Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd;\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data = pd.read_csv(\"titanic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Survived     418 non-null    int64  \n",
      " 2   Pclass       418 non-null    int64  \n",
      " 3   Name         418 non-null    object \n",
      " 4   Sex          418 non-null    object \n",
      " 5   Age          332 non-null    float64\n",
      " 6   SibSp        418 non-null    int64  \n",
      " 7   Parch        418 non-null    int64  \n",
      " 8   Ticket       418 non-null    object \n",
      " 9   Fare         417 non-null    float64\n",
      " 10  Cabin        91 non-null     object \n",
      " 11  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 39.3+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_data.info()"
   ]
  },
  {
   "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>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>892</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare Cabin Embarked  \n",
       "0   330911   7.8292   NaN        Q  \n",
       "1   363272   7.0000   NaN        S  \n",
       "2   240276   9.6875   NaN        Q  \n",
       "3   315154   8.6625   NaN        S  \n",
       "4  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age             86\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             1\n",
       "Cabin          327\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
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       "      <td>Wirz, Mr. Albert</td>\n",
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       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
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       "      <td>Spector, Mr. Woolf</td>\n",
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       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>1308</td>\n",
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       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
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       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
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       "<p>418 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0            892         0       3   \n",
       "1            893         1       3   \n",
       "2            894         0       2   \n",
       "3            895         0       3   \n",
       "4            896         1       3   \n",
       "..           ...       ...     ...   \n",
       "413         1305         0       3   \n",
       "414         1306         1       1   \n",
       "415         1307         0       3   \n",
       "416         1308         0       3   \n",
       "417         1309         0       3   \n",
       "\n",
       "                                             Name     Sex   Age  SibSp  Parch  \\\n",
       "0                                Kelly, Mr. James    male  34.5      0      0   \n",
       "1                Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                       Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                                Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4    Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "..                                            ...     ...   ...    ...    ...   \n",
       "413                            Spector, Mr. Woolf    male   NaN      0      0   \n",
       "414                  Oliva y Ocana, Dona. Fermina  female  39.0      0      0   \n",
       "415                  Saether, Mr. Simon Sivertsen    male  38.5      0      0   \n",
       "416                           Ware, Mr. Frederick    male   NaN      0      0   \n",
       "417                      Peter, Master. Michael J    male   NaN      1      1   \n",
       "\n",
       "                 Ticket      Fare Cabin Embarked  \n",
       "0                330911    7.8292   NaN        Q  \n",
       "1                363272    7.0000   NaN        S  \n",
       "2                240276    9.6875   NaN        Q  \n",
       "3                315154    8.6625   NaN        S  \n",
       "4               3101298   12.2875   NaN        S  \n",
       "..                  ...       ...   ...      ...  \n",
       "413           A.5. 3236    8.0500   NaN        S  \n",
       "414            PC 17758  108.9000  C105        C  \n",
       "415  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
       "416              359309    8.0500   NaN        S  \n",
       "417                2668   22.3583   NaN        C  \n",
       "\n",
       "[418 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Age'].fillna(titanic_data['Age'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Fare'].fillna(titanic_data['Fare'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_data['Cabin'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0          892         0       3   \n",
       "1          893         1       3   \n",
       "2          894         0       2   \n",
       "3          895         0       3   \n",
       "4          896         1       3   \n",
       "\n",
       "                                           Name     Sex   Age  SibSp  Parch  \\\n",
       "0                              Kelly, Mr. James    male  34.5      0      0   \n",
       "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
       "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
       "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
       "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
       "\n",
       "    Ticket     Fare    Cabin Embarked  \n",
       "0   330911   7.8292  Unknown        Q  \n",
       "1   363272   7.0000  Unknown        S  \n",
       "2   240276   9.6875  Unknown        Q  \n",
       "3   315154   8.6625  Unknown        S  \n",
       "4  3101298  12.2875  Unknown        S  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manua\\AppData\\Local\\Temp\\ipykernel_22472\\3937775855.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(x='Sex', data=titanic_data, palette='Set1')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Passenger Gender Distribution')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Passenger demographics\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Counterplot for 'Sex'\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.countplot(x='Sex', data=titanic_data, palette='Set1')\n",
    "plt.title('Passenger Gender Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manua\\AppData\\Local\\Temp\\ipykernel_22472\\682368044.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.countplot(x='Pclass', data=titanic_data, palette='Set2')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot for 'Pclass'\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.countplot(x='Pclass', data=titanic_data, palette='Set2')\n",
    "plt.title('Passenger Class Distribution')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analyze Survival Rates based on sex\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "sns.countplot(x='Survived', hue='Sex', data=titanic_data, palette='pastel')\n",
    "plt.title('Survival Count by Gender')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize ticket class and cabin locations\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# Plot a countplot for 'Pclass' based on 'Survived'\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.countplot(x='Pclass', hue='Survived', data=titanic_data, palette='dark')\n",
    "plt.title('Survival Count by Passenger Class')\n",
    "\n",
    "# Plot a heatmap for 'Cabin' data\n",
    "plt.subplot(1, 2, 2)\n",
    "cabin_survival = titanic_data[['Cabin', 'Survived']].dropna()\n",
    "sns.heatmap(pd.crosstab(cabin_survival['Cabin'], cabin_survival['Survived']), cmap='coolwarm', annot=True, fmt='d')\n",
    "plt.title('Survival Heatmap by Cabin')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}