{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#library\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bdd = pd.read_csv('Subject6_smoking.csv',encoding='utf8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Smoker</th>\n",
       "      <th>Status</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>47.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Smoker Status   Age\n",
       "0    Yes  Alive  21.0\n",
       "1    Yes  Alive  19.3\n",
       "2     No   Dead  57.5\n",
       "3     No  Alive  47.1\n",
       "4    Yes  Alive  81.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdd.head()"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Smoker_No</th>\n",
       "      <th>Smoker_Yes</th>\n",
       "      <th>Status_Alive</th>\n",
       "      <th>Status_Dead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Smoker_No  Smoker_Yes  Status_Alive  Status_Dead\n",
       "0  21.0          0           1             1            0\n",
       "1  19.3          0           1             1            0\n",
       "2  57.5          1           0             0            1\n",
       "3  47.1          1           0             1            0\n",
       "4  81.4          0           1             1            0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff=pd.get_dummies(bdd)\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dff.drop(dff.columns[[1,3]], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Age</th>\n",
       "      <th>Smoker_Yes</th>\n",
       "      <th>Status_Dead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Smoker_Yes  Status_Dead\n",
       "0  21.0           1            0\n",
       "1  19.3           1            0\n",
       "2  57.5           0            1\n",
       "3  47.1           0            0\n",
       "4  81.4           1            0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Smoker  Status\n",
       "0  21.0       1       0\n",
       "1  19.3       1       0\n",
       "2  57.5       0       1\n",
       "3  47.1       0       0\n",
       "4  81.4       1       0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['Age','Smoker','Status']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a1b59e908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XgF9DVmIFp113qWSxJ/WY1lSmr0B5etd19ldyXWbuEp2HJBbnHO+0D/Legivs22wTFdF5BPACmFBTWb5bdJBEyrgRd3yK5HwAdtfk0yZkcmkDAGcm9rD1khHT1YdsD7XPqAnHEBadiSTGx+1Z3sVtP45cYrs7O0NLGwCyAdwnOkSiZdyI2+72jAXwc8me3Zh/0lXXMpNihB1hAyZXbQmuit29p9y1dTidgWJMX/psvlsiS83vOxfQ9/Z+82sqy98SHSJRMmrEHd9ocwEAn2vK6XOptL+tzZRvu9ayqnSB/zb/+x2FuzLtB7uR1XSaO1bsvSB4mvmRLCrtb/m16ACJlFHFDWAKgFFK/nDNXDh6hugwqaxWKc26QPnj8CUdN+zd6nc0iM5DDq0hKPt/3LQ4MN+02vW68wyb6Dwp6rjSlWtS/tC4nsqYqRK722MBcBsAnrtgxalKbvEE0ZmM5LTQK7tvtj5tGWKNpvVadyNpDUmBP/hO4E85L3VokiG3pw+0z6Bvhzd86WXSiPt4ALnmwaOtVNq995L1lOKZfHX+Le2n7/JFWIfoPJmsI8JCtzfN6vRoD9v+krWcSrvnpgD4jugQiZARI2672+ME8DsAbTnHX3ySuXDkNNGZjMyshWI3RB6su8RZNdhqAs2lDpCgisijrZPD9zh+4AjJrkwadCXSV9CXBxq6+DLlf74HgFmy50hKwYhJosMYXUSyyr+xXlcyM3wvXvC66QyUJIvEoD7eXOabGbxHviPrRheVdr+MA3Ci6BD9lfbfAHa3xwRgEYC9jgnzpjJJztT1rAnXLudZr7OsKlngv82/zjd4p5YJv74NIFVD7Lm9pd7Z/jvYza6bs7xKPi3PTIwrRQfor7Qvbuhb23MgySFLUdmxosOko1qlNOtC8x9GLOm4sWWL30krUPpJ45y/0lLknd9xq3a98zfZe81DqbAT68zSlWsMfaeotC7u+C7JcgCd9rLjxklma5boTOnsc/OUglPkPxdd6V3W0BBS9orOYzScc7zblu89pf0m9UrH77PrLKPpt8PkMAG4THSI/kjr4gYwAoAbQIt16ITJosNkilcsJxXN4qsLKtrLd/kikk90HiP41JvlPavtuvBFtnuyq21HU2En3+WlK9cYtv8MG7yH5gGISlanWXYVjBIdJqMwCY9Zlw6frj7seLDdUxtSERIdKRVt6bD5LmxZHlpieSB7vX0m3aVo4JQCWCg6RF+lbXHb3R4FwGwAzbZR09xGumN7OolIVvk263UlnvC9+Jd3bK2qQRWdKRXU+s0dVzSfHzxFeSRrnWMhLakU40zRAfoqbYsbwGgACoCoeYh7vOgwmc4r51l/ZKkome+vDL7rG5KxK1Aag7L/J81nBOZJj7pedS2m7elinSY6QF+lc3FPARBjJrNsyip0iw5DdHXKCNdF5jtHnNWxsvUrvytjVqC0hqXg/zUt8M/mqx3Puy6wg6XzPz3DGFW6ck2Z6BB9kZbfPXa3R4Y+TdJiGzVtNJNNZtGZyIG+MB+Tf6r8YNHl3ssadgfNabsCpSPCQnc0zez0xB62PpG1granpx5DHjyVlsUNoASAA0BYKRxFFyVT2GuWE4tm49GC/2tftMubRitQgioi9zcd4zs28ifzfVnXOqOSle7OnJoyo7gZY7mMsVTfNj4JgAYAJlfBcMFZyJEwCU9YLxw+XX3E8af2WYZegRKNQf1L81jfrOBd8m+zfpYVNGWl6+AoXcwtXbnGcNcaevRNxRh7kzGWxRjLA7AewGrG2J3JjdYvkwF4mWI1STaXoXdIZZKoZJFvt15T4gnfi+e9ZTVGWoES07j2z70l3tn+O/BLV0VWuzKIVjEZgxl6XxhKT0cD2ZxzH4AlAFZzzqchRQ9qiZ+7PRxAp6XIPYQxiUY8BuOV86w/sdxcOt9fGXzHV5TSK1A0zvmrLUO88ztuif3YeVt2s3koTWIbz1TRAXqrp99kJsZYEYDzAPw8iXkSYWj8T27KG0ajbQOrU0a4LsbvXUd3rm+9nf0pNMHpKxadqQvnHOva872rcIV9q2NStug8pF+miA7QWz0t7lsAvALgXc75R4yxUQCqkxerX4oAMAAwZQ2i4k4DG5Rj8k7HAzjR+7+GW8x/UYptkQKReT73uryr1EutnzlmU2Gnh6NEB+itHhU35/zvAP7e7fUdAM5JVqh+GgMgAgCS1UW32Uojr1sWFr3OT8DF7X/bdYN9TXa2WRvQQ8O2dlh9q8IXmt9znpgN2pyeTgy3lrtHd8BhjK0G8K0P5Jyn3AlbdrfnVgA2AJ0F5df/ULI6qLzTkKKFYz+KPFR3mXNdoc2EpK4K2OlXOn8TOFt+2XWW4VYfkB4bXFNZ3iQ6RE/19MLdiwDWxB//A5AFoDNZofoqfoxrIYAgADDF4hSbiCRLVLLId1h/WOIJ3y891z4uKStQmoKy/4am8sBcabWTSjvtGWpatadTJc91f50x9gyA15OSqH/M0M8niTHFamKyiX6hTXM+Ocdyvfx/pXcGdnXcpt3VNsdVP1xirF+bXdrCLHiXd772hHOZQ8uiRSIZIld0gN7o63elG/pZ16nGifiUjilrkEtwFjKA6k3DXZfgd66JHRtafivdF+7LCpTOKMIPth0bfdBxpSOSZaOdjpkl/YqbMdaBA+e4GwH8LCmJ+seFeE7ZkUvTJBloo/no/NPxABZ432i81fy4PNQWGXSk54RURB5vmxS+y/YDRyArm35Ly0zpV9ycc6OMXveVNbPY6YzjDLbWcsKQtXwelrb/fddP7f/JyjFr31q6F41B/Vvr2MDvrFc72l30G1qGM1Rx93TL+/968rYU4ETX15TCu+3IAGESnrJ+d/gM9RHnve3H1QZV/aJ1TOPav/aO8B7nvx2/oO3pRGeo4j7siJsxZgVgB1DAGMtFfGML9FUlKbOLrRsFXRm5pomNQlJFVLLIv7NeXfJg+KLIsrbH2l9QTnXUOsto8wzpzlDXNI40VXIlgB9BL+lPsP+L8wG4L4m5+mpfWXONipscqEPONt/tuo7OZicH4xcdoDcOW9yc87sA3MUYu4Zzfs8AZeoPjq6LqFyjqRJCSE+lT3F34ZzfwxibCGACAGu3tz+RrGB9tH+UTVMlhJCeS7/iZozdDGA+9OL+L/SbbL4LIGWLm6sRw5zlTAgRLiA6QG/0dMv7dwAsBNDIOV8G4BggJY/Z2TdVonqbvIKzEEKMw1Aj7p4Wd5BzrgFQGWNZAJoApOK9HMNdL8Q6WwJci0VFhiGEGEaj6AC90dPi/pgxlgPgIeirSz4F8GHSUvVdO7rt8OSREI26CSE9sUV0gN7o6cXJq+IvPsAYexlAFuf8i+TF6jMvuq3H1CIBr2R1CD10nxCS8vbUVJYbapDX652TnPMazvkXKbpzsutMFQkAtLC/XWwcQogBbBUdoLcOW9yMMWv8zu4FjLFcxlhe/FGKFNw5Gaiu0gC0In7hNBbwtolNRAgxAENNkwC93znZpQOpuXMSAPZAP3I2GN27s85WcozoPISQ1Ga44j7SVMk6ALMB3MA5HwVgFYCNAN4C8HSSs/VVLfTzVRCu/7Ke00YcQsjhfSY6QG8dqbgfBBCO75ycC+A2AI9Dvwj452SH66NtiH9dPBpStYCvQXAeQkjqCgF4T3SI3jpSccuc89b4y98F8GfO+XOc819Cv5t6KtqFbitLVF/zToFZCCGpbV1NZXlIdIjeOmJxM8a65sEXAljb7X2pejO+VuinF1oAINqyi4qbEHIoqbg67oiOVNzPAHiLMfYC9DunvwMAjLEx0KdLUk6guopDn4fPBoBw/Zc7Od1UgRBycOlX3JzzXwO4HsBjAOZ0K0AJwDXJjdYvmwHYAH3re6yztVZwHkJI6vEC+Fh0iL444nQH5/yDg7wt1Res13R/JbJn+0aTK79UTBRCSIp6qaayPCY6RF/09KwSo2mAfmiMEwCC26o207JAQsg3PC46QF+lZXHH57nfAJAHADF/WzDW0bJDbCpCSArZDeA10SH6Ki2LO249ui0LDDdUbxSYhRCSWv5i1GkSIL2LuwnATuh3pEdwW9VXXFPpfG5CCKAvuDCstC3u+HTJmwByAEALdYQjzbWfCg1FCEkFVTWV5V+JDtEfaVvcceuhH/MqA4B/05vv00VKQjLevaID9FdaF3eguqoV+jkEgwFAbav3qm0Nm8WmIoQIVA19Y6GhpXVxx70KwIz4hUr/lncNd6AMISRhfmPki5Jd0r64A9VVddCnTAYBQGT3lka1Yy8tDSQk8+wA8KToEImQ9sUd91/Ez+gGgMDW998WmIUQIsZtNZXlqugQiZApxb0V+jb4XAAI1XxWG21r+FJoIkLIQNoJA++U/KaMKO740sDnET8xEAA6Pn/pVa7F0uKnLyHkiG6sqSxPm30cGVHccRugH/eqrzBprWsPN2x9X2wkQsgAeK2msvxvokMkUsYUd3zU/Qz0GyzIANDx6Zp3tGioQ2gwQkjScM5DAK4SnSPRMqa4ASBQXVUPfXlgMQDwSCAa3P6RYQ+aIYQcHmOssqayfJvoHImWUcUd9yL0G4TaAMC/6Y0Nqo+WBxKSbjjn1QAqRedIhowr7kB1VSeAvwIY0vU230fPv8BjUcPdMJQQcnCcc84Yu6qmsjwsOksyZFxxx62DvkRQv1DZ3ugLVFe9JDYSISRRGGN311SWvy46R7JkZHEHqqtiAB6Gfus2KwD4N639ItpaT2d2E2JwnGtfAPiZ6BzJlJHFDQCB6qo90Le/FiN+jon3/Wdf1MKBNqHBCCF9xjkPMCadn65TJF0ytrjj3gHwEeKrTLRQR7jj8//+g2ua4Q+hISQTMcZW1FSWp/2u6Iwu7kB1lQZ9G6wf8V2V4brNuwPV7/9HaDBCSK9xTXugprLc8Ee29kRGFzcABKqrfADuh36nHDMA+Df+b324/qt3hAYjhPQYj6nrmCRdJzrHQMn44gaAQHXVVgBPABiG+H8T7wfPro227aabLhCS4ngsup3JpvKayvKI6CwDhYp7vzcBvAygpOsN7e88+c9YwLtbWCJCyGHxWLSFycqCmsrydtFZBhIVd1z8LJNnAXwGYDgA8GhIbX/v6We0SMgnNBwh5Fu4FguCSQtrKst3is4y0Ki4uwlUV6kAHgLQgPjmnJivudP34fNPcjUSEBqOELIP51oMWuys2t8uXi86iwhU3N8QqK7yA7gLgAr9giUie7Y1e6v+8TiVNyHicc45V6Mran939quis4hCxX0QgeqqZgC/h34QlV7ejduavFXPUXkTIhDnmqaFOlfs/P2Sx0RnEYmK+xAC1VU10E8WsyK+xjvSWN3k/fD5J7gaCYrMRkgm4poWi3W0rNh11/mPis4iGuOci86Q0uxuzyjo5x4EAHgBwFxUNjj72CWXMpNiExouQ0Vb6tD879v3va62NyJnzkWQXfnwvvs0oi27MOSSO2Epch/0+b6PX0Dn+lcADjiPOQVZM84EALS9uRrBHZ/AXDgSBYuuBwB0blwLLdSBrOlnJv8LI4fEtVhM9TVfWv/A8qdEZ0kFNOI+gkB11Q4At0O/S7w+8m7Yssf74fOP091zxFDyh6F42T0oXnYPii79I5higX3sLJgLSjDo7JtgGX7UIZ8baa5B5/pXMOSSO1F02T0Ibv8Q0dZ6aGE/wvVfoviye8G5hkhzDbRoGP6Nr8M1pXwAvzryTVxTVdXXdD6V9n5U3D0QL+/f4hvl3f72Ew/Hgh17hIbLcKHa9VByimDKLoRSMBxK/rDDfny0pQ6W4nGQFCuYJMMyfCIC1e8DYOAxFZxzcDUCJsnwffg8XNMWg8mmgfliyLfwWDSs+vaeXf/Ain+IzpJKqLh7KFBdtR16eVsADAL0c7zb1j70qOpr3i40XAbzf/k27OPn9vjjzQUlCO3aiFjQBy0aQnDHx4j59kKy2GEvm42Gx66FKXswmMWBSMNW2N0zk5ieHI4W9rdFW3bNrX9g+Yuis6QamuPuJbvbMwTAjwHkAtB3VUqylDNnabl5UOlUkdkyDY9FUXffpShefh9kR+6+tzc+vRK5Jyw/5Bx3x/pX0fnZGjDFCqVgOJjJgryFlx/wMS0v3Q3X1HKEG7ch9PVnUApLkTP7/KRr0zWcAAAL20lEQVR+PWQ/taOlJtJYPb/puVtrRWdJRTTi7qVAdVUjgF8D+Br69ngGLaa1v/3Ef4K161+nH4QDJ7jjE5gHjz6gtHvCdczJKPreXRiy9HZIVheU3OID3h/Zo/8CZcodCv/GtRh01kpEm2sRba1PWHZyaNGWXe90bnj9aCrtQ6Pi7oP4iYJ3AvgAQCkAGQA6Pn7hPf+mN/5G968cGP7Nb8HRi2mSLjG/fqyF6mtCYOv7sE+Yd8D72995EtlzlgKaCnBNfyOTwNW0PptfOM41Ht695aG2N1ef0P72E52i86QymirpB7vbIwE4G8CZ0KdNQgCg5A3LyfKcc65szy4+3PNJ32nREOrvX4ah338YksUBAAhsXYfW1x5ELOiFZHHCXDgSg797K9SOFrS8fDcGn7sKAND41I3Qgh2AJCN3wQrYSifv+7yBre8j0vQ1cuZcCABoW/sIgl9/CqWwFIPO+OnAf6EZQouGQ+HdX92455mb7hGdxQiouPvJ7vYwALMAXAa9uPcCAJMVOWvmuSdZhozxiMxHSKpTvXt2Br/+7LyWl++pEp3FKKi4E8Tu9gwDcDX0w6nqAGgAYC+bM84x/vgzmaxYReYjJNVwLaaFdm54rfPzly/yf/XOXtF5jISKO4Hsbo8NwAUA5qPb1Ikpb1hOtmfJd2R7zlCB8QhJGbFghy+w5d1bg9s/uitQXRUVncdoqLgT7FBTJ2ASc007Y7Z1+MT5TKIdHSRzRfbs2Ny5+Y2LfVXPfyo6i1FRcSeJ3e0ZCn3qpAhAPfRjYqEMKs13TV202OTMGyEyHyEDTQv7/YFtHz4Z+OqdG+Mrs0gfUXEnkd3tsQBYBOAMAB0AWrre55x8+gxb6eQTmWwyi8pHyEDgXNPC9V9u9m9686exzpZXA9VVmuhMRkfFPQDsbk8pgOXQb4lWDyAKAKbc4uys6WcuMmUNGiMwHiFJo3a27PVvXPvXcP2XlYHqKtrBlCBU3APE7vYoAE4GsAT63Hdz1/scE+YdZRt97EmS2ZYtKh8hicRj0WhwxydVnRvX3gRNfY9G2YlFxT3A4nPfywC4ATQCCAIAUywm1+TTZlqGTjiepk+IUXHOeaRpxw7/5rceUlvrHgxUV2XU3dcHChW3AHa3RwZwPIBzod9hpwHxi5eyq8DhmnL6AqWgZApjjAmMSUivRNt21/k3vfm/yJ5tdwDYHKiuonJJEipugexujxPAafFHFPoInAOAuWjsYOfRJ55schWMEhiRkCNSO1v3+je/VRXeteEhAK8EqqvorJ4ko+JOAfGjYr8DYAb026O1dr3PNubY0fYxnnmyI3e4qHyEHIwW8vsCW9d9Fqj+4FGAvxiormo98rNIIlBxp4j4xp2xAJZCPy52L/QlhAAA26hpI23uWfNMzrwSQREJAQBoIb83WPPp5sCW957lauTvtFpk4FFxp5j4/Pd0AOcAKIQ++t63WcFaMnmEfezseaYsmkIhAysW8DYHtn34ZXDbh6+Dx/4KYBvNY4tBxZ2i7G6PCcAx0KdQigC0xx8AAMvwo4c5yo6ba8ouPPhtXghJENXXvDuw9f3NodrP1wF4HsAGWt4nFhV3iouPwCdBH4EPgz4H3tb1fvPg0YPsY2cfqxQMP4ZJJkVQTJJmOOdcbdtd6//qnc2Rhq1vA3gBwBYaYacGKm6DiN+0YSL0DTwl0DfxNCG+CkWyZVkdE+ZPsRSNnSFZ7L27lxchcVyNBMINW6sDW9ZVq97GNwH8F0ANFXZqoeI2mHiBu6HvwpwKIAa9wCNdH2Mbc+xoa8nkGabswWNpLTjpCbWzpS5Us357YFvV14hF34K+rK9BdC5ycFTcBmZ3ewoBzAVwIgAL9GmUffPgppwhWXb3zElK4ahJstU5SFBMkqK4GgmEG7dtCVR/8LXaWlcH4GUAHwSqq9qO9FwiFhV3GrC7PVboFzJPBzAC+i7MZnQbhZuLy4bYSqdOUgqGT5QUq0tMUiIaj6mRaNvu6tDODbtDtZ/vhhbbBOBVAJsC1VWq6HykZ6i400h8LXgJgNkA5kDfTh+GviY8BgBgjNlGTiu1DJ84ScktHs9kk0VUXjIwuBaLqu2N20K7Nu4K1Xy2h6uRduhlXRWormo+0vNJ6qHiTlN2t8cMoAzAcdDXhcvQN/S0IX5BkykWk23UjDHmIaPHmrKHjJWU+O3SieFxTYupvqZt4bpNu4I7Pmnk0VAQQBWAD6CvDqHRtYFRcWcAu9vjAHAU9Hthjou/uQP6fPi+bwDL8IlDLUPHlyl5w8bKNtfgAQ9K+kWLBNvV9oZt4d1bmkI7N7TyaCgE4BMA66CXdVhwRJIgVNwZxu725EFfFz4T+uoUBn06pRXxGzwA+k0erCXHjDUPKh0rO3NLaI146uGaFot1ttZG99Z+Hdr5RUu0ZZcfgAZgPYD3AHwZqK4Kik1JkoGKO4PFTyd0Q59KmQZAgT4CbwPg3/eBskmyFI8vtgweXWrKLSqRHbkj6Mzwgce5pmlBX4Pqbd4Z2bO9ObTzCy+PhlTo/68+hl7Y1YHqKv/hPxMxOipuAmDfFvuR0Df5zAJQEH9XFPqUyv6RG5OYZej4IvPg0aVKblGJ7MwbwWTFOtCZ0x2PRcMxf/sutb1xZ6Tp6z3h+i8DXA1L8Xdvhz5nvQVAPW1BzyxU3ORb4qtT8gCUApgAfalhXvzdKvQiD3R/jlJQkmcuLC0yZQ8pkl35RbItq4iZzLYBjG1oXItFtVBnc8zf3qj6mhojDVtbIk07IuCcQZ/OagHwRfyxPVBd1Sk0MBGKipscUbzIc6EvNRwPvcgHQZ9WYdB/Ve+EPle+jyl3aLa5cGSRKbeoyOTMGyJZXQXMbM1hTJKQwbRI0KsFfXvUztY9qndPY3Tvzrbo3toIOHdg/8XiWsRLGsDOQHWVV1hgknKouEmf2N2eHADFAIZCX6niBuDE/jIPQl+5cuDdUCRZUvKG5Zhyi/NNrvx82ZGbL9lceZLFkc8Ua1Y6bNHnnHMeDfm0cKBNC3W2aUFfW8zf1qb6mtujLTuDWrBDAWCGfiGxa5nmdgAboRd2Hd1FhhwOFTdJiPio3AX9CNqh0G8K4YY+Utegl7kEfVQehD7VEu3+OZhiMZmyCl2yM88l2bOdsi3LJVkcLmaxOyWzzcUUq0syWZzMpAiZguGccx6LBrga9vNoxM+jIb8WCfp5JOjXwn5/LNDuVdv3tEXb6oOIqRboG6DM2D+K5gDqoJf0DgB7oN+uzk+HOJHeoOImSRXfjp8HID/+GB5/DAVgw/5S6xpph6Fv1Y/EX/72RhEmMcnqtEgWh1my2C3MbLdIZquZKVaLpFgszGQxM5NihiTL+gdLEsAYDjZFw7UYj0WjPBaNcjX+Zywa5dFwhKuRKFcjUR4JRmIBbyDmbw2CcxP0Mu4aNZuh/0DqujgoQb8G0AigHsBu6PPTTQD20sYXkghU3ESI+AjdBr3Mc6BPs+RCnzvvKvnc+Mdo+HbBd12006Bv51fjf3Z9LMeBI93uzwX0KQrpIH9K3Z7T/XNI8ed3Yv+Z6G3Qz4Rp6vZ6e6C66oDfJAhJNCpuktLsbo8CfQrGCf0ERAv0UW7Xy45uDzv0kbAJB5bxN2nQR/Mh7J+6CUGfvglBn8KJxt/u7/YI0LI7kgqouAkhxGAyelkWIYQYERU3SUuMsVMZY1sYY9sYYytF5yEkkWiqhKQdxpgMYCuAk6Avv/sIwAWc881CgxGSIDTiJunoWADbOOc7OOcRAH8FcKbgTIQkDBU3SUdDAezq9npd/G2EpAUqbpKODrZtnuYESdqg4ibpqA767swuw6DvYCQkLVBxk3T0EQA3Y2wkY8wM4HwA/xaciZCEMYkOQEiicc5VxtgPAbwCfffko5zzTYJjEZIwtByQEEIMhqZKCCHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYKi4CSHEYP4fSBijhIe8dTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Status'].value_counts().plot.pie(autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a1953a3c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Smoker'].value_counts().plot.pie(autopct='%1.1f%%', shadow=True, startangle=140)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Smoker\n",
       "0    230\n",
       "1    139\n",
       "Name: Status, dtype: uint8"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1=df.groupby([\"Smoker\"])[\"Status\"].sum().round(0)\n",
    "Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Age'].between(18,34), 'Classe_age'] = '18-34'\n",
    "df.loc[df['Age'].between(34,54), 'Classe_age'] = '34-54'\n",
    "df.loc[df['Age'].between(55,64), 'Classe_age'] = '55-64'\n",
    "df.loc[df['Age']>65, 'Classe_age'] = '+ 65'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Age</th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Status</th>\n",
       "      <th>Classe_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>81.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>+ 65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  Smoker  Status Classe_age\n",
       "0  21.0       1       0      18-34\n",
       "1  19.3       1       0      18-34\n",
       "2  57.5       0       1      55-64\n",
       "3  47.1       0       0      34-54\n",
       "4  81.4       1       0       + 65"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q2=df.groupby(['Classe_age','Status']).size()\n",
    "Q2=Q2.unstack()"
   ]
  },
  {
   "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>Status</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classe_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>+ 65</th>\n",
       "      <td>34</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-34</th>\n",
       "      <td>387</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34-54</th>\n",
       "      <td>378</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-64</th>\n",
       "      <td>145</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Status        0    1\n",
       "Classe_age          \n",
       "+ 65         34  207\n",
       "18-34       387   11\n",
       "34-54       378   60\n",
       "55-64       145   91"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a19484da0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "Q2.plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#la mortalite est tres elevee entre classe d'age 18-34/34-54"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hideOutput": true
   },
   "outputs": [],
   "source": [
    "#Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc =[\"Smoker\" , 'Age']\n",
    "dffeature = df[fc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dftarget =  df.loc[:, df.columns == 'Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## v1.0 split train set and test set\n",
    "features, targets = dffeature, dftarget\n",
    "train_features, test_features, train_targets, test_targets = train_test_split(features, targets,\n",
    "                                                                              train_size=0.8,\n",
    "                                                                              test_size=0.2,\n",
    "                                                                              random_state=42,\n",
    "                                                                              shuffle = True,\n",
    "                                                                              stratify=targets\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py:578: 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": [
    "modelf = model.fit(train_features, train_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = modelf.predict(test_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction : 81.74904942965779\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.90      0.88       189\n",
      "          1       0.71      0.59      0.65        74\n",
      "\n",
      "avg / total       0.81      0.82      0.81       263\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Model Accuracy:how often is the classifier correct\n",
    "print(\"prediction : {}\".format(accuracy_score(test_targets, y_pred)*100))\n",
    "\n",
    "# Check the model performance\n",
    "print(classification_report(test_targets, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix = confusion_matrix(test_targets,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[171,  18],\n",
       "       [ 30,  44]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 216x216 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix, annot = True)\n",
    "plt.figure(figsize=(3,3))\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}