrJPSB8D/UJTfX9aOtwFfLD4s3gDMKt5LmnQaqt2AKWoHcHLZ6wWUPmHT4J6OiBMzc2fxaeEzRflQ93EH8OYB5T8oyhcMUr8mRUQjpXD7jcz830Wx9/IIZObeiPgBpeeaZ0dEQ/FHR/n199/LHRHRABxDadj9cP/fHw0/D94I/EFEvBNoAWZR6sHwPkqqtgeB9wxSPtTkiX8MzANek5ndEfE4pZ9rcPgHa1nB9mDvO9j+/wA8TanHtI5ST3K/zrLtXir7G33gMdMY+pqHm0iy/Dx9Za/7ytpRB7w+Mw9W0C6pquzBHZu7gcVRmj20idKEKRuq3KbJbAPQP3vvxcC3y8o/UDwX8jpgXzHs9g7g7RExJ0oTIr0duKPYtz8iXlcMJfpA2blqSnF964CtmXlN2S7v5SgVzw3NLranUfoUeiuwiRf+IBp4L/vv8XuA7xfPKW8ALozS7MCLKA0Nu4uj5OdBZn4iMxdk5kJK1/j9zPxjvI+Squ/7QHNEfKi/ICJeS2niu/cVj6nMA36X0s+bYyiNSOmOiBXAqWXnOiUi+ntnVwKby/a9r+z7j4dpzy+Ak4o2UDx/2/9B386il/b9QH0F17YfqLintBjttSMi3l28d3OUniP+Jwa/F5X6B+Aj/S8i4tWjOFaaUPbgjkFm9kTERyiFh3rgxsx8sMrNmhQiYj2lHsPjImIHpRl8PwfcGhGrgCeB9xbVNwLvpDTJTDvwQYDM3B0Rf0HpD16AqzKzf+Kqf0dppuZpwHeKr1r0Rkq//O4vhgNBaUZI7+XonQjcFKVZeuuAWzPz9oh4CLglIv4S+BmlDxQovv91RGyj1ON4IUBmPhgRtwIPUZrl+sOZ2QtwlP88uBzvo6QqysyMiH8F/LcoLTHWATxO6ZnWGcB9lHpU/ywzfxMR3wD+T0RsoTQc+Bdlp9sKXBwRfwU8QrEqQaE5Iu6k9Ltk5TDt6Somcbq2+GD1IKUPV78M/E1EvJfSh4NtFVze9cB3ImLnEM/hDub9wF9FxFVAN6W/Ff6W0rDqgffilRWe80+BLxXDuhsoBebLKjxWmlBR+kBdkiRJOnoVkzrenpnLBtn3OHB2Zj47wc2SNEoOUZYkSZIk1QR7cCVJkiT9/+3dWYgcVRSH8e/vFhUXEIMb6GA0AfctggtiID67QpAQfBJENDEgPor4IIqI+4L6oOC+IIgoxLjHLdGQxWgQxIDBGBGUiLvm+FC3tRk6Yw+MUdvvB01X3aq6594aaOZwuvpKI8EKriRJkiRpJJjgSpIkSZJGggmuJEmSJGkkmOBKk5Rk/ySPJfkkyYdJnk8yczvEvSbJlW372iRzp7j/K9paeb39DUn2ncoYkiRJ0t/JBFeahCShW0vu1aqaUVVH0K1Pu9/2HEdVXV1VS6e42yuA3f/yLEmSJOlfygRXmpw5wC9VdU+voapWAcuS3JjkgyRr2wLvJNkjyUtJVrb2s1v7WJL1SR5MsibJU73qaauc3pBkeXsdNn4QSR5IckHbnp3krSSr2/l7tv7faHFXJjm1nXtmkldbvPVJHk5nIXAg8EqSV8bFGkvyUZL7kqxLsqQtXE+Sw5IsbbFXJpnR+ht0L85M8lqSJ5J8nOT6JPPbmNcmmdHOm57k6SQr2uu0qf4jSpIkaTSZ4EqTcxTw/oD284DjgGOBucCNSQ4AfgTOraoT6JLjm1oVGGAWcG9VHQNsAS7t629LVZ0M3AHcsq3BJNkFeBxYVFW92D8AXwJntbjzgNv6Ljuerlp7BHAocFpV3QZ8DsypqjkDQh0O3FlVRwLfAOe39odb+7HAqcCmCe4FrW0RcDSwAJjZ5nk/cHk751bg5qqa3eLcv635S5IkSf1McKWpcTrwaFX9VlWbgdeA2UCA65KsAZYCB/Hn15k/q6o32/ZDrY+eR/veT5kg7ixgU1WtAKiqLVX1K7AzcF+StcCTdMlsz/Kq2lhVW4FVwNgQ8/u0VaqhS/DHkuwJHFRVz7TYP1bV9xPcC4AVVbWpqn4CPgGWtPa1feOYC9yRZBXwLLBXiyVJkiRNaKd/egDSf8w64IIB7RnQBjAfmA6cWFW/JNkA7NqO1bhza4jtQXEHHV8MbKarmO5AV0nu+alv+zeG+xwYf81ubHvO22of38/Wvv2tfePYATilqn4YYlySJEnSH6zgSpPzMjAtycW9hiSzga+BeUl2TDIdOANYDuwNfNmS2znAIX19HZykV529EFjWd2xe3/vbE4xnPXBgGwPt+dudWtxNrUq7ANhxiLl9CwxdKa2qLcDGJOe02NPac8SvM/heDGsJcFlvJ8lxk7hWkiRJ/2MmuNIkVFUB5wJntWWC1gHXAI8Aa4DVdEnwVVX1Bd0zqicleY+umru+r7uPgIva15f3Ae7uOzYtybt0z6sunmA8P9MlwbcnWQ28SFchvqv1/Q4wE/huiOndC7ww/kem/sICYGGbw1vA/nS/Mj3oXgxrId09W5PkQ+CSSVwrSZKk/7F0/69L2p6SjAHPVdVRA45tAE6qqq+287AkSZKk/zQruJIkSZKkkWAFV5IkSZI0EqzgSpIkSZJGggmuJEmSJGkkmOBKkiRJkkaCCa4kSZIkaSSY4EqSJEmSRsLvickEE/rM3FEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['CoapplicantIncome']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['CoapplicantIncome'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "df=train.dropna()\n",
    "sns.distplot(df['LoanAmount']);\n",
    "\n",
    "plt.subplot(122)\n",
    "train['LoanAmount'].plot.box(figsize=(16,5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1db8ac3278>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender=pd.crosstab(train['Gender'],train['Loan_Status'])\n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qp2C7HFjRsJGkocBE4MsR8dfSlGdmWSmm5zAP6Cepr6SOwCigurCBpKOB24AzIuLd0pdpZi2t0XCIiM3AOGAusBiYHRELJU2WdEa+2Q1AF+BBSS9Lqt7O6cxsN1HU6tMRMQeY02DfVQWPh5a4LjPLmGdImlmSw8HMkhwOZpbkcDCzJIeDmSU5HMwsyeFgZkkOBzNLcjiYWZLDwcySHA5mluRwMLMkh4OZJTkczCzJ4WBmSQ4HM0tyOJhZksPBzJIcDmaW5HAwsySHg5klORzMLMnhYGZJDgczS3I4mFmSw8HMkhwOZpbkcDCzJIeDmSU5HMwsyeFgZkkOBzNLKiocJA2TtETSUkkTEsc7SXogf/wFSRWlLtTMWlaj4SCpPTANGA4cDoyWdHiDZucB70fE54CbgetLXaiZtaxieg6DgKURsSwiNgL3A2c2aHMmcGf+8UPAEEkqXZlm1tI6FNGmN7C8YLsOOGZ7bSJis6Q1QA9gVWEjSVVAVX5zraQlTSl6N9GTBt9/c5L7aqW0p//sDimmUTHhkOoBRBPaEBEzgBlFvOZuT1JtRFRmXYftPP/scooZVtQBfQq2y4EV22sjqQPQDXivFAWaWTaKCYd5QD9JfSV1BEYB1Q3aVAP/nn98NvDbiNim52Bmu49GhxX5awjjgLlAe2BWRCyUNBmojYhqYCZwt6Sl5HoMo5qz6N1Emxg+7aH8swPk/+DNLMUzJM0syeFgZkkOBzNLcjhYmydpnKR9849vk/QHSUOyritrDgczqIqIDySdQm6274XAlIxrypzDoYQklUt6RFK9pJWSfimpPOu6rFFbbtkNB34REfPxvw2/ASX2C3ITwg4i9z/Qo/l91rq9ImkOcDrwmKQuJKb/tzWe51BCkl6OiKMa22etS35Zgi+S+/Txe5J6An0i4qWMS8uUew6ltUrSuZLa57/OBVZnXZTtWER8DBxK7loDwN7434Z7DqUk6WDgVuBYct3SZ4FvR8SbmRZmOyTpVmAv4MSIOExSd2BuRPxjxqVlqpiPbFuRIuIt4Iys67CddlxEDJT0EkB+aNEx66Ky5nAoAUlX7eBwRMQ1LVaMNcUmSe3IX4SU1AP4JNuSstfmx1Ulsi7xBbm1NS/Pqigr2jTgl0CZpB8A/4vXQfU1h1KT1BX4NrlgmA3cFBHvZluVpeRvX34rIt6QdAQwlNyqZk9GxB+zrS57HlaUSP4i1njga+QW2x0YEe9nW5U14g7gCUl3AlMiYmHG9bQq7jmUgKQbgLPILRIyLSLWZlySFUnSPsBVwDDgbgquNUTET7KqqzVwz6E0LgX+ClwJTCxYlV/kLkjum1Vh1qhN5K4RdQK64guRWzkcSiAifGF3NyRpGPATclPeB0bE+oxLalU8rLA2S9IzwAW+1pDmcDCzJHeHzSzJ4WBmSQ4HM0tyOLQhkkLS3QXbHfKrVv26BOd+difbT5L03V19XWs+Doe2ZR3QX9Le+e2Tgbd35gT534VauN0eICKOK0mF1mo4HNqex4BT849HA/dtOSBpkKRnJb2U//ML+f1jJT0o6VFy041PklQj6V7g1XybtQXnuUzSPEkL8h9k2rJ/oqQlkp4EvtD836rtCk+CanvuB67KDyUGALOAf8of+xO5BU82SxoK/Aj4av7YscCA/FoHJwGDgP4R8XrhyfMrOPfLHxdQLelEcr2WUcDR5P7evQjMb7bv0naZw6GNiYgFkirI9RrmNDjcDbhTUj9yaxvsVXDsNxHxXsH2HxoGQ94p+a8t6y92IRcWXYFHtsxClNTwN7VbK+NhRdtUDdxIwZAi7xqgJiL6k1uJuXPBsXUN2jbc3kLAjyPiqPzX5yJiZv6YZ9ztRhwObdMsYHJEvNpgfzf+doFybBPPPRf4Rn55dyT1lrQ/8HvgXyXtnV/z4vQmnt9aiIcVbVBE1AE/TRyaQm5YMR74bRPP/YSkw4Dn8p9OXQucGxEvSnoAeBl4E3imScVbi/FnK8wsycMKM0tyOJhZksPBzJIcDmaW5HAwsySHg5klORzMLMnhYGZJ/w+5zGZKS1o4mwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married=pd.crosstab(train['Married'],train['Loan_Status'])\n",
    "Dependents=pd.crosstab(train['Dependents'],train['Loan_Status'])\n",
    "Education=pd.crosstab(train['Education'],train['Loan_Status'])\n",
    "Self_Employed=pd.crosstab(train['Self_Employed'],train['Loan_Status'])\n",
    "\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()\n",
    "\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()\n",
    "\n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()\n",
    "\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status'])\n",
    "Property_Area=pd.crosstab(train['Property_Area'],train['Loan_Status'])\n",
    "\n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4))\n",
    "plt.show()\n",
    "\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1db89f4208>"
      ]
     },
     "execution_count": 18,
     "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": [
    "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EopUz6fR6+ZRkZmZFKTcU3pO048oZSf8HeD+fkszMrCjlXlP4HnCjpFfS+c2BkfmUZGZmRWk2FCR1ADoD2wLbkLyS85mIWJZzbWZm1sqaDYWIWCHp1xHxeZIhtM3MrJ0q95rCXZK+Jkm5VmNmZoUq95rCWKA78KGk90m6kCIi1s+tMjMza3XlDp3dM+9CzMyseOUOnS1JR0r6aTrfT9LQfEszM7PWVu41hd8BnwcOT+cXARflUpGZmRWm3GsKO0fEjpIeA4iItyR1zrEuMzMrQLlnCsvSQfACQFJvYEVzG0naV9KzkuZIOmU17UZICkl1ZdZjZmY5KDcUzgduATaR9DPgQeDnq9sgDZGLgP2AQcAoSYMaadcTOAl4pAV1m5lZDsq9+2iipJnAcJLbUb8SEU83s9lQYE5EzAWQdB1wEDC7Qbv/Ac4BftCSws3MrPJWGwqSugLHA1uRvGDnkohYXua++wLzSubrgZ0b7H8HoF9E/FWSQ8HMrGDNdR9dBdSRBMJ+wK9asO/Gnn7O3uucjql0LnByszuSxkiaIWnG/PnzW1CCmZm1RHPdR4Mi4rMAki4HHm3BvuuBfiXztcArJfM9gcHAvenoGZsBkyQdGBEzSncUEROACQB1dXWBmZnlorkzhWwk1BZ0G600HRgoaUB6++phwKSS/S2MiF4R0T8i+gMPA6sEgpmZtZ7mzhSGSHonnRbQLZ1vduyjiFgu6QTgTqCG5B3PT0kaD8yIiElNbWtmZsVYbShERM3a7DwiJgOTGyw7rYm2e6zNZ5mZ2dor9zkFMzNbBzgUzMws41AwM7OMQ8HMzDIOBTMzyzgUzMwsU+77FMzMql7/JdcUXUJZXizws32mYGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllfEsqvk3NzGwlnymYmVnGoWBmZhmHgpmZZXxNwSrO12jMqpdDwawNc8Baa3P3kZmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWyTUUJO0r6VlJcySd0sj6sZJmS5olaaqkLfOsx8zMVi+3UJBUA1wE7AcMAkZJGtSg2WNAXURsB9wEnJNXPWZm1rw8zxSGAnMiYm5EfABcBxxU2iAi7omIxensw0BtjvWYmVkz8gyFvsC8kvn6dFlTjgZub2yFpDGSZkiaMX/+/AqWaGZmpfIMBTWyLBptKB0J1AG/bGx9REyIiLqIqOvdu3cFSzQzs1J5vqO5HuhXMl8LvNKwkaS9gVOB3SNiaY71mJlZM/I8U5gODJQ0QFJn4DBgUmkDSTsAlwAHRsTrOdZiZmZlyC0UImI5cAJwJ/A0cENEPCVpvKQD02a/BHoAN0p6XNKkJnZnZmatIM/uIyJiMjC5wbLTSqb3zvPzzcysZfxEs5mZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZplcQ0HSvpKelTRH0imNrO8i6fp0/SOS+udZj5mZrV5uoSCpBrgI2A8YBIySNKhBs6OBtyJiK+Bc4Oy86jEzs+bleaYwFJgTEXMj4gPgOuCgBm0OAq5Kp28ChktSjjWZmdlq5BkKfYF5JfP16bJG20TEcmAhsHGONZmZ2Wp0zHHfjf3GH2vQBkljgDHp7CJJz65lba2hF/BGJXeodbtzzd9n5fi7rKxq+T63LKdRnqFQD/Qrma8FXmmiTb2kjsAGwJsNdxQRE4AJOdWZC0kzIqKu6DraC3+flePvsrLa2/eZZ/fRdGCgpAGSOgOHAZMatJkEfCOdHgH8PSJWOVMwM7PWkduZQkQsl3QCcCdQA1wREU9JGg/MiIhJwOXAHyXNITlDOCyveszMrHl5dh8REZOByQ2WnVYyvQQ4JM8aClRV3V1VwN9n5fi7rKx29X3KvTVmZraSh7kwM7OMQ8HMzDK5XlMwM2uPJPUlue8/O4ZGxP3FVVQ5DoUKkvTJiJhbdB3tiaTuEfFe0XW0B+35QNaaJJ0NjARmAx+miwNoF9+lLzRXkKT7SYbumE7yD+SBiHiy2Kqqk6RdgMuAHhGxhaQhwHER8Z2CS6tKTR3IIuLA4qqqTumICttFxNKia8mDzxQqKCJ2Sx/U2wnYA/ibpB4RsVGxlVWlc4EvkT7wGBFPSNqt2JKq2leAbdrrgayVzQU6Ae3yu3QoVJCkXYEvpD8bAn8FHii0qCoWEfMaDJr7YVNtrVnt+kDWGiRdQNJNtBh4XNJUSr7PiDipqNoqyaFQWfcBM4BfAJPTIcNtzcxLu5AiPfs6CXi64JqqzrpyIGslM9I/Z7LqkD3thq8pVJCkDYFhwG4kXUgrgGkR8dNCC6tCknoB5wF7k4ymexfwvYhYUGhhVUbSN1a3PiKuWt16W/c4FCpM0qeB3Um6kHYBXo6I3YutyswqRdKTrDrE/0KSM4kzq/0XF4dCBUl6HngWeJDkWsIj7kJaM5LOb2TxQpLBFP/S2vVUu/Z+IGtNks4hub51TbroMJKz2YXArhFxQFG1VYJDoYIkdYiIFUXX0R5ImgBsC9yYLvoa8BTJ+zfmRsT3i6qtGrX3A1lrkvSPiBjW2DJJT0bEZ4uqrRJ8obmy+qQX9oaR/Fb2IEk/eH2xZVWlrYC90te0Iun3JNcV9gH87EfLDWtwIHuy5EB2ZGFVVaceknaOiEcAJA0FeqTrlhdXVmV47KPK+gPJXQl9SB5iuy1dZi3XF+heMt8d6BMRH+LbKtdED0k7r5xpbweyVnYMcJmkFyS9SPKQ5bGSupPceVjVfKZQWb0jojQErpTkbo41cw7JLZT3knRz7Ab8PP0fb0qRhVWpY4ArJPUg+T7fAY5pLwey1hQR04HPStqApAv+7ZLVNxRUVsX4mkIFSZoCXAlcmy4aBXwzIoYXVlQVk7Q5MJTkIPZoRDR8x7e1UBMHMiuDpCMj4k+Sxja2PiJ+09o15cFnCpX1LeBCkiEaAngI+GahFVW3JcCrQFdgK0lbeQC3lmnqQLbySfH2ciBrJSu7M3sWWkXOHAoVFBEvAx8bYCztPvptMRVVL0nHAN8DaoHHgc8B04C9iqyrCq0TB7LWEBGXpH+eUXQteXL3Uc4kvRwRWxRdR7VJ76vfCXg4IraXtC1wRkSMLLg0W8dJ6g0cC/Tn48OQf6uomirJZwr5U/NNrBFLImKJJCR1iYhnJG1TdFHVpomHADMe+2iN/IXk4dQptMNBGh0K+fOp2JqpT8eSuhW4W9JbgC80t9zMkukzgNOLKqQdWS8iflR0EXlx91EFSHqXxg/+ArpFhMN3LUjaHdgAuMPDhqw5SY9FxA5F11HtJJ0JPBQRk4uuJQ8OBWtzJHUAZkXE4KJraU8k/TMidiy6jmpV8sufSC7gLwWWpfMREesXWF7F+DdYa3MiYoWkJyRtkd7RZVa4iFgn7uByKFhbtTnwlKRHgfdWLvQ7hVumQdfmepLeWbmKdvTbrVWOu4+sTUqvI6wiIu5r7VrM1iUOBWuzJG0JDIyIKZLWA2oi4t2i6zJrzzxKqrVJko4FbgIuSRf1Jbk91axQkn4l6TNF15EXh4K1Vd8leS/FOwAR8RywSaEVmSWeASZIekTS8ekgg+2GQ8HaqqWlzyRI6ogfBLQ2ICIuS19YdBTJUBezJF0jac9iK6sMh4K1VfdJ+jHQTdI+JK/lvK3gmswAkFRD8rrYbYE3gCeAsZKuK7SwCvCFZmuT0gfYjga+SHL75J3AZeF/sFYwSb8BDgD+DlweEY+WrHs2Iqp6jC4/p2Bt1UHA1RFxadGFmK2k5EUUbwFDImJxI02GtnJJFefuI2urDgT+LemPkr6cXlMwK1R6pvqVJgKBiFjYyiVVnEPB2qSI+CawFcm1hMOB5yVdVmxVZgA8LGmnoovIi68pWJsmqROwL8mrTr8QEb0KLsnWcZJmA9sAL5IMwbJyyJDtiqyrUhwK1iZJ2hc4jOT1m/cA1wF3R8TyQguzdV76pP0qIuKl1q4lD+4+srZqNHALyTAX3wDeBc4rtCIzsoN/P2CvdHox7ehY6jMFa7MkbQ+MAkYCLwA3R8QFxVZl6zpJpwN1wDYRsbWkPsCN6QNtVc93dFibImlrkm6jUcAC4HqSX17axdOi1i58FdgB+CdARLwiqd28a8GhYG3NMyQvRT8gIuYASPqvYksy+5gPIiIkBYCk7kUXVEntph/M2o2vAf8fuEfSpZKGk9zdYdZW3CDpEmDDdDTfKUC7ecjS1xSsTUp/+/oKSTfSXsBVwC0RcVehhZkB6Xhc2RAsEXF3wSVVjEPB2jxJGwGHACMjYq+i67F1k6QLgWsi4qGia8mTQ8HMrAySvkdyE8TmJDdAXBsRjxdbVeU5FMzMWiB9eO2w9KcrcC1wXUT8u9DCKsShYGa2hiTtAFwBbBcRNUXXUwm++8jMrAUkdZJ0gKSJwO3Av0nummsXfKZgZlaG9I6jUcCXgUdJxuO6NSLeK7SwCnMomJmVQdI9wDXAnyPizaLryYtDwczMMr6mYGZmGYeCmZllHApWVSR9VVJI2nYt9nGlpBHp9GWSBlWuQpD04wbziyq5f7M8ORSs2owCHiR5cGitRcQxETG7Evsq8ePmm5i1TQ4FqxqSegDDgKNJQ0HSHpLul3SLpNmSLpbUIV23SNKvJf1T0lRJvRvZ572S6tLpfdO2T0iami4bKukhSY+lf26TLh8t6WZJd0h6TtI56fKzgG6SHk/vYy/9rD3Sz7tJ0jOSJkpSum6ndP9PSHpUUk9JXSX9QdKT6efvWfLZt0q6TdILkk6QNDZt83A6VhSSPpXWN1PSA2tzdmXrkIjwj3+q4gc4Erg8nX4I2BHYA1gCfBKoAe4GRqRtAjginT4NuDCdvrKkzb0kb9HqDcwDBqTLN0r/XB/omE7vTXI7IiSvC50LbEAy1MFLQL903aIGdS9K/9wDWAjUkvxCNg3YFeic7mun0s8ETgb+kC7bFng5/azRwBygZ1r3QuD4tN25wPfT6akkrzMF2Bn4e9H/Df3T9n/8kh2rJqOA36bT16XzfwMejYi5AJKuJTnQ3gSsIBm4DOBPwM2r2ffngPsj4gWA+Og+9A2AqyQNJAmZTiXbTI2Ihennzga2JAmW1Xk0IurTbR4H+pMc1F+NiOnpZ7+Trt8VuCBd9oykl4Ct0/3cExHvAu9KWgjcli5/EtguPavaBbgxPRkB6NJMbWYOBasOkjYmea/C4PSNVzUkB+nJ6Z+lmnr4ZnUP5aiJ9f9DcgD+qqT+JGcWKy0tmf6Q8v5/amybpj57dS8XKt3PipL5Fek+OwBvR8T2ZdRklvE1BasWI4CrI2LLiOgfEf2AF0jOCoZKGpBeSxhJciEakn/fI9Lpw0uWN2YasLukAZC9wwGSM4X/pNOjy6x1maROzTfLPAP0kbRT+tk9JXUE7geOSJdtDWwBPFvODtOzjRckHZJuL0lDWlCTraMcClYtRgG3NFj2Z5KD/TTgLOBfJEGxst17wGckzSQ5yxjf1M4jYj4wBrhZ0hN81O10DvALSf8gOTspxwRgVsMLzav57A9IwuyC9LPvJrl28DugRtKTaT2jI2Jp03taxRHA0ek+nwIOasG2to7yMBdW1STtAfwgIvZvZN2iiOjR+lWZVS+fKZiZWcZnCmZmlvGZgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaW+V8iUOVl8kSzAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['Low','Average','High', 'Very high']\n",
    "train['Income_bin']=pd.cut(df['ApplicantIncome'],bins,labels=group)\n",
    "\n",
    "Income_bin=pd.crosstab(train['Income_bin'],train['Loan_Status'])\n",
    "Income_bin.div(Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('ApplicantIncome')\n",
    "P = plt.ylabel('Percentage')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "bins=[0,1000,3000,42000]\n",
    "group=['Low','Average','High']\n",
    "train['Coapplicant_Income_bin']=pd.cut(df['CoapplicantIncome'],bins,labels=group)\n",
    "Coapplicant_Income_bin=pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "bins=[0,2500,4000,6000,81000]\n",
    "group=['Low','Average','High', 'Very high']\n",
    "train['Total_Income_bin']=pd.cut(train['Total_Income'],bins,labels=group)\n",
    "Total_Income_bin=pd.crosstab(train['Total_Income_bin'],train['Loan_Status'])\n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('Total_Income')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEpCAYAAAB8/T7dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHAxJREFUeJzt3XuYVmW9//H3V1BQ8JCCBwREC/NAmsbGClNJLfmV2i4E7WCWhZVK/nRXtq0kq53aSSl3ioet9bMM3bo3tlFMN5rtPIBpmJiFZDLhT5EKRURFv/uP52H5OBzmGWYWa2Z4v66Ly2et515rvjPP5Xzmvu91rxWZiSRJAJtUXYAkqeswFCRJBUNBklQwFCRJBUNBklQwFCRJBUNBklQwFCRJBUNBklToXXUB7TVgwIAcNmxY1WVIUrdy3333PZ2ZA9tq1+1CYdiwYcyZM6fqMiSpW4mIPzfTzuEjSVLBUJAkFQwFSVKh280pSFKjl156iZaWFlasWFF1KV1C3759GTx4MJtuuul6HW8oSOrWWlpa2HLLLRk2bBgRUXU5lcpMlixZQktLC7vuuut6naO04aOIuCIinoqI363l/YiIKRExPyLmRsT+ZdUiqedasWIF22233UYfCAARwXbbbdehXlOZcwpXAkes4/2xwPD6v4nAD0usRVIPZiC8qqM/i9JCITN/Cfx1HU2OBn6UNXcD20TETmXVI0lqW5VzCjsDCxu2W+r7nmjdMCImUutNMHTo0A1SXGHy1hv2621ok5dWXUG5evLn52dX8+5psKgbTjIP2q/qCtaoyktS19THyTU1zMypmTkyM0cOHNjmKm1JG7n+w0dv8K/5jQsvY+8x49jnsPG8+fBjuec3DwJwwaVXs/z559s8/oILLmD58uVll9mmKkOhBRjSsD0YWFRRLZK03u6a81t+fuud/ObmnzD31mnc+rMfMmTQjgBccNlPWP582z0ZQwGmA8fXr0J6K7A0M1cbOpKkzvDnlkUcOv4k9jlsPIeOP4nH/1L7dXPjLXdwwHuPZ793HcdhEz7Fk4uXADD5Oxfz8dMnc8i4T7Lb245kyuU/Xeu5n3jqaQZsuw19+mwGwIBtX8egHQcy5fKfsujJxYw55iTGjJsIwKfP/BdGjv0Qe++9N2effTYAU6ZMYdGiRYwZM4YxY8YA0L9//+L81113HSeccAIA1157LSNGjGDffffloIMO6twfEuVekvpT4C7gjRHREhEnRsSnIuJT9SYzgAXAfOBS4DNl1SJJp5x1HsePey9zb53Gh94/lklf/hYAB47aj7tvvIr7b/kpxx79bs7/16uKY34//zFmXn0R9/7Xj/jqd6fy0ksvrfHc7zr4bSxc9CS7H/g+PvPFb3LHXfcBMOnE4xi0w0BmXXsJs66bCsA3vnAyc266mrlz53LHHXcwd+5cJk2axKBBg5g1axazZs1a5/dxzjnnMHPmTH77298yffr0zvjRvEZpE82ZeVwb7ydwcllfX5Ia3XXfg1x/2bcB+MgH3sPnvz4FgJYnnmTCp8/kiaee5sUXX2LXoYOKY95z6IH06bMZffpsxvYDXseTi//K4EE7rHbu/v224L6br+bOe+5n1q9nM+HTZ3LuF0/lhAlHrdZ22o2/YOrV17MyNuWJJ55g3rx57LPPPk1/H6NHj+aEE05g/PjxvP/972/vj6FN3vtI0kZp1eX8p375fE752AQevG0al5x3FiteeLFos2o4CKBXr16sfPnltZ6vV69eHPL2kXz1nz7ND77+Bf59xm2rtfnT43/h25f8iNt+djFz587lPe95z1oXmjWuN2hsc/HFF/P1r3+dhQsX8uY3v5klS5Y0/T03w1CQtFF4+8h9uOY/ZwJw9fU3ceCo2iWhS59Zxs471q5qvOraG9fr3I/Mf4w/Lni82H7goUfYZXBt2dWW/fvx7LLaBPIzzz5Hv803Z+ut+vPkk09y0003FcdsueWWPPvss8X2DjvswMMPP8wrr7zCDTfcUOx/9NFHOeCAAzjnnHMYMGAACxc2Xtnfcd77SFKPs/z5FQx+y6s3VDh94oeZ8rXP8/HTJ/Oti3/EwG1fx799bzIAk884iWNO+gI77ziQt+7/Jv60sP0XQS5bvpxTv3Q+f3/mWXr37sUbhg1h6vlfAmDih97P2A+fyk7bD2DWdVPZb8Qe7D1mHLvtvhejR7966ezEiRMZO3YsO+20E7NmzeLcc8/lve99L0OGDGHEiBEsW7YMgM997nP88Y9/JDM59NBD2XfffTvwk1pd1Ib2u4+RI0fmBn3yWk9e/AQugOrO/OwAePjd09hzl+1LLqYEJS5ee/jhh9lzzz1fsy8i7svMkW0d6/CRJKng8JEkNWnJX//OoRM+tdr+2352Mdttu00FFXU+Q0GSmrTdttvwwC+uqbqMUjl8JEkqGAqSpIKhIEkqOKcgaaM3bErn3qD5sUmD2mwTEZx++ul85zvfAeDb3/42y5YtY/LkyZ1aS3vZU5CkCvTp04frr7+ep59+uupSXsNQkKQK9O7dm4kTJ/K9732v6lJew1CQpIqcfPLJXH311Sxd2nVWpxsKklSRrbbaiuOPP54pU6ZUXUrBUJCkCp122mlcfvnlPPfcc1WXAhgKklSpbbfdlvHjx3P55ZdXXQrgJamS1NQlpGU644wz+MEPflBpDasYCpJUgVXPR4DaA3WWL19eYTWvcvhIklQwFCRJBUNBklQwFCRJBUNBklQwFCRJBS9JlaSph3Tu+Sbevs63M5N3HHggZ511FmPHjgVg2rRpXHHFFdx8882dW0s7GQqStIFFBBdffDHHHHMMY8aM4eWXX+ass86qPBDAUJCkSowYMYIjjzyS8847j+eee47jjz+e17/+9VWXZShIUlXOPvts9t9/fzbbbDPmzJlTdTmAoSBJlenXrx8TJkygf//+9OnTp+pyAK8+kqRKbbLJJmyySdf5Vdx1KpEkVa7U4aOIOAK4EOgFXJaZ57Z6fyhwFbBNvc2ZmTmjzJokaTUTb6+6gi6jtFCIiF7ARcDhQAswOyKmZ+a8hmZfAqZl5g8jYi9gBjCsrJokqauZPHly1SW8RpnDR6OA+Zm5IDNfBK4Bjm7VJoGt6q+3BhaVWI8kqQ1lDh/tDCxs2G4BDmjVZjJwS0ScCvQDDiuxHklSG8rsKcQa9mWr7eOAKzNzMPB/gB9HxGo1RcTEiJgTEXMWL15cQqmSuq8ks/Wvlo1XR38WZYZCCzCkYXswqw8PnQhMA8jMu4C+wIDWJ8rMqZk5MjNHDhw4sKRyJXVHfZcuYMlzKw0GaoGwZMkS+vbtu97nKHP4aDYwPCJ2Bf4CHAt8sFWbx4FDgSsjYk9qoWBXQFLTBv/mPFr4Aou33o01D1B0UUsfLuW0ffv2ZfDgwet9fGmhkJkrI+IUYCa1y02vyMyHIuIcYE5mTgfOAC6NiP9LbWjphDTuJbXDpi/+nV3v/mLVZbTf5KVVV7BGpa5TqK85mNFq31caXs8DRpdZgySpea5oliQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUsFQkCQVDAVJUqHpUIiIzSPijWUWI0mqVlOhEBFHAg8AN9e33xwR08ssTJK04TXbU5gMjAL+DpCZDwDDyilJklSVZkNhZWYuLbUSSVLlejfZ7ncR8UGgV0QMByYBvy6vLElSFZrtKZwK7A28APwUeAY4rayiJEnVaKqnkJnLgbPq/yRJPVRToRARNwLZavdSYA5wSWauWMtxRwAXAr2AyzLz3DW0GU9tIjuB32bmB5uuXpLUqZqdU1gADKQ2dAQwAXgS2B24FPhI6wMiohdwEXA40ALMjojpmTmvoc1w4IvA6Mz8W0Rsv77fiCSp45oNhf0y86CG7Rsj4peZeVBEPLSWY0YB8zNzAUBEXAMcDcxraPNJ4KLM/BtAZj7VvvIlSZ2p2YnmgRExdNVG/fWA+uaLazlmZ2Bhw3ZLfV+j3YHdI+J/IuLu+nDTaiJiYkTMiYg5ixcvbrJkSVJ7NdtTOAP4VUQ8CgSwK/CZiOgHXLWWY2IN+1rPS/QGhgOHAIOBOyNiRGb+/TUHZU4FpgKMHDmy9TkkSZ2k2auPZtTH//eg9sv+9w2Tyxes5bAWYEjD9mBg0Rra3J2ZLwF/iohHqIXE7CbrlyR1ovbcJXU48EZgH2B8RBzfRvvZwPCI2DUiNgOOBVrfL+k/gDEAETGA2nDSgnbUJEnqRM1ekno2tSGevYAZwFjgV8CP1nZMZq6MiFOAmdQuSb0iMx+KiHOAOZk5vf7euyJiHvAy8LnMXNKB70eS1AHNzimMA/YF7s/Mj0XEDsBlbR2UmTOohUjjvq80vE7g9Po/SVLFmh0+ej4zXwFWRsRWwFPAbuWVJUmqQrM9hTkRsQ21hWr3AcuAe0urSpJUiWavPvpM/eXFEXEzsFVmzi2vLElSFZp98tptq15n5mOZObdxnySpZ1hnTyEi+gJbAAMi4nW8uiBtK2BQybVJkjawtoaPTqL23IRB1OYSVoXCM9RudidJ6kHWGQqZeSFwYUScmpnf30A1SZIq0uxE8/cj4u3AsMZjMnOti9ckSd1Psyuafwy8HniA2spjqN3czlCQpB6k2XUKI4G96iuQJUk9VLMrmn8H7FhmIZKk6jXbUxgAzIuIe4EXVu3MzKNKqUqSVIlmQ2FymUVIkrqGZq8+uiMidgGGZ+atEbEFtdthS5J6kGZvc/FJ4Drgkvqunak9IEeS1IM0O9F8MjCa2kpmMvOPwPZlFSVJqkazofBCZr64aiMielNbpyBJ6kGaDYU7IuKfgc0j4nDgWuDG8sqSJFWh2VA4E1gMPEjtJnkzgC+VVZQkqRrNXpK6OXBFZl4KEBG96vuWl1WYJGnDa7ancBu1EFhlc+DWzi9HklSlZkOhb2YuW7VRf71FOSVJkqrSbCg8FxH7r9qIiLcAz5dTkiSpKs3OKXwWuDYiFtW3dwImlFOSJKkqbYZCRGwCbAbsAbyR2iM5f5+ZL5VcmyRpA2szFDLzlYj4Tma+jdottCVJPVSzcwq3RMQHIiJKrUaSVKlm5xROB/oBL0fE89SGkDIztyqtMknSBtfsrbO3LLsQSVL1mr11dkTEhyPiy/XtIRExqtzSJEkbWrNzCv8KvA34YH17GXBRKRVJkirT7JzCAZm5f0TcD5CZf4uIzUqsS5JUgWZ7Ci/Vb4KXABExEHilrYMi4oiIeCQi5kfEmetoNy4iMiJGNlmPJKkEzfYUpgA3ANtHxDeAcbRx6+x6iFwEHA60ALMjYnpmzmvVbktgEnBPO2uX1IMNW/GTqkso1WNVF7AWzV59dHVE3AccSu1y1Pdl5sNtHDYKmJ+ZCwAi4hrgaGBeq3ZfA84H/qk9hUuSOt86QyEi+gKfAt5A7QE7l2TmyibPvTOwsGG7BTig1fn3A4Zk5s8jYq2hEBETgYkAQ4cObfLLS5Laq605hauAkdQCYSzw7Xace02rn4vnOtfvqfQ94Iy2TpSZUzNzZGaOHDhwYDtKkCS1R1vDR3tl5psAIuJy4N52nLsFGNKwPRhY1LC9JTACuL1+94wdgekRcVRmzmnH15EkdZK2egrFnVDbMWy0ymxgeETsWr989VhgesP5lmbmgMwclpnDgLsBA0GSKtRWT2HfiHim/jqAzevbbd77KDNXRsQpwEygF7VnPD8UEecAczJz+tqOlSRVY52hkJm9OnLyzJwBzGi17ytraXtIR76WJKnjml28JknaCBgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqRCW89T2OgNW/GTqkso1WNVFyCpS7GnIEkqGAqSpIKhIEkqOKegHq0nzwk9VnUB6pHsKUiSCoaCJKlgKEiSCoaCJKlgKEiSCoaCJKlgKEiSCoaCJKlgKEiSCoaCJKlgKEiSCoaCJKlgKEiSCoaCJKlQaihExBER8UhEzI+IM9fw/ukRMS8i5kbEbRGxS5n1SJLWrbRQiIhewEXAWGAv4LiI2KtVs/uBkZm5D3AdcH5Z9UiS2lZmT2EUMD8zF2Tmi8A1wNGNDTJzVmYur2/eDQwusR5JUhvKDIWdgYUN2y31fWtzInDTmt6IiIkRMSci5ixevLgTS5QkNSozFGIN+3KNDSM+DIwEvrWm9zNzamaOzMyRAwcO7MQSJUmNynxGcwswpGF7MLCodaOIOAw4Czg4M18osR5JUhvK7CnMBoZHxK4RsRlwLDC9sUFE7AdcAhyVmU+VWIskqQmlhUJmrgROAWYCDwPTMvOhiDgnIo6qN/sW0B+4NiIeiIjpazmdJGkDKHP4iMycAcxote8rDa8PK/PrS5LaxxXNkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKhgKkqSCoSBJKpQaChFxREQ8EhHzI+LMNbzfJyJ+Vn//nogYVmY9kqR1Ky0UIqIXcBEwFtgLOC4i9mrV7ETgb5n5BuB7wHll1SNJaluZPYVRwPzMXJCZLwLXAEe3anM0cFX99XXAoRERJdYkSVqH3iWee2dgYcN2C3DA2tpk5sqIWApsBzzd2CgiJgIT65vLIuKRUiruGgbQ6vsvU9g360x+dt1bT//8dmmmUZmhsKa/+HM92pCZU4GpnVFUVxcRczJzZNV1qP387Lo3P7+aMoePWoAhDduDgUVraxMRvYGtgb+WWJMkaR3KDIXZwPCI2DUiNgOOBaa3ajMd+Gj99TjgvzNztZ6CJGnDKG34qD5HcAowE+gFXJGZD0XEOcCczJwOXA78OCLmU+shHFtWPd3IRjFM1kP52XVvfn5A+Ie5JGkVVzRLkgqGgiSpYChIkgqGgiSpUObiNTUpInbLzAVV16H1FxH9MvO5qutQ+0XEztRW+xa/DzPzl9VVVC2vPuoCIuKX1G75MRv4JXBnZj5YbVVqRkS8HbgM6J+ZQyNiX+CkzPxMxaWpCRFxHjABmAe8XN+dmXlUdVVVy1DoIuoL/P4BOAQ4idovmW0rLUptioh7qC28nJ6Z+9X3/S4zR1RbmZpRv4/aPpn5QtW1dBUOH3UBEXEg8I76v22AnwN3VlqUmpaZC1vd3PfltbVVl7MA2BQwFOoMha7hDmAO8E1gRv1W4+oeFtaHkLLe25sEPFxxTWpDRHyf2s03lwMPRMRtNARDZk6qqraqOXzUBUTENsBo4CBqQ0ivAHdl5pcrLUxtiogBwIXAYdTu+nsL8NnMXFJpYVqniPjout7PzKvW9X5PZih0ERGxJ3AwtSGktwOPZ+bB1VYlaWNjKHQBEfEo8AjwK2pzCfc4hNQ9RMSUNexeSu2mj/+5oetR+0TEg6z+DJel1IZzv74x9vgMhS4gIjbJzFeqrkPtFxFTgT2Aa+u7PgA8RO05IQsy87SqalPbIuJ8ahcG/KS+61hqw4BLgQMz88iqaquKodAFRMRg4PvU5hWSWo/hs5nZUmlhalNE/DfwrsxcWd/uTW1e4XDgwczcq8r6tG4R8T+ZOXpN+yLiwcx8U1W1VcXbXHQN/0btgUODqC1iu7G+T13fzkC/hu1+wKDMfBkvc+wO+kdE8ez4iBgF9K9vrqympGp5SWrXMDAzG0Pgyohw2KF7OJ/aJY23Uxt2OAj4l4joB9xaZWFqyieAKyKiP7XP7xngE/XP75uVVlYRh4+6gIi4FbgS+Gl913HAxzLz0MqKUtMiYidgFLVfKvdmZutnkauLi4itqf0+/HvVtVTNUOgCImIo8APgbdTmFH4NTMrMxystTE2JiNcBw4G+q/ZtzDdU6w4i4sOZ+f8i4vQ1vZ+Z393QNXUVDh91AfVf/q+5AVd9+OiCaipSsyLiE8BngcHAA8BbgbuAd1ZZl9q0ah5oy0qr6ILsKXRREfF4Zg6tug6tW/06938A7s7MN0fEHsBXM3NCxaVJ68WeQtcVbTdRF7AiM1dEBBHRJzN/HxFvrLoordtaFh0WNuZ7HxkKXZdduO6hpX7vqv8AfhERfwOcaO767mt4/VXg7KoK6WocPqpQRDzLmn/5B7B5Zhra3UhEHAxsDdzsbUq6j4i4f9WzMGRPoVKZ6SRXNxYRmwBzVz1QJzPvqLgkrR//Mm7gimZpPdXvV/Xb+iXFUo9gT0HqmJ2AhyLiXuC5VTs35mf8dgethm63iIhnVr1F7RnNW1VTWfWcU5A6oD6PsBqHktRdGQpSB0XELsDwzLw1IrYAemXms1XXJa0P5xSkDoiITwLXAZfUd+1M7fJUqVsyFKSOOZnaczCeAcjMPwLbV1qR1AGGgtQxLzSuSag/ZMcxWXVbhoLUMXdExD8Dm0fE4dQey3ljxTVJ682JZqkD6gvYTgTeRe1yxpnAZen/WOqmDAWpAyLiH4EZmemjN9UjOHwkdcxRwB8i4scR8Z76nILUbdlTkDooIjYFxgITgAOBX2TmJ6qtSlo/hoLUCerBcATwceAdmTmg4pKk9eLwkdQBEXFERFwJPAqMA6YCO1ZalNQBhoLUMScAN1C7zcVHgWeBCyutSOoAQ0HqgMw8FvgzcE5EPAZ8Dfh9pUVJHeCVEtJ6iIjdgWOB44AlwM+ozdGNqbQwqYOcaJbWQ0S8AtwJnJiZ8+v7FmTmbtVWJnWMw0fS+vkA8P+BWRFxaUQcSm1Fs9St2VOQOiAi+gHvozaM9E7gKuCGzLyl0sKk9WQoSJ0kIrYFjgEmZOY7q65HWh+GgiSp4JyCJKlgKEiSCoaCepyIWLYBvsY/RkRGxB5lf6026jgtIraosgb1LIaCtH6OA35FbQFblU4DDAV1GkNBG4WI2CUibouIufX/Dq3vPzIi7omI+yPi1ojYob5/ckRcERG3R8SCiJjUcK7+wGhqT1w7tmH/IRFxR0RMi4g/RMS5EfGhiLg3Ih6MiNe3UcuVETGu4XzLGs57e0RcFxG/j4iro2YSMIjaWolZpf8QtVEwFLSx+AHwo8zcB7gamFLf/yvgrZm5H3AN8PmGY/YA3g2MAs6u3x4bausSbs7MPwB/jYj9G47ZF/gs8CbgI8DumTkKuAw4tY1a1mU/ar2CvYDdgNGZOQVYBIzx9hrqLIaCNhZvA35Sf/1jag/DARgMzIyIB4HPAXs3HPNfmflCZj4NPAXsUN9/HLUAof7f4xqOmZ2ZT9Qfz/kosGoR24PAsDZqWZd7M7MlM18BHmg4l9SpvCGeNlarFuh8H/huZk6PiEOAyQ1tGp+7/DLQOyK2o7ZyeUREJNALyIj4/BqOeaVh+xXW/v/bqlpWUv9DLSIC2Gxdtazrm5PWlz0FbSx+zavj/x+iNmwEsDXwl/rrjzZxnnHUhn52ycxhmTkE+BPN/bXfVi2PAW+pvz4a2JS2PQts2Y6vLa2ToaCeaIuIaGn4dzowCfhYRMylNtb/2XrbycC1EXEn8HQT5z6O2kN1Gv078MF21Le2Wi4FDo6Ie4EDgOeaONdU4CYnmtVZvM2FJKlgT0GSVDAUJEkFQ0GSVDAUJEkFQ0GSVDAUJEkFQ0GSVPhf8LvMKKRy8OAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,100,200,700]\n",
    "group=['Low','Average','High']\n",
    "train['LoanAmount_bin']=pd.cut(df['LoanAmount'],bins,labels=group)\n",
    "LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
    "plt.xlabel('LoanAmount')\n",
    "P = plt.ylabel('Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['Income_bin', 'Coapplicant_Income_bin', 'LoanAmount_bin', 'Total_Income_bin', 'Total_Income'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Dependents'].replace('3+', 3,inplace=True)\n",
    "test['Dependents'].replace('3+', 3,inplace=True)\n",
    "train['Loan_Status'].replace('N', 0,inplace=True)\n",
    "train['Loan_Status'].replace('Y', 1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = train.corr()                       \n",
    "f, ax = plt.subplots(figsize=(9, 6))\n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
    "train['Married'].fillna(train['Married'].mode()[0], inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)\n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "84.0       4\n",
       "240.0      4\n",
       "120.0      3\n",
       "36.0       2\n",
       "60.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Gender'].fillna(train['Gender'].mode()[0], inplace=True)\n",
    "test['Dependents'].fillna(train['Dependents'].mode()[0], inplace=True)\n",
    "test['Self_Employed'].fillna(train['Self_Employed'].mode()[0], inplace=True)\n",
    "test['Credit_History'].fillna(train['Credit_History'].mode()[0], inplace=True)\n",
    "test['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0], inplace=True)\n",
    "test['LoanAmount'].fillna(train['LoanAmount'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "train['LoanAmount_log'] = np.log(train['LoanAmount'])   #log transformation\n",
    "train['LoanAmount_log'].hist(bins=20)\n",
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop('Loan_ID',axis=1)\n",
    "test=test.drop('Loan_ID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.get_dummies(X)\n",
    "train=pd.get_dummies(train)\n",
    "test=pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_cv, y_train, y_cv = train_test_split(X,y, test_size =0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7891891891891892"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_cv,pred_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission=pd.read_csv(\"Sample_Submission_ZAuTl8O_FK3zQHh.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('logistic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8306451612903226\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.8114754098360656\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7950819672131147\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.8278688524590164\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = LogisticRegression(random_state=1)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)\n",
    "pred=model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SV7TRu3y0eDrivkC3zFwDHAVcCOzSCbVJkiRJW71ybpt3D5AR8Tbgh8ChwJ0VrUqSJEnqIsoJ1GszcxVwOvDNzLwY6FPZsiRJkqSuoZxAvToi/gX4V+DeUluPypUkSZIkdR3lPinxeOBrmfl0RPQH7qpsWZIkSVLX0N5dPgDIzNkRcQlwYEQcAszPzC9XvjRJkiRp67fJQB0R7wJ+BCyk6R7Ub42If83M31e6OEmSJGlrt8lADfwX8N7MbACIiENpCthDK1mYJEmS1BWUM4d6x3VhGiAznwR2rFxJkiRJUtdRzgj1nyLiuzSNSgOcDTxauZIkSZKkrqOcQP0x4BLgcprmUD8EfKuSRUmSJEldRbuBOiIOA94GTMjMr3VOSVLHu3Pac0yctXCLt29YvIK62poOrEiSJG0rNjqHOiKupOmx42cD90fEeZ1WldTBJs5aSMPiFVu8fV1tDSPqfUCoJEnaUHsj1GcDgzJzZUT0BiYDt3ZOWVLHq6utYeyFR1W7DEmStI1p7y4ff8/MlQCZuXQTfSVJkqTtUnsj1AdExN2l9wG8rcUymXl6RSuTJEmSuoD2AvUHWi3fWMlCJEmSpK5oo4E6Mx/ozEIkSZKkrsh50ZIkSVIBBmpJkiSpgLIDdUS8qZKFSJIkSV3RJgN1RAyLiCeAeaXlwRHho8clSZIkyhuhvgE4BVgGkJmPAcdXsihJkiSpqygnUO+Qmc+2altTiWIkSZKkrqa9+1Cv83xEDAMyIroBFwNzK1uWJEmS1DWUM0L9ceBTwH7AEuDIUpskSZK03dvkCHVmvgCM6oRaJEmSpC5nk4E6Ir4HZOv2zLygIhVJkiRJXUg5c6j/p8X7nsBpwPOVKUeSJEnqWsqZ8jG25XJE/Ai4v2IVSZIkSV3Iljx6vD+wf0cXIkmSJHVF5cyhXs7/zaHeAXgJuKKSRUmSJEldRbuBOiICGAwsLDWtzcwNLlCUJEmStlftTvkohecJmbmm9DJMS5IkSS2UM4d6ekQcXvFKJEmSpC5oo4E6ItZNB/lHmkL1UxHxp4h4NCL+VM7OI+Lk0nbzI2Kj864jYmREZEQM3bzyJUmSpOpqbw71dOBw4P1bsuOI6AbcBJwINAIzImJSZja06rcbcAkwbUuOI0mSJFVTe4E6ADLzL1u472HA/Mx8GiAixgAjgIZW/f4f8DXgM1t4HEmSJKlq2gvUvSPiUxtbmZnXb2LffVj/iYqNwDtbdoiIIcC+mXlvRGw0UEfEBcAFAPvtt98mDitJkiR1nvYCdTdgV0oj1Vugre2a7xISETsA/wWcu6kdZeYtwC0AQ4cO9U4jkiRJ2mq0F6gXZ+Y1BfbdCOzbYrkvsKjF8m7AQODBpttd81ZgUkScmpkzCxxXkiRJ6jTt3TZvS0em15kBHBQR/SNiR2AUMGndysx8JTN7ZWa/zOwHTAUM05IkSepS2gvUJxTZcWauBi4C7gOeBMZl5pyIuCYiTi2yb0mSJGlrsdEpH5n5UtGdZ+ZkYHKrti9spO9xRY8nSZIkdbZynpQoSZIkaSMM1JIkSVIBBmpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQVYKCWJEmSCjBQS5IkSQUYqCVJkqQCDNSSJElSARt99Li0Nblz2nNMnLVwi7dvWLyCutqaDqxIkiSpiSPU6hImzlpIw+IVW7x9XW0NI+r7dGBFkiRJTRyhVpdRV1vD2AuPqnYZkiRJ63GEWpIkSSrAQC1JkiQVYKCWJEmSCjBQS5IkSQUYqCVJkqQCDNSSJElSAQZqSZIkqQADtSRJklSAgVqSJEkqwEAtSZIkFWCgliRJkgowUEuSJEkFGKglSZKkAgzUkiRJUgEGakmSJKkAA7UkSZJUgIFakiRJKsBALUmSJBVgoJYkSZIKMFBLkiRJBRioJUmSpAIM1JIkSVIBBmpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQVYKCWJEmSCjBQS5IkSQUYqCVJkqQCDNSSJElSAQZqSZIkqQADtSRJklSAgVqSJEkqwEAtSZIkFWCgliRJkgroXu0C1DnunPYcE2ctrHYZW6xh8QrqamuqXYYkSdIGHKHeTkyctZCGxSuqXcYWq6utYUR9n2qXIUmStAFHqLcjdbU1jL3wqGqXIUmStE1xhFqSJEkqwEAtSZIkFWCgliRJkgowUEuSJEkFGKglSZKkAgzUkiRJUgEGakmSJKkAA7UkSZJUgIFakiRJKsBALUmSJBVgoJYkSZIKMFBLkiRJBRioJUmSpAIM1JIkSVIBBmpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQVYKCWJEmSCjBQS5IkSQVUNFBHxMkR8VREzI+IK9pY/6mIaIiIxyPigYjYv5L1SJIkSR2tYoE6IroBNwHDgTrgrIioa9XtUWBoZg4CxgNfq1Q9kiRJUiVUcoR6GDA/M5/OzDeAMcCIlh0yc0pmvlpanAr0rWA9kiRJUoerZKDuAzzfYrmx1LYx5wO/bGtFRFwQETMjYubSpUs7sERJkiSpmEoG6mijLdvsGPEhYChwXVvrM/OWzByamUN79+7dgSVKkiRJxXSv4L4bgX1bLPcFFrXuFBH/BFwFHJuZf69gPZIkSVKHq+QI9QzgoIjoHxE7AqOASS07RMQQ4LvAqZn5QgVrkSRJkiqiYoE6M1cDFwH3AU8C4zJzTkRcExGnlrpdB+wK/DQiZkXEpI3sTpIkSdoqVXLKB5k5GZjcqu0LLd7/UyWPL0mSJFWaT0qUJEmSCqjoCLU6zp3TnmPirIVbvH3D4hXU1dZ0YEWSJEkCR6i7jImzFtKweMUWb19XW8OI+vZuAy5JkqQt4Qh1F1JXW8PYC4+qdhmSJElqwRFqSZIkqQADtSRJklSAUz46iRcVSpIkbZscoe4kXlQoSZK0bXKEuhN5UaEkSdK2xxFqSZIkqQADtSRJklSAUz7K5EWFkiRJaosj1GXyokJJkiS1xRHqzeBFhZIkSWrNEWpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQVYKCWJEmSCjBQS5IkSQUYqCVJkqQCDNSSJElSAQZqSZIkqQADtSRJklSAgVqSJEkqwEAtSZIkFWCgliRJkgowUEuSJEkFGKglSZKkAgzUkiRJUgEGakmSJKkAA7UkSZJUgIFakiRJKsBALUmSJBVgoJYkSZIKMFBLkiRJBRioJUmSpAIM1JIkSVIBBmpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQVYKCWJEmSCjBQS5IkSQUYqCVJkqQCDNSSJElSAQZqSZIkqQADtSRJklSAgVqSJEkqwEAtSZIkFWCgliRJkgowUEuSJEkFGKglSZKkAgzUkiRJUgEGakmSJKkAA7UkSZJUgIFakiRJKsBALUmSJBVgoJYkSZIKMFBLkiRJBRioJUmSpAIM1JIkSVIBBmpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQV0L3aBUiStD1atWoVjY2NvP7669UuRdru9ezZk759+9KjR48t2t5ALUlSFTQ2NrLbbrvRr18/IqLa5Ujbrcxk2bJlNDY20r9//y3ah1M+JEmqgtdff50999zTMC1VWUSw5557FvptkSPUZarbp6baJUiStjGGaWnrUPRn0UBdpi++b0C1S5AkSdJWqKJTPiLi5Ih4KiLmR8QVbax/U0SMLa2fFhH9KlmPJEnacrvuuisAixYtYuTIkW32Oe6445g5c2a7+/nmN7/Jq6++2rz83ve+l5dffrnjCu1kv/rVrzj44IM58MAD+cpXvtJmn8suu4z6+nrq6+t5+9vfzpvf/GYApkyZ0txeX19Pz549ueeeewA4++yzOfjggxk4cCDnnXceq1atAuDBBx9k9913b97mmmuuaT5Ov379OOyww6ivr2fo0KHr1fCtb32Lgw8+mAEDBnD55ZcDMH369Ob9DB48mAkTJjT3/+///m8GDhzIgAED+OY3v9nc/tJLL3HiiSdy0EEHceKJJ7J8+XIAli9fzmmnncagQYMYNmwYs2fPBuCpp55a7zPW1NQ07+/MM89sbu/Xrx/19fXr1fzcc8+x66678vWvf3299jVr1jBkyBBOOeWU5rbzzz+fwYMHM2jQIEaOHMn//u//Nq8bN24cdXV1DBgwgA9+8INtn8giMrMiL6Ab8BfgAGBH4DGgrlWfTwDfKb0fBYzd1H6POOKIlCSpq2toaKh2CZttl1122WSfY489NmfMmNFun/333z+XLl3aUWVV1erVq/OAAw7Iv/zlL/n3v/89Bw0alHPmzGl3mxtuuCFHjx69QfuyZctyjz32yJUrV2Zm5i9+8Ytcu3Ztrl27NkeNGpXf/va3MzNzypQp+c///M9t7ntj3+1vfvObPOGEE/L111/PzMwlS5ZkZubKlStz1apVmZm5aNGi7N27d65atSqfeOKJHDBgQPP6E044IefOnZuZmf/+7/+e1157bWZmXnvttXn55ZdnZuZnPvOZ/NKXvpSZmU8++WS++93vbvP72nvvvXPBggUbrPvUpz6VV1999Xptp59+eo4cOTKvu+669dq/8Y1v5FlnnbXe9/DKK680v7/sssuaa5w7d27W19fnSy+9tN5nb62tn0lgZpaReys55WMYMD8znwaIiDHACKChRZ8RwJdK78cDN0ZElD6AJEnbhat/PoeGRSs6dJ91+9S0O13xs5/9LPvvvz+f+MQnAPjSl77EbrvtxoUXXsiIESNYvnw5q1at4j//8z8ZMWLEetsuWLCAU045hdmzZ/Paa68xevRoGhoaOPTQQ3nttdea+3384x9nxowZvPbaa4wcOZKrr76aG264gUWLFnH88cfTq1cvpkyZQr9+/Zg5cya9evXi+uuv59ZbbwXgIx/5CJdeeikLFixg+PDh/OM//iN/+MMf6NOnDxMnTmSnnXba6OebPn06l156Ka+99ho77bQTt912GwcffDC33347M2fO5MYbbwTglFNO4TOf+QzHHXccv/rVr7jyyitZs2YNvXr14oEHHtjk9zx9+nQOPPBADjjgAABGjRrFxIkTqaur2+g2d911F1dfffUG7ePHj2f48OHsvPPOQNPI/TrDhg2jsbFxk/VszM0338wVV1zBm970JgD22msvgOZjQdOFsuvmEj/55JMceeSRzeuPPfZYJkyYwOWXX87EiRN58MEHATjnnHM47rjj+OpXv0pDQwOf+9znADjkkENYsGABS5YsYe+9924+xgMPPMDb3vY29t9///Xqy0zGjRvHb37zm+a2e+65hwMOOIBddtllvb6NjY384he/4KqrruL6669vbq+pqWne12uvvdb8Wb73ve/xyU9+kj322GO9z96RKjnlow/wfIvlxlJbm30yczXwCrBn6x1FxAURMTMiZi5durRC5UqStP0YNWoUY8eObV4eN24c//Iv/0LPnj2ZMGECf/rTn5gyZQqf/vSnaW+c6+abb2bnnXfm8ccf56qrruKRRx5pXvflL3+ZmTNn8vjjj/Pb3/6Wxx9/nEsuuYR99tmHKVOmMGXKlPX29cgjj3Dbbbcxbdo0pk6dyve+9z0effRRAObNm8cnP/lJ5syZw5vf/GZ+9rOftfv5DjnkEB566CEeffRRrrnmGq688sp2+y9dupSPfvSj/OxnP+Oxxx7jpz/9KbDhlIx1r3/4h38AYOHChey7777N++nbty8LFy7c6HGeffZZnnnmGd797ndvsG7MmDGcddZZG7SvWrWKH/3oR5x88snNbX/84x8ZPHgww4cPZ86cOc3tEcF73vMejjjiCG655Zbm9rlz5/Lwww/zzne+k2OPPZYZM2Y0r5s2bRoDBgzgsMMO4zvf+Q7du3dn4MCBPPTQQyxbtoxXX32VyZMn8/zzTbFuyZIl1NbWAlBbW8sLL7wAwODBg7n77ruBpn9oPPvssxv8I2Bjn/Hhhx9m77335qCDDgJg5cqVfPWrX+WLX/ziBn0vvfRSvva1r7HDDhvG2NGjR/PWt76VP//5z1x88cXNn33u3LkcffTRHHnkkfzqV7/aYLuiKjlC3dblkq1/IsvpQ2beAtwCMHToUEevJUnblGpc+D5kyBBeeOEFFi1axNKlS9ljjz3Yb7/9WLVqFVdeeSUPPfRxIl7BAAAQUElEQVQQO+ywAwsXLmTJkiW89a1vbXM/Dz30EJdccgkAgwYNYtCgQc3rxo0bxy233MLq1atZvHgxDQ0N661v7Xe/+x2nnXZa84jk6aefzsMPP8ypp55K//79m+fXHnHEESxYsKDdz/fKK69wzjnnMG/ePCKief7xxkydOpVjjjmm+T7Eb3nLWwA4/vjjmTVr1ka3a+sfG+3dMWLMmDGMHDmSbt26rde+ePFinnjiCU466aQNtvnEJz7BMcccw7ve9S4ADj/8cJ599ll23XVXJk+ezPvf/37mzZsHwO9//3v22WcfXnjhBU488UQOOeQQjjnmGFavXs3y5cuZOnUqM2bM4IwzzuDpp58mInjnO9/JnDlzePLJJznnnHMYPnw4hx56KJ/97Gc58cQT2XXXXRk8eDDdu7cfG6+44gr+7d/+jfr6eg477DCGDBmy3jZvvPEGkyZN4tprr91g27vuumu9oP3FL36Ryy67rHne/jr33nsve+21F0cccUTzKHlLt912G2vWrOHiiy9m7NixjB49mtWrVzNv3jwefPBBGhsbede73sXs2bOb57F3hEoG6kZg3xbLfYFFG+nTGBHdgd2BlypYkyRJKhk5ciTjx4/nr3/9K6NGjQLgJz/5CUuXLuWRRx6hR48e9OvXb5P3520rQD7zzDN8/etfZ8aMGeyxxx6ce+65m9xPeyPh66YqAHTr1m29qSVt+Y//+A+OP/54JkyYwIIFCzjuuOMA6N69O2vXrm3ut66mzGzzc0yZMoXLLrtsg/add96ZP/zhD/Tt27d55BaapiPss88+G61rzJgx3HTTTRu0jxs3jtNOO22DJ/VdffXVLF26lO9+97vNbeumNkDTtJBPfOITvPjii/Tq1av52HvttRennXYa06dP55hjjqFv376cfvrpRATDhg1jhx124MUXX6R3797N+zr00EPZZZddmD17NkOHDuX888/n/PPPB+DKK6+kb9++AOy9994sXryY2tpaFi9e3DyFoqamhttuu635++zfv/96D0r55S9/yeGHH77eFBCA1atXc/fdd6/3241p06Yxfvx4Lr/8cl5++WV22GEHevbsycKFC5k0aRKTJ0/m9ddfZ8WKFXzoQx/ixz/+cfO23bp148wzz+S6665j9OjR9O3blyOPPJIePXrQv39/Dj74YObNm8c73vGOjZ6nzVXJKR8zgIMion9E7EjTRYeTWvWZBJxTej8S+I3zpyVJ6hyjRo1izJgxjB8/vvmuHa+88gp77bUXPXr0YMqUKTz77LPt7uOYY47hJz/5CQCzZ8/m8ccfB2DFihXssssu7L777ixZsoRf/vKXzdvstttu/O1vf2tzX/fccw+vvvoqK1euZMKECc2jshtz4403Ns+HbumVV16hT5+mmaa33357c3u/fv2YNWsWa9eu5fnnn2f69OkAHHXUUfz2t7/lmWeeAZruZAH/N0Ld+vWHP/wBgHe84x3MmzePZ555hjfeeIMxY8Zw6qmntlnrU089xfLlyznqqKM2WNd6hBbg+9//Pvfddx933XXXetMb/vrXvzb/42P69OmsXbuWPffck5UrVzZ/rytXruTXv/41AwcOBOD9739/8/zkuXPn8sYbb9CrVy+eeeYZVq9eDTRNR3nqqafo168fQPNUjueee4677767ub5TTz2VO+64A4A77rijeY79yy+/zBtvvNFc+zHHHLNe+G/rMwL8z//8D4ccckhzYIemKSALFixgwYIFXHrppVx55ZVcdNFFXHvttTQ2NrJgwQLGjBnDu9/9bn784x+TmcyfPx9oCvM///nPOeSQQ5o/+7rpRS+++CJz585tnvPeUSo2Qp2ZqyPiIuA+mu74cWtmzomIa2i6YnIS8APgRxExn6aR6VGVqkeSJK1vwIAB/O1vf6NPnz7Nc2LPPvts3ve+9zF06FDq6+ubQ8nGfPzjH2f06NEMGjSI+vp6hg0bBjTNpx0yZAgDBgzggAMO4Oijj27e5oILLmD48OHU1tauN4/68MMP59xzz23ex0c+8hGGDBnS7vSOP//5z+vte53LL7+cc845h+uvv369+cpHH300/fv357DDDmPgwIEcfvjhAPTu3ZtbbrmF008/nbVr17LXXntx//33b+IbbBrxvvHGGznppJNYs2YN5513HgMGNE3h+cIXvsDQoUObA/Zdd93FqFGjNhgJX7BgAc8//zzHHnvseu0f+9jH2H///ZsD+Omnn84XvvAFxo8fz80330z37t3ZaaedGDNmDBHBkiVLOO2004CmUd8PfvCDzfOuzzvvPM477zwGDhzIjjvuyB133EFE8Lvf/Y6vfOUr9OjRgx122IFvf/vb9OrVC4APfOADLFu2jB49enDTTTc1X9R3xRVXcMYZZ/CDH/yA/fbbr3m++ZNPPsmHP/xhunXrRl1dHT/4wQ+aP8urr77K/fffv95I+zobm1e9OTKTc845hxUrVpCZDB48mJtvvhmAk046iV//+tfU1dXRrVs3rrvuOvbcc4NL9gqJrjYgPHTo0NzU/S0lSdraPfnkkxx66KHVLqPLO+WUU7j77rvZcccdq12Kuri2fiYj4pHMHLqRTZr5pERJktRl3XvvvdUuQarskxIlSZKkbZ2BWpKkKulq0y6lbVXRn0UDtSRJVdCzZ0+WLVtmqJaqLDNZtmwZPXv23OJ9OIdakqQq6Nu3L42NjfgEYKn6evbsud5t+zaXgVqSpCpY95AJSV2fUz4kSZKkAgzUkiRJUgEGakmSJKmALvekxIhYCjxbpcP3Al6s0rHVOTzH2wfP8/bB87zt8xxvH6p5nvfPzN6b6tTlAnU1RcTMch4/qa7Lc7x98DxvHzzP2z7P8fahK5xnp3xIkiRJBRioJUmSpAIM1JvnlmoXoIrzHG8fPM/bB8/zts9zvH3Y6s+zc6glSZKkAhyhliRJkgowUEuSJEkFGKhbiYiTI+KpiJgfEVe0sf5NETG2tH5aRPTr/CpVVBnn+VMR0RARj0fEAxGxfzXqVDGbOs8t+o2MiIyIrfq2TNpQOec4Is4o/TzPiYg7O7tGFVfGn9n7RcSUiHi09Of2e6tRp7ZcRNwaES9ExOyNrI+IuKH0/8DjEXF4Z9fYHgN1CxHRDbgJGA7UAWdFRF2rbucDyzPzQOC/gK92bpUqqszz/CgwNDMHAeOBr3VulSqqzPNMROwGXAJM69wKVVQ55zgiDgI+BxydmQOASzu9UBVS5s/y54FxmTkEGAV8u3OrVAe4HTi5nfXDgYNKrwuAmzuhprIZqNc3DJifmU9n5hvAGGBEqz4jgDtK78cDJ0REdGKNKm6T5zkzp2Tmq6XFqUDfTq5RxZXz8wzw/2j6B9PrnVmcOkQ55/ijwE2ZuRwgM1/o5BpVXDnnOYGa0vvdgUWdWJ86QGY+BLzUTpcRwA+zyVTgzRFR2znVbZqBen19gOdbLDeW2trsk5mrgVeAPTulOnWUcs5zS+cDv6xoRaqETZ7niBgC7JuZ93ZmYeow5fwsvx14e0T8PiKmRkR7I2DaOpVznr8EfCgiGoHJwMWdU5o60eb+3d2pule7gK1MWyPNre8rWE4fbd3KPocR8SFgKHBsRStSJbR7niNiB5qmbZ3bWQWpw5Xzs9ydpl8RH0fTb5oejoiBmflyhWtTxynnPJ8F3J6Z34iIo4Aflc7z2sqXp06yVecvR6jX1wjs22K5Lxv+2qi5T0R0p+lXS+39ikJbn3LOMxHxT8BVwKmZ+fdOqk0dZ1PneTdgIPBgRCwAjgQmeWFil1Lun9kTM3NVZj4DPEVTwFbXUc55Ph8YB5CZfwR6Ar06pTp1lrL+7q4WA/X6ZgAHRUT/iNiRpgsbJrXqMwk4p/R+JPCb9Ok4Xc0mz3NpKsB3aQrTzrnsmto9z5n5Smb2ysx+mdmPprnyp2bmzOqUqy1Qzp/Z9wDHA0REL5qmgDzdqVWqqHLO83PACQARcShNgXppp1apSpsEfLh0t48jgVcyc3G1i1rHKR8tZObqiLgIuA/oBtyamXMi4hpgZmZOAn5A06+S5tM0Mj2qehVrS5R5nq8DdgV+Wrrm9LnMPLVqRWuzlXme1YWVeY7vA94TEQ3AGuDfM3NZ9arW5irzPH8a+F5EXEbTNIBzHezqWiLiLpqmZvUqzYX/ItADIDO/Q9Pc+PcC84FXgdHVqbRtPnpckiRJKsApH5IkSVIBBmpJkiSpAAO1JEmSVICBWpIkSSrAQC1JkiQVYKCWpM0QEWsiYlaLV792+vaLiNkdcMwHI+KpiHis9Ajtg7dgHx+LiA+X3p8bEfu0WPf9iKjr4DpnRER9GdtcGhE7Fz22JFWTgVqSNs9rmVnf4rWgk457dmYOBu6g6T7pmyUzv5OZPywtngvs02LdRzKzoUOq/L86v015dV4KGKgldWkGakkqqDQS/XBE/Kn0+oc2+gyIiOmlUe3HI+KgUvuHWrR/NyK6beJwDwEHlrY9ISIejYgnIuLWiHhTqf0rEdFQOs7XS21fiojPRMRIYCjwk9IxdyqNLA+NiI9HxNda1HxuRHxrC+v8I9Cnxb5ujoiZETEnIq4utV1CU7CfEhFTSm3viYg/lr7Hn0bErps4jiRVnYFakjbPTi2me0wotb0AnJiZhwNnAje0sd3HgP/OzHqaAm1j6RHJZwJHl9rXAGdv4vjvA56IiJ7A7cCZmXkYTU++/XhEvAU4DRiQmYOA/2y5cWaOB2bSNJJcn5mvtVg9Hji9xfKZwNgtrPNkmh77vc5VmTkUGAQcGxGDMvMGYBFwfGYeX3o0+OeBfyp9lzOBT23iOJJUdT56XJI2z2ulUNlSD+DG0pzhNcDb29juj8BVEdEXuDsz50XECcARwIzSI+53oimct+UnEfEasAC4GDgYeCYz55bW3wF8ErgReB34fkT8Ari33A+WmUsj4umIOBKYVzrG70v73Zw6d6HpEdGHt2g/IyIuoOnvnVqgDni81bZHltp/XzrOjjR9b5K0VTNQS1JxlwFLgME0/ebv9dYdMvPOiJgG/DNwX0R8BAjgjsz8XBnHODszZ65biIg92+qUmasjYhhwAjAKuAh492Z8lrHAGcCfgQmZmdGUbsuuE3gM+ApwE3B6RPQHPgO8IzOXR8TtQM82tg3g/sw8azPqlaSqc8qHJBW3O7A4M9cC/0rT6Ox6IuIA4OnSNIdJNE19eAAYGRF7lfq8JSL2L/OYfwb6RcSBpeV/BX5bmnO8e2ZOpumCv7butPE3YLeN7Pdu4P3AWTSFaza3zsxcRdPUjSNL00VqgJXAKxGxNzB8I7VMBY5e95kiYueIaGu0X5K2KgZqSSru28A5ETGVpukeK9vocyYwOyJmAYcAPyzdWePzwK8j4nHgfpqmQ2xSZr4OjAZ+GhFPAGuB79AUTu8t7e+3NI2et3Y78J11FyW22u9yoAHYPzOnl9o2u87S3OxvAJ/JzMeAR4E5wK00TSNZ5xbglxExJTOX0nQHkrtKx5lK03clSVu1yMxq1yBJkiR1WY5QS5IkSQUYqCVJkqQCDNSSJElSAQZqSZIkqQADtSRJklSAgVqSJEkqwEAtSZIkFfD/Ae4AFJe/PSS1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(yvl,  pred)\n",
    "auc = metrics.roc_auc_score(yvl, pred)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(fpr,tpr,label=\"validation, auc=\"+str(auc))\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test\n",
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Logistic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "test['Total_Income']=test['ApplicantIncome']+test['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": [
    "sns.distplot(train['Total_Income']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": [
    "train['Total_Income_log'] = np.log(train['Total_Income'])\n",
    "sns.distplot(train['Total_Income_log']);\n",
    "test['Total_Income_log'] = np.log(test['Total_Income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['EMI']=train['LoanAmount']/train['Loan_Amount_Term']\n",
    "test['EMI']=test['LoanAmount']/test['Loan_Amount_Term']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "sns.distplot(train['EMI']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Balance Income']=train['Total_Income']-(train['EMI']*1000) # Multiply with 1000 to make the units equal \n",
    "test['Balance Income']=test['Total_Income']-(test['EMI']*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": [
    "sns.distplot(train['Balance Income']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)\n",
    "test=test.drop(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Loan_Status',1)\n",
    "y = train.Loan_Status                # Save target variable in separate dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7983870967741935\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.8225806451612904\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7786885245901639\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.7868852459016393\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.819672131147541\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = LogisticRegression(random_state=1)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)\n",
    "pred=model.predict_proba(xvl)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test            # filling Loan_Status with predictions\n",
    "submission['Loan_ID']=test_original['Loan_ID'] # filling Loan_ID with test Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Log2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 of kfold 5\n",
      "accuracy_score 0.7258064516129032\n",
      "\n",
      "2 of kfold 5\n",
      "accuracy_score 0.7419354838709677\n",
      "\n",
      "3 of kfold 5\n",
      "accuracy_score 0.7049180327868853\n",
      "\n",
      "4 of kfold 5\n",
      "accuracy_score 0.680327868852459\n",
      "\n",
      "5 of kfold 5\n",
      "accuracy_score 0.7049180327868853\n"
     ]
    }
   ],
   "source": [
    "i=1\n",
    "kf = StratifiedKFold(n_splits=5,random_state=1,shuffle=True)\n",
    "for train_index,test_index in kf.split(X,y):\n",
    "     print('\\n{} of kfold {}'.format(i,kf.n_splits))\n",
    "     xtr,xvl = X.loc[train_index],X.loc[test_index]\n",
    "     ytr,yvl = y[train_index],y[test_index]\n",
    "    \n",
    "     model = tree.DecisionTreeClassifier(random_state=1)\n",
    "     model.fit(xtr, ytr)\n",
    "     pred_test = model.predict(xvl)\n",
    "     score = accuracy_score(yvl,pred_test)\n",
    "     print('accuracy_score',score)\n",
    "     i+=1\n",
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test            # filling Loan_Status with predictions\n",
    "submission['Loan_ID']=test_original['Loan_ID'] # filling Loan_ID with test Loan_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replacing 0 and 1 with N and Y\n",
    "submission['Loan_Status'].replace(0, 'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1, 'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting submission file to .csv format\n",
    "pd.DataFrame(submission, columns=['Loan_ID','Loan_Status']).to_csv('Decision Tree.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/bibek/anaconda3/lib/python3.7/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}