Breast Cancer Prediction - Modelling and performance evaluation - Final version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification of Human Breast as Bening or Malignant using Machine Learning Algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As part of this project, we will use Logistic Regression, SVM with rbf kernel functions, and Random Forest for classification of a tumor in human breast as bening or malignant.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's import required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "##\n",
    "## First, we will import all the necessary libraries that we will need in the code.\n",
    "##\n",
    "import pandas                   as pd\n",
    "import numpy                    as np\n",
    "import matplotlib               as mpl\n",
    "import matplotlib.pyplot        as plt\n",
    "import seaborn                  as sb\n",
    "from   sklearn                  import preprocessing \n",
    "from   sklearn.model_selection  import train_test_split\n",
    "from   sklearn                  import svm\n",
    "from   sklearn.metrics          import classification_report, confusion_matrix\n",
    "import itertools\n",
    "from   sklearn.tree             import DecisionTreeClassifier\n",
    "from   sklearn                  import metrics\n",
    "import xgboost                  as xgb\n",
    "from   sklearn.model_selection  import KFold\n",
    "from   sklearn.model_selection  import cross_val_score\n",
    "from   sklearn.model_selection  import cross_val_predict\n",
    "from   sklearn.model_selection  import validation_curve\n",
    "from   sklearn                  import tree\n",
    "import graphviz \n",
    "from   sklearn.model_selection  import learning_curve\n",
    "from   sklearn.linear_model     import LogisticRegression\n",
    "from   sklearn.ensemble         import RandomForestClassifier\n",
    "from numpy                      import loadtxt\n",
    "from   mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from   sklearn.linear_model     import LogisticRegression\n",
    "from   sklearn.mixture          import GaussianMixture\n",
    "from   sklearn.random_projection import GaussianRandomProjection as GRP\n",
    "import random \n",
    "from   keras.models             import Sequential\n",
    "from   keras.layers             import Dense\n",
    "import tensorflow               as tf\n",
    "from   keras.utils              import plot_model\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the dataset from the local drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##\n",
    "## Provide the directory where metadata file is stored and read the file from that library. Displaying the initial few rows.\n",
    "##\n",
    "cancer_path = 'D:/First Semester - MSBA - UTD/Kaggle Datasets for Practice/breast-cancer-wisconsin-data/breast-cancer-data.csv'\n",
    "cancer_df = pd.read_csv(cancer_path)\n",
    "cancer_df.drop(['id','Unnamed: 32'], axis=1, inplace=True)\n",
    "cancer_df.head() # Displays the first 5 rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Diagnosis has values 'M' or 'B'. So, changing the values to 1 and 0 respectively. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
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       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
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      "text/plain": [
       "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0          1        17.99         10.38          122.80     1001.0   \n",
       "1          1        20.57         17.77          132.90     1326.0   \n",
       "2          1        19.69         21.25          130.00     1203.0   \n",
       "3          1        11.42         20.38           77.58      386.1   \n",
       "4          1        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         22.54          16.67           152.20   \n",
       "\n",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##\n",
    "## Replacing Churn field's value with 1 for Yes and 0 for No.\n",
    "##\n",
    "cancer_df['diagnosis'] = cancer_df['diagnosis'].replace(to_replace=['M', 'B'], value=[1,0])\n",
    "cancer_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check if there are any NULLS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                  0\n",
       "radius_mean                0\n",
       "texture_mean               0\n",
       "perimeter_mean             0\n",
       "area_mean                  0\n",
       "smoothness_mean            0\n",
       "compactness_mean           0\n",
       "concavity_mean             0\n",
       "concave points_mean        0\n",
       "symmetry_mean              0\n",
       "fractal_dimension_mean     0\n",
       "radius_se                  0\n",
       "texture_se                 0\n",
       "perimeter_se               0\n",
       "area_se                    0\n",
       "smoothness_se              0\n",
       "compactness_se             0\n",
       "concavity_se               0\n",
       "concave points_se          0\n",
       "symmetry_se                0\n",
       "fractal_dimension_se       0\n",
       "radius_worst               0\n",
       "texture_worst              0\n",
       "perimeter_worst            0\n",
       "area_worst                 0\n",
       "smoothness_worst           0\n",
       "compactness_worst          0\n",
       "concavity_worst            0\n",
       "concave points_worst       0\n",
       "symmetry_worst             0\n",
       "fractal_dimension_worst    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##\n",
    "## Now let's check the statistical summary of the dataframe\n",
    "##\n",
    "cancer_df[cancer_df.isnull().any(axis=1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Heat-Map showing the correlation of variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##\n",
    "## Dropping un-wanted columns or rows.\n",
    "##\n",
    "corr=cancer_df.corr()\n",
    "plt.figure(figsize=(20,10))\n",
    "sb.heatmap(corr,xticklabels=corr.columns,yticklabels=corr.columns,linewidths=.1,cmap=\"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing the mean of each feature under each group of diagnosis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>diagnosis</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>radius_mean</td>\n",
       "      <td>12.146524</td>\n",
       "      <td>17.462830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_mean</td>\n",
       "      <td>17.914762</td>\n",
       "      <td>21.604906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_mean</td>\n",
       "      <td>78.075406</td>\n",
       "      <td>115.365377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_mean</td>\n",
       "      <td>462.790196</td>\n",
       "      <td>978.376415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_mean</td>\n",
       "      <td>0.092478</td>\n",
       "      <td>0.102898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_mean</td>\n",
       "      <td>0.080085</td>\n",
       "      <td>0.145188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_mean</td>\n",
       "      <td>0.046058</td>\n",
       "      <td>0.160775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_mean</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>0.087990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_mean</td>\n",
       "      <td>0.174186</td>\n",
       "      <td>0.192909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_mean</td>\n",
       "      <td>0.062867</td>\n",
       "      <td>0.062680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_se</td>\n",
       "      <td>0.284082</td>\n",
       "      <td>0.609083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_se</td>\n",
       "      <td>1.220380</td>\n",
       "      <td>1.210915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_se</td>\n",
       "      <td>2.000321</td>\n",
       "      <td>4.323929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_se</td>\n",
       "      <td>21.135148</td>\n",
       "      <td>72.672406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_se</td>\n",
       "      <td>0.007196</td>\n",
       "      <td>0.006780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_se</td>\n",
       "      <td>0.021438</td>\n",
       "      <td>0.032281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_se</td>\n",
       "      <td>0.025997</td>\n",
       "      <td>0.041824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_se</td>\n",
       "      <td>0.009858</td>\n",
       "      <td>0.015060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_se</td>\n",
       "      <td>0.020584</td>\n",
       "      <td>0.020472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_se</td>\n",
       "      <td>0.003636</td>\n",
       "      <td>0.004062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_worst</td>\n",
       "      <td>13.379801</td>\n",
       "      <td>21.134811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_worst</td>\n",
       "      <td>23.515070</td>\n",
       "      <td>29.318208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>87.005938</td>\n",
       "      <td>141.370330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_worst</td>\n",
       "      <td>558.899440</td>\n",
       "      <td>1422.286321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_worst</td>\n",
       "      <td>0.124959</td>\n",
       "      <td>0.144845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_worst</td>\n",
       "      <td>0.182673</td>\n",
       "      <td>0.374824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_worst</td>\n",
       "      <td>0.166238</td>\n",
       "      <td>0.450606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_worst</td>\n",
       "      <td>0.074444</td>\n",
       "      <td>0.182237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_worst</td>\n",
       "      <td>0.270246</td>\n",
       "      <td>0.323468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>0.079442</td>\n",
       "      <td>0.091530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "diagnosis                         0            1\n",
       "radius_mean               12.146524    17.462830\n",
       "texture_mean              17.914762    21.604906\n",
       "perimeter_mean            78.075406   115.365377\n",
       "area_mean                462.790196   978.376415\n",
       "smoothness_mean            0.092478     0.102898\n",
       "compactness_mean           0.080085     0.145188\n",
       "concavity_mean             0.046058     0.160775\n",
       "concave points_mean        0.025717     0.087990\n",
       "symmetry_mean              0.174186     0.192909\n",
       "fractal_dimension_mean     0.062867     0.062680\n",
       "radius_se                  0.284082     0.609083\n",
       "texture_se                 1.220380     1.210915\n",
       "perimeter_se               2.000321     4.323929\n",
       "area_se                   21.135148    72.672406\n",
       "smoothness_se              0.007196     0.006780\n",
       "compactness_se             0.021438     0.032281\n",
       "concavity_se               0.025997     0.041824\n",
       "concave points_se          0.009858     0.015060\n",
       "symmetry_se                0.020584     0.020472\n",
       "fractal_dimension_se       0.003636     0.004062\n",
       "radius_worst              13.379801    21.134811\n",
       "texture_worst             23.515070    29.318208\n",
       "perimeter_worst           87.005938   141.370330\n",
       "area_worst               558.899440  1422.286321\n",
       "smoothness_worst           0.124959     0.144845\n",
       "compactness_worst          0.182673     0.374824\n",
       "concavity_worst            0.166238     0.450606\n",
       "concave points_worst       0.074444     0.182237\n",
       "symmetry_worst             0.270246     0.323468\n",
       "fractal_dimension_worst    0.079442     0.091530"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_mean_by_class = cancer_df.groupby(['diagnosis']).mean().T\n",
    "cancer_mean_by_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_mean_by_class.rename(columns={0:'No', 1:'Yes'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Computing the percentage change in feature within the categories for feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>diagnosis</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "      <th>Per_Chg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>concavity_mean</td>\n",
       "      <td>0.046058</td>\n",
       "      <td>0.160775</td>\n",
       "      <td>249.072995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_se</td>\n",
       "      <td>21.135148</td>\n",
       "      <td>72.672406</td>\n",
       "      <td>243.846204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_mean</td>\n",
       "      <td>0.025717</td>\n",
       "      <td>0.087990</td>\n",
       "      <td>242.141814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_worst</td>\n",
       "      <td>0.166238</td>\n",
       "      <td>0.450606</td>\n",
       "      <td>171.060959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_worst</td>\n",
       "      <td>558.899440</td>\n",
       "      <td>1422.286321</td>\n",
       "      <td>154.479826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_worst</td>\n",
       "      <td>0.074444</td>\n",
       "      <td>0.182237</td>\n",
       "      <td>144.796717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_se</td>\n",
       "      <td>2.000321</td>\n",
       "      <td>4.323929</td>\n",
       "      <td>116.161737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_se</td>\n",
       "      <td>0.284082</td>\n",
       "      <td>0.609083</td>\n",
       "      <td>114.403514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_mean</td>\n",
       "      <td>462.790196</td>\n",
       "      <td>978.376415</td>\n",
       "      <td>111.408198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_worst</td>\n",
       "      <td>0.182673</td>\n",
       "      <td>0.374824</td>\n",
       "      <td>105.189070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_mean</td>\n",
       "      <td>0.080085</td>\n",
       "      <td>0.145188</td>\n",
       "      <td>81.292962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>87.005938</td>\n",
       "      <td>141.370330</td>\n",
       "      <td>62.483542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_se</td>\n",
       "      <td>0.025997</td>\n",
       "      <td>0.041824</td>\n",
       "      <td>60.881774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_worst</td>\n",
       "      <td>13.379801</td>\n",
       "      <td>21.134811</td>\n",
       "      <td>57.960579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_se</td>\n",
       "      <td>0.009858</td>\n",
       "      <td>0.015060</td>\n",
       "      <td>52.779492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_se</td>\n",
       "      <td>0.021438</td>\n",
       "      <td>0.032281</td>\n",
       "      <td>50.577451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_mean</td>\n",
       "      <td>78.075406</td>\n",
       "      <td>115.365377</td>\n",
       "      <td>47.761482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_mean</td>\n",
       "      <td>12.146524</td>\n",
       "      <td>17.462830</td>\n",
       "      <td>43.768130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_worst</td>\n",
       "      <td>23.515070</td>\n",
       "      <td>29.318208</td>\n",
       "      <td>24.678377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_mean</td>\n",
       "      <td>17.914762</td>\n",
       "      <td>21.604906</td>\n",
       "      <td>20.598341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_worst</td>\n",
       "      <td>0.270246</td>\n",
       "      <td>0.323468</td>\n",
       "      <td>19.693908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_worst</td>\n",
       "      <td>0.124959</td>\n",
       "      <td>0.144845</td>\n",
       "      <td>15.913749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>0.079442</td>\n",
       "      <td>0.091530</td>\n",
       "      <td>15.215968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_se</td>\n",
       "      <td>0.003636</td>\n",
       "      <td>0.004062</td>\n",
       "      <td>11.725753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_mean</td>\n",
       "      <td>0.092478</td>\n",
       "      <td>0.102898</td>\n",
       "      <td>11.268500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_mean</td>\n",
       "      <td>0.174186</td>\n",
       "      <td>0.192909</td>\n",
       "      <td>10.748837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_se</td>\n",
       "      <td>0.007196</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>5.778395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_se</td>\n",
       "      <td>1.220380</td>\n",
       "      <td>1.210915</td>\n",
       "      <td>0.775618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_se</td>\n",
       "      <td>0.020584</td>\n",
       "      <td>0.020472</td>\n",
       "      <td>0.541230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_mean</td>\n",
       "      <td>0.062867</td>\n",
       "      <td>0.062680</td>\n",
       "      <td>0.297930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "diagnosis                        No          Yes     Per_Chg\n",
       "concavity_mean             0.046058     0.160775  249.072995\n",
       "area_se                   21.135148    72.672406  243.846204\n",
       "concave points_mean        0.025717     0.087990  242.141814\n",
       "concavity_worst            0.166238     0.450606  171.060959\n",
       "area_worst               558.899440  1422.286321  154.479826\n",
       "concave points_worst       0.074444     0.182237  144.796717\n",
       "perimeter_se               2.000321     4.323929  116.161737\n",
       "radius_se                  0.284082     0.609083  114.403514\n",
       "area_mean                462.790196   978.376415  111.408198\n",
       "compactness_worst          0.182673     0.374824  105.189070\n",
       "compactness_mean           0.080085     0.145188   81.292962\n",
       "perimeter_worst           87.005938   141.370330   62.483542\n",
       "concavity_se               0.025997     0.041824   60.881774\n",
       "radius_worst              13.379801    21.134811   57.960579\n",
       "concave points_se          0.009858     0.015060   52.779492\n",
       "compactness_se             0.021438     0.032281   50.577451\n",
       "perimeter_mean            78.075406   115.365377   47.761482\n",
       "radius_mean               12.146524    17.462830   43.768130\n",
       "texture_worst             23.515070    29.318208   24.678377\n",
       "texture_mean              17.914762    21.604906   20.598341\n",
       "symmetry_worst             0.270246     0.323468   19.693908\n",
       "smoothness_worst           0.124959     0.144845   15.913749\n",
       "fractal_dimension_worst    0.079442     0.091530   15.215968\n",
       "fractal_dimension_se       0.003636     0.004062   11.725753\n",
       "smoothness_mean            0.092478     0.102898   11.268500\n",
       "symmetry_mean              0.174186     0.192909   10.748837\n",
       "smoothness_se              0.007196     0.006780    5.778395\n",
       "texture_se                 1.220380     1.210915    0.775618\n",
       "symmetry_se                0.020584     0.020472    0.541230\n",
       "fractal_dimension_mean     0.062867     0.062680    0.297930"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_mean_by_class['Per_Chg'] = abs((cancer_mean_by_class['Yes'] - cancer_mean_by_class['No'])/ cancer_mean_by_class['No']*100)\n",
    "cancer_mean_by_class.sort_values(\"Per_Chg\", axis = 0, ascending = False, \n",
    "                 inplace = True, na_position ='first')\n",
    "cancer_mean_by_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's segregate X and Y datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = cancer_df.iloc[:,1:31]\n",
    "y_train = cancer_df['diagnosis']\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Backward ellimination process for feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('radius_mean',\n",
       " 'texture_mean',\n",
       " 'area_mean',\n",
       " 'compactness_mean',\n",
       " 'concavity_mean',\n",
       " 'texture_se',\n",
       " 'perimeter_worst',\n",
       " 'concavity_worst')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_back = SFS(LogisticRegression(), k_features=8, forward=False, floating=False, scoring = 'accuracy', cv=0)\n",
    "cancer_back.fit(x_train, y_train)\n",
    "cancer_back.k_feature_names_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Partitioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>concavity_mean</th>\n",
       "      <th>area_se</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>perimeter_se</th>\n",
       "      <th>radius_se</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>compactness_se</th>\n",
       "      <th>concavity_se</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>fractal_dimension_se</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>153.40</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>8.589</td>\n",
       "      <td>1.0950</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>17.33</td>\n",
       "      <td>10.38</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>25.38</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.04904</td>\n",
       "      <td>0.05373</td>\n",
       "      <td>184.60</td>\n",
       "      <td>0.006193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>74.08</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>3.398</td>\n",
       "      <td>0.5435</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>23.41</td>\n",
       "      <td>17.77</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>24.99</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.01308</td>\n",
       "      <td>0.01860</td>\n",
       "      <td>158.80</td>\n",
       "      <td>0.003532</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>94.03</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>4.585</td>\n",
       "      <td>0.7456</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>25.53</td>\n",
       "      <td>21.25</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>23.57</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.04006</td>\n",
       "      <td>0.03832</td>\n",
       "      <td>152.50</td>\n",
       "      <td>0.004571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>27.23</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>3.445</td>\n",
       "      <td>0.4956</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>26.50</td>\n",
       "      <td>20.38</td>\n",
       "      <td>567.7</td>\n",
       "      <td>14.91</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.07458</td>\n",
       "      <td>0.05661</td>\n",
       "      <td>98.87</td>\n",
       "      <td>0.009208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>94.44</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>5.438</td>\n",
       "      <td>0.7572</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>16.67</td>\n",
       "      <td>14.34</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>22.54</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.02461</td>\n",
       "      <td>0.05688</td>\n",
       "      <td>152.20</td>\n",
       "      <td>0.005115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   concavity_mean  area_se  concave points_mean  concavity_worst  \\\n",
       "0          0.3001   153.40              0.14710           0.7119   \n",
       "1          0.0869    74.08              0.07017           0.2416   \n",
       "2          0.1974    94.03              0.12790           0.4504   \n",
       "3          0.2414    27.23              0.10520           0.6869   \n",
       "4          0.1980    94.44              0.10430           0.4000   \n",
       "\n",
       "   perimeter_se  radius_se  compactness_worst  texture_worst  texture_mean  \\\n",
       "0         8.589     1.0950             0.6656          17.33         10.38   \n",
       "1         3.398     0.5435             0.1866          23.41         17.77   \n",
       "2         4.585     0.7456             0.4245          25.53         21.25   \n",
       "3         3.445     0.4956             0.8663          26.50         20.38   \n",
       "4         5.438     0.7572             0.2050          16.67         14.34   \n",
       "\n",
       "   area_worst  radius_worst  fractal_dimension_worst  smoothness_worst  \\\n",
       "0      2019.0         25.38                  0.11890            0.1622   \n",
       "1      1956.0         24.99                  0.08902            0.1238   \n",
       "2      1709.0         23.57                  0.08758            0.1444   \n",
       "3       567.7         14.91                  0.17300            0.2098   \n",
       "4      1575.0         22.54                  0.07678            0.1374   \n",
       "\n",
       "   symmetry_worst  compactness_se  concavity_se  perimeter_worst  \\\n",
       "0          0.4601         0.04904       0.05373           184.60   \n",
       "1          0.2750         0.01308       0.01860           158.80   \n",
       "2          0.3613         0.04006       0.03832           152.50   \n",
       "3          0.6638         0.07458       0.05661            98.87   \n",
       "4          0.2364         0.02461       0.05688           152.20   \n",
       "\n",
       "   fractal_dimension_se  \n",
       "0              0.006193  \n",
       "1              0.003532  \n",
       "2              0.004571  \n",
       "3              0.009208  \n",
       "4              0.005115  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cancer_df = cancer_df[['concavity_mean','area_se', 'concave points_mean', 'concavity_worst', 'perimeter_se', \n",
    "                         'radius_se', 'compactness_worst', 'texture_worst','texture_mean','area_worst','radius_worst',\n",
    "                        'fractal_dimension_worst', 'smoothness_worst', 'symmetry_worst','compactness_se',\n",
    "                        'concavity_se','perimeter_worst','fractal_dimension_se']]\n",
    "y_cancer_df = cancer_df[['diagnosis']]\n",
    "x_cancer_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>area_se</th>\n",
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       "      <th>concavity_worst</th>\n",
       "      <th>perimeter_se</th>\n",
       "      <th>radius_se</th>\n",
       "      <th>compactness_worst</th>\n",
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       "      <th>texture_mean</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>compactness_se</th>\n",
       "      <th>concavity_se</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>fractal_dimension_se</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>concavity_mean</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.884103</td>\n",
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       "      <td>0.631925</td>\n",
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       "      <td>0.302418</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.449301</td>\n",
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       "    <tr>\n",
       "      <td>area_se</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.937655</td>\n",
       "      <td>0.951830</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.196497</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.757373</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.284840</td>\n",
       "      <td>0.270895</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.127071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concave points_mean</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.752399</td>\n",
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       "      <td>0.698050</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.257584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_worst</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.380585</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.368366</td>\n",
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       "      <td>0.573975</td>\n",
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       "      <td>0.532520</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.379975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_se</td>\n",
       "      <td>0.660391</td>\n",
       "      <td>0.937655</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>0.418899</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.972794</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.200371</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.697201</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.416322</td>\n",
       "      <td>0.362482</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.244143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_se</td>\n",
       "      <td>0.631925</td>\n",
       "      <td>0.951830</td>\n",
       "      <td>0.698050</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>0.972794</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.715065</td>\n",
       "      <td>0.049559</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.356065</td>\n",
       "      <td>0.332358</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.227754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_worst</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.390159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_worst</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.196497</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.200371</td>\n",
       "      <td>0.194799</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.100241</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>-0.003195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>texture_mean</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.054458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>area_worst</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>-0.022736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radius_worst</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.757373</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.697201</td>\n",
       "      <td>0.715065</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.359921</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.204607</td>\n",
       "      <td>0.186904</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>-0.037488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>0.049559</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>0.590973</td>\n",
       "      <td>0.439329</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.591328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>smoothness_worst</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.170568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>symmetry_worst</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.111094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>compactness_se</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.284840</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.416322</td>\n",
       "      <td>0.356065</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.143003</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.204607</td>\n",
       "      <td>0.590973</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.801268</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.803269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>concavity_se</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.270895</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.362482</td>\n",
       "      <td>0.332358</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.100241</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.186904</td>\n",
       "      <td>0.439329</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.801268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>0.727372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>fractal_dimension_se</td>\n",
       "      <td>0.449301</td>\n",
       "      <td>0.127071</td>\n",
       "      <td>0.257584</td>\n",
       "      <td>0.379975</td>\n",
       "      <td>0.244143</td>\n",
       "      <td>0.227754</td>\n",
       "      <td>0.390159</td>\n",
       "      <td>-0.003195</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.022736</td>\n",
       "      <td>-0.037488</td>\n",
       "      <td>0.591328</td>\n",
       "      <td>0.170568</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.803269</td>\n",
       "      <td>0.727372</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         concavity_mean   area_se  concave points_mean  \\\n",
       "concavity_mean                 1.000000  0.617427             0.921391   \n",
       "area_se                        0.617427  1.000000             0.690299   \n",
       "concave points_mean            0.921391  0.690299             1.000000   \n",
       "concavity_worst                0.884103  0.385100             0.752399   \n",
       "perimeter_se                   0.660391  0.937655             0.710650   \n",
       "radius_se                      0.631925  0.951830             0.698050   \n",
       "compactness_worst              0.754968  0.283257             0.667454   \n",
       "texture_worst                  0.299879  0.196497             0.292752   \n",
       "texture_mean                   0.302418  0.259845             0.293464   \n",
       "area_worst                     0.675987  0.811408             0.809630   \n",
       "radius_worst                   0.688236  0.757373             0.830318   \n",
       "fractal_dimension_worst        0.514930  0.017539             0.368661   \n",
       "smoothness_worst               0.448822  0.125389             0.452753   \n",
       "symmetry_worst                 0.409464  0.074126             0.375744   \n",
       "compactness_se                 0.670279  0.284840             0.490424   \n",
       "concavity_se                   0.691270  0.270895             0.439167   \n",
       "perimeter_worst                0.729565  0.761213             0.855923   \n",
       "fractal_dimension_se           0.449301  0.127071             0.257584   \n",
       "\n",
       "                         concavity_worst  perimeter_se  radius_se  \\\n",
       "concavity_mean                  0.884103      0.660391   0.631925   \n",
       "area_se                         0.385100      0.937655   0.951830   \n",
       "concave points_mean             0.752399      0.710650   0.698050   \n",
       "concavity_worst                 1.000000      0.418899   0.380585   \n",
       "perimeter_se                    0.418899      1.000000   0.972794   \n",
       "radius_se                       0.380585      0.972794   1.000000   \n",
       "compactness_worst               0.892261      0.341919   0.287103   \n",
       "texture_worst                   0.368366      0.200371   0.194799   \n",
       "texture_mean                    0.301025      0.281673   0.275869   \n",
       "area_worst                      0.543331      0.730713   0.751548   \n",
       "radius_worst                    0.573975      0.697201   0.715065   \n",
       "fractal_dimension_worst         0.686511      0.085433   0.049559   \n",
       "smoothness_worst                0.518523      0.130054   0.141919   \n",
       "symmetry_worst                  0.532520      0.109930   0.094543   \n",
       "compactness_se                  0.639147      0.416322   0.356065   \n",
       "concavity_se                    0.662564      0.362482   0.332358   \n",
       "perimeter_worst                 0.618344      0.721031   0.719684   \n",
       "fractal_dimension_se            0.379975      0.244143   0.227754   \n",
       "\n",
       "                         compactness_worst  texture_worst  texture_mean  \\\n",
       "concavity_mean                    0.754968       0.299879      0.302418   \n",
       "area_se                           0.283257       0.196497      0.259845   \n",
       "concave points_mean               0.667454       0.292752      0.293464   \n",
       "concavity_worst                   0.892261       0.368366      0.301025   \n",
       "perimeter_se                      0.341919       0.200371      0.281673   \n",
       "radius_se                         0.287103       0.194799      0.275869   \n",
       "compactness_worst                 1.000000       0.360832      0.277830   \n",
       "texture_worst                     0.360832       1.000000      0.912045   \n",
       "texture_mean                      0.277830       0.912045      1.000000   \n",
       "area_worst                        0.438296       0.345842      0.343546   \n",
       "radius_worst                      0.475820       0.359921      0.352573   \n",
       "fractal_dimension_worst           0.810455       0.219122      0.119205   \n",
       "smoothness_worst                  0.568187       0.225429      0.077503   \n",
       "symmetry_worst                    0.614441       0.233027      0.105008   \n",
       "compactness_se                    0.678780       0.143003      0.191975   \n",
       "concavity_se                      0.484858       0.100241      0.143293   \n",
       "perimeter_worst                   0.529408       0.365098      0.358040   \n",
       "fractal_dimension_se              0.390159      -0.003195      0.054458   \n",
       "\n",
       "                         area_worst  radius_worst  fractal_dimension_worst  \\\n",
       "concavity_mean             0.675987      0.688236                 0.514930   \n",
       "area_se                    0.811408      0.757373                 0.017539   \n",
       "concave points_mean        0.809630      0.830318                 0.368661   \n",
       "concavity_worst            0.543331      0.573975                 0.686511   \n",
       "perimeter_se               0.730713      0.697201                 0.085433   \n",
       "radius_se                  0.751548      0.715065                 0.049559   \n",
       "compactness_worst          0.438296      0.475820                 0.810455   \n",
       "texture_worst              0.345842      0.359921                 0.219122   \n",
       "texture_mean               0.343546      0.352573                 0.119205   \n",
       "area_worst                 1.000000      0.984015                 0.079647   \n",
       "radius_worst               0.984015      1.000000                 0.093492   \n",
       "fractal_dimension_worst    0.079647      0.093492                 1.000000   \n",
       "smoothness_worst           0.209145      0.216574                 0.617624   \n",
       "symmetry_worst             0.209146      0.243529                 0.537848   \n",
       "compactness_se             0.199371      0.204607                 0.590973   \n",
       "concavity_se               0.188353      0.186904                 0.439329   \n",
       "perimeter_worst            0.977578      0.993708                 0.138957   \n",
       "fractal_dimension_se      -0.022736     -0.037488                 0.591328   \n",
       "\n",
       "                         smoothness_worst  symmetry_worst  compactness_se  \\\n",
       "concavity_mean                   0.448822        0.409464        0.670279   \n",
       "area_se                          0.125389        0.074126        0.284840   \n",
       "concave points_mean              0.452753        0.375744        0.490424   \n",
       "concavity_worst                  0.518523        0.532520        0.639147   \n",
       "perimeter_se                     0.130054        0.109930        0.416322   \n",
       "radius_se                        0.141919        0.094543        0.356065   \n",
       "compactness_worst                0.568187        0.614441        0.678780   \n",
       "texture_worst                    0.225429        0.233027        0.143003   \n",
       "texture_mean                     0.077503        0.105008        0.191975   \n",
       "area_worst                       0.209145        0.209146        0.199371   \n",
       "radius_worst                     0.216574        0.243529        0.204607   \n",
       "fractal_dimension_worst          0.617624        0.537848        0.590973   \n",
       "smoothness_worst                 1.000000        0.493838        0.227394   \n",
       "symmetry_worst                   0.493838        1.000000        0.277878   \n",
       "compactness_se                   0.227394        0.277878        1.000000   \n",
       "concavity_se                     0.168481        0.197788        0.801268   \n",
       "perimeter_worst                  0.236775        0.269493        0.260516   \n",
       "fractal_dimension_se             0.170568        0.111094        0.803269   \n",
       "\n",
       "                         concavity_se  perimeter_worst  fractal_dimension_se  \n",
       "concavity_mean               0.691270         0.729565              0.449301  \n",
       "area_se                      0.270895         0.761213              0.127071  \n",
       "concave points_mean          0.439167         0.855923              0.257584  \n",
       "concavity_worst              0.662564         0.618344              0.379975  \n",
       "perimeter_se                 0.362482         0.721031              0.244143  \n",
       "radius_se                    0.332358         0.719684              0.227754  \n",
       "compactness_worst            0.484858         0.529408              0.390159  \n",
       "texture_worst                0.100241         0.365098             -0.003195  \n",
       "texture_mean                 0.143293         0.358040              0.054458  \n",
       "area_worst                   0.188353         0.977578             -0.022736  \n",
       "radius_worst                 0.186904         0.993708             -0.037488  \n",
       "fractal_dimension_worst      0.439329         0.138957              0.591328  \n",
       "smoothness_worst             0.168481         0.236775              0.170568  \n",
       "symmetry_worst               0.197788         0.269493              0.111094  \n",
       "compactness_se               0.801268         0.260516              0.803269  \n",
       "concavity_se                 1.000000         0.226680              0.727372  \n",
       "perimeter_worst              0.226680         1.000000             -0.001000  \n",
       "fractal_dimension_se         0.727372        -0.001000              1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_cancer_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final features selected for computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_cancer_df = cancer_df[['concavity_mean','area_se', 'concave points_se', 'concavity_worst', 'perimeter_worst',\n",
    "                          'texture_worst','fractal_dimension_worst','smoothness_worst','compactness_se','radius_mean']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = x_cancer_df.columns\n",
    "scale = preprocessing.StandardScaler()\n",
    "\n",
    "x_cancer_norm_df = scale.fit_transform(x_cancer_df)\n",
    "x_cancer_norm_df = pd.DataFrame(x_cancer_norm_df, columns=names)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_cancer, x_test_cancer, y_train_cancer, y_test_cancer = train_test_split(x_cancer_norm_df, y_cancer_df,test_size=0.3, random_state=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_c = np.asarray(x_train_cancer)\n",
    "y_train_c = np.asarray(y_train_cancer)\n",
    "\n",
    "x_test_c  = np.asarray(x_test_cancer)\n",
    "y_test_c  = np.asarray(y_test_cancer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modelling with various algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:724: 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"
     ]
    }
   ],
   "source": [
    "Logit_model = LogisticRegression()\n",
    "Logit_model_fit = Logit_model.fit(x_train_c, y_train_c)\n",
    "logit_predictions = Logit_model.predict(x_test_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Model Accuracy:  0.9766081871345029\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Model Accuracy: \", metrics.accuracy_score(y_test_c, logit_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report Logistic regression - Cancer data\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       106\n",
      "           1       1.00      0.94      0.97        65\n",
      "\n",
      "    accuracy                           0.98       171\n",
      "   macro avg       0.98      0.97      0.97       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Report Logistic regression - Cancer data')\n",
    "print(classification_report(y_test_c, logit_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(y_test_c, logit_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:724: 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",
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "svm_model_p_c = svm.SVC(kernel='rbf')\n",
    "svm_model_p_c.fit = svm_model_p_c.fit(x_train_c, y_train_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_svm_pol_c = svm_model_p_c.predict(x_test_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Accuracy Polynomial:  0.9883040935672515\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM Accuracy Polynomial: \", metrics.accuracy_score(y_test_c, yhat_svm_pol_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report Logistic regression - Cancer data\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       106\n",
      "           1       0.98      0.98      0.98        65\n",
      "\n",
      "    accuracy                           0.99       171\n",
      "   macro avg       0.99      0.99      0.99       171\n",
      "weighted avg       0.99      0.99      0.99       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Report Logistic regression - Cancer data')\n",
    "print(classification_report(y_test_c, yhat_svm_pol_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(y_test_c, yhat_svm_pol_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy:  0.9824561403508771\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarth\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: 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",
      "  \n"
     ]
    }
   ],
   "source": [
    "model_rf = RandomForestClassifier(n_estimators=100, bootstrap = True, max_features = 5)\n",
    "model_rf.fit(x_train_c, y_train_c)\n",
    "model_rf_predict = model_rf.predict(x_test_c)\n",
    "print(\"Random Forest Accuracy: \", metrics.accuracy_score(y_test_c, model_rf_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report Churn Random Forest\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.99      0.99       106\n",
      "           1       0.98      0.97      0.98        65\n",
      "\n",
      "    accuracy                           0.98       171\n",
      "   macro avg       0.98      0.98      0.98       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Report Churn Random Forest')\n",
    "print(classification_report(y_test_c, model_rf_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(y_test_c, model_rf_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
      "Number of devices: 1\n"
     ]
    }
   ],
   "source": [
    "strategy = tf.distribute.MirroredStrategy()\n",
    "print ('Number of devices: {}'.format(strategy.num_replicas_in_sync))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the distribution scope context manager\n",
    "with strategy.scope():\n",
    "    # Define a model to fit the above data\n",
    "    model_3 = tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(units=11, input_dim=10),\n",
    "        tf.keras.layers.Dense(units=1, activation='sigmoid')\n",
    "    ])\n",
    "    \n",
    "    # Compile the model\n",
    "    model_3.compile(loss='binary_crossentropy', optimizer='sgd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 298 samples, validate on 100 samples\n",
      "Epoch 1/50\n",
      "298/298 [==============================] - 2s 5ms/sample - loss: 0.5375 - val_loss: 0.3224\n",
      "Epoch 2/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.2765 - val_loss: 0.2395\n",
      "Epoch 3/50\n",
      "298/298 [==============================] - 0s 885us/sample - loss: 0.2045 - val_loss: 0.2065\n",
      "Epoch 4/50\n",
      "298/298 [==============================] - 0s 952us/sample - loss: 0.1669 - val_loss: 0.1888\n",
      "Epoch 5/50\n",
      "298/298 [==============================] - 0s 870us/sample - loss: 0.1438 - val_loss: 0.1785\n",
      "Epoch 6/50\n",
      "298/298 [==============================] - 0s 902us/sample - loss: 0.1276 - val_loss: 0.1732\n",
      "Epoch 7/50\n",
      "298/298 [==============================] - 0s 979us/sample - loss: 0.1157 - val_loss: 0.1686\n",
      "Epoch 8/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.1064 - val_loss: 0.1657\n",
      "Epoch 9/50\n",
      "298/298 [==============================] - 0s 952us/sample - loss: 0.0991 - val_loss: 0.1637\n",
      "Epoch 10/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0931 - val_loss: 0.1622\n",
      "Epoch 11/50\n",
      "298/298 [==============================] - 0s 961us/sample - loss: 0.0880 - val_loss: 0.1617\n",
      "Epoch 12/50\n",
      "298/298 [==============================] - 0s 898us/sample - loss: 0.0838 - val_loss: 0.1614\n",
      "Epoch 13/50\n",
      "298/298 [==============================] - 0s 871us/sample - loss: 0.0801 - val_loss: 0.1613\n",
      "Epoch 14/50\n",
      "298/298 [==============================] - 0s 858us/sample - loss: 0.0768 - val_loss: 0.1617\n",
      "Epoch 15/50\n",
      "298/298 [==============================] - 0s 845us/sample - loss: 0.0740 - val_loss: 0.1620\n",
      "Epoch 16/50\n",
      "298/298 [==============================] - 0s 885us/sample - loss: 0.0715 - val_loss: 0.1625\n",
      "Epoch 17/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0692 - val_loss: 0.1630\n",
      "Epoch 18/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0672 - val_loss: 0.1636\n",
      "Epoch 19/50\n",
      "298/298 [==============================] - 0s 912us/sample - loss: 0.0654 - val_loss: 0.1644\n",
      "Epoch 20/50\n",
      "298/298 [==============================] - 0s 938us/sample - loss: 0.0638 - val_loss: 0.1651\n",
      "Epoch 21/50\n",
      "298/298 [==============================] - 0s 965us/sample - loss: 0.0623 - val_loss: 0.1660\n",
      "Epoch 22/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0609 - val_loss: 0.1669\n",
      "Epoch 23/50\n",
      "298/298 [==============================] - 0s 992us/sample - loss: 0.0596 - val_loss: 0.1677\n",
      "Epoch 24/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0585 - val_loss: 0.1687\n",
      "Epoch 25/50\n",
      "298/298 [==============================] - 0s 965us/sample - loss: 0.0574 - val_loss: 0.1696\n",
      "Epoch 26/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0564 - val_loss: 0.1706\n",
      "Epoch 27/50\n",
      "298/298 [==============================] - 0s 871us/sample - loss: 0.0555 - val_loss: 0.1716\n",
      "Epoch 28/50\n",
      "298/298 [==============================] - 0s 912us/sample - loss: 0.0547 - val_loss: 0.1725\n",
      "Epoch 29/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0539 - val_loss: 0.1735\n",
      "Epoch 30/50\n",
      "298/298 [==============================] - 0s 979us/sample - loss: 0.0531 - val_loss: 0.1744\n",
      "Epoch 31/50\n",
      "298/298 [==============================] - 0s 952us/sample - loss: 0.0524 - val_loss: 0.1753\n",
      "Epoch 32/50\n",
      "298/298 [==============================] - 0s 871us/sample - loss: 0.0518 - val_loss: 0.1761\n",
      "Epoch 33/50\n",
      "298/298 [==============================] - 0s 885us/sample - loss: 0.0511 - val_loss: 0.1771\n",
      "Epoch 34/50\n",
      "298/298 [==============================] - 0s 889us/sample - loss: 0.0505 - val_loss: 0.1780\n",
      "Epoch 35/50\n",
      "298/298 [==============================] - 0s 979us/sample - loss: 0.0500 - val_loss: 0.1789\n",
      "Epoch 36/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0495 - val_loss: 0.1798\n",
      "Epoch 37/50\n",
      "298/298 [==============================] - 0s 925us/sample - loss: 0.0490 - val_loss: 0.1807\n",
      "Epoch 38/50\n",
      "298/298 [==============================] - 0s 965us/sample - loss: 0.0485 - val_loss: 0.1815\n",
      "Epoch 39/50\n",
      "298/298 [==============================] - 0s 983us/sample - loss: 0.0481 - val_loss: 0.1824\n",
      "Epoch 40/50\n",
      "298/298 [==============================] - 0s 911us/sample - loss: 0.0477 - val_loss: 0.1833\n",
      "Epoch 41/50\n",
      "298/298 [==============================] - 0s 925us/sample - loss: 0.0473 - val_loss: 0.1841\n",
      "Epoch 42/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0469 - val_loss: 0.1849\n",
      "Epoch 43/50\n",
      "298/298 [==============================] - 0s 1ms/sample - loss: 0.0466 - val_loss: 0.1858\n",
      "Epoch 44/50\n",
      "298/298 [==============================] - 0s 902us/sample - loss: 0.0461 - val_loss: 0.1865\n",
      "Epoch 45/50\n",
      "298/298 [==============================] - 0s 845us/sample - loss: 0.0460 - val_loss: 0.1873\n",
      "Epoch 46/50\n",
      "298/298 [==============================] - 0s 898us/sample - loss: 0.0455 - val_loss: 0.1879\n",
      "Epoch 47/50\n",
      "298/298 [==============================] - 0s 818us/sample - loss: 0.0453 - val_loss: 0.1887\n",
      "Epoch 48/50\n",
      "298/298 [==============================] - 0s 911us/sample - loss: 0.0450 - val_loss: 0.1895\n",
      "Epoch 49/50\n",
      "298/298 [==============================] - 0s 898us/sample - loss: 0.0447 - val_loss: 0.1905\n",
      "Epoch 50/50\n",
      "298/298 [==============================] - 0s 871us/sample - loss: 0.0445 - val_loss: 0.1912\n"
     ]
    }
   ],
   "source": [
    "model2h_fit = model_3.fit(x_train_c, y_train_c, validation_split=0.25, epochs=50, batch_size=5, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model2h_fit.history['loss'])\n",
    "plt.plot(model2h_fit.history['val_loss'])\n",
    "plt.title('NN Model - no hidden tanh - Learning curves')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_predict = model_3.predict_classes(x_test_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Accuracy:  0.9766081871345029\n"
     ]
    }
   ],
   "source": [
    "print(\"Neural Network Accuracy: \", metrics.accuracy_score(y_test_c, nn_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(y_test_c, nn_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report  NN - no Hidden Layer\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98       106\n",
      "           1       0.98      0.95      0.97        65\n",
      "\n",
      "    accuracy                           0.98       171\n",
      "   macro avg       0.98      0.97      0.98       171\n",
      "weighted avg       0.98      0.98      0.98       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Report  NN - no Hidden Layer')\n",
    "print(classification_report(y_test_c, nn_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion Matrix function defination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cm(y_true, y_pred, figsize=(7,5)):\n",
    "    cm = confusion_matrix(y_true, y_pred, labels=np.unique(y_true))\n",
    "    cm_sum = np.sum(cm, axis=1, keepdims=True)\n",
    "    cm_perc = cm / cm_sum.astype(float) * 100\n",
    "    annot = np.empty_like(cm).astype(str)\n",
    "    nrows, ncols = cm.shape\n",
    "    for i in range(nrows):\n",
    "        for j in range(ncols):\n",
    "            c = cm[i, j]\n",
    "            p = cm_perc[i, j]\n",
    "            if i == j:\n",
    "                s = cm_sum[i]\n",
    "                annot[i, j] = '%.1f%%\\n%d/%d' % (p, c, s)\n",
    "            elif c == 0:\n",
    "                annot[i, j] = ''\n",
    "            else:\n",
    "                annot[i, j] = '%.1f%%\\n%d' % (p, c)\n",
    "    cm = pd.DataFrame(cm, index=np.unique(y_true), columns=np.unique(y_true))\n",
    "    cm.index.name = 'Actual'\n",
    "    cm.columns.name = 'Predicted'\n",
    "    fig, ax = plt.subplots(figsize=figsize)\n",
    "    sb.heatmap(cm, cmap='OrRd', annot=annot, fmt='', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}