My Solution for Coronavirus Problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:\\mystuff\\data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>99.254765</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>98.954284</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101.647720</td>\n",
       "      <td>0</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101.318212</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.139178</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        fever  bodyPain  age  runnyNose  diffBreath  infectionProb\n",
       "0   99.254765         1   85          1           1              1\n",
       "1   98.954284         0   55          0          -1              0\n",
       "2  101.647720         0   84          0           0              1\n",
       "3  101.318212         1   12          0           1              1\n",
       "4   99.139178         1   24          0           0              0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2884</th>\n",
       "      <td>101.712022</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2885</th>\n",
       "      <td>100.435787</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2886</th>\n",
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       "      <th>2887</th>\n",
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       "    <tr>\n",
       "      <th>2888</th>\n",
       "      <td>99.519023</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "           fever  bodyPain  age  runnyNose  diffBreath  infectionProb\n",
       "2884  101.712022         0   42          1          -1              1\n",
       "2885  100.435787         0   97          1           1              0\n",
       "2886  100.750620         0    5          0           0              1\n",
       "2887   99.927393         0   96          1           1              0\n",
       "2888   99.519023         0   15          1           0              0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2889 entries, 0 to 2888\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   fever          2889 non-null   float64\n",
      " 1   bodyPain       2889 non-null   int64  \n",
      " 2   age            2889 non-null   int64  \n",
      " 3   runnyNose      2889 non-null   int64  \n",
      " 4   diffBreath     2889 non-null   int64  \n",
      " 5   infectionProb  2889 non-null   int64  \n",
      "dtypes: float64(1), int64(5)\n",
      "memory usage: 135.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        99.254765\n",
       "1        98.954284\n",
       "2       101.647720\n",
       "3       101.318212\n",
       "4        99.139178\n",
       "           ...    \n",
       "2884    101.712022\n",
       "2885    100.435787\n",
       "2886    100.750620\n",
       "2887     99.927393\n",
       "2888     99.519023\n",
       "Name: fever, Length: 2889, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fever']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1    1027\n",
       " 1     948\n",
       " 0     914\n",
       "Name: diffBreath, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diffBreath'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>99.999273</td>\n",
       "      <td>0.510903</td>\n",
       "      <td>49.899965</td>\n",
       "      <td>0.491866</td>\n",
       "      <td>-0.027345</td>\n",
       "      <td>0.500519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.134316</td>\n",
       "      <td>0.499968</td>\n",
       "      <td>28.847724</td>\n",
       "      <td>0.500020</td>\n",
       "      <td>0.826508</td>\n",
       "      <td>0.500086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>98.000300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.036548</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.993046</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>100.962298</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>101.992313</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             fever     bodyPain          age    runnyNose   diffBreath  \\\n",
       "count  2889.000000  2889.000000  2889.000000  2889.000000  2889.000000   \n",
       "mean     99.999273     0.510903    49.899965     0.491866    -0.027345   \n",
       "std       1.134316     0.499968    28.847724     0.500020     0.826508   \n",
       "min      98.000300     0.000000     1.000000     0.000000    -1.000000   \n",
       "25%      99.036548     0.000000    25.000000     0.000000    -1.000000   \n",
       "50%      99.993046     1.000000    49.000000     0.000000     0.000000   \n",
       "75%     100.962298     1.000000    74.000000     1.000000     1.000000   \n",
       "max     101.992313     1.000000   100.000000     1.000000     1.000000   \n",
       "\n",
       "       infectionProb  \n",
       "count    2889.000000  \n",
       "mean        0.500519  \n",
       "std         0.500086  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         1.000000  \n",
       "75%         1.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_split(data, ratio):\n",
    "    np.random.seed(42)\n",
    "    shuffled = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * ratio)\n",
    "    test_indices = shuffled[:test_set_size]\n",
    "    train_indices = shuffled[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 5, 4, 6, 1, 3])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation((7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = data_split(df, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
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       "    <tr>\n",
       "      <th>1811</th>\n",
       "      <td>98.228244</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>101.091649</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>101.981402</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>100.425669</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>100.502494</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>100.823703</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>99.092296</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>101.223455</td>\n",
       "      <td>1</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>100.944881</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>100.862392</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2312 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           fever  bodyPain  age  runnyNose  diffBreath  infectionProb\n",
       "1811   98.228244         1   70          0           0              1\n",
       "1835  101.091649         1   69          1           0              0\n",
       "965   101.981402         0   60          0           0              1\n",
       "1234  100.425669         0   61          0          -1              1\n",
       "662   100.502494         0    3          1           0              1\n",
       "...          ...       ...  ...        ...         ...            ...\n",
       "1638  100.823703         0   16          1           0              1\n",
       "1095   99.092296         1   10          1           1              0\n",
       "1130  101.223455         1   84          1           0              0\n",
       "1294  100.944881         0   44          0           1              1\n",
       "860   100.862392         1   62          0           0              1\n",
       "\n",
       "[2312 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>fever</th>\n",
       "      <th>bodyPain</th>\n",
       "      <th>age</th>\n",
       "      <th>runnyNose</th>\n",
       "      <th>diffBreath</th>\n",
       "      <th>infectionProb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>101.649511</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1206</th>\n",
       "      <td>98.852075</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2382</th>\n",
       "      <td>100.808617</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>99.756587</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2885</th>\n",
       "      <td>100.435787</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>100.181416</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894</th>\n",
       "      <td>101.252918</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>98.792579</td>\n",
       "      <td>1</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>99.712089</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>100.604908</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>577 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           fever  bodyPain  age  runnyNose  diffBreath  infectionProb\n",
       "471   101.649511         0   36          0           0              0\n",
       "1206   98.852075         1   34          1           1              1\n",
       "2382  100.808617         0   52          1          -1              0\n",
       "2013   99.756587         1   55          1           1              1\n",
       "2885  100.435787         0   97          1           1              0\n",
       "...          ...       ...  ...        ...         ...            ...\n",
       "109   100.181416         0   91          1           1              1\n",
       "1894  101.252918         0    9          1           1              0\n",
       "163    98.792579         1   68          1           0              0\n",
       "1924   99.712089         0   76          0           0              0\n",
       "252   100.604908         1   63          0           1              0\n",
       "\n",
       "[577 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[['fever', 'bodyPain', 'age', 'runnyNose', 'diffBreath']].to_numpy()\n",
    "X_test = test[['fever', 'bodyPain', 'age', 'runnyNose', 'diffBreath']].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = train[['infectionProb']].to_numpy().reshape(2312,)\n",
    "Y_test = test[['infectionProb']].to_numpy().reshape(577,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputFeatures = [100, 1, 22, -1, 0]\n",
    "infProb =clf.predict_proba([inputFeatures])[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5134932968526814"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infProb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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