loan predictions resume.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "s=pd.read_csv('t1train.csv')\n",
    "s.drop(['Loan_ID'],inplace=True,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</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",
       "      <td>2</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",
       "      <td>3</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",
       "      <td>4</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>609</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>610</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>611</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",
       "      <td>612</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",
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       "      <td>613</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",
       "<p>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0      Male      No          0      Graduate            No             5849   \n",
       "1      Male     Yes          1      Graduate            No             4583   \n",
       "2      Male     Yes          0      Graduate           Yes             3000   \n",
       "3      Male     Yes          0  Not Graduate            No             2583   \n",
       "4      Male      No          0      Graduate            No             6000   \n",
       "..      ...     ...        ...           ...           ...              ...   \n",
       "609  Female      No          0      Graduate            No             2900   \n",
       "610    Male     Yes         3+      Graduate            No             4106   \n",
       "611    Male     Yes          1      Graduate            No             8072   \n",
       "612    Male     Yes          2      Graduate            No             7583   \n",
       "613  Female      No          0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0         NaN             360.0             1.0   \n",
       "1               1508.0       128.0             360.0             1.0   \n",
       "2                  0.0        66.0             360.0             1.0   \n",
       "3               2358.0       120.0             360.0             1.0   \n",
       "4                  0.0       141.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0        71.0             360.0             1.0   \n",
       "610                0.0        40.0             180.0             1.0   \n",
       "611              240.0       253.0             360.0             1.0   \n",
       "612                0.0       187.0             360.0             1.0   \n",
       "613                0.0       133.0             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "0           Urban           Y  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Gender'].fillna(s['Gender'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Credit_History'].fillna(s['Credit_History'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Dependents'].fillna(s['Dependents'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Self_Employed'].fillna(s['Self_Employed'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['LoanAmount'].fillna(\n",
    "    \n",
    "    value=128.0,\n",
    "    axis=0,\n",
    "    inplace=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
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       "      <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",
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       "      <td>Urban</td>\n",
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       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <td>611</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",
       "      <td>612</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",
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       "      <td>Y</td>\n",
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       "      <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",
       "<p>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0      Male      No          0      Graduate            No             5849   \n",
       "1      Male     Yes          1      Graduate            No             4583   \n",
       "2      Male     Yes          0      Graduate           Yes             3000   \n",
       "3      Male     Yes          0  Not Graduate            No             2583   \n",
       "4      Male      No          0      Graduate            No             6000   \n",
       "..      ...     ...        ...           ...           ...              ...   \n",
       "609  Female      No          0      Graduate            No             2900   \n",
       "610    Male     Yes         3+      Graduate            No             4106   \n",
       "611    Male     Yes          1      Graduate            No             8072   \n",
       "612    Male     Yes          2      Graduate            No             7583   \n",
       "613  Female      No          0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0       128.0             360.0             1.0   \n",
       "1               1508.0       128.0             360.0             1.0   \n",
       "2                  0.0        66.0             360.0             1.0   \n",
       "3               2358.0       120.0             360.0             1.0   \n",
       "4                  0.0       141.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0        71.0             360.0             1.0   \n",
       "610                0.0        40.0             180.0             1.0   \n",
       "611              240.0       253.0             360.0             1.0   \n",
       "612                0.0       187.0             360.0             1.0   \n",
       "613                0.0       133.0             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "0           Urban           Y  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Loan_Amount_Term'].fillna(s['Loan_Amount_Term'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
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       "     Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0      Male      No          0      Graduate            No             5849   \n",
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       "2      Male     Yes          0      Graduate           Yes             3000   \n",
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       "..      ...     ...        ...           ...           ...              ...   \n",
       "609  Female      No          0      Graduate            No             2900   \n",
       "610    Male     Yes         3+      Graduate            No             4106   \n",
       "611    Male     Yes          1      Graduate            No             8072   \n",
       "612    Male     Yes          2      Graduate            No             7583   \n",
       "613  Female      No          0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0       128.0             360.0             1.0   \n",
       "1               1508.0       128.0             360.0             1.0   \n",
       "2                  0.0        66.0             360.0             1.0   \n",
       "3               2358.0       120.0             360.0             1.0   \n",
       "4                  0.0       141.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0        71.0             360.0             1.0   \n",
       "610                0.0        40.0             180.0             1.0   \n",
       "611              240.0       253.0             360.0             1.0   \n",
       "612                0.0       187.0             360.0             1.0   \n",
       "613                0.0       133.0             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "0           Urban           Y  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[614 rows x 12 columns]"
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     "execution_count": 264,
     "metadata": {},
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   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Married']=pd.get_dummies(s['Married'],drop_first=True)\n",
    "s['Education']=pd.get_dummies(s['Education'],drop_first=True)\n",
    "s['Self_Employed']=pd.get_dummies(s['Self_Employed'],drop_first=True)\n",
    "s['Property_Area']=pd.get_dummies(s['Property_Area'],drop_first=True)\n",
    "s['Credit_History']=pd.get_dummies(s['Credit_History'],drop_first=True)\n",
    "s['Dependents']=pd.get_dummies(s['Dependents'],drop_first=True)\n",
    "s['Gender']=pd.get_dummies(s['Gender'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "0         1        0           0          0              0             5849   \n",
       "1         1        1           1          0              0             4583   \n",
       "2         1        1           0          0              1             3000   \n",
       "3         1        1           0          1              0             2583   \n",
       "4         1        0           0          0              0             6000   \n",
       "..      ...      ...         ...        ...            ...              ...   \n",
       "609       0        0           0          0              0             2900   \n",
       "610       1        1           0          0              0             4106   \n",
       "611       1        1           1          0              0             8072   \n",
       "612       1        1           0          0              0             7583   \n",
       "613       0        0           0          0              1             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0       128.0             360.0               1   \n",
       "1               1508.0       128.0             360.0               1   \n",
       "2                  0.0        66.0             360.0               1   \n",
       "3               2358.0       120.0             360.0               1   \n",
       "4                  0.0       141.0             360.0               1   \n",
       "..                 ...         ...               ...             ...   \n",
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       "610                0.0        40.0             180.0               1   \n",
       "611              240.0       253.0             360.0               1   \n",
       "612                0.0       187.0             360.0               1   \n",
       "613                0.0       133.0             360.0               0   \n",
       "\n",
       "     Property_Area Loan_Status  \n",
       "0                0           Y  \n",
       "1                0           N  \n",
       "2                0           Y  \n",
       "3                0           Y  \n",
       "4                0           Y  \n",
       "..             ...         ...  \n",
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       "610              0           Y  \n",
       "611              0           Y  \n",
       "612              0           Y  \n",
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       "\n",
       "[614 rows x 12 columns]"
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     },
     "execution_count": 266,
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  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
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       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['Total_income']=s['ApplicantIncome']+s['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "s['EMI']=(s['LoanAmount']*0.09*(1.09**s['Loan_Amount_Term']))/(1.09**(s['Loan_Amount_Term']-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.drop(['ApplicantIncome','CoapplicantIncome'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
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       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
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       "\n",
       "     Total_income      EMI  \n",
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  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('s11.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
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       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "0         1        1           0          0              0       110.0   \n",
       "1         1        1           1          0              0       126.0   \n",
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       "3         1        1           0          0              0       100.0   \n",
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       "366       1        0           0          0              1        98.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area  Total_income      EMI  \n",
       "0               360.0               1              0          5720  10.7910  \n",
       "1               360.0               1              0          4576  12.3606  \n",
       "2               360.0               1              0          6800  20.4048  \n",
       "3               360.0               1              0          4886   9.8100  \n",
       "4               360.0               1              0          3276   7.6518  \n",
       "..                ...             ...            ...           ...      ...  \n",
       "362             360.0               1              0          5786  11.0853  \n",
       "363             360.0               1              0          4867  11.2815  \n",
       "364             360.0               1              1          5243  12.3606  \n",
       "365             360.0               1              0          7393  15.4998  \n",
       "366             180.0               1              0          9200   9.6138  \n",
       "\n",
       "[367 rows x 11 columns]"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "final=pd.concat([s,test],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "final=final.loc[:,~final.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Loan_Status</th>\n",
       "      <th>Total_income</th>\n",
       "      <th>EMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>5849.0</td>\n",
       "      <td>12.5568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>12.5568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>6.4746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>11.7720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>13.8321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>609</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>6.9651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>610</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>4106.0</td>\n",
       "      <td>3.9240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>611</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>8312.0</td>\n",
       "      <td>24.8193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>612</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Y</td>\n",
       "      <td>7583.0</td>\n",
       "      <td>18.3447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>613</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>4583.0</td>\n",
       "      <td>13.0473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "0         1        0           0          0              0       128.0   \n",
       "1         1        1           1          0              0       128.0   \n",
       "2         1        1           0          0              1        66.0   \n",
       "3         1        1           0          1              0       120.0   \n",
       "4         1        0           0          0              0       141.0   \n",
       "..      ...      ...         ...        ...            ...         ...   \n",
       "609       0        0           0          0              0        71.0   \n",
       "610       1        1           0          0              0        40.0   \n",
       "611       1        1           1          0              0       253.0   \n",
       "612       1        1           0          0              0       187.0   \n",
       "613       0        0           0          0              1       133.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area Loan_Status  \\\n",
       "0               360.0               1              0           Y   \n",
       "1               360.0               1              0           N   \n",
       "2               360.0               1              0           Y   \n",
       "3               360.0               1              0           Y   \n",
       "4               360.0               1              0           Y   \n",
       "..                ...             ...            ...         ...   \n",
       "609             360.0               1              0           Y   \n",
       "610             180.0               1              0           Y   \n",
       "611             360.0               1              0           Y   \n",
       "612             360.0               1              0           Y   \n",
       "613             360.0               0              1           N   \n",
       "\n",
       "     Total_income      EMI  \n",
       "0          5849.0  12.5568  \n",
       "1          6091.0  12.5568  \n",
       "2          3000.0   6.4746  \n",
       "3          4941.0  11.7720  \n",
       "4          6000.0  13.8321  \n",
       "..            ...      ...  \n",
       "609        2900.0   6.9651  \n",
       "610        4106.0   3.9240  \n",
       "611        8312.0  24.8193  \n",
       "612        7583.0  18.3447  \n",
       "613        4583.0  13.0473  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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",
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       "\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>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Total_income</th>\n",
       "      <th>EMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\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>110.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>5720</td>\n",
       "      <td>10.7910</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4576</td>\n",
       "      <td>12.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6800</td>\n",
       "      <td>20.4048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4886</td>\n",
       "      <td>9.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3276</td>\n",
       "      <td>7.6518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5786</td>\n",
       "      <td>11.0853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4867</td>\n",
       "      <td>11.2815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5243</td>\n",
       "      <td>12.3606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7393</td>\n",
       "      <td>15.4998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9200</td>\n",
       "      <td>9.6138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "0         1        1           0          0              0       110.0   \n",
       "1         1        1           1          0              0       126.0   \n",
       "2         1        1           0          0              0       208.0   \n",
       "3         1        1           0          0              0       100.0   \n",
       "4         1        0           0          1              0        78.0   \n",
       "..      ...      ...         ...        ...            ...         ...   \n",
       "362       1        1           0          1              1       113.0   \n",
       "363       1        1           0          0              0       115.0   \n",
       "364       1        0           0          0              0       126.0   \n",
       "365       1        1           0          0              0       158.0   \n",
       "366       1        0           0          0              1        98.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area  Total_income      EMI  \n",
       "0               360.0               1              0          5720  10.7910  \n",
       "1               360.0               1              0          4576  12.3606  \n",
       "2               360.0               1              0          6800  20.4048  \n",
       "3               360.0               1              0          4886   9.8100  \n",
       "4               360.0               1              0          3276   7.6518  \n",
       "..                ...             ...            ...           ...      ...  \n",
       "362             360.0               1              0          5786  11.0853  \n",
       "363             360.0               1              0          4867  11.2815  \n",
       "364             360.0               1              1          5243  12.3606  \n",
       "365             360.0               1              0          7393  15.4998  \n",
       "366             180.0               1              0          9200   9.6138  \n",
       "\n",
       "[367 rows x 11 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = s.drop(['Loan_Status'],axis=1)\n",
    "y = s['Loan_Status']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 11)"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614,)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 11)"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_train' 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-1-e7a6915396ee>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msvm\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSVC\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSVC\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----> 3\u001b[1;33m \u001b[0mmodel\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[0;32m      4\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "model = SVC()\n",
    "model.fit(X_train,y_train)\n",
    "predictions = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.tree import DecisionTreeClassifier\n",
    "#dtree = DecisionTreeClassifier(criterion='gini')\n",
    "#dtree.fit(X_train,y_train)\n",
    "#predictions1 = dtree.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.neighbors import KNeighborsClassifier\n",
    "#knn = KNeighborsClassifier(n_neighbors=1)\n",
    "#knn.fit(X_train,y_train)\n",
    "#pred = knn.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframe=pd.DataFrame(predictions, columns=['Loan_Status']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataframe.to_csv('sss.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Dependents</th>\n",
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       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Total_income</th>\n",
       "      <th>EMI</th>\n",
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       "      <td>4576</td>\n",
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       "      <td>6800</td>\n",
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       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>4886</td>\n",
       "      <td>9.8100</td>\n",
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       "      <td>...</td>\n",
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       "      <td>362</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5786</td>\n",
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       "    <tr>\n",
       "      <td>363</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4867</td>\n",
       "      <td>11.2815</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5243</td>\n",
       "      <td>12.3606</td>\n",
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       "      <td>365</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7393</td>\n",
       "      <td>15.4998</td>\n",
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       "    <tr>\n",
       "      <td>366</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>9.6138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "0         1        1           0          0              0       110.0   \n",
       "1         1        1           1          0              0       126.0   \n",
       "2         1        1           0          0              0       208.0   \n",
       "3         1        1           0          0              0       100.0   \n",
       "4         1        0           0          1              0        78.0   \n",
       "..      ...      ...         ...        ...            ...         ...   \n",
       "362       1        1           0          1              1       113.0   \n",
       "363       1        1           0          0              0       115.0   \n",
       "364       1        0           0          0              0       126.0   \n",
       "365       1        1           0          0              0       158.0   \n",
       "366       1        0           0          0              1        98.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area  Total_income      EMI  \n",
       "0               360.0               1              0          5720  10.7910  \n",
       "1               360.0               1              0          4576  12.3606  \n",
       "2               360.0               1              0          6800  20.4048  \n",
       "3               360.0               1              0          4886   9.8100  \n",
       "4               360.0               1              0          3276   7.6518  \n",
       "..                ...             ...            ...           ...      ...  \n",
       "362             360.0               1              0          5786  11.0853  \n",
       "363             360.0               1              0          4867  11.2815  \n",
       "364             360.0               1              1          5243  12.3606  \n",
       "365             360.0               1              0          7393  15.4998  \n",
       "366             180.0               1              0          9200   9.6138  \n",
       "\n",
       "[367 rows x 11 columns]"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350    Y\n",
       "377    Y\n",
       "163    Y\n",
       "609    Y\n",
       "132    Y\n",
       "      ..\n",
       "380    Y\n",
       "446    Y\n",
       "144    Y\n",
       "290    Y\n",
       "353    N\n",
       "Name: Loan_Status, Length: 185, dtype: object"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
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       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Total_income</th>\n",
       "      <th>EMI</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4310.0</td>\n",
       "      <td>12.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5614.0</td>\n",
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       "    <tr>\n",
       "      <td>609</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>6.9651</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2718.0</td>\n",
       "      <td>6.8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5833.0</td>\n",
       "      <td>12.5568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>446</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4652.0</td>\n",
       "      <td>10.7910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11757.0</td>\n",
       "      <td>18.3447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5491.0</td>\n",
       "      <td>13.6359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>10.3005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "350       1        1           0          0              0       228.0   \n",
       "377       1        1           0          0              0       130.0   \n",
       "163       1        1           0          0              0       158.0   \n",
       "609       0        0           0          0              0        71.0   \n",
       "132       1        0           0          0              0        70.0   \n",
       "..      ...      ...         ...        ...            ...         ...   \n",
       "380       1        1           0          0              0       128.0   \n",
       "446       1        1           0          1              0       110.0   \n",
       "144       1        1           0          0              0       187.0   \n",
       "290       1        1           0          0              0       139.0   \n",
       "353       0        1           0          0              1       105.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area  Total_income      EMI  \n",
       "350             360.0               1              1        9083.0  22.3668  \n",
       "377             360.0               1              1        4310.0  12.7530  \n",
       "163             360.0               1              0        5614.0  15.4998  \n",
       "609             360.0               1              0        2900.0   6.9651  \n",
       "132             360.0               1              1        2718.0   6.8670  \n",
       "..                ...             ...            ...           ...      ...  \n",
       "380             360.0               1              1        5833.0  12.5568  \n",
       "446             360.0               1              0        4652.0  10.7910  \n",
       "144             180.0               1              0       11757.0  18.3447  \n",
       "290             360.0               1              0        5491.0  13.6359  \n",
       "353             360.0               0              0        5500.0  10.3005  \n",
       "\n",
       "[185 rows x 11 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logmodel.fit(X_train, y_train)\n",
    "#print(\"model score: %.3f\" % logmodel.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Total_income</th>\n",
       "      <th>EMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>553</td>\n",
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       "      <td>5686.0</td>\n",
       "      <td>15.2055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>360.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>4300.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>9.5157</td>\n",
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       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12543.0</td>\n",
       "      <td>22.0725</td>\n",
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       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3237.0</td>\n",
       "      <td>2.9430</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10047.0</td>\n",
       "      <td>12.5568</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>429 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "553       1        1           0          1              0       181.0   \n",
       "601       1        1           0          1              0       155.0   \n",
       "261       1        0           0          0              0       134.0   \n",
       "496       1        1           0          1              0       107.0   \n",
       "529       1        0           0          1              0       130.0   \n",
       "..      ...      ...         ...        ...            ...         ...   \n",
       "71        1        1           0          1              1        97.0   \n",
       "106       1        1           0          0              0       225.0   \n",
       "270       0        0           0          0              0        30.0   \n",
       "435       0        0           0          0              0       128.0   \n",
       "102       1        1           0          0              0       128.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area  Total_income      EMI  \n",
       "553             360.0               0              0        4787.0  17.7561  \n",
       "601             360.0               1              0        5686.0  15.2055  \n",
       "261             360.0               1              1        4269.0  13.1454  \n",
       "496             360.0               1              0        4300.0  10.4967  \n",
       "529             360.0               1              1        6783.0  12.7530  \n",
       "..                ...             ...            ...           ...      ...  \n",
       "71              360.0               1              1        3750.0   9.5157  \n",
       "106             360.0               1              0       12543.0  22.0725  \n",
       "270             360.0               1              0        3237.0   2.9430  \n",
       "435             240.0               1              1       10047.0  12.5568  \n",
       "102             360.0               1              0       13650.0  12.5568  \n",
       "\n",
       "[429 rows x 11 columns]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "553    N\n",
       "601    Y\n",
       "261    Y\n",
       "496    Y\n",
       "529    Y\n",
       "      ..\n",
       "71     Y\n",
       "106    Y\n",
       "270    Y\n",
       "435    Y\n",
       "102    Y\n",
       "Name: Loan_Status, Length: 429, dtype: object"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
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       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Total_income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2900.0</td>\n",
       "      <td>6.9651</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2718.0</td>\n",
       "      <td>6.8670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5833.0</td>\n",
       "      <td>12.5568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>446</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4652.0</td>\n",
       "      <td>10.7910</td>\n",
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       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11757.0</td>\n",
       "      <td>18.3447</td>\n",
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       "    <tr>\n",
       "      <td>290</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5491.0</td>\n",
       "      <td>13.6359</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>105.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>10.3005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married  Dependents  Education  Self_Employed  LoanAmount  \\\n",
       "350       1        1           0          0              0       228.0   \n",
       "377       1        1           0          0              0       130.0   \n",
       "163       1        1           0          0              0       158.0   \n",
       "609       0        0           0          0              0        71.0   \n",
       "132       1        0           0          0              0        70.0   \n",
       "..      ...      ...         ...        ...            ...         ...   \n",
       "380       1        1           0          0              0       128.0   \n",
       "446       1        1           0          1              0       110.0   \n",
       "144       1        1           0          0              0       187.0   \n",
       "290       1        1           0          0              0       139.0   \n",
       "353       0        1           0          0              1       105.0   \n",
       "\n",
       "     Loan_Amount_Term  Credit_History  Property_Area  Total_income      EMI  \n",
       "350             360.0               1              1        9083.0  22.3668  \n",
       "377             360.0               1              1        4310.0  12.7530  \n",
       "163             360.0               1              0        5614.0  15.4998  \n",
       "609             360.0               1              0        2900.0   6.9651  \n",
       "132             360.0               1              1        2718.0   6.8670  \n",
       "..                ...             ...            ...           ...      ...  \n",
       "380             360.0               1              1        5833.0  12.5568  \n",
       "446             360.0               1              0        4652.0  10.7910  \n",
       "144             180.0               1              0       11757.0  18.3447  \n",
       "290             360.0               1              0        5491.0  13.6359  \n",
       "353             360.0               0              0        5500.0  10.3005  \n",
       "\n",
       "[185 rows x 11 columns]"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350    Y\n",
       "377    Y\n",
       "163    Y\n",
       "609    Y\n",
       "132    Y\n",
       "      ..\n",
       "380    Y\n",
       "446    Y\n",
       "144    Y\n",
       "290    Y\n",
       "353    N\n",
       "Name: Loan_Status, Length: 185, dtype: object"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "553    N\n",
       "601    Y\n",
       "261    Y\n",
       "496    Y\n",
       "529    Y\n",
       "      ..\n",
       "71     Y\n",
       "106    Y\n",
       "270    Y\n",
       "435    Y\n",
       "102    Y\n",
       "Name: Loan_Status, Length: 429, dtype: object"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350    Y\n",
       "377    Y\n",
       "163    Y\n",
       "609    Y\n",
       "132    Y\n",
       "      ..\n",
       "380    Y\n",
       "446    Y\n",
       "144    Y\n",
       "290    Y\n",
       "353    N\n",
       "Name: Loan_Status, Length: 185, dtype: object"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "#logModel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "#param_grid = [    \n",
    " #   {'penalty' : ['l1', 'l2', 'elasticnet', 'none'],\n",
    "  #  'C' : np.logspace(-4, 4, 20),\n",
    "   # 'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],\n",
    "    #'max_iter' : [100, 1000,2500, 5000]\n",
    "    #}\n",
    "#]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#Init signature:\n",
    "#LogisticRegression(\n",
    "#    penalty='l2',\n",
    "#    dual=False,\n",
    "#    tol=0.0001,\n",
    "#    C=1.0,\n",
    "#    fit_intercept=True,\n",
    "#    intercept_scaling=1,\n",
    "#    class_weight=None,\n",
    "#    random_state=None,\n",
    "#    solver='lbfgs',\n",
    "#    max_iter=100,\n",
    "#    multi_class='auto',\n",
    "#    verbose=0,\n",
    "#    warm_start=False,\n",
    "#    n_jobs=None,\n",
    "#    l1_ratio=None,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf = GridSearchCV(logModel, param_grid = param_grid, cv = 3, verbose=True, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "#best_clf = clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "#best_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print (f'Accuracy - : {best_clf.score(X,y):.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predic=best_clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre=pd.DataFrame(predictions)\n",
    "#sub_df=pd.read_csv('s1traintest.csv')\n",
    "#datasets=pd.concat([sub_df['Loan_ID'],pre],axis=1)\n",
    "#datasets.columns=['Loan_ID','Loan_Status']\n",
    "#datasets.to_csv('go22.csv',index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cpre=pd.DataFrame(predic)\n",
    "#sub_df1=pd.read_csv('s1traintest.csv')\n",
    "#datasets1=pd.concat([sub_df['Loan_ID'],cpre],axis=1)\n",
    "#datasets1.columns=['Loan_ID','Loan_Status']\n",
    "#datasets1.to_csv('go33.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pro=pd.DataFrame(pred)\n",
    "#sub_df2=pd.read_csv('s1traintest.csv')\n",
    "#datasets1=pd.concat([sub_df2['Loan_ID'],pro],axis=1)\n",
    "#datasets1.columns=['Loan_ID','Loan_Status']\n",
    "#datasets1.to_csv('gotit.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pro1=pd.DataFrame(predictions1)\n",
    "#sub_df2=pd.read_csv('s1traintest.csv')\n",
    "#datasets1=pd.concat([sub_df2['Loan_ID'],pro1],axis=1)\n",
    "#datasets1.columns=['Loan_ID','Loan_Status']\n",
    "datasets1.to_csv('gee1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=10000)\n",
    "rfc.fit(X_train,y_train)\n",
    "predictions2 = rfc.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "pro4=pd.DataFrame(predictions1)\n",
    "sub_df2=pd.read_csv('s1traintest.csv')\n",
    "datasets1=pd.concat([sub_df2['Loan_ID'],pro4],axis=1)\n",
    "datasets1.columns=['Loan_ID','Loan_Status']\n",
    "datasets1.to_csv('gee222.csv',index=False)"
   ]
  },
  {
   "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": []
  },
  {
   "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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