titanic_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic=sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
      "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
      "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
      "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
      "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
      "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
      "\n",
      "     who  adult_male deck  embark_town alive  alone  \n",
      "0    man        True  NaN  Southampton    no  False  \n",
      "1  woman       False    C    Cherbourg   yes  False  \n",
      "2  woman       False  NaN  Southampton   yes   True  \n",
      "3  woman       False    C  Southampton   yes  False  \n",
      "4    man        True  NaN  Southampton    no   True  \n"
     ]
    }
   ],
   "source": [
    "print(titanic.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alone\n",
      "False    0.505650\n",
      "True     0.303538\n",
      "Name: survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(titanic.groupby('alone')['survived'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      (18.0, 50.0]\n",
       "1      (18.0, 50.0]\n",
       "2      (18.0, 50.0]\n",
       "3      (18.0, 50.0]\n",
       "4      (18.0, 50.0]\n",
       "           ...     \n",
       "886    (18.0, 50.0]\n",
       "887    (18.0, 50.0]\n",
       "888             NaN\n",
       "889    (18.0, 50.0]\n",
       "890    (18.0, 50.0]\n",
       "Name: age, Length: 891, dtype: category\n",
       "Categories (3, interval[int64]): [(0, 18] < (18, 50] < (50, 100]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age=pd.cut(titanic['age'],[0,18,50,100])\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>First</th>\n",
       "      <th>Second</th>\n",
       "      <th>Third</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">female</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 50]</th>\n",
       "      <td>0.967213</td>\n",
       "      <td>0.912281</td>\n",
       "      <td>0.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 100]</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">male</th>\n",
       "      <th>(0, 18]</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.215686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 50]</th>\n",
       "      <td>0.442857</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>0.139896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 100]</th>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "class                First    Second     Third\n",
       "sex    age                                    \n",
       "female (0, 18]    0.909091  1.000000  0.511628\n",
       "       (18, 50]   0.967213  0.912281  0.413793\n",
       "       (50, 100]  1.000000  0.666667  1.000000\n",
       "male   (0, 18]    0.800000  0.600000  0.215686\n",
       "       (18, 50]   0.442857  0.069444  0.139896\n",
       "       (50, 100]  0.192308  0.083333  0.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.pivot_table('survived',['sex',age],'class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
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       "      <th>alone</th>\n",
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       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  sibsp  parch     fare embarked  class    who  \\\n",
       "0         0       3  22.0      1      0   7.2500        S  Third    man   \n",
       "1         1       1  38.0      1      0  71.2833        C  First  woman   \n",
       "2         1       3  26.0      0      0   7.9250        S  Third  woman   \n",
       "3         1       1  35.0      1      0  53.1000        S  First  woman   \n",
       "4         0       3  35.0      0      0   8.0500        S  Third    man   \n",
       "\n",
       "   adult_male deck  embark_town alive  alone  \n",
       "0        True  NaN  Southampton    no  False  \n",
       "1       False    C    Cherbourg   yes  False  \n",
       "2       False  NaN  Southampton   yes   True  \n",
       "3       False    C  Southampton   yes  False  \n",
       "4        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex = pd.get_dummies(titanic['sex'],drop_first=True)\n",
    "\n",
    "titanic.drop(['sex'],axis=1,inplace=True)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic=pd.concat([titanic,sex],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop(['adult_male','who'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop(['deck'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic=pd.concat([titanic,sex],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>S</td>\n",
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       "      <th>1</th>\n",
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       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  sibsp  parch     fare embarked  class  embark_town  \\\n",
       "0         0       3  22.0      1      0   7.2500        S  Third  Southampton   \n",
       "1         1       1  38.0      1      0  71.2833        C  First    Cherbourg   \n",
       "2         1       3  26.0      0      0   7.9250        S  Third  Southampton   \n",
       "3         1       1  35.0      1      0  53.1000        S  First  Southampton   \n",
       "4         0       3  35.0      0      0   8.0500        S  Third  Southampton   \n",
       "\n",
       "  alive  alone  male  \n",
       "0    no  False     1  \n",
       "1   yes  False     0  \n",
       "2   yes   True     0  \n",
       "3   yes  False     0  \n",
       "4    no   True     1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.drop(['embarked','embark_town'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4379: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().replace(\n"
     ]
    }
   ],
   "source": [
    "titanic[['class']].replace('Second',2,inplace=True)\n",
    "titanic[['class']].replace('First',1,inplace=True)\n",
    "titanic[['class']].replace('Third',3,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype   \n",
      "---  ------  --------------  -----   \n",
      " 0   class   891 non-null    category\n",
      "dtypes: category(1)\n",
      "memory usage: 1.1 KB\n"
     ]
    }
   ],
   "source": [
    "titanic[['class']].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch     fare class  male\n",
       "0           0       3  22.0      1      0   7.2500     3     1\n",
       "1           1       1  38.0      1      0  71.2833     1     0\n",
       "2           1       3  26.0      0      0   7.9250     3     0\n",
       "3           1       1  35.0      1      0  53.1000     1     0\n",
       "4           0       3  35.0      0      0   8.0500     3     1\n",
       "..        ...     ...   ...    ...    ...      ...   ...   ...\n",
       "886         0       2  27.0      0      0  13.0000     2     1\n",
       "887         1       1  19.0      0      0  30.0000     1     0\n",
       "888         0       3   NaN      1      2  23.4500     3     0\n",
       "889         1       1  26.0      0      0  30.0000     1     1\n",
       "890         0       3  32.0      0      0   7.7500     3     1\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.drop(['alive','alone'],axis=1,inplace=True)\n",
    "\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch     fare class  male\n",
       "0           0       3  22.0      1      0   7.2500     3     1\n",
       "1           1       1  38.0      1      0  71.2833     1     0\n",
       "2           1       3  26.0      0      0   7.9250     3     0\n",
       "3           1       1  35.0      1      0  53.1000     1     0\n",
       "4           0       3  35.0      0      0   8.0500     3     1\n",
       "..        ...     ...   ...    ...    ...      ...   ...   ...\n",
       "885         0       3  39.0      0      5  29.1250     3     0\n",
       "886         0       2  27.0      0      0  13.0000     2     1\n",
       "887         1       1  19.0      0      0  30.0000     1     0\n",
       "889         1       1  26.0      0      0  30.0000     1     1\n",
       "890         0       3  32.0      0      0   7.7500     3     1\n",
       "\n",
       "[714 rows x 8 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.dropna(inplace=True)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>13.0000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass   age  sibsp  parch     fare class  male\n",
       "0         3  22.0      1      0   7.2500     3     1\n",
       "1         1  38.0      1      0  71.2833     1     0\n",
       "2         3  26.0      0      0   7.9250     3     0\n",
       "3         1  35.0      1      0  53.1000     1     0\n",
       "4         3  35.0      0      0   8.0500     3     1\n",
       "..      ...   ...    ...    ...      ...   ...   ...\n",
       "885       3  39.0      0      5  29.1250     3     0\n",
       "886       2  27.0      0      0  13.0000     2     1\n",
       "887       1  19.0      0      0  30.0000     1     0\n",
       "889       1  26.0      0      0  30.0000     1     1\n",
       "890       3  32.0      0      0   7.7500     3     1\n",
       "\n",
       "[714 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x =titanic.drop(['survived'],axis=1)\n",
    "df_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived\n",
       "0           0\n",
       "1           1\n",
       "2           1\n",
       "3           1\n",
       "4           0\n",
       "..        ...\n",
       "885         0\n",
       "886         0\n",
       "887         1\n",
       "889         1\n",
       "890         0\n",
       "\n",
       "[714 rows x 1 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_y =titanic[['survived']]\n",
    "df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(df_x,df_y,test_size=0.30,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log=LogisticRegression()\n",
    "log.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict=log.predict(x_test)\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>215 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived\n",
       "149         0\n",
       "407         1\n",
       "53          1\n",
       "369         1\n",
       "818         0\n",
       "..        ...\n",
       "676         0\n",
       "493         0\n",
       "479         1\n",
       "278         0\n",
       "174         0\n",
       "\n",
       "[215 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[107  19]\n",
      " [ 30  59]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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