17.1_Credit_Risk_Analysis-11.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from collections import Counter\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "f = open(\"data_all.pkl\",\"rb\")\n",
    "data_all = pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function get_dummies in module pandas.core.reshape.reshape:\n",
      "\n",
      "get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False)\n",
      "    Convert categorical variable into dummy/indicator variables\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    data : array-like, Series, or DataFrame\n",
      "    prefix : string, list of strings, or dict of strings, default None\n",
      "        String to append DataFrame column names\n",
      "        Pass a list with length equal to the number of columns\n",
      "        when calling get_dummies on a DataFrame. Alternativly, `prefix`\n",
      "        can be a dictionary mapping column names to prefixes.\n",
      "    prefix_sep : string, default '_'\n",
      "        If appending prefix, separator/delimiter to use. Or pass a\n",
      "        list or dictionary as with `prefix.`\n",
      "    dummy_na : bool, default False\n",
      "        Add a column to indicate NaNs, if False NaNs are ignored.\n",
      "    columns : list-like, default None\n",
      "        Column names in the DataFrame to be encoded.\n",
      "        If `columns` is None then all the columns with\n",
      "        `object` or `category` dtype will be converted.\n",
      "    sparse : bool, default False\n",
      "        Whether the dummy columns should be sparse or not.  Returns\n",
      "        SparseDataFrame if `data` is a Series or if all columns are included.\n",
      "        Otherwise returns a DataFrame with some SparseBlocks.\n",
      "    \n",
      "        .. versionadded:: 0.16.1\n",
      "    drop_first : bool, default False\n",
      "        Whether to get k-1 dummies out of k categorical levels by removing the\n",
      "        first level.\n",
      "    \n",
      "        .. versionadded:: 0.18.0\n",
      "    Returns\n",
      "    -------\n",
      "    dummies : DataFrame or SparseDataFrame\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> import pandas as pd\n",
      "    >>> s = pd.Series(list('abca'))\n",
      "    \n",
      "    >>> pd.get_dummies(s)\n",
      "       a  b  c\n",
      "    0  1  0  0\n",
      "    1  0  1  0\n",
      "    2  0  0  1\n",
      "    3  1  0  0\n",
      "    \n",
      "    >>> s1 = ['a', 'b', np.nan]\n",
      "    \n",
      "    >>> pd.get_dummies(s1)\n",
      "       a  b\n",
      "    0  1  0\n",
      "    1  0  1\n",
      "    2  0  0\n",
      "    \n",
      "    >>> pd.get_dummies(s1, dummy_na=True)\n",
      "       a  b  NaN\n",
      "    0  1  0    0\n",
      "    1  0  1    0\n",
      "    2  0  0    1\n",
      "    \n",
      "    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n",
      "                        'C': [1, 2, 3]})\n",
      "    \n",
      "    >>> pd.get_dummies(df, prefix=['col1', 'col2'])\n",
      "       C  col1_a  col1_b  col2_a  col2_b  col2_c\n",
      "    0  1       1       0       0       1       0\n",
      "    1  2       0       1       1       0       0\n",
      "    2  3       1       0       0       0       1\n",
      "    \n",
      "    >>> pd.get_dummies(pd.Series(list('abcaa')))\n",
      "       a  b  c\n",
      "    0  1  0  0\n",
      "    1  0  1  0\n",
      "    2  0  0  1\n",
      "    3  1  0  0\n",
      "    4  1  0  0\n",
      "    \n",
      "    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True))\n",
      "       b  c\n",
      "    0  0  0\n",
      "    1  1  0\n",
      "    2  0  1\n",
      "    3  0  0\n",
      "    4  0  0\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    Series.str.get_dummies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.get_dummies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    a\n",
      "1    b\n",
      "2    c\n",
      "3    a\n",
      "4    a\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "x = pd.Series(list('abcaa'))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   a  b  c\n",
       "0  1  0  0\n",
       "1  0  1  0\n",
       "2  0  0  1\n",
       "3  1  0  0\n",
       "4  1  0  0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   b  c\n",
       "0  0  0\n",
       "1  1  0\n",
       "2  0  1\n",
       "3  0  0\n",
       "4  0  0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(x,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</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>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       144.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_all_new = pd.get_dummies(data_all.drop(['Loan_ID'],axis=1),drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>Gender_Male</th>\n",
       "      <th>Married_Yes</th>\n",
       "      <th>Dependents_1</th>\n",
       "      <th>Dependents_2</th>\n",
       "      <th>Dependents_3+</th>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <th>Loan_Status_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    </tr>\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>1</td>\n",
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       "      <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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>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>1</td>\n",
       "      <td>1</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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       144.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History  Gender_Male  Married_Yes  Dependents_1  Dependents_2  \\\n",
       "0             1.0            1            0             0             0   \n",
       "1             1.0            1            1             1             0   \n",
       "2             1.0            1            1             0             0   \n",
       "3             1.0            1            1             0             0   \n",
       "4             1.0            1            0             0             0   \n",
       "\n",
       "   Dependents_3+  Education_Not Graduate  Self_Employed_Yes  \\\n",
       "0              0                       0                  0   \n",
       "1              0                       0                  0   \n",
       "2              0                       0                  1   \n",
       "3              0                       1                  0   \n",
       "4              0                       0                  0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  Loan_Status_Y  \n",
       "0                        0                    1              1  \n",
       "1                        0                    0              0  \n",
       "2                        0                    1              1  \n",
       "3                        0                    1              1  \n",
       "4                        0                    1              1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_all_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data_all_new.drop(['Loan_Status_Y'],axis = 1)\n",
    "y = data_all_new['Loan_Status_Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
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      "text/plain": [
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0       144.0             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History  Gender_Male  Married_Yes  Dependents_1  Dependents_2  \\\n",
       "0             1.0            1            0             0             0   \n",
       "1             1.0            1            1             1             0   \n",
       "2             1.0            1            1             0             0   \n",
       "3             1.0            1            1             0             0   \n",
       "4             1.0            1            0             0             0   \n",
       "\n",
       "   Dependents_3+  Education_Not Graduate  Self_Employed_Yes  \\\n",
       "0              0                       0                  0   \n",
       "1              0                       0                  0   \n",
       "2              0                       0                  1   \n",
       "3              0                       1                  0   \n",
       "4              0                       0                  0   \n",
       "\n",
       "   Property_Area_Semiurban  Property_Area_Urban  \n",
       "0                        0                    1  \n",
       "1                        0                    0  \n",
       "2                        0                    1  \n",
       "3                        0                    1  \n",
       "4                        0                    1  "
      ]
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     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "X.head()"
   ]
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