sklearn_log_reg_notes2.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3ecf70fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73052fcb",
   "metadata": {},
   "outputs": [
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv('titanic_train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fa16d7e",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
       "0          False     False   False  False  False  False  False  False   False   \n",
       "1          False     False   False  False  False  False  False  False   False   \n",
       "2          False     False   False  False  False  False  False  False   False   \n",
       "3          False     False   False  False  False  False  False  False   False   \n",
       "4          False     False   False  False  False  False  False  False   False   \n",
       "..           ...       ...     ...    ...    ...    ...    ...    ...     ...   \n",
       "886        False     False   False  False  False  False  False  False   False   \n",
       "887        False     False   False  False  False  False  False  False   False   \n",
       "888        False     False   False  False  False   True  False  False   False   \n",
       "889        False     False   False  False  False  False  False  False   False   \n",
       "890        False     False   False  False  False  False  False  False   False   \n",
       "\n",
       "      Fare  Cabin  Embarked  \n",
       "0    False   True     False  \n",
       "1    False  False     False  \n",
       "2    False   True     False  \n",
       "3    False  False     False  \n",
       "4    False   True     False  \n",
       "..     ...    ...       ...  \n",
       "886  False   True     False  \n",
       "887  False  False     False  \n",
       "888  False   True     False  \n",
       "889  False  False     False  \n",
       "890  False   True     False  \n",
       "\n",
       "[891 rows x 12 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05fd3cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57bde965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "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": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(data=train,x='Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f29cb7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(data=train,x='Survived',hue='Sex',palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "454727da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(data=train,x='Survived',hue='Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82ef5872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 8,
     "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": [
    "sns.histplot(train['Age'].dropna(),bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a626dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "train['Age'].plot.hist(bins=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "03e36037",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8958d2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "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": [
    "sns.countplot(data=train,x='SibSp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1be80f8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(bins=40,figsize=(10,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3d988de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "070e7acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train['Fare'].iplot(kind='hist',bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99223f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2f16774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    38.233441\n",
       "2    29.877630\n",
       "3    25.140620\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby('Pclass')['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96ee6eed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a99b678b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c85d0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CAhUXF2vChAlKS0szMfQpSkpK1NzcPChjDWVNTU2S/vMpFvQtISGB1woA8KUYKVvDhw/X/ffff8rXy8vLTQzXp+bmZr36+pvqDnK9kb74Tvx7KrzyzkGbkzifP9RmdwQAwBDiiYuadgfj9NmUH9sdAy4x4s1n7I4AABhCuKooAACAQZQtAAAAgyhbAAAABlG2AAAADKJsAQAAGETZAgAAMIiyBQAAYBBlCwAAwCDKFgAAgEGULQAAAIMoWwAAAAZRtgAAAAyibAEAABhE2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBBlC0AAACDKFsAAAAGUbYAAAAMomwBAAAYRNkCAAAwiLIFAABgEGULAADAIMoWAACAQZQtAAAAgyhbAAAABlG2AAAADKJsAQAAGPSlylZLS4tefPFFHTx4UJZlmc4EAADgGlH9fUN5eblqamr0ySef6LrrrtMHH3ygwsLCwcgGAAAw5PW7svXss8/q8ccf16hRo7R48WK99tprg5ELAADAFfotWydPG/p8PknS8OHDzSYCAABwkX5PI15zzTXKzMzUgQMHdNttt2nmzJmDkQsAAMAV+i1bWVlZmj59ut5++21NmDBBl1xyyWDkAgAAcIV+y9aqVat6/ntdXZ2io6N1/vnnKzMzU6NHjz7jzx05ckQ33HCDHnvsMUVFRWnlypXy+XyaNGmSioqK5Pdz1QkAAOB+/TaecDisc889V7Nnz9YFF1ygQ4cO6dixY8rLyzvjz0QiERUWFmrEiBGSpPXr1ys3N1fbt2+XZVmqra0duN8AAADAwfpd2Wpra1NxcbEkacaMGVqyZIlyc3OVmZl5xp/ZsGGDbrrpJj3yyCOSpIaGBiUnJ0uSUlJStHv3bqWmpvY5bjgcVmNj45f+Rc4kFAp97ecA/lsoFBqQ+YnBdfLvAf92cDPmufP0W7Y6Ozv1zjvvaOLEiXrnnXcUCoXU3t5+xhKzY8cOxcXFacaMGT1ly7Ksnk8zxsTEqKOjo99ggUBAiYmJX+V3Oa1gMCjp6Nd+HuCLgsHggMxPDK5//z0Q/3ZwNea5Pfoqt/2WrcLCQq1YsUKHDx/WiBEjdP3116uyslJLly497ff/4Q9/kM/n01//+lc1NjYqLy9PbW1tPY93dXUpNjb2f/g1AAAAhp5+92xddtllWrNmjaZPn65PP/1UR44cUWZmptLS0k77/du2bVN5ebm2bt2qxMREbdiwQSkpKaqvr5f07032SUlJA/tbAAAAONQZV7aOHTumZ599Vtu2bdPw4cPV2dmp2trank3vX0VeXp4KCgpUXFysCRMmnLGoAQAAuM0Zy9YPf/hD/fjHP9amTZs0btw43XrrrV+5aG3durXnv5eXl//vKQEAAIaoM5atRYsW6ZlnntH+/fs1b968ntv2AAAA4Ms7456tn/70p6qoqFBWVpaeeeYZvfHGG9q4caPefvvtwcwHAAAwpPW7QT45OVkbN25UTU2Nzj//fN15552DkQsAAMAVvvQ9c2JjY5WVlaWdO3cajAMAAOAu3KAQAADAIMoWAACAQf1eQX6oa2trkz90RCPefMbuKHAJf+iI2tqG2x0DADBEsLIFAABgkOtXtuLi4vRe+zF9NuXHdkeBS4x48xnFxcXZHQMAMESwsgUAAGAQZQsAAMAg159GBLygpKREzc3NdsdwvKamJklSTk6OzUmGhoSEBF4rYABQtgAXaG5u1ttv/EMXjTxhdxRHi7V8kqTPWl62OYnzfdA5zO4IgGtQtgCXuGjkCeUnddodAy6x7u8j7Y4AuAZ7tgAAAAyibAEAABhE2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBBlC0AAACDKFsAAAAGUbYAAAAMomwBAAAYRNkCAAAwiLIFAABgEGULAADAIMoWAACAQVF2BwAA4MsoKSlRc3Oz3TEcr6mpSZKUk5Njc5KhISEhwfhrRdkCAAwJzc3NerXhVWmM3Ukc7vNzVq/uf9XeHEPBx4MzDGULADB0jJG6f9Btdwq4hP+FwdlNxZ4tAAAAgyhbAAAABlG2AAAADKJsAQAAGGRkg/yJEyeUn5+v9957T8OGDdP69etlWZZWrlwpn8+nSZMmqaioSH4/XQ8AALibkbL1/PPPS5KeeOIJ1dfX95St3NxcTZs2TYWFhaqtrVVqaqqJ4QEAABzDyNLSzJkztXbtWknSgQMHFB8fr4aGBiUnJ0uSUlJStGfPHhNDAwAAOIqx62xFRUUpLy9PNTU1Kikp0fPPPy+fzydJiomJUUdHR58/Hw6H1djY+LVzhEKhr/0cwH8LhUIDMj8HSigUYgMmBpwT5zkw0AZjnhu9qOmGDRv0y1/+UjfeeKPC4XDP17u6uhQbG9vnzwYCASUmJn7tDMFgUNLRr/08wBcFg8EBmZ8DJRgM6jO7Q8B1nDjP1W53CrjNQM3zvgqbkTfDO3fu1JYtWyRJZ511lnw+ny699FLV19dLkurq6pSUlGRiaAAAAEcxsrI1a9YsrVq1SpmZmTp+/LhWr16tiRMnqqCgQMXFxZowYYLS0tJMDA14Ultbm/7VMUzr/j7S7ihwifc7humctja7YwCuYKRsBYNBPfjgg6d8vby83MRwAAAAjsWNqAEXiIuLU/DoO8pP6rQ7Clxi3d9HakRcnN0xAFfgA0wAAAAGUbYAAAAMomwBAAAYRNkCAAAwiLIFAABgEGULAADAIE9c+sEfatOIN5+xO4aj+SKfSpKs6LNsTuJ8/lCbpPPtjgEAGCJcX7YSEhLsjjAkNDU1SZImTaRE9O985hUA4EtzfdnKycmxO8KQcPJ1KikpsTkJAADuwp4tAAAAgyhbAAAABlG2AAAADKJsAQAAGETZAgAAMIiyBQAAYBBlCwAAwCDKFgAAgEGULQAAAIMoWwAAAAa5/nY9AAB3aGtrkz6W/C+wToAB8rHUdlab8WGYsQAAAAaxsgUAGBLi4uL0/qfvq/sH3XZHgUv4X/ArLi7O/DjGRwAAAPAwVrYAl/igc5jW/X2k3TEc7ZNjPknS6OGWzUmc74POYZpsdwjAJShbgAskJCTYHWFI+L+mJknSeeMm2ZzE+SaLeQUMFMoW4AI5OTl2RxgSTr5OJSUlNicB4CXs2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBBlC0AAACDKFsAAAAGUbYAAAAMomwBAAAYRNkCAAAwaMCvIB+JRLR69Wrt379fx44d07Jly5SQkKCVK1fK5/Np0qRJKioqkt9PzwMAAO434GWroqJCY8aM0caNG9Xe3q7rr79e3/rWt5Sbm6tp06apsLBQtbW1Sk1NHeihAQAAHGfAy1Z6errS0tJ6jocNG6aGhgYlJydLklJSUrR79+5+y1Y4HFZjY+NAx8MZhEIhSeI1h6sxz4e2k/9+wEAKhULG/yYMeNmKiYmRJHV2dionJ0e5ubnasGGDfD5fz+MdHR39Pk8gEFBiYuJAx8MZBINBSeI1h6sxz4e2YDAotdudAm4TDAYH5G9CX4XNyMapjz76SIsWLdLcuXM1Z86cXvuzurq6FBsba2JYAAAAxxnwstXa2qolS5ZoxYoVmjdvniRpypQpqq+vlyTV1dUpKSlpoIcFAABwpAEvW5s3b9bRo0f10EMPKSsrS1lZWcrNzVVpaanmz5+vSCTSa08XAACAmw34nq38/Hzl5+ef8vXy8vKBHgoAAMDxuNgVAACAQZQtAAAAgwb8NCIAAMZ8LPlfYJ2gT599/p8jbE0xNHws6QLzw1C2AABDQkJCgt0RhoSmpiZJ0qQLJtmcZAi4YHDmFWULADAk5OTk2B1hSDj5OpWUlNicBCexFgsAAGAQZQsAAMAgyhYAAIBBlC0AAACDKFsAAAAGUbYAAAAMomwBAAAYRNkCAAAwiLIFAABgEGULAADAIMoWAACAQZQtAAAAgyhbAAAABlG2AAAADKJsAQAAGETZAgAAMIiyBQAAYBBlCwAAwCDKFgAAgEGULQAAAIMoWwAAAAZRtgAAAAyibAEAABhE2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBBlC0AAACDKFsAAAAGUbYAAAAMMla2XnvtNWVlZUmS3n//fS1YsEALFy5UUVGRuru7TQ0LAADgKEbK1qOPPqr8/HyFw2FJ0vr165Wbm6vt27fLsizV1taaGBYAAMBxjJStiy66SKWlpT3HDQ0NSk5OliSlpKRoz549JoYFAABwnCgTT5qWlqYPP/yw59iyLPl8PklSTEyMOjo6+n2OcDisxsZGE/FwGqFQSJJ4zeFqzHN4AfPceYyUrf/m9/9nAa2rq0uxsbH9/kwgEFBiYqLJWPiCYDAoSbzmcDXmObyAeW6PvsrtoHwaccqUKaqvr5ck1dXVKSkpaTCGBQAAsN2glK28vDyVlpZq/vz5ikQiSktLG4xhAQAAbGfsNOKFF16oJ598UpI0fvx4lZeXmxoKAADAsbioKQAAgEGULQAAAIMoWwAAAAZRtgAAAAyibAEAABhE2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBBlC0AAACDKFsAAAAGUbYAAAAMomwBAAAYRNkCAAAwiLIFAABgEGULAADAIMoWAACAQZQtAAAAgyhbAAAABlG2AAAADIqyOwAAd6qurlZlZaXdMXppamqSJOXk5Nic5FSzZ89Wenq63TEAGMDKFgDPCAaD6urq0tGjR+2OAsBDWNkCYER6errjVmpmz54tSTp8+LAef/xxe8MA8AxWtgB4wt/+9jd1dnZKkjo7O/XKK6/YnAiAV1C2AHjCmjVreh0XFBTYEwSA51C2AHjCyVWtMx0DgCmULQCeMHLkyD6PAcAUyhYAT/jv04hr1661JwgAz6FsAfCEMWPG9DoePXq0PUEAeA5lC4AnrFu3rtfx3XffbVMSAF5D2QLgCS0tLX0eA4AplC0AnjBu3Lg+jwHAFMoWAE9YtGhRr+Obb77ZpiQAvIbb9diAG/R+NdygFwNh8+bNvY4feughXX311TalAeAllC1IksaOHWt3BMCow4cP9zo+dOiQTUkAeA1lywZOvEEvAAAwY9DKVnd3t9asWaN//vOfGj58uNatW6eLL754sIYH4HHBYFChUKjXMfB1sS3kq/HqtpBB2yD/pz/9SceOHdPvfvc73XHHHbrvvvsGa2gAOOW6Wvfcc49NSQCzxo4dy9YQhxm0la1XXnlFM2bMkCRNnTpVb7zxRp/fHw6H1djYOBjRAHjAqFGjFAgEFA6HFQgEFAwG+RuDr+3iiy/WsmXL7I4xpHjxf3eDVrY6Ozt73fh12LBhOn78uKKiTh8hEAgoMTFxsOIB8IB77rlHd955p+677z7+vgAYUH2VyEErWyNHjlRXV1fPcXd39xmLFgCYkJycrBdeeMHuGAA8ZtD2bF1++eWqq6uTJO3du1eTJ08erKEBAABsM2hLS6mpqdq9e7duuukmWZale++9d7CGBgAAsM2glS2/33/Kp4EAAADcjnsjAgAAGETZAgAAMIiyBQAAYBBlCwAAwCDKFgAAgEGULQAAAIMoWwAAAAZRtgAAAAyibAEAABhE2QIAADBo0G7X81WFw2E1NjbaHQMAAKBf4XD4jI/5LMuyBjELAACAp3AaEQAAwCDKFgAAgEGULQAAAIMoWwAAAAZRtgAAAAyibAEAABhE2UKP1157TVlZWXbHAIyIRCJasWKFFi5cqHnz5qm2ttbuSMCAO3HihFatWqWbbrpJmZmZ+uCDD+yOBDn4oqYYXI8++qgqKip01lln2R0FMKKiokJjxozRxo0b1d7eruuvv14/+tGP7I4FDKjnn39ekvTEE0+ovr5e69ev18MPP2xzKrCyBUnSRRddpNLSUrtjAMakp6frZz/7Wc/xsGHDbEwDmDFz5kytXbtWknTgwAHFx8fbnAgSK1v4XFpamj788EO7YwDGxMTESJI6OzuVk5Oj3NxcewMBhkRFRSkvL081NTUqKSmxOw7EyhYAD/noo4+0aNEizZ07V3PmzLE7DmDMhg0b9Nxzz6mgoEChUMjuOJ5H2QLgCa2trVqyZIlWrFihefPm2R0HMGLnzp3asmWLJOmss86Sz+fjlLkDULYAeMLmzZt19OhRPfTQQ8rKylJWVpY+++wzu2MBA2rWrFl68803lZmZqVtuuUWrV69WIBCwO5bn+SzLsuwOAQAA4FasbAEAABhE2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBBlC0AQ1J9fb2uvPLKnss43Hjjjdq6detpvzcrK0vvvPPOICcEgH/jdj0AhqwrrrhCDzzwgCTp2LFjSk9P19y5cxUbG2tzMgD4D8oWAFfo7OyU3+/XW2+9pU2bNsmyLJ133nnatGlTz/ccPHhQa9asUTgc1scff6zbb79dM2fO1AMPPKCXXnpJ3d3duuaaa7R48WJt27ZNO3fulN/v1+WXX668vDwbfzsAQxllC8CQ9dJLLykrK0s+n0/R0dEqKCjQunXr9MADD2jixInatm1br9OH7777rm6++WZNmzZN//jHP1RaWqqZM2dq586dKi8v13nnnacdO3ZIknbs2KGCggJNnTpV27dv1/HjxxUVxZ9MAF8dfzkADFlfPI140urVqzVx4kRJUmZmZq/HzjnnHD388MN66qmn5PP5dPz4cUlScXGxiouL1draqhkzZkiS1q9fr8cee0ybNm3S1KlTxc02APyv2CAPwFXOPfdctbS0SJIeeeQR1dTU9Dz24IMPau7cudq4caOmTZsmy7J07NgxVVdXq7i4WGVlZfrjH/+o/fv368knn9Rdd92l8vJyNTY26tVXX7XpNwIw1LGyBcBV7rrrLq1evVp+v1/nnHOOFi9erN/85jeSpPT0dN1zzz3asmWLvvGNb6i9vV3Dhw/X6NGjNXfuXI0ePVpXXXWVvvnNb+qSSy7RvHnzdPbZZ+u8887Td7/7XZt/MwBDFTeiBgAAMIjTiAAAAAZRtgAAAAyibAEAABhE2QIAADCIsgUAAGAQZQsAAMAgyhYAAIBB/w8XU7nA9IZKBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(data=train,x='Pclass',y='Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cefea5d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_val(x):\n",
    "    Age=x[0]\n",
    "    Pclass=x[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        \n",
    "        if Pclass==1:\n",
    "            return 38.233\n",
    "            \n",
    "        elif Pclass==2:\n",
    "            return 29.878\n",
    "            \n",
    "        else:\n",
    "            return 25.141\n",
    "            \n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08679cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train[['Age','Pclass']].apply(change_val,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb6df319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca5d8370",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d9a36a82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cb2582a1",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "66858086",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a6ef0f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d42ce438",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex= pd.get_dummies(train['Sex'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ed560730",
   "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>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   male\n",
       "0     1\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "947236f4",
   "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",
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      ],
      "text/plain": [
       "   Q  S\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  1\n",
       "4  0  1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark=pd.get_dummies(train['Embarked'],drop_first=True)\n",
    "embark.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "995fef09",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "103e19b0",
   "metadata": {},
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      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S  \\\n",
       "0            1         0       3  22.0      1      0   7.2500     1  0  1   \n",
       "1            2         1       1  38.0      1      0  71.2833     0  0  0   \n",
       "2            3         1       3  26.0      0      0   7.9250     0  0  1   \n",
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       "4            5         0       3  35.0      0      0   8.0500     1  0  1   \n",
       "\n",
       "   male  Q  S  \n",
       "0     1  0  1  \n",
       "1     0  0  0  \n",
       "2     0  0  1  \n",
       "3     0  0  1  \n",
       "4     1  0  1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.concat([train,sex,embark],axis=1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f32135f4",
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   "outputs": [
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      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S  male  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1     1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0     0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1     0  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1     0  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1     1  0  1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
    "train.drop(['PassengerId'],axis=1,inplace=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "88b3f939",
   "metadata": {},
   "outputs": [
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       "   Survived  Pclass   Age  SibSp  Parch  male  Q  S  male  Q  S\n",
       "0         0       3  22.0      1      0     1  0  1     1  0  1\n",
       "1         1       1  38.0      1      0     0  0  0     0  0  0\n",
       "2         1       3  26.0      0      0     0  0  1     0  0  1\n",
       "3         1       1  35.0      1      0     0  0  1     0  0  1\n",
       "4         0       3  35.0      0      0     1  0  1     1  0  1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(['Fare'],axis=1,inplace=True)\n",
    "train.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "32ccb08b",
   "metadata": {},
   "outputs": [],
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    "X=train.drop('Survived',axis=1)\n",
    "y=train['Survived']"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8e619c52",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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    "y.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b6dfa983",
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   "outputs": [
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       "   Pclass   Age  SibSp  Parch  male  Q  S  male  Q  S\n",
       "0       3  22.0      1      0     1  0  1     1  0  1\n",
       "1       1  38.0      1      0     0  0  0     0  0  0\n",
       "2       3  26.0      0      0     0  0  1     0  0  1\n",
       "3       1  35.0      1      0     0  0  1     0  0  1\n",
       "4       3  35.0      0      0     1  0  1     1  0  1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5f1d31ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e7499d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2cb18c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2af7ee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2eec4cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "68694cd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c3b24e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6b461966",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.91      0.87       163\n",
      "           1       0.84      0.70      0.76       104\n",
      "\n",
      "    accuracy                           0.83       267\n",
      "   macro avg       0.83      0.81      0.82       267\n",
      "weighted avg       0.83      0.83      0.83       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "617107b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "02581808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[149,  14],\n",
       "       [ 31,  73]], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d361b145",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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