Titanic_Disaster_LogisticR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": [
    "#1. import and check data\n",
    "train_data = pd.read_csv(\"/Users/fanyingtang/Desktop/homework_1/project_2/train.csv\")\n",
    "train_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()\n",
    "# consider Pclass, Sex, Age, SibSp, Parch, Fare and Embarked. \n",
    "# Age and Embarked has some missing data\n",
    "\n",
    "# Not considering Name, Ticket, Cabin for the following reasons\n",
    "# Name is complicated and may contain typo. \n",
    "# Ticket format is very heterogeneous and confusing.\n",
    "# Majority of the Cabin info of is missing.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fanyingtang/anaconda3/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Quick, Mrs. Frederick Charles (Jane Richards)</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Name   Sex    Ticket Cabin  \\\n",
       "count                                             891   891       891   204   \n",
       "unique                                            891     2       681   147   \n",
       "top     Quick, Mrs. Frederick Charles (Jane Richards)  male  CA. 2343    G6   \n",
       "freq                                                1   577         7     4   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x117448208>"
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     "execution_count": 6,
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YM8i76iYpisaWTtu+FKjriZ5yB0XNQ1/HEhF/Bbwc+I3MvL3O/jNzR2belJnX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\nH7NdhWKp4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117553fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. Determine which factors may affect the survival rate:\n",
    "\n",
    "#2.1 check whether age affects survival by figure\n",
    "age_fig = sns.FacetGrid(train_data, col='Survived')\n",
    "age_fig.map(plt.hist, 'Age', bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Sex  Survived\n",
      "0  female  0.742038\n",
      "1    male  0.188908\n"
     ]
    }
   ],
   "source": [
    "#2.2 check whether sex affects survival\n",
    "sex_data = train_data[[\"Sex\", \"Survived\"]].groupby(['Sex'], \\\n",
    "                                        as_index=False).mean().sort_values(by='Survived', \\\n",
    "                                                                           ascending=False)\n",
    "print(sex_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JDq6qovnq7u6q2tR3yA1tmSRJ0kSacXACzgKeADy9d2dVndPz8qtJvgJ8CzgM+MQsPk+S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\ncVwMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119fd2b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.3 check whether Pclass affects survival rate\n",
    "Pclass_fig = sns.FacetGrid(train_data, col='Survived')\n",
    "Pclass_fig.map(plt.hist, 'Pclass', bins=3)\n",
    "\n",
    "\n",
    "train_data[[\"Pclass\", \"Survived\"]].groupby(['Pclass'], \\\n",
    "                                          as_index=False).mean().sort_values(by='Survived', \\\n",
    "                                                                             ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.4 check the effects of Sibling numbers\n",
    "train_data[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], \\\n",
    "                                          as_index=False).mean().sort_values(by='Survived', \\\n",
    "                                                                             ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.5 check the effects of parents numbers\n",
    "train_data[[\"Parch\", \"Survived\"]].groupby(['Parch'], \\\n",
    "                                          as_index=False).mean().sort_values(by='Survived', \\\n",
    "                                                                             ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.336957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.336957"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.6 check the effects of Embarked sites\n",
    "train_data[[\"Embarked\", \"Survived\"]].groupby(['Embarked'], \\\n",
    "                                          as_index=False).mean().sort_values(by='Survived', \\\n",
    "                                                                             ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ahwHHtEFk8jTcTWluZ06SQ2iu+rdf2/7TNJes/niSg2lOx/0n4KODDr+0bgVYtmwZu+yy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\nmJ17oxULyPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a939c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.7 check the effects of fare\n",
    "victim = train_data.Fare[train_data.Survived == 0].value_counts()\n",
    "survivor = train_data.Fare[train_data.Survived == 1].value_counts()\n",
    "\n",
    "fareMax = train_data.Fare.max()\n",
    "q1 = train_data.Fare.quantile(0.25)\n",
    "q2 = train_data.Fare.quantile(0.5)\n",
    "q3 = train_data.Fare.quantile(0.75)\n",
    "\n",
    "bins_ad = []\n",
    "bins = []\n",
    "survive_rate = []\n",
    "bins_ad.append(train_data.Survived[train_data.Fare <= q1].value_counts())\n",
    "# return two values 0 --num of not survived; 1 -- num of survived\n",
    "bins_ad.append(train_data.Survived[train_data.Fare <= q2].value_counts())\n",
    "bins_ad.append(train_data.Survived[train_data.Fare <= q3].value_counts())\n",
    "bins_ad.append(train_data.Survived[train_data.Fare <= fareMax].value_counts())\n",
    "\n",
    "# initiate with the first quarter\n",
    "bins.append(bins_ad[0]) \n",
    "survive_rate.append(bins[0][1]/(bins[0][0]+bins[0][1]))\n",
    "\n",
    "for i in range(3):\n",
    "    bins.append(bins_ad[i+1] - bins_ad[i])\n",
    "    survive_rate.append(bins[i+1][1]/(bins[i+1][0]+bins[i+1][1]))\n",
    "    \n",
    "    \n",
    "xs = [0,0.25,0.5,0.75]\n",
    "ys = survive_rate\n",
    "plt.bar(xs,ys,width = 0.25)\n",
    "\n",
    "plt.title(\"Effects of Fare on Surival Rate\")\n",
    "plt.xlabel(\"Fare Quantile\") \n",
    "plt.ylabel(\"Survival Rate\") \n",
    "plt.show()\n",
    "# plt.savefig(\"Fare.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Sex            891 non-null int64\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Fare           891 non-null float64\n",
      "Embarked       889 non-null float64\n",
      "dtypes: float64(3), int64(6)\n",
      "memory usage: 62.7 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Sex            418 non-null int64\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Fare           417 non-null float64\n",
      "Embarked       418 non-null int64\n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 26.2 KB\n"
     ]
    }
   ],
   "source": [
    "# 3. Clean up data\n",
    "# Import test data\n",
    "test_data = pd.read_csv(\"/Users/fanyingtang/Desktop/homework_1/project_2/test.csv\")\n",
    "\n",
    "# 3.1 Change \"Embarked\", \"Sex\" into numbers\n",
    "train_data['Sex'] = train_data['Sex'].map( {'female': 1, 'male': 0} )\n",
    "test_data['Sex'] = test_data['Sex'].map( {'female': 1, 'male': 0} )\n",
    "\n",
    "train_data['Embarked'] = train_data['Embarked'].map( {'S': 2, 'C': 1, 'Q':0} )\n",
    "test_data['Embarked'] = test_data['Embarked'].map( {'S': 2, 'C': 1, 'Q':0} )\n",
    "\n",
    "\n",
    "# 3.2 Remove columns \"Name\", \"Ticket\", \"Cabin\"\n",
    "train_data_select = train_data.drop(['Ticket', 'Cabin', 'Name'], axis=1)\n",
    "test_data_select = test_data.drop(['Ticket', 'Cabin', 'Name'], axis=1)\n",
    "\n",
    "#train_data_select.head()\n",
    "#test_data_select.head()\n",
    "train_data_select.info()\n",
    "test_data_select.info()\n",
    "# train data has missing Age, Embarked values\n",
    "# test data has missing Age, Fare values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Sex            891 non-null int64\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Fare           891 non-null float64\n",
      "Embarked       891 non-null float64\n",
      "dtypes: float64(3), int64(6)\n",
      "memory usage: 62.7 KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Sex            418 non-null int64\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Fare           418 non-null float64\n",
      "Embarked       418 non-null int64\n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 26.2 KB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fanyingtang/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "/Users/fanyingtang/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/fanyingtang/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:9: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/fanyingtang/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:12: 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: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# 3.3 Fill up NA values for \"Age\", \"Embarked\", and \"Fare\" based on the median value of existing data.\n",
    "median_test_age = test_data_select[\"Age\"].median()\n",
    "test_data_select[\"Age\"][np.isnan(test_data_select[\"Age\"])] = median_test_age\n",
    "\n",
    "median_train_age = train_data_select[\"Age\"].median()\n",
    "train_data_select[\"Age\"][np.isnan(train_data_select[\"Age\"])] = median_train_age\n",
    "\n",
    "median_test_fare  = test_data_select[\"Fare\"].median()\n",
    "test_data_select[\"Fare\"][np.isnan(test_data_select[\"Fare\"])] = median_test_fare\n",
    "\n",
    "median_train_embark  = train_data_select[\"Embarked\"].median()\n",
    "train_data_select[\"Embarked\"][np.isnan(train_data_select[\"Embarked\"])] = median_train_embark\n",
    "\n",
    "train_data_select.info()\n",
    "test_data_select.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 4. Do logistic regression and submit the data\n",
    "X_train = train_data_select.drop(['Survived'], axis = 1)\n",
    "Y_train = train_data_select['Survived']\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "test_pred = logreg.predict(test_data_select)\n",
    "\n",
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_data[\"PassengerId\"],\n",
    "        \"Survived\": test_pred\n",
    "    })\n",
    "\n",
    "submission.to_csv('pred.csv', index=False)"
   ]
  },
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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