Preprocessing_modeling_refinements_evaluations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3272.0</td>\n",
       "      <td>3455.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>90000.0</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>...</td>\n",
       "      <td>14331.0</td>\n",
       "      <td>14948.0</td>\n",
       "      <td>15549.0</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>50000.0</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>...</td>\n",
       "      <td>28314.0</td>\n",
       "      <td>28959.0</td>\n",
       "      <td>29547.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>50000.0</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>...</td>\n",
       "      <td>20940.0</td>\n",
       "      <td>19146.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>36681.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>679.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "1   2   120000.0    2          2         2   26     -1      2      0      0   \n",
       "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
       "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
       "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "              ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
       "0             ...                    0.0        0.0        0.0       0.0   \n",
       "1             ...                 3272.0     3455.0     3261.0       0.0   \n",
       "2             ...                14331.0    14948.0    15549.0    1518.0   \n",
       "3             ...                28314.0    28959.0    29547.0    2000.0   \n",
       "4             ...                20940.0    19146.0    19131.0    2000.0   \n",
       "\n",
       "   PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0     689.0       0.0       0.0       0.0       0.0   \n",
       "1    1000.0    1000.0    1000.0       0.0    2000.0   \n",
       "2    1500.0    1000.0    1000.0    1000.0    5000.0   \n",
       "3    2019.0    1200.0    1100.0    1069.0    1000.0   \n",
       "4   36681.0   10000.0    9000.0     689.0     679.0   \n",
       "\n",
       "   default.payment.next.month  \n",
       "0                           1  \n",
       "1                           1  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import numpy as np\n",
    "\n",
    "credit=pd.read_csv(\"Credit.csv\")\n",
    "credit.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We don't need the ID column,so lets drop it.\n",
    "credit = credit.drop([\"ID\"],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#changing the name of  pay_0 column to pay_1 to make the numbering correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "credit.rename(columns={'PAY_0':'PAY_1'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default.payment.next.month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  PAY_3  PAY_4  \\\n",
       "0    20000.0    2          2         1   24      2      2     -1     -1   \n",
       "\n",
       "   PAY_5             ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  \\\n",
       "0     -2             ...                    0.0        0.0        0.0   \n",
       "\n",
       "   PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0       0.0     689.0       0.0       0.0       0.0       0.0   \n",
       "\n",
       "   default.payment.next.month  \n",
       "0                           1  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing Unwanted categorical levels as mentioned in data exploration\n",
    "credit['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Changing Education levels 0,5,6 to others(4)\n",
    "credit[\"EDUCATION\"]=credit[\"EDUCATION\"].map({0:4,1:1,2:2,3:3,4:4,5:4,6:4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    14030\n",
       "1    10585\n",
       "3     4917\n",
       "4      468\n",
       "Name: EDUCATION, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13659\n",
       "3      323\n",
       "0       54\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#similarly chaning marriage level 0 to level 3\n",
    "credit[\"MARRIAGE\"]=credit[\"MARRIAGE\"].map({0:3,1:1,2:2,3:3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15964\n",
       "1    13659\n",
       "3      377\n",
       "Name: MARRIAGE, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.06673129,  0.01107916,  0.03071698,  0.01828816,  0.06729232,\n",
       "        0.10502206,  0.04590552,  0.03639505,  0.02706571,  0.02889421,\n",
       "        0.02720425,  0.05080295,  0.04753515,  0.04445718,  0.04384626,\n",
       "        0.04408402,  0.04320006,  0.04511946,  0.042764  ,  0.04264839,\n",
       "        0.04227342,  0.04214917,  0.04652524])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check which features are important\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "X = credit.drop('default.payment.next.month',axis=1)\n",
    "Y = credit['default.payment.next.month']\n",
    "clf = ExtraTreesClassifier()\n",
    "clf = clf.fit(X, Y)\n",
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21fe2edd5c0>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WuiJpbbqb6sHCGDMzK4lWzji6gcF0Z9QsYCginpH034EhSZuB88ADABFxStIQcBq4BmxL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21fe2ed1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#feature scaling check\n",
    "credit['BILL_AMT1'].hist(bins=60, color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01010101,  1.        ,  0.33333333, ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.11111111,  1.        ,  0.33333333, ...,  0.        ,\n",
       "         0.00378311,  1.        ],\n",
       "       [ 0.08080808,  1.        ,  0.33333333, ...,  0.00234451,\n",
       "         0.00945777,  0.        ],\n",
       "       ..., \n",
       "       [ 0.02020202,  0.        ,  0.33333333, ...,  0.00468901,\n",
       "         0.00586382,  1.        ],\n",
       "       [ 0.07070707,  0.        ,  0.66666667, ...,  0.12417444,\n",
       "         0.00341236,  1.        ],\n",
       "       [ 0.04040404,  0.        ,  0.33333333, ...,  0.00234451,\n",
       "         0.00189155,  1.        ]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit_transform(credit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIMIT_BAL                     0\n",
       "SEX                           0\n",
       "EDUCATION                     0\n",
       "MARRIAGE                      0\n",
       "AGE                           0\n",
       "PAY_1                         0\n",
       "PAY_2                         0\n",
       "PAY_3                         0\n",
       "PAY_4                         0\n",
       "PAY_5                         0\n",
       "PAY_6                         0\n",
       "BILL_AMT1                     0\n",
       "BILL_AMT2                     0\n",
       "BILL_AMT3                     0\n",
       "BILL_AMT4                     0\n",
       "BILL_AMT5                     0\n",
       "BILL_AMT6                     0\n",
       "PAY_AMT1                      0\n",
       "PAY_AMT2                      0\n",
       "PAY_AMT3                      0\n",
       "PAY_AMT4                      0\n",
       "PAY_AMT5                      0\n",
       "PAY_AMT6                      0\n",
       "default.payment.next.month    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "credit.apply(lambda x: sum(x.isnull()),axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Initial Model training(except feed forward neural network)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(credit, test_size = 0.3)\n",
    "\n",
    "X_train = train.drop('default.payment.next.month',axis=1)\n",
    "\n",
    "y_train = train['default.payment.next.month']\n",
    "\n",
    "X_test =  test.drop('default.payment.next.month',axis=1)\n",
    "\n",
    "y_test = test['default.payment.next.month']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=10, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train benchmark model(Log Regression)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "clf0 = LogisticRegression(random_state=10)\n",
    "clf0.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.777\n",
      "Precision: 0.603729\n",
      "Recall: 0.777\n",
      "f1_score: 0.679492402926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "y_pred = clf0.predict(X_test)\n",
    "accuracy_regr=accuracy_score(y_test, y_pred)\n",
    "precision_regr=precision_score(y_test, y_pred,average='weighted')\n",
    "recall_regr=recall_score(y_test, y_pred,average='weighted')\n",
    "f1_regr=f1_score(y_test,y_pred,average='weighted')\n",
    "print(\"accuracy:\", accuracy_regr)\n",
    "print(\"Precision:\", precision_regr)\n",
    "print(\"Recall:\", recall_regr)\n",
    "print('f1_score:',f1_regr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=10, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train other classifiers\n",
    "#We start with randomforest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf1=RandomForestClassifier(random_state=10)\n",
    "clf1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.807222222222\n",
      "Precision: 0.785871777618\n",
      "Recall: 0.807222222222\n",
      "f1_score: 0.783502576417\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = clf1.predict(X_test)\n",
    "accuracy_rf=accuracy_score(y_test, y_pred1)\n",
    "precision_rf=precision_score(y_test, y_pred1,average='weighted')\n",
    "recall_rf=recall_score(y_test, y_pred1,average='weighted')\n",
    "f1_rf=f1_score(y_test,y_pred1,average='weighted')\n",
    "print(\"accuracy:\", accuracy_rf)\n",
    "print(\"Precision:\", precision_rf)\n",
    "print(\"Recall:\", recall_rf)\n",
    "print('f1_score:',f1_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=10, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gradeint boost\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf2=GradientBoostingClassifier(random_state=10)\n",
    "clf2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.821444444444\n",
      "Precision: 0.805451059511\n",
      "Recall: 0.821444444444\n",
      "f1_score: 0.800503291948\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = clf2.predict(X_test)\n",
    "accuracy_gb=accuracy_score(y_test, y_pred2)\n",
    "precision_gb=precision_score(y_test, y_pred2,average='weighted')\n",
    "recall_gb=recall_score(y_test, y_pred2,average='weighted')\n",
    "f1_gb=f1_score(y_test,y_pred2,average='weighted')\n",
    "print(\"accuracy:\", accuracy_gb)\n",
    "print(\"Precision:\", precision_gb)\n",
    "print(\"Recall:\", recall_gb)\n",
    "print('f1_score:',f1_gb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=50, random_state=10)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adaboost\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "clf3 = AdaBoostClassifier(random_state=10)\n",
    "clf3.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.818555555556\n",
      "Precision: 0.802242885074\n",
      "Recall: 0.818555555556\n",
      "f1_score: 0.793428250173\n"
     ]
    }
   ],
   "source": [
    "y_pred3 = clf3.predict(X_test)\n",
    "accuracy_ab=accuracy_score(y_test, y_pred3)\n",
    "precision_ab=precision_score(y_test, y_pred3,average='weighted')\n",
    "recall_ab=recall_score(y_test, y_pred3,average='weighted')\n",
    "f1_ab=f1_score(y_test,y_pred3,average='weighted')\n",
    "print(\"accuracy:\", accuracy_ab)\n",
    "print(\"Precision:\", precision_ab)\n",
    "print(\"Recall:\", recall_ab)\n",
    "print('f1_score:',f1_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('rf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_we...thm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=50, random_state=10))],\n",
       "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#voting classifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "clf4 = VotingClassifier(estimators=[('rf',clf1),('gb', clf2),('ab',clf3)])\n",
    "clf4.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.819777777778\n",
      "Precision: 0.803495820308\n",
      "Recall: 0.819777777778\n",
      "f1_score: 0.79663926916\n"
     ]
    }
   ],
   "source": [
    "y_pred4 = clf4.predict(X_test)\n",
    "accuracy_vc=accuracy_score(y_test, y_pred4)\n",
    "precision_vc=precision_score(y_test, y_pred4,average='weighted')\n",
    "recall_vc=recall_score(y_test, y_pred4,average='weighted')\n",
    "f1_vc=f1_score(y_test,y_pred4,average='weighted')\n",
    "print(\"accuracy:\", accuracy_vc)\n",
    "print(\"Precision:\", precision_vc)\n",
    "print(\"Recall:\", recall_vc)\n",
    "print('f1_score:',f1_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#appending all socres\n",
    "arraycuracy=[]\n",
    "arraycuracy.append(accuracy_regr)\n",
    "arraycuracy.append(accuracy_rf)\n",
    "arraycuracy.append(accuracy_gb)\n",
    "arraycuracy.append(accuracy_ab)\n",
    "arraycuracy.append(accuracy_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "precisionray=[]\n",
    "precisionray.append(precision_regr)\n",
    "precisionray.append(precision_rf)\n",
    "precisionray.append(precision_gb)\n",
    "precisionray.append(precision_ab)\n",
    "precisionray.append(precision_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "recallray=[]\n",
    "recallray.append(recall_regr)\n",
    "recallray.append(recall_rf)\n",
    "recallray.append(recall_gb)\n",
    "recallray.append(recall_ab)\n",
    "recallray.append(recall_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1ray=[]\n",
    "f1ray.append(f1_regr)\n",
    "f1ray.append(f1_rf)\n",
    "f1ray.append(f1_gb)\n",
    "f1ray.append(f1_ab)\n",
    "f1ray.append(f1_vc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f90fdd8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets compare Accuracy,precison,racall\n",
    "import matplotlib.pyplot as plt\n",
    "classifier_names=('Log_reg','random_forst','grad_boost','adaboost','voting_class')\n",
    "y_axis=np.arange(len(classifier_names))\n",
    "plt.bar(y_axis, arraycuracy, alpha=0.4,color='navy',ecolor='navy')\n",
    "plt.xticks(y_axis, classifier_names)\n",
    "plt.ylabel('Accuracy Scores')\n",
    "plt.title('Classifier comparison wrt Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sjqw9EjMz67gq5xSOBt4r6SHgWVITUkTEobVGZmZm/a5KUnh77VGYmdkWocptLh4CdgNO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\nbfW0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f92a26400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_axis=np.arange(len(classifier_names))\n",
    "plt.bar(y_axis,precisionray, alpha=0.4,color='purple',ecolor='darkgreen')\n",
    "plt.xticks(y_axis, classifier_names)\n",
    "plt.ylabel('Precision Scores')\n",
    "plt.title('Classifier comparison wrt Precision')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PJR2o7iMdGKeQvpyyY/n5bEgKB5MS48Ic13WlNj5fesx3SFcz/wqcUyr/b+Ad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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f92ab56d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_axis=np.arange(len(classifier_names))\n",
    "plt.bar(y_axis,recallray, alpha=0.4,color='brown',ecolor='black')\n",
    "plt.xticks(y_axis, classifier_names)\n",
    "plt.ylabel('recall Scores')\n",
    "plt.title('Classifier comparison wrt Recall')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xTZNC7jaaHxF7Aff1TEhmZtZbmnYfRcQrwAJJg3soHjMz60WtXH20IzBf0gzg2c7CiHhX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\nIXXqDGg0xgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f92b3ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_axis=np.arange(len(classifier_names))\n",
    "plt.bar(y_axis,f1ray, alpha=0.6,color='orange',ecolor='black')\n",
    "plt.xticks(y_axis, classifier_names)\n",
    "plt.ylabel('f1 Scores')\n",
    "plt.title('Classifier comparison wrt f1')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Adaboost: {'n_estimators': 15}\n"
     ]
    }
   ],
   "source": [
    "#Some fine REFINEMENTS to GBboost,Rfforest,and Adaboost\n",
    "#PERFROM GRIDSEARCH CV on RF,GB and Adaboost to find best parameter values on 10 fold cross validation set\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import fbeta_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "\n",
    "parameters_ada ={'n_estimators' :[15,25,50],\n",
    "            }\n",
    "\n",
    "\n",
    "\n",
    "clf = GridSearchCV(AdaBoostClassifier(random_state=10), parameters_ada,cv=10,scoring='accuracy')\n",
    "clf.fit(X_train, y_train)\n",
    "print(\"Best parameters for Adaboost:\",clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for GradeintBoosting: {'min_samples_split': 15, 'n_estimators': 50}\n",
      "Best parameters for RandomForest: {'min_samples_split': 15, 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "parameters_boost ={'n_estimators' :[15,25,50],\n",
    "             'min_samples_split' :[3,10,15]\n",
    "}\n",
    "\n",
    "clf00 = GridSearchCV(GradientBoostingClassifier(random_state=10), cv=10, param_grid=parameters_boost, scoring='accuracy')\n",
    "clf00.fit(X_train,y_train)\n",
    "print(\"Best parameters for GradeintBoosting:\",clf00.best_params_)\n",
    "\n",
    "\n",
    "clf001 = GridSearchCV(RandomForestClassifier(random_state=10), cv=10, param_grid=parameters_boost,scoring='accuracy')\n",
    "clf001.fit(X_train,y_train)\n",
    "print(\"Best parameters for RandomForest:\",clf001.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Use the above parameter values for our Weighted voting CLassifier\n",
    "\n",
    "#First we have to calibrate our classsifers\n",
    "\n",
    "#Isotonic claibration\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "clfvc1 = RandomForestClassifier(n_estimators=50,min_samples_split=15,random_state=10)\n",
    "clfvc2=AdaBoostClassifier(n_estimators=15,random_state=10)\n",
    "clfvc3=GradientBoostingClassifier(n_estimators=50,min_samples_split=15,random_state=10)\n",
    "clf_isotonic1 = CalibratedClassifierCV(clfvc1, method='isotonic',cv=10)\n",
    "clf_isotonic2 = CalibratedClassifierCV(clfvc2, method='isotonic',cv=10)\n",
    "clf_isotonic3 = CalibratedClassifierCV(clfvc3, method='isotonic',cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('rf', CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, m..., subsample=1.0, verbose=0,\n",
       "              warm_start=False),\n",
       "            cv=10, method='isotonic'))],\n",
       "         flatten_transform=None, n_jobs=1, voting='soft',\n",
       "         weights=[1, 1, 1])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Inputing the istonic calibration to voting classifier with weights.\n",
    "import time\n",
    "t0 = time.clock()\n",
    "clfvc = VotingClassifier(estimators=[('rf',clf_isotonic1),('ab', clf_isotonic2),('gb',clf_isotonic3)],voting='soft',weights=[1,1,2])\n",
    "clfvc.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predvc = clfvc.predict(X_test)\n",
    "accuracy_vciso=accuracy_score(y_test, y_predvc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "precision_vciso=precision_score(y_test, y_predvc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.821666666667\n",
      "Precision: 0.805790548828\n",
      "Recall: 0.821666666667\n",
      "f1_score: 0.800497169577\n",
      "Training_time: 59.46775733333334\n"
     ]
    }
   ],
   "source": [
    "#Results of weighted voting classifer\n",
    "y_predvc = clfvc.predict(X_test)\n",
    "accuracy_vciso=accuracy_score(y_test, y_predvc)\n",
    "precision_vciso=precision_score(y_test, y_predvc,average='weighted')\n",
    "recall_vciso=recall_score(y_test, y_predvc,average='weighted')\n",
    "f1_vciso=f1_score(y_test,y_predvc,average='weighted')\n",
    "print(\"accuracy:\", accuracy_vciso)\n",
    "print(\"Precision:\", precision_vciso)\n",
    "print(\"Recall:\", recall_vciso)\n",
    "print('f1_score:',f1_vciso)\n",
    "print('Training_time:',time.clock() - t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=15,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=50,\n",
       "              presort='auto', random_state=10, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training just the Gradient Boost(clfvc3) with tuned parameters from above\n",
    "t1 = time.clock()\n",
    "clfvc3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.822888888889\n",
      "Precision: 0.807469736307\n",
      "Recall: 0.822888888889\n",
      "f1_score: 0.801832784112\n",
      "Training_time: 1.9779253333335873\n"
     ]
    }
   ],
   "source": [
    "#results of Tuned Gradient Boosting classifer\n",
    "y_predgb2 = clfvc3.predict(X_test)\n",
    "accuracy_gb2=accuracy_score(y_test, y_predgb2)\n",
    "precision_gb2=precision_score(y_test, y_predgb2,average='weighted')\n",
    "recall_gb2=recall_score(y_test, y_predgb2,average='weighted')\n",
    "f1_gb2=f1_score(y_test,y_predgb2,average='weighted')\n",
    "print(\"accuracy:\", accuracy_gb2)\n",
    "print(\"Precision:\", precision_gb2)\n",
    "print(\"Recall:\", recall_gb2)\n",
    "print('f1_score:',f1_gb2)\n",
    "print('Training_time:',time.clock() - t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82220287299291184"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#FINAL EVALUATION USING Stratified 10-fold CV on VC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#accuracy\n",
    "scores = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80021882447113324"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "#f1\n",
    "scores1 = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='f1_weighted')\n",
    "scores1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80645997468495467"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Precision\n",
    "scores2 = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='precision_weighted')\n",
    "scores2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.82220287299291184"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall\n",
    "scores3 = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='recall_weighted')\n",
    "scores3.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.822202872993\n",
      "Precision: 0.806459974685\n",
      "Recall: 0.822202872993\n",
      "F1: 0.800218824471\n"
     ]
    }
   ],
   "source": [
    "#corss validated final results of tuned Gradient Boosting\n",
    "print(\"Accuracy:\",scores.mean())\n",
    "print(\"Precision:\",scores2.mean())\n",
    "print(\"Recall:\",scores3.mean())\n",
    "print(\"F1:\",scores1.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68193712829259523"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets crossvalidate log regression \n",
    "#f1\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "scores4 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='f1_weighted')\n",
    "scores4.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77876670255926317"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy\n",
    "scores5 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10)\n",
    "scores5.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60652374650316576"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#precision \n",
    "scores6 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='precision_weighted')\n",
    "scores6.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.77876670255926317"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall\n",
    "scores7 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='recall_weighted')\n",
    "scores7.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.778766702559\n",
      "Precision: 0.606523746503\n",
      "Recall: 0.778766702559\n",
      "F1: 0.681937128293\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",scores5.mean())\n",
    "print(\"Precision:\",scores6.mean())\n",
    "print(\"Recall:\",scores7.mean())\n",
    "print(\"F1:\",scores4.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DnAEAMIzD6gGAUJbMedbqEQCgV7FyBgDAMCFXzn6/X8uXL9exY8cUHh6uVatWKSEhQZLU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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21f919a60b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Free form visualization of all metrics of Gradient boosting(final model) and Log_regression(benchmark model)\n",
    "import seaborn as sns\n",
    "all_scores=[]\n",
    "all_scores.append(scores)\n",
    "all_scores.append(scores2)\n",
    "all_scores.append(scores3)\n",
    "all_scores.append(scores1)\n",
    "eval1=[]\n",
    "eval1.append('Accuracy')\n",
    "eval1.append('Precision')\n",
    "eval1.append('Recall')\n",
    "eval1.append('F1')\n",
    "ax=sns.boxplot(y=all_scores,x=eval1,palette=(\"RdBu_r\"),linewidth=2.0,width=0.6)\n",
    "#ax.set(ylim=(0.76, 0.82))"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}