Loan-Defaulter-classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the data and other imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.utils import shuffle\n",
    "# might be needed depending on your version of Jupyter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('lending_club_loan_two.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396030 entries, 0 to 396029\n",
      "Data columns (total 27 columns):\n",
      "loan_amnt               396030 non-null float64\n",
      "term                    396030 non-null object\n",
      "int_rate                396030 non-null float64\n",
      "installment             396030 non-null float64\n",
      "grade                   396030 non-null object\n",
      "sub_grade               396030 non-null object\n",
      "emp_title               373103 non-null object\n",
      "emp_length              377729 non-null object\n",
      "home_ownership          396030 non-null object\n",
      "annual_inc              396030 non-null float64\n",
      "verification_status     396030 non-null object\n",
      "issue_d                 396030 non-null object\n",
      "loan_status             396030 non-null object\n",
      "purpose                 396030 non-null object\n",
      "title                   394275 non-null object\n",
      "dti                     396030 non-null float64\n",
      "earliest_cr_line        396030 non-null object\n",
      "open_acc                396030 non-null float64\n",
      "pub_rec                 396030 non-null float64\n",
      "revol_bal               396030 non-null float64\n",
      "revol_util              395754 non-null float64\n",
      "total_acc               396030 non-null float64\n",
      "initial_list_status     396030 non-null object\n",
      "application_type        396030 non-null object\n",
      "mort_acc                358235 non-null float64\n",
      "pub_rec_bankruptcies    395495 non-null float64\n",
      "address                 396030 non-null object\n",
      "dtypes: float64(12), object(15)\n",
      "memory usage: 81.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0eed9946d0>"
      ]
     },
     "execution_count": 9,
     "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": [
    "#distribution of our target variable\n",
    "sns.countplot(x=\"loan_status\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`there is class imbalance we have more cases of paid off then charged off `"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0eed719b50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribution of loan amount\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.distplot(df[\"loan_amnt\"],kde=False,bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b8547e990>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising correlation matrix\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "**`Here we can see that loan installment is correlated to to our loan amount and the reason being loan installment is calculated using some kind of formula which is calculated on the loan amount`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0eed3a2450>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating scatter plot of installment and loan amount\n",
    "sns.scatterplot(x=\"installment\",y=\"loan_amnt\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0eed3d03d0>"
      ]
     },
     "execution_count": 13,
     "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": [
    "# creating boxplot \n",
    "sns.boxplot(x=\"loan_status\",y=\"loan_amnt\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charged Off</th>\n",
       "      <td>77673.0</td>\n",
       "      <td>15126.300967</td>\n",
       "      <td>8505.090557</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8525.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fully Paid</th>\n",
       "      <td>318357.0</td>\n",
       "      <td>13866.878771</td>\n",
       "      <td>8302.319699</td>\n",
       "      <td>500.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>19225.0</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std     min     25%      50%  \\\n",
       "loan_status                                                                 \n",
       "Charged Off   77673.0  15126.300967  8505.090557  1000.0  8525.0  14000.0   \n",
       "Fully Paid   318357.0  13866.878771  8302.319699   500.0  7500.0  12000.0   \n",
       "\n",
       "                 75%      max  \n",
       "loan_status                    \n",
       "Charged Off  20000.0  40000.0  \n",
       "Fully Paid   19225.0  40000.0  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking mean value of loan approved \n",
    "df.groupby(\"loan_status\")[\"loan_amnt\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "**`From above graph and statistics we can say that there is not a much difference between the defaulters when we take loan amount into consideration`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'A', 'C', 'E', 'D', 'F', 'G'], dtype=object)"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking unique values of grade\n",
    "df[\"grade\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B4', 'B5', 'B3', 'A2', 'C5', 'C3', 'A1', 'B2', 'C1', 'A5', 'E4',\n",
       "       'A4', 'A3', 'D1', 'C2', 'B1', 'D3', 'D5', 'D2', 'E1', 'E2', 'E5',\n",
       "       'F4', 'E3', 'D4', 'G1', 'F5', 'G2', 'C4', 'F1', 'F3', 'G5', 'G4',\n",
       "       'F2', 'G3'], dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking unique values for subgrade\n",
    "df[\"sub_grade\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b8510eb50>"
      ]
     },
     "execution_count": 229,
     "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": [
    "#checking plot for grade\n",
    "sns.countplot(x=\"grade\",hue=\"loan_status\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`here we can see that "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b850a6590>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating sub grade plot\n",
    "plt.figure(figsize=(12,4))\n",
    "sub_order=sorted(df[\"sub_grade\"].unique())\n",
    "sns.countplot(x='sub_grade',data=df,order=sub_order,palette=\"coolwarm\",hue=\"loan_status\",\n",
    "              edgecolor=sns.color_palette(\"dark\", 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observation\n",
    "\n",
    "**`It looks like F and G subgrades don't get paid back that often`.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing fully paid with one and charge off as 0\n",
    "df[\"loan_status\"]=df[\"loan_status\"].apply(lambda x : 1 if x==\"Fully Paid\" else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b85103e10>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bAasSSNIWwDnAeyLi6bz7OGAHYFdgY1ashnpGRMyMiKGIGJoyxRccZmaljNrGEBGvG+45SQ9J2iIiFucv/oeHOW4D4NvAxyPiurbXXpwf/lHSWcDHVqn0ZmZWu16rkuYAM/LjGcDszgMkrQ1cDHwtIi7oeG6LfC9S+8TtPZbHzMx61GtiOAmYLmkhMD1vI2lI0un5mLcDrwIO79It9VxJtwG3AZOBf+2xPGZm1iNFRL/LsMqGhoZi/vz5/S6GmdlqRdKNETE02nEe+WxmZhVODGZmVrFaViVJWgLcP4ZTJwO/rrk4gxpvIv/bHM/xJmq8rSNi1P7+q2ViGCtJ81emfs3xxlcsx3M8x2s2nquSzMyswonBzMwqBi0xzHS81TKW4zme4zUYb6DaGMzMbHSDdsVgZmajcGIwM7MKJwYzM6twYrBxq9vCTSMt5mRm9eh1zWfLJG080vMR8WiNsXaIiDsl7TJMrJ/UFatL7G0i4t7R9tVkHtD5b+y2rxaStgEWR8STefvZpFUK7ysRb6KS9NaRno+I/y0Ut9H3T9IewB0RsTRvrw/sGBHXF4j1HOAPrUXOJK0BrBsRv687FgxAYpC0GfBZ4HkRsb+kHYE9I+KMmkPdCASgLs8F8PwaYx0NHAl8YZhYe9cYq9NFrPjFfCHwsroCSNoc2BJ4tqSXsvz/dANgvbridHEB8PK27T/nfbuWCijpcuDgiHg8b08CvhkR+xaI9W+kqe3/AFwKvAT4aER8veZQfz3CcwEUSQw0//79J9XPwhNd9tVlLvA64Hd5ez3ge1T/vbWZ8IkBOBs4Czg+b/8MOA+oNTFExDZ1vt4osY7MD/dv/TpqkbRuiZiSdgBeCGzY8YtwA6DumPsChwNbAf/Rtn8p8M81x2q3VkQ81dqIiKfyQlMlTW4lhRzzMUkjrp3eg9dHxD9JeguwCDgYuBKoNTFExHvqfL1V0PT7p2jr7x8RT0sq9Z26bkS0kgIR8TtJxX4kDUJimBwR50s6DiAilkn6c91B+lS9cy0r/jrptq8O2wNvAjai+otwKfD+OgNFxCxglqS3RcRFdb72KJZIenNEzAHI65qXnhjtaUlTI+IXOebWpF/VJTwr378B+EZEPJoWTyxH0htJPyie+fEQEScWCtf0+/dzSX9HukoA+CDw80KxnpC0S+t7RNLLSFd+RQxCYnhC0ibkD1uuF/xNgTiNVe90VLW0J4FiVS0RMRuYLWnPiJhXIkYXl0h6JzCNtr/Vgl8sHyCtKviVvL0IOKxQrJbjgR9Kujpvv4r0d1TCtyTdSfpC+aCkKcCTo5wzZpJOI/09vhY4HTgI+HGpeDT//n0A+BLwcdJnfC7l3ruPAhdIejBvbwEcUijWxB/5nL84vwzsRFpTegqpTveWQvHW7Va907mvxxgzSFUtQ8ANbU8tBc6OiIvritUl9hTSFcI0ql/W7y0Q61JSEr+RVF/citUt+dYZ97mkz8bSknHa4k0G9iC1pcyLiGK/cnMbxm8j4s+5QXP9iPhVoVi3RsSL2+6fC/xvRLy+RLy2uI2+f02R9CzSlbuAOyPiT6ViDcIVwx3Aq1n+H3oXZbvpNlG9Mxm4JN/aG7wDKN3WMRu4BriCti/rQraKiP0Kx3iGpM8C/9bREPwPEfHxgjEF7Ac8PyJOlDRV0m4RUfsv61wn/SFgKumX7fNIn4tL6o6Vtao6fi/pecAjFPz7bPr9a/hH0oeAcyPi9rw9SdKhEfHVumPBYIxjmBcRyyLijoi4PWfZ2qtCJG2e6/2eLemlknbJt9dQf/XOc/PtZcDfki4rn0e6tN2x5lid1ouIYyLi/Ii4qHUrFOtaSS8q9Nrd7N/ZEEyqjy/pq8CewKF5eylwaqFYZwFPsbwnyyJSL6VSLpG0EfB54CfAfcA3CsZr+v2bDWxI+pH07bZbCe/v8m+rtW2v3YS9YuhDl8fGetJExKcAJH0P2KWtH/UJpO55JV0i6Q0R8Z3CcQD2Ag6XdC/wR9J7GBHx4kLx1pS0TkT8EZ7pB196QN3uEbGLpJvgmV5JpXrSbBsRh0g6NMf6gwq2PkfEp/PDiyRdQupZU6J9r6Xp92+9iDim4Ou3W0PSM72gJK0JFOtxNWETAw13eexTT5qppF+ALU+RLmtLOgr4Z0l/BP7E8i/rDQrE2r/Aa47k68BcSWeRquXeC8wqHPNP+UPe+sBPAZ4uFOup/GXZirUtKeEWkbtOf5CU4IPUyP6fdba3dWj6/WvyR9JlwPm5QT9ItQOXlgo2CI3PjXZ5lPRJunQ3LNGTRtLxwNuBi3PMtwDnRcTn6o7VL5L2AraLiLPyl+ZzC42ybsXbH9iHlPC+FxGXlYqV472L1LtkF9KX2EHAxyOi9is/SdNJPWh2JA2OegVweERcVXesHO980g+x1jiJQ4FJEXFwiXg5ZmPvn6SlwHNIybXoj6Q80vlvaPu3AadHRJF2vgmfGKDZvtSS/qFtc11S3/8FJRqkcrxdgFfmzR9ExE0l4nTEfDErNrjVPpo1J9khYPuI+KvcgHlBRLyi7lj9lAcPtj7wcyNiQcFYm7C8B9R1hXtA3RIRLxltn40/E7kqCWi+L3VnV0pJ/w7MKRjvJ6SGvUZIOhN4Mam3V6vKo9Q0B28BXkr+90XEg0rz0RSRx7h8GXgBqf52TeCJQtVkrV+Bt0bETsCdJWLkOJ2DLxfn+6l5cF2pv5+bJO0REdflcuwO/KhQrMbevy7/nxUl/j8lbQd8jnS11/4Dt86pdp4x4RMD8PK2vtSfkvQFys3V0s161DtPUr/tERGlez61PBURIalVJ/6cwvG+AryD1IA/RBoc9ZelguUpFG5R28jnQhqdW0vSbfl1nwUcJukXeXtr4Kd1xurQ1PvXj7nKzgI+CZxC+pH7HrrPy1aLQUgMrYaupvpStz4UkH6xTAFKjdTth3mSdoyIkh/wlvMl/RewkaT3kxoT/7tkwIi4W9Kaue72LEnXloxH6mp8h6QfkyZha5XjzXUFiDy3VkS8tq7XHMWbVuYgSZNyt8vaNPH+rez/p6TpEXF5TWGfHRFzc8+k+4ETJF1DSha1G4TE8K2OvtRB2S+X9g/FMuChiFjW2lHiw9CwWaTk8CsKdyGNiH/PDaa/JQ3E+kSNH7Rufp+7it6sNBPpYlLjYkmfKvz6z2gbJNU+AKz2QVL5i2tlzKXegZ/9eP9GcjJQ19/rk7nqcaGkDwMPAKUmW5zYjc/5P3KPiLg2b69D+b7Uo5XpJxFRZD2BJki6m3QpfRtt3SpX4ctgLDE3oNrQXdvaFh1xtgYeItVP/z1p8NJXI+LuEvFWskzzImLPml7r5ojYuWPfTRHx0jpefwzlqTX2eHv/6vz3SdoVWECaxPLTpPFYn2+139RtQl8x5DrcL5BGlpIHvhTrt72Syk5nWd4vIs9eWZqkvyFVw/2BlIRE/WtbPKMtuT1Jl1/yki6KiLeViD2COqc0b3SQ1Eqo9VfpOHz/avv3RURrTrTfkdoXKiR9OSI+Ule8CZ0Ysu9Jehtp8q7xcHk0HsrQizsl/Q/wLdqSbInuqsDHgBeW7FK5ivrRiaDOv5fv0eAgqXFoInUC6VRrF+5BSAxHk+oZl0l6krIjdQfBs0kJoX2GzFLdVe8BiixdOEare1L/J1Jvmr9l+SCpoo35o2j66rnp9+++huPVZsInhogYsd+7pBdGxB1NlYfVvCopml2d6zjSRHrXU706+bsGy9Bvdf69fCQivgic9syLS0cBX6wxxqqsf75PnXGbopVc0zoiRjxuPJvwiWElnEMNPSMm+oehJc9/cwQrjiQvMbL7v4Dv09HQ3UdFkrrShI+7kX7R3hDV9RHeXWOoGayYBA7vsq9XK7X+ealOBCOo6/3r15rWI6n1b9OJob7/0PH6YajbOaRRuvuSGobfReotUcKyiDi60GuPRe0zaUp6H/AJUgIU8GVJJ0bEmQCR59/vMcahwDuBbSS1dxzYgDSup1bR4Prnq6iW96/hq2YAJB3cOX9Wx756r/rGR3ts/6zu3Ueb1uqCp+Wrcj0LuCwiah/tKekzwP2s2NBdqrvqK4ATSCN012J5e1SxRktJd5FG5z+StzcBro2I7WuMsTVpUOfngGPbnlpKmpJjWdcT64n9ZtJypQBXRUTtiwJ1DCqtPEXZadobm4et2/dUye8uXzEU0MSHoY9aywk+Lmkn4FeUm+r7nfn+uLZ9xbqrAmeQ+r9XlhItbBHpC7plKfDLOgPkbpz3A3t2VFvdVTgpnATsCpybdx0l6RURcdwIp43FSo20rlsT87Dl2WLfAGwp6UttT21AGkBbhBNDdT2DnjX4YeiXmXnE7MdJkwM+F/iXEoH6UCXxm4j4bhOBJLWqyB4Arpc0m/RlfQCFJnmUdARpCoWu1VYFvAHYOSKezvFnATdRTfQ9Kzm4chRNzMP2IDAfeDPpB0vLUtKPmCImfFWSpLkRsc9o+2qMdyvVD8OawE0lL2fHE0kzIi1a1MtrrFSvj7rlpL4m6cPdXnVVYrbMEee4ibxKX80xi1dbdcS7FXhNq+ovd9C4qtRnoanZVdviXR8Ru0u6Dngrqb3m9ojYruY4awJfi4h31fm6I5mwVwy598x6wOT8C7d9ac/nFQ6/EdCqB9+wcKzx5ih6XzWrX70+ds/3Qx3xam8/KfHFvxKKV1t1+Bxp6u0rSZ+/V1Hz1UKHRmfHZcU1rYNUpVSriPizpE0krR0RtdZwDGfCXjHk/tkfJSWBB1ieGH4L/HdEfKVQ3EOBk4DKhyEivlki3njTz7l3Vkf5S7Pbin8lGvO/BryItIh9e7XVz3LM/xj+7DHH3IJUtSrg+o6uuHXHmh8RQ62OEXnftRHx8lIx22IXnYdNaZbhXUjVt+2z8Nb+nsEEvmLIA3m+KOkjEfHlBuN+Q9JVLP8wHFPywzAO9fxLo63+vXuAQh8GSZsBnwWeFxH7S9oR2DMizigRL/tY2+N1gbdRrlHxnnxrmZ3viyx+lLvGfgOYExFPjHZ8DRqdXVXSYV32ERFfKxDuwXxbg0LvV7sJe8XQTtLLWXEpyhJvXj8+DONKHVcM/ah/z3G/S1oQ5fiIeImktUjtQy8qEW+EclwdEa9uMmYJkl5NWs/6jaQrk/OASyLiyRFPHHu8brOrnhoR94x44tjjtf/gXJc0ePUnEXFQiXg55vqkLri/KxUDBiAxSDoH2Ba4meVdEKPUtApNfxjGG0lfiYgP97scYyHphojYtT25dZuquuaY7SPm1yDVjX+xRINwk9VWHXHXJLXTvB/Yr2Bj8FG5pmDEfaVI2hA4J2pcZKnttXciDS5t/b38Gjis1HQ+E7Yqqc0QsGNTM6tGxNXA1R0fhjNJjd6rvVyX+jZWvAI7Md/XlhQann4D4IncU6c1LfUeQOm1O1oj5iFVId1H+jeX0GS1FQCSnk3qTHAIqY68144JI2lqyo/h/B6otUdSm5nA0RFxJYCk15AmQCzSfjIIieF2YHOWL4BeXMMfhqbNJn1Z3kj5tS2anH4D0ky8c4BtJf2ItCxrsWqBbEfgg8BepARxDanfeu0i4saOXT+SdHWJWACSziP19LoUOJXUVbX2Oa+anvKjLe63WJ7U1yC9l+cXCvecVlIAiIirVHAN9EGoSroS2JlUrdPeN732y70cr/3DcD6FPgz9Iun2iNipoViNTb/RFnMt0jKiIo0M/tMop/Qa73xST7nWgMhDgUkRcXCBWJ3VVi8DvlRwHMN+wOWR1l8upl9TfuRq45ZlwP0RsahQrItJXWLPybv+DzAUEQeWiDcIVwwnNBzvLOCdpT8MfXStpBdFxG0NxGpy+o1W1VXl17uk0wq3D20fES9p275S0i2FYrVP9LgMuJdy1VYAPwCOkzQ1Io6UtB3p31vrFDEdU35sRuoRCLCg5JQfudq4Ke8lrUp3Een9+wGpmqyICX/F0DRJ65GqJIp+GPpF0k9Jg4buJV2BFZuoTGnm0YtIfe/PJk+/ERH/VXesHO980q/Mr+ddxX69t8U8Gzgt8tq9knYHZkTEB2uOswap6+2P6nzdUWKeR0pGh0XETrmKdV6pxnxJBwP/DlxF+rt8JfCPEXFhoXhvBU4GNs3xii0CJmkIOJ5q216Rzx1M4MQg6YfxGcQEAAAI4UlEQVQRsZekpVR7YhRdwa3pD0PT8mX7CqLAfDWStomIe0fbV2O8Wzp+vXfdV3PMBaSqq1/kXVNJ7ShPU/MHX9K8iNizrtdbiXitAWftvbyK/X/mK63pEfFw3p4CXFEw3t3AX0dEyXavVqy7SJ0HbqdtbZISnzuYwFVJEbFXvi8+GKTDthFxSG4QIyL+IGm1XrWtXesPUdKm1LtQfTcXseIiSheS6sZLuEnSHh2/3kv/wt6v8Ou3a3r986fyD6NWL69tKdthYY1WUsgeIbWllPJQE0khWxIR32oo1sRNDH3U9IehUUpTin+BNNXIw6S1CxaQupTWFWOH/Hobqjqh3gaUTUa7A4dJ+gXp/dsaWKA833+Jy/ZSv/iG0dj65/nH0GmkThh/Ielc0oL1h9cdq813JV1GGmAKqVfgdwrGm59rCP4f1Y4tJeby+qSk04G5DcRyYqhTnz4MTfs0sAfpEv2lkl5Lqouv0/akOfY3ojqh3lLSuJBS9gMmkeqmITXwPV4wXqO6XT2XupqNiFCar+z1pL8XAUdFxK9LxGuFJS0Hu1eONzPHLmUD0tiF13eUocSX9XuAHYBnsbwqqdiEkhO2jaFfJN1I9cNwXeEPQ6Pa6o1vAV4aEU9L+nFE7FYg1p4RMa/u1x0h3lHA+0gfNgEHkiZcbGyurZKU1l74RNv2GqSRukWmc5Z0KnB2RNxQ4vW7xOu2ytmtJa70mibptmhwahZfMdTvOuD5EfHtfhekkMclPZc0EOtcSQ9TbvTsWyTdAfyBdBX2EuCjEfH1kU8bsyOAPSLPcSXpZGAeaY7/iWCqpOMi4nN5BPsFpL7xpbwW+BtJ95NmBC3Sg03S35K6GT9faQ2IlvUp2EaUG7ffz4qzAJQYmX+dpB0j4qcFXnsFvmKoWe7O+VekftXFPgz9kkdbtuqn30WaqOzcyIu/1Bzr5ojYWdJbSL/e/x64smAvk9uAXVvjFvK4hhua/KVWUq42Ohe4jfSl/d2IOKVgvEZ6sCnNUTSJLgPcotD64DnutaQfSJWlYCPiogKxFpDmfCveTRx8xVDC/v0uQEkR8UTbIKJHSF8upaYdeFa+fwPwjYh4tHAHr7NIy2xenLcPJK0DvVqT1F698kVSPfyPSHN67RIFVqiD5hrWI62B8Bvqb+sazXoRcUxDsZrsveYrBls1kt5OWrHqKgoPIlJaavNAUlXSbqTG6EsiYvcRT+wt5i4sb7z8QUTcVCpWU/K0MMOJklOMTGSS/pW0NGrJnk994cRgq6QPg4gmAb+NtLzhesAGMVgLH9k4lQfPPodUtfMnCg+ebZKrkmxVNT2I6AXAtDy5XUuRRZYmOo0yZbqtmohYP09MuB3lB3s2yonBVtWlTQ0i0jCLLOHEMFZNTpk+4eW5vI4CtiL9je4BXEtayW215qokW2V5WoVXsLwe/uJRThlrnAU0uMjSRKcGp0wfBK1ebKSxSjvnEfufiohD+ly0nvmKwVZZ7o5Xe5e8LhpfZGmCa3LK9EHwZEQ8KQlJ60TEnZKKrG3RNCcGWyldZql95inKNbhNBn4qqZFFlgbAXsDhkhrpCz8AFknaiDRX0uWSHgMe7HOZauGqJBu3VF0h6xnR7AIpE0aTU6YPmvy3uiFwaUQ81e/y9MqJwWzAdE6ZHhG/GOFwG0AluxmajYmkH+b7pZJ+23ZbKum3/S7f6krSmyUtJE2rcDVwH/DdvhbKxiVfMZgNiDw4cW86pkyPiCP7XDQbZ3zFYDY4/pTntVpD0hoRcSUwIZactXq5V5LZ4GhyynRbjbkqyWxANDlluq3enBjMBoikzUkz1QZprQlPSGgrcBuD2YDIc/v8GHgrcBBpVbASq43Zas5XDGYDQtJdwMtbVUeSNiGtJzAhpnGw+viKwWxwLAKWtm0vBX7Zp7LYOOZeSWYTnKSj88MHSEuXzia1MRxAqloyq3BiMJv41s/39+Rby+w+lMVWA25jMDMAJH05Ij7S73JY/7mNwcxaXtHvAtj44MRgZmYVTgxmZlbhxGBmLep3AWx8cGIws5Yv9rsANj44MZgNCEmX5zWKW9uTJF3W2o6Is/tSMBt3nBjMBsfkiHi8tRERjwGb9rE8Nk45MZgNjqclTW1tSNqaNALarMIjn80Gx/HADyVdnbdfBXhZT1uBRz6bDRBJk4E9SD2Q5kXEr/tcJBuHXJVkNiAkCdgP2CUivgWsJ2m3PhfLxiFfMZgNCEn/CTwN7B0RL5A0CfheROza56LZOOM2BrPBsXtE7CLpJki9kiSt3e9C2fjjqiSzwfEnSWuSeyJJmkK6gjCrcGIwGxxfAi4GNpX0GeCHwGf7WyQbj9zGYDZAJO0A7EPqlTQ3Ihb0uUg2DjkxmA0ASWsAt0bETv0ui41/rkoyGwAR8TRwS/vIZ7PhuFeS2eDYArhD0o+BJ1o7I+LN/SuSjUdODGaD41P9LoCtHtzGYGYASJoXEXv2uxzWf25jMLOWdftdABsfnBjMrMXVBwY4MZiZWQcnBjNrUb8LYOODeyWZDRBJmwO7kaqNboiIX7U9/e7+lMrGG18xmA0ISe8Dfgy8FTgIuE7Se1vPR8Tt/SqbjS/urmo2ICTdBbw8Ih7J25sA10bE9v0tmY03vmIwGxyLgKVt20uBX/apLDaOuY3BbIKTdHR++ABwvaTZpDaGA0hVS2YVTgxmE9/6+f6efGuZ3Yey2GrAbQxmZlbhKwazASHpSrqMbo6IvftQHBvHnBjMBsfH2h6vC7wNWNanstg45qokswEm6eqIeHW/y2Hji68YzAaEpI3bNtcAhoDN+1QcG8ecGMwGx40sb2NYBtwHHNG30ti45cRgNjh2BD4I7EVKENcA8/taIhuX3MZgNiAknQ/8Fjg37zoUmBQRB/evVDYeOTGYDQhJt0TES0bbZ+a5kswGx02S9mhtSNod+FEfy2PjlK8YzAaEpAXA9sAv8q6pwALgaSAi4sX9KpuNL04MZgNC0tYjPR8R9zdVFhvfnBjMzKzCbQxmZlbhxGBmZhVODGZmVuHEYGZmFf8feFomut8W8kkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating corelation plot\n",
    "df.corr()[\"loan_status\"].sort_values().drop(\"loan_status\").plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b84ed5790>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#verification status graph\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x=\"verification_status\",data=df,hue=\"loan_status\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Here we can see a trend that more verified people defaulted than the not verified which questions the autheticity of the verification process of the compnay `**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "emp_title               5.789208\n",
       "emp_length              4.621115\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking missing values in %\n",
    "(df.isnull().sum()/len(df))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Teacher                                    4389\n",
       "Manager                                    4250\n",
       "Registered Nurse                           1856\n",
       "RN                                         1846\n",
       "Supervisor                                 1830\n",
       "Sales                                      1638\n",
       "Project Manager                            1505\n",
       "Owner                                      1410\n",
       "Driver                                     1339\n",
       "Office Manager                             1218\n",
       "manager                                    1145\n",
       "Director                                   1089\n",
       "General Manager                            1074\n",
       "Engineer                                    995\n",
       "teacher                                     962\n",
       "driver                                      882\n",
       "Vice President                              857\n",
       "Operations Manager                          763\n",
       "Administrative Assistant                    756\n",
       "Accountant                                  748\n",
       "President                                   742\n",
       "owner                                       697\n",
       "Account Manager                             692\n",
       "Police Officer                              686\n",
       "supervisor                                  673\n",
       "Attorney                                    667\n",
       "Sales Manager                               665\n",
       "sales                                       645\n",
       "Executive Assistant                         642\n",
       "Analyst                                     623\n",
       "                                           ... \n",
       "machina opertor                               1\n",
       " Claims Adjuster                              1\n",
       "AVP / Business Development                    1\n",
       "Unity Integrity Lead                          1\n",
       "Allied Barton Private Security                1\n",
       "Mize&Company Inc.                             1\n",
       "security Manager                              1\n",
       "Donor Relations Manager                       1\n",
       "The University of Alabama at Birmingham       1\n",
       "Bright Horizons LDAC                          1\n",
       "MOTOR VEHICLE REPRESENTATIVE                  1\n",
       "Analytical Specialist                         1\n",
       "Banking Specialist                            1\n",
       "Process Machine Operator                      1\n",
       "NetJets Aviation                              1\n",
       "Analyst Supervisor                            1\n",
       "GHX Industrial                                1\n",
       "Manager Student Computing Services            1\n",
       "Blanker Operator                              1\n",
       "Diary Buyer/Specialist                        1\n",
       "FISH & RICHARDSON                             1\n",
       "MEDISYS                                       1\n",
       "City of Ferndale Police Department            1\n",
       "STG INC                                       1\n",
       "Site Coordinator/Assistant Professor          1\n",
       "J P Morgan chase                              1\n",
       "Bridgeforce, Inc                              1\n",
       "HCC Surety Group                              1\n",
       "Cirrus Logic                                  1\n",
       "Motel 6 Hesperia                              1\n",
       "Name: emp_title, Length: 173105, dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"emp_title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the emp title since too many professions are there\n",
    "df.drop(\"emp_title\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1 year',\n",
       " '10+ years',\n",
       " '2 years',\n",
       " '3 years',\n",
       " '4 years',\n",
       " '5 years',\n",
       " '6 years',\n",
       " '7 years',\n",
       " '8 years',\n",
       " '9 years',\n",
       " '< 1 year']"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking unique values in employ length\n",
    "sorted(df[\"emp_length\"].dropna().unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating an ordered list of employes\n",
    "emp_order=['< 1 year',\n",
    " '1 year',\n",
    " '2 years',\n",
    " '3 years',\n",
    " '4 years',\n",
    " '5 years',\n",
    " '6 years',\n",
    " '7 years',\n",
    " '8 years',\n",
    " '9 years',\n",
    " '10+ years'\n",
    " ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b851db650>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zk8eOHbuKzZEkSZKG3op61P8OmAtsWX73/lwKnLYqF8zMPwD3R8SbStLuwALgMqB35ZZDyjUo6R8uq7/sAjxRhsZcCexRPkBsCOwBXFmOPRkRu5TVXj7cUJYkSZK0Rhi5vIOZ+XXg6xFxTGZ+YwivewxwbkSMBu4CDqP60PCjiDgCuA/Yv+S9HNgHWAz8qeQlMx+NiC8CN5V8J2bmo2X7Y8BZwNrAz8uPJEmStMZYbqDeKzO/ERFvBbobz8nMc1blopk5D5jcz6Hd+8mbwIwByjkTOLOf9DnAtqtSN0mSJKkOBhWoR8QPgDcA84BlJbl36UNJkiRJQ2xQgTpV7/fWpXdbkiRJUpMNdh3124FXN7MikiRJkl402B71TYAFEXEj8HRvYmbu25RaSZIkScPcYAP1E5pZCUmSJEkvNdhVX37d7IpIkiRJetFgV315kmqVF4DRwCjgfzJz/WZVTJIkSRrOBtujvl7jfkRMA6Y0pUaSJEmSBr3qy0tk5o+B3Ya4LpIkSZKKwQ59+euG3VdQravumuqSJElSkwx21ZcPNGw/B9wDTB3y2kiSJEkCBj9G/bBmV0SSJEnSiwY1Rj0iuiLikoh4OCIeioiLIqKr2ZWTJEmShqvBTib9PnAZ8BpgHPCTkiZJkiSpCQYbqI/NzO9n5nPl5yxgbBPrJUmSJA1rgw3U/xgRB0fEiPJzMPBIMysmSZIkDWeDDdQPB/4G+AOwBNgPcIKpJEmS1CSDXZ7xi8AhmfkYQERsBPwbVQAvSZIkaYgNtkd9+94gHSAzHwV2bE6VJEmSJA02UH9FRGzYu1N61AfbGy9JkiRpJQ022P534HcRcSGQVOPV/6VptZIkSZKGucHemfSciJgD7AYE8NeZuaCpNZMkSZKGsUEPXymBucG5JEmS1AKDHaMuSZIkqYUM1CVJkqQaMlCXJEmSashAXZIkSaohA3VJkiSphgzUJUmSpBoyUJckSZJqyEBdkiRJqiEDdUmSJKmG2haoR8SIiLglIn5a9sdHxA0RsSgiLoiI0SV9rbK/uBzvbijjMyX9zojYsyF9r5K2OCKOb3XbJEmSpNXVzh71mcDChv2vAKdk5gTgMeCIkn4E8FhmvhE4peQjIrYGpgPbAHsB3yrB/wjgNGBvYGvgwJJXkiRJWmO0JVCPiC7gfcD3yn4AuwEXlixnA9PK9tSyTzm+e8k/FTg/M5/OzLuBxcCU8rM4M+/KzGeA80teSZIkaY3Rrh71/wN8Cni+7G8MPJ6Zz5X9HmBc2R4H3A9Qjj9R8r+Q3uecgdJfJiKOiog5ETFn6dKlq9smSZIkaci0PFCPiPcDD2fm3MbkfrLmCo6tbPrLEzNPz8zJmTl57Nixy6m1JEmS1Foj23DNtwH7RsQ+wBhgfaoe9g0iYmTpNe8CHiz5e4AtgJ6IGAm8Cni0Ib1X4zkDpUuSJElrhJb3qGfmZzKzKzO7qSaD/iozDwKuBvYr2Q4BLi3bl5V9yvFfZWaW9OllVZjxwATgRuAmYEJZRWZ0ucZlLWiaJEmSNGTa0aM+kE8D50fEl4BbgDNK+hnADyJiMVVP+nSAzLwjIn4ELACeA2Zk5jKAiDgauBIYAZyZmXe0tCWSJEnSamproJ6Zs4HZZfsuqhVb+ub5C7D/AOf/C/Av/aRfDlw+hFWVJEmSWso7k0qSJEk1ZKAuSZIk1ZCBuiRJklRDBuqSJElSDRmoS5IkSTVkoC5JkiTVkIG6JEmSVEMG6pIkSVINGahLkiRJNWSgLkmSJNWQgbokSZJUQwbqkiRJUg0ZqEuSJEk1ZKAuSZIk1ZCBuiRJklRDBuqSJElSDRmoS5IkSTU0st0VkIaD+07crqnlv/Zz85taviRJaj171CVJkqQaMlCXJEmSashAXZIkSaohA3VJkiSphgzUJUmSpBoyUJckSZJqyOUZ26zZy/aBS/epPVySUpKk1WOPuiRJklRDBuqSJElSDRmoS5IkSTXkGHVJkiTVzqTjzmlq+Zes19Tih4Q96pIkSVINGahLkiRJNdTyQD0itoiIqyNiYUTcEREzS/pGETErIhaV3xuW9IiIUyNicUTcFhE7NZR1SMm/KCIOaUifFBHzyzmnRkS0up2SJEnS6mjHGPXngE9m5s0RsR4wNyJmAYcCV2XmSRFxPHA88Glgb2BC+dkZ+Dawc0RsBHwemAxkKeeyzHys5DkKuB64HNgL+HkL2yhJw4Lr5UtS87Q8UM/MJcCSsv1kRCwExgFTgXeVbGcDs6kC9anAOZmZwPURsUFEbF7yzsrMRwFKsL9XRMwG1s/M60r6OcA0DNRrw3/skiRJK9bWMeoR0Q3sCNwAbFaC+N5gftOSbRxwf8NpPSVteek9/aRLkiRJa4y2BeoRsS5wEfCJzPzv5WXtJy1XIb2/OhwVEXMiYs7SpUtXVGVJkiSpZdqyjnpEjKIK0s/NzItL8kMRsXlmLilDWx4u6T3AFg2ndwEPlvR39UmfXdK7+sn/Mpl5OnA6wOTJk/sN5iVpsBzWJUkaSu1Y9SWAM4CFmfm1hkOXAb0rtxwCXNqQ/uGy+ssuwBNlaMyVwB4RsWFZIWYP4Mpy7MmI2KVc68MNZUmSJElrhHb0qL8N+BAwPyLmlbTPAicBP4qII4D7gP3LscuBfYDFwJ+AwwAy89GI+CJwU8l3Yu/EUuBjwFnA2lSTSJ1IKkmSpDVKO1Z9uZb+x5ED7N5P/gRmDFDWmcCZ/aTPAbZdjWpKkiRJbeWdSSVJkqQaastkUkmS1kTNnjAM9Zs0PBzbLNWFPeqSJElSDRmoS5IkSTXk0BdJkqQGDvdRXdijLkmSJNWQgbokSZJUQwbqkiRJUg0ZqEuSJEk1ZKAuSZIk1ZCrvkiSJA1zzV7pxlVuVo096pIkSVINGahLkiRJNWSgLkmSJNWQgbokSZJUQwbqkiRJUg0ZqEuSJEk1ZKAuSZIk1ZCBuiRJklRDBuqSJElSDRmoS5IkSTU0st0VkOpg0nHnNLX8S9ZravGSJKkDGaivgAGcOpWvbUmS6s2hL5IkSVINGahLkiRJNeTQF72MQyKkztDsv2Xw71mSmslAXdKwYNAqSVrTGKhLkjrGcPxG0DYPvTq2WcOTY9QlSZKkGrJHXZIkqeb8FmF4skddkiRJqqGODdQjYq+IuDMiFkfE8e2ujyRJkrQyOjJQj4gRwGnA3sDWwIERsXV7ayVJkiQNXkcG6sAUYHFm3pWZzwDnA1PbXCdJkiRp0Do1UB8H3N+w31PSJEmSpDVCZGa76zDkImJ/YM/M/EjZ/xAwJTOP6ZPvKOCosvsm4M6WVrSyCfDHNly3nWzz8GCbh4fh1ubh1l6wzcOFbW6d12Xm2MFk7NTlGXuALRr2u4AH+2bKzNOB01tVqf5ExJzMnNzOOrSabR4ebPPwMNzaPNzaC7Z5uLDN9dSpQ19uAiZExPiIGA1MBy5rc50kSZKkQevIHvXMfC4ijgauBEYAZ2bmHW2uliRJkjRoHRmoA2Tm5cDl7a7HILR16E2b2ObhwTYPD8OtzcOtvWCbhwvbXEMdOZlUkiRJWtN16hh1SZIkaY1moN5EEbF/RNwREc9HRK1nFa+siDgzIh6OiNvbXZdmiogtIuLqiFhYnsuZ7a5TM0XEmIi4MSJuLe39Qrvr1CoRMSIibomIn7a7Lq0QEfdExPyImBcRc9pdn1aIiA0i4sKI+H35m35Lu+vUTBHxpvL89v78d0R8ot31araIOLa8f90eEedFxJh216nZImJmae8dnfIcDxRnRMRGETErIhaV3xu2q46tYKA+BCJidESs08+h24G/Bq5pcX0iIpr93J4F7NXka7xMRLR6XsVzwCczcytgF2BGRGzdigu3oa0ATwO7ZeYOwERgr4jYpRUXblN7G80EFrbygjVo87szc2Irlydrc5u/DlyRmVsCO9Ci57tdbc7MO8vzOxGYBPwJuKQV125XmyNiHPBxYHJmbku1oMT0Fl27XW3eFjiS6q7sOwDvj4gJLbp2M9t8Fv3HGccDV2XmBOCqst+3XmdFxLuaWLd+RcSIoS7TQH01RMRWEfHvVDdK+qu+xzNzYWYu9yZKEfGDiJjasH9uROxbeve+GhE3RcRtEfF35fi6EXFVRNxcesOmlvTu0kP0LeBmXrqO/JDLzGuAR5fTrvUi4u6IGFX21y89eKMi4g0RcUVEzI2I30TEliXPByLihtKr+cuI2KyknxARp0fEL4BzmtmuvjJzSWbeXLafpPrH/pK73DazrRGxTenhnldeB019883KU2V3VPl5yUSW0qabG/YnRMTcsj0pIn5d2ntlRGxe0o8sr+VbI+KiiHhlST8rIr4WEVcDX4mIdzb0/t0SEes1s70NbegC3gd8b4DjHdfmFemk13Vv/YFdgTMAMvOZzHy8k9vcx+7Af2XmvcOgzSOBtaMKIl9Jn/uodODf81bA9Zn5p8x8Dvg18ME+bV7jnuflxBlTgbPL9tnAtJUtOyKOiIhTGvaPjIivle2DG9rz3SjBd0R8OyLmRJ9vm8vj+LmIuBbYPyI+HhELymNx/srW7WUy05+V+AHWAQ4DrgV+C3wEWG8F58ym+nTf37F3Aj8u268C7qZ6kzkK+KeSvhYwBxhfjq1f0jcBFgMBdAPPA7u08LHoBm5fzvHvA9PK9lHAv5ftq4AJZXtn4Fdle0NenOD8kYb8JwBzgbXb/Nx3A/f1Pv6taCvwDeCgsj26FY8BVQ/UPOAp4CsD5LkamFi2vwwcQxXU/w4YW9IPoFoaFWDjhnO/BBxTts8CfgqMKPs/Ad5WttcFRrboub2QqsfxXcBPh0mb76b6UD8XOGqAPJ30up4I3Fge/1uoPpSt08lt7tOuM4GjO/15LteaSfX+tRQ4d4A8HfP3TBWo/z9gY6oPJtcB3+iE55l+4gzg8T77j/Vz3lnAu5ZT7jrAfwGjyv7vgO3KY/mThvRvAR8u2xuV3yOo4rrty/49wKcayn4QWKtsb7C6j0G7v3ZdEy0BbgM+kpm/X93CMvPXEXFaRGxKNUzmoqzWgd8D2D4i9itZXwVMoLrr6pcjYleqwHwcsFnJc29mXr+6dRpC3wM+BfyY6sPNkRGxLvBW4D8jojffWuV3F3BB6cEYTRVI9LosM//cklr3o9T7IuATmfnf/WRpVluvA/53VD2+F2fmoiFsVr8ycxkwMSI2AC6JiG0zs+9chO8Bh0XEP1D9M5sCvAnYFphV2juC6u8FYNuI+BKwAdU/rysbyvrPck2oPvx+LSLOpWpvz9C38KUi4v3Aw5k5N5b/VWnHtLl4W2Y+WN57ZkXE77PqwWrUMa9rqk6OnagCrBsi4utUX5n/c598ndRmoBqeCewLfGaALB3T5qjGK0+l6th6vNT/4Mz8jz5ZO+bvOTMXRsRXgFlUH1BupRq22VfHPM/9iYg9ga+U3dcCb4+Ip4CnM3PnxryZ+T8R8SuqYUILqQLz+VHdg2cScFN5PNYGHi6n/U1EHEX1XrI5sDVVPAhwQUPxtwHnRsSPqR7r1dPsT3qd9gPsUZ6QhcDngNcN4pzZDNCjXo5/GjgWuAHYpqRdBOzZT95Dy/V7P+3dQ/WJs5vl9G436bFY4TWp3jDeCdxY9tcHlizncdq3bL8LmF22TwD+sY3P+SiqN+V/aEdbgTdQjbm8i2r8eCvb/vn+HntgDFUPzlTgRyVtO+C6Acq5G9ihbB8KnFW2zwL265N3u/I30QNs2YI2/mu51j3AH6jG8f5HJ7e5n7oO+DfWKa9r4NXAPQ377wB+1sltbrjuVOAXK8jTEW0G9gfOaNj/MPCtfvJ18t/zl4G/74Tnmf571O8ENi/bmwN39nPeWSynR73k2Rm4lCq4//uSdgzwr/3kHU81gmHDhvIPLdv3AJs05B0BvBs4pdR1tb5VcYz6SsrMX2TmAcDbgSeAS8vYre7VKPYs4BOl/N47qF4JfKxhPNlfRTVh9VVUvX/PRsS7gdetxnVb4RzgPKqv3MiqN/ruiNgfXpj4ukPJ+yrggbJ9SKsr2p+oPlKfASzMzK+tIPuQtzUiXg/clZmnApcB269qWwYjIsaWnnQiYm3gPcDLvjnKzL9QvUa/TWkv1RvS2CgraZSxj9uUY+sBS8rr+aDlXP8NmTk/M79xrr2BAAAFdElEQVRCNdxry6Fp2cAy8zOZ2ZWZ3VSTzn6VmQf3k69j2hwR60QZO1veV/agmvzenzX+dQ2QmX8A7o+IN5Wk3YEFA2TviDY3OJCqPcvTKW2+D9glIl5Z3r93p59Jw53091yuu2n5/Vqqb+cHer474Xm+rKE+h1AF2ystM2+gms/3t7z4eF0F7NfweG4UEa+j+kDzP8ATUY3V37u/MqNayGOLzLya6tuL3m9gVpmB+irKzEcy8+tZzab/LLCsb56I+GBE9ABvAX4WEVf2zVPKeojqjeT7Dcnfo/oncnNUSxN9l+rrlnOByVEtp3YQ/QRRrRAR51F93fWmiOiJiCMGyHou1Ti3xjeNg4AjIuJW4A6qHg2oPqX/Z0T8BvhjUyq+8t4GfAjYLV6cILTPAHmb0dYDgNsjYh7VG36zJ9NuDlwdEbcBNwGzMnOg5QrPpZpo+guoJucB+1FNqLqVapz7W0vef6b6xmgWy3/NfiKqJcZuBf4M/Hw12zPUOqXNmwHXlmveSNWzfMUAeTvhdd3rGKqvpG+jGrP+5QHydUybo5r0+F7g4hVk7Yg2l+DrQqr5F/Op4pyB7j7ZKX/PABdFxAKq8dUzMvOxAfKtMc/zcuKMk4D3RsQiqtf2SatxmR8Bv+19vDJzAfBPwC/K+8Qsqt77W6nmttxBNd/jtwOUNwL4j4iYX/Kfkn0mra8s70xaA+WNdD6wU2Y+0e76DKWoxthPzcwPtbsuzTac2goQEf8IvCoz+47x7VjDtM3D6nUNtrnddWkV/54V1X0zTsnMq9pdl4E4mbTNIuI9VJ/OvtaBQfo3qL4eGqgHumMMp7YCRMQlVGMSd2t3XVplmLZ5WL2uwTa3uy6t4t/z8FaGeN4I3FrnIB3sUZckSZJqyTHqkiRJUg0ZqEuSJEk1ZKAuSZIk1ZCBuiRJklRDBuqSpJUW1a25h7rMiY33KYiIE8oSepI0LBmoS5LqYiIuHSdJLzBQl6QOEhEHR8SN5S66342IERHxVER8JSLmRsQvI2JKRMyOiLsiYt9y3qERcWlEXBERd0bE51fimsdFxE0RcVtEfKGkdUfEwoj4vxFxR0T8IiLWLsfeXPJeFxFfLXdyHA2cCBxQ6n5AKX7rhrp+fIgfLkmqNQN1SeoQEbEV1e2835aZE4FlVLcHXweYnZmTgCeBL1HdevuDVMFxrykl/0Rg/4iYPIhr7gFMKOdOBCZFxK7l8ATgtMzcBngc+F8l/fvARzPzLaWOvbdv/xxwQWZOzMwLSt4tgT1L+Z+PiFEr96hI0prLO5NKUufYHZgE3BQRAGsDDwPPAFeUPPOBpzPz2YiYD3Q3nD8rMx8BiIiLgbcDc1ZwzT3Kzy1lf12qAP0+4O7MnFfS5wLd5Y6A62Xm70r6D4H3L6f8n2Xm08DTEfEwsBnQs4I6SVJHMFCXpM4RwNmZ+ZmXJEb8Y754G+rngacBMvP5iGj8P9D3VtWDuXV1AP+amd/tc83u3usUy6g+OMQgymzUtwz/b0kaNhz6Ikmd4ypgv4jYFCAiNoqI163E+e8t56wNTAN+O4hzrgQOj4h1yzXH9V6/P5n5GPBkROxSkqY3HH4SWG8l6itJHc1AXZI6RGYuAP4J+EVE3AbMAjZfiSKuBX4AzAMuyswVDXshM39BNXzlujKU5kJWHGwfAZweEddR9bA/UdKvppo82jiZVJKGrXjx21BJ0nAVEYcCkzPz6BZca93MfKpsHw9snpkzm31dSVrTONZPktRq74uIz1D9D7oXOLS91ZGkerJHXZLUr4jYmGrce1+7964OI0lqHgN1SZIkqYacTCpJkiTVkIG6JEmSVEMG6pIkSVINGahLkiRJNWSgLkmSJNXQ/wcX10VdmzX4CgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating employ length count plot setting hue as loan status\n",
    "plt.figure(figsize=(12,4))\n",
    "sns.countplot(x=\"emp_length\",data=df,order=emp_order,hue=\"loan_status\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count of people those who didnt pay considering there emp length\n",
    "emp_co=df[df[\"loan_status\"]==0].groupby(\"emp_length\").count()[\"loan_status\"]\n",
    "\n",
    "#count of people those who did pay considering there emp length\n",
    "emp_fp=df[df[\"loan_status\"]==1].groupby(\"emp_length\").count()[\"loan_status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b85978610>"
      ]
     },
     "execution_count": 241,
     "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": [
    "# % of people whoc didnt pay there loan considering there emp length\n",
    "co_pre=emp_co*100/emp_fp\n",
    "\n",
    "co_pre.plot(kind=\"bar\",colormap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Here this is not a very deciding factor whether someone will charged off or not because distribution is somewhat uniform across the employe length`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hence dropping emp length\n",
    "df.drop(\"emp_length\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_amnt               0.000000\n",
       "term                    0.000000\n",
       "int_rate                0.000000\n",
       "installment             0.000000\n",
       "grade                   0.000000\n",
       "sub_grade               0.000000\n",
       "home_ownership          0.000000\n",
       "annual_inc              0.000000\n",
       "verification_status     0.000000\n",
       "issue_d                 0.000000\n",
       "loan_status             0.000000\n",
       "purpose                 0.000000\n",
       "title                   0.443148\n",
       "dti                     0.000000\n",
       "earliest_cr_line        0.000000\n",
       "open_acc                0.000000\n",
       "pub_rec                 0.000000\n",
       "revol_bal               0.000000\n",
       "revol_util              0.069692\n",
       "total_acc               0.000000\n",
       "initial_list_status     0.000000\n",
       "application_type        0.000000\n",
       "mort_acc                9.543469\n",
       "pub_rec_bankruptcies    0.135091\n",
       "address                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.isnull().sum()/len(df))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              vacation\n",
       "1    debt_consolidation\n",
       "2           credit_card\n",
       "3           credit_card\n",
       "4           credit_card\n",
       "5    debt_consolidation\n",
       "6      home_improvement\n",
       "7           credit_card\n",
       "8    debt_consolidation\n",
       "9    debt_consolidation\n",
       "Name: purpose, dtype: object"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"purpose\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   Vacation\n",
       "1         Debt consolidation\n",
       "2    Credit card refinancing\n",
       "3    Credit card refinancing\n",
       "4      Credit Card Refinance\n",
       "5         Debt consolidation\n",
       "6           Home improvement\n",
       "7       No More Credit Cards\n",
       "8         Debt consolidation\n",
       "9         Debt Consolidation\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "#since reflecting the same information hence dropping the column\n",
    "df.drop(\"title\",inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     139777\n",
       "1.0      60416\n",
       "2.0      49948\n",
       "3.0      38049\n",
       "4.0      27887\n",
       "5.0      18194\n",
       "6.0      11069\n",
       "7.0       6052\n",
       "8.0       3121\n",
       "9.0       1656\n",
       "10.0       865\n",
       "11.0       479\n",
       "12.0       264\n",
       "13.0       146\n",
       "14.0       107\n",
       "15.0        61\n",
       "16.0        37\n",
       "17.0        22\n",
       "18.0        18\n",
       "19.0        15\n",
       "20.0        13\n",
       "24.0        10\n",
       "22.0         7\n",
       "21.0         4\n",
       "25.0         4\n",
       "27.0         3\n",
       "23.0         2\n",
       "32.0         2\n",
       "26.0         2\n",
       "31.0         2\n",
       "30.0         1\n",
       "28.0         1\n",
       "34.0         1\n",
       "Name: mort_acc, dtype: int64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mortage accounts count\n",
    "df[\"mort_acc\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_rate               -0.082583\n",
       "dti                    -0.025439\n",
       "revol_util              0.007514\n",
       "pub_rec                 0.011552\n",
       "pub_rec_bankruptcies    0.027239\n",
       "loan_status             0.073111\n",
       "open_acc                0.109205\n",
       "installment             0.193694\n",
       "revol_bal               0.194925\n",
       "loan_amnt               0.222315\n",
       "annual_inc              0.236320\n",
       "total_acc               0.381072\n",
       "mort_acc                1.000000\n",
       "Name: mort_acc, dtype: float64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling the mortage value\n",
    "df.corr()[\"mort_acc\"].sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Here we can see that mort acc is +vely correlated with total acc and the correlation is not that much high which is a good thing to predict mort_acc using total_acc`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"mort_acc\"]=df[\"mort_acc\"].fillna(np.mean(df[\"mort_acc\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping remaining data\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables and Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['term', 'grade', 'sub_grade', 'home_ownership', 'verification_status',\n",
       "       'issue_d', 'purpose', 'earliest_cr_line', 'initial_list_status',\n",
       "       'application_type', 'address'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes([\"object\"]).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting 36 mont to 36 numeric and same for 60\n",
    "df[\"term\"]=df[\"term\"].apply(lambda x:int(x[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping dropping grade\n",
    "df.drop(\"grade\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the sub grade into dummy variables\n",
    "dummies=pd.get_dummies(df[\"sub_grade\"],drop_first=True)\n",
    "\n",
    "#concatinating with orginal data\n",
    "df=pd.concat([df.drop(\"sub_grade\",axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "#doing same thing for other categorical variables\n",
    "dummies=pd.get_dummies(df[['verification_status', 'application_type','initial_list_status','purpose']],drop_first=True)\n",
    "\n",
    "#concatinating with orginal data\n",
    "df=pd.concat([df.drop(['verification_status', 'application_type','initial_list_status','purpose'],axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MORTGAGE    181592\n",
       "RENT        141604\n",
       "OWN          34752\n",
       "OTHER           34\n",
       "NONE            29\n",
       "ANY              3\n",
       "Name: home_ownership, dtype: int64"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the home ownership\n",
    "df[\"home_ownership\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing none and any with other\n",
    "df[\"home_ownership\"]=df[\"home_ownership\"].replace([\"NONE\",\"ANY\"],\"OTHER\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping and adding \n",
    "dummies=pd.get_dummies(df[\"home_ownership\"],drop_first=True)\n",
    "\n",
    "#concatinating with orginal data\n",
    "df=pd.concat([df.drop(\"home_ownership\",axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grabbing only last zip code of the address\n",
    "df[\"Zip\"]=df[\"address\"].apply(lambda x:x[-5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70466    51552\n",
       "30723    51180\n",
       "22690    51030\n",
       "48052    50556\n",
       "00813    41156\n",
       "29597    40858\n",
       "05113    40752\n",
       "11650    10412\n",
       "93700    10347\n",
       "86630    10171\n",
       "Name: Zip, dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting value counts to verify whether to convert into dummy or not\n",
    "df[\"Zip\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting into dummy variable\n",
    "dummies=pd.get_dummies(df[\"Zip\"],drop_first=True)\n",
    "\n",
    "#concatinating with orginal data\n",
    "df=pd.concat([df.drop(\"Zip\",axis=1),dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping address column\n",
    "df.drop(\"address\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping issue date\n",
    "df.drop(\"issue_d\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "#earliesr cr line extracting the year only\n",
    "df[\"earliest_cr_line\"]=df[\"earliest_cr_line\"].apply(lambda x:int(x[-4:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sampling the data\n",
    "data=df.sample(frac=.5,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing data into feature and target\n",
    "X=data.drop(\"loan_status\",axis=1).values\n",
    "y=data[\"loan_status\"].values\n",
    "\n",
    "#spliting the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalizing the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling the data\n",
    "scaler=MinMaxScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense,Dropout\n",
    "import logging\n",
    "logging.getLogger('tensorflow').setLevel(logging.ERROR)\n",
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "#running model\n",
    "#creating model \n",
    "model = Sequential()\n",
    "\n",
    "#adding layer\n",
    "model.add(Dense(78,activation=\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(39,activation=\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(19,activation=\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(19,activation=\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(units=1,activation=\"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer=\"adam\")\n",
    "\n",
    "#parameters for early stopping\n",
    "early_stop=EarlyStopping(monitor=\"val_loss\",mode=\"min\",verbose=1,patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 143205 samples, validate on 35802 samples\n",
      "Epoch 1/25\n",
      "143205/143205 [==============================] - 33s 229us/sample - loss: 0.3508 - val_loss: 0.2728\n",
      "Epoch 2/25\n",
      "143205/143205 [==============================] - 20s 141us/sample - loss: 0.2762 - val_loss: 0.2682\n",
      "Epoch 3/25\n",
      "143205/143205 [==============================] - 22s 150us/sample - loss: 0.2706 - val_loss: 0.2670\n",
      "Epoch 4/25\n",
      "143205/143205 [==============================] - 21s 145us/sample - loss: 0.2680 - val_loss: 0.2663\n",
      "Epoch 5/25\n",
      "143205/143205 [==============================] - 21s 149us/sample - loss: 0.2665 - val_loss: 0.2656\n",
      "Epoch 6/25\n",
      "143205/143205 [==============================] - 21s 149us/sample - loss: 0.2657 - val_loss: 0.2659\n",
      "Epoch 7/25\n",
      "143205/143205 [==============================] - 23s 157us/sample - loss: 0.2649 - val_loss: 0.2653\n",
      "Epoch 8/25\n",
      "143205/143205 [==============================] - 23s 158us/sample - loss: 0.2644 - val_loss: 0.2655\n",
      "Epoch 9/25\n",
      "143205/143205 [==============================] - 24s 167us/sample - loss: 0.2633 - val_loss: 0.2658\n",
      "Epoch 10/25\n",
      "143205/143205 [==============================] - 24s 166us/sample - loss: 0.2628 - val_loss: 0.2653\n",
      "Epoch 11/25\n",
      "143205/143205 [==============================] - 29s 202us/sample - loss: 0.2629 - val_loss: 0.2647\n",
      "Epoch 12/25\n",
      "143205/143205 [==============================] - 30s 209us/sample - loss: 0.2623 - val_loss: 0.2646\n",
      "Epoch 13/25\n",
      "143205/143205 [==============================] - 29s 203us/sample - loss: 0.2613 - val_loss: 0.2649\n",
      "Epoch 14/25\n",
      "143205/143205 [==============================] - 30s 209us/sample - loss: 0.2614 - val_loss: 0.2648\n",
      "Epoch 15/25\n",
      "143205/143205 [==============================] - 29s 201us/sample - loss: 0.2606 - val_loss: 0.2646\n",
      "Epoch 16/25\n",
      "143205/143205 [==============================] - 28s 197us/sample - loss: 0.2605 - val_loss: 0.2648\n",
      "Epoch 17/25\n",
      "143205/143205 [==============================] - 29s 202us/sample - loss: 0.2596 - val_loss: 0.2653\n",
      "Epoch 00017: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f2b859eded0>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#running the model\n",
    "model.fit(x=X_train,y=y_train,epochs=25,batch_size=256,validation_data=(X_test,y_test),callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b87e08390>"
      ]
     },
     "execution_count": 273,
     "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": [
    "#plotting the losses\n",
    "loss=pd.DataFrame(model.history.history)\n",
    "\n",
    "loss.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.44      0.61      7286\n",
      "           1       0.87      1.00      0.93     28516\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     35802\n",
      "   macro avg       0.93      0.72      0.77     35802\n",
      "weighted avg       0.90      0.88      0.87     35802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix,roc_curve,roc_auc_score,auc\n",
    "\n",
    "predict=model.predict_classes(X_test)\n",
    "print(classification_report(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.44      0.61      7286\n",
      "           1       0.87      1.00      0.93     28516\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     35802\n",
      "   macro avg       0.93      0.72      0.77     35802\n",
      "weighted avg       0.90      0.88      0.87     35802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here We cannot use accuracy as measure to assess our classifier because our classifier is then taking advantage of the fact that our data is quite skewed. Here we have almost 80% data wo people paid loan and rest didnt pay the loan.\n",
    "\n",
    "Hence we will use matrics like precision, recall and F1-score and confusion matrix\n",
    "\n",
    "**`confusion matrix`** - a matrix which tells us how many time our classifier got confused and predicted wrongly\n",
    "\n",
    "**`Precision`**- The ratio of correct positive predictions to the total predicted positives. Here is this case out of total predicted paid loan how many actually paid the loan\n",
    "\n",
    "**`Recall`**-Also called Sensitivity, Probability of Detection, True Positive Rate The ratio of correct positive predictions to the total positives examples. in layman term it means out of actual prediction how many times the classifier was correct\n",
    "\n",
    "**`In Our Case`**\n",
    "if a loan would default — It would be better to have a high Recall as the banks don’t want to lose money and would be a good idea to alarm the bank even if there is a slight doubt about defaulter.Low precision, in this case, might be okay.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3219</td>\n",
       "      <td>4067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>28461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0      1\n",
       "0  3219   4067\n",
       "1    55  28461"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "#trying other Models\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=[LinearSVC(),\n",
    "     LogisticRegression(random_state=0,solver='lbfgs',max_iter=1000),\n",
    "     GaussianNB(),\n",
    "     RandomForestClassifier(random_state=0,n_estimators=10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_clf(x):\n",
    "    cl=x\n",
    "    cl.fit(X_train,y_train)\n",
    "    predict=cl.predict(X_test)\n",
    "    print(classification_report(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.45      0.62      7286\n",
      "           1       0.88      1.00      0.93     28516\n",
      "\n",
      "   micro avg       0.89      0.89      0.89     35802\n",
      "   macro avg       0.92      0.72      0.77     35802\n",
      "weighted avg       0.89      0.89      0.87     35802\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.46      0.62      7286\n",
      "           1       0.88      0.99      0.93     28516\n",
      "\n",
      "   micro avg       0.88      0.88      0.88     35802\n",
      "   macro avg       0.91      0.73      0.78     35802\n",
      "weighted avg       0.89      0.88      0.87     35802\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.59      0.62      7286\n",
      "           1       0.90      0.92      0.91     28516\n",
      "\n",
      "   micro avg       0.85      0.85      0.85     35802\n",
      "   macro avg       0.77      0.75      0.76     35802\n",
      "weighted avg       0.85      0.85      0.85     35802\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.52      0.62      7286\n",
      "           1       0.89      0.96      0.92     28516\n",
      "\n",
      "   micro avg       0.87      0.87      0.87     35802\n",
      "   macro avg       0.83      0.74      0.77     35802\n",
      "weighted avg       0.86      0.87      0.86     35802\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x in clf:\n",
    "    model_clf(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating function which samples equal classes\n",
    "def sampling_k_elements(group, k=77000):\n",
    "    if len(group) < k:\n",
    "        return group\n",
    "    return group.sample(k)\n",
    "\n",
    "data = df.groupby('loan_status').apply(sampling_k_elements).reset_index(drop=True)\n",
    "data=shuffle(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing data into feature and target\n",
    "X=data.drop(\"loan_status\",axis=1).values\n",
    "y=data[\"loan_status\"].values\n",
    "\n",
    "#spliting the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)\n",
    "\n",
    "#scaling the data\n",
    "#scaling the data\n",
    "scaler=MinMaxScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 119262 samples, validate on 29816 samples\n",
      "Epoch 1/25\n",
      "119262/119262 [==============================] - 31s 259us/sample - loss: 0.4580 - val_loss: 0.3665\n",
      "Epoch 2/25\n",
      "119262/119262 [==============================] - 19s 163us/sample - loss: 0.3742 - val_loss: 0.3596\n",
      "Epoch 3/25\n",
      "119262/119262 [==============================] - 23s 190us/sample - loss: 0.3674 - val_loss: 0.3592\n",
      "Epoch 4/25\n",
      "119262/119262 [==============================] - 22s 186us/sample - loss: 0.3651 - val_loss: 0.3573\n",
      "Epoch 5/25\n",
      "119262/119262 [==============================] - 22s 184us/sample - loss: 0.3637 - val_loss: 0.3575\n",
      "Epoch 6/25\n",
      "119262/119262 [==============================] - 24s 205us/sample - loss: 0.3624 - val_loss: 0.3570\n",
      "Epoch 7/25\n",
      "119262/119262 [==============================] - 26s 221us/sample - loss: 0.3617 - val_loss: 0.3569\n",
      "Epoch 8/25\n",
      "119262/119262 [==============================] - 25s 213us/sample - loss: 0.3611 - val_loss: 0.3562\n",
      "Epoch 9/25\n",
      "119262/119262 [==============================] - 25s 214us/sample - loss: 0.3604 - val_loss: 0.3557\n",
      "Epoch 10/25\n",
      "119262/119262 [==============================] - 26s 216us/sample - loss: 0.3604 - val_loss: 0.3555\n",
      "Epoch 11/25\n",
      "119262/119262 [==============================] - 25s 212us/sample - loss: 0.3590 - val_loss: 0.3576\n",
      "Epoch 12/25\n",
      "119262/119262 [==============================] - 23s 197us/sample - loss: 0.3591 - val_loss: 0.3557\n",
      "Epoch 13/25\n",
      "119262/119262 [==============================] - 25s 214us/sample - loss: 0.3581 - val_loss: 0.3559\n",
      "Epoch 14/25\n",
      "119262/119262 [==============================] - 24s 202us/sample - loss: 0.3576 - val_loss: 0.3552\n",
      "Epoch 15/25\n",
      "119262/119262 [==============================] - 25s 206us/sample - loss: 0.3572 - val_loss: 0.3558\n",
      "Epoch 16/25\n",
      "119262/119262 [==============================] - 23s 194us/sample - loss: 0.3574 - val_loss: 0.3555\n",
      "Epoch 17/25\n",
      "119262/119262 [==============================] - 24s 201us/sample - loss: 0.3572 - val_loss: 0.3555\n",
      "Epoch 18/25\n",
      "119262/119262 [==============================] - 22s 184us/sample - loss: 0.3566 - val_loss: 0.3555\n",
      "Epoch 19/25\n",
      "119262/119262 [==============================] - 22s 185us/sample - loss: 0.3559 - val_loss: 0.3557\n",
      "Epoch 00019: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f2b8790a750>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#running the model\n",
    "model.fit(x=X_train,y=y_train,epochs=25,batch_size=256,validation_data=(X_test,y_test),callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2b87306310>"
      ]
     },
     "execution_count": 292,
     "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": [
    "#plotting the losses\n",
    "loss=pd.DataFrame(model.history.history)\n",
    "\n",
    "loss.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.78      0.79     14453\n",
      "           1       0.80      0.82      0.81     15363\n",
      "\n",
      "   micro avg       0.80      0.80      0.80     29816\n",
      "   macro avg       0.80      0.80      0.80     29816\n",
      "weighted avg       0.80      0.80      0.80     29816\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predict=model.predict_classes(X_test)\n",
    "print(classification_report(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.80      0.80     14453\n",
      "           1       0.81      0.81      0.81     15363\n",
      "\n",
      "   micro avg       0.81      0.81      0.81     29816\n",
      "   macro avg       0.81      0.81      0.81     29816\n",
      "weighted avg       0.81      0.81      0.81     29816\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.80      0.80     14453\n",
      "           1       0.81      0.81      0.81     15363\n",
      "\n",
      "   micro avg       0.81      0.81      0.81     29816\n",
      "   macro avg       0.81      0.81      0.81     29816\n",
      "weighted avg       0.81      0.81      0.81     29816\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.57      0.69     14453\n",
      "           1       0.70      0.94      0.80     15363\n",
      "\n",
      "   micro avg       0.76      0.76      0.76     29816\n",
      "   macro avg       0.80      0.75      0.75     29816\n",
      "weighted avg       0.79      0.76      0.75     29816\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.79      0.77     14453\n",
      "           1       0.79      0.75      0.77     15363\n",
      "\n",
      "   micro avg       0.77      0.77      0.77     29816\n",
      "   macro avg       0.77      0.77      0.77     29816\n",
      "weighted avg       0.77      0.77      0.77     29816\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x in clf:\n",
    "    model_clf(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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