{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BIKES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "day = pd.read_csv(\"data/day.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = day.drop([\"dteday\", \"instant\", \"casual\", 'registered', 'cnt', 'yr'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['season', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit',\n",
       "       'temp', 'atemp', 'hum', 'windspeed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_raw = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data.season = data.season.map({1: \"spring\", 2: \"summer\", 3: \"fall\", 4: 'winter'})\n",
    "data.weathersit = data.weathersit.map({1: \"clear, partly cloudy\", 2: 'mist, cloudy', 3: 'light snow, light rain', 4:'heavy rain, snow and fog'})\n",
    "data.mnth = pd.to_datetime(data.mnth, format=\"%m\").dt.strftime(\"%b\")\n",
    "data.weekday = pd.to_datetime(data.weekday, format=\"%w\").dt.strftime(\"%a\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data_dummies = pd.get_dummies(data, columns=['season', 'mnth', 'weekday', 'weathersit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>season_fall</th>\n",
       "      <th>season_spring</th>\n",
       "      <th>season_summer</th>\n",
       "      <th>season_winter</th>\n",
       "      <th>...</th>\n",
       "      <th>mnth_Mar</th>\n",
       "      <th>mnth_May</th>\n",
       "      <th>mnth_Nov</th>\n",
       "      <th>mnth_Oct</th>\n",
       "      <th>mnth_Sep</th>\n",
       "      <th>weekday_Mon</th>\n",
       "      <th>weathersit_clear, partly cloudy</th>\n",
       "      <th>weathersit_light snow, light rain</th>\n",
       "      <th>weathersit_mist, cloudy</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   holiday  workingday      temp     atemp       hum  windspeed  season_fall  \\\n",
       "0        0           0  0.344167  0.363625  0.805833   0.160446          0.0   \n",
       "1        0           0  0.363478  0.353739  0.696087   0.248539          0.0   \n",
       "2        0           1  0.196364  0.189405  0.437273   0.248309          0.0   \n",
       "3        0           1  0.200000  0.212122  0.590435   0.160296          0.0   \n",
       "4        0           1  0.226957  0.229270  0.436957   0.186900          0.0   \n",
       "\n",
       "   season_spring  season_summer  season_winter  ...   mnth_Mar  mnth_May  \\\n",
       "0            1.0            0.0            0.0  ...        0.0       0.0   \n",
       "1            1.0            0.0            0.0  ...        0.0       0.0   \n",
       "2            1.0            0.0            0.0  ...        0.0       0.0   \n",
       "3            1.0            0.0            0.0  ...        0.0       0.0   \n",
       "4            1.0            0.0            0.0  ...        0.0       0.0   \n",
       "\n",
       "   mnth_Nov  mnth_Oct  mnth_Sep  weekday_Mon  weathersit_clear, partly cloudy  \\\n",
       "0       0.0       0.0       0.0          1.0                              0.0   \n",
       "1       0.0       0.0       0.0          1.0                              0.0   \n",
       "2       0.0       0.0       0.0          1.0                              1.0   \n",
       "3       0.0       0.0       0.0          1.0                              1.0   \n",
       "4       0.0       0.0       0.0          1.0                              1.0   \n",
       "\n",
       "   weathersit_light snow, light rain  weathersit_mist, cloudy   cnt  \n",
       "0                                0.0                      1.0   985  \n",
       "1                                0.0                      1.0   801  \n",
       "2                                0.0                      0.0  1349  \n",
       "3                                0.0                      0.0  1562  \n",
       "4                                0.0                      0.0  1600  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andy/checkout/scikit-learn/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_raw.values, day.cnt.values, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "ridge = RidgeCV().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.52947089808569869"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51677323867022462"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.668775389005\n",
      "0.516835225432\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeRegressor(max_depth=5).fit(X_train, y_train)\n",
    "print(tree.score(X_train, y_train))\n",
    "print(tree.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.947307995615\n",
      "0.595926183484\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor(n_estimators=500).fit(X_train, y_train)\n",
    "print(forest.score(X_train, y_train))\n",
    "print(forest.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_raw.cnt = day.cnt\n",
    "data_dummies.cnt = day.cnt\n",
    "\n",
    "data_raw.to_csv(\"data/bike_day_raw.csv\", index=None)\n",
    "data_dummies.to_csv(\"data/bike_day_dummies.csv\", index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOANS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/andy/anaconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (19,55) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/loan.csv\")[::23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38582, 74)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
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       "      <th>open_rv_24m</th>\n",
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       "      <td>4975.0</td>\n",
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       "      <td>60 months</td>\n",
       "      <td>19.42</td>\n",
       "      <td>214.62</td>\n",
       "      <td>E</td>\n",
       "      <td>E3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0   1077501    1296599     5000.0       5000.0           4975.0   36 months   \n",
       "23  1069700    1304810    10000.0      10000.0          10000.0   36 months   \n",
       "46  1069465    1304521     5000.0       5000.0           5000.0   36 months   \n",
       "69  1069073    1303718    15000.0      15000.0          15000.0   36 months   \n",
       "92  1065674    1299785     8200.0       8200.0           8200.0   60 months   \n",
       "\n",
       "    int_rate  installment grade sub_grade     ...      total_bal_il il_util  \\\n",
       "0      10.65       162.87     B        B2     ...               NaN     NaN   \n",
       "23     11.71       330.76     B        B3     ...               NaN     NaN   \n",
       "46      8.90       158.77     A        A5     ...               NaN     NaN   \n",
       "69     14.65       517.42     C        C3     ...               NaN     NaN   \n",
       "92     19.42       214.62     E        E3     ...               NaN     NaN   \n",
       "\n",
       "   open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
       "0          NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "23         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "46         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "69         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "92         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "\n",
       "   total_cu_tl inq_last_12m  \n",
       "0          NaN          NaN  \n",
       "23         NaN          NaN  \n",
       "46         NaN          NaN  \n",
       "69         NaN          NaN  \n",
       "92         NaN          NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "counts = data.notnull().sum(axis=0).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "columns = counts[:52].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data[columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>out_prncp</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <th>total_pymnt</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>initial_list_status</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>dti</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>...</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>title</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42550</th>\n",
       "      <td>4145.19</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3854.81</td>\n",
       "      <td>4990.03</td>\n",
       "      <td>4990.030000</td>\n",
       "      <td>4145.19</td>\n",
       "      <td>f</td>\n",
       "      <td>7203.0</td>\n",
       "      <td>15.75</td>\n",
       "      <td>CO</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>My Life Saving Loan :)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>Oct-2015</td>\n",
       "      <td>PARTS MANAGER</td>\n",
       "      <td>20800.0</td>\n",
       "      <td>15949.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42573</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16000.00</td>\n",
       "      <td>16900.62</td>\n",
       "      <td>16900.617682</td>\n",
       "      <td>0.00</td>\n",
       "      <td>w</td>\n",
       "      <td>6868.0</td>\n",
       "      <td>7.39</td>\n",
       "      <td>MA</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Debt Consolidation</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>Nov-2014</td>\n",
       "      <td>Senior Software Engineer</td>\n",
       "      <td>66800.0</td>\n",
       "      <td>369614.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42596</th>\n",
       "      <td>13859.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6140.28</td>\n",
       "      <td>11734.08</td>\n",
       "      <td>11734.080000</td>\n",
       "      <td>13859.72</td>\n",
       "      <td>w</td>\n",
       "      <td>13772.0</td>\n",
       "      <td>17.03</td>\n",
       "      <td>TX</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Credit card refinancing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.6</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>Project Manager</td>\n",
       "      <td>33100.0</td>\n",
       "      <td>75993.0</td>\n",
       "      <td>3712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42619</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4631.49</td>\n",
       "      <td>12732.64</td>\n",
       "      <td>12732.640000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>w</td>\n",
       "      <td>27597.0</td>\n",
       "      <td>8.58</td>\n",
       "      <td>NY</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Consolidation for Self-Publication</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.9</td>\n",
       "      <td>May-2015</td>\n",
       "      <td>Administrator</td>\n",
       "      <td>39500.0</td>\n",
       "      <td>181757.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42642</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>34218.71</td>\n",
       "      <td>34218.711966</td>\n",
       "      <td>0.00</td>\n",
       "      <td>f</td>\n",
       "      <td>17756.0</td>\n",
       "      <td>9.41</td>\n",
       "      <td>MN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Credit card refinancing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Mar-2015</td>\n",
       "      <td>Engineer/Conductor</td>\n",
       "      <td>29100.0</td>\n",
       "      <td>161166.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       out_prncp  total_rec_late_fee  total_rec_prncp  total_pymnt_inv  \\\n",
       "42550    4145.19                15.0          3854.81          4990.03   \n",
       "42573       0.00                 0.0         16000.00         16900.62   \n",
       "42596   13859.72                 0.0          6140.28         11734.08   \n",
       "42619       0.00                 0.0          4631.49         12732.64   \n",
       "42642       0.00                 0.0         30000.00         34218.71   \n",
       "\n",
       "        total_pymnt  out_prncp_inv initial_list_status  revol_bal    dti  \\\n",
       "42550   4990.030000        4145.19                   f     7203.0  15.75   \n",
       "42573  16900.617682           0.00                   w     6868.0   7.39   \n",
       "42596  11734.080000       13859.72                   w    13772.0  17.03   \n",
       "42619  12732.640000           0.00                   w    27597.0   8.58   \n",
       "42642  34218.711966           0.00                   f    17756.0   9.41   \n",
       "\n",
       "      addr_state     ...      pub_rec open_acc  \\\n",
       "42550         CO     ...          1.0      9.0   \n",
       "42573         MA     ...          0.0     12.0   \n",
       "42596         TX     ...          0.0     14.0   \n",
       "42619         NY     ...          3.0     19.0   \n",
       "42642         MN     ...          0.0     12.0   \n",
       "\n",
       "                                    title collections_12_mths_ex_med  \\\n",
       "42550              My Life Saving Loan :)                        0.0   \n",
       "42573                  Debt Consolidation                        0.0   \n",
       "42596             Credit card refinancing                        0.0   \n",
       "42619  Consolidation for Self-Publication                        0.0   \n",
       "42642             Credit card refinancing                        0.0   \n",
       "\n",
       "      revol_util last_pymnt_d                 emp_title  total_rev_hi_lim  \\\n",
       "42550       34.6     Oct-2015             PARTS MANAGER           20800.0   \n",
       "42573       10.3     Nov-2014  Senior Software Engineer           66800.0   \n",
       "42596       41.6     Jan-2016           Project Manager           33100.0   \n",
       "42619       69.9     May-2015             Administrator           39500.0   \n",
       "42642       61.0     Mar-2015        Engineer/Conductor           29100.0   \n",
       "\n",
       "      tot_cur_bal tot_coll_amt  \n",
       "42550     15949.0          0.0  \n",
       "42573    369614.0          0.0  \n",
       "42596     75993.0       3712.0  \n",
       "42619    181757.0          0.0  \n",
       "42642    161166.0          0.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bad_statuses = [\"Charged Off \", \"Default\", \"Does not meet the credit policy. Status:Charged Off\", \"In Grace Period\",\n",
    "                \"Default Receiver\", \"Late (16-30 days)\", \"Late (31-120 days)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "data['bad_status'] = data.loan_status.isin(bad_statuses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.drop([\"url\", \"title\", \"id\", \"emp_title\", \"loan_status\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['out_prncp', 'total_rec_late_fee', 'total_rec_prncp', 'total_pymnt_inv',\n",
       "       'total_pymnt', 'out_prncp_inv', 'initial_list_status', 'revol_bal',\n",
       "       'dti', 'addr_state', 'zip_code', 'purpose', 'pymnt_plan', 'issue_d',\n",
       "       'verification_status', 'annual_inc', 'home_ownership', 'emp_length',\n",
       "       'sub_grade', 'grade', 'installment', 'int_rate', 'term',\n",
       "       'funded_amnt_inv', 'funded_amnt', 'loan_amnt', 'member_id',\n",
       "       'total_rec_int', 'recoveries', 'last_pymnt_amnt',\n",
       "       'collection_recovery_fee', 'application_type', 'policy_code',\n",
       "       'inq_last_6mths', 'acc_now_delinq', 'last_credit_pull_d', 'total_acc',\n",
       "       'delinq_2yrs', 'earliest_cr_line', 'pub_rec', 'open_acc',\n",
       "       'collections_12_mths_ex_med', 'revol_util', 'last_pymnt_d',\n",
       "       'total_rev_hi_lim', 'tot_cur_bal', 'tot_coll_amt', 'bad_status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "out_prncp                     float64\n",
       "total_rec_late_fee            float64\n",
       "total_rec_prncp               float64\n",
       "total_pymnt_inv               float64\n",
       "total_pymnt                   float64\n",
       "out_prncp_inv                 float64\n",
       "initial_list_status            object\n",
       "revol_bal                     float64\n",
       "dti                           float64\n",
       "addr_state                     object\n",
       "zip_code                       object\n",
       "purpose                        object\n",
       "pymnt_plan                     object\n",
       "issue_d                        object\n",
       "verification_status            object\n",
       "annual_inc                    float64\n",
       "home_ownership                 object\n",
       "emp_length                     object\n",
       "sub_grade                      object\n",
       "grade                          object\n",
       "installment                   float64\n",
       "int_rate                      float64\n",
       "term                           object\n",
       "funded_amnt_inv               float64\n",
       "funded_amnt                   float64\n",
       "loan_amnt                     float64\n",
       "member_id                       int64\n",
       "total_rec_int                 float64\n",
       "recoveries                    float64\n",
       "last_pymnt_amnt               float64\n",
       "collection_recovery_fee       float64\n",
       "application_type               object\n",
       "policy_code                   float64\n",
       "inq_last_6mths                float64\n",
       "acc_now_delinq                float64\n",
       "last_credit_pull_d             object\n",
       "total_acc                     float64\n",
       "delinq_2yrs                   float64\n",
       "earliest_cr_line               object\n",
       "pub_rec                       float64\n",
       "open_acc                      float64\n",
       "collections_12_mths_ex_med    float64\n",
       "revol_util                    float64\n",
       "last_pymnt_d                   object\n",
       "total_rev_hi_lim              float64\n",
       "tot_cur_bal                   float64\n",
       "tot_coll_amt                  float64\n",
       "bad_status                       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debt_consolidation    489197\n",
       "credit_card           195527\n",
       "home_improvement       46956\n",
       "other                  36707\n",
       "major_purchase         14143\n",
       "medical                 7457\n",
       "small_business          7451\n",
       "car                     6666\n",
       "moving                  4549\n",
       "vacation                4140\n",
       "house                   3001\n",
       "wedding                  876\n",
       "renewable_energy         432\n",
       "educational                1\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.purpose.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "float_columns = data.dtypes[data.dtypes == \"float64\"].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_float = data[float_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32719, 30)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_float.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data_float.values\n",
    "y = data.bad_status.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.972329760789\n",
      "0.975794621027\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(lr.score(X_train, y_train))\n",
    "print(lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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R8at0PCPdC7AvUBqhu4ls81uAx4CDgG2AS1IfHgWmVWnjwFJdEfErSa+l82OB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\n/we/T2gGMMa7ngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8618c8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.barh(range(X.shape[1]), lr.coef_.ravel())\n",
    "plt.yticks(np.arange(X.shape[1]) + .5, data_float_hard.columns.tolist(), va=\"center\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_float_hard = data_float.drop(['total_rec_late_fee', \"revol_util\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data_float_hard.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SHELTER ANIMALS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/shelter_train.csv\")\n",
    "test = pd.read_csv(\"data/shelter_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>AnimalID</th>\n",
       "      <th>Name</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>OutcomeType</th>\n",
       "      <th>OutcomeSubtype</th>\n",
       "      <th>AnimalType</th>\n",
       "      <th>SexuponOutcome</th>\n",
       "      <th>AgeuponOutcome</th>\n",
       "      <th>Breed</th>\n",
       "      <th>Color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A671945</td>\n",
       "      <td>Hambone</td>\n",
       "      <td>2014-02-12 18:22:00</td>\n",
       "      <td>Return_to_owner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Shetland Sheepdog Mix</td>\n",
       "      <td>Brown/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A656520</td>\n",
       "      <td>Emily</td>\n",
       "      <td>2013-10-13 12:44:00</td>\n",
       "      <td>Euthanasia</td>\n",
       "      <td>Suffering</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Spayed Female</td>\n",
       "      <td>1 year</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Cream Tabby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A686464</td>\n",
       "      <td>Pearce</td>\n",
       "      <td>2015-01-31 12:28:00</td>\n",
       "      <td>Adoption</td>\n",
       "      <td>Foster</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 years</td>\n",
       "      <td>Pit Bull Mix</td>\n",
       "      <td>Blue/White</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A683430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-07-11 19:09:00</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Cat</td>\n",
       "      <td>Intact Male</td>\n",
       "      <td>3 weeks</td>\n",
       "      <td>Domestic Shorthair Mix</td>\n",
       "      <td>Blue Cream</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A667013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013-11-15 12:52:00</td>\n",
       "      <td>Transfer</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Dog</td>\n",
       "      <td>Neutered Male</td>\n",
       "      <td>2 years</td>\n",
       "      <td>Lhasa Apso/Miniature Poodle</td>\n",
       "      <td>Tan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  AnimalID     Name             DateTime      OutcomeType OutcomeSubtype  \\\n",
       "0  A671945  Hambone  2014-02-12 18:22:00  Return_to_owner            NaN   \n",
       "1  A656520    Emily  2013-10-13 12:44:00       Euthanasia      Suffering   \n",
       "2  A686464   Pearce  2015-01-31 12:28:00         Adoption         Foster   \n",
       "3  A683430      NaN  2014-07-11 19:09:00         Transfer        Partner   \n",
       "4  A667013      NaN  2013-11-15 12:52:00         Transfer        Partner   \n",
       "\n",
       "  AnimalType SexuponOutcome AgeuponOutcome                        Breed  \\\n",
       "0        Dog  Neutered Male         1 year        Shetland Sheepdog Mix   \n",
       "1        Cat  Spayed Female         1 year       Domestic Shorthair Mix   \n",
       "2        Dog  Neutered Male        2 years                 Pit Bull Mix   \n",
       "3        Cat    Intact Male        3 weeks       Domestic Shorthair Mix   \n",
       "4        Dog  Neutered Male        2 years  Lhasa Apso/Miniature Poodle   \n",
       "\n",
       "         Color  \n",
       "0  Brown/White  \n",
       "1  Cream Tabby  \n",
       "2   Blue/White  \n",
       "3   Blue Cream  \n",
       "4          Tan  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bank marketing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"data/bank-additional/bank-additional-full.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "admin.           10422\n",
       "blue-collar       9254\n",
       "technician        6743\n",
       "services          3969\n",
       "management        2924\n",
       "retired           1720\n",
       "entrepreneur      1456\n",
       "self-employed     1421\n",
       "housemaid         1060\n",
       "unemployed        1014\n",
       "student            875\n",
       "unknown            330\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "job                object\n",
       "marital            object\n",
       "education          object\n",
       "default            object\n",
       "housing            object\n",
       "loan               object\n",
       "contact            object\n",
       "month              object\n",
       "day_of_week        object\n",
       "duration            int64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "poutcome           object\n",
       "emp.var.rate      float64\n",
       "cons.price.idx    float64\n",
       "cons.conf.idx     float64\n",
       "euribor3m         float64\n",
       "nr.employed       float64\n",
       "y                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target = data.y\n",
    "data = data.drop(\"y\", axis=1)\n",
    "bla = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
       "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed',\n",
       "       'job_admin.', 'job_blue-collar', 'job_entrepreneur', 'job_housemaid',\n",
       "       'job_management', 'job_retired', 'job_self-employed', 'job_services',\n",
       "       'job_student', 'job_technician', 'job_unemployed', 'job_unknown',\n",
       "       'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'marital_unknown', 'education_basic.4y', 'education_basic.6y',\n",
       "       'education_basic.9y', 'education_high.school', 'education_illiterate',\n",
       "       'education_professional.course', 'education_university.degree',\n",
       "       'education_unknown', 'default_no', 'default_unknown', 'default_yes',\n",
       "       'housing_no', 'housing_unknown', 'housing_yes', 'loan_no',\n",
       "       'loan_unknown', 'loan_yes', 'contact_cellular', 'contact_telephone',\n",
       "       'month_apr', 'month_aug', 'month_dec', 'month_jul', 'month_jun',\n",
       "       'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep',\n",
       "       'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu',\n",
       "       'day_of_week_tue', 'day_of_week_wed', 'poutcome_failure',\n",
       "       'poutcome_nonexistent', 'poutcome_success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bla.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = bla.values\n",
    "y = target.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.908905506458\n",
      "0.91201320773\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(lr.score(X_train, y_train))\n",
    "print(lr.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1apK7tgIY4CtVdTPw0xacs2LANc6tqh8BJLmonfc2BThdyuYPquqq9voYuuUs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pmZmdkA5nXArWF4HXCz5qS6JMM6fdPM6svrgJuZWd1kSZiq6cvpm2bWzDwC3qIkDQMOiYgTe1nP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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb860a32630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 12))\n",
    "plt.barh(range(X.shape[1]), lr.coef_.ravel())\n",
    "plt.yticks(np.arange(X.shape[1]) + .5, bla.columns.tolist(), va=\"center\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99993525622349555"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=100).fit(X_train, y_train)\n",
    "rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91317859570748761"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bla['target'] = target\n",
    "bla.to_csv(\"data/bank-campaign.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}