titanic_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2c3e77a",
   "metadata": {},
   "outputs": [],
   "source": [
    "---ABHAY CHAND--- \n",
    "DATA SCIENCE INTERN \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f0cc802",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "85703eaf",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('titanic.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "253c4ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
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       "      <th>888</th>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
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       "      <td>Q</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38a68c49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5fdd535d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
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       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
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       "    <tr>\n",
       "      <th>Pclass</th>\n",
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       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
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       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a95faed5",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "962afb26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5eba2f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8dbb7cf6",
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63a83f40",
   "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",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].isnull().sum()\n",
    "data['Embarked'].isnull().sum()\n",
    "data['Cabin'].isnull().sum()\n",
    "data= data.dropna(subset=['Age'])\n",
    "data= data.dropna(subset=['Embarked'])\n",
    "data= data.dropna(subset=['Cabin'])\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21c1b80f",
   "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",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8afe695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ce09a1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId\n",
      "[  2   4   7  11  12  22  24  28  53  55  63  67  76  89  93  97  98 103\n",
      " 111 119 124 125 137 138 140 149 152 171 175 178 184 194 195 196 206 210\n",
      " 216 219 225 231 246 249 252 253 258 263 264 269 270 274 276 292 293 298\n",
      " 300 306 308 310 311 312 319 320 326 328 330 332 333 337 338 340 341 342\n",
      " 346 357 367 370 371 378 391 394 395 413 430 431 435 436 439 446 450 453\n",
      " 454 457 461 463 474 485 487 488 493 497 499 505 506 513 516 517 521 524\n",
      " 537 540 541 545 551 557 559 572 573 578 582 584 586 588 592 600 610 619\n",
      " 622 626 628 631 633 642 646 648 660 663 672 680 682 690 691 699 700 701\n",
      " 702 708 711 713 716 717 718 725 731 738 742 743 746 749 752 760 764 766\n",
      " 773 780 782 783 790 797 803 807 810 821 824 836 854 858 863 868 872 873\n",
      " 880 888 890]\n",
      "Survived\n",
      "[1 0]\n",
      "Pclass\n",
      "[1 3 2]\n",
      "Name\n",
      "['Cumings, Mrs. John Bradley (Florence Briggs Thayer)'\n",
      " 'Futrelle, Mrs. Jacques Heath (Lily May Peel)' 'McCarthy, Mr. Timothy J'\n",
      " 'Sandstrom, Miss. Marguerite Rut' 'Bonnell, Miss. Elizabeth'\n",
      " 'Beesley, Mr. Lawrence' 'Sloper, Mr. William Thompson'\n",
      " 'Fortune, Mr. Charles Alexander'\n",
      " 'Harper, Mrs. Henry Sleeper (Myna Haxtun)'\n",
      " 'Ostby, Mr. Engelhart Cornelius' 'Harris, Mr. Henry Birkhardt'\n",
      " 'Nye, Mrs. (Elizabeth Ramell)' 'Moen, Mr. Sigurd Hansen'\n",
      " 'Fortune, Miss. Mabel Helen' 'Chaffee, Mr. Herbert Fuller'\n",
      " 'Goldschmidt, Mr. George B' 'Greenfield, Mr. William Bertram'\n",
      " 'White, Mr. Richard Frasar' 'Porter, Mr. Walter Chamberlain'\n",
      " 'Baxter, Mr. Quigg Edmond' 'Webber, Miss. Susan'\n",
      " 'White, Mr. Percival Wayland' 'Newsom, Miss. Helen Monypeny'\n",
      " 'Futrelle, Mr. Jacques Heath' 'Giglio, Mr. Victor'\n",
      " 'Navratil, Mr. Michel (\"Louis M Hoffman\")'\n",
      " 'Pears, Mrs. Thomas (Edith Wearne)' 'Van der hoef, Mr. Wyckoff'\n",
      " 'Smith, Mr. James Clinch' 'Isham, Miss. Ann Elizabeth'\n",
      " 'Becker, Master. Richard F' 'Navratil, Master. Michel M'\n",
      " 'Brown, Mrs. James Joseph (Margaret Tobin)' 'Lurette, Miss. Elise'\n",
      " 'Strom, Miss. Telma Matilda' 'Blank, Mr. Henry' 'Newell, Miss. Madeleine'\n",
      " 'Bazzani, Miss. Albina' 'Hoyt, Mr. Frederick Maxfield'\n",
      " 'Harris, Mrs. Henry Birkhardt (Irene Wallach)'\n",
      " 'Minahan, Dr. William Edward' 'Beckwith, Mr. Richard Leonard'\n",
      " 'Strom, Mrs. Wilhelm (Elna Matilda Persson)' 'Stead, Mr. William Thomas'\n",
      " 'Cherry, Miss. Gladys' 'Taussig, Mr. Emil' 'Harrison, Mr. William'\n",
      " 'Graham, Mrs. William Thompson (Edith Junkins)' 'Bissette, Miss. Amelia'\n",
      " 'Natsch, Mr. Charles H' 'Andrews, Miss. Kornelia Theodosia'\n",
      " 'Bishop, Mrs. Dickinson H (Helen Walton)' 'Levy, Mr. Rene Jacques'\n",
      " 'Allison, Miss. Helen Loraine'\n",
      " 'Baxter, Mrs. James (Helene DeLaudeniere Chaput)'\n",
      " 'Allison, Master. Hudson Trevor'\n",
      " 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)'\n",
      " 'Francatelli, Miss. Laura Mabel' 'Hays, Miss. Margaret Bechstein'\n",
      " 'Ryerson, Miss. Emily Borie' 'Wick, Miss. Mary Natalie'\n",
      " 'Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)'\n",
      " 'Young, Miss. Marie Grice' 'Ball, Mrs. (Ada E Hall)'\n",
      " 'Hippach, Miss. Jean Gertrude' 'Partner, Mr. Austen'\n",
      " 'Graham, Mr. George Edward' 'Pears, Mr. Thomas Clinton'\n",
      " 'Burns, Miss. Elizabeth Margaret' 'Blackwell, Mr. Stephen Weart'\n",
      " 'Navratil, Master. Edmond Roger' 'Fortune, Miss. Alice Elizabeth'\n",
      " 'Brown, Miss. Amelia \"Mildred\"' 'Bowerman, Miss. Elsie Edith'\n",
      " 'Warren, Mrs. Frank Manley (Anna Sophia Atkinson)'\n",
      " 'Aubart, Mme. Leontine Pauline' 'Harder, Mr. George Achilles'\n",
      " 'Widener, Mr. Harry Elkins' 'Carter, Mr. William Ernest'\n",
      " 'Newell, Miss. Marjorie'\n",
      " 'Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)'\n",
      " 'Minahan, Miss. Daisy E' 'Pickard, Mr. Berk (Berk Trembisky)'\n",
      " 'Bjornstrom-Steffansson, Mr. Mauritz Hakan' 'Silvey, Mr. William Baird'\n",
      " 'Carter, Miss. Lucile Polk' 'Fortune, Mr. Mark'\n",
      " 'Dodge, Master. Washington' 'Peuchen, Major. Arthur Godfrey'\n",
      " 'Foreman, Mr. Benjamin Laventall' 'Goldenberg, Mr. Samuel L'\n",
      " 'Millet, Mr. Francis Davis' 'Anderson, Mr. Harry' 'Gee, Mr. Arthur H'\n",
      " 'Jerwan, Mrs. Amin S (Marie Marthe Thuillard)' 'Bishop, Mr. Dickinson H'\n",
      " 'Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)'\n",
      " 'Kent, Mr. Edward Austin' 'Molson, Mr. Harry Markland'\n",
      " 'Eustis, Miss. Elizabeth Mussey'\n",
      " 'Allison, Mrs. Hudson J C (Bessie Waldo Daniels)' 'Maioni, Miss. Roberta'\n",
      " 'Penasco y Castellana, Mr. Victor de Satode' 'McGough, Mr. James Robert'\n",
      " 'Walker, Mr. William Anderson' 'Lemore, Mrs. (Amelia Milley)'\n",
      " 'Perreault, Miss. Anne' 'Hippach, Mrs. Louis Albert (Ida Sophia Fischer)'\n",
      " 'Butt, Major. Archibald Willingham' 'Frolicher, Miss. Hedwig Margaritha'\n",
      " 'Crosby, Miss. Harriet R' 'Douglas, Mr. Walter Donald'\n",
      " 'Thayer, Mr. John Borland Jr'\n",
      " 'Duff Gordon, Lady. (Lucille Christiana Sutherland) (\"Mrs Morgan\")'\n",
      " 'Taussig, Mrs. Emil (Tillie Mandelbaum)'\n",
      " 'Appleton, Mrs. Edward Dale (Charlotte Lamson)'\n",
      " 'Flynn, Mr. John Irwin (\"Irving\")'\n",
      " 'Silvey, Mrs. William Baird (Alice Munger)'\n",
      " 'Thayer, Mrs. John Borland (Marian Longstreth Morris)'\n",
      " 'Ross, Mr. John Hugo' 'Taussig, Miss. Ruth'\n",
      " 'Frolicher-Stehli, Mr. Maxmillian'\n",
      " 'Stephenson, Mrs. Walter Bertram (Martha Eustis)'\n",
      " 'Duff Gordon, Sir. Cosmo Edmund (\"Mr Morgan\")'\n",
      " 'Shutes, Miss. Elizabeth W' 'Becker, Miss. Marion Louise'\n",
      " 'Kimball, Mr. Edwin Nelson Jr' 'Sutton, Mr. Frederick'\n",
      " 'Longley, Miss. Gretchen Fiske' 'Barkworth, Mr. Algernon Henry Wilson'\n",
      " 'Stahelin-Maeglin, Dr. Max' 'Sagesser, Mlle. Emma'\n",
      " 'Harper, Mr. Henry Sleeper' 'Simonius-Blumer, Col. Oberst Alfons'\n",
      " 'Newell, Mr. Arthur Webster' 'Colley, Mr. Edward Pomeroy'\n",
      " 'Davidson, Mr. Thornton' 'Cardeza, Mr. Thomas Drake Martinez'\n",
      " 'Hassab, Mr. Hammad' 'Madill, Miss. Georgette Alexandra'\n",
      " 'Dick, Mr. Albert Adrian' 'Thayer, Mr. John Borland'\n",
      " 'Humblen, Mr. Adolf Mathias Nicolai Olsen'\n",
      " 'Astor, Mrs. John Jacob (Madeleine Talmadge Force)'\n",
      " 'Silverthorne, Mr. Spencer Victor' 'Calderhead, Mr. Edward Pennington'\n",
      " 'Mayne, Mlle. Berthe Antonine (\"Mrs de Villiers\")'\n",
      " 'Taylor, Mr. Elmer Zebley' 'Soholt, Mr. Peter Andreas Lauritz Andersen'\n",
      " 'Endres, Miss. Caroline Louise' 'Troutt, Miss. Edwina Celia \"Winnie\"'\n",
      " 'Chambers, Mr. Norman Campbell' 'Allen, Miss. Elisabeth Walton'\n",
      " 'Lesurer, Mr. Gustave J' 'Cavendish, Mr. Tyrell William'\n",
      " 'Ryerson, Miss. Susan Parker \"Suzette\"' 'Crosby, Capt. Edward Gifford'\n",
      " 'Marvin, Mr. Daniel Warner' 'Moor, Master. Meier'\n",
      " 'Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)'\n",
      " 'Carter, Mrs. William Ernest (Lucile Polk)'\n",
      " 'Hogeboom, Mrs. John C (Anna Andrews)' 'Mack, Mrs. (Mary)'\n",
      " 'Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)'\n",
      " 'Dick, Mrs. Albert Adrian (Vera Gillespie)' 'Long, Mr. Milton Clyde'\n",
      " 'Guggenheim, Mr. Benjamin' 'Leader, Dr. Alice (Farnham)'\n",
      " 'Carter, Master. William Thornton II' 'Andrews, Mr. Thomas Jr'\n",
      " 'Chambers, Mrs. Norman Campbell (Bertha Griggs)'\n",
      " 'Hays, Mrs. Charles Melville (Clara Jennings Gregg)' 'Moor, Mrs. (Beila)'\n",
      " 'Compton, Miss. Sara Rebecca' 'Lines, Miss. Mary Conover'\n",
      " 'Daly, Mr. Peter Denis '\n",
      " 'Swift, Mrs. Frederick Joel (Margaret Welles Barron)'\n",
      " 'Roebling, Mr. Washington Augustus II'\n",
      " 'Beckwith, Mrs. Richard Leonard (Sallie Monypeny)'\n",
      " 'Carlsson, Mr. Frans Olof'\n",
      " 'Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)'\n",
      " 'Graham, Miss. Margaret Edith' 'Behr, Mr. Karl Howell']\n",
      "Sex\n",
      "['female' 'male']\n",
      "Age\n",
      "[38.   35.   54.    4.   58.   34.   28.   19.   49.   65.   45.   29.\n",
      " 25.   23.   46.   71.   21.   47.   24.   32.5  37.   36.5  22.   61.\n",
      " 56.   50.    1.    3.   44.    2.   40.   31.   32.   62.   30.   52.\n",
      " 63.   36.    0.92 17.   18.   16.   45.5  41.   60.   27.   33.   14.\n",
      " 64.   48.   55.   39.   53.   42.   80.   15.   70.    6.   51.   57.\n",
      " 43.   11.   26.  ]\n",
      "SibSp\n",
      "[1 0 3 2]\n",
      "Parch\n",
      "[0 1 2 4]\n",
      "Ticket\n",
      "['PC 17599' '113803' '17463' 'PP 9549' '113783' '248698' '113788' '19950'\n",
      " 'PC 17572' '113509' '36973' 'C.A. 29395' '348123' 'W.E.P. 5734'\n",
      " 'PC 17754' 'PC 17759' '35281' '110465' 'PC 17558' '27267' '11752'\n",
      " 'PC 17593' '230080' '113776' '111240' '17764' 'PC 17595' '230136'\n",
      " 'PC 17610' 'PC 17569' '347054' '112277' '35273' '11813' '19943' '19928'\n",
      " '11751' '113514' '110152' '110413' '112059' 'PC 17582' 'PC 17760'\n",
      " 'PC 17596' '13502' '11967' 'SC/Paris 2163' '113781' 'PC 17758' 'PC 17485'\n",
      " '11767' 'PC 17608' '36928' '16966' '28551' '111361' '113043' '113784'\n",
      " '248733' '113505' '110813' 'PC 17477' '11765' '113503' '113760'\n",
      " 'SOTON/O.Q. 392078' '110564' '13507' '33638' '113786' '113051' '17453'\n",
      " '13509' '19952' '111320' 'SC/AH Basle 541' '11771' '113787' '36947'\n",
      " 'PC 17473' '36967' 'C.A. 34260' '12749' '113050' '13568' 'WE/P 5735'\n",
      " 'PC 17761' '17421' '11755' '11769' 'PC 17474' '13049' '13567' '11753'\n",
      " '36963' '27042' '13214' '13213' '5727' 'F.C. 12750' 'PC 17755' '24160'\n",
      " '17474' '348121' 'PC 17757' 'PC 17475' 'PC 17476' 'PC 17482' '19996'\n",
      " '348124' '34218' '113806' '19877' '113773' '392096' 'S.O./P.P. 3'\n",
      " '113501' '17465' '112050' 'PC 17756' 'PC 17592' '113055' '17466'\n",
      " 'PC 17590' '695' '112053' '111369']\n",
      "Fare\n",
      "[ 71.2833  53.1     51.8625  16.7     26.55    13.      35.5    263.\n",
      "  76.7292  61.9792  83.475   10.5      7.65    61.175   34.6542  63.3583\n",
      "  77.2875  52.     247.5208  26.2833  79.2     26.      66.6     33.5\n",
      "  30.6958  28.7125  39.      27.7208 146.5208  10.4625  31.     113.275\n",
      "  76.2917  90.      52.5542  86.5     79.65     0.     153.4625 135.6333\n",
      "  29.7     77.9583  91.0792  12.875  151.55   108.9     56.9292  83.1583\n",
      " 262.375  164.8667 134.5     57.9792  28.5     55.      75.25    69.3\n",
      "  55.4417 211.5    120.       8.05    55.9     81.8583  30.5     27.75\n",
      "  89.1042  38.5     13.7917  78.2667  26.2875  34.0208  93.5     49.5\n",
      "  71.     106.425  110.8833  39.6     51.4792  26.3875  40.125   32.3208\n",
      "  30.      25.5875 512.3292 211.3375  57.     227.525   49.5042  78.85\n",
      "  12.475   25.9292  39.4     50.4958   5.    ]\n",
      "Cabin\n",
      "['C85' 'C123' 'E46' 'G6' 'C103' 'D56' 'A6' 'C23 C25 C27' 'D33' 'B30' 'C83'\n",
      " 'F33' 'F G73' 'E31' 'A5' 'D10 D12' 'D26' 'C110' 'B58 B60' 'E101' 'D47'\n",
      " 'B86' 'F2' 'C2' 'B19' 'A7' 'C49' 'F4' 'B4' 'B80' 'A31' 'D36' 'D15' 'C93'\n",
      " 'C78' 'D35' 'C87' 'B77' 'E67' 'B94' 'C125' 'C99' 'C118' 'D7' 'B49' 'D'\n",
      " 'C22 C26' 'C65' 'E36' 'C54' 'B57 B59 B63 B66' 'C7' 'E34' 'C32' 'B18'\n",
      " 'C124' 'C91' 'E40' 'T' 'E33' 'D37' 'B35' 'E50' 'C82' 'B96 B98' 'E10'\n",
      " 'C52' 'E44' 'A34' 'C104' 'C111' 'C92' 'E38' 'E12' 'E63' 'B37' 'C30' 'D20'\n",
      " 'B79' 'E25' 'D46' 'B73' 'B38' 'B39' 'B22' 'C86' 'C70' 'A16' 'C101' 'C68'\n",
      " 'A10' 'E68' 'B41' 'A20' 'D19' 'D50' 'D9' 'A23' 'B50' 'A26' 'D48' 'E58'\n",
      " 'B71' 'B51 B53 B55' 'D49' 'B5' 'B20' 'F G63' 'C62 C64' 'E24' 'C90' 'C126'\n",
      " 'C45' 'E8' 'B101' 'C46' 'D30' 'E121' 'D11' 'E77' 'B3' 'D6' 'B82 B84'\n",
      " 'D17' 'A36' 'B69' 'E49' 'D28' 'E17' 'A24' 'C50' 'B42' 'C148']\n",
      "Embarked\n",
      "['C' 'S' 'Q']\n"
     ]
    }
   ],
   "source": [
    "for i in data.columns:\n",
    "    print(i)\n",
    "    print(data[i].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cbde5868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2,   4,   7,  11,  12,  22,  24,  28,  53,  55,  63,  67,  76,\n",
       "        89,  93,  97,  98, 103, 111, 119, 124, 125, 137, 138, 140, 149,\n",
       "       152, 171, 175, 178, 184, 194, 195, 196, 206, 210, 216, 219, 225,\n",
       "       231, 246, 249, 252, 253, 258, 263, 264, 269, 270, 274, 276, 292,\n",
       "       293, 298, 300, 306, 308, 310, 311, 312, 319, 320, 326, 328, 330,\n",
       "       332, 333, 337, 338, 340, 341, 342, 346, 357, 367, 370, 371, 378,\n",
       "       391, 394, 395, 413, 430, 431, 435, 436, 439, 446, 450, 453, 454,\n",
       "       457, 461, 463, 474, 485, 487, 488, 493, 497, 499, 505, 506, 513,\n",
       "       516, 517, 521, 524, 537, 540, 541, 545, 551, 557, 559, 572, 573,\n",
       "       578, 582, 584, 586, 588, 592, 600, 610, 619, 622, 626, 628, 631,\n",
       "       633, 642, 646, 648, 660, 663, 672, 680, 682, 690, 691, 699, 700,\n",
       "       701, 702, 708, 711, 713, 716, 717, 718, 725, 731, 738, 742, 743,\n",
       "       746, 749, 752, 760, 764, 766, 773, 780, 782, 783, 790, 797, 803,\n",
       "       807, 810, 821, 824, 836, 854, 858, 863, 868, 872, 873, 880, 888,\n",
       "       890], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PassengerId'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d540d829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59cfd773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pclass'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "885cd014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38.  , 35.  , 54.  ,  4.  , 58.  , 34.  , 28.  , 19.  , 49.  ,\n",
       "       65.  , 45.  , 29.  , 25.  , 23.  , 46.  , 71.  , 21.  , 47.  ,\n",
       "       24.  , 32.5 , 37.  , 36.5 , 22.  , 61.  , 56.  , 50.  ,  1.  ,\n",
       "        3.  , 44.  ,  2.  , 40.  , 31.  , 32.  , 62.  , 30.  , 52.  ,\n",
       "       63.  , 36.  ,  0.92, 17.  , 18.  , 16.  , 45.5 , 41.  , 60.  ,\n",
       "       27.  , 33.  , 14.  , 64.  , 48.  , 55.  , 39.  , 53.  , 42.  ,\n",
       "       80.  , 15.  , 70.  ,  6.  , 51.  , 57.  , 43.  , 11.  , 26.  ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa0be466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 3, 2], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SibSp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3fd3b80b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 4], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Parch'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c39037b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 71.2833,  53.1   ,  51.8625,  16.7   ,  26.55  ,  13.    ,\n",
       "        35.5   , 263.    ,  76.7292,  61.9792,  83.475 ,  10.5   ,\n",
       "         7.65  ,  61.175 ,  34.6542,  63.3583,  77.2875,  52.    ,\n",
       "       247.5208,  26.2833,  79.2   ,  26.    ,  66.6   ,  33.5   ,\n",
       "        30.6958,  28.7125,  39.    ,  27.7208, 146.5208,  10.4625,\n",
       "        31.    , 113.275 ,  76.2917,  90.    ,  52.5542,  86.5   ,\n",
       "        79.65  ,   0.    , 153.4625, 135.6333,  29.7   ,  77.9583,\n",
       "        91.0792,  12.875 , 151.55  , 108.9   ,  56.9292,  83.1583,\n",
       "       262.375 , 164.8667, 134.5   ,  57.9792,  28.5   ,  55.    ,\n",
       "        75.25  ,  69.3   ,  55.4417, 211.5   , 120.    ,   8.05  ,\n",
       "        55.9   ,  81.8583,  30.5   ,  27.75  ,  89.1042,  38.5   ,\n",
       "        13.7917,  78.2667,  26.2875,  34.0208,  93.5   ,  49.5   ,\n",
       "        71.    , 106.425 , 110.8833,  39.6   ,  51.4792,  26.3875,\n",
       "        40.125 ,  32.3208,  30.    ,  25.5875, 512.3292, 211.3375,\n",
       "        57.    , 227.525 ,  49.5042,  78.85  ,  12.475 ,  25.9292,\n",
       "        39.4   ,  50.4958,   5.    ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Fare'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1922d8ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5ee52019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      False\n",
       "3      False\n",
       "6      False\n",
       "10     False\n",
       "11     False\n",
       "       ...  \n",
       "871    False\n",
       "872    False\n",
       "879    False\n",
       "887    False\n",
       "889    False\n",
       "Length: 183, dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e8bf2327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c5648849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    183\n",
       "Survived         2\n",
       "Pclass           3\n",
       "Name           183\n",
       "Sex              2\n",
       "Age             63\n",
       "SibSp            4\n",
       "Parch            4\n",
       "Ticket         127\n",
       "Fare            93\n",
       "Cabin          133\n",
       "Embarked         3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58a1861b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2      1\n",
       "572    1\n",
       "578    1\n",
       "582    1\n",
       "584    1\n",
       "      ..\n",
       "328    1\n",
       "330    1\n",
       "332    1\n",
       "333    1\n",
       "890    1\n",
       "Name: PassengerId, Length: 183, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PassengerId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "98fc63c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    123\n",
       "0     60\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5222deae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    158\n",
       "2     15\n",
       "3     10\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "380546b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cumings, Mrs. John Bradley (Florence Briggs Thayer)     1\n",
       "Appleton, Mrs. Edward Dale (Charlotte Lamson)           1\n",
       "Silvey, Mrs. William Baird (Alice Munger)               1\n",
       "Thayer, Mrs. John Borland (Marian Longstreth Morris)    1\n",
       "Ross, Mr. John Hugo                                     1\n",
       "                                                       ..\n",
       "Ball, Mrs. (Ada E Hall)                                 1\n",
       "Hippach, Miss. Jean Gertrude                            1\n",
       "Partner, Mr. Austen                                     1\n",
       "Graham, Mr. George Edward                               1\n",
       "Behr, Mr. Karl Howell                                   1\n",
       "Name: Name, Length: 183, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2f8b7ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      95\n",
       "female    88\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a6f63603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.0    11\n",
       "24.0     9\n",
       "19.0     6\n",
       "35.0     6\n",
       "31.0     5\n",
       "        ..\n",
       "71.0     1\n",
       "3.0      1\n",
       "1.0      1\n",
       "36.5     1\n",
       "26.0     1\n",
       "Name: Age, Length: 63, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6e3785b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    110\n",
       "1     64\n",
       "2      6\n",
       "3      3\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SibSp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c766dc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    122\n",
       "1     37\n",
       "2     23\n",
       "4      1\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Parch'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "33465820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.5500     7\n",
       "53.1000     5\n",
       "90.0000     4\n",
       "10.5000     4\n",
       "13.0000     4\n",
       "           ..\n",
       "211.5000    1\n",
       "8.0500      1\n",
       "81.8583     1\n",
       "27.7500     1\n",
       "5.0000      1\n",
       "Name: Fare, Length: 93, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Fare'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ecd66be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr=data.corr()\n",
    "sns.heatmap(corr,annot=True,cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a5cb4977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(20,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d8b6d20b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.countplot(x='Pclass',data = data)\n",
    "plt.xticks(rotation =90)\n",
    "plt.title('Countplot for Pclass')             \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a00e2494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1]), [Text(0, 0, 'female'), Text(1, 0, 'male')])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (10,8)\n",
    "sns.countplot(x=data['Sex'])\n",
    "plt.xticks(rotation =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "767afc27",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARIAAACaCAYAAABlo8ciAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAezElEQVR4nO3dd3gU57X48e9RBVRRRQUkihDNCIQwYGwCccMkuDvGNtclxXHaTXEKN/n94jg3yXXyJL4xadhxnBgnwQ6u4IAN7timFwnRm0BdQr23PfePXTlYUVlpd7aI9/M8+2i1Mzt7ZgeOZuYtR1QVwzAMVwR4OwDDMPyfSSSGYbjMJBLDMFxmEolhGC4zicQwDJeZRGIYhsuCvB3AYMXFxWl6erq3wzCMi87evXvPq2p8b8v8LpGkp6ezZ88eb4dhGBcdETnb1zLLLm1EZKyIvC0iR0TkkIh8vZd1RERWi8hJEckTkWyr4jEMwzpWnpF0Ag+q6j4RiQD2ishWVT18wTrXARmOxzzgD46fhmH4EcvOSFS1VFX3OZ43AEeAlB6r3QCsVbsdQLSIJFkVk2EY1vDIPRIRSQdmAzt7LEoBCi/4vcjxWqkn4jL80993nvN2CADcOW+ct0PwGZY3/4pIOPAC8A1Vre+5uJe3/NsoQhG5X0T2iMieyspKK8I0DMMFliYSEQnGnkT+pqov9rJKETD2gt9TgZKeK6nqE6qao6o58fG9tj4ZhuFFVrbaCPAn4IiqPtrHahuAux2tN/OBOlU1lzWG4WesvEeyEPgP4KCIHHC89n1gHICqrgE2AcuAk0AzcJ+F8RiGYRHLEomqvk/v90AuXEeBr1gVg2EYnmHG2hiG4TKTSAzDcJlJJIZhuMwkEsMwXGYSiWEYLjOJxDAMl5lEYhiGy0wiMQzDZSaRGIbhMpNIDMNwmUkkhmG4zCQSwzBcZhKJYRguM4nEMAyX+V1dG8PwFlXlWHkDewpqON/YxsbcEpbNTGLF3LEEB17cf5Mv7r03DCd1dNl4bk8ha7efpaimmbjwUGqa2/n/L+dzw28/oKS2xdshepU5IzGMAXTabDyz4yynKhq5eloiizLiCQwQ7pw3jtfyy/jO+lxu/cOHvPDly0iKGuntcL3CyjlbnxKRChHJ72P5YhGpE5EDjscPrYrFMFyxMbeUkxWN3JydypLMBAID/jXx39IZY1h3/3zqWzv5wto9tHZ0eTFS77Hy0uYvwNIB1tmmqrMcjx9bGIthDEl+cR27C6pZlBHPnLTRva4zIyWKx1bMIr+4nl9tOebhCH2DlZX23gOqrdq+YVittaOLjbklpESP5Oppif2ue+XURFbOH8eT75/hQGGtZwL0Id6+2bpARHJFZLOITPdyLIbxMW8eKaexrZMbZ6V87HKmL6uum0pceCgPbTiEzfZvdd6GNW8mkn1AmqpmAb8BXu5rRVNpz/C06qZ2dpyuJid9NCmjnbuBGh4axKqlU8gtrOX1Q2UWR+hbvJZIVLVeVRsdzzcBwSIS18e6ptKe4VFvHa1ABD45pf9Lmp5unJ3ChPgwHnvzxEV1VuK1RCIiYxzV+BCRSx2xVHkrHsPoVtfSQW5hLXPTY4gaGTyo9wYGCF9dMomjZQ28caTcogh9j5XNv+uA7UCmiBSJyOdE5AERecCxyq1AvojkAquBFY6CWYbhVR+ePI+iXD6p1xPkAV2flUxa7Ch+89ZJLpZ/0lZW2rtjgOW/BX5r1ecbxlC0dnSxq6CaGSlRjA4LGdI2ggID+PLiiXzvhYN8cLKKyzOGlpD8ibdbbQzDp+w6U01bp41FGa7di7thVgoxYSE8s6PAPYH5OKcSiYi8ICKfEhGTeIxhy6bKzjNVjI8LIznata7uI4ID+UzOWLYeLqe0bviPw3E2MfwBuBM4ISKPiMgUC2MyDK84VdFITXMH88bHuGV7d80bhwLrdp5zy/Z8mVOJRFXfUNW7gGygANgqIh+KyH0iMrjb2obho3YVVDMqJJBpSZFu2d7YmFEsyUxg3e5C2jttbtmmr3L6UkVEYoF7gc8D+4HHsCeWrZZEZhge1NDawZHSerLHjSbIjXOLrJw/jsqGNt46Orybgp29R/IisA0YBSxX1etV9TlV/RoQbmWAhuEJ+87VYlOYm+6ey5puizLiiY8I5fm9xW7drq9xtvn3SUfv04+ISKiqtqlqjgVxGYbHqCr7ztaQFjuK+IhQt247KDCAm2an8NT7Z6hqbCM23L3b9xXOnsP9pJfXtrszEMPwlrL6Viob25g1NtqS7d+SnUqnTXnlQIkl2/cF/SYSRzf2OcBIEZktItmOx2LslzmG4ffyiuoIEJieHGXJ9jPHRDAjJZIX9hVZsn1fMNClzbXYb7CmAo9e8HoD8H2LYjIMj1FV8opqmRgfTniodTOP3pKdysMbD3O0rJ4pY9zTKuRL+j0jUdWnVXUJcK+qLrngcb2qvuihGA3DMkU1LdQ0dzAzNdrSz7k+K5nAAGFj7vC8vOk3BYvISlX9K5AuIt/quVxVH+3lbYbhN/KKagkMELf1HelLbHgol02MZWNuKd++JhPHwPdhY6CbrWGOn+FARC8Pw/BbNlXyiuvITIxgZEig5Z+3fGYy56qbOVhcZ/lneVq/ZySq+rjj58OeCccwPKegqomG1k5mplpzk7Wna6eP4QcvH2Rjbonll1Ke5myHtF+ISKSIBIvImyJyXkRWWh2cYVgpr6iO4EDx2M3PqFHBLMqI59W80mE3e5qz/UiuUdV64NNAETAZ+I5lURmGxbpsSn5xHVOTIgkJ8tyg9uVZyZTWtbLvXI3HPtMTnP0GuwfmLQPWqaopM2H4tVOVjTS3d5Hl4UuMq6YlEhoUMOxab5xNJBtF5CiQA7wpIvFAa39vcKLSnojIahE5KSJ5IpI9uNANY+jyimoZERxARoJnh4qFhwbxySkJ/PNgGV3D6PLG2WkEVgELgBxV7QCagBsGeNtf6L/S3nVAhuNxP/Y5TwzDch1dNg6V1DM9KcqtI32dtTwrmfONbew8PXzmOh9MV76p2PuTXPietX2trKrviUh6P9u7AVjrmPB5h4hEi0iSqpYOIibDGLQT5Q20ddo81lrT05LMBEaFBLIxr4TLhjjBtK9xttXmGeCXwOXAXMfD1VG/KUDhBb8XOV7r7fNNgSzDbXKL6ggLCWRCvHdmwBgZEsjV0xLZnF9GR9fwmPDI2TOSHGCam8tF9Na1r9ftq+oTwBMAOTk5w+fC0kl/95Gp+u6cN87bIbisrbOLo2X2CYycKcNpleUzk3nlQAnvnzzPkswEr8XhLs5eIOYDY9z82UXA2At+TwWG161sw+ccLW2go0u93iHsislxRIwI4tXc4XEl7+wZSRxwWER2AW3dL6rq9S589gbgqyLyLDAPqDP3Rwyr5RXVEjkiiLRY786CERoUyNLpY3gtv4zWjhmMCLa+i76VnE0kPxrshh2V9hYDcSJSBDyEoz+Kqq4BNmHvl3ISaAbuG+xnGMZgtLR3cby8kQUTYwnwgUFzy7OSWb+3iHePV3LtdHef8HuWU4lEVd8VkTQgQ1XfEJFRQL8p1IlKewp8xelIDcNFh0vr6FL1WmtNT5dNjCUmLIRX80r9PpE422rzBeB54HHHSynAyxbFZBiWyC2qIyYshBQXi1+5S1BgANfNGMMbh8tpbu/0djgucfZm61eAhUA9gKqeAPz/VrNx0Whs6+RURSMzU6N8ai6Q5VnJtHR08dbRCm+H4hJnE0mbqrZ3/+LolHbRNcMa/iu/uA4Fr7fW9DQ3PYaEiFC/H3vjbCJ5V0S+j30S6KuB9cBG68IyDPfKK6olISKUMZEjvB3KxwQGCJ+amcTbxyppaO3wdjhD5mwiWQVUAgeBL2Jvcfl/VgVlQGtHFztPV/HC3iL2n6uh4HwTncOkF6Sn1TS3U1DVTJZF5SZctTwrmfZOG1sP+281PmdbbWwi8jLwsqqaPuoWOlfVzO/ePsmG3BJaOro+tiwkKIBZY6NZPDme6FEhXorQ/+QW1gJ4fMoAZ80eG01K9Eg25pZwc3aqt8MZkoEmfxbs/T++ir1Lu4hIF/AbVf2xB+K7aKgqj793mke3HEcEbpqdwtXTEpkYH86G3BIqG9o4VFLPvrM17Dtbw7JLkpg3Psanbhz6IlXlQGEtabGjiAnzzeQrInw6K4k/bTtDbXO7X/6RGOjS5hvYW2vmqmqsqsZg74W6UES+aXVwF4u6lg7uf2Yvj2w+ypVTE3jvu0t45JaZXDk1kfS4MOLCQ5maFMmtc1L51tWTmRAfxobcEv6+6xxtnV0Df8BFrLSulYoG66roucvymcl02pRNB8u8HcqQDJRI7gbuUNUz3S+o6mlgpWOZ4aKapnbueGIHbx+t4KHl0/j9Xdkk9nNDMHpUCHcvSOe6GWM4UlrPnz8ooLXDJJO+HCisJVCESyyqoucu05MjmZQQzot+Wo1voEQSrKrne77ouE8S3Mv6xiBUN7Vz55M7OVnZyJP35HDfwvFOXaoEiHBFRjwr5o6jqKaZP71/hpZ2k0x6sqmSW1TL5MRwRllYRc8dRIRb56Sy56z9xrq/GSiRtA9xmTGA1o4uPvuX3ZyqbOSPd+eweAhDyWekRLFyfhplda38bddZOm2mVedCpyvt5SZmjRvt7VCcctPsFAIEv6wRPFAiyRKR+l4eDcAlnghwOLLZlAfX55JbVMvqFbP5xOT4IW9ryphIbs5O4XRlEy/vL8a9U8b4twOFtYQGBTBljH/UckuMHMEVGfG8uK/Y78pVDFT7N1BVI3t5RKiqubQZosfePME/80pZtXQKS2e4Plhr9rjRXDk1gX3navng5L9diV6U2jq7yC+pY0ZyFMFemJd1qG6Zk0pxbQs7/Gw+V//5hoeJd49XsvqtE9ySncr9iya4bbufzExgWlIkrx0q41yV/11ju1t+cR3tnTbmpPnHZU23a6YlEjEiiOf97PLGJBIPKqlt4RvP7iczMYKf3DjDrX1ARIRbslOJHhXCut2FNLX592hSV+0uqCEuPNTrExgN1ojgQD49M5nNB8to9KNjaBKJh3TZlK8/u5/2Thu/uyvbkqLVI0MCuePScTS2dbJ+byG2i/R+SXl9K+eqm5mbPtovO+zdOieVlo4uNh30nwkDLU0kIrJURI45imCt6mX5YhGpE5EDjscPrYzHm9a8e4rdBTX8+IYZTLRw9vKU6JF86pIkjpc38v6Ji/N+yd6zNQSI/d6RP8oeF82khHCfmfTbGZYlEhEJBH6HvRDWNOAOEZnWy6rbVHWW4zEsu93nF9fxv1uP86lLkrg5u9eKG241b3wM05Mj2Xq4nOLaFss/z5d0dNnYd66GqUmRhPt435G+iAgr543jQGEtB4vqvB2OU6w8I7kUOKmqpx1zmTzLwNX5hp2W9i6+/ux+YsND+OlN7r0v0hcR4abZKYSFBvLc7nO0d148/Uvyiupobu9i/oRYb4fikpvnpDIqJJBndhR4OxSnWJlInC2AtUBEckVks4hMtzAer3hk8xFOVTbxq9tmeXQw1qiQIG7LGUtVYzv/POjfk+Y4S1XZfuo8CRGhTIgL83Y4LokcEcyNs1N45UAJdc2+P0+JlYnEmQJY+4A0Vc0CfkMf88D6a6W9d45V8PT2s3x24Xguz/B8acaJ8eFckRHP7oIaDpX4xymyK85WNVNS18qCibF+eZO1p5Xz0mjrtPGPPYUDr+xlViaSAQtgqWq9qjY6nm8CgkXk3/7HqeoTqpqjqjnx8UPvBepJ1U3tfOf5PCYnhvPdpZlei+OqaQmkRI/kxX3F1LX4/l82V3x4uooRwQHMHuufN1l7mpYcybzxMfz5gzM+X9rTykSyG8gQkfEiEgKswF4U6yMiMsYx5wkicqkjHv/q0tcLVWXVC3nUNXfw69tne7X4UVBAALfnjKXTZhvWTcLVTe0cLqljbloMIUHDp1fDA5+YSEldq8/P6WrZN66qndgnRHodOAL8Q1UPicgDIvKAY7VbgXwRyQVWAyvcXF/YK9bvKWLL4XK+fe1kpiVHejsc4iJCWT4zmdOVTcO2Sfjd45WICAsnef4S0kqLM+PJTIzg8XdP+/Q4KktTt6puUtXJqjpRVX/qeG2No9IeqvpbVZ2uqlmqOl9VP7QyHk84W9XEwxsPsWBCLJ+/3H1d4F01J20005Mj2XK4jOKa4dUkXNfSwb5zNcxJG03kyOE1BExE+OInJnCsvIG3j/luyYrhcw7oAzq7bHzzuQMEBAi/+kwWAV6sdt9Td5NwxIhgntszvJqEt52oRFVZlOEf988Ga3lWMinRI3nszZM+e1ZiEokb/f6dU+w7V8tPbpxBso9Uc7uQvUk4larGdl7N8+1rbmfVt3Swu6CarNRon52T1VXBgQF8/coMcgtref2Qb840bxKJm2w/VcWv3zjODbOSuWGW9b1Xh2pCXDiLJsez52wN+cX+3yT8xpFybDa4cmqit0Ox1M3ZKUyMD+NXW47R5YNzlZhE4gYV9a18bd1+0uPC+OlNvj/f01VTE0kdPZKX9vt3k3BZXSt7z9Ywf0LMsD0b6RYUGMCD12RyoqKRl/YXezucf2MSiYs6u2x8bd1+Gts6+MNdc/xifEdggHB7zli6bMr6Pf7bJPzaoVJCgwNYMuXiKEO9dPoYslKj+PlrR6n3sap8JpG46JdbjrPzTDU/u+kSMv1kSj+A2PBQlmclcfp8E+/4cGtAX/KL6zhe3siSzARGhfh+8naHgADhv2+cwfnGNh7dctzb4XyMSSQueGl/EWvePcUdl47zywpp2eNGM3tsNG8cqfCr+yUt7V1szC0hKWoEl00cXv1GBjIzNZqV89JYu73Ap46ZSSRDtOtMNd97/iDzJ8Tw8PX+OdZQRLhxdgrjYkaxfm8hRTXN3g7JKZvyS2lq7+Tm7FQCfaiJ3VO+fW0mMWEhfOf5PJ8pkGYSyRAcK2vg/mf2kDp6JGtWzvHrLtnBgQHcNW8cYaFBPP1hAZUNbd4OqV+v5Zex92wNl0+KI8UHm9g9IWpkMD+/ZSZHSuv5+eZj3g4HMIlk0ArON7HyTzsJCQzgz/fN9cs6rT1FjAjms5eNB+CpD+z1Z33RmfNNfGd9LqmjR3LVMG/uHciVUxO5Z0EaT31whrePev8el0kkg3DmfBN3PbmTLpvyt8/PIy3Wv+e8uFBcRCj3LRxPa0cXf9x2muom30omTW2dfOmvewkMFO64dBxBflRiwir/tWwqU8ZE8J/P7udEeYNXYzFHw0mHSuq4bc2HtHR0sfazl5KR6D8tNM5Kjh7J5y4fT2uHjcffO0V5fau3QwLsVQnvf2YPJyoa+fXtsxg9DM4C3WFEcCBP3pPDiOBA7v3zbioavHe8TCJxwtbD5dz++A5CgwJZ/8ACZqT4dkFqV6SOHsUXFk0AtU9Yfays3qvxdHTZ+M91+/ngZBW/uGXmkEqbDmepo0fx1D1zqW5q5z+e3OW1ZGISST86u2w8uvU4X1i7h/FxYTz/pQWWzgDvK8ZEjuBLiycSGxbC2u1neetouVe6Zde1dHDvn3ex5XA5Dy2fxi1z/K+J3RMuSY3iyXtyOFfdzGfWbKew2vOtbyaR9OFkRSO3rtnO6jftVfHWP7CApKiLp5UgelQI9y+aSJajn8kft53mdGWjxz7/ZEUDt635kJ2nq/nlbVnct3C8xz7bHy2cFMdfPz+P6qZ2bvzdB2w74dkpSU0i6aG+tYNHNh9l2eptFFQ1sfqO2fzytpleneXMW0KCAvhMzlhunzuWyoY2rv31e/xs0xFLu2d3dtl4cttplq1+n8qGNtZ+9lJuNWciTpmTNpoXv3wZMWEh3P3ULh7ZfJSWds/0M7k4+hY7oaapnWd2nOUvHxZQ3dTOzdkprFo6hYTIEd4OzeuyUqOZEBfGqcpG/rjtNOt2nWPl/DTuvSydRDd9Pzab8tqhMn615RinKpu4amoCP7v5EhIizPc/GJMSInjlqwt5eMNh1rx7ig0HivnedVP41CVJlrZ0iZUTpYjIUuAxIBB4UlUf6bFcHMuXAc3Avaq6r79t5uTk6J49e9wSX2tHF9tPV/Hy/mJeP1RGa4eNJZnxfPPqycxMjXbLZ7iDr1Rcu3PeOA6V1PH7t0+xOd9eTnLhpDiWz0zmislxg770U1WOlzfy+qEynttdSHFtCxkJ4Tx4TSbXTk/scyZ4X/o+fNmuM9U8tOEQR0rrGRszknsWpHP9rOQhJ2cR2auqOb0usyqROCrtHQeuxj6j/G7gDlU9fME6y4CvYU8k84DHVHVef9t1JZHUNXdwuLSeg8W1vH+yil1nqmjtsBE1MphPz0zinsvSmeyDzbq++B/nbFUTz+8t4qX9xRQ5pm5Mjx3F9JQoJidEkBgZSkJkKFEjQwDFpvbEXVHfRlFNC0dK6zlQWEuZo4n58klxrLh0LNfNSBqw27svfh++qsumbD1czhPv2SfdChCYmx7D/AmxzJsQw+yxo52uQ91fIrHy0uajSnuOILor7R2+YJ0bgLWOCZ93iEi0iCSpqkvVk09WNPD+ifOU1rdSVtdKaW0rhTXNlNb9q2lsUkI4K+aOY9HkOBZOiiM06OK7B+KKtNgwHrwmk29dPZmjZfbve+/ZGvKKavlnXv+HTwTGx4aRkz6aKzLiWDQ5/qK6ke1JgQHC0hljWDpjDCfKG9iQW8JbRytY/dYJ9E0IEBgfF8aGr15OmAtTYFiZSHqrtNfzbKOvanwuJZLdBTX8aONhQgIDSIwKJSlqJPMnxDI5MYKpSRFMS440195uIiJMTYpkalIkX3C81tbZxfnGdirqW6lr6SBAhAARQoICSIgIJTFyhNN/BQ33yUiM4MFrMnnwmkz7hNlna9h/roaimhaXkghYm0icqbTnzDqIyP3A/Y5fG0XEmZFKccD5E06s6AfiAK/XkbjLPZvxiX1xh7uGz77E/e8Kp/Yjra8FViaSASvtObkOqvoE8MRgPlxE9vR1PedvzL74puGyL+7YD69W2nP8frfYzQfqXL0/YhiG51l2RqKqnSLSXWkvEHiqu9KeY/kaYBP2FpuT2Jt/77MqHsMwrGNphzRHYfBNPV5bc8FzBb5i0ccP6lLIx5l98U3DZV9c3g9LO6QZhnFxMGNtDMNw2bBJJCISIyJbReSE4+foPtYrEJGDInJARNzT195NRGSpiBwTkZMisqqX5SIiqx3L80Qk2xtxDsSJ/VgsInWOY3BARH7ojTidISJPiUiFiOT3sdwvjgk4tS9DPy6qOiwewC+AVY7nq4Cf97FeARDn7Xh7iSsQOAVMAEKAXGBaj3WWAZux97+ZD+z0dtxD3I/FwKvejtXJ/VkEZAP5fSz3+WMyiH0Z8nEZNmck2LvbP+14/jRwo/dCGZKPhhSoajvQPaTgQh8NKVDVHUC0iCR5OtABOLMffkNV3wOq+1nFH44J4NS+DNlwSiSJ6uiD4vjZ15x8CmwRkb2OHrO+oq/hAoNdx9ucjXGBiOSKyGYR8c/CQHb+cEwGY0jHxa/mIxGRN4AxvSz6wSA2s1BVS0QkAdgqIkcdmdrb3DakwMuciXEfkKaqjY4R4C8DGVYHZhF/OCbOGvJx8aszElW9SlVn9PJ4BSjvPqV0/Oy12Ieqljh+VgAvYT8V9wVuG1LgZQPGqKr1qtroeL4JCBYRf6296Q/HxCmuHBe/SiQD2ADc43h+D/BKzxVEJExEIrqfA9cAvd7B9oLhMqRgwP0QkTGOSa0QkUux/zus8nik7uEPx8QprhwXv7q0GcAjwD9E5HPAOeA2ABFJxj472zIgEXjJ8V0FAX9X1de8FO/H6DAZUuDkftwKfElEOoEWYIU6mg18jYisw96aESciRcBDQDD4zzHp5sS+DPm4mJ6thmG4bDhd2hiG4SUmkRiG4TKTSAzDcJlJJIZhuMwkEsMwXGYSidEnEfmBiBxyjGo9ICL91hxycpvX9zYieIjb8lwxYqNfpvnX6JWILAAeBRarapujh2NId8/gAd4bpKqdHoixUVXDrf4cY2DmjMToSxJwXlXbAFT1vGOMUkF3t2kRyRGRdxzPfyQiT4jIFmCtiOy8cNCXiLwjInNE5F4R+a2IRDm2FeBYPkpECkUkWEQmishrjoGV20RkimOd8SKyXUR2i8h/e/j7MPphEonRly3AWBE5LiK/F5FPOPGeOcANqnon9ukDPgMfjX1KVtW93Suqah32uUq6t7sceF1VO7DPIfo1VZ0DfBv4vWOdx4A/qOpcoMzlPTTcxiQSo1eOwVtzsBcmqwSeE5F7B3jbBlVtcTz/B45hCtgTyvpe1n8OuN3xfIXjM8KBy4D1InIAeBz72RHAQmCd4/kzg9kfw1rDaayN4Waq2gW8A7wjIgexD4bs5F9/gHrWPW264L3FIlIlIjOxJ4sv9vIRG4D/EZEY7EnrLSAMqFXVWX2FNbS9MaxkzkiMXolIpohcOBfFLOAs9qkq5zheu2WAzTwLfBeIUtWDPRc6znp2Yb9keVVVu1S1HjgjIt2DLkVEshxv+QD7mQu4rYKo4Q4mkRh9CQeeFpHDIpIHTAN+BDwMPCYi24CuAbbxPPb/+P/oZ53ngJWOn93uAj4nIrnAIf41VePXga+IyG4ganC7Y1jJNP8ahuEyc0ZiGIbLTCIxDMNlJpEYhuEyk0gMw3CZSSSGYbjMJBLDMFxmEolhGC4zicQwDJf9H/yHIaiBlcyWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 4,2\n",
    "\n",
    "sns.distplot(data['Survived'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "89c37adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1464"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = data.filter(['Survived'])\n",
    "dataset = data.values\n",
    "training_data_len=math.ceil(len(data) * 8)\n",
    "training_data_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "80e31f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, nan, 1, ..., 71.2833, 'C85', 'C'],\n",
       "       [4, nan, 1, ..., 53.1, 'C123', 'S'],\n",
       "       [7, nan, 1, ..., 51.8625, 'E46', 'S'],\n",
       "       ...,\n",
       "       [880, 0.7, 1, ..., 83.1583, 'C50', 'C'],\n",
       "       [888, 0.7, 1, ..., 30.0, 'B42', 'S'],\n",
       "       [890, 0.7, 1, ..., 30.0, 'C148', 'C']], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "457ac72a",
   "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",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2       NaN       1   \n",
       "3              4       NaN       1   \n",
       "6              7       NaN       1   \n",
       "10            11       NaN       3   \n",
       "11            12       NaN       1   \n",
       "..           ...       ...     ...   \n",
       "871          872  0.733333       1   \n",
       "872          873  0.700000       1   \n",
       "879          880  0.700000       1   \n",
       "887          888  0.700000       1   \n",
       "889          890  0.700000       1   \n",
       "\n",
       "                                                  Name     Sex  \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  \n",
       "6                              McCarthy, Mr. Timothy J    male  \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female  \n",
       "11                            Bonnell, Miss. Elizabeth  female  \n",
       "..                                                 ...     ...  \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  \n",
       "872                           Carlsson, Mr. Frans Olof    male  \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  \n",
       "887                       Graham, Miss. Margaret Edith  female  \n",
       "889                              Behr, Mr. Karl Howell    male  \n",
       "\n",
       "[183 rows x 5 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.iloc[:, 0:5]\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "87045764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[       nan],\n",
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       "       [0.7       ],\n",
       "       [0.66666667],\n",
       "       [0.66666667],\n",
       "       [0.7       ],\n",
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       "       [0.66666667],\n",
       "       [0.66666667],\n",
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       "       [0.73333333],\n",
       "       [0.73333333],\n",
       "       [0.73333333],\n",
       "       [0.7       ],\n",
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       "       [0.7       ],\n",
       "       [0.7       ],\n",
       "       [0.7       ],\n",
       "       [0.7       ]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set = data.iloc[:, 1:2].values\n",
    "training_set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0d9d9e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "data_training_scaled = scaler.fit_transform(training_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "87cc02e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_set = []\n",
    "labels = []\n",
    "for i in range(60, 586):\n",
    "  features_set.append(data_training_scaled[i - 60:i, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5b3557d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_6256\\4245798851.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  features_set, labels = np.array(features_set), np.array(labels)\n"
     ]
    }
   ],
   "source": [
    "features_set, labels = np.array(features_set), np.array(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f1b52352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(526, 1)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_set = np.reshape(features_set, (features_set.shape[0], 1))\n",
    "features_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e5672511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\admin\\anaconda3\\lib\\site-packages (2.12.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.12.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow) (2.12.0)\n",
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      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (0.31.0)\n",
      "Requirement already satisfied: wrapt<1.15,>=1.11.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (1.12.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (61.2.0)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (16.0.0)\n",
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      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (4.1.1)\n",
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      "Requirement already satisfied: packaging in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (21.3)\n",
      "Requirement already satisfied: keras<2.13,>=2.12.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (2.12.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (1.54.0)\n",
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      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.12.0->tensorflow) (0.4.0)\n",
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      "Requirement already satisfied: ml-dtypes>=0.0.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow) (0.1.0)\n",
      "Requirement already satisfied: scipy>=1.7 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from jax>=0.3.15->tensorflow-intel==2.12.0->tensorflow) (1.7.3)\n",
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      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.17.3)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.0.3)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.27.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (0.7.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (1.0.0)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (2022.6.15)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (3.3)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (1.26.9)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow-intel==2.12.0->tensorflow) (3.2.2)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from packaging->tensorflow-intel==2.12.0->tensorflow) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "48001354",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.models import Sequential\n",
    "from tensorflow.python.keras.layers import Dense\n",
    "from tensorflow.python.keras.layers import LSTM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5874a858",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f4f09347",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "930ff863",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_total = pd.concat((data['Survived'], data['Survived']), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "af0e7fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(243,)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_inputs = data_total[len(data_total) - len(data) - 60:].values\n",
    "test_inputs.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d5c660c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_inputs = test_inputs.reshape(-1, 1)\n",
    "test_inputs = scaler.transform(test_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "944bf989",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_feature = []\n",
    "for i in range(60, 89):\n",
    "  test_feature.append(test_inputs[i-60:i, 0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5d66196f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1740, 1)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_feature = np.array(test_feature)\n",
    "test_feature = np.reshape(test_feature, (test_feature.shape[0] - test_feature.shape[1], 1))\n",
    "test_feature.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d4d58f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "classification = model.predict(test_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a6adb5ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.75],\n",
       "       [0.75],\n",
       "       [0.75],\n",
       "       ...,\n",
       "       [ nan],\n",
       "       [ nan],\n",
       "       [ nan]], dtype=float32)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5b055a3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(183, 5)\n",
      "(182, 5)\n"
     ]
    }
   ],
   "source": [
    "x_train = data[0:1256]\n",
    "y_train = data[1:1257]\n",
    "print(x_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "49276c0f",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2       NaN       1   \n",
       "3              4       NaN       1   \n",
       "6              7       NaN       1   \n",
       "10            11       NaN       3   \n",
       "11            12       NaN       1   \n",
       "..           ...       ...     ...   \n",
       "871          872  0.733333       1   \n",
       "872          873  0.700000       1   \n",
       "879          880  0.700000       1   \n",
       "887          888  0.700000       1   \n",
       "889          890  0.700000       1   \n",
       "\n",
       "                                                  Name     Sex  \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  \n",
       "6                              McCarthy, Mr. Timothy J    male  \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female  \n",
       "11                            Bonnell, Miss. Elizabeth  female  \n",
       "..                                                 ...     ...  \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  \n",
       "872                           Carlsson, Mr. Frans Olof    male  \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  \n",
       "887                       Graham, Miss. Margaret Edith  female  \n",
       "889                              Behr, Mr. Karl Howell    male  \n",
       "\n",
       "[183 rows x 5 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7db19002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9765919947103443"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dc0e17c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.60059780821406"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3bbbeb70",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6440b479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.87262077473541"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(size=1000, scale=100).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84c4560f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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