Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer=load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(cancer.data,columns=cancer.feature_names)"
   ]
  },
  {
   "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></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</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",
       "      <th>1</th>\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>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</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",
       "      <th>2</th>\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>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</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",
       "      <th>3</th>\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>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</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",
       "      <th>4</th>\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>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.12780</td>\n",
       "      <td>0.17000</td>\n",
       "      <td>0.15780</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>0.07613</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.25</td>\n",
       "      <td>19.98</td>\n",
       "      <td>119.60</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.09463</td>\n",
       "      <td>0.10900</td>\n",
       "      <td>0.11270</td>\n",
       "      <td>0.07400</td>\n",
       "      <td>0.1794</td>\n",
       "      <td>0.05742</td>\n",
       "      <td>...</td>\n",
       "      <td>22.88</td>\n",
       "      <td>27.66</td>\n",
       "      <td>153.20</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>0.1442</td>\n",
       "      <td>0.2576</td>\n",
       "      <td>0.3784</td>\n",
       "      <td>0.1932</td>\n",
       "      <td>0.3063</td>\n",
       "      <td>0.08368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13.71</td>\n",
       "      <td>20.83</td>\n",
       "      <td>90.20</td>\n",
       "      <td>577.9</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.16450</td>\n",
       "      <td>0.09366</td>\n",
       "      <td>0.05985</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.07451</td>\n",
       "      <td>...</td>\n",
       "      <td>17.06</td>\n",
       "      <td>28.14</td>\n",
       "      <td>110.60</td>\n",
       "      <td>897.0</td>\n",
       "      <td>0.1654</td>\n",
       "      <td>0.3682</td>\n",
       "      <td>0.2678</td>\n",
       "      <td>0.1556</td>\n",
       "      <td>0.3196</td>\n",
       "      <td>0.11510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13.00</td>\n",
       "      <td>21.82</td>\n",
       "      <td>87.50</td>\n",
       "      <td>519.8</td>\n",
       "      <td>0.12730</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.18590</td>\n",
       "      <td>0.09353</td>\n",
       "      <td>0.2350</td>\n",
       "      <td>0.07389</td>\n",
       "      <td>...</td>\n",
       "      <td>15.49</td>\n",
       "      <td>30.73</td>\n",
       "      <td>106.20</td>\n",
       "      <td>739.3</td>\n",
       "      <td>0.1703</td>\n",
       "      <td>0.5401</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.4378</td>\n",
       "      <td>0.10720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.46</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.08543</td>\n",
       "      <td>0.2030</td>\n",
       "      <td>0.08243</td>\n",
       "      <td>...</td>\n",
       "      <td>15.09</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0580</td>\n",
       "      <td>1.1050</td>\n",
       "      <td>0.2210</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "5        12.45         15.70           82.57      477.1          0.12780   \n",
       "6        18.25         19.98          119.60     1040.0          0.09463   \n",
       "7        13.71         20.83           90.20      577.9          0.11890   \n",
       "8        13.00         21.82           87.50      519.8          0.12730   \n",
       "9        12.46         24.04           83.97      475.9          0.11860   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760         0.30010              0.14710         0.2419   \n",
       "1           0.07864         0.08690              0.07017         0.1812   \n",
       "2           0.15990         0.19740              0.12790         0.2069   \n",
       "3           0.28390         0.24140              0.10520         0.2597   \n",
       "4           0.13280         0.19800              0.10430         0.1809   \n",
       "5           0.17000         0.15780              0.08089         0.2087   \n",
       "6           0.10900         0.11270              0.07400         0.1794   \n",
       "7           0.16450         0.09366              0.05985         0.2196   \n",
       "8           0.19320         0.18590              0.09353         0.2350   \n",
       "9           0.23960         0.22730              0.08543         0.2030   \n",
       "\n",
       "   mean fractal dimension           ...             worst radius  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "5                 0.07613           ...                    15.47   \n",
       "6                 0.05742           ...                    22.88   \n",
       "7                 0.07451           ...                    17.06   \n",
       "8                 0.07389           ...                    15.49   \n",
       "9                 0.08243           ...                    15.09   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "5          23.75           103.40       741.6            0.1791   \n",
       "6          27.66           153.20      1606.0            0.1442   \n",
       "7          28.14           110.60       897.0            0.1654   \n",
       "8          30.73           106.20       739.3            0.1703   \n",
       "9          40.68            97.65       711.4            0.1853   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "5             0.5249           0.5355                0.1741          0.3985   \n",
       "6             0.2576           0.3784                0.1932          0.3063   \n",
       "7             0.3682           0.2678                0.1556          0.3196   \n",
       "8             0.5401           0.5390                0.2060          0.4378   \n",
       "9             1.0580           1.1050                0.2210          0.4366   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "5                  0.12440  \n",
       "6                  0.08368  \n",
       "7                  0.11510  \n",
       "8                  0.10720  \n",
       "9                  0.20750  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.DataFrame(cancer.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mean radius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mean texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mean perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mean area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mean smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mean compactness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mean concavity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mean concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mean symmetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>mean fractal dimension</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>radius error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>texture error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>perimeter error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>area error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>smoothness error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>compactness error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>concavity error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>concave points error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>symmetry error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>fractal dimension error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>worst radius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>worst texture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>worst perimeter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>worst area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>worst smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>worst compactness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>worst concavity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>worst concave points</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>worst symmetry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>worst fractal dimension</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0\n",
       "0               mean radius\n",
       "1              mean texture\n",
       "2            mean perimeter\n",
       "3                 mean area\n",
       "4           mean smoothness\n",
       "5          mean compactness\n",
       "6            mean concavity\n",
       "7       mean concave points\n",
       "8             mean symmetry\n",
       "9    mean fractal dimension\n",
       "10             radius error\n",
       "11            texture error\n",
       "12          perimeter error\n",
       "13               area error\n",
       "14         smoothness error\n",
       "15        compactness error\n",
       "16          concavity error\n",
       "17     concave points error\n",
       "18           symmetry error\n",
       "19  fractal dimension error\n",
       "20             worst radius\n",
       "21            worst texture\n",
       "22          worst perimeter\n",
       "23               worst area\n",
       "24         worst smoothness\n",
       "25        worst compactness\n",
       "26          worst concavity\n",
       "27     worst concave points\n",
       "28           worst symmetry\n",
       "29  worst fractal dimension"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train,Y_test=train_test_split(cancer.data,cancer.target,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfa=pd.DataFrame(cancer.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(455, 30)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(114, 30)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.37142857142857144\n",
      "0.8505494505494505\n",
      "0.8967032967032967\n",
      "0.9098901098901099\n",
      "0.9120879120879121\n",
      "0.9142857142857143\n",
      "0.9120879120879121\n",
      "0.9142857142857143\n",
      "0.9274725274725275\n",
      "0.9252747252747253\n",
      "0.9274725274725275\n",
      "0.9252747252747253\n",
      "0.9296703296703297\n",
      "0.9384615384615385\n",
      "0.9428571428571428\n",
      "0.9384615384615385\n",
      "0.945054945054945\n",
      "0.9494505494505494\n",
      "0.9516483516483516\n",
      "0.9516483516483516\n",
      "0.9538461538461539\n",
      "0.9538461538461539\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "for i in range(22):\n",
    "    log_reg=LogisticRegression(max_iter=i)\n",
    "    log_reg.fit(X_train,Y_train)\n",
    "    print(log_reg.score(X_train,Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9538461538461539"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.956140350877193"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg.score(X_test,Y_test)"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}