Titanic_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv(\"train.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</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>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  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"test.csv\")\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n",
      "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
      "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(train_data.keys())\n",
    "print(test_data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women who survived: 0.7420382165605095\n"
     ]
    }
   ],
   "source": [
    "women = train_data.loc[train_data.Sex == 'female'][\"Survived\"]\n",
    "rate_women = sum(women)/len(women)\n",
    "\n",
    "print(\"% of women who survived:\", rate_women)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of men who survived: 0.18890814558058924\n"
     ]
    }
   ],
   "source": [
    "men = train_data.loc[train_data.Sex == 'male'][\"Survived\"]\n",
    "rate_men = sum(men)/len(men)\n",
    "\n",
    "print(\"% of men who survived:\", rate_men)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NaN_table(train_dataa, test_data):\n",
    "    print(\"Training Data Frame\")\n",
    "    print(pd.isnull(train_data).sum()) \n",
    "    print(\" \")\n",
    "    print(\"Testing Data Frame\")\n",
    "    print(pd.isnull(test_data).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Frame\n",
      "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\n",
      " \n",
      "Testing Data Frame\n",
      "PassengerId      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\n"
     ]
    }
   ],
   "source": [
    "NaN_table(train_data, test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data Frame\n",
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Fare             0\n",
      "Embarked         2\n",
      "dtype: int64\n",
      " \n",
      "Testing Data Frame\n",
      "PassengerId     0\n",
      "Pclass          0\n",
      "Name            0\n",
      "Sex             0\n",
      "Age            86\n",
      "SibSp           0\n",
      "Parch           0\n",
      "Fare            1\n",
      "Embarked        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "train_data.drop(labels = [\"Cabin\", \"Ticket\"], axis = 1, inplace = True)\n",
    "test_data.drop(labels = [\"Cabin\", \"Ticket\"], axis = 1, inplace = True)\n",
    "NaN_table(train_data, test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8f446adbd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(train_data[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your submission was successfully saved!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "y = train_data[\"Survived\"]\n",
    "\n",
    "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
    "X = pd.get_dummies(train_data[features])\n",
    "X_test = pd.get_dummies(test_data[features])\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
    "model.fit(X, y)\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
    "output.to_csv('my_submission.csv', index=False)\n",
    "print(\"Your submission was successfully saved!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}