Logistic_Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7695146958304853\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression Classification\n",
    "from pandas import read_csv\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "filename = 'pima-indians-diabetes.data.csv'\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(filename, names=names)\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]\n",
    "num_folds = 10\n",
    "kfold = KFold(n_splits=10, random_state=7)\n",
    "model = LogisticRegression()\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             preg        plas        pres        skin        test        mass  \\\n",
       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "             pedi         age       class  \n",
       "count  768.000000  768.000000  768.000000  \n",
       "mean     0.471876   33.240885    0.348958  \n",
       "std      0.331329   11.760232    0.476951  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.243750   24.000000    0.000000  \n",
       "50%      0.372500   29.000000    0.000000  \n",
       "75%      0.626250   41.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as ply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.7012987  0.81818182 0.74025974 0.71428571 0.77922078 0.75324675\n",
      " 0.85714286 0.80519481 0.72368421 0.80263158]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2941ad90128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ply.plot(results)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pima-indians-diabetes.data.csv\n"
     ]
    }
   ],
   "source": [
    "print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     preg  plas  pres  skin  test  mass   pedi  age  class\n",
      "0       6   148    72    35     0  33.6  0.627   50      1\n",
      "1       1    85    66    29     0  26.6  0.351   31      0\n",
      "2       8   183    64     0     0  23.3  0.672   32      1\n",
      "3       1    89    66    23    94  28.1  0.167   21      0\n",
      "4       0   137    40    35   168  43.1  2.288   33      1\n",
      "5       5   116    74     0     0  25.6  0.201   30      0\n",
      "6       3    78    50    32    88  31.0  0.248   26      1\n",
      "7      10   115     0     0     0  35.3  0.134   29      0\n",
      "8       2   197    70    45   543  30.5  0.158   53      1\n",
      "9       8   125    96     0     0   0.0  0.232   54      1\n",
      "10      4   110    92     0     0  37.6  0.191   30      0\n",
      "11     10   168    74     0     0  38.0  0.537   34      1\n",
      "12     10   139    80     0     0  27.1  1.441   57      0\n",
      "13      1   189    60    23   846  30.1  0.398   59      1\n",
      "14      5   166    72    19   175  25.8  0.587   51      1\n",
      "15      7   100     0     0     0  30.0  0.484   32      1\n",
      "16      0   118    84    47   230  45.8  0.551   31      1\n",
      "17      7   107    74     0     0  29.6  0.254   31      1\n",
      "18      1   103    30    38    83  43.3  0.183   33      0\n",
      "19      1   115    70    30    96  34.6  0.529   32      1\n",
      "20      3   126    88    41   235  39.3  0.704   27      0\n",
      "21      8    99    84     0     0  35.4  0.388   50      0\n",
      "22      7   196    90     0     0  39.8  0.451   41      1\n",
      "23      9   119    80    35     0  29.0  0.263   29      1\n",
      "24     11   143    94    33   146  36.6  0.254   51      1\n",
      "25     10   125    70    26   115  31.1  0.205   41      1\n",
      "26      7   147    76     0     0  39.4  0.257   43      1\n",
      "27      1    97    66    15   140  23.2  0.487   22      0\n",
      "28     13   145    82    19   110  22.2  0.245   57      0\n",
      "29      5   117    92     0     0  34.1  0.337   38      0\n",
      "..    ...   ...   ...   ...   ...   ...    ...  ...    ...\n",
      "738     2    99    60    17   160  36.6  0.453   21      0\n",
      "739     1   102    74     0     0  39.5  0.293   42      1\n",
      "740    11   120    80    37   150  42.3  0.785   48      1\n",
      "741     3   102    44    20    94  30.8  0.400   26      0\n",
      "742     1   109    58    18   116  28.5  0.219   22      0\n",
      "743     9   140    94     0     0  32.7  0.734   45      1\n",
      "744    13   153    88    37   140  40.6  1.174   39      0\n",
      "745    12   100    84    33   105  30.0  0.488   46      0\n",
      "746     1   147    94    41     0  49.3  0.358   27      1\n",
      "747     1    81    74    41    57  46.3  1.096   32      0\n",
      "748     3   187    70    22   200  36.4  0.408   36      1\n",
      "749     6   162    62     0     0  24.3  0.178   50      1\n",
      "750     4   136    70     0     0  31.2  1.182   22      1\n",
      "751     1   121    78    39    74  39.0  0.261   28      0\n",
      "752     3   108    62    24     0  26.0  0.223   25      0\n",
      "753     0   181    88    44   510  43.3  0.222   26      1\n",
      "754     8   154    78    32     0  32.4  0.443   45      1\n",
      "755     1   128    88    39   110  36.5  1.057   37      1\n",
      "756     7   137    90    41     0  32.0  0.391   39      0\n",
      "757     0   123    72     0     0  36.3  0.258   52      1\n",
      "758     1   106    76     0     0  37.5  0.197   26      0\n",
      "759     6   190    92     0     0  35.5  0.278   66      1\n",
      "760     2    88    58    26    16  28.4  0.766   22      0\n",
      "761     9   170    74    31     0  44.0  0.403   43      1\n",
      "762     9    89    62     0     0  22.5  0.142   33      0\n",
      "763    10   101    76    48   180  32.9  0.171   63      0\n",
      "764     2   122    70    27     0  36.8  0.340   27      0\n",
      "765     5   121    72    23   112  26.2  0.245   30      0\n",
      "766     1   126    60     0     0  30.1  0.349   47      1\n",
      "767     1    93    70    31     0  30.4  0.315   23      0\n",
      "\n",
      "[768 rows x 9 columns]\n"
     ]
    },
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             preg        plas        pres        skin        test        mass  \\\n",
       "count  768.000000  768.000000  768.000000  768.000000  768.000000  768.000000   \n",
       "mean     3.845052  120.894531   69.105469   20.536458   79.799479   31.992578   \n",
       "std      3.369578   31.972618   19.355807   15.952218  115.244002    7.884160   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      1.000000   99.000000   62.000000    0.000000    0.000000   27.300000   \n",
       "50%      3.000000  117.000000   72.000000   23.000000   30.500000   32.000000   \n",
       "75%      6.000000  140.250000   80.000000   32.000000  127.250000   36.600000   \n",
       "max     17.000000  199.000000  122.000000   99.000000  846.000000   67.100000   \n",
       "\n",
       "             pedi         age       class  \n",
       "count  768.000000  768.000000  768.000000  \n",
       "mean     0.471876   33.240885    0.348958  \n",
       "std      0.331329   11.760232    0.476951  \n",
       "min      0.078000   21.000000    0.000000  \n",
       "25%      0.243750   24.000000    0.000000  \n",
       "50%      0.372500   29.000000    0.000000  \n",
       "75%      0.626250   41.000000    1.000000  \n",
       "max      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataframe)\n",
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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