Loan_Problem_Soluion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv('test.csv')\n",
    "train_data=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
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       "      <td>1500</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "3  LP001035   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "3             NaN         Urban  \n",
       "4             1.0         Urban  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
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       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001054</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2165</td>\n",
       "      <td>3422</td>\n",
       "      <td>152.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes          0      Graduate            No   \n",
       "1  LP001022   Male     Yes          1      Graduate            No   \n",
       "2  LP001031   Male     Yes          2      Graduate            No   \n",
       "4  LP001051   Male      No          0  Not Graduate            No   \n",
       "5  LP001054   Male     Yes          0  Not Graduate           Yes   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "5             2165               3422       152.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  \n",
       "0             1.0         Urban  \n",
       "1             1.0         Urban  \n",
       "2             1.0         Urban  \n",
       "4             1.0         Urban  \n",
       "5             1.0         Urban  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
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       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>LP002979</td>\n",
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       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
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       "      <td>40.0</td>\n",
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       "      <td>LP002983</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>240.0</td>\n",
       "      <td>253.0</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>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</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>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>LP002978</td>\n",
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       "      <td>LP002979</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>Y</td>\n",
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       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</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>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</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>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 480 entries, 1 to 613\n",
      "Data columns (total 13 columns):\n",
      "Loan_ID              480 non-null object\n",
      "Gender               480 non-null object\n",
      "Married              480 non-null object\n",
      "Dependents           480 non-null object\n",
      "Education            480 non-null object\n",
      "Self_Employed        480 non-null object\n",
      "ApplicantIncome      480 non-null int64\n",
      "CoapplicantIncome    480 non-null float64\n",
      "LoanAmount           480 non-null float64\n",
      "Loan_Amount_Term     480 non-null float64\n",
      "Credit_History       480 non-null float64\n",
      "Property_Area        480 non-null object\n",
      "Loan_Status          480 non-null object\n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 52.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    332\n",
       "N    148\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2102f5c6da0>"
      ]
     },
     "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_data['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "train_data['Gender'].value_counts().plot.bar(title='Gender')\n",
    "plt.subplot(2,2,2)\n",
    "train_data['Married'].value_counts().plot.bar(title='Married')\n",
    "plt.subplot(2,2,3)\n",
    "train_data['Self_Employed'].value_counts().plot.bar(title='Self_Employed')\n",
    "plt.subplot(2,2,4)\n",
    "train_data['Credit_History'].value_counts().plot.bar(title='Credit_History')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,3,5)\n",
    "train_data['Gender'].value_counts().plot.bar(title='Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.subplot(1,3,1)\n",
    "train_data['Dependents'].value_counts().plot.bar(title='Dependents')\n",
    "plt.subplot(1,3,2)\n",
    "train_data['Education'].value_counts().plot.bar(title='Education')\n",
    "plt.subplot(1,3,3)\n",
    "train_data['Property_Area'].value_counts().plot.bar(title='Property_Area')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "train_data['ApplicantIncome'].value_counts().plot.box(title='Appl In')\n",
    "plt.subplot(2,2,2)\n",
    "train_data['CoapplicantIncome'].value_counts().plot.box(title='CoapplicantIncome')\n",
    "plt.subplot(2,2,3)\n",
    "train_data['LoanAmount'].value_counts().plot.box(title='LoanAmount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "X=['carrier','ATC', 'Weather', 'Security', 'Inbound']\n",
    "Y=[23.02,30.40,4.03,0.112,42.43]\n",
    "plt.bar(X,Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2103178def0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "Gender=pd.crosstab(train_data['Gender'], train_data['LoanAmount'])\n",
    "#train_data.plot.bar(Gender)\n",
    "Gender.plot(kind='bar')\n",
    "#Gender.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_data['Gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-870762f6eaa5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGender\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    866\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    867\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 868\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4373\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4374\u001b[0m             return self._engine.get_value(s, k,\n\u001b[1;32m-> 4375\u001b[1;33m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m   4376\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4377\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    " type(train_data.Gender[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(train_data.LoanAmount[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.LoanAmount=train_data.LoanAmount.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(train_data.LoanAmount[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(train_data.Gender[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "before =type(train_data.LoanAmount[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = type(train_data.ApplicantIncome[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 480 entries, 1 to 613\n",
      "Data columns (total 13 columns):\n",
      "Loan_ID              480 non-null object\n",
      "Gender               480 non-null object\n",
      "Married              480 non-null object\n",
      "Dependents           480 non-null object\n",
      "Education            480 non-null object\n",
      "Self_Employed        480 non-null object\n",
      "ApplicantIncome      480 non-null int64\n",
      "CoapplicantIncome    480 non-null float64\n",
      "LoanAmount           480 non-null int64\n",
      "Loan_Amount_Term     480 non-null float64\n",
      "Credit_History       480 non-null float64\n",
      "Property_Area        480 non-null object\n",
      "Loan_Status          480 non-null object\n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 52.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_data.Gender[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_data.ApplicantIncome[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4583.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.ApplicantIncome[1].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</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",
       "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",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\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",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \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",
       "5             1.0         Urban           Y  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_data.Loan_Status[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'StringMethods' object has no attribute 'astype'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-82-2eda9f381ee6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Loan_Status'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLoan_Status\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'StringMethods' object has no attribute 'astype'"
     ]
    }
   ],
   "source": [
    "train_data['Loan_Status']=train_data.Loan_Status.str.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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"
    }
   ],
   "source": [
    "\n",
    "Cross =pd.crosstab(train_data['Gender'], train_data['Loan_Status'])\n",
    "Cross.div(Gender.sum(1).astype('float'), axis=0).plot(kind='bar', stacked=True, figsize=(4,4))\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Gender Vs LoanStatus')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210370d9080>"
      ]
     },
     "execution_count": 43,
     "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_data.groupby(['ApplicantIncome','Loan_Status']).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train_data.drop('Loan_Status', axis=1)\n",
    "Y=train_data.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
       "      <th>Loan_ID_LP001003</th>\n",
       "      <th>Loan_ID_LP001005</th>\n",
       "      <th>Loan_ID_LP001006</th>\n",
       "      <th>Loan_ID_LP001008</th>\n",
       "      <th>Loan_ID_LP001011</th>\n",
       "      <th>...</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Dependents_3+</th>\n",
       "      <th>Education_Graduate</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_No</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Rural</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\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",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History  Loan_ID_LP001003  Loan_ID_LP001005  Loan_ID_LP001006  \\\n",
       "1             1.0                 1                 0                 0   \n",
       "2             1.0                 0                 1                 0   \n",
       "3             1.0                 0                 0                 1   \n",
       "4             1.0                 0                 0                 0   \n",
       "5             1.0                 0                 0                 0   \n",
       "\n",
       "   Loan_ID_LP001008  Loan_ID_LP001011  ...  Dependents_1  Dependents_2  \\\n",
       "1                 0                 0  ...             1             0   \n",
       "2                 0                 0  ...             0             0   \n",
       "3                 0                 0  ...             0             0   \n",
       "4                 1                 0  ...             0             0   \n",
       "5                 0                 1  ...             0             1   \n",
       "\n",
       "   Dependents_3+  Education_Graduate  Education_Not Graduate  \\\n",
       "1              0                   1                       0   \n",
       "2              0                   1                       0   \n",
       "3              0                   0                       1   \n",
       "4              0                   1                       0   \n",
       "5              0                   1                       0   \n",
       "\n",
       "   Self_Employed_No  Self_Employed_Yes  Property_Area_Rural  \\\n",
       "1                 1                  0                    1   \n",
       "2                 0                  1                    0   \n",
       "3                 1                  0                    0   \n",
       "4                 1                  0                    0   \n",
       "5                 0                  1                    0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  \n",
       "1                        0                    0  \n",
       "2                        0                    1  \n",
       "3                        0                    1  \n",
       "4                        0                    1  \n",
       "5                        0                    1  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Y[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'astype'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-75-3b43016b1a0a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mY\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'float'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'astype'"
     ]
    }
   ],
   "source": [
    "Y(Y[1].astype('float'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    N\n",
       "2    Y\n",
       "3    Y\n",
       "4    Y\n",
       "5    Y\n",
       "Name: Loan_Status, dtype: object"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Y.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.get_dummies(X)\n",
    "X1=pd.get_dummies(train_data)\n",
    "X2=pd.get_dummies(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-77-e9b10c1700b8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mLnrReg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mLnrReg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    461\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[1;32m--> 463\u001b[1;33m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[0;32m    464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    725\u001b[0m         \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0my_numeric\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'O'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Y'"
     ]
    }
   ],
   "source": [
    "LnrReg=LinearRegression()\n",
    "LnrReg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'LnrReg' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-68-41508bd1d534>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLnrReg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'LnrReg' is not defined"
     ]
    }
   ],
   "source": [
    "pred=LnrReg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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