Titanic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1- Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training data first, then check the data format"
   ]
  },
  {
   "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",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_csv('data/train.csv')\n",
    "data_test = pd.read_csv('data/test.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "----------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()\n",
    "print('-'*40)\n",
    "data_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Survived'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First look at the overall survival ratio\n",
    "fig = plt.figure(figsize=(6,6))\n",
    "train_data['Survived'].value_counts().plot.pie(autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-Missing value processing\n",
    "When analyzing the data, it is important to note whether there are missing values in it.\n",
    "\n",
    "Some machine learning algorithms can handle missing values, such as neural networks, and some cannot. For missing values, there are several general ways to handle them.\n",
    "\n",
    "(1) If the dataset is large but has few missing values, the rows with missing values can be deleted.\n",
    "\n",
    "(2) If the attribute is not very important relative to the learning, you can assign a mean or plurality to the missing values. For example, if the attribute Embarked (there are three embarkation locations) has two missing values, you can use the plural to assign the value\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/14/nc2ygsds1flfyq_lf5n38g600000gn/T/ipykernel_27414/3714205079.py:2: 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",
      "  train_data['Embarked'][train_data['Embarked'].isnull()] = train_data['Embarked'].dropna().mode().values\n"
     ]
    }
   ],
   "source": [
    "train_data['Embarked'][train_data['Embarked'].isnull()]\n",
    "train_data['Embarked'][train_data['Embarked'].isnull()] = train_data['Embarked'].dropna().mode().values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(3) For nominal attributes, you can assign a value that represents the absence, such as 'U0'. Because the absence itself may also represent some implicit information. For example, the missing property Cabin may mean that there is no cabin. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Cabin'] = train_data['Cabin'].fillna('U0') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(4) Use models such as regression Random Forest to predict the values of missing attributes. Since Age is a fairly important feature in this dataset (as can be seen by first analyzing Age), it is very important to ensure a certain accuracy in filling in the missing values, which can also have a large impact on the results. In general, entries with complete data are used as the training set for the model as a way to predict missing values. For this current data, either random forest can be used to predict or linear regression can be used to predict. Here the random forest prediction model is used, and the numerical attributes in the dataset are selected as features (because sklearn's model can only handle numerical attributes, so only numerical features are selected here first, but in practical applications non-numerical features need to be converted to numerical features)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "age_df = train_data[['Age','Survived','Fare', 'Parch', 'SibSp', 'Pclass']]\n",
    "age_df_notnull = age_df.loc[(train_data['Age'].notnull())]\n",
    "age_df_isnull = age_df.loc[(train_data['Age'].isnull())]\n",
    "X = age_df_notnull.values[:,1:]\n",
    "Y = age_df_notnull.values[:,0]\n",
    "# use RandomForestRegression to train data\n",
    "RFR = RandomForestRegressor(n_estimators=1000,n_jobs=-1)\n",
    "RFR.fit(X,Y)\n",
    "predictAges = RFR.predict(age_df_isnull.values[:,1:])\n",
    "train_data.loc[train_data['Age'].isnull(), ['Age']]= predictAges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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          891 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        891 non-null    object \n",
      " 11  Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Next, look at the complementary data\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3-Preliminary data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3-1-The relationship between gender and survival or not (Sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            81\n",
       "        1           233\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex','Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "survived_by_sex = train_data[['Sex','Survived']].groupby('Sex').mean()\n",
    "type(survived_by_sex)\n",
    "survived_by_sex.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is the survival rate of different genders, which can be seen in the Titanic accident, or reflect the Lady First."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3-2-The relationship between cabin class and survival or not Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Survived\n",
       "1       0            80\n",
       "        1           136\n",
       "2       0            97\n",
       "        1            87\n",
       "3       0           372\n",
       "        1           119\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Pclass','Survived'])['Pclass'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "train_data[['Pclass','Survived']].groupby(['Pclass']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass  Survived\n",
       "female  1       0             3\n",
       "                1            91\n",
       "        2       0             6\n",
       "                1            70\n",
       "        3       0            72\n",
       "                1            72\n",
       "male    1       0            77\n",
       "                1            45\n",
       "        2       0            91\n",
       "                1            17\n",
       "        3       0           300\n",
       "                1            47\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.groupby(['Sex', 'Pclass', 'Survived'])['Survived'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Pclass,Sex'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "train_data[['Sex','Pclass','Survived']].groupby(['Pclass','Sex']).mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3-3 The relationship between age and survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tamanna/opt/anaconda3/envs/env/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "/Users/tamanna/opt/anaconda3/envs/env/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='Age'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(18,8))\n",
    "sns.violinplot(\"Pclass\",\"Age\",hue=\"Survived\",data=train_data, split=True, ax=ax[0])\n",
    "sns.violinplot(\"Sex\", \"Age\", hue=\"Survived\", data=train_data, split=True, ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of the overall age distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "train_data['Age'].hist(bins=70)\n",
    "\n",
    "plt.subplot(122)\n",
    "train_data.boxplot(column='Age', showfliers=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of survival and non-survival at different ages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fbbbaab3be0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 915.875x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train_data,hue='Survived',aspect=4)\n",
    "facet.map(sns.kdeplot, 'Age', shade=True)\n",
    "facet.set(xlim=(0, train_data['Age'].max()))\n",
    "facet.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}