titanic_kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named seaborn",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-5a921ddd2a61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named seaborn"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# pandas\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# numpy, matplotlib, seaborn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "# numpy, matplotlib, seaborn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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</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</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</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</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</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      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
       "2                             Heikkinen, Miss. Laina  female   26      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
       "4                           Allen, Mr. William Henry    male   35      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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get titanic & test csv files as a DataFrame\n",
    "titanic_df = pd.read_csv(\"/home/priya/Downloads/titanic/train.csv\")\n",
    "test_df    = pd.read_csv(\"/home/priya/Downloads/titanic/test.csv\")\n",
    "\n",
    "# preview the data\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 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: 90.5+ KB\n",
      "----------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()\n",
    "print(\"----------------------------\")\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unnecessary columns, these columns won't be useful in analysis and prediction\n",
    "titanic_df = titanic_df.drop(['PassengerId','Name','Ticket'], axis=1)\n",
    "test_df    = test_df.drop(['Name','Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f30b3bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f2fd9250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked\n",
    "\n",
    "# only in titanic_df, fill the two missing values with the most occurred value, which is \"S\".\n",
    "titanic_df[\"Embarked\"] = titanic_df[\"Embarked\"].fillna(\"S\")\n",
    "\n",
    "# plot\n",
    "sns.factorplot('Embarked','Survived', data=titanic_df,size=4,aspect=3)\n",
    "\n",
    "fig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,5))\n",
    "\n",
    "# sns.factorplot('Embarked',data=titanic_df,kind='count',order=['S','C','Q'],ax=axis1)\n",
    "# sns.factorplot('Survived',hue=\"Embarked\",data=titanic_df,kind='count',order=[1,0],ax=axis2)\n",
    "sns.countplot(x='Embarked', data=titanic_df, ax=axis1)\n",
    "sns.countplot(x='Survived', hue=\"Embarked\", data=titanic_df, order=[1,0], ax=axis2)\n",
    "\n",
    "# group by embarked, and get the mean for survived passengers for each value in Embarked\n",
    "embark_perc = titanic_df[[\"Embarked\", \"Survived\"]].groupby(['Embarked'],as_index=False).mean()\n",
    "sns.barplot(x='Embarked', y='Survived', data=embark_perc,order=['S','C','Q'],ax=axis3)\n",
    "\n",
    "# Either to consider Embarked column in predictions,\n",
    "# and remove \"S\" dummy variable, \n",
    "# and leave \"C\" & \"Q\", since they seem to have a good rate for Survival.\n",
    "\n",
    "# OR, don't create dummy variables for Embarked column, just drop it, \n",
    "# because logically, Embarked doesn't seem to be useful in prediction.\n",
    "\n",
    "embark_dummies_titanic  = pd.get_dummies(titanic_df['Embarked'])\n",
    "embark_dummies_titanic.drop(['S'], axis=1, inplace=True)\n",
    "\n",
    "embark_dummies_test  = pd.get_dummies(test_df['Embarked'])\n",
    "embark_dummies_test.drop(['S'], axis=1, inplace=True)\n",
    "\n",
    "titanic_df = titanic_df.join(embark_dummies_titanic)\n",
    "test_df    = test_df.join(embark_dummies_test)\n",
    "\n",
    "titanic_df.drop(['Embarked'], axis=1,inplace=True)\n",
    "test_df.drop(['Embarked'], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f295d350>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkiRJao1Kw19mfhf4xS7Na4B1ZXtd2Z9uX1/63QgsjYiRKuuTJEmSpLaodNrnbhyemeMAmbmjJ+AtB+7sOW97aRuf5/okSZozy5atYnz8jrrLWFRGRo5gx45tdZchSQtOHeFvV3u1+nWn05nrOiQtIP4OGNzExISfWw26wW+vvuq0G+PjQ45lzRt/d2oxqSP8jUfESGaOR8Qy4O7Svh1Y2XPeitI2o9HR0QpLlNR0/g4YXKfT8XPTouFY1nzxd6eaamxsbOA+87HUw1D5mbYBOLVsnwpc09P+xwAR8WJg5/T0UEmSJEnSvql6qYcvAScAT42InwHnAZ8AvhwRpwF3AGsBMvPaiHh1RPyY7lIP76yyNkmSJElqk0rDX2a+ZTeHXrGb899XYTmSJEmS1FrzMe1TkiRJklQzw58kSZIktYDhT5IkSZJawPAnSZIkSS1g+JMkSZKkFjD8SZIkSVILGP4kSZIkqQUMf5IkSZLUAoY/SZIkSWoBw58kSZIktYDhT5IkSZJawPAnSZIkSS1g+JMkSZKkFjD8SZIkSVILGP4kSZIkqQX2r+uNI2IbcC8wCTySmcdFxCHAFcARwDZgbWbeW1eNkiRJkrRY1HnlbxI4ITOPyczjStvZwMbMDOAG4JzaqpMkSZKkRaTO8Dc0w/uvAdaV7XXAKfNakSRJkiQtUnWGvyngmxFxc0S8u7SNZOY4QGbuAA6vrTpJkiRJWkRqu+cPOD4zxyLit4HrIiLpBsJeu+7/SqfTqbQ4Sc3m74DBTUxM+Llp0XAsa774u1OLSW3hLzPHyp/3RMTXgOOA8YgYyczxiFgG3L27/qOjo/NUqaQm8nfA4Dqdjp+bFg3HsuaLvzvVVGNjYwP3qWXaZ0QcFBFPLttPAv4A2AJsAE4tp70DuKaO+iRJkiRpsanryt8IcHVETJUavpiZ10XELcCVEXEacAewtqb6JEmSJGlRqSX8ZeZPgaNnaP858Ir5r0iStGzZKsbH76i7DEmSVJE6H/giSXtpCUNDQ3UXsUjt9jlb2muOVUlSMxj+JC1AD2FIqYIhRZKkxazOdf4kSZIkSfPE8CdJkiRJLeC0T0mStMB4328VRkaOYMeObXWXIalChj9JkrTAeN9vFcbHDdTSYue0T0mSJElqAcOfJEmSJLWA4U+SJEmSWsDwJ0mSJEktsGAf+LJy5XPrLmFRGR72Jm9JktrNp6hWwaeoqkkWbPi7664r6y5hUVmy5KPAj+ouQ5Ik1canqFbBp6iqSRZs+AOv/M2l4eFD6y5BkiRJUoW850+SJEmSWmABX/mTJEmSms57KaswPHwQk5MP1l1GrW655ZaB+zQy/EXEq4BP070yeUlmfrLmkiRJkqS94L2UVZicHMLPddPAPRo37TMihoG/BV5J98a+P4qI59RblSRJkiQtbI0Lf8BxwO2ZeUdmPgJcDqypuSZJkiRJWtCaOO1zOXBnz/5ddAPhbzj44NfNW0Ft8PDDP6i7BEmSJEkVamL468sNN3y47hIWqcHnDms2t+DnOtf8TKvh51oNP9e552daDT/Xavi5VsPPdW80MfxtB57Ws7+itP3K6tWrfWSSJEmSJA2gieHvZuBZEXEEMAa8GfijekuSJEmSpIWtcQ98yczHgPcB1wE/Ai7PzNvqrUqSJEmSFrahqam2r48hSZIkSYtfE6d97pELwKspIuIS4LXAeGY+v7QdAlwBHAFsA9Zm5r21FalWiogVwHpgBJgELs7MixyfaoKIWAJ8BziA7t9DrsrMj0TEKrrLOx1K9ykOb8/MR2srVK1V1py+BbgrM1/v2FRTRMQ24F663+2PZOZxg363N27a5564ALwa5lK6Y7HX2cDGzAzgBuCcea9KgkeBszLzucBLgDPK70rHp2qXmQ8BJ2bmMcDRwMkR8SLgk8D5mflsYCfwrhrLVLudCWzt2XdsqikmgRMy85jMnF4Kb6Dv9gUV/nABeDVIZn4X+MUuzWuAdWV7HXDKvBYlAZm5IzM3l+37gdvoPjnZ8alGyMwHy+YSulf/poATga+U9nXAG2ooTS1XZk68Gvh8T/Pv49hUMwzx+Pw20Hf7Qgt/My0Av7ymWqSZHJ6Z49D9CzhweM31qOXKdKWjge8DI45PNUFEDEfErcAO4HrgJ8DOzJwsp9wFjNZVn1rtU8Bf0P0HCSLiqcAvHJtqiCngmxFxc0S8u7QN9N2+0MKftND4RCXVJiKeDFwFnFmuAO46Hh2fqkVmTpZpnyvozurxFg7VLiJeQ/c+/s10r7BMc31pNcXxmfkCulenz4iIlzLgd/tCC3+zLgAv1Ww8IkYAImIZcHfN9ailImJ/usHvssy8pjQ7PtUomXkf8G2696Y+pdzbD36/qx7HA6+PiP8B/onudM8LgaWOTTVBZo6VP+8Bvkb3H88G+m5faOHvVwvAR8QBdBeA31BzTWq3IX7zXwQ3AKeW7XcA1+zaQZonXwC2ZuaFPW2OT9UuIg6LiKVl+0DgJLoP1/gW8KZymuNT8y4zz83Mp2XmM+j+HfOGzHwbjk01QEQcVGb0EBFPAv4A2MKA3+0Lbp2/stTDhfx6qYdP1FySWioivgScADwVGAfOo/uvMF8GVgJ30H3c7s66alQ7RcTxdB+lv4Xu9I8p4FzgJuBKHJ+qUUQ8j+5DCYbLzxWZ+VcR8XS6D3I7BLgVeFt5uJs07yLiZcAHylIPjk3VrozDq+l+p+8PfDEzPxERhzLAd/uCC3+SJEmSpMEttGmfkiRJkqS9YPiTJEmSpBYw/EmSJElSCxj+JEmSJKkFDH+SJEmS1AKGP0mSJElqAcOfJEmSJLXA/wPmAIsEUKydRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f2fd3cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f2fd3910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# only for test_df, since there is a missing \"Fare\" values\n",
    "test_df[\"Fare\"].fillna(test_df[\"Fare\"].median(), inplace=True)\n",
    "\n",
    "# convert from float to int\n",
    "titanic_df['Fare'] = titanic_df['Fare'].astype(int)\n",
    "test_df['Fare']    = test_df['Fare'].astype(int)\n",
    "\n",
    "# get fare for survived & didn't survive passengers \n",
    "fare_not_survived = titanic_df[\"Fare\"][titanic_df[\"Survived\"] == 0]\n",
    "fare_survived     = titanic_df[\"Fare\"][titanic_df[\"Survived\"] == 1]\n",
    "\n",
    "# get average and std for fare of survived/not survived passengers\n",
    "avgerage_fare = DataFrame([fare_not_survived.mean(), fare_survived.mean()])\n",
    "std_fare      = DataFrame([fare_not_survived.std(), fare_survived.std()])\n",
    "\n",
    "# plot\n",
    "titanic_df['Fare'].plot(kind='hist', figsize=(15,3),bins=100, xlim=(0,50))\n",
    "\n",
    "avgerage_fare.index.names = std_fare.index.names = [\"Survived\"]\n",
    "avgerage_fare.plot(yerr=std_fare,kind='bar',legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:30: 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",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:31: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f27daad0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f28bbd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age \n",
    "\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))\n",
    "axis1.set_title('Original Age values - Titanic')\n",
    "axis2.set_title('New Age values - Titanic')\n",
    "\n",
    "# axis3.set_title('Original Age values - Test')\n",
    "# axis4.set_title('New Age values - Test')\n",
    "\n",
    "# get average, std, and number of NaN values in titanic_df\n",
    "average_age_titanic   = titanic_df[\"Age\"].mean()\n",
    "std_age_titanic       = titanic_df[\"Age\"].std()\n",
    "count_nan_age_titanic = titanic_df[\"Age\"].isnull().sum()\n",
    "\n",
    "# get average, std, and number of NaN values in test_df\n",
    "average_age_test   = test_df[\"Age\"].mean()\n",
    "std_age_test       = test_df[\"Age\"].std()\n",
    "count_nan_age_test = test_df[\"Age\"].isnull().sum()\n",
    "\n",
    "# generate random numbers between (mean - std) & (mean + std)\n",
    "rand_1 = np.random.randint(average_age_titanic - std_age_titanic, average_age_titanic + std_age_titanic, size = count_nan_age_titanic)\n",
    "rand_2 = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test, size = count_nan_age_test)\n",
    "\n",
    "# plot original Age values\n",
    "# NOTE: drop all null values, and convert to int\n",
    "titanic_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)\n",
    "# test_df['Age'].dropna().astype(int).hist(bins=70, ax=axis1)\n",
    "\n",
    "# fill NaN values in Age column with random values generated\n",
    "titanic_df[\"Age\"][np.isnan(titanic_df[\"Age\"])] = rand_1\n",
    "test_df[\"Age\"][np.isnan(test_df[\"Age\"])] = rand_2\n",
    "\n",
    "# convert from float to int\n",
    "titanic_df['Age'] = titanic_df['Age'].astype(int)\n",
    "test_df['Age']    = test_df['Age'].astype(int)\n",
    "        \n",
    "# plot new Age Values\n",
    "titanic_df['Age'].hist(bins=70, ax=axis2)\n",
    "# test_df['Age'].hist(bins=70, ax=axis4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f02f2b0e490>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f295d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f2a37fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .... continue with plot Age column\n",
    "\n",
    "# peaks for survived/not survived passengers by their age\n",
    "facet = sns.FacetGrid(titanic_df, hue=\"Survived\",aspect=4)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, titanic_df['Age'].max()))\n",
    "facet.add_legend()\n",
    "\n",
    "# average survived passengers by age\n",
    "fig, axis1 = plt.subplots(1,1,figsize=(18,4))\n",
    "average_age = titanic_df[[\"Age\", \"Survived\"]].groupby(['Age'],as_index=False).mean()\n",
    "sns.barplot(x='Age', y='Survived', data=average_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cabin\n",
    "# It has a lot of NaN values, so it won't cause a remarkable impact on prediction\n",
    "titanic_df.drop(\"Cabin\",axis=1,inplace=True)\n",
    "test_df.drop(\"Cabin\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python2.7/dist-packages/pandas/core/indexing.py:117: 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",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x7f02f3177910>,\n",
       " <matplotlib.text.Text at 0x7f02f279e310>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f22d97d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Family\n",
    "\n",
    "# Instead of having two columns Parch & SibSp, \n",
    "# we can have only one column represent if the passenger had any family member aboard or not,\n",
    "# Meaning, if having any family member(whether parent, brother, ...etc) will increase chances of Survival or not.\n",
    "titanic_df['Family'] =  titanic_df[\"Parch\"] + titanic_df[\"SibSp\"]\n",
    "titanic_df['Family'].loc[titanic_df['Family'] > 0] = 1\n",
    "titanic_df['Family'].loc[titanic_df['Family'] == 0] = 0\n",
    "\n",
    "test_df['Family'] =  test_df[\"Parch\"] + test_df[\"SibSp\"]\n",
    "test_df['Family'].loc[test_df['Family'] > 0] = 1\n",
    "test_df['Family'].loc[test_df['Family'] == 0] = 0\n",
    "\n",
    "# drop Parch & SibSp\n",
    "titanic_df = titanic_df.drop(['SibSp','Parch'], axis=1)\n",
    "test_df    = test_df.drop(['SibSp','Parch'], axis=1)\n",
    "\n",
    "# plot\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,sharex=True,figsize=(10,5))\n",
    "\n",
    "# sns.factorplot('Family',data=titanic_df,kind='count',ax=axis1)\n",
    "sns.countplot(x='Family', data=titanic_df, order=[1,0], ax=axis1)\n",
    "\n",
    "# average of survived for those who had/didn't have any family member\n",
    "family_perc = titanic_df[[\"Family\", \"Survived\"]].groupby(['Family'],as_index=False).mean()\n",
    "sns.barplot(x='Family', y='Survived', data=family_perc, order=[1,0], ax=axis2)\n",
    "\n",
    "axis1.set_xticklabels([\"With Family\",\"Alone\"], rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f213bcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sex\n",
    "\n",
    "# As we see, children(age < ~16) on aboard seem to have a high chances for Survival.\n",
    "# So, we can classify passengers as males, females, and child\n",
    "def get_person(passenger):\n",
    "    age,sex = passenger\n",
    "    return 'child' if age < 16 else sex\n",
    "    \n",
    "titanic_df['Person'] = titanic_df[['Age','Sex']].apply(get_person,axis=1)\n",
    "test_df['Person']    = test_df[['Age','Sex']].apply(get_person,axis=1)\n",
    "\n",
    "# No need to use Sex column since we created Person column\n",
    "titanic_df.drop(['Sex'],axis=1,inplace=True)\n",
    "test_df.drop(['Sex'],axis=1,inplace=True)\n",
    "\n",
    "# create dummy variables for Person column, & drop Male as it has the lowest average of survived passengers\n",
    "person_dummies_titanic  = pd.get_dummies(titanic_df['Person'])\n",
    "person_dummies_titanic.columns = ['Child','Female','Male']\n",
    "person_dummies_titanic.drop(['Male'], axis=1, inplace=True)\n",
    "\n",
    "person_dummies_test  = pd.get_dummies(test_df['Person'])\n",
    "person_dummies_test.columns = ['Child','Female','Male']\n",
    "person_dummies_test.drop(['Male'], axis=1, inplace=True)\n",
    "\n",
    "titanic_df = titanic_df.join(person_dummies_titanic)\n",
    "test_df    = test_df.join(person_dummies_test)\n",
    "\n",
    "fig, (axis1,axis2) = plt.subplots(1,2,figsize=(10,5))\n",
    "\n",
    "# sns.factorplot('Person',data=titanic_df,kind='count',ax=axis1)\n",
    "sns.countplot(x='Person', data=titanic_df, ax=axis1)\n",
    "\n",
    "# average of survived for each Person(male, female, or child)\n",
    "person_perc = titanic_df[[\"Person\", \"Survived\"]].groupby(['Person'],as_index=False).mean()\n",
    "sns.barplot(x='Person', y='Survived', data=person_perc, ax=axis2, order=['male','female','child'])\n",
    "\n",
    "titanic_df.drop(['Person'],axis=1,inplace=True)\n",
    "test_df.drop(['Person'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9fIstsp9FilemBXw6cBPQ2cyWAGcBpyaWSiRhDRvClVeuffzll2Hx4uznkeKUaQEvdPfewA+Azu6+j7svTDCXSOJOPx0efTTsttGiRVj4/Z//DOsMay0JyYZMC/g9M7sZ2BP4T4J5RLKqb1/YfHNo2RLuuCOsKfGPf4QS/uc/Y6eTQpdpAXcGniBMRbxnZteZ2T7JxRLJvlQqrCMxciS8+y7suy+8/37sVFLIMipgd1/h7jPcfSDQDdgcmJNoMpEIUim4+mo466xQvj17qoQlORmvB2xm+5rZDUAF0JRwa7JIwUmlwqaf55wDCxeGkfC778ZOJYUoox0xzOx94CVgBvC/7q6FeKSgpVJwxRVhreErrggj4aeegh12iJ1MCkmmWxJ1cfdliSYRyTGpVFg/okGDsKramhLu1Cl2MikU69sR41x3vxy42MzWWrXH3UcmlkwkB6RScMklYSR8ySVhOuLpp2HHHWMnk0KwvhHwG+mPLyYdRCRXpVJw0UWhhC+6KJTwU0+FxXxENsU6C9jdH0p/+qq7z89CHpGclEqFpSwbNIDx47+bjujcOXYyyWeZzgFfmd69+B5guru/lmAmkZyUSsFvf/vdxzUl/KMfRQ4meSvT64B7Ab2AfwM3mdmrWg9YitUFF4RlKz/8MJTwggWxE0m+yvg6YHf/l7tfQ1iE52Ugazsii+SacePg4ovho4+gVy94Tf8mlI2Q6XXAPwKOBo4APgGmA7WsqCpSPH796zAn/KtfhRJ+8kno0iV2Ksknmc4B3wpMA/q4e2WCeUTyypgx4eqIc8+F/fYLJdy1a+xUki/WW8Bm1gB4z91/n4U8Innnf/83lPDo0aGEn3gCunWLnUrywXrngN19FbCtmTXOQh6RvHTOOWH9iKVLYf/9Yb4u2pQMZDoF8R7wvJk9CHy7DoS7T0wklUgeOvvsMCd85pmhhB9/HHbbLXYqyWWZXgXxD+Dh9Pktqz1EpJqRI+Haa+Gzz6B3b5g3L3YiyWUZjYDdfXzSQUQKxYgRYU749NNDCc+aBXvsETuV5KJML0N7ivSOyNW5+371nkikAJx2WpiOOPVUOPBA+MtfYK+9YqeSXJPpHPDoap83JVwP/E39xxEpHKecEkbCw4dDnz6hhH/609ipJJdkOgVRUePQ82Y2N4E8IgXl5JNDCZ98cijhRx+FfbSboqRlOgXRutrTEmA3oFUiiUQKzLBhoYSHDQu7MD/6KPToETuV5IJMpyAq+G4O+BvgfWBYEoFECtEJJ4QSPuEEOOgg+POfw7rCUtzWtyPG7sAid/+f9POhhPnf94HXM/kBZtYXuJowcp7k7pfVcd4RwN3Ablp7WArR0KGhhI8/Hg4+GB5+OKwhIcVrfdcB3wR8DWBmPwN+B0wGPgduXt83N7MS4DqgD1AGDDGztZawNrMWwEjghQ0JL5JvjjsO/vQn+PJLOOQQmD07diKJaX0F3MDdl6Y/Pxq42d3vdffzgEy2JuwOvO3uC919JWFBnwG1nDcBuBT4KsPcInnrmGNgyhT46qtQwk88ETuRxLLeAjazNdMU+wPV/3+dyfxxO2BRteeL08e+ZWbdgPbu/mgG30+k3h1xRHhk05AhcOedsHIl9O8fbtaQ4rO+Er0LmGNmHwP/BZ4FMLNOhGmITWJmKWAiMLTa4VRd51dWaiVMqX9jx4aP2f7r1aMHXH99U04/fUsOPRQmTVpKr176R2AhKi0trfX4+jblvNjMngS2AWa5+5orIUqAMzL4uUuADtWet08fW6MlYW746XQZbw08YGaH1vZGXF2/hEi+OuUU2GorGDwYTjyxDTNnhjfopDikqqrWusO43qTXEnbC9MUHwFxgiLu/Ucf5TwGj3P2lmq9VVFRUlZeXJ5ZVJKaZM2HQoHCVxL33Qr9+sRNJPav1X/YZ7wm3MdJrCY8AZgELgGnu/oaZjTez2v6KVbGOKQiRQnX44XDPPVBVBQMHwkMPxU4k2ZDoCLg+aQQsxeChh757Q/Duu2FAbdcMST7K/ghYRDZM//5hOiKVgiOPDJ9L4VIBi+SYQw6B++8Py1kOGhTmhKUwqYBFctBBB8EDD4QSPvroMB0hhUcFLJKj+vSBBx+ERo3CjRvTp8dOJPVNBSySww48MLwx16gR/PzncNddsRNJfVIBi+S43r3D8pVNmsCxx8LUqbETSX1RAYvkgf32g0cegaZN4Re/gDvuiJ1I6oMKWCRP9OwZSrhZs7C28OTJsRPJplIBi+SRffcNWxpttlnYXeO222Inkk2hAhbJMz16wGOPQfPmYZ+5SZNiJ5KNpQIWyUN77x1KuEULOOkkuHm9+9NILlIBi+Spn/40LOS++eZhWcsbb4ydSDaUClgkj+25Jzz+OLRqBb/8JdxwQ+xEsiFUwCJ5rnv370r49NPhuutiJ5JMqYBFCsDuu4fNPbfYAs44A37/+9iJJBMqYJECsdtu8OSTsOWWcNZZcNVVsRPJ+qiARQrIrruGEm7dGkaNgiuvjJ1I1kUFLFJgunWD2bOhTRsYPRquuCJ2IqmLClikAHXtGkp4q63g3HPh0ktjJ5LaqIBFClSXLvDUU/CDH8DYsXDJJbETSU0qYJEC9pOfhBL+4Q/hN7+BCRNiJ5LqVMAiBa6sLJRw27Zw/vkwfnzsRLKGClikCPz4x/D007D11vDb38IFF0BVVexUogIWKRKdO4cS3mYbuPDCMBpWCcelAhYpImYwZw60awcXXQTjxqmEY1IBixSZHXcMI+H27cOVEWPHqoRjUQGLFKFOnUIJb7stXHYZjBmjEo5BBSxSpHbYIZRwhw7hbrnRo1XC2aYCFili228f5oS32w4mTgzrR6iEs0cFLFLkOnYMJdyxI1x9dVhJTSWcHSpgEWG77UIJb789XHNNWFNYJZw8FbCIAGEu+Omnw9zw9deH3TVWr46dqrCpgEXkW9tuG0p4xx3hD3+A005TCSdJBSwi39O+fVg7Yqed4Kab4NRTVcJJUQGLyFratQsjYTO45RYYPlwlnISGSf8AM+sLXE0o+0nuflmN108BTgdWAV8Aw939zaRzici6bbNNKOFevWDSJFi1Cv74R2jQIHaywpHoCNjMSoDrgD5AGTDEzDrXOG2qu3dx927AFYC2EhTJEVtvHUr4xz+G22+HE08MRSz1I+kpiO7A2+6+0N1XAtOAAdVPcPf/VHvaAtA/dERySNu2YU64rAz+9Cc4/niVcH1JegqiHbCo2vPFhFL+HjM7DRgFNAL2SziTiGygH/4wlPD++8OUKWE+ePJkaJj4JGZhy4k/Pne/AbjBzAYD5wHH13ZeZWVlNmOJSA133lnCoEFtuPPORixfvoJrrvlMJZyB0tLSWo8n/Ue3BOhQ7Xn79LG6TAdurOvFun4JEcmO0lJ49lno3RseeGAzmjTZjKlTNRLeWEnPAc8DOpnZdmbWGBgMPFj9BDPrVO1pP+CthDOJyCZo0waefBK6dYMZM2DIEFi5Mnaq/JTo/7fcfZWZjQBm8d1laG+Y2Xhgnrs/DIwws97A18CnwNAkM4nIpmvdGp54Ag48EO65J8wJT5sGjRrFTpZfUlV5suJGRUVFVXl5eewYIlLNp5+GEn7xRTjsMJg+HRo3jp0qJ6VqO6g74URko225JTz+OHTvDvffD0cdBV9/HTtV/lABi8gm2WILmDUL9tgDHnwQjjwSvvoqdqr8oAIWkU3WqlUo4b32gocegiOOgC+/3Pjvd8YZ4VHoVMAiUi823xweewz23hv+/GcYOHDjS/jee8Oj0KmARaTetGwJjz4K++wTPh52GPz3v7FT5S4VsIjUqzUl/LOfhRHxgAEq4bqogEWk3rVoAY88Aj17hqsk+veHFStip8o9KmARSUTz5mEueL/9wp1z/frB8uWxU+UWFbCIJGazzcJVEfvvH1ZTUwl/nwpYRBK1poQPOCAs7n7wwfCf/6z3y4qCClhEEtesGTzwAPTpA888AwcdBF98ETtVfCpgEcmKZs3C7coHHQTPPRc+LlsWO1VcKmARyZqmTWHmTDjkEHj+eejbt7hLWAUsIlnVpEm4y61/f/jrX8O0xOefx04VhwpYRLKuSZOwjvCAAfDCC2FJy88+i50q+1TAIhJF48ZhR43DDoO5c8NVEp9+GjtVdqmARSSaNSU8cGBY1L13b1i6NHaq7FEBi0hUjRqF7YyOPBLmz4eddoIPPgiPoUMLex0JFbCIRNeoEdx5J+y4I3zyyXfHp06FUaPi5UqaClhEckKjRmvfIbdqVWGvC6wCFpGc0bTp2seaNMl+jmxRAYtIzjjnnLWPFfIURMPYAURE1jjttDAK/uUvw/Nrr4Xhw+NmSlKqqqoqdoaMVFRUVJWXl8eOISJZUFoaPlZWxs1Rj1K1HdQUhIhIJCpgEZFIVMAiIpGogEVEIlEBi4hEogIWEYlEBSwiEokKWEQkEhWwiEgkKmARkUgSXwvCzPoCVxPKfpK7X1bj9bOBk4CVwL+BE919UdK5RERiS3QEbGYlwHVAH6AMGGJmnWucNh8od/ddgHuBK5LMJCKSK5IeAXcH3nb3hQBmNg0YALy55gR3n1Pt/BeAYxLOJCKSE5Iu4HZA9emExYRSrssw4NFEE4lIzjviiNgJsiNn1gM2s2OBcmDfus6pLKC16USkbmPHho+F8p986Zr1NWtIuoCXAB2qPW+fPvY9ZtYbGAv8zN1X1vXN6volRETyUdIFPA/oZGbbAR8Ag4Eh1U8ws27AjUAfd/9k7W8hIlKYEr0Kwt1XASOAWcACYJq7v2Fm482sX/q0y4HmwN1m9pKZ3Z9kJhGRXKEtiUREkqctiUREcokKWEQkEhWwiEgkKmARkUhUwCIikaiARUQiUQGLiESiAhYRiUQFLCISiQpYRCQSFbCISCQqYBGRSFTAIiKRqIBFRCJRAYuIRKICFhGJRAUsIhKJClhEJBIVsIhIJCpgEZFIVMAiIpGogEVEIlEBi4hEogIWEYlEBSwiEokKWEQkEhWwiEgkKmARkUhUwCIikaiARUQiUQGLiESiAhYRiUQFLCISiQpYRCQSFbCISCQNk/4BZtYXuJpQ9pPc/bIar/dIv94FONrd70s6k4hILkh0BGxmJcB1QB+gDBhiZp1rnLYQGApMTTKLiEiuSXoE3B14290XApjZNGAA8OaaE9z9n+nXqhLOIiKSU5KeA24HLKr2fHH6mIhI0Ut8Drg+VVRUxI4gIrIxqsrLy1M1DyZdwEuADtWet08f22C1hRcRyWdJF/A8oJOZbQd8AAwGhqzjfJWsiBSNVFVVsu99pS9D+z3fXYZ2qZmNB+a5+8NmthswE9gC+BL4l7vvnGgoEZEckHgBi4hI7fLqTbh8Z2aTgH7Ah+7eJXaeYmdm7YE/AW2B1cAt7n5N3FRiZk2AZ4DGhI66x93Hx02VDN2KnF23EW5KkdzwDTDK3cuAvYDTa7lRSLLM3b8Cerl7N2AX4CAz6x45ViJUwFnk7s8Bn8bOIYG7/8vdX05//h/gDXSdek5w9xXpT5sQRsEFOVeqKQgRwMw6EkZbf4scRfh2GYMKYAfgenefFzlSIjQClqJnZi2Ae4Az0yNhiczdV6enINoDe5jZj2NnSoIKWIqamTUklO8d7v5A7Dzyfe6+DHgK6Bs7SxJUwNmXQjec5JJbgdfd/fexg0hgZluZWav0582AA6i2gFch0XXAWWRmdwI9gTbAh8AF7n5b1FBFzMz2Jlzu9CrhTZ4q4Nfu/peowYqcme0MTCYMEEuA6e5+cdxUyVABi4hEoikIEZFIVMAiIpGogEVEIlEBi4hEogIWEYlEBSwiEonWgpCCYWargL8DjYDXgaHu/mUd514AfOHuE7MYUeR7NAKWQrLc3XdN76iyEjg1diCRddEIWArVs8DOAGb2C+AcwqLrr7j70OonmtlJwHDCyPkd4Dh3/9LMjgLOJ6wb/Lm790wvCnNb+twS4Ah3/0eWficpMBoBSyFJwbcL7BwEvJouzN8APdOra51Zy9fd6+7d06+/CQxLHz8PODB9/ND0sVOBq92eqo0vAAABK0lEQVR9V2A3YHFiv40UPI2ApZA0M7P56c+fASYRCnOGu38K4O6f1fJ1XcxsAmFj2ObAY+njzwGTzWwGcF/62F+B36S3M5rp7u8k86tIMdAIWArJivQc8K7ufpa7f5Ph190GnJbep+9CoCmAu59GGD1vC1SY2ZbufhfQn7CD9yNm1rPefwspGipgKSS1LfM5GzjKzFoDmNmWtZzTAviXmTUCjllz0My2d/d57n4B8BGwrZn9j7u/5+7XAg8A2lxVNpqmIKSQrLW0n7u/bmYXA3PM7BvgJeDEGqedD8wllOzfgJbp41eY2Y7pz59w91fMbIyZHUe4yuIDoCCXSZTs0HKUIiKRaApCRCQSFbCISCQqYBGRSFTAIiKRqIBFRCJRAYuIRKICFhGJ5P8DJQmxpeJ3L9MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f02f24d0590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pclass\n",
    "\n",
    "# sns.factorplot('Pclass',data=titanic_df,kind='count',order=[1,2,3])\n",
    "sns.factorplot('Pclass','Survived',order=[1,2,3], data=titanic_df,size=5)\n",
    "\n",
    "# create dummy variables for Pclass column, & drop 3rd class as it has the lowest average of survived passengers\n",
    "pclass_dummies_titanic  = pd.get_dummies(titanic_df['Pclass'])\n",
    "pclass_dummies_titanic.columns = ['Class_1','Class_2','Class_3']\n",
    "pclass_dummies_titanic.drop(['Class_3'], axis=1, inplace=True)\n",
    "\n",
    "pclass_dummies_test  = pd.get_dummies(test_df['Pclass'])\n",
    "pclass_dummies_test.columns = ['Class_1','Class_2','Class_3']\n",
    "pclass_dummies_test.drop(['Class_3'], axis=1, inplace=True)\n",
    "\n",
    "titanic_df.drop(['Pclass'],axis=1,inplace=True)\n",
    "test_df.drop(['Pclass'],axis=1,inplace=True)\n",
    "\n",
    "titanic_df = titanic_df.join(pclass_dummies_titanic)\n",
    "test_df    = test_df.join(pclass_dummies_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define training and testing sets\n",
    "\n",
    "X_train = titanic_df.drop(\"Survived\",axis=1)\n",
    "Y_train = titanic_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\",axis=1).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.80695847362514028"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = logreg.predict(X_test)\n",
    "\n",
    "logreg.score(X_train, Y_train)# Support Vector Machines\n",
    "\n",
    "# svc = SVC()\n",
    "\n",
    "# svc.fit(X_train, Y_train)\n",
    "\n",
    "# Y_pred = svc.predict(X_test)\n",
    "\n",
    "# svc.score(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96745230078563416"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forests\n",
    "\n",
    "#random_forest = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#random_forest.fit(X_train, Y_train)\n",
    "\n",
    "#Y_pred = random_forest.predict(X_test)\n",
    "\n",
    "#random_forest.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8271604938271605"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "knn.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "# gaussian = GaussianNB()\n",
    "\n",
    "# gaussian.fit(X_train, Y_train)\n",
    "\n",
    "# Y_pred = gaussian.predict(X_test)\n",
    "\n",
    "# gaussian.score(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.024000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fare</td>\n",
       "      <td>0.001079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.563096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.258950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Family</td>\n",
       "      <td>-0.217962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Child</td>\n",
       "      <td>1.727648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Female</td>\n",
       "      <td>2.738828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Class_1</td>\n",
       "      <td>1.989213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Class_2</td>\n",
       "      <td>1.114351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Features  Coefficient Estimate\n",
       "0      Age             -0.024000\n",
       "1     Fare              0.001079\n",
       "2        C              0.563096\n",
       "3        Q              0.258950\n",
       "4   Family             -0.217962\n",
       "5    Child              1.727648\n",
       "6   Female              2.738828\n",
       "7  Class_1              1.989213\n",
       "8  Class_2              1.114351"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get Correlation Coefficient for each feature using Logistic Regression\n",
    "coeff_df = DataFrame(titanic_df.columns.delete(0))\n",
    "coeff_df.columns = ['Features']\n",
    "coeff_df[\"Coefficient Estimate\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "# preview\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "submission.to_csv('t1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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