keshavramaswamy/loan_uci.ipynb

## loan_uci.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.style.use('ggplot')\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.rcParams['figure.figsize'] = (20, 10)\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('/Users/kramaswamy/Downloads/DefaultOfCreditCardClients.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14331</td>\n",
       "      <td>14948</td>\n",
       "      <td>15549</td>\n",
       "      <td>1518</td>\n",
       "      <td>1500</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>28314</td>\n",
       "      <td>28959</td>\n",
       "      <td>29547</td>\n",
       "      <td>2000</td>\n",
       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19394</td>\n",
       "      <td>19619</td>\n",
       "      <td>20024</td>\n",
       "      <td>2500</td>\n",
       "      <td>1815</td>\n",
       "      <td>657</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>542653</td>\n",
       "      <td>483003</td>\n",
       "      <td>473944</td>\n",
       "      <td>55000</td>\n",
       "      <td>40000</td>\n",
       "      <td>38000</td>\n",
       "      <td>20239</td>\n",
       "      <td>13750</td>\n",
       "      <td>13770</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0   1   2   3   4   5   6   7   8   9  ...      14      15      16  \\\n",
       "0   90000   2   2   2  34   0   0   0   0   0 ...   14331   14948   15549   \n",
       "1   50000   2   2   1  37   0   0   0   0   0 ...   28314   28959   29547   \n",
       "2   50000   1   2   1  57  -1   0  -1   0   0 ...   20940   19146   19131   \n",
       "3   50000   1   1   2  37   0   0   0   0   0 ...   19394   19619   20024   \n",
       "4  500000   1   1   2  29   0   0   0   0   0 ...  542653  483003  473944   \n",
       "\n",
       "      17     18     19     20     21     22  23  \n",
       "0   1518   1500   1000   1000   1000   5000   0  \n",
       "1   2000   2019   1200   1100   1069   1000   0  \n",
       "2   2000  36681  10000   9000    689    679   0  \n",
       "3   2500   1815    657   1000   1000    800   0  \n",
       "4  55000  40000  38000  20239  13750  13770   0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "5      280\n",
       "4      123\n",
       "6       51\n",
       "0       14\n",
       "Name: 2, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c4d4c90>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z0Woq3j/7zWMOV9N2rpHUh6cRMT2Oi++cxt3tGry/+XgNXpeXqNn62NpJGJfN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\nBJoQwjD+P/Y/D+pYm4YdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4d4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,5), dpi=200)\n",
    "ax = plt.subplot(111)\n",
    "data[1].value_counts(normalize=True).plot(kind='pie', ax=ax, autopct='%1.1f%%', startangle=270, fontsize=17, title='random')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x118448150>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175991d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data[0])\n",
    "plt.title('asass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1175a2d90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170ce990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[0].plot(kind='hist',title='asasa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "###data.columns = # add column names herelike this :['abc','def','ghi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#train.columns = ['loan_id','gender','married','dependents','education','self_employed','income','co_income','amount','amount_term','credit_history','property_area','loan_status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    18112\n",
       "1    11888\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1207e9e50>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAEDhhB0AAEDhhB0AAEDhhB0AAEDhhB0AAEDhhB0AAEDhhB0AAEDhhB0AAEDh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12718fcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tmp=pd.DataFrame(data.groupby([23,5]).size(), columns=['count'])\n",
    "tmp.reset_index(inplace=True)\n",
    "tmp=tmp.pivot(index=23,columns=5,values='count')\n",
    "fig, axes = plt.subplots(1,1,figsize=(15,15))\n",
    "tmp.plot(ax=axes,kind='bar', stacked=True,title='asassa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "acc_data = pd.DataFrame()\n",
    "acc_data['error_rates'] = [0.1,0.12,0.13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11982e8d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AABIShQAAAAASEoUAAAAAEhKFAAAAABIShQAAAAASEoUAAAAAEhKFAAAAABIS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\nSBQCAAAASOj/AQjlN9nv8ebJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a80eb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_data.error_rates.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_data_fold_one = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_data_fold_two = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_data_fold_three = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sample_data_fold_one['first fold'] =[0.1,0.11,0.12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sample_data_fold_three['second fold'] =[0.11,0.134,0.72]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_data_fold_two['third fold'] =[0.6,0.15,0.82]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = ['svnm','asa','asasas']\n",
    "sample_data_fold_one['class'] = x\n",
    "sample_data_fold_two['class'] = x\n",
    "sample_data_fold_three['class'] = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first fold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>svnm</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asa</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asasas</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        first fold\n",
       "class             \n",
       "svnm          0.10\n",
       "asa           0.11\n",
       "asasas        0.12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data_fold_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_data_fold_one.set_index(['class'],inplace=True)\n",
    "sample_data_fold_two.set_index(['class'],inplace=True)\n",
    "\n",
    "sample_data_fold_three.set_index(['class'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11cb4d190>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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4/sXLAQAAAADAtzZvtqply2CdPGlVixbJGjo0SXf7X2UT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jvYvl0vCyIikbR6tYWrr3bS1GRi/vxmHnzQj8USmSzNwWbueeUOxpdfS3OwmQWX/46nr3qGo23H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\ntFhffunQ4MFJuueeBP3nP7Vz/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb7c150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sample_data_fold_one.plot(kind='line',title='Average Phone call duration per customer',color='red')\n",
    "sample_data_fold_two.plot(kind='line',title='Average Phone call duration per customer',color='blue',ax=ax)\n",
    "sample_data_fold_three.plot(kind='line',title='Average Phone call duration per customer',color='green',ax=ax)\n",
    "ax.set_ylabel('Phone Call Duration (in minutes)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [python2]",
   "language": "python",
   "name": "Python [python2]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}