Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00dfeb30",
   "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": "efc28eed",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a74d7b78",
   "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>Cabin</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>NaN</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>C85</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>NaN</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>C123</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>NaN</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 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",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7598502b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d0d0915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c95e933",
   "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": 7,
   "id": "a7de03e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Sex',data=train,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "344db8e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Survived',hue='Pclass',data=train,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a076c0cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age', ylabel='Count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(train['Age'].dropna(),kde=False,color='red',bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fa23e85c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Age'].hist(bins=20,color='red',alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2628480",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='SibSp',data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c1d501e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['Fare'].hist(color='green',bins=40,figsize=(8,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "64cf2c89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ISingzz//fC688MJMnjw573nPe/LpT386Tz/9dJJk3bp1mTNnTiZNmpRp06bl1ltvLSMiADQVVwsGGoW7OzCQlVLA/9t/+2/Ztm1b7rnnntx3331paWnJZZddls7OzsybNy+nnHJKHnrooSxatCiLFy/O+vXry4gJAE3D1YKBRuLuDgxUhRfwn//851m3bl2uvPLKvP71r8/w4cNz+eWXZ/78+Vm5cmVGjBiRuXPnprW1NVOmTMmsWbNy8803Fx0TAJqGqwUDjcjdHRiICr8I2/r16zNu3Lh8//vfzz/8wz9k+/btOeGEE/K5z30ujz/+eCZMmNBr/XHjxmX58uV93k6tVuuvyLCH3fevWq1mfwMOWvV6PV/60pdecfxLX/qS6ZrAQWnw4MG54IILcu211+a8887L4MGD/c7GAbOv+1bhBbyzszO//OUv8453vCO33357urq6cuGFF+Zzn/tcRo0alba2tl7rV6vVbNu2rc/b2bBhQ39Fhj289NJLPY/Xr1+foUOHlpgG4LV78skn8+CDD+4xXqvV8uCDD+buu+/O6NGjS0gGsP+GDRuWhQsXJkkeffTRcsNASijgQ4YMSZJceumlGTp0aIYPH57Pfvaz+fCHP5zZs2fvcdGXrq6uDBs2rM/baW9v3+PKh9Bftm/f3vN44sSJe7xxBHCwqNfrueeee/Lwww/3eve+paUlRx99dGbMmOEMOAC8ilqttk8ngQsv4OPGjUt3d3d27tzZc9awu7s7SfIf/+N/zPe+971e62/cuDHjx4/v83ZaWloUcA6Y3fct+xpwsJs/f37OOOOMXmOVSiXz589Pa2vhvyoA9JtVq1blmmuuyfnnn5+pU6eWHQeKvwjbe9/73vzJn/xJLrnkkrz44ot59tlnc+211+bkk0/OzJkzs3Xr1tx4443ZuXNnHnjggdx555059dRTi44JAE1jzJgxexTwM844w9WCgYOa2ysyEBVewAcPHpxly5alpaUlM2bMyIwZMzJ69OhcccUVGTlyZG644YasWLEikydPzsKFC7Nw4cIcd9xxRccEAAAOYm6vyEBUqb98z5EGUavV8uijj+Zd73qXacEcMNu3b89JJ52UJLn33nt9Bhw4qG3evDlnnHFGr8+At7a25nvf+56z4MBByXGNou1rDy38DDgAMHDU6/VcffXVrzjeYO/TA03AcY2BTAEHgCa2adOmrFmzZo/7l9ZqtaxZsyabNm0qKRnAa+O4xkCmgANAExs7dmwmT568x3S5lpaWTJ48OWPHji0pGcBr47jGQKaAA0ATq1QqueCCC15x3D3AgYON4xoDmQIOAE1uzJgx6ejo6PmltFKppKOjw4WKgIOW4xoDlQIOAOTMM8/MqFGjkiSHH354Ojo6Sk4EsH8c1xiIFHAAINVqNQsWLMjo0aMzf/78VKvVsiMB7BfHNQai1rIDAAADw9SpUzN16tSyYwD0G8c1BhpnwAGAJMk3v/nNTJ06Nd/85jfLjgIADUkBBwDy/PPP56abbkp3d3duuummPP/882VHAoCGo4ADALnooovS3d2dJOnu7s7FF19cciIAaDwKOAA0ubVr12b9+vW9xtatW5e1a9eWlAgAGpMCDgBNrLu7O5dddtlel1122WU9Z8UBgP2ngANAE1u9enU6Ozv3uqyzszOrV68uOBEANC4FHACa2JQpU/KGN7xhr8ve8IY3ZMqUKQUnAoDGpYADQBMbNGhQLr/88r0uu/zyyzNokF8VAKC/+KkKAE3umGOOycSJE3uNvfOd78wxxxxTUiIAaEwKOACQK6+8suds96BBg7J48eKSEwHsv1WrVmX27NlZtWpV2VEgiQIOACQZMWJEzjzzzAwaNChnnnlmRowYUXYkgP3S1dWVJUuW5Mknn8ySJUvS1dVVdiRQwAGA35s3b15WrVqVefPmlR0FYL/ddNNN2bp1a5Jk69atWbZsWcmJQAEHAAAazObNm7Ns2bLU6/UkSb1ez7Jly7J58+aSk9HsFHAAAKBh1Ov1XH311a84/nIphzIo4AAAQMPYtGlT1qxZk1qt1mu8VqtlzZo12bRpU0nJIGktOwAADDT1er0pL9bz8lmhSqVScpLiVavVpvy+oRGNHTs2kydPztq1a3uV8JaWlhxzzDEZO3Zsielodgo4AOymXq/n7LPPzoYNG8qOQoEmTpyYpUuXKuHQACqVSi644IKcccYZex33OqdMpqADwB/wyxnAwW3MmDHp6OjoOZ5XKpV0dHRkzJgxJSej2TkDDgC7qVQqWbp0adNNQd++fXtmzpyZJLnrrrvS1tZWcqJimYIOjefMM8/MD3/4wzzzzDM5/PDD09HRUXYkUMAB4A9VKpWmK6C7a2tra+rvH2gM1Wo1CxYsyDXXXJPzzz8/1Wq17EiggAMAAI1p6tSpmTp1atkxoIfPgAMAAEABFHAAAAAogAIOAAAABVDAAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVoLTsAAABwYNXr9XR1dZUdo3D1ej1JUqlUSk5SvGq12pTf90CngAMAQAOr1+s5++yzs2HDhrKjUKCJEydm6dKlSvgAYwo6AAA0OCUMBgZnwAEAoIFVKpUsXbq06aagb9++PTNnzkyS3HXXXWlrays5UbFMQR+YSing//RP/5T58+dn6NChPWMnn3xylixZknXr1uULX/hCNm7cmJEjR+ZTn/pU5syZU0ZMAABoCJVKpekK6O7a2tqa+vtn4CilgG/YsCEf+tCHsnjx4l7jnZ2dmTdvXs4999ycfvrpeeihh3LOOefkyCOPzMSJE8uICgAAAP2ilM+Ab9iwIe94xzv2GF+5cmVGjBiRuXPnprW1NVOmTMmsWbNy8803l5ASAAAA+k/hZ8C7u7vzi1/8Im1tbbn++utTq9Vy4oknZv78+Xn88cczYcKEXuuPGzcuy5cv7/N2arVaf0WGPey+f9VqNfsbcNBzXAMajeMaRdrX/avwAv7ss8/mz/7szzJjxoxcd911ee655/K5z30uCxYsyOGHH77HZzOq1Wq2bdvW5+24zQIH0ksvvdTzeP369b2uZwBwMHJcAxqN4xoDUeEFfNSoUb2mlLe1tWXBggX58Ic/nNmzZ+9xdcaurq4MGzasz9tpb29PS0vLfueFvdm+fXvP44kTJ7qoB3DQc1wDGo3jGkWq1Wr7dBK48AL+2GOP5a677soFF1zQc1n8HTt2ZNCgQZk4cWK+853v9Fp/48aNGT9+fJ+309LSooBzwOy+b9nXgEbguAY0Gsc1BqLCL8I2YsSI3Hzzzbn++uuza9eubNmyJUuWLMl/+S//JTNmzMjWrVtz4403ZufOnXnggQdy55135tRTTy06JgAAAPSrws+Ajx49Ot/4xjdyzTXXZOnSpRk6dGj+83/+z1mwYEGGDh2aG264IYsWLcp1112XQw89NAsXLsxxxx1XdMw+q9fre0yfp3HtPqVp98c0vmq12jN7BwAA+qKU+4Afe+yxueWWW/a6rL29/RWXDWRdXV056aSTyo5BCWbOnFl2BAp07733+gwZAACvSSn3AQcAAIBmU8oZ8Eb365F/nXplSNkxONDq9d//bTpyw6vUd+TNz32+7BgAABzkFPADoF4ZooA3A70bAADoA1PQAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAEHAACAAijgAAAAUAAFHAAAAAqggAMAAEABFHAAAAAogAIOAAAABVDAAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAV4zQX82Wef7c8cAAAA0ND6VMB37dqVa6+9NkcffXSmTZuWJ554IqeeemqefvrpA5UPAAAAGkKfCvhXv/rVPPDAA/nKV76SwYMH57DDDsvo0aOzaNGiA5UPAAAAGkJrX1a+88478w//8A854ogjUqlUcsghh2Tx4sWZPn36gcoHAAAADaFPZ8C3bduWQw89NElSr9eTJNVqNYMGuZYbAAAA/DF9as7vete78nd/93dJkkqlkiRZtmxZ2tvb+z8ZAAAANJA+TUG/9NJL89GPfjS33357XnzxxXzgAx/Iiy++mL//+78/UPkAAACgIfSpgP/Jn/xJfvjDH+a+++7Lli1bMnr06Lzvfe/L8OHDD1Q+AAAAaAh9moK+ZcuWPPfcc3nXu96VD3zgA3n3u9+d3/72t3nmmWeyY8eOPm+8Vqulo6MjF110Uc/YunXrMmfOnEyaNCnTpk3Lrbfe2ufnBQAAgIGmT2fAp0+fnu7u7r0uGzRoUN773vfmqquu6rlQ26v5u7/7u6xduzZvfvObkySdnZ2ZN29ezj333Jx++ul56KGHcs455+TII4/MxIkT+xIVAAAABpQ+nQG/+OKL8973vjd33XVX1q1blx/+8Ic58cQTc8455+T222/P8OHDs3jx4n16rtWrV2flypX5y7/8y56xlStXZsSIEZk7d25aW1szZcqUzJo1KzfffHPfvisAAAAYYPpUwL/zne/k6quvzp/+6Z9myJAhedvb3parrroq//N//s9MmDAhl19+eX7yk5+86vP85je/yaWXXpqrr746bW1tPeOPP/54JkyY0GvdcePG5bHHHutLTAAAABhw+jQF/bnnnktLS0uvsUqlkt/85jdJkra2tlecov6y7u7uLFiwIGeddVaOOuqoXstefPHFXoU8+f19xrdt29aXmEl+//nyIhW9PaActVrN652GtPt+bT8HGoHjGkXa1/2rTwX8hBNOyAUXXJBLL700b3rTm7Jly5Z88YtfzPHHH58dO3bka1/7Wt7+9rf/0ef4xje+kSFDhqSjo2OPZW1tbXnhhRd6jXV1dWXYsGF9iZkk2bBhQ5//zf546aWXCt0eUI7169dn6NChZceAfrf7zzH7OdAIHNcYiPpUwP/mb/4mF1xwQWbMmJFKpZIked/73pdFixZl7dq1+ed//udcc801f/Q57rjjjjz99NM55phjkvy+YCfJ//pf/ysXXnhh7r///l7rb9y4MePHj+9LzCRJe3v7HmfrD6Tt27cXti2gPBMnTtxjpg40gt1/jtnPgUbguEaRarXaPp0E7lMBHzFiRL797W/nqaeeypNPPpl6vZ7bbrst06ZNy6OPPpo77rjjVZ9jxYoVvb5++RZkV155ZZ577rksWbIkN954Y+bOnZuHH344d955Z77+9a/3JWaSpKWlpdACXuS2gPIUfWyBouy+X9vPgUbguMZA1KcC/rInnngi3/72t/PjH/8448ePz4IFC/olzMiRI3PDDTdk0aJFue6663LooYdm4cKFOe644/rl+QEAAKAs+1zAu7u7s2LFivz93/99Hn/88ezatSvf+MY3csIJJ+xXgCuvvLLX1+3t7bnlllv26zkBAABgoNmn25B95zvfyfTp07NkyZJMnz49//zP/5zhw4fvccswAAAAYO/26Qz44sWL85GPfCQXXXRRhgwZcqAzAQAAQMPZpzPgl112WdasWZMTTzwx1157bZ566qmeq6ADAAAAr26fCvjcuXPzwx/+MNdcc002btyY6dOn57e//W1Wr17thvYAAACwD/apgL9sypQp+drXvpYf/ehH+djHPpYrr7wyJ5xwwh4XUgMAAAB661MBf9mb3/zmLFiwID/5yU9y/vnn58EHH+zvXAAAANBQXlMBf9mQIUNy2mmn5bbbbuuvPAAAANCQ9quAAwAAAPtGAQcAAIACKOAAAABQAAUcAAAACtBadoBGVKnvKDsC0I+8pgEA6A8KeD+p1+s9j9/83OdLTAIcSLu/1gEAoC9MQQcAAIACOAPeTyqVSs/jX4/869QrQ0pMA/SnSn1Hz8yW3V/rAADQFwr4AVCvDFHAAQAA6MUUdAAAACiAM+AA7FW9Xk9XV1fZMSjI9u3b9/qYxletVn28BqAgCjgAe9XV1ZWTTjqp7BiUYObMmWVHoED33ntv2trayo4B0BRMQQcAAIACOAMOwKvaNOXjqbcMLjsGB9rL97k3HbnhVWo7M3b19WXHAGg6CjgAr6reMlgBBwDYT6agAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAEHAACAAijgAAAAUAAFHAAAAAqggAMAAEABFHAAAAAogAIOAAAABVDAAQAAoACtZQcAAIAi1Ov1dHV1lR2Dgmzfvn2vj2l81Wo1lUql7Bh7pYADANAUurq6ctJJJ5UdgxLMnDmz7AgU6N57701bW1vZMfbKFHQAAAAogDPgAAA0nU2f+GDqg/0q3PDq9d//PUCnI9N/Kjt3Zey3flB2jFflqAMAQNOpD25VwIHCmYIOAAAABVDAAQAAoAAKOAAAABRAAQcAAIACKOAAAABQgFIK+OrVqzNnzpy8+93vzvHHH5/LL788XV1dSZJ169Zlzpw5mTRpUqZNm5Zbb721jIgAAADQrwov4M8++2w++clP5owzzsjatWtz++2358EHH8w3v/nNdHZ2Zt68eTnllFPy0EMPZdGiRVm8eHHWr19fdEwAAADoV4Xf/PDQQw/NT3/60wwfPjz1ej3PP/98XnrppRx66KFZuXJlRowYkblz5yZJpkyZklmzZuXmm2/OxIkTi44KAAAA/abwAp4kw4cPT5KceOKJeeqpp3LMMcdk9uzZ+fKXv5wJEyb0WnfcuHFZvnx5n7dRq9X6JetA3R5Qjlqt1jSv92b5PqHZOa4BjaaM49q+bq+UAv6ylStXprOzM/Pnz8+5556bI444Im1tbb3WqVar2bZtW5+fe8OGDf0Vc5+89NJLhW4PKMf69eszdOjQsmMUwnENmoPjGtBoBvJxrdQCXq1WU61Ws2DBgsyZMycdHR154YUXeq3T1dWVYcOG9fm529vb09LS0l9RX9X27dsL2xZQnokTJ+7xRmGjclyD5uC4BjSaMo5rtVptn04CF17AH3nkkVxyySX5wQ9+kCFDhiRJduzYkcGDB2fcuHG5//77e62/cePGjB8/vs/baWlpKbSAF7ktoDxFH1vK1CzfJzQ7xzWg0Qzk41rhV0E/8sgj09XVlauvvjo7duzIr3/961x11VU57bTTMmPGjGzdujU33nhjdu7cmQceeCB33nlnTj311KJjAgAAQL8q/Az4sGHDcv311+eKK67I8ccfn9e97nWZNWtWzjnnnAwZMiQ33HBDFi1alOuuuy6HHnpoFi5cmOOOO67omADsplLbWXYEoB95TQOUo5TPgI8bNy433HDDXpe1t7fnlltuKTgRAH+oXq/3PB67+voSkwAH0u6vdQAOrMKnoAMAAEAzKvUq6AAMXJVKpefxpikfT71lcIlpgP5Uqe3smdmy+2sdgANLAQfgVdVbBivgAAD7yRR0AAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAEHAACAAijgAAAAUAAFHAAAAAqggAMAAEABWssO0Igq9R1lR6AI9frv/65Uys3BAec1DQBAf1DAD4A3P/f5siMAAAAwwJiCDgAAAAVwBryfVKvV3HvvvWXHoCDbt2/PzJkzkyR33XVX2traSk5EUarVatkRAAA4SCng/aRSqShhTaqtrc3/PQAA8KpMQQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKEBr2QEAAKBolZ27yo4A9KOD5TWtgAMA0BTq9XrP47Hf+kGJSYADaffX+kBjCjoAAAAUwBlwAACaQqVS6Xm86RMfTH2wX4WhUVR27uqZ2bL7a32gcdQBAKDp1Ae3KuBA4UxBBwAAgAJ42w+AV1Wp7Sw7AkV4+aI1A3jqHv3DaxqgHAo4AK9q7Orry44AAHDQMwUdAAAACuAMOAB7Va1Wc++995Ydg4Js3749M2fOTJLcddddaWtrKzkRRalWq2VHAGgaCjgAe1WpVJSwJtXW1ub/HgAOAFPQAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAKUUsAfe+yxnHXWWTn22GNz/PHH58ILL8yzzz6bJFm3bl3mzJmTSZMmZdq0abn11lvLiAgAAAD9qvAC3tXVlY9//OOZNGlSVq1albvuuivPP/98LrnkknR2dmbevHk55ZRT8tBDD2XRokVZvHhx1q9fX3RMAAAA6FeFF/AtW7bkqKOOyjnnnJMhQ4Zk5MiROf300/PQQw9l5cqVGTFiRObOnZvW1tZMmTIls2bNys0331x0TAAAAOhXhRfwt73tbbn++uvT0tLSM3b33Xfn7W9/ex5//PFMmDCh1/rjxo3LY489VnRMAAAA6FetZW68Xq/ny1/+cu67775897vfzU033ZS2trZe61Sr1Wzbtq3Pz12r1forJuxh9/2rVqvZ34CDnuMazcB+Dc2hjJ9j+7q90gr47373u1x88cX5xS9+ke9+97s58sgj09bWlhdeeKHXel1dXRk2bFifn3/Dhg39FRX28NJLL/U8Xr9+fYYOHVpiGoD957hGM9h9Pwca10D+OVZKAf/Vr36VT3ziE3nTm96U5cuX59BDD02STJgwIffff3+vdTdu3Jjx48f3eRvt7e29prlDf9q+fXvP44kTJ+4xcwPgYOO4RjPYfT8HGlcZP8dqtdo+nQQuvIB3dnbmox/9aI477rgsWrQogwb9/x9Dnz59epYsWZIbb7wxc+fOzcMPP5w777wzX//61/u8nZaWFgWcA2b3fcu+BjQCxzWagf0amsNA/jlWeAG/7bbbsmXLlvzoRz/KihUrei372c9+lhtuuCGLFi3Kddddl0MPPTQLFy7McccdV3RMAAAA6FeFF/CzzjorZ5111isub29vzy233FJgIgAAADjwCr8NGQAAADQjBRwAAAAKoIADAABAARRwAAAAKIACDgAAAAUo/CroAABQtsrOXWVHoAj1+u//rlTKzcEBd7C8phVwAACazthv/aDsCEATMgUdAAAACuAMOAAATaFarebee+8tOwYF2b59e2bOnJkkueuuu9LW1lZyIopSrVbLjvCKFHAAAJpCpVJRwppUW1ub/3sGBFPQAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAEHAACAAijgAAAAUAAFHAAAAAqggAMAAEABFHAAAAAogAIOAAAABVDAAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUoNQC/uyzz2b69OlZs2ZNz9i6desyZ86cTJo0KdOmTcutt95aYkIAAADoH6UV8Icffjinn356fvWrX/WMdXZ2Zt68eTnllFPy0EMPZdGiRVm8eHHWr19fVkwAAADoF6UU8Ntvvz3z58/Peeed12t85cqVGTFiRObOnZvW1tZMmTIls2bNys0331xGTAAAAOg3rWVsdOrUqZk1a1ZaW1t7lfDHH388EyZM6LXuuHHjsnz58j5vo1ar7XdOeCW771+1Ws3+Bhz0HNeARuO4RpH2df8qpYAffvjhex1/8cUX09bW1musWq1m27Ztfd7Ghg0bXlM22BcvvfRSz+P169dn6NChJaYB2H+Oa0CjcVxjICqlgL+Stra2vPDCC73Gurq6MmzYsD4/V3t7e1paWvorGvSyffv2nscTJ07c440jgION4xrQaBzXKFKtVtunk8ADqoBPmDAh999/f6+xjRs3Zvz48X1+rpaWFgWcA2b3fcu+BjQCxzWg0TiuMRANqPuAT58+PVu3bs2NN96YnTt35oEHHsidd96ZU089texoAAAAsF8GVAEfOXJkbrjhhqxYsSKTJ0/OwoULs3Dhwhx33HFlRwMAAID9UvoU9F/+8pe9vm5vb88tt9xSUhoAAAA4MAbUGXAAAABoVAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAEHAACAAijgAAAAUAAFHAAAAAqggAMAAEABFHAAAAAogAIOAAAABVDAAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAEUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAK0Fp2AAAYaOr1erq6usqOUajt27fv9XGzqFarqVQqZccAoMEp4ACwm3q9nrPPPjsbNmwoO0ppZs6cWXaEwk2cODFLly5VwgE4oExBB4A/oIQBAAeCM+AAsJtKpZKlS5c23RT0JPn85z+fH//4xznxxBPz13/912XHKZQp6AAUQQEHgD9QqVTS1tZWdoxCPfnkk/nxj3+cJPnxj3+czs7OjB49uuRUANBYTEEHAHL22Wf3+vpTn/pUSUkAoHEp4ADQ5H70ox/l6aef7jX21FNP5Uc/+lFJiQCgMSngANDEarVaFi9evNdlixcvTq1WKzgRADQuBRwAmtgdd9yRXbt27XXZrl27cscddxScCAAalwIOAE3sQx/6UFpb935N1tbW1nzoQx8qOBEANC4FHACaWEtLSy6++OK9Lrv44ovT0tJScCIAaFwKOAA0ufe///17nAUfPHhw3v/+95eUCAAakwIOAE1u7dq1e3wOfOfOnVm7dm1JiQCgMSngANDEuru7c9lll+112WWXXZbu7u6CEwFA41LAAaCJrV69Op2dnXtd1tnZmdWrVxecCAAalwIOAE1sypQpecMb3rDXZW94wxsyZcqUghMBQONSwAGgiQ0aNCiXX375XpddfvnlGTTIrwoA0F/8VAWAJnfMMcfkz/7sz3qNvf3tb88xxxxTUiIAaEwKOACwx1XQ//BrAGD/tb76KvDH1ev1dHV1lR2jUNu3b9/r42ZRrVZTqVTKjgH0k7Vr1+Zf//Vfe4398pe/zNq1a50FB4B+pICzX+r1es4+++xs2LCh7CilmTlzZtkRCjdx4sQsXbpUCYcG0N3dnYsuumivyy666KKsXLnS58ABoJ/4icp+U8IADl73339/tm3bttdl27Zty/33319wIgBoXM6As18qlUqWLl3adFPQk9+f/U+a8w0IU9Chcbx8LHuty4GDg48M+sggA8OALOC/+c1vctlll+XBBx9MS0tLPvjBD+Zzn/tcWlsHZNymV6lU0tbWVnYMAF6DMWPG7NdyYODzkUEfGWTgGJBT0D/72c/mkEMOyb/8y79k+fLlWb16dW688cayYwFAw3nrW9+ao446aq/LjjrqqLz1rW8tOBFwIChhMDBU6gNsbtmmTZvyl3/5l/nJT36SI444IknyT//0T1myZEnuu+++V/33tVotjz76aN71rnelpaXlQMcFgIPe5s2b8+EPf3iP8e9///vOgEODaMYp6ImPDDbj912Wfe2hA25O9+OPP54RI0b0lO8k+dM//dNs2bIlv/3tb/P6179+n56nVqsdqIgA0FDe+MY35vTTT88//uM/9oydfvrpeeMb3+jnKTSQIUOGlB2BAnV3d5cdoans68/LAVfAX3zxxT0+T/zy19u2bdvnAt7Mn3EBgL469thjc/vtt2fHjh0ZOnRojj322Dz66KNlxwKAhjLgCvghhxyyx1UKX/562LBh+/w87e3tpqADQB98/vOfz7XXXpvzzjsvxx57bNlxAOCgUavV9ukk8IAr4OPHj8/zzz+frVu3ZtSoUUmS//t//29Gjx6d173udfv8PC0tLQo4APTBn//5n+fP//zPy44BAA1rwF0F/S1veUuOPvroXHHFFfnd736XJ554Il//+tdz2mmnlR0NAAAAXrMBV8CT5LrrrsuuXbty0kkn5cMf/nBOOOGEfPrTny47FgAAALxmA24KepKMGjUq1113XdkxAAAAoN8MyDPgAAAA0GgUcAAAACiAAg4AAAAFUMABAACgAAo4AAAAFEABBwAAgAIo4AAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAEHAACAAijgAAAAUIDWsgP0t3q9niSp1WolJwEAAKAZvNw/X+6jr6ThCnh3d3eSZMOGDSUnAQAAoJm83EdfSaX+ahX9INPd3Z1du3Zl0KBBqVQqZccBAACgwdXr9XR3d6e1tTWDBr3yJ70broADAADAQOQibAAAAFAABRwAAAAKoIADAABAARRwAAAAKIACDgAAAAVQwAEAAKAACjgAAAAUQAGH1+jZZ5/N9OnTs2bNmrKjAOyXxx57LGeddVaOPfbYHH/88bnwwgvz7LPPlh0LYL+sXr06c+bMybvf/e4cf/zxufzyy9PV1VV2LJqcAg6vwcMPP5zTTz89v/rVr8qOArBfurq68vGPfzyTJk3KqlWrctddd+X555/PJZdcUnY0gNfs2WefzSc/+cmcccYZWbt2bW6//fY8+OCD+eY3v1l2NJqcAg59dPvtt2f+/Pk577zzyo4CsN+2bNmSo446Kuecc06GDBmSkSNH5vTTT89DDz1UdjSA1+zQQw/NT3/608yePTuVSiXPP/98XnrppRx66KFlR6PJKeDQR1OnTs0999yTD3zgA2VHAdhvb3vb23L99denpaWlZ+zuu+/O29/+9hJTAey/4cOHJ0lOPPHEzJo1K4cffnhmz55dciqanQIOfXT44YentbW17BgA/a5er+faa6/Nfffdl0svvbTsOAD9YuXKlfnJT36SQYMG5dxzzy07Dk1OAQcA8rvf/S7nnntu7rzzznz3u9/NkUceWXYkgH5RrVZzxBFHZMGCBfmXf/mXdHZ2lh2JJqaAA0CT+9WvfpVTTz01v/vd77J8+XLlGzjoPfLII/lP/+k/ZceOHT1jO3bsyODBg9PW1lZiMpqdAg4ATayzszMf/ehH8+53vzvf/va3XaAIaAhHHnlkurq6cvXVV2fHjh359a9/nauuuiqnnXZahgwZUnY8mpgPsgJAE7vtttuyZcuW/OhHP8qKFSt6LfvZz35WUiqA/TNs2LBcf/31ueKKK3L88cfnda97XWbNmpVzzjmn7Gg0uUq9Xq+XHQIAAAAanSnoAAAAUAAFHAAAAAqggAMAAEABFHAAAAAogAIOAAAABVDAAQAAoAAKOAAAABSgtewAAMBrN23atDzzzDNpbf39j/R6vZ7hw4dn1qxZWbBgQQYNeuX32qdNm5bPfOYzmT17dlFxAaCpKeAAcJD727/9214l+pe//GU+9rGPpa2tLeeee26JyQCA3ZmCDgAN5sgjj8x73vOe/J//83+ybdu2fP7zn8+UKVNyzDHH5BOf+ER+/etf7/FvnnrqqXz2s5/NtGnT8s53vjMnnXRSli9f3rP8e9/7Xk4++eQcc8wxmTVrVm699daeZV/96ldz4okn5thjj82pp56ae++9t5DvEwAONgo4ADSQnTt3Zs2aNXnggQdy/PHH5/Of/3w2bNiQ2267LT/96U8zatSonH/++Xv8u4ULF2bw4MH54Q9/mEceeSR/9Vd/lcsvvzwvvvhinnjiiSxevDjf/OY3s3bt2lx44YW5/PLL8/TTT+eBBx7IP/7jP+bWW2/NmjVrMmfOnFx66aXZuXNnCd89AAxspqADwEHub//2b3PFFVf0fD169OicddZZOf3003P00Udn6dKleeMb35gkufjii7Np06Y9nuMLX/hChg0blsGDB2fLli0ZNmxYurq60tnZmZaWltTr9dxyyy2ZMWNGpkyZkkcffTSDBg3Kr3/963R2dub73/9+/uIv/iJz5szJ6aefnkqlUtj3DwAHCwUcAA5yf/M3f7PXC6k988wz2bFjR970pjf1jL3+9a9Pe3v7Hus+8cQT+eIXv5h/+7d/y1ve8paMHTs2SdLd3Z0xY8Zk2bJluf7663P22WenVqtl9uzZWbBgQSZNmpSvfvWrPcur1Wo6OjryqU996o9eAA4AmpECDgAN6rDDDsuQIUPy7//+73nb296WJPnNb36Tb33rW/nsZz/bs97OnTvzyU9+Mueff34+8pGPpFKp5Oc//3l+8IMf9PybWq2Wr33ta+nu7s4jjzySc889N29961vzF3/xFznssMPy7W9/Ozt27Mjq1avzmc98Jm9/+9vzvve9r4TvGgAGLm9NA0CDGjRoUE455ZR89atfzVNPPZWXXnopX/7yl/Poo4+mWq32rLdz5850dXWlWq2mUqlky5YtWbJkSc+yLVu25L/+1/+a1atXZ9CgQTniiCOSJCNHjsyGDRvy8Y9/PI899liGDBmSww47rGcZANCbM+AA0MAuuuiiXHvttZkzZ066urpy7LHH5itf+UqvdQ455JBcccUV+cpXvpIvfOELOeyww/LhD384GzduzL/+679mxowZ+eu//uv89//+3/P000/nda97XT7ykY/k/e9/fyqVSv7t3/4tn/rUp/Lcc8/lsMMOyyWXXJJ3vvOdJX3HADBwVer1er3sEAAAANDoTEEHAACAAijgAAAAUAAFHAAAAAqggAMAAEABFHAAAAAogAIOAAAABVDAAQAAoAAKOAAAABRAAQcAAIACKOAAAABQAAUcAAAACqCAAwAAQAH+P9q9Knk+krZxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.boxplot(x='Pclass',y='Age',data=train,palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "59054662",
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age=cols[0]\n",
    "    Pclass=cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        if Pclass ==1:\n",
    "            return 37\n",
    "        elif Pclass ==2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "    else:\n",
    "        \n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a46a79e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age'] = train[['Age','Pclass']].apply(impute_age,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4c1be9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef544ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9e7e414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6cd0ccf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Q      S\n",
       "0  False   True\n",
       "1  False  False\n",
       "2  False   True\n",
       "3  False   True\n",
       "4  False   True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(train['Embarked'],drop_first=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c470d87b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sex=pd.get_dummies(train['Sex'],drop_first=True)\n",
    "embark=pd.get_dummies(train['Embarked'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8df941e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4913d217",
   "metadata": {},
   "outputs": [
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       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
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    "train = pd.concat([train,sex,embark],axis=1)"
   ]
  },
  {
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   "execution_count": 24,
   "id": "fb9769c6",
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       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare   male      Q  \\\n",
       "0            1         0       3  22.0      1      0   7.2500   True  False   \n",
       "1            2         1       1  38.0      1      0  71.2833  False  False   \n",
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   "cell_type": "code",
   "execution_count": 25,
   "id": "b1559f34",
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   "outputs": [],
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    "train['male'] = train['male'].astype(int)"
   ]
  },
  {
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   "execution_count": 26,
   "id": "a02223a3",
   "metadata": {},
   "outputs": [
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       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male      Q  \\\n",
       "0            1         0       3  22.0      1      0   7.2500     1  False   \n",
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       "\n",
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     "execution_count": 26,
     "metadata": {},
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   "id": "e0a53b0d",
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    "train['Q'] =train['Q'].astype(int)"
   ]
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   "cell_type": "code",
   "execution_count": 28,
   "id": "a7492071",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['S']=train['S'].astype(int)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "60e71c13",
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   "outputs": [
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
       "0            1       3  22.0      1      0   7.2500     1  0  1\n",
       "1            2       1  38.0      1      0  71.2833     0  0  0\n",
       "2            3       3  26.0      0      0   7.9250     0  0  1\n",
       "3            4       1  35.0      1      0  53.1000     0  0  1\n",
       "4            5       3  35.0      0      0   8.0500     1  0  1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop('Survived',axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "44dd2958",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Survived'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a5e94bb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e0798c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(train.drop('Survived',axis=1),train['Survived'],test_size=0.30,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "50b5e0e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "648df1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4220862f",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression(solver='liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d19a5eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression(max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6385ad1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\ana\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d3429497",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cdae2667",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "624aab38",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7fa5415a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[134,  20],\n",
       "       [ 42,  72]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "95cb9d5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "77dc6eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7686567164179104"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy =accuracy_score(y_test,predictions)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "14f43aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c531294f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e5efea0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.87      0.81       154\n",
      "           1       0.78      0.63      0.70       114\n",
      "\n",
      "    accuracy                           0.77       268\n",
      "   macro avg       0.77      0.75      0.76       268\n",
      "weighted avg       0.77      0.77      0.76       268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  }
 ],
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