Titanic Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2bd4555d",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import math\n",
    "\n",
    "titanic_data = pd.read_csv('titanic.csv')\n",
    "titanic_data.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "983a6313",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of passenger in original data:891\n"
     ]
    }
   ],
   "source": [
    "print(\"No of passenger in original data:\" +str(len(titanic_data.index) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d4b5f7",
   "metadata": {},
   "source": [
    "## Analyzing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3694f767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c809fdbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", hue=\"Sex\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2491c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Survived\", hue=\"Pclass\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "510d22b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_data[\"Age\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28c00ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_data[\"Fare\"].plot.hist(bins=20, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ea42131",
   "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": [
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9fdbcfcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SibSp', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGwCAYAAABPSaTdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAqJ0lEQVR4nO3df3BU9b3/8deakCWEZCUJ7LKXgAGjRRL8Ebxp4g+4/AiDAjr4FSlU6YAdLD80JYhFWs11MBG4ApUIV7woCEPjndqgTitNsBCL1GmIpkLk+qPSC9SssRh2EwibEM73D8e9XUIEQsJZPjwfM2eGPeezm/fZ0clzzu5mHZZlWQIAADDUFXYPAAAA0JWIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYLdruASLBqVOn9MUXXyg+Pl4Oh8PucQAAwDmwLEsNDQ3yer264or2r98QO5K++OILpaSk2D0GAADogEOHDqlfv37tHid2JMXHx0v65slKSEiweRoAAHAuAoGAUlJSQr/H20PsSKGXrhISEogdAAAuMWd7CwpvUAYAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYLRouwe4FGQ++ordI3S5quUP2D0CAABdwvYrO3//+9/1wx/+UElJSerRo4duuOEGVVVVhY5blqWCggJ5vV7FxsZqxIgRqqmpCXuMYDCoefPmKTk5WXFxcZo4caIOHz58sU8FAABEIFtjp76+Xrfccou6deumt956Sx999JGeffZZXXnllaE1y5Yt04oVK1RcXKzKykp5PB6NGTNGDQ0NoTV5eXkqLS1VSUmJdu3apcbGRo0fP16tra02nBUAAIgktr6MtXTpUqWkpOjll18O7bvqqqtC/7YsS6tWrdLixYs1adIkSdLGjRvldru1ZcsWzZo1S36/X+vXr9emTZs0evRoSdLmzZuVkpKi7du3a+zYsW1+bjAYVDAYDN0OBAJddIYAAMButl7ZeeONNzRs2DDde++96tOnj2688Ua9+OKLoeMHDhyQz+dTbm5uaJ/T6dTw4cO1e/duSVJVVZVaWlrC1ni9XqWnp4fWnK6oqEgulyu0paSkdNEZAgAAu9kaO59//rnWrl2rtLQ0/f73v9dDDz2khx9+WK+88s0bgn0+nyTJ7XaH3c/tdoeO+Xw+xcTEqFevXu2uOd2iRYvk9/tD26FDhzr71AAAQISw9WWsU6dOadiwYSosLJQk3XjjjaqpqdHatWv1wAP/9+kgh8MRdj/LstrsO913rXE6nXI6nRc4PQAAuBTYemWnb9++uu6668L2DR48WAcPHpQkeTweSWpzhaauri50tcfj8ai5uVn19fXtrgEAAJcvW2Pnlltu0ccffxy275NPPtGAAQMkSampqfJ4PCovLw8db25uVkVFhXJyciRJmZmZ6tatW9ia2tpa7du3L7QGAABcvmx9GeunP/2pcnJyVFhYqMmTJ+vPf/6z1q1bp3Xr1kn65uWrvLw8FRYWKi0tTWlpaSosLFSPHj00depUSZLL5dLMmTOVn5+vpKQkJSYmasGCBcrIyAh9OgsAAFy+bI2dm2++WaWlpVq0aJGeeuoppaamatWqVZo2bVpozcKFC9XU1KTZs2ervr5eWVlZKisrU3x8fGjNypUrFR0drcmTJ6upqUmjRo3Shg0bFBUVZcdpAQCACOKwLMuyewi7BQIBuVwu+f1+JSQktDnO10UAABB5zvb7+1u2f10EAABAVyJ2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRbY6egoEAOhyNs83g8oeOWZamgoEBer1exsbEaMWKEampqwh4jGAxq3rx5Sk5OVlxcnCZOnKjDhw9f7FMBAAARyvYrO0OGDFFtbW1o27t3b+jYsmXLtGLFChUXF6uyslIej0djxoxRQ0NDaE1eXp5KS0tVUlKiXbt2qbGxUePHj1dra6sdpwMAACJMtO0DREeHXc35lmVZWrVqlRYvXqxJkyZJkjZu3Ci3260tW7Zo1qxZ8vv9Wr9+vTZt2qTRo0dLkjZv3qyUlBRt375dY8eOPePPDAaDCgaDoduBQKALzgwAAEQC26/sfPrpp/J6vUpNTdWUKVP0+eefS5IOHDggn8+n3Nzc0Fqn06nhw4dr9+7dkqSqqiq1tLSErfF6vUpPTw+tOZOioiK5XK7QlpKS0kVnBwAA7GZr7GRlZemVV17R73//e7344ovy+XzKycnRkSNH5PP5JElutzvsPm63O3TM5/MpJiZGvXr1anfNmSxatEh+vz+0HTp0qJPPDAAARApbX8YaN25c6N8ZGRnKzs7WoEGDtHHjRn3/+9+XJDkcjrD7WJbVZt/pzrbG6XTK6XRewOQAAOBSYfvLWP8sLi5OGRkZ+vTTT0Pv4zn9Ck1dXV3oao/H41Fzc7Pq6+vbXQMAAC5vERU7wWBQ+/fvV9++fZWamiqPx6Py8vLQ8ebmZlVUVCgnJ0eSlJmZqW7duoWtqa2t1b59+0JrAADA5c3Wl7EWLFigCRMmqH///qqrq9OSJUsUCAQ0ffp0ORwO5eXlqbCwUGlpaUpLS1NhYaF69OihqVOnSpJcLpdmzpyp/Px8JSUlKTExUQsWLFBGRkbo01kAAODyZmvsHD58WD/4wQ/0j3/8Q71799b3v/99vffeexowYIAkaeHChWpqatLs2bNVX1+vrKwslZWVKT4+PvQYK1euVHR0tCZPnqympiaNGjVKGzZsUFRUlF2nBQAAIojDsizL7iHsFggE5HK55Pf7lZCQ0OZ45qOv2DDVxVW1/AG7RwAA4Lyc7ff3tyLqPTsAAACdjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0SImdoqKiuRwOJSXlxfaZ1mWCgoK5PV6FRsbqxEjRqimpibsfsFgUPPmzVNycrLi4uI0ceJEHT58+CJPDwAAIlVExE5lZaXWrVunoUOHhu1ftmyZVqxYoeLiYlVWVsrj8WjMmDFqaGgIrcnLy1NpaalKSkq0a9cuNTY2avz48Wptbb3YpwEAACKQ7bHT2NioadOm6cUXX1SvXr1C+y3L0qpVq7R48WJNmjRJ6enp2rhxo44fP64tW7ZIkvx+v9avX69nn31Wo0eP1o033qjNmzdr79692r59e7s/MxgMKhAIhG0AAMBMtsfOnDlzdOedd2r06NFh+w8cOCCfz6fc3NzQPqfTqeHDh2v37t2SpKqqKrW0tISt8Xq9Sk9PD605k6KiIrlcrtCWkpLSyWcFAAAiha2xU1JSovfff19FRUVtjvl8PkmS2+0O2+92u0PHfD6fYmJiwq4Inb7mTBYtWiS/3x/aDh06dKGnAgAAIlS0XT/40KFDeuSRR1RWVqbu3bu3u87hcITdtiyrzb7TnW2N0+mU0+k8v4EBAMAlybYrO1VVVaqrq1NmZqaio6MVHR2tiooKPffcc4qOjg5d0Tn9Ck1dXV3omMfjUXNzs+rr69tdAwAALm+2xc6oUaO0d+9eVVdXh7Zhw4Zp2rRpqq6u1sCBA+XxeFReXh66T3NzsyoqKpSTkyNJyszMVLdu3cLW1NbWat++faE1AADg8mbby1jx8fFKT08P2xcXF6ekpKTQ/ry8PBUWFiotLU1paWkqLCxUjx49NHXqVEmSy+XSzJkzlZ+fr6SkJCUmJmrBggXKyMho84ZnAABwebItds7FwoUL1dTUpNmzZ6u+vl5ZWVkqKytTfHx8aM3KlSsVHR2tyZMnq6mpSaNGjdKGDRsUFRVl4+QAACBSOCzLsuwewm6BQEAul0t+v18JCQltjmc++ooNU11cVcsfsHsEAADOy9l+f3/L9r+zAwAA0JWIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGC0DsXOyJEjdfTo0Tb7A4GARo4ceaEzAQAAdJoOxc7OnTvV3NzcZv+JEyf0xz/+8YKHAgAA6CzR57P4ww8/DP37o48+ks/nC91ubW3Vtm3b9C//8i+dNx0AAMAFOq/YueGGG+RwOORwOM74clVsbKxWr17dacMBAABcqPOKnQMHDsiyLA0cOFB//vOf1bt379CxmJgY9enTR1FRUZ0+JAAAQEedV+wMGDBAknTq1KkuGQYAAKCznVfs/LNPPvlEO3fuVF1dXZv4eeKJJy54MAAAgM7Qodh58cUX9ZOf/ETJycnyeDxyOByhYw6Hg9gBAAARo0Oxs2TJEj399NN67LHHOnseAACATtWhv7NTX1+ve++9t7NnAQAA6HQdip17771XZWVlnT0LAABAp+vQy1hXX321fvGLX+i9995TRkaGunXrFnb84Ycf7pThAAAALlSHYmfdunXq2bOnKioqVFFREXbM4XAQOwAAIGJ0KHYOHDjQ2XMAAAB0iQ69ZwcAAOBS0aErOzNmzPjO4y+99FKHhgEAAOhsHYqd+vr6sNstLS3at2+fjh49esYvCAUAALBLh2KntLS0zb5Tp05p9uzZGjhw4AUPBQAA0Fk67T07V1xxhX76059q5cqVnfWQAAAAF6xT36D817/+VSdPnuzMhwQAALggHXoZa/78+WG3LctSbW2tfvvb32r69OmdMhgAAEBn6FDsfPDBB2G3r7jiCvXu3VvPPvvsWT+pBQAAcDF1KHZ27NjR2XMAAAB0iQ7Fzre++uorffzxx3I4HLrmmmvUu3fvzpoLAACgU3ToDcrHjh3TjBkz1LdvX91+++267bbb5PV6NXPmTB0/fvycH2ft2rUaOnSoEhISlJCQoOzsbL311luh45ZlqaCgQF6vV7GxsRoxYoRqamrCHiMYDGrevHlKTk5WXFycJk6cqMOHD3fktAAAgIE6FDvz589XRUWF3nzzTR09elRHjx7V66+/roqKCuXn55/z4/Tr10/PPPOM9uzZoz179mjkyJG66667QkGzbNkyrVixQsXFxaqsrJTH49GYMWPU0NAQeoy8vDyVlpaqpKREu3btUmNjo8aPH6/W1taOnBoAADCMw7Is63zvlJycrF//+tcaMWJE2P4dO3Zo8uTJ+uqrrzo8UGJiopYvX64ZM2bI6/UqLy9Pjz32mKRvruK43W4tXbpUs2bNkt/vV+/evbVp0ybdd999kqQvvvhCKSkp+t3vfqexY8ee088MBAJyuVzy+/1KSEhoczzz0Vc6fD6XiqrlD9g9AgAA5+Vsv7+/1aErO8ePH5fb7W6zv0+fPuf1MtY/a21tVUlJiY4dO6bs7GwdOHBAPp9Pubm5oTVOp1PDhw/X7t27JUlVVVVqaWkJW+P1epWenh5acybBYFCBQCBsAwAAZupQ7GRnZ+vJJ5/UiRMnQvuampr07//+78rOzj6vx9q7d6969uwpp9Ophx56SKWlpbruuuvk8/kkqU1Uud3u0DGfz6eYmBj16tWr3TVnUlRUJJfLFdpSUlLOa2YAAHDp6NCnsVatWqVx48apX79+uv766+VwOFRdXS2n06mysrLzeqxrr71W1dXVOnr0qF577TVNnz5dFRUVoeMOhyNsvWVZbfad7mxrFi1aFPaHEQOBAMEDAIChOhQ7GRkZ+vTTT7V582b9z//8jyzL0pQpUzRt2jTFxsae12PFxMTo6quvliQNGzZMlZWV+uUvfxl6n47P51Pfvn1D6+vq6kJXezwej5qbm1VfXx92daeurk45OTnt/kyn0ymn03lecwIAgEtTh2KnqKhIbrdbP/7xj8P2v/TSS/rqq69CodIRlmUpGAwqNTVVHo9H5eXluvHGGyVJzc3Nqqio0NKlSyVJmZmZ6tatm8rLyzV58mRJUm1trfbt26dly5Z1eAYAAGCODsXOCy+8oC1btrTZP2TIEE2ZMuWcY+fxxx/XuHHjlJKSooaGBpWUlGjnzp3atm2bHA6H8vLyVFhYqLS0NKWlpamwsFA9evTQ1KlTJUkul0szZ85Ufn6+kpKSlJiYqAULFigjI0OjR4/uyKkBAADDdCh2Tn9p6Vu9e/dWbW3tOT/Ol19+qfvvv1+1tbVyuVwaOnSotm3bpjFjxkiSFi5cqKamJs2ePVv19fXKyspSWVmZ4uPjQ4+xcuVKRUdHa/LkyWpqatKoUaO0YcMGRUVFdeTUAACAYToUOykpKXr33XeVmpoatv/dd9+V1+s958dZv379dx53OBwqKChQQUFBu2u6d++u1atXa/Xq1ef8cwEAwOWjQ7Hz4IMPKi8vTy0tLRo5cqQk6e2339bChQvP6y8oAwAAdLUOxc7ChQv19ddfa/bs2Wpubpb0zRWWxx57TIsWLerUAQEAAC5Eh2LH4XBo6dKl+sUvfqH9+/crNjZWaWlpfJwbAABEnA7Fzrd69uypm2++ubNmAQAA6HQd+roIAACASwWxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMFm33ALi0HXwqw+4Rulz/J/baPQIA4ALYemWnqKhIN998s+Lj49WnTx/dfffd+vjjj8PWWJalgoICeb1excbGasSIEaqpqQlbEwwGNW/ePCUnJysuLk4TJ07U4cOHL+apAACACGVr7FRUVGjOnDl67733VF5erpMnTyo3N1fHjh0LrVm2bJlWrFih4uJiVVZWyuPxaMyYMWpoaAitycvLU2lpqUpKSrRr1y41NjZq/Pjxam1tteO0AABABLH1Zaxt27aF3X755ZfVp08fVVVV6fbbb5dlWVq1apUWL16sSZMmSZI2btwot9utLVu2aNasWfL7/Vq/fr02bdqk0aNHS5I2b96slJQUbd++XWPHjr3o5wUAACJHRL1B2e/3S5ISExMlSQcOHJDP51Nubm5ojdPp1PDhw7V7925JUlVVlVpaWsLWeL1epaenh9acLhgMKhAIhG0AAMBMERM7lmVp/vz5uvXWW5Weni5J8vl8kiS32x221u12h475fD7FxMSoV69e7a45XVFRkVwuV2hLSUnp7NMBAAARImJiZ+7cufrwww/1q1/9qs0xh8MRdtuyrDb7TvddaxYtWiS/3x/aDh061PHBAQBARIuI2Jk3b57eeOMN7dixQ/369Qvt93g8ktTmCk1dXV3oao/H41Fzc7Pq6+vbXXM6p9OphISEsA0AAJjJ1tixLEtz587Vb37zG/3hD39Qampq2PHU1FR5PB6Vl5eH9jU3N6uiokI5OTmSpMzMTHXr1i1sTW1trfbt2xdaAwAALl+2fhprzpw52rJli15//XXFx8eHruC4XC7FxsbK4XAoLy9PhYWFSktLU1pamgoLC9WjRw9NnTo1tHbmzJnKz89XUlKSEhMTtWDBAmVkZIQ+nQUAAC5ftsbO2rVrJUkjRowI2//yyy/rRz/6kSRp4cKFampq0uzZs1VfX6+srCyVlZUpPj4+tH7lypWKjo7W5MmT1dTUpFGjRmnDhg2Kioq6WKcCAAAilMOyLMvuIewWCATkcrnk9/vP+P6dzEdfsWGqi6tq+QMduh9fFwEAsMvZfn9/KyLeoAwAANBViB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYzdbYeeeddzRhwgR5vV45HA5t3bo17LhlWSooKJDX61VsbKxGjBihmpqasDXBYFDz5s1TcnKy4uLiNHHiRB0+fPgingUAAIhktsbOsWPHdP3116u4uPiMx5ctW6YVK1aouLhYlZWV8ng8GjNmjBoaGkJr8vLyVFpaqpKSEu3atUuNjY0aP368WltbL9ZpAACACBZt5w8fN26cxo0bd8ZjlmVp1apVWrx4sSZNmiRJ2rhxo9xut7Zs2aJZs2bJ7/dr/fr12rRpk0aPHi1J2rx5s1JSUrR9+3aNHTv2op0LAACITBH7np0DBw7I5/MpNzc3tM/pdGr48OHavXu3JKmqqkotLS1ha7xer9LT00NrziQYDCoQCIRtAADATBEbOz6fT5LkdrvD9rvd7tAxn8+nmJgY9erVq901Z1JUVCSXyxXaUlJSOnl6AAAQKSI2dr7lcDjCbluW1Wbf6c62ZtGiRfL7/aHt0KFDnTIrAACIPBEbOx6PR5LaXKGpq6sLXe3xeDxqbm5WfX19u2vOxOl0KiEhIWwDAABmitjYSU1NlcfjUXl5eWhfc3OzKioqlJOTI0nKzMxUt27dwtbU1tZq3759oTUAAODyZuunsRobG/XZZ5+Fbh84cEDV1dVKTExU//79lZeXp8LCQqWlpSktLU2FhYXq0aOHpk6dKklyuVyaOXOm8vPzlZSUpMTERC1YsEAZGRmhT2cBAIDLm62xs2fPHv3bv/1b6Pb8+fMlSdOnT9eGDRu0cOFCNTU1afbs2aqvr1dWVpbKysoUHx8fus/KlSsVHR2tyZMnq6mpSaNGjdKGDRsUFRV10c8HAABEHodlWZbdQ9gtEAjI5XLJ7/ef8f07mY++YsNUF1fV8gc6dL+DT2V08iSRp/8Te+0eAQBwBmf7/f2tiH3PDgAAQGcgdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGC0aLsHAEx1y+pb7B6hS7077127RwCAc8KVHQAAYDRiBwAAGI3YAQAARiN2AACA0XiDMgBEiOL8N+0eocvNfXaC3SPgMkTsALjoKm4fbvcIXWr4OxV2jwDgn/AyFgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwmjGfxlqzZo2WL1+u2tpaDRkyRKtWrdJtt91m91gAAHSp/U//we4RutzgxSMv6P5GxM6rr76qvLw8rVmzRrfccoteeOEFjRs3Th999JH69+9v93gAgAv09A//n90jdLnFm39t9wjGMuJlrBUrVmjmzJl68MEHNXjwYK1atUopKSlau3at3aMBAACbXfJXdpqbm1VVVaWf/exnYftzc3O1e/fuM94nGAwqGAyGbvv9fklSIBA44/rWYFMnTRu52jv3s2k40drJk0Sejj43J5tOdvIkkaWjz4skHTvJc3MmTcHjnTxJ5Onoc3OipaWTJ4k8HX1uGk8c6+RJIk97z823+y3L+u4HsC5xf//73y1J1rvvvhu2/+mnn7auueaaM97nySeftCSxsbGxsbGxGbAdOnToO1vhkr+y8y2HwxF227KsNvu+tWjRIs2fPz90+9SpU/r666+VlJTU7n0ulkAgoJSUFB06dEgJCQm2zhJpeG7ax3PTPp6b9vHctI/n5swi7XmxLEsNDQ3yer3fue6Sj53k5GRFRUXJ5/OF7a+rq5Pb7T7jfZxOp5xOZ9i+K6+8sqtG7JCEhISI+A8pEvHctI/npn08N+3juWkfz82ZRdLz4nK5zrrmkn+DckxMjDIzM1VeXh62v7y8XDk5OTZNBQAAIsUlf2VHkubPn6/7779fw4YNU3Z2ttatW6eDBw/qoYcesns0AABgMyNi57777tORI0f01FNPqba2Vunp6frd736nAQMG2D3aeXM6nXryySfbvMwGnpvvwnPTPp6b9vHctI/n5swu1efFYVln+7wWAADApeuSf88OAADAdyF2AACA0YgdAABgNGIHAAAYjdiJMGvWrFFqaqq6d++uzMxM/fGPf7R7JNu98847mjBhgrxerxwOh7Zu3Wr3SBGjqKhIN998s+Lj49WnTx/dfffd+vjjj+0eKyKsXbtWQ4cODf3xs+zsbL311lt2jxVxioqK5HA4lJeXZ/cotisoKJDD4QjbPB6P3WNFhJMnT+rnP/+5UlNTFRsbq4EDB+qpp57SqVOn7B7tnBA7EeTVV19VXl6eFi9erA8++EC33Xabxo0bp4MHD9o9mq2OHTum66+/XsXFxXaPEnEqKio0Z84cvffeeyovL9fJkyeVm5urY8fM/2LAs+nXr5+eeeYZ7dmzR3v27NHIkSN11113qaamxu7RIkZlZaXWrVunoUOH2j1KxBgyZIhqa2tD2969e+0eKSIsXbpU//mf/6ni4mLt379fy5Yt0/Lly7V69Wq7RzsnfPQ8gmRlZemmm27S2rVrQ/sGDx6su+++W0VFRTZOFjkcDodKS0t199132z1KRPrqq6/Up08fVVRU6Pbbb7d7nIiTmJio5cuXa+bMmXaPYrvGxkbddNNNWrNmjZYsWaIbbrhBq1atsnssWxUUFGjr1q2qrq62e5SIM378eLndbq1fvz6075577lGPHj20adMmGyc7N1zZiRDNzc2qqqpSbm5u2P7c3Fzt3r3bpqlwqfH7/ZK++aWO/9Pa2qqSkhIdO3ZM2dnZdo8TEebMmaM777xTo0ePtnuUiPLpp5/K6/UqNTVVU6ZM0eeff273SBHh1ltv1dtvv61PPvlEkvSXv/xFu3bt0h133GHzZOfGiL+gbIJ//OMfam1tbfPlpW63u82XnAJnYlmW5s+fr1tvvVXp6el2jxMR9u7dq+zsbJ04cUI9e/ZUaWmprrvuOrvHsl1JSYnef/99VVZW2j1KRMnKytIrr7yia665Rl9++aWWLFminJwc1dTUKCkpye7xbPXYY4/J7/fre9/7nqKiotTa2qqnn35aP/jBD+we7ZwQOxHG4XCE3bYsq80+4Ezmzp2rDz/8ULt27bJ7lIhx7bXXqrq6WkePHtVrr72m6dOnq6Ki4rIOnkOHDumRRx5RWVmZunfvbvc4EWXcuHGhf2dkZCg7O1uDBg3Sxo0bNX/+fBsns9+rr76qzZs3a8uWLRoyZIiqq6uVl5cnr9er6dOn2z3eWRE7ESI5OVlRUVFtruLU1dW1udoDnG7evHl644039M4776hfv352jxMxYmJidPXVV0uShg0bpsrKSv3yl7/UCy+8YPNk9qmqqlJdXZ0yMzND+1pbW/XOO++ouLhYwWBQUVFRNk4YOeLi4pSRkaFPP/3U7lFs9+ijj+pnP/uZpkyZIumbGPzf//1fFRUVXRKxw3t2IkRMTIwyMzNVXl4etr+8vFw5OTk2TYVIZ1mW5s6dq9/85jf6wx/+oNTUVLtHimiWZSkYDNo9hq1GjRqlvXv3qrq6OrQNGzZM06ZNU3V1NaHzT4LBoPbv36++ffvaPYrtjh8/riuuCE+GqKioS+aj51zZiSDz58/X/fffr2HDhik7O1vr1q3TwYMH9dBDD9k9mq0aGxv12WefhW4fOHBA1dXVSkxMVP/+/W2czH5z5szRli1b9Prrrys+Pj50ZdDlcik2Ntbm6ez1+OOPa9y4cUpJSVFDQ4NKSkq0c+dObdu2ze7RbBUfH9/mPV1xcXFKSkq67N/rtWDBAk2YMEH9+/dXXV2dlixZokAgcElcuehqEyZM0NNPP63+/ftryJAh+uCDD7RixQrNmDHD7tHOjYWI8vzzz1sDBgywYmJirJtuusmqqKiweyTb7dixw5LUZps+fbrdo9nuTM+LJOvll1+2ezTbzZgxI/T/Uu/eva1Ro0ZZZWVldo8VkYYPH2498sgjdo9hu/vuu8/q27ev1a1bN8vr9VqTJk2yampq7B4rIgQCAeuRRx6x+vfvb3Xv3t0aOHCgtXjxYisYDNo92jnh7+wAAACj8Z4dAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQCXLIfDoa1bt0qS/va3v8nhcKi6utrWmQBEHmIHQMSqq6vTrFmz1L9/fzmdTnk8Ho0dO1Z/+tOfJEm1tbUaN27ceT3ma6+9pqysLLlcLsXHx2vIkCHKz8/vivEBRAi+CBRAxLrnnnvU0tKijRs3auDAgfryyy/19ttv6+uvv5YkeTye83q87du3a8qUKSosLNTEiRPlcDj00Ucf6e233+6K8QFECL4bC0BEOnr0qHr16qWdO3dq+PDhZ1zjcDhUWlqqu+++W3/729+UmpqqX/3qV3ruuef0/vvva9CgQXr++ec1YsQISVJeXp7+8pe/aMeOHe3+3IKCAm3dulU/+clPtGTJEh05ckR33nmnXnzxRV155ZVdcKYAuhovYwGISD179lTPnj21detWBYPBc77fo48+qvz8fH3wwQfKycnRxIkTdeTIEUnfXAmqqanRvn37vvMxPvvsM/33f/+33nzzTW3btk3V1dWaM2fOBZ0PAPsQOwAiUnR0tDZs2KCNGzfqyiuv1C233KLHH39cH3744Xfeb+7cubrnnns0ePBgrV27Vi6XS+vXr5ckzZs3TzfffLMyMjJ01VVXacqUKXrppZfaxNSJEye0ceNG3XDDDbr99tu1evVqlZSUyOfzddn5Aug6xA6AiHXPPffoiy++0BtvvKGxY8dq586duummm7Rhw4Z275OdnR36d3R0tIYNG6b9+/dLkuLi4vTb3/5Wn332mX7+85+rZ8+eys/P17/+67/q+PHjofv1799f/fr1C3vMU6dO6eOPP+78kwTQ5YgdABGte/fuGjNmjJ544gnt3r1bP/rRj/Tkk0+e12M4HI6w24MGDdKDDz6o//qv/9L777+vjz76SK+++upZ73/64wC4NBA7AC4p1113nY4dO9bu8ffeey/075MnT6qqqkrf+9732l1/1VVXqUePHmGPefDgQX3xxReh23/60590xRVX6JprrrnA6QHYgY+eA4hIR44c0b333qsZM2Zo6NChio+P1549e7Rs2TLddddd7d7v+eefV1pamgYPHqyVK1eqvr5eM2bMkPTNJ62OHz+uO+64QwMGDNDRo0f13HPPqaWlRWPGjAk9Rvfu3TV9+nT9x3/8hwKBgB5++GFNnjz5vD/qDiAyEDsAIlLPnj2VlZWllStX6q9//ataWlqUkpKiH//4x3r88cfbvd8zzzyjpUuX6oMPPtCgQYP0+uuvKzk5WZI0fPhwPf/883rggQf05ZdfqlevXrrxxhtVVlama6+9NvQYV199tSZNmqQ77rhDX3/9te644w6tWbOmy88ZQNfg7+wAwD/59u/s8LUTgDl4zw4AADAasQMAAIzGy1gAAMBoXNkBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGO3/AwWrPB8P7nkwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"SibSp\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4fdcfe",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1665556d",
   "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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "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]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    titanic_data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4332f2db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a274b491",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    sns.heatmap(titanic_data.isnull(), yticklabels = False, cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf4f28e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Age'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjMAAAGwCAYAAABcnuQpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAwpUlEQVR4nO3dfXRU9YH/8c9N0JmJJEFRZoiEGDViIdCiPDQEBatAqfKwIKIJHli1q4K6Abso0tbgQyJxQRQsVRaBClmsDzzYqiHqEpWHbrCLIniwWhaCGqP5YRJCZiLJ/f3BMiWGpwiZ772Z9+uce07m3puZz8wZmE++9zv3WrZt2wIAAHCpGNMBAAAATgVlBgAAuBplBgAAuBplBgAAuBplBgAAuBplBgAAuBplBgAAuFo70wFaW2Njo7744gvFx8fLsizTcQAAwEmwbVs1NTVKSkpSTMzxx17afJn54osvlJycbDoGAAD4AcrKytSlS5fj7tPmy0x8fLykQy9GQkKC4TQAAOBkVFdXKzk5Ofw5fjxtvswcPrSUkJBAmQEAwGVOZooIE4ABAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrGS0zBw8e1K9//WulpqbK5/Ppwgsv1EMPPaTGxsbwPrZtKzc3V0lJSfL5fBo8eLC2b99uMDUAAHASo2Vm9uzZ+v3vf68FCxbo448/VkFBgR5//HHNnz8/vE9BQYHmzp2rBQsWqLS0VIFAQEOGDFFNTY3B5AAAwCks27ZtUw9+3XXXye/3a/HixeF1Y8eOVVxcnJ5//nnZtq2kpCTl5OTovvvukySFQiH5/X7Nnj1bt99++wkfo7q6WomJiaqqqnLthSZt21YwGDSeIRQKSZI8Hs9JXfirNXm9XuMZAACtpyWf30avmj1w4ED9/ve/1yeffKJLLrlEH3zwgd577z3NmzdPkrRr1y6Vl5dr6NCh4d/xeDwaNGiQNm7ceNQyEwqFwh+60qEXw+2CwaCGDRtmOoajFBUVyefzmY4BAHAAo2XmvvvuU1VVlS699FLFxsaqoaFBjz76qG666SZJUnl5uSTJ7/c3+T2/36/du3cf9T7z8/M1a9as1g0OAAAcw2iZeeGFF7R8+XIVFhaqR48e2rp1q3JycpSUlKSJEyeG9/v+4QTbto95iGHGjBmaNm1a+HZ1dbWSk5Nb5wlEiNfrVVFRkdEMwWBQo0aNkiStWbNGXq/XaB7Tjw8AcA6jZebf/u3fdP/99+vGG2+UJPXs2VO7d+9Wfn6+Jk6cqEAgIOnQCE3nzp3Dv1dRUdFstOYwj8cjj8fT+uEjyLIsRx1S8Xq9jsoDAIhuRr/NdODAAcXENI0QGxsb/mp2amqqAoGAiouLw9vr6+tVUlKiAQMGRDQrAABwJqMjMyNGjNCjjz6qrl27qkePHvqf//kfzZ07V7fccoukQyMSOTk5ysvLU1pamtLS0pSXl6e4uDhlZWWZjA4AABzCaJmZP3++fvOb32jy5MmqqKhQUlKSbr/9dv32t78N7zN9+nTV1dVp8uTJ2rdvn/r3769169YpPj7eYHIAAOAURs8zEwlt4TwzTlBXVxf+ejhfiwYAtLaWfH5zbSYAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBkAAE6TDRs2aNy4cdqwYYPpKFGFMgMAwGkQDAY1Z84cffXVV5ozZ46CwaDpSFGDMgMAwGmwfPlyVVZWSpIqKyu1YsUKw4miB2UGAIBTtHfvXq1YsUK2bUuSbNvWihUrtHfvXsPJogNlBgCAU2Dbtp544oljrj9ccNB6KDMAAJyC3bt3q7S0VA0NDU3WNzQ0qLS0VLt37zaULHpQZgAAOAUpKSnq27evYmNjm6yPjY1Vv379lJKSYihZ9KDMAABwCizL0tSpU4+53rIsA6miC2UGAIBT1KVLF2VnZ4eLi2VZys7O1vnnn284WXSgzAAAcBpMmDBBHTt2lCSde+65ys7ONpwoelBmAAA4Dbxer+699175/X5NmzZNXq/XdKSo0c50AAAA2orMzExlZmaajhF1GJkBAACuRpkBAACuRpkBAACuRpkBAOA02bBhg8aNG6cNGzaYjhJVKDMAAJwGwWBQc+bM0VdffaU5c+YoGAyajhQ1KDMAXIu/guEky5cvV2VlpSSpsrJSK1asMJwoelBmALgSfwXDSfbu3asVK1aEr5Bt27ZWrFihvXv3Gk4WHYyWmQsuuECWZTVbpkyZIunQmyE3N1dJSUny+XwaPHiwtm/fbjIyAIfgr2A4hW3beuKJJ465/nDBQesxWmZKS0v15Zdfhpfi4mJJ0rhx4yRJBQUFmjt3rhYsWKDS0lIFAgENGTJENTU1JmMDMIy/guEku3fvVmlpqRoaGpqsb2hoUGlpqXbv3m0oWfQwWmbOO+88BQKB8PKnP/1JF110kQYNGiTbtjVv3jzNnDlTY8aMUXp6upYtW6YDBw6osLDwmPcZCoVUXV3dZAHQdvBXMJwmJSVFffv2VWxsbJP1sbGx6tevn1JSUgwlix6OmTNTX1+v5cuX65ZbbpFlWdq1a5fKy8s1dOjQ8D4ej0eDBg3Sxo0bj3k/+fn5SkxMDC/JycmRiA8gQvgrGE5jWZamTp16zPWHr6SN1uOYMrN69Wp9++23mjRpkiSpvLxckuT3+5vs5/f7w9uOZsaMGaqqqgovZWVlrZYZQOTxVzCcqEuXLsrOzg4XF8uylJ2drfPPP99wsujgmDKzePFiDR8+XElJSU3Wf7/R2rZ93Jbr8XiUkJDQZAHQdvBXMJxqwoQJ6tixoyTp3HPPVXZ2tuFE0cMRZWb37t168803ddttt4XXBQIBSWo2ClNRUdFstAZAdOGvYDiR1+vVvffeK7/fr2nTpsnr9ZqOFDUcUWaWLFmiTp066dprrw2vS01NVSAQCH/DSTo0r6akpEQDBgwwEROAg/BXMJwoMzNTL774ojIzM01HiSrGy0xjY6OWLFmiiRMnql27duH1lmUpJydHeXl5WrVqlT766CNNmjRJcXFxysrKMpgYgBPwVzCAw9qdeJfW9eabb2rPnj265ZZbmm2bPn266urqNHnyZO3bt0/9+/fXunXrFB8fbyApAKfJzMzkL2AAsuw2flKG6upqJSYmqqqqisnAp6Curk7Dhg2TJBUVFcnn8xlOBABoy1ry+W38MBMAAMCpoMwAAABXo8wAAABXo8wAAABXo8wAcK0NGzZo3Lhx2rBhg+koAAyizABwpWAwqDlz5uirr77SnDlzFAwGTUcCYAhlBoArLV++XJWVlZKkyspKrVixwnAiAKZQZgC4zt69e7VixQodPk2WbdtasWKF9u7dazgZABMoMwBcxbZtPfHEE8dc38bPAwqHYx6XGZQZAK6ye/dulZaWqqGhocn6hoYGlZaWavfu3YaSIdoxj8scygwAV0lJSVHfvn0VGxvbZH1sbKz69eunlJQUQ8kQ7ZjHZQ5lBoCrWJalqVOnHnO9ZVkGUiHaMY/LLMoMANfp0qWLsrOzw8XFsixlZ2fr/PPPN5wM0Yh5XOZRZgC40oQJE9SxY0dJ0rnnnqvs7GzDiRCtmMdlHmUGgCt5vV7de++98vv9mjZtmrxer+lIiFLM4zKPMgPAtTIzM/Xiiy8qMzPTdBREMeZxmUeZAQDgFDGPyyzKDAAAp8GECRPUvn17SVJ8fDzzuCKIMgMAwGnCISUzKDMAAJwGy5cvV01NjSSppqaGk+ZFEGUGAIBTxEnzzKLMAHAtLuoHJ+CkeeZRZgC4Ehf1g1Nw0jzzKDMAXImL+sEpOGmeeZQZAK7D/AQ4CSfNM48yA8BVmJ8AJ+KkeWZRZgC4CvMT4FTXX399kzIzduxYw4miB2UGgKswPwFO9dJLL6mxsVGS1NjYqJdfftlwouhBmQHgKsxPgBMdnsd1JOZxRQ5lBoDrHJ6fcCTmJ8AU5nGZR5kB4ErXX3+9YmIO/RcWExPD/AQYwzwu84yXmc8//1wTJkxQx44dFRcXp5/85Cd6//33w9tt21Zubq6SkpLk8/k0ePBgbd++3WBiAE7w0ksvNflqNvMTYArzuMwzWmb27dunzMxMnXHGGXr99de1Y8cOzZkzRx06dAjvU1BQoLlz52rBggUqLS1VIBDQkCFDwhfzAhB9OM8MnIR5XOYZLTOzZ89WcnKylixZon79+umCCy7Q1VdfrYsuukjSof+g5s2bp5kzZ2rMmDFKT0/XsmXLdODAARUWFh71PkOhkKqrq5ssANoO5ifAiTjPjFlGy8zatWvVp08fjRs3Tp06dVLv3r21aNGi8PZdu3apvLxcQ4cODa/zeDwaNGiQNm7ceNT7zM/PV2JiYnhJTk5u9ecBIHKYnwCnmjBhguLj4yVJCQkJzSapo/UYLTN///vftXDhQqWlpamoqEh33HGH7rnnHv3hD3+QJJWXl0uS/H5/k9/z+/3hbd83Y8YMVVVVhZeysrLWfRIAIurw/ITDk38PY34CnODwyODh880gMoyWmcbGRl122WXKy8tT7969dfvtt+uXv/ylFi5c2GS/7x9vtG37mMcgPR6PEhISmiwA2o7D8xC+fzjJtm3mJ8Co5cuXa//+/ZKk/fv3c/HTCDJaZjp37qzu3bs3WfejH/1Ie/bskSQFAgFJajYKU1FR0Wy0BkB0s22b+TIwhknpZhktM5mZmdq5c2eTdZ988kl4mDg1NVWBQEDFxcXh7fX19SopKdGAAQMimhWAMxye6Pv9ERjLspgADCOYlG6e0TIzdepUbd68WXl5efr0009VWFioZ599VlOmTJF06D+nnJwc5eXladWqVfroo480adIkxcXFKSsry2R0AIYcngD8/TkJjY2NTACGEUxKN89omenbt69WrVql//zP/1R6eroefvhhzZs3r8kM8OnTpysnJ0eTJ09Wnz599Pnnn2vdunXhGeMAogsnKIPT8J40z7Lb+PhXdXW1EhMTVVVVxWTgU1BXV6dhw4ZJkoqKiuTz+QwnQjTbu3evbr755iZ/Cbdr107PP/885/WAEbwnT7+WfH4bv5wBALQUF5qE03DSPLMoMwBcacSIEU1uX3fddYaSAIccvs6gJJ177rmcNC+CKDMAXOmhhx5qcvvhhx82lAQ4xOv16t5775Xf79e0adPk9XpNR4oa7UwHAICW2rJli7Zt29Zk3YcffqgtW7aoT58+hlIBh045kpmZaTpG1GFkBoCrNDY2Kjc396jbcnNzOY08EIUoMwBcZdOmTaqurj7qturqam3atCnCiQCYRpkB4CoZGRnH/JpmYmKiMjIyIpwI+IcNGzZo3Lhx2rBhg+koUYUyA8BVYmJijnmYadasWc2upg1ESjAY1Jw5c/TVV19pzpw5CgaDpiNFDf7VA3CdPn36qGfPnk3W9erVS5dddpmhRMChq2ZXVlZKkiorK7lqdgRRZgC40qOPPhoehYmJidEjjzxiOBGiGVfNNosyA8CVOnTooAkTJigmJkYTJkxQhw4dTEdClOKq2eZxnhkALWbbtvH5ALZta+zYsRo7dqw8Ho/q6uqM5vF6veFT2SO6HL5q9vcdedXsCy64IPLBoghlBkCLBYPB8IVHcQgXYI1eh6+a/de//rXJhSZjY2N1+eWXc9XsCOAwEwAAp8CyLE2dOvWY6xmxa32MzABoMa/Xq6KiIqMZgsGgRo0aJUlas2aN8evgmH58mHX4qtnPP/+8bNvmqtkRRpkB0GKWZTnqkIrX63VUHkSnCRMm6LXXXtM333zDVbMjjMNMAACcBlw12xxGZgAAOE24arYZjMwAAABXo8wAAABXo8wAAABXo8wAAABXo8wAAABXo8wAAABXo8wAAABXo8wAAABXo8wAAABXo8wAAABX43IGAADXs21bwWDQeIZQKCRJ8ng8sizLaB6v12s8Q6RQZgAArhcMBjVs2DDTMRylqKgoaq4mz2EmAADgakZHZnJzczVr1qwm6/x+v8rLyyUdGrKbNWuWnn32We3bt0/9+/fX008/rR49epiICwBwKK/Xq6KiIqMZgsGgRo0aJUlas2aNvF6v0TymHz+SjB9m6tGjh958883w7djY2PDPBQUFmjt3rpYuXapLLrlEjzzyiIYMGaKdO3cqPj7eRFwAgANZluWoQyper9dRedo644eZ2rVrp0AgEF7OO+88SYdGZebNm6eZM2dqzJgxSk9P17Jly3TgwAEVFhYaTg0AAJzCeJn529/+pqSkJKWmpurGG2/U3//+d0nSrl27VF5erqFDh4b39Xg8GjRokDZu3HjM+wuFQqqurm6yAACAtstomenfv7/+8Ic/qKioSIsWLVJ5ebkGDBigysrK8LwZv9/f5HeOnFNzNPn5+UpMTAwvycnJrfocAACAWUbLzPDhwzV27Fj17NlT11xzjf785z9LkpYtWxbe5/vfkbdt+7jfm58xY4aqqqrCS1lZWeuEBwAAjmB8AvCRzjrrLPXs2VN/+9vfNHr0aElSeXm5OnfuHN6noqKi2WjNkTwejzwez2nL5IQTMTnBka8Br8ch0XRCKgBwMkeVmVAopI8//lhXXHGFUlNTFQgEVFxcrN69e0uS6uvrVVJSotmzZ0csEydiau7wVw+jXTSdkAoAnMxomfnVr36lESNGqGvXrqqoqNAjjzyi6upqTZw4UZZlKScnR3l5eUpLS1NaWpry8vIUFxenrKwsk7EBAICDGC0ze/fu1U033aRvvvlG5513nn76059q8+bNSklJkSRNnz5ddXV1mjx5cvikeevWrTN2jpnay7KlGEcNZkWObUuNBw/9HNNOitbDK40HddZfV5hOAQA4gtFP5pUrVx53u2VZys3NVW5ubmQCnUhMOyn2DNMpDDrTdAAAAJoxfp4ZAACAU0GZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArkaZAQAArvaDy0x9fb127typgwcPns48AAAALdLiMnPgwAHdeuutiouLU48ePbRnzx5J0j333KPHHnvstAcEAAA4nhaXmRkzZuiDDz7Q+vXr5fV6w+uvueYavfDCC6c1HAAAwIm0a+kvrF69Wi+88IJ++tOfyrKs8Pru3bvrs88+O63hAAAATqTFIzNff/21OnXq1Gx9bW1tk3IDAAAQCS0uM3379tWf//zn8O3DBWbRokXKyMg4fckAAABOQosPM+Xn5+vnP/+5duzYoYMHD+rJJ5/U9u3btWnTJpWUlLRGRgAAgGNq8cjMgAEDtGHDBh04cEAXXXSR1q1bJ7/fr02bNunyyy9vjYwAAADH9IPOM9OzZ08tW7ZMH330kXbs2KHly5erZ8+epxQkPz9flmUpJycnvM62beXm5iopKUk+n0+DBw/W9u3bT+lxAABA29LiMlNdXX3UpaamRvX19T8oRGlpqZ599ln16tWryfqCggLNnTtXCxYsUGlpqQKBgIYMGaKampof9DgAAKDtaXGZ6dChg84+++xmS4cOHeTz+ZSSkqIHH3xQjY2NJ3V/+/fvV3Z2thYtWqSzzz47vN62bc2bN08zZ87UmDFjlJ6ermXLlunAgQMqLCxsaWwAANBGtbjMLF26VElJSXrggQe0evVqrVq1Sg888IDOP/98LVy4UP/yL/+ip5566qTPBjxlyhRde+21uuaaa5qs37Vrl8rLyzV06NDwOo/Ho0GDBmnjxo3HvL9QKNRs1AgAALRdLf4207JlyzRnzhzdcMMN4XUjR45Uz5499cwzz+itt95S165d9eijj+qBBx447n2tXLlSf/3rX1VaWtpsW3l5uSTJ7/c3We/3+7V79+5j3md+fr5mzZrVkqcEAABcrMUjM5s2bVLv3r2bre/du7c2bdokSRo4cGD4mk3HUlZWpn/913/V8uXLm1wW4fu+fyI+27aPe3K+GTNmqKqqKryUlZUdNwcAAHC3FpeZLl26aPHixc3WL168WMnJyZKkysrKJvNfjub9999XRUWFLr/8crVr107t2rVTSUmJnnrqKbVr1y48InN4hOawioqKZqM1R/J4PEpISGiyAACAtqvFh5n+/d//XePGjdPrr7+uvn37yrIslZaW6uOPP9bLL78s6dC3k8aPH3/c+7n66qu1bdu2Juv++Z//WZdeeqnuu+8+XXjhhQoEAiouLg6PBNXX16ukpESzZ89uaWwAANBGtbjMjBw5Up988okWLlyoTz75RLZta/jw4Vq9erW+/fZbSdKdd955wvuJj49Xenp6k3VnnXWWOnbsGF6fk5OjvLw8paWlKS0tTXl5eYqLi1NWVlZLYwMAgDaqxWVGklJSUsLfVvr222+1YsUKjR07Vlu3blVDQ8NpCzd9+nTV1dVp8uTJ2rdvn/r3769169YpPj7+tD0GAABwtx9UZiTp7bff1nPPPadXXnlFKSkpGjt2rP7jP/7jlMKsX7++yW3LspSbm6vc3NxTul8AANB2tajM7N27V0uXLtVzzz2n2tpa3XDDDfruu+/08ssvq3v37q2VEQAA4JhO+ttMv/jFL9S9e3ft2LFD8+fP1xdffKH58+e3ZjYAAIATOumRmXXr1umee+7RnXfeqbS0tNbMBAAAcNJOusy8++67eu6559SnTx9deumluvnmm0/49eu2wLbtf9xo+M5cEDjDEe+BJu8NAIAxJ11mMjIylJGRoSeffFIrV67Uc889p2nTpqmxsVHFxcVKTk5uk98yCoVC4Z/P+h8ucIl/CIVCiouLMx0DAKJei88AHBcXp1tuuUXvvfeetm3bpnvvvVePPfaYOnXqpJEjR7ZGRgAAgGP6wV/NlqRu3bqpoKBA+fn5evXVV/Xcc8+drlyO4fF4wj/X9s6SYs8wmAbGNXwXHqE78r0BADDnlMrMYbGxsRo9erRGjx59Ou7OUZpc1DL2DMoMwo53wVMAQOScljIDIHJs21YwGDQdw7gjXwNej0O8Xi8lG1GJMgO4TDAY1LBhw0zHcJRRo0aZjuAIRUVF8vl8pmMAEdfiCcAAAABOwsgM4GJPX/mtPLHReb4b25bqGw/9fGaMFK1HV0INlqa808F0DMAoygzgYp5YW95Y0ynM4YCKJEVnmQWOxGEmAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgapQZAADgalxoEgBwSmzbVjAYNB3DuCNfA16PQ7xer6wIXNKeMgMAOCXBYFDDhg0zHcNRRo0aZTqCIxQVFcnna/3r23OYCQAAuBojMwCA06ZhREP0frLYkhr+7+dYSa1/dMWZDkqxr8ZG9CGj9S0HAGgN7RTdnyxnmA4QnTjMBAAAXM1omVm4cKF69eqlhIQEJSQkKCMjQ6+//np4u23bys3NVVJSknw+nwYPHqzt27cbTAwAAJzGaJnp0qWLHnvsMW3ZskVbtmzRz372M40aNSpcWAoKCjR37lwtWLBApaWlCgQCGjJkiGpqakzGBgAADmK0zIwYMUK/+MUvdMkll+iSSy7Ro48+qvbt22vz5s2ybVvz5s3TzJkzNWbMGKWnp2vZsmU6cOCACgsLTcYGAAAO4pg5Mw0NDVq5cqVqa2uVkZGhXbt2qby8XEOHDg3v4/F4NGjQIG3cuPGY9xMKhVRdXd1kAQAAbZfxMrNt2za1b99eHo9Hd9xxh1atWqXu3burvLxckuT3+5vs7/f7w9uOJj8/X4mJieElOTm5VfMDAACzjJeZbt26aevWrdq8ebPuvPNOTZw4UTt27Ahv//5pkG3bPu6pkWfMmKGqqqrwUlZW1mrZAQCAecbPBnDmmWfq4osvliT16dNHpaWlevLJJ3XfffdJksrLy9W5c+fw/hUVFc1Ga47k8Xjk8XhaNzRgkG3b4Z9DDcfZEVHhyPfAke8NIJoYLzPfZ9u2QqGQUlNTFQgEVFxcrN69e0uS6uvrVVJSotmzZxtOCZgTCoXCP09552yDSeA0oVBIcXFxpmMAEWe0zDzwwAMaPny4kpOTVVNTo5UrV2r9+vV64403ZFmWcnJylJeXp7S0NKWlpSkvL09xcXHKysoyGRsAADiI0TLz1Vdf6eabb9aXX36pxMRE9erVS2+88YaGDBkiSZo+fbrq6uo0efJk7du3T/3799e6desUHx9vMjZg1JGHUZ++cp88kb0EChwm1PCPEToOsSNaGS0zixcvPu52y7KUm5ur3NzcyAQCXODICfCeWMlLmcH/Od6XI4C2zPi3mQAAAE4FZQYAALgaZQYAALgaZQYAALia484z42iNB00nMMe2//H8Y9pJ0TrRMJrfAwDgUJSZFjjrrytMRwAAAN/DYSYAAOBqjMycgNfrVVFRkekYxgWDQY0aNUqStGbNGnm9XsOJzOM1AABnoMycgGVZ8vl8pmM4itfr5TUBADgGh5kAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrUWYAAICrcZ4ZAMApsW37Hze4fBmOeA80eW+0IsoMAOCUhEKh8M+xr8YaTAKnCYVCiouLa/XH4TATAABwNUZmAACnxOPxhH9uGNHAJ0u0O/iPEboj3xutibccAOCUWJb1jxvtxCcLwpq8N1oRbznAxUINlqTITLBzGtuW6hsP/XxmjBSh/zMd59B7AIhulBnAxaa808F0BAAwjgnAAADA1RiZAVzG6/WqqKjIdAzjgsGgRo0aJUlas2aNvF6v4UTm8RogWlFmAJexLEs+n890DEfxer28JkAU4zATAABwNcoMAABwNcoMAABwNcoMAABwNcoMAABwNcoMAABwNaNlJj8/X3379lV8fLw6deqk0aNHa+fOnU32sW1bubm5SkpKks/n0+DBg7V9+3ZDiQEAgNMYLTMlJSWaMmWKNm/erOLiYh08eFBDhw5VbW1teJ+CggLNnTtXCxYsUGlpqQKBgIYMGaKamhqDyQEAgFMYPWneG2+80eT2kiVL1KlTJ73//vu68sorZdu25s2bp5kzZ2rMmDGSpGXLlsnv96uwsFC33357s/sMhUIKhULh29XV1a37JAAAgFGOmjNTVVUlSTrnnHMkSbt27VJ5ebmGDh0a3sfj8WjQoEHauHHjUe8jPz9fiYmJ4SU5Obn1gwMAAGMcU2Zs29a0adM0cOBApaenS5LKy8slSX6/v8m+fr8/vO37ZsyYoaqqqvBSVlbWusEBAIBRjrk201133aUPP/xQ7733XrNtlmU1uW3bdrN1h3k8Hnk8nlbJCAAAnMcRIzN333231q5dq//6r/9Sly5dwusDgYAkNRuFqaioaDZaAwAAopPRMmPbtu666y698sorevvtt5Wamtpke2pqqgKBgIqLi8Pr6uvrVVJSogEDBkQ6LgAAcCCjh5mmTJmiwsJCrVmzRvHx8eERmMTERPl8PlmWpZycHOXl5SktLU1paWnKy8tTXFycsrKyTEYHAAAOYbTMLFy4UJI0ePDgJuuXLFmiSZMmSZKmT5+uuro6TZ48Wfv27VP//v21bt06xcfHRzgtAABwIqNlxrbtE+5jWZZyc3OVm5vb+oEAAKfmoOkABtmSGv7v51hJR/+eSttn4D3gmG8zAQDcL/bVWNMREIUc8W0mAACAH4qRGQDAKfF6vSoqKjIdw7hgMKhRo0ZJktasWSOv12s4kXmReg0oMwCAU2JZlnw+n+kYjuL1enlNIojDTAAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNUoMwAAwNWMlpl33nlHI0aMUFJSkizL0urVq5tst21bubm5SkpKks/n0+DBg7V9+3YzYQEAgCMZLTO1tbX68Y9/rAULFhx1e0FBgebOnasFCxaotLRUgUBAQ4YMUU1NTYSTAgAAp2pn8sGHDx+u4cOHH3WbbduaN2+eZs6cqTFjxkiSli1bJr/fr8LCQt1+++1H/b1QKKRQKBS+XV1dffqDAwAAx3DsnJldu3apvLxcQ4cODa/zeDwaNGiQNm7ceMzfy8/PV2JiYnhJTk6ORFwAAGCIY8tMeXm5JMnv9zdZ7/f7w9uOZsaMGaqqqgovZWVlrZoTAACYZfQw08mwLKvJbdu2m607ksfjkcfjae1YAADAIRw7MhMIBCSp2ShMRUVFs9EaAAAQvRxbZlJTUxUIBFRcXBxeV19fr5KSEg0YMMBgMgAA4CRGDzPt379fn376afj2rl27tHXrVp1zzjnq2rWrcnJylJeXp7S0NKWlpSkvL09xcXHKysoymBoAADiJ0TKzZcsWXXXVVeHb06ZNkyRNnDhRS5cu1fTp01VXV6fJkydr37596t+/v9atW6f4+HhTkQEAgMMYLTODBw+WbdvH3G5ZlnJzc5Wbmxu5UAAAwFUcO2cGAADgZFBmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq1FmAACAq7UzHQCA+9i2rWAwaDTDkY9vOoskeb1eWZZlOgYQlSgzAFosGAxq2LBhpmOEjRo1ynQEFRUVyefzmY4BRCUOMwEAAFdjZAZAi3m9XhUVFRnNkJ2drW+++SZ8+7zzztPy5cuN5fF6vcYeG4h2lBkALWZZltFDKq+//nqTIiNJX3/9tdavX6/hw4cbSgXAFA4zAXCVhoYGFRQUHHVbQUGBGhoaIpwIgGmUGQCusnbt2mMWloaGBq1duzbCiQCYRpkB4CojR45UbGzsUbe1a9dOI0eOjHAiAKZRZgC4SmxsrMaNG3fUbePHjz9m0QHQdlFmALhKY2OjXnvttaNue/XVV9XY2BjhRABMo8wAcJVNmzapurr6qNuqq6u1adOmCCcCYBplBoCrZGRkKCEh4ajbEhMTlZGREeFEAEyjzABwlZiYGN10001H3ZaVlaWYGP5bA6INJ81zAS7q1xwX9YtejY2Nev7554+6bdmyZRo/fjyFBogylBkX4KJ+zXFRv+i1ceNGHThw4KjbDhw4oI0bN2rgwIERTgXAJFeUmd/97nd6/PHH9eWXX6pHjx6aN2+errjiCtOxABhg2/YpbUfbxAh2c9E0gu34MvPCCy8oJydHv/vd75SZmalnnnlGw4cP144dO9S1a1fT8SLCCRf1s21boVBIkuTxeIz/A+GiftHr/PPPP6XtaJsYwW4umkawHV9m5s6dq1tvvVW33XabJGnevHkqKirSwoULlZ+fbzhdZJi+qN9hcXFxpiMASk1NVbdu3bRz585m2y699FKlpqYaSAXAJEeXmfr6er3//vu6//77m6wfOnSoNm7ceNTfCYVC4REEScc8HwUAd7IsSw8++KCysrKabXvwwQeNjxrCDEawm4umEWxHl5lvvvlGDQ0N8vv9Tdb7/X6Vl5cf9Xfy8/M1a9asSMQDYEiXLl10ww036I9//GN43fjx4znEFMUYwY5urvj+4vfbrW3bx2y8M2bMUFVVVXgpKyuLREQAEXbbbbeFP7zi4uJ06623Gk4EwBRHl5lzzz1XsbGxzUZhKioqmo3WHObxeJSQkNBkAdD2eL1e/fa3v5Xf79dvfvObqBpSB9CUo8vMmWeeqcsvv1zFxcVN1hcXF2vAgAGGUgFwiszMTL344ovKzMw0HQWAQY6eMyNJ06ZN080336w+ffooIyNDzz77rPbs2aM77rjDdDQAAOAAji8z48ePV2VlpR566CF9+eWXSk9P12uvvaaUlBTT0QAAgANYdhs/XWZ1dbUSExNVVVXF/BkAAFyiJZ/fjp4zAwAAcCKUGQAA4GqUGQAA4GqUGQAA4GqUGQAA4GqUGQAA4GqUGQAA4GqOP2neqTp8Gp3q6mrDSQAAwMk6/Ll9MqfDa/NlpqamRpKUnJxsOAkAAGipmpoaJSYmHnefNn8G4MbGRn3xxReKj4+XZVmm47hadXW1kpOTVVZWxtmU4Qi8J+E0vCdPH9u2VVNTo6SkJMXEHH9WTJsfmYmJiVGXLl1Mx2hTEhIS+EcKR+E9CafhPXl6nGhE5jAmAAMAAFejzAAAAFejzOCkeTwePfjgg/J4PKajAJJ4T8J5eE+a0eYnAAMAgLaNkRkAAOBqlBkAAOBqlBkAAOBqlBkAAOBqlBmc0DvvvKMRI0YoKSlJlmVp9erVpiMhyuXn56tv376Kj49Xp06dNHr0aO3cudN0LESxhQsXqlevXuGT5WVkZOj11183HStqUGZwQrW1tfrxj3+sBQsWmI4CSJJKSko0ZcoUbd68WcXFxTp48KCGDh2q2tpa09EQpbp06aLHHntMW7Zs0ZYtW/Szn/1Mo0aN0vbt201Hiwp8NRstYlmWVq1apdGjR5uOAoR9/fXX6tSpk0pKSnTllVeajgNIks455xw9/vjjuvXWW01HafPa/LWZALR9VVVVkg59eACmNTQ06MUXX1Rtba0yMjJMx4kKlBkArmbbtqZNm6aBAwcqPT3ddBxEsW3btikjI0PBYFDt27fXqlWr1L17d9OxogJlBoCr3XXXXfrwww/13nvvmY6CKNetWzdt3bpV3377rV5++WVNnDhRJSUlFJoIoMwAcK27775ba9eu1TvvvKMuXbqYjoMod+aZZ+riiy+WJPXp00elpaV68skn9cwzzxhO1vZRZgC4jm3buvvuu7Vq1SqtX79eqamppiMBzdi2rVAoZDpGVKDM4IT279+vTz/9NHx7165d2rp1q8455xx17drVYDJEqylTpqiwsFBr1qxRfHy8ysvLJUmJiYny+XyG0yEaPfDAAxo+fLiSk5NVU1OjlStXav369XrjjTdMR4sKfDUbJ7R+/XpdddVVzdZPnDhRS5cujXwgRD3Lso66fsmSJZo0aVJkwwCSbr31Vr311lv68ssvlZiYqF69eum+++7TkCFDTEeLCpQZAADgapwBGAAAuBplBgAAuBplBgAAuBplBgAAuBplBgAAuBplBgAAuBplBgAAuBplBgAAuBplBoBrTJo0SaNHjzYdA4DDUGYARNSkSZNkWZYsy9IZZ5yhCy+8UL/61a9UW1trOhoAl+JCkwAi7uc//7mWLFmi7777Tu+++65uu+021dbWauHChaajAXAhRmYARJzH41EgEFBycrKysrKUnZ2t1atXS5K2b9+ua6+9VgkJCYqPj9cVV1yhzz777Kj388Ybb2jgwIHq0KGDOnbsqOuuu67JvvX19brrrrvUuXNneb1eXXDBBcrPzw9vz83NVdeuXeXxeJSUlKR77rmnVZ83gNbByAwA43w+n7777jt9/vnnuvLKKzV48GC9/fbbSkhI0IYNG3Tw4MGj/l5tba2mTZumnj17qra2Vr/97W/1T//0T9q6datiYmL01FNPae3atfrjH/+orl27qqysTGVlZZKkl156SU888YRWrlypHj16qLy8XB988EEknzaA04QyA8Co//7v/1ZhYaGuvvpqPf3000pMTNTKlSt1xhlnSJIuueSSY/7u2LFjm9xevHixOnXqpB07dig9PV179uxRWlqaBg4cKMuylJKSEt53z549CgQCuuaaa3TGGWeoa9eu6tevX+s8SQCtisNMACLuT3/6k9q3by+v16uMjAxdeeWVmj9/vrZu3aorrrgiXGRO5LPPPlNWVpYuvPBCJSQkKDU1VdKhoiIdmmy8detWdevWTffcc4/WrVsX/t1x48aprq5OF154oX75y19q1apVxxwBAuBslBkAEXfVVVdp69at2rlzp4LBoF555RV16tRJPp+vRfczYsQIVVZWatGiRfrLX/6iv/zlL5IOzZWRpMsuu0y7du3Sww8/rLq6Ot1www26/vrrJUnJycnauXOnnn76afl8Pk2ePFlXXnmlvvvuu9P7ZAG0OsoMgIg766yzdPHFFyslJaXJKEyvXr307rvvnlShqKys1Mcff6xf//rXuvrqq/WjH/1I+/bta7ZfQkKCxo8fr0WLFumFF17Qyy+/rP/3//6fpENzdUaOHKmnnnpK69ev16ZNm7Rt27bT90QBRARzZgA4xl133aX58+frxhtv1IwZM5SYmKjNmzerX79+6tatW5N9zz77bHXs2FHPPvusOnfurD179uj+++9vss8TTzyhzp076yc/+YliYmL04osvKhAIqEOHDlq6dKkaGhrUv39/xcXF6fnnn5fP52syrwaAOzAyA8AxOnbsqLffflv79+/XoEGDdPnll2vRokVHnUMTExOjlStX6v3331d6erqmTp2qxx9/vMk+7du31+zZs9WnTx/17dtX//u//6vXXntNMTEx6tChgxYtWqTMzEz16tVLb731ll599VV17NgxUk8XwGli2bZtmw4BAADwQzEyAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXI0yAwAAXO3/A/Iah8W53fsCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Pclass\",y=\"Age\", data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82e0332b",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "13d5ae39",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop(\"Cabin\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "90740ae9",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d4d4d0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4581b509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(titanic_data.isnull(), yticklabels=False, cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26beb1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    sns.heatmap(titanic_data.isnull(), yticklabels = False, cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a315a6c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a97aead",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1a135825",
   "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>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "885       1     0\n",
       "886       0     1\n",
       "887       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[712 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(titanic_data['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0daeb512",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex= pd.get_dummies(titanic_data['Sex'], drop_first=True)\n",
    "sex.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "92c14814",
   "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>C</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C  Q  S\n",
       "0  0  0  1\n",
       "1  1  0  0\n",
       "2  0  0  1\n",
       "3  0  0  1\n",
       "4  0  0  1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark=pd.get_dummies(titanic_data[\"Embarked\"])\n",
    "embark.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7e1991a6",
   "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>Q</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark=pd.get_dummies(titanic_data[\"Embarked\"], drop_first=True)\n",
    "embark.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1ab4c424",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  2  3\n",
       "0  0  0  1\n",
       "1  1  0  0\n",
       "2  0  0  1\n",
       "3  1  0  0\n",
       "4  0  0  1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pcl=pd.get_dummies(titanic_data[\"Pclass\"])\n",
    "Pcl.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "31c4867c",
   "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>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2  3\n",
       "0  0  1\n",
       "1  0  0\n",
       "2  0  1\n",
       "3  0  0\n",
       "4  0  1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pcl=pd.get_dummies(titanic_data[\"Pclass\"],drop_first=True)\n",
    "Pcl.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c9760510",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data=pd.concat([titanic_data,sex,embark,Pcl], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "37275a7a",
   "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",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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  male  Q  S  2  3  \n",
       "0      0         A/5 21171   7.2500        S     1  0  1  0  1  \n",
       "1      0          PC 17599  71.2833        C     0  0  0  0  0  \n",
       "2      0  STON/O2. 3101282   7.9250        S     0  0  1  0  1  \n",
       "3      0            113803  53.1000        S     0  0  1  0  0  \n",
       "4      0            373450   8.0500        S     1  0  1  0  1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f756b47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data.drop(['Sex','Embarked','PassengerId','Name','Ticket', 'Pclass'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76536a1d",
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age  SibSp  Parch     Fare  male  Q  S  2  3\n",
       "0         0  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1         1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2         1  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3         1  35.0      1      0  53.1000     0  0  1  0  0\n",
       "4         0  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87182eef",
   "metadata": {},
   "source": [
    "## Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c43b1de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=titanic_data.drop(\"Survived\",axis=1)\n",
    "y=titanic_data[\"Survived\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b58f9074",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "712d6a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bf79fdda",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "089c0dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel=LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d5372ae9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: 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-1 {color: black;background-color: white;}#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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "370a1ad2",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[46], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mlogmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:419\u001b[0m, in \u001b[0;36mLinearClassifierMixin.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;124;03mPredict class labels for samples in X.\u001b[39;00m\n\u001b[0;32m    407\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;124;03m    Vector containing the class labels for each sample.\u001b[39;00m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    418\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[1;32m--> 419\u001b[0m scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(scores\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    421\u001b[0m     indices \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(scores \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mint\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:400\u001b[0m, in \u001b[0;36mLinearClassifierMixin.decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    397\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    398\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[1;32m--> 400\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    401\u001b[0m scores \u001b[38;5;241m=\u001b[39m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n\u001b[0;32m    402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39mreshape(scores, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m scores\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m scores\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:529\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    464\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m    465\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    466\u001b[0m     X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m    471\u001b[0m ):\n\u001b[0;32m    472\u001b[0m     \u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m \n\u001b[0;32m    474\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    527\u001b[0m \u001b[38;5;124;03m        validated.\u001b[39;00m\n\u001b[0;32m    528\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    531\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m    532\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    533\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    534\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    535\u001b[0m         )\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:406\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    403\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    405\u001b[0m fitted_feature_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_names_in_\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 406\u001b[0m X_feature_names \u001b[38;5;241m=\u001b[39m \u001b[43m_get_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fitted_feature_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m X_feature_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    409\u001b[0m     \u001b[38;5;66;03m# no feature names seen in fit and in X\u001b[39;00m\n\u001b[0;32m    410\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1903\u001b[0m, in \u001b[0;36m_get_feature_names\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m   1901\u001b[0m \u001b[38;5;66;03m# mixed type of string and non-string is not supported\u001b[39;00m\n\u001b[0;32m   1902\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(types) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m types:\n\u001b[1;32m-> 1903\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   1904\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names are only supported if all input features have string names, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1905\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut your input has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtypes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m as feature name / column name types. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1906\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you want feature names to be stored and validated, you must convert \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1907\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthem all to strings, by using X.columns = X.columns.astype(str) for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1908\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexample. Otherwise you can remove feature / column names from your input \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1909\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata, or convert them all to a non-string data type.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1910\u001b[0m     )\n\u001b[0;32m   1912\u001b[0m \u001b[38;5;66;03m# Only feature names of all strings are supported\u001b[39;00m\n\u001b[0;32m   1913\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(types) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m types[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[1;31mTypeError\u001b[0m: Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type."
     ]
    }
   ],
   "source": [
    "\n",
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "20976687",
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "This LogisticRegression instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[44], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mlogmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:419\u001b[0m, in \u001b[0;36mLinearClassifierMixin.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;124;03mPredict class labels for samples in X.\u001b[39;00m\n\u001b[0;32m    407\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;124;03m    Vector containing the class labels for each sample.\u001b[39;00m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    418\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[1;32m--> 419\u001b[0m scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(scores\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    421\u001b[0m     indices \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(scores \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mint\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:397\u001b[0m, in \u001b[0;36mLinearClassifierMixin.decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    378\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecision_function\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[0;32m    379\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    380\u001b[0m \u001b[38;5;124;03m    Predict confidence scores for samples.\u001b[39;00m\n\u001b[0;32m    381\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    395\u001b[0m \u001b[38;5;124;03m        this class would be predicted.\u001b[39;00m\n\u001b[0;32m    396\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 397\u001b[0m     \u001b[43mcheck_is_fitted\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    398\u001b[0m     xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[0;32m    400\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(X, accept_sparse\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcsr\u001b[39m\u001b[38;5;124m\"\u001b[39m, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1390\u001b[0m, in \u001b[0;36mcheck_is_fitted\u001b[1;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[0;32m   1385\u001b[0m     fitted \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m   1386\u001b[0m         v \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mvars\u001b[39m(estimator) \u001b[38;5;28;01mif\u001b[39;00m v\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m v\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1387\u001b[0m     ]\n\u001b[0;32m   1389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fitted:\n\u001b[1;32m-> 1390\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m NotFittedError(msg \u001b[38;5;241m%\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m(estimator)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m})\n",
      "\u001b[1;31mNotFittedError\u001b[0m: This LogisticRegression instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator."
     ]
    }
   ],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "014aec69",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[46], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m predictions \u001b[38;5;241m=\u001b[39m \u001b[43mlogmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:419\u001b[0m, in \u001b[0;36mLinearClassifierMixin.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    405\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    406\u001b[0m \u001b[38;5;124;03mPredict class labels for samples in X.\u001b[39;00m\n\u001b[0;32m    407\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;124;03m    Vector containing the class labels for each sample.\u001b[39;00m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    418\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[1;32m--> 419\u001b[0m scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecision_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(scores\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    421\u001b[0m     indices \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(scores \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mint\u001b[39m)\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_base.py:400\u001b[0m, in \u001b[0;36mLinearClassifierMixin.decision_function\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    397\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m    398\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[1;32m--> 400\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    401\u001b[0m scores \u001b[38;5;241m=\u001b[39m safe_sparse_dot(X, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcoef_\u001b[38;5;241m.\u001b[39mT, dense_output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintercept_\n\u001b[0;32m    402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39mreshape(scores, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m scores\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m scores\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:529\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    464\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_data\u001b[39m(\n\u001b[0;32m    465\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    466\u001b[0m     X\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno_validation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    470\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m    471\u001b[0m ):\n\u001b[0;32m    472\u001b[0m     \u001b[38;5;124;03m\"\"\"Validate input data and set or check the `n_features_in_` attribute.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m \n\u001b[0;32m    474\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    527\u001b[0m \u001b[38;5;124;03m        validated.\u001b[39;00m\n\u001b[0;32m    528\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 529\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    531\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_tags()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires_y\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m    532\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    533\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m estimator \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    534\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequires y to be passed, but the target y is None.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    535\u001b[0m         )\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py:406\u001b[0m, in \u001b[0;36mBaseEstimator._check_feature_names\u001b[1;34m(self, X, reset)\u001b[0m\n\u001b[0;32m    403\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    405\u001b[0m fitted_feature_names \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeature_names_in_\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 406\u001b[0m X_feature_names \u001b[38;5;241m=\u001b[39m \u001b[43m_get_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    408\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fitted_feature_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m X_feature_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    409\u001b[0m     \u001b[38;5;66;03m# no feature names seen in fit and in X\u001b[39;00m\n\u001b[0;32m    410\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:1903\u001b[0m, in \u001b[0;36m_get_feature_names\u001b[1;34m(X)\u001b[0m\n\u001b[0;32m   1901\u001b[0m \u001b[38;5;66;03m# mixed type of string and non-string is not supported\u001b[39;00m\n\u001b[0;32m   1902\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(types) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m types:\n\u001b[1;32m-> 1903\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   1904\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names are only supported if all input features have string names, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1905\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut your input has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtypes\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m as feature name / column name types. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1906\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf you want feature names to be stored and validated, you must convert \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1907\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthem all to strings, by using X.columns = X.columns.astype(str) for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1908\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexample. Otherwise you can remove feature / column names from your input \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1909\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata, or convert them all to a non-string data type.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1910\u001b[0m     )\n\u001b[0;32m   1912\u001b[0m \u001b[38;5;66;03m# Only feature names of all strings are supported\u001b[39;00m\n\u001b[0;32m   1913\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(types) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m types[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "\u001b[1;31mTypeError\u001b[0m: Feature names are only supported if all input features have string names, but your input has ['int', 'str'] as feature name / column name types. If you want feature names to be stored and validated, you must convert them all to strings, by using X.columns = X.columns.astype(str) for example. Otherwise you can remove feature / column names from your input data, or convert them all to a non-string data type."
     ]
    }
   ],
   "source": [
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d3e1c14",
   "metadata": {},
   "outputs": [],
   "source": [
    "d"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}