Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing packages...\n"
     ]
    }
   ],
   "source": [
    "print('Importing packages...')\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing models from scikit learn module..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda2\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 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": [
    "print('Importing models from scikit learn module..')\n",
    "from sklearn.linear_model import LogisticRegression,LinearRegression\n",
    "from sklearn.cross_validation import KFold   \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "from sklearn import metrics\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading data...\n"
     ]
    }
   ],
   "source": [
    "print('Reading data...')\n",
    "dfTrain = pd.read_excel('training.xlsx')\n",
    "dfTest = pd.read_excel('output.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.487636</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-1.569017</td>\n",
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       "      <td>-0.244476</td>\n",
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       "      <th>18</th>\n",
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       "      <td>-0.244670</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-0.165990</td>\n",
       "      <td>0.221510</td>\n",
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       "      <td>-0.003940</td>\n",
       "      <td>-0.391440</td>\n",
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       "      <td>-0.126408</td>\n",
       "      <td>0.261092</td>\n",
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       "      <td>-0.697384</td>\n",
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       "      <td>1.784383</td>\n",
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       "      <td>-0.021543</td>\n",
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       "      <th>28</th>\n",
       "      <td>0</td>\n",
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       "      <td>-0.3875</td>\n",
       "      <td>-0.045862</td>\n",
       "      <td>-0.433362</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.002547</td>\n",
       "      <td>0.384953</td>\n",
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       "      <td>0.124304</td>\n",
       "      <td>1.661804</td>\n",
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       "      <th>9970</th>\n",
       "      <td>0</td>\n",
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       "      <td>-0.076759</td>\n",
       "      <td>1.460741</td>\n",
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       "      <td>-0.5</td>\n",
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       "      <td>0.144660</td>\n",
       "      <td>-0.242840</td>\n",
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       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.051204</td>\n",
       "      <td>1.588704</td>\n",
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       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.059438</td>\n",
       "      <td>0.328062</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.018731</td>\n",
       "      <td>-1.556231</td>\n",
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       "      <th>9976</th>\n",
       "      <td>0</td>\n",
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       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.031626</td>\n",
       "      <td>-0.355874</td>\n",
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       "      <th>9977</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.252080</td>\n",
       "      <td>0.639580</td>\n",
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       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.022638</td>\n",
       "      <td>-1.514862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>-1.537186</td>\n",
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       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.235457</td>\n",
       "      <td>-1.302043</td>\n",
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       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.073508</td>\n",
       "      <td>-1.463992</td>\n",
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       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.153816</td>\n",
       "      <td>-1.691316</td>\n",
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       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.086907</td>\n",
       "      <td>-0.300593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.331262</td>\n",
       "      <td>1.868762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.142758</td>\n",
       "      <td>0.244742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.028871</td>\n",
       "      <td>-1.566371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.115243</td>\n",
       "      <td>-0.272257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.013926</td>\n",
       "      <td>0.373574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.154083</td>\n",
       "      <td>-0.541583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.178585</td>\n",
       "      <td>1.358915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.039444</td>\n",
       "      <td>1.498056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.014156</td>\n",
       "      <td>1.551656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.069382</td>\n",
       "      <td>0.456882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.291733</td>\n",
       "      <td>-1.245767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.387015</td>\n",
       "      <td>-1.924515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.208812</td>\n",
       "      <td>-1.328688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.065560</td>\n",
       "      <td>-0.453060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.139305</td>\n",
       "      <td>0.248195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  un  un-1   bn      an     noise        Rn\n",
       "0     0   1     0  1.0  0.1000 -0.405274 -0.305274\n",
       "1     0  -1     1 -0.5 -0.3875  0.010171 -0.377329\n",
       "2     0   1    -1  0.5  0.3875 -0.141262  0.246238\n",
       "3     0   1     1  1.5 -1.5375 -0.433456 -1.970956\n",
       "4     0  -1     1 -0.5 -0.3875  0.069772 -0.317728\n",
       "5     0   1    -1  0.5  0.3875 -0.179888  0.207612\n",
       "6     0  -1     1 -0.5 -0.3875 -0.326357 -0.713857\n",
       "7     0   1    -1  0.5  0.3875 -0.182146  0.205354\n",
       "8     0  -1     1 -0.5 -0.3875 -0.100136 -0.487636\n",
       "9     0  -1    -1 -1.5  1.5375  0.132197  1.669697\n",
       "10    0  -1    -1 -1.5  1.5375 -0.065527  1.471973\n",
       "11    0   1    -1  0.5  0.3875  0.562926  0.950426\n",
       "12    0   1     1  1.5 -1.5375 -0.203016 -1.740516\n",
       "13    0   1     1  1.5 -1.5375 -0.031517 -1.569017\n",
       "14    0   1     1  1.5 -1.5375 -0.139130 -1.676630\n",
       "15    0  -1     1 -0.5 -0.3875  0.058049 -0.329451\n",
       "16    0   1    -1  0.5  0.3875 -0.054325  0.333175\n",
       "17    0  -1     1 -0.5 -0.3875 -0.244476 -0.631976\n",
       "18    0  -1    -1 -1.5  1.5375 -0.244670  1.292830\n",
       "19    0   1    -1  0.5  0.3875 -0.165990  0.221510\n",
       "20    0  -1     1 -0.5 -0.3875 -0.003940 -0.391440\n",
       "21    0  -1    -1 -1.5  1.5375  0.250765  1.788265\n",
       "22    0  -1    -1 -1.5  1.5375 -0.016305  1.521195\n",
       "23    0  -1    -1 -1.5  1.5375 -0.217285  1.320215\n",
       "24    0   1    -1  0.5  0.3875 -0.126408  0.261092\n",
       "25    0  -1     1 -0.5 -0.3875 -0.309884 -0.697384\n",
       "26    0  -1    -1 -1.5  1.5375  0.246883  1.784383\n",
       "27    0   1    -1  0.5  0.3875 -0.409043 -0.021543\n",
       "28    0  -1     1 -0.5 -0.3875 -0.045862 -0.433362\n",
       "29    0   1    -1  0.5  0.3875 -0.002547  0.384953\n",
       "...  ..  ..   ...  ...     ...       ...       ...\n",
       "9969  0  -1    -1 -1.5  1.5375  0.124304  1.661804\n",
       "9970  0  -1    -1 -1.5  1.5375 -0.076759  1.460741\n",
       "9971  0   1    -1  0.5  0.3875  0.043916  0.431416\n",
       "9972  0  -1     1 -0.5 -0.3875  0.144660 -0.242840\n",
       "9973  0  -1    -1 -1.5  1.5375  0.051204  1.588704\n",
       "9974  0   1    -1  0.5  0.3875 -0.059438  0.328062\n",
       "9975  0   1     1  1.5 -1.5375 -0.018731 -1.556231\n",
       "9976  0  -1     1 -0.5 -0.3875  0.031626 -0.355874\n",
       "9977  0   1    -1  0.5  0.3875  0.252080  0.639580\n",
       "9978  0   1     1  1.5 -1.5375  0.022638 -1.514862\n",
       "9979  0   1     1  1.5 -1.5375  0.000314 -1.537186\n",
       "9980  0   1     1  1.5 -1.5375  0.235457 -1.302043\n",
       "9981  0   1     1  1.5 -1.5375  0.073508 -1.463992\n",
       "9982  0   1     1  1.5 -1.5375 -0.153816 -1.691316\n",
       "9983  0  -1     1 -0.5 -0.3875  0.086907 -0.300593\n",
       "9984  0  -1    -1 -1.5  1.5375  0.331262  1.868762\n",
       "9985  0   1    -1  0.5  0.3875 -0.142758  0.244742\n",
       "9986  0   1     1  1.5 -1.5375 -0.028871 -1.566371\n",
       "9987  0  -1     1 -0.5 -0.3875  0.115243 -0.272257\n",
       "9988  0   1    -1  0.5  0.3875 -0.013926  0.373574\n",
       "9989  0  -1     1 -0.5 -0.3875 -0.154083 -0.541583\n",
       "9990  0  -1    -1 -1.5  1.5375 -0.178585  1.358915\n",
       "9991  0  -1    -1 -1.5  1.5375 -0.039444  1.498056\n",
       "9992  0  -1    -1 -1.5  1.5375  0.014156  1.551656\n",
       "9993  0   1    -1  0.5  0.3875  0.069382  0.456882\n",
       "9994  0   1     1  1.5 -1.5375  0.291733 -1.245767\n",
       "9995  0   1     1  1.5 -1.5375 -0.387015 -1.924515\n",
       "9996  0   1     1  1.5 -1.5375  0.208812 -1.328688\n",
       "9997  0  -1     1 -0.5 -0.3875 -0.065560 -0.453060\n",
       "9998  0   1    -1  0.5  0.3875 -0.139305  0.248195\n",
       "\n",
       "[9999 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dfTrain"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "dfTrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>un</th>\n",
       "      <th>un-1</th>\n",
       "      <th>bn</th>\n",
       "      <th>an</th>\n",
       "      <th>noise</th>\n",
       "      <th>Rn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9999.0</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006501</td>\n",
       "      <td>0.006401</td>\n",
       "      <td>0.009701</td>\n",
       "      <td>-0.009831</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>-0.009170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000029</td>\n",
       "      <td>0.999980</td>\n",
       "      <td>1.120806</td>\n",
       "      <td>1.124188</td>\n",
       "      <td>0.201295</td>\n",
       "      <td>1.139578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-1.537500</td>\n",
       "      <td>-0.768341</td>\n",
       "      <td>-2.305841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-1.537500</td>\n",
       "      <td>-0.134999</td>\n",
       "      <td>-1.124474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.387500</td>\n",
       "      <td>0.000084</td>\n",
       "      <td>-0.020863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.387500</td>\n",
       "      <td>0.134788</td>\n",
       "      <td>0.884939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.537500</td>\n",
       "      <td>0.697444</td>\n",
       "      <td>2.178832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0           un         un-1           bn           an  \\\n",
       "count  9999.0  9999.000000  9999.000000  9999.000000  9999.000000   \n",
       "mean      0.0     0.006501     0.006401     0.009701    -0.009831   \n",
       "std       0.0     1.000029     0.999980     1.120806     1.124188   \n",
       "min       0.0    -1.000000    -1.000000    -1.500000    -1.537500   \n",
       "25%       0.0    -1.000000    -1.000000    -0.500000    -1.537500   \n",
       "50%       0.0     1.000000     1.000000     0.500000    -0.387500   \n",
       "75%       0.0     1.000000     1.000000     1.500000     0.387500   \n",
       "max       0.0     1.000000     1.000000     1.500000     1.537500   \n",
       "\n",
       "             noise           Rn  \n",
       "count  9999.000000  9999.000000  \n",
       "mean      0.000661    -0.009170  \n",
       "std       0.201295     1.139578  \n",
       "min      -0.768341    -2.305841  \n",
       "25%      -0.134999    -1.124474  \n",
       "50%       0.000084    -0.020863  \n",
       "75%       0.134788     0.884939  \n",
       "max       0.697444     2.178832  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTrain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_cols=['Rn']\n",
    "train_target = pd.DataFrame(dfTrain['un'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finalTrain=dfTrain[train_cols];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(np.array(finalTrain), np.array(train_target), test_size=0.30,random_state=59)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = LogisticRegression()  # logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:547: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set:\n",
      "0.508\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set:')\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler  # scaling the data so that neural network works better\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(100,100,50,30,10),solver='adam',max_iter=1000,verbose=True)  # neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda2\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:912: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.58356710\n",
      "Iteration 2, loss = 0.36132029\n",
      "Iteration 3, loss = 0.13557250\n",
      "Iteration 4, loss = 0.05094768\n",
      "Iteration 5, loss = 0.05017641\n",
      "Iteration 6, loss = 0.04941623\n",
      "Iteration 7, loss = 0.04500122\n",
      "Iteration 8, loss = 0.04616869\n",
      "Iteration 9, loss = 0.04794667\n",
      "Iteration 10, loss = 0.04639116\n",
      "Training loss did not improve more than tol=0.000100 for two consecutive epochs. Stopping.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100, 50, 30, 10), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=1000, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=True, warm_start=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions=mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of neural network classifier on test set:\n",
      "0.986666666667\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of neural network classifier on test set:')\n",
    "print(mlp.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9867519370573623"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTrain['inverse_Rn']=100/dfTrain['Rn'] # adding an extra column of square of output\n",
    "                                        #dfTrain['log_amount'] = np.log(dfTrain['AMOUNT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>un</th>\n",
       "      <th>un-1</th>\n",
       "      <th>bn</th>\n",
       "      <th>an</th>\n",
       "      <th>noise</th>\n",
       "      <th>Rn</th>\n",
       "      <th>inverse_Rn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>-0.405274</td>\n",
       "      <td>-0.305274</td>\n",
       "      <td>-327.574060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.010171</td>\n",
       "      <td>-0.377329</td>\n",
       "      <td>-265.020627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.141262</td>\n",
       "      <td>0.246238</td>\n",
       "      <td>406.111218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.433456</td>\n",
       "      <td>-1.970956</td>\n",
       "      <td>-50.736798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.069772</td>\n",
       "      <td>-0.317728</td>\n",
       "      <td>-314.734503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.179888</td>\n",
       "      <td>0.207612</td>\n",
       "      <td>481.666782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.326357</td>\n",
       "      <td>-0.713857</td>\n",
       "      <td>-140.084135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.182146</td>\n",
       "      <td>0.205354</td>\n",
       "      <td>486.963662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.100136</td>\n",
       "      <td>-0.487636</td>\n",
       "      <td>-205.070975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.132197</td>\n",
       "      <td>1.669697</td>\n",
       "      <td>59.891102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.065527</td>\n",
       "      <td>1.471973</td>\n",
       "      <td>67.936041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.562926</td>\n",
       "      <td>0.950426</td>\n",
       "      <td>105.215945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.203016</td>\n",
       "      <td>-1.740516</td>\n",
       "      <td>-57.454225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.031517</td>\n",
       "      <td>-1.569017</td>\n",
       "      <td>-63.734165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.139130</td>\n",
       "      <td>-1.676630</td>\n",
       "      <td>-59.643453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.058049</td>\n",
       "      <td>-0.329451</td>\n",
       "      <td>-303.535024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.054325</td>\n",
       "      <td>0.333175</td>\n",
       "      <td>300.142183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.244476</td>\n",
       "      <td>-0.631976</td>\n",
       "      <td>-158.233904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.244670</td>\n",
       "      <td>1.292830</td>\n",
       "      <td>77.349662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.165990</td>\n",
       "      <td>0.221510</td>\n",
       "      <td>451.446081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.003940</td>\n",
       "      <td>-0.391440</td>\n",
       "      <td>-255.467039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.250765</td>\n",
       "      <td>1.788265</td>\n",
       "      <td>55.920125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.016305</td>\n",
       "      <td>1.521195</td>\n",
       "      <td>65.737771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.217285</td>\n",
       "      <td>1.320215</td>\n",
       "      <td>75.745245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.126408</td>\n",
       "      <td>0.261092</td>\n",
       "      <td>383.007335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.309884</td>\n",
       "      <td>-0.697384</td>\n",
       "      <td>-143.393035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.246883</td>\n",
       "      <td>1.784383</td>\n",
       "      <td>56.041773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.409043</td>\n",
       "      <td>-0.021543</td>\n",
       "      <td>-4641.787862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.045862</td>\n",
       "      <td>-0.433362</td>\n",
       "      <td>-230.753991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.002547</td>\n",
       "      <td>0.384953</td>\n",
       "      <td>259.772306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.124304</td>\n",
       "      <td>1.661804</td>\n",
       "      <td>60.175558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.076759</td>\n",
       "      <td>1.460741</td>\n",
       "      <td>68.458388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.043916</td>\n",
       "      <td>0.431416</td>\n",
       "      <td>231.794664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.144660</td>\n",
       "      <td>-0.242840</td>\n",
       "      <td>-411.793231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.051204</td>\n",
       "      <td>1.588704</td>\n",
       "      <td>62.944402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.059438</td>\n",
       "      <td>0.328062</td>\n",
       "      <td>304.820834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.018731</td>\n",
       "      <td>-1.556231</td>\n",
       "      <td>-64.257801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.031626</td>\n",
       "      <td>-0.355874</td>\n",
       "      <td>-280.998220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.252080</td>\n",
       "      <td>0.639580</td>\n",
       "      <td>156.352693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.022638</td>\n",
       "      <td>-1.514862</td>\n",
       "      <td>-66.012624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>-1.537186</td>\n",
       "      <td>-65.053927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.235457</td>\n",
       "      <td>-1.302043</td>\n",
       "      <td>-76.802368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.073508</td>\n",
       "      <td>-1.463992</td>\n",
       "      <td>-68.306367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.153816</td>\n",
       "      <td>-1.691316</td>\n",
       "      <td>-59.125572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.086907</td>\n",
       "      <td>-0.300593</td>\n",
       "      <td>-332.675989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.331262</td>\n",
       "      <td>1.868762</td>\n",
       "      <td>53.511373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.142758</td>\n",
       "      <td>0.244742</td>\n",
       "      <td>408.594176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.028871</td>\n",
       "      <td>-1.566371</td>\n",
       "      <td>-63.841836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>0.115243</td>\n",
       "      <td>-0.272257</td>\n",
       "      <td>-367.299992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.013926</td>\n",
       "      <td>0.373574</td>\n",
       "      <td>267.684713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.154083</td>\n",
       "      <td>-0.541583</td>\n",
       "      <td>-184.643921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.178585</td>\n",
       "      <td>1.358915</td>\n",
       "      <td>73.588106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>-0.039444</td>\n",
       "      <td>1.498056</td>\n",
       "      <td>66.753194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.5375</td>\n",
       "      <td>0.014156</td>\n",
       "      <td>1.551656</td>\n",
       "      <td>64.447263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.069382</td>\n",
       "      <td>0.456882</td>\n",
       "      <td>218.875033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.291733</td>\n",
       "      <td>-1.245767</td>\n",
       "      <td>-80.271843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.387015</td>\n",
       "      <td>-1.924515</td>\n",
       "      <td>-51.961131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.208812</td>\n",
       "      <td>-1.328688</td>\n",
       "      <td>-75.262228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.3875</td>\n",
       "      <td>-0.065560</td>\n",
       "      <td>-0.453060</td>\n",
       "      <td>-220.721531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>-0.139305</td>\n",
       "      <td>0.248195</td>\n",
       "      <td>402.909789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  un  un-1   bn      an     noise        Rn   inverse_Rn\n",
       "0     0   1     0  1.0  0.1000 -0.405274 -0.305274  -327.574060\n",
       "1     0  -1     1 -0.5 -0.3875  0.010171 -0.377329  -265.020627\n",
       "2     0   1    -1  0.5  0.3875 -0.141262  0.246238   406.111218\n",
       "3     0   1     1  1.5 -1.5375 -0.433456 -1.970956   -50.736798\n",
       "4     0  -1     1 -0.5 -0.3875  0.069772 -0.317728  -314.734503\n",
       "5     0   1    -1  0.5  0.3875 -0.179888  0.207612   481.666782\n",
       "6     0  -1     1 -0.5 -0.3875 -0.326357 -0.713857  -140.084135\n",
       "7     0   1    -1  0.5  0.3875 -0.182146  0.205354   486.963662\n",
       "8     0  -1     1 -0.5 -0.3875 -0.100136 -0.487636  -205.070975\n",
       "9     0  -1    -1 -1.5  1.5375  0.132197  1.669697    59.891102\n",
       "10    0  -1    -1 -1.5  1.5375 -0.065527  1.471973    67.936041\n",
       "11    0   1    -1  0.5  0.3875  0.562926  0.950426   105.215945\n",
       "12    0   1     1  1.5 -1.5375 -0.203016 -1.740516   -57.454225\n",
       "13    0   1     1  1.5 -1.5375 -0.031517 -1.569017   -63.734165\n",
       "14    0   1     1  1.5 -1.5375 -0.139130 -1.676630   -59.643453\n",
       "15    0  -1     1 -0.5 -0.3875  0.058049 -0.329451  -303.535024\n",
       "16    0   1    -1  0.5  0.3875 -0.054325  0.333175   300.142183\n",
       "17    0  -1     1 -0.5 -0.3875 -0.244476 -0.631976  -158.233904\n",
       "18    0  -1    -1 -1.5  1.5375 -0.244670  1.292830    77.349662\n",
       "19    0   1    -1  0.5  0.3875 -0.165990  0.221510   451.446081\n",
       "20    0  -1     1 -0.5 -0.3875 -0.003940 -0.391440  -255.467039\n",
       "21    0  -1    -1 -1.5  1.5375  0.250765  1.788265    55.920125\n",
       "22    0  -1    -1 -1.5  1.5375 -0.016305  1.521195    65.737771\n",
       "23    0  -1    -1 -1.5  1.5375 -0.217285  1.320215    75.745245\n",
       "24    0   1    -1  0.5  0.3875 -0.126408  0.261092   383.007335\n",
       "25    0  -1     1 -0.5 -0.3875 -0.309884 -0.697384  -143.393035\n",
       "26    0  -1    -1 -1.5  1.5375  0.246883  1.784383    56.041773\n",
       "27    0   1    -1  0.5  0.3875 -0.409043 -0.021543 -4641.787862\n",
       "28    0  -1     1 -0.5 -0.3875 -0.045862 -0.433362  -230.753991\n",
       "29    0   1    -1  0.5  0.3875 -0.002547  0.384953   259.772306\n",
       "...  ..  ..   ...  ...     ...       ...       ...          ...\n",
       "9969  0  -1    -1 -1.5  1.5375  0.124304  1.661804    60.175558\n",
       "9970  0  -1    -1 -1.5  1.5375 -0.076759  1.460741    68.458388\n",
       "9971  0   1    -1  0.5  0.3875  0.043916  0.431416   231.794664\n",
       "9972  0  -1     1 -0.5 -0.3875  0.144660 -0.242840  -411.793231\n",
       "9973  0  -1    -1 -1.5  1.5375  0.051204  1.588704    62.944402\n",
       "9974  0   1    -1  0.5  0.3875 -0.059438  0.328062   304.820834\n",
       "9975  0   1     1  1.5 -1.5375 -0.018731 -1.556231   -64.257801\n",
       "9976  0  -1     1 -0.5 -0.3875  0.031626 -0.355874  -280.998220\n",
       "9977  0   1    -1  0.5  0.3875  0.252080  0.639580   156.352693\n",
       "9978  0   1     1  1.5 -1.5375  0.022638 -1.514862   -66.012624\n",
       "9979  0   1     1  1.5 -1.5375  0.000314 -1.537186   -65.053927\n",
       "9980  0   1     1  1.5 -1.5375  0.235457 -1.302043   -76.802368\n",
       "9981  0   1     1  1.5 -1.5375  0.073508 -1.463992   -68.306367\n",
       "9982  0   1     1  1.5 -1.5375 -0.153816 -1.691316   -59.125572\n",
       "9983  0  -1     1 -0.5 -0.3875  0.086907 -0.300593  -332.675989\n",
       "9984  0  -1    -1 -1.5  1.5375  0.331262  1.868762    53.511373\n",
       "9985  0   1    -1  0.5  0.3875 -0.142758  0.244742   408.594176\n",
       "9986  0   1     1  1.5 -1.5375 -0.028871 -1.566371   -63.841836\n",
       "9987  0  -1     1 -0.5 -0.3875  0.115243 -0.272257  -367.299992\n",
       "9988  0   1    -1  0.5  0.3875 -0.013926  0.373574   267.684713\n",
       "9989  0  -1     1 -0.5 -0.3875 -0.154083 -0.541583  -184.643921\n",
       "9990  0  -1    -1 -1.5  1.5375 -0.178585  1.358915    73.588106\n",
       "9991  0  -1    -1 -1.5  1.5375 -0.039444  1.498056    66.753194\n",
       "9992  0  -1    -1 -1.5  1.5375  0.014156  1.551656    64.447263\n",
       "9993  0   1    -1  0.5  0.3875  0.069382  0.456882   218.875033\n",
       "9994  0   1     1  1.5 -1.5375  0.291733 -1.245767   -80.271843\n",
       "9995  0   1     1  1.5 -1.5375 -0.387015 -1.924515   -51.961131\n",
       "9996  0   1     1  1.5 -1.5375  0.208812 -1.328688   -75.262228\n",
       "9997  0  -1     1 -0.5 -0.3875 -0.065560 -0.453060  -220.721531\n",
       "9998  0   1    -1  0.5  0.3875 -0.139305  0.248195   402.909789\n",
       "\n",
       "[9999 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_cols=['inverse_Rn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "finalTrain=dfTrain[train_cols];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(np.array(finalTrain), np.array(train_target), test_size=0.30,random_state=59)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf=LogisticRegression()  #logistic Rgression __ again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set:\n",
      "0.491333333333\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set:')\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49140100811014242"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mlp_1= MLPClassifier(hidden_layer_sizes=(30,30,30),solver='adam')  # another neural network model with different number of hidden layers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(30, 30, 30), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of neural network classifier on test set:\n",
      "0.490333333333\n"
     ]
    }
   ],
   "source": [
    "y_pred_1 = mlp_1.predict(X_test)\n",
    "print('Accuracy of neural network classifier on test set:')\n",
    "print(mlp_1.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test,y_pred_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm  # svm classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_clf = svm.SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred=svm_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of svm classifier on test set:\n",
      "0.973333333333\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of svm classifier on test set:')\n",
    "print(svm_clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736972797387824"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>un_original</th>\n",
       "      <th>un-1_original</th>\n",
       "      <th>_original</th>\n",
       "      <th>an_original</th>\n",
       "      <th>noise_original</th>\n",
       "      <th>Rn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.059918</td>\n",
       "      <td>-1.597418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.095018</td>\n",
       "      <td>-1.442482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.129054</td>\n",
       "      <td>-1.666554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>0.113154</td>\n",
       "      <td>-1.424346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.5375</td>\n",
       "      <td>-0.031022</td>\n",
       "      <td>-1.568522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   un_original  un-1_original  _original  an_original  noise_original  \\\n",
       "0            1              1        1.5      -1.5375       -0.059918   \n",
       "1            1              1        1.5      -1.5375        0.095018   \n",
       "2            1              1        1.5      -1.5375       -0.129054   \n",
       "3            1              1        1.5      -1.5375        0.113154   \n",
       "4            1              1        1.5      -1.5375       -0.031022   \n",
       "\n",
       "         Rn  \n",
       "0 -1.597418  \n",
       "1 -1.442482  \n",
       "2 -1.666554  \n",
       "3 -1.424346  \n",
       "4 -1.568522  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_cols=['Rn']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_test=dfTest[test_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions=mlp.predict(final_test) # using the neural network classifier for predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  1  1  1  1 -1  1  1 -1 -1  1 -1  1  1  1  1 -1 -1 -1 -1  1  1 -1 -1  1\n",
      " -1 -1 -1 -1 -1  1 -1  1  1  1  1  1  1 -1 -1 -1  1 -1  1 -1  1 -1 -1 -1 -1\n",
      " -1 -1 -1 -1 -1  1  1  1  1  1  1 -1  1 -1  1 -1 -1  1  1  1  1  1 -1 -1  1\n",
      " -1 -1 -1 -1  1 -1  1  1  1 -1 -1  1 -1  1  1 -1  1 -1 -1 -1 -1  1  1 -1 -1\n",
      " -1 -1 -1 -1 -1  1  1 -1 -1  1 -1  1 -1  1  1  1  1 -1  1 -1 -1  1  1  1  1\n",
      "  1 -1  1 -1  1 -1  1  1 -1  1  1 -1 -1  1 -1  1 -1 -1 -1  1 -1  1  1 -1 -1\n",
      " -1  1  1 -1  1  1  1 -1  1  1 -1  1  1  1 -1  1 -1 -1  1 -1  1 -1  1 -1  1\n",
      "  1 -1 -1  1 -1 -1 -1  1  1 -1  1 -1  1  1 -1 -1  1  1  1  1 -1  1 -1 -1  1\n",
      "  1 -1 -1 -1  1  1  1  1 -1  1  1  1  1 -1 -1 -1  1  1 -1 -1 -1  1  1  1  1\n",
      "  1  1 -1 -1 -1  1 -1  1  1  1 -1 -1 -1 -1 -1  1 -1  1  1 -1 -1  1  1  1  1\n",
      " -1  1  1  1  1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1  1 -1 -1  1 -1  1 -1 -1 -1 -1\n",
      "  1  1  1  1  1 -1 -1 -1  1 -1  1  1 -1  1 -1  1 -1 -1 -1 -1 -1  1  1  1  1\n",
      "  1 -1 -1 -1 -1  1  1 -1 -1  1 -1 -1 -1 -1  1 -1 -1  1  1 -1  1  1  1  1  1\n",
      "  1 -1 -1 -1  1  1  1  1 -1  1  1  1  1 -1 -1 -1 -1  1 -1  1 -1  1 -1 -1  1\n",
      " -1 -1  1 -1 -1  1 -1 -1  1  1 -1  1  1 -1  1 -1  1  1 -1 -1  1 -1  1  1 -1\n",
      " -1 -1 -1  1 -1 -1  1 -1  1 -1  1 -1 -1 -1  1  1 -1 -1  1 -1 -1  1  1 -1 -1\n",
      "  1  1 -1  1  1 -1 -1  1  1  1  1  1  1  1  1 -1 -1 -1  1  1 -1 -1  1 -1  1\n",
      " -1 -1  1  1 -1 -1  1  1  1  1  1 -1 -1 -1 -1  1  1  1  1  1 -1 -1 -1 -1 -1\n",
      " -1  1  1 -1 -1  1  1  1 -1 -1 -1  1  1  1 -1  1  1 -1 -1  1 -1  1 -1 -1 -1\n",
      "  1 -1 -1  1 -1 -1 -1 -1 -1  1  1 -1  1 -1 -1 -1 -1  1 -1  1 -1 -1  1 -1 -1\n",
      " -1]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTest['Predictions']=predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>un_original</th>\n",
       "      <th>un-1_original</th>\n",
       "      <th>_original</th>\n",
       "      <th>an_original</th>\n",
       "      <th>noise_original</th>\n",
       "      <th>Rn</th>\n",
       "      <th>Predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>501.00000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>501.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.00998</td>\n",
       "      <td>-0.005988</td>\n",
       "      <td>-0.012974</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>-0.007283</td>\n",
       "      <td>0.004219</td>\n",
       "      <td>-0.021956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.00095</td>\n",
       "      <td>1.000982</td>\n",
       "      <td>1.121754</td>\n",
       "      <td>1.125191</td>\n",
       "      <td>0.200711</td>\n",
       "      <td>1.137725</td>\n",
       "      <td>1.000758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-1.537500</td>\n",
       "      <td>-0.567932</td>\n",
       "      <td>-2.105432</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.500000</td>\n",
       "      <td>-0.387500</td>\n",
       "      <td>-0.147818</td>\n",
       "      <td>-0.776222</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.387500</td>\n",
       "      <td>-0.002823</td>\n",
       "      <td>0.018622</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.537500</td>\n",
       "      <td>0.129770</td>\n",
       "      <td>1.130661</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.537500</td>\n",
       "      <td>0.680215</td>\n",
       "      <td>2.151620</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       un_original  un-1_original   _original  an_original  noise_original  \\\n",
       "count    501.00000     501.000000  501.000000   501.000000      501.000000   \n",
       "mean      -0.00998      -0.005988   -0.012974     0.011502       -0.007283   \n",
       "std        1.00095       1.000982    1.121754     1.125191        0.200711   \n",
       "min       -1.00000      -1.000000   -1.500000    -1.537500       -0.567932   \n",
       "25%       -1.00000      -1.000000   -1.500000    -0.387500       -0.147818   \n",
       "50%       -1.00000      -1.000000   -0.500000     0.387500       -0.002823   \n",
       "75%        1.00000       1.000000    0.500000     1.537500        0.129770   \n",
       "max        1.00000       1.000000    1.500000     1.537500        0.680215   \n",
       "\n",
       "               Rn  Predictions  \n",
       "count  501.000000   501.000000  \n",
       "mean     0.004219    -0.021956  \n",
       "std      1.137725     1.000758  \n",
       "min     -2.105432    -1.000000  \n",
       "25%     -0.776222    -1.000000  \n",
       "50%      0.018622    -1.000000  \n",
       "75%      1.130661     1.000000  \n",
       "max      2.151620     1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfTest.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "writer=pd.ExcelWriter('predictions.xlsx',engine='xlsxwriter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfTest.to_excel(writer,'Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 13] Permission denied: 'predictions.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-0470dbbeb1e7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Admin\\Anaconda2\\lib\\site-packages\\pandas\\io\\excel.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1552\u001b[0m         \"\"\"\n\u001b[0;32m   1553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1554\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1555\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1556\u001b[0m     def write_cells(self, cells, sheet_name=None, startrow=0, startcol=0,\n",
      "\u001b[1;32mC:\\Users\\Admin\\Anaconda2\\lib\\site-packages\\xlsxwriter\\workbook.pyc\u001b[0m in \u001b[0;36mclose\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    309\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfileclosed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfileclosed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_store_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mset_size\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Admin\\Anaconda2\\lib\\site-packages\\xlsxwriter\\workbook.pyc\u001b[0m in \u001b[0;36m_store_workbook\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m         xlsx_file = ZipFile(self.filename, \"w\", compression=ZIP_DEFLATED,\n\u001b[1;32m--> 625\u001b[1;33m                             allowZip64=self.allow_zip64)\n\u001b[0m\u001b[0;32m    626\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m         \u001b[1;31m# Add XML sub-files to the Zip file with their Excel filename.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Admin\\Anaconda2\\lib\\zipfile.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, file, mode, compression, allowZip64)\u001b[0m\n\u001b[0;32m    754\u001b[0m             \u001b[0mmodeDict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'r'\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'a'\u001b[0m \u001b[1;33m:\u001b[0m \u001b[1;34m'r+b'\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    755\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 756\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodeDict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    757\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIOError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    758\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'a'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: [Errno 13] Permission denied: 'predictions.xlsx'"
     ]
    }
   ],
   "source": [
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm  # svm classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf = svm.SVC(kernel='poly',degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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