LoanPredict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('/Users/sunxun/Downloads/train_u6lujuX_CVtuZ9i.csv')\n",
    "test = pd.read_csv('/Users/sunxun/Downloads/test_Y3wMUE5_7gLdaTN.csv')\n",
    "train_original = train.copy()\n",
    "test_original = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Gender', 'Married', 'Dependents', 'Education',\n",
       "       'Self_Employed', 'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 13), (367, 12))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    422\n",
       "N    192\n",
       "Name: Loan_Status, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    0.687296\n",
       "N    0.312704\n",
       "Name: Loan_Status, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16782d68>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Loan_Status'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16b779e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Gender'].value_counts(normalize=True).plot.bar(figsize=(20,10), title='Genders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a16b95ac8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAAAADPP/AVTC5vQP9FnkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Married'].value_counts(normalize=True).plot.bar(figsize=(20,10), title='Married')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(131)\n",
    "train['Dependents'].value_counts(normalize=True).plot.bar(figsize=(24,6),title='Dependents')\n",
    "plt.subplot(132) \n",
    "train['Education'].value_counts(normalize=True).plot.bar(title='Education')\n",
    "plt.subplot(133)\n",
    "train['Property_Area'].value_counts(normalize=True).plot.bar(title='Property_Area')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17966e80>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "sns.distplot(train['ApplicantIncome'])\n",
    "plt.subplot(122)\n",
    "train['ApplicantIncome'].plot.box(figsize=(16,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, ' ')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.boxplot(column='ApplicantIncome', by='Education')\n",
    "plt.suptitle(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18a9a940>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1) \n",
    "plt.subplot(141) \n",
    "df=train.dropna() \n",
    "sns.distplot(df['LoanAmount']);\n",
    "plt.subplot(142)\n",
    "train['LoanAmount'].plot.box(figsize=(16, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a18d15860>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Gender = pd.crosstab(train['Gender'], train['Loan_Status'])\n",
    "Gender.div(Gender.sum(1).astype(float), axis=0).plot(kind='bar', stacked='True', figsize=(4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Married = pd.crosstab(train['Married'], train['Loan_Status'])\n",
    "Married.div(Married.sum(1).astype(float), axis=0).plot(kind='bar', stacked='True',figsize=(4,4))\n",
    "plt.show()\n",
    "Dependents = pd.crosstab(train['Dependents'], train['Loan_Status'])\n",
    "Dependents.div(Dependents.sum(1).astype(float), axis=0).plot(kind='bar', stacked='True',figsize=(4,4))\n",
    "plt.show()\n",
    "Self_Employed = pd.crosstab(train['Self_Employed'], train['Loan_Status'])\n",
    "Self_Employed.div(Self_Employed.sum(1).astype(float), axis=0).plot(kind='bar', stacked='True',figsize=(4,4))\n",
    "plt.show()\n",
    "Property_Area = pd.crosstab(train['Property_Area'], train['Loan_Status'])\n",
    "Property_Area.div(Property_Area.sum(1).astype(float), axis=0).plot(kind='bar', stacked='True',figsize=(4,4))\n",
    "plt.show()\n",
    "Credit_History=pd.crosstab(train['Credit_History'],train['Loan_Status']) \n",
    "Education=pd.crosstab(train['Education'],train['Loan_Status']) \n",
    "Credit_History.div(Credit_History.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4)) \n",
    "plt.show() \n",
    "Education.div(Education.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True, figsize=(4,4)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a19764898>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.groupby('Loan_Status')['ApplicantIncome'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Persentage')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0, 1000, 3000, 42000]\n",
    "group=['low', 'average', 'high']\n",
    "train['Coapplicant_Income_bin']=pd.cut(df['CoapplicantIncome'], bins, labels=group)\n",
    "Coapplicant_Income_bin=pd.crosstab(train['Coapplicant_Income_bin'],train['Loan_Status'])\n",
    "Coapplicant_Income_bin.div(Coapplicant_Income_bin.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('CoapplicantIncome')\n",
    "plt.ylabel('Persentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Persentage')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Total_Income']=train['ApplicantIncome']+train['CoapplicantIncome']\n",
    "bins=[0, 2500,4000,6000,81000]\n",
    "group=['low','average','high','very high']\n",
    "train['Total_Income_bin']=pd.cut(train['Total_Income'],bins,labels=group)\n",
    "Total_Income_bin=pd.crosstab(train['Total_Income_bin'],train['Loan_Status'])\n",
    "Total_Income_bin.div(Total_Income_bin.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('TotalIncome')\n",
    "plt.ylabel('Persentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Persentage')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,100,200,700]\n",
    "group=['low','average','high']\n",
    "train['LoanAmount_bin']=pd.cut(df['LoanAmount'],bins,labels=group)\n",
    "LoanAmount_bin=pd.crosstab(train['LoanAmount_bin'],train['Loan_Status'])\n",
    "LoanAmount_bin.div(LoanAmount_bin.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.xlabel('LoanAmount')\n",
    "plt.ylabel('Persentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop(['Total_Income_bin','LoanAmount_bin','Total_Income','Total_Income_bin','Coapplicant_Income_bin'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Dependents'].replace('3+','3',inplace=True)\n",
    "train['Loan_Status'].replace('N',0,inplace=True)\n",
    "train['Loan_Status'].replace('Y',1,inplace=True)\n",
    "test['Dependents'].replace('3+','3',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = train.corr() \n",
    "f, ax = plt.subplots(figsize=(9, 6)) \n",
    "sns.heatmap(matrix, vmax=.8, square=True, cmap=\"BuPu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Gender'].fillna(train['Gender'].mode()[0],inplace=True)\n",
    "train['Married'].fillna(train['Married'].mode()[0],inplace=True)\n",
    "train['Dependents'].fillna(train['Dependents'].mode()[0],inplace=True)\n",
    "train['Self_Employed'].fillna(train['Self_Employed'].mode()[0],inplace=True)\n",
    "train['Credit_History'].fillna(train['Credit_History'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    512\n",
       "180.0     44\n",
       "480.0     15\n",
       "300.0     13\n",
       "84.0       4\n",
       "240.0      4\n",
       "120.0      3\n",
       "36.0       2\n",
       "60.0       2\n",
       "12.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Loan_Amount_Term'].fillna(train['Loan_Amount_Term'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['LoanAmount'].fillna(train['LoanAmount'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['LoanAmount_log'] = np.log(train['LoanAmount']) \n",
    "train['LoanAmount_log'].hist(bins=20) \n",
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=train.drop('Loan_ID',axis=1)\n",
    "test=test.drop('Loan_ID',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=train.drop('Loan_Status',1)\n",
    "y=train.Loan_Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.get_dummies(X)\n",
    "train=pd.get_dummies(train)\n",
    "test=pd.get_dummies(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_cv,y_train,y_cv = train_test_split(X, y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_cv = model.predict(x_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8432432432432433"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_cv, pred_cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('/Users/sunxun/Downloads/Sample_Submission_ZAuTl8O_FK3zQHh.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome             0\n",
       "CoapplicantIncome           0\n",
       "LoanAmount                  5\n",
       "Loan_Amount_Term            6\n",
       "Credit_History             29\n",
       "LoanAmount_log              5\n",
       "Gender_Female               0\n",
       "Gender_Male                 0\n",
       "Married_No                  0\n",
       "Married_Yes                 0\n",
       "Dependents_0                0\n",
       "Dependents_1                0\n",
       "Dependents_2                0\n",
       "Dependents_3                0\n",
       "Education_Graduate          0\n",
       "Education_Not Graduate      0\n",
       "Self_Employed_No            0\n",
       "Self_Employed_Yes           0\n",
       "Property_Area_Rural         0\n",
       "Property_Area_Semiurban     0\n",
       "Property_Area_Urban         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['LoanAmount'].fillna(test['LoanAmount'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['LoanAmount_log'] = np.log(test['LoanAmount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome             0\n",
       "CoapplicantIncome           0\n",
       "LoanAmount                  0\n",
       "Loan_Amount_Term            6\n",
       "Credit_History             29\n",
       "LoanAmount_log              0\n",
       "Gender_Female               0\n",
       "Gender_Male                 0\n",
       "Married_No                  0\n",
       "Married_Yes                 0\n",
       "Dependents_0                0\n",
       "Dependents_1                0\n",
       "Dependents_2                0\n",
       "Dependents_3                0\n",
       "Education_Graduate          0\n",
       "Education_Not Graduate      0\n",
       "Self_Employed_No            0\n",
       "Self_Employed_Yes           0\n",
       "Property_Area_Rural         0\n",
       "Property_Area_Semiurban     0\n",
       "Property_Area_Urban         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Credit_History'].fillna(test['Credit_History'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360.0    311\n",
       "180.0     22\n",
       "480.0      8\n",
       "300.0      7\n",
       "240.0      4\n",
       "84.0       3\n",
       "6.0        1\n",
       "120.0      1\n",
       "36.0       1\n",
       "350.0      1\n",
       "12.0       1\n",
       "60.0       1\n",
       "Name: Loan_Amount_Term, dtype: int64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Loan_Amount_Term'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Loan_Amount_Term'].fillna(test['Loan_Amount_Term'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome            0\n",
       "CoapplicantIncome          0\n",
       "LoanAmount                 0\n",
       "Loan_Amount_Term           0\n",
       "Credit_History             0\n",
       "LoanAmount_log             0\n",
       "Gender_Female              0\n",
       "Gender_Male                0\n",
       "Married_No                 0\n",
       "Married_Yes                0\n",
       "Dependents_0               0\n",
       "Dependents_1               0\n",
       "Dependents_2               0\n",
       "Dependents_3               0\n",
       "Education_Graduate         0\n",
       "Education_Not Graduate     0\n",
       "Self_Employed_No           0\n",
       "Self_Employed_Yes          0\n",
       "Property_Area_Rural        0\n",
       "Property_Area_Semiurban    0\n",
       "Property_Area_Urban        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status']=pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_ID']=test_original['Loan_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission['Loan_Status'].replace(0,'N',inplace=True)\n",
    "submission['Loan_Status'].replace(1,'Y',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(submission,columns=['Loan_ID','Loan_Status']).to_csv('/Users/sunxun/Desktop/logistic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Loan_ID', 'Loan_Status'], dtype='object')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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