EDA and Feature Extraction.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;sns.set()\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import validation_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function- Create Table for Missing Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_missing_data_table(df):\n",
    "    total = df.isnull().sum().sort_values(ascending=False)\n",
    "    percent = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    return missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function- Plot Learning Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function-Plot Validation Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_validation_curve(estimator, title, X, y, param_name, param_range, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    train_scores, test_scores = validation_curve(estimator, X, y, param_name, param_range, cv)\n",
    "    train_mean = np.mean(train_scores, axis=1)\n",
    "    train_std = np.std(train_scores, axis=1)\n",
    "    test_mean = np.mean(test_scores, axis=1)\n",
    "    test_std = np.std(test_scores, axis=1)\n",
    "    plt.plot(param_range, train_mean, color='r', marker='o', markersize=5, label='Training score')\n",
    "    plt.fill_between(param_range, train_mean + train_std, train_mean - train_std, alpha=0.15, color='r')\n",
    "    plt.plot(param_range, test_mean, color='g', linestyle='--', marker='s', markersize=5, label='Validation score')\n",
    "    plt.fill_between(param_range, test_mean + test_std, test_mean - test_std, alpha=0.15, color='g')\n",
    "    plt.grid() \n",
    "    plt.xscale('log')\n",
    "    plt.legend(loc='best') \n",
    "    plt.xlabel('Parameter') \n",
    "    plt.ylabel('Score') \n",
    "    plt.ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Titanic\\\\train.csv\")\n",
    "df_raw=df.copy()#Save original data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Shape: (891, 12)\n"
     ]
    }
   ],
   "source": [
    "print(\"Data Shape: {}\".format(df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>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>20.125000</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>28.000000</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>38.000000</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   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Min and max values. This can give us an idea about the range of values and is helpful to detect outliers. In our case, all the min and max values seem reasonable and in a reasonable range of values. The only exception could eventually be the max value of 'Fare', but for now we will leave it as it is.\n",
    "\n",
    ">Mean and standard deviation. The mean shows us the central tendency of the distribution, while the standard deviation quantifies its amount of variation. For example, a low standard deviation suggests that data points tend to be close to the mean. Giving a quick look to our values, there's nothing that looks like obviously wrong.\n",
    "\n",
    ">Count. This is important to give us a first perception about the volume of missing data. Here, we can see that some 'Age' data is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>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>Harris, Mrs. Henry Birkhardt (Irene Wallach)</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>C23 C25 C27</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  \\\n",
       "count                                            891   891       891   \n",
       "unique                                           891     2       681   \n",
       "top     Harris, Mrs. Henry Birkhardt (Irene Wallach)  male  CA. 2343   \n",
       "freq                                               1   577         7   \n",
       "\n",
       "              Cabin Embarked  \n",
       "count           204      889  \n",
       "unique          147        3  \n",
       "top     C23 C25 C27        S  \n",
       "freq              4      644  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>0.771044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>0.198653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.002245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total   Percent\n",
       "Cabin          687  0.771044\n",
       "Age            177  0.198653\n",
       "Embarked         2  0.002245\n",
       "Fare             0  0.000000\n",
       "Ticket           0  0.000000\n",
       "Parch            0  0.000000\n",
       "SibSp            0  0.000000\n",
       "Sex              0  0.000000\n",
       "Name             0  0.000000\n",
       "Pclass           0  0.000000\n",
       "Survived         0  0.000000\n",
       "PassengerId      0  0.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_missing_data_table(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Cabin' has too many missing values (>25%). Dogma! We need to delete this variable right away.\n",
    "\n",
    ">'Age' can be imputed. For now, I'll associate a value that allows me to know that I'm imputing data. Later, I'll revise this strategy.\n",
    "\n",
    ">Due to the low percentage of missing values, I'll delete the observations where we don't know 'Embarked'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Cabin\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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      "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 Embarked  \n",
       "0      0         A/5 21171   7.2500        S  \n",
       "1      0          PC 17599  71.2833        C  \n",
       "2      0  STON/O2. 3101282   7.9250        S  \n",
       "3      0            113803  53.1000        S  \n",
       "4      0            373450   8.0500        S  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fill missing values in age with a specific value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age Imputation\n",
    "\n",
    "The steps to implement this new imputation method are:\n",
    "\n",
    ">Extract titles from 'Name'.\n",
    "\n",
    ">Plot a figure with both features and confirm that there is a connection between titles and age.\n",
    "\n",
    ">For each title, get people's average age and use it to fill missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>PassengerId</th>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Mr</td>\n",
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      ],
      "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 Embarked Title  \n",
       "0      0         A/5 21171   7.2500        S    Mr  \n",
       "1      0          PC 17599  71.2833        C   Mrs  \n",
       "2      0  STON/O2. 3101282   7.9250        S  Miss  \n",
       "3      0            113803  53.1000        S   Mrs  \n",
       "4      0            373450   8.0500        S    Mr  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract titles from name\n",
    "df['Title']=0\n",
    "for i in df:\n",
    "    df['Title']=df_raw['Name'].str.extract('([A-Za-z]+)\\.', expand=False)  # Use REGEX to define a search pattern\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'Countess',\n",
       "       'Jonkheer'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Title\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.barplot(y=df[\"Age\"],x=df[\"Title\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Bar plot gives us an estimate of central tendency for a numeric variable (height of each rectangle) and an indication of the uncertainty around that estimate (error bars in black).\n",
    "\n",
    ">Apart from Rev and Dr, which have a larger error bar, the mean value seems to accurately represent the data of all the other features. This validates our approach.\n",
    "\n",
    ">Here [https://www.biologyforlife.com/interpreting-error-bars.html] you can find a short and sweet intro to error bars interpretation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Capt        70.000000\n",
       "Col         58.000000\n",
       "Countess    33.000000\n",
       "Don         40.000000\n",
       "Dr          42.000000\n",
       "Jonkheer    38.000000\n",
       "Lady        48.000000\n",
       "Major       48.500000\n",
       "Master       4.574167\n",
       "Miss        21.773973\n",
       "Mlle        24.000000\n",
       "Mme         24.000000\n",
       "Mr          32.368090\n",
       "Mrs         35.898148\n",
       "Ms          28.000000\n",
       "Rev         43.166667\n",
       "Sir         49.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "means=df.groupby(\"Title\")[\"Age\"].mean()\n",
    "means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Capt': 70.0,\n",
       " 'Col': 58.0,\n",
       " 'Countess': 33.0,\n",
       " 'Don': 40.0,\n",
       " 'Dr': 42.0,\n",
       " 'Jonkheer': 38.0,\n",
       " 'Lady': 48.0,\n",
       " 'Major': 48.5,\n",
       " 'Master': 4.574166666666667,\n",
       " 'Miss': 21.773972602739725,\n",
       " 'Mlle': 24.0,\n",
       " 'Mme': 24.0,\n",
       " 'Mr': 32.368090452261306,\n",
       " 'Mrs': 35.898148148148145,\n",
       " 'Ms': 28.0,\n",
       " 'Rev': 43.166666666666664,\n",
       " 'Sir': 49.0}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#transform means to dictionary\n",
    "map_means=means.to_dict()\n",
    "map_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Embarked</th>\n",
       "      <th>Title</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>S</td>\n",
       "      <td>Mr</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>C</td>\n",
       "      <td>Mrs</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>S</td>\n",
       "      <td>Miss</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>S</td>\n",
       "      <td>Mrs</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>S</td>\n",
       "      <td>Mr</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 Embarked Title  \n",
       "0      0         A/5 21171   7.2500        S    Mr  \n",
       "1      0          PC 17599  71.2833        C   Mrs  \n",
       "2      0  STON/O2. 3101282   7.9250        S  Miss  \n",
       "3      0            113803  53.1000        S   Mrs  \n",
       "4      0            373450   8.0500        S    Mr  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_nan_age=df.loc[np.isnan(df[\"Age\"])].index\n",
    "df.loc[idx_nan_age,'Age']=df['Title'].map(map_means)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Identify Imputed Data\n",
    "df[\"Imputed\"]=0\n",
    "df.at[idx_nan_age,\"Imputed\"]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>Imputed</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>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</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>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</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>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</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>35.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</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>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>32.368090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
       "      <td>female</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PP 9549</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Saundercock, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5. 2151</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Andersson, Mr. Anders Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347082</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
       "      <td>female</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>350406</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Master. Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>32.368090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
       "      <td>female</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Masselmani, Mrs. Fatima</td>\n",
       "      <td>female</td>\n",
       "      <td>35.898148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Fynney, Mr. Joseph J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beesley, Mr. Lawrence</td>\n",
       "      <td>male</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>Q</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Miss. Torborg Danira</td>\n",
       "      <td>female</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Emir, Mr. Farred Chehab</td>\n",
       "      <td>male</td>\n",
       "      <td>32.368090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.773973</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Q</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
       "      <td>male</td>\n",
       "      <td>32.368090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>862</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
       "      <td>female</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.773973</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>865</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
       "      <td>female</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>867</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>C</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>868</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>869</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>32.368090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>Master</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>871</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>872</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>873</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>874</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>875</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>878</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>879</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>32.368090</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>882</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>884</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Rev</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.773973</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\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",
       "5              6         0       3   \n",
       "6              7         0       1   \n",
       "7              8         0       3   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "10            11         1       3   \n",
       "11            12         1       1   \n",
       "12            13         0       3   \n",
       "13            14         0       3   \n",
       "14            15         0       3   \n",
       "15            16         1       2   \n",
       "16            17         0       3   \n",
       "17            18         1       2   \n",
       "18            19         0       3   \n",
       "19            20         1       3   \n",
       "20            21         0       2   \n",
       "21            22         1       2   \n",
       "22            23         1       3   \n",
       "23            24         1       1   \n",
       "24            25         0       3   \n",
       "25            26         1       3   \n",
       "26            27         0       3   \n",
       "27            28         0       1   \n",
       "28            29         1       3   \n",
       "29            30         0       3   \n",
       "..           ...       ...     ...   \n",
       "861          862         0       2   \n",
       "862          863         1       1   \n",
       "863          864         0       3   \n",
       "864          865         0       2   \n",
       "865          866         1       2   \n",
       "866          867         1       2   \n",
       "867          868         0       1   \n",
       "868          869         0       3   \n",
       "869          870         1       3   \n",
       "870          871         0       3   \n",
       "871          872         1       1   \n",
       "872          873         0       1   \n",
       "873          874         0       3   \n",
       "874          875         1       2   \n",
       "875          876         1       3   \n",
       "876          877         0       3   \n",
       "877          878         0       3   \n",
       "878          879         0       3   \n",
       "879          880         1       1   \n",
       "880          881         1       2   \n",
       "881          882         0       3   \n",
       "882          883         0       3   \n",
       "883          884         0       2   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "5                                     Moran, Mr. James    male  32.368090   \n",
       "6                              McCarthy, Mr. Timothy J    male  54.000000   \n",
       "7                       Palsson, Master. Gosta Leonard    male   2.000000   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.000000   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.000000   \n",
       "10                     Sandstrom, Miss. Marguerite Rut  female   4.000000   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.000000   \n",
       "12                      Saundercock, Mr. William Henry    male  20.000000   \n",
       "13                         Andersson, Mr. Anders Johan    male  39.000000   \n",
       "14                Vestrom, Miss. Hulda Amanda Adolfina  female  14.000000   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.000000   \n",
       "16                                Rice, Master. Eugene    male   2.000000   \n",
       "17                        Williams, Mr. Charles Eugene    male  32.368090   \n",
       "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.000000   \n",
       "19                             Masselmani, Mrs. Fatima  female  35.898148   \n",
       "20                                Fynney, Mr. Joseph J    male  35.000000   \n",
       "21                               Beesley, Mr. Lawrence    male  34.000000   \n",
       "22                         McGowan, Miss. Anna \"Annie\"  female  15.000000   \n",
       "23                        Sloper, Mr. William Thompson    male  28.000000   \n",
       "24                       Palsson, Miss. Torborg Danira  female   8.000000   \n",
       "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.000000   \n",
       "26                             Emir, Mr. Farred Chehab    male  32.368090   \n",
       "27                      Fortune, Mr. Charles Alexander    male  19.000000   \n",
       "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  21.773973   \n",
       "29                                 Todoroff, Mr. Lalio    male  32.368090   \n",
       "..                                                 ...     ...        ...   \n",
       "861                        Giles, Mr. Frederick Edward    male  21.000000   \n",
       "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.000000   \n",
       "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  21.773973   \n",
       "864                             Gill, Mr. John William    male  24.000000   \n",
       "865                           Bystrom, Mrs. (Karolina)  female  42.000000   \n",
       "866                       Duran y More, Miss. Asuncion  female  27.000000   \n",
       "867               Roebling, Mr. Washington Augustus II    male  31.000000   \n",
       "868                        van Melkebeke, Mr. Philemon    male  32.368090   \n",
       "869                    Johnson, Master. Harold Theodor    male   4.000000   \n",
       "870                                  Balkic, Mr. Cerin    male  26.000000   \n",
       "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.000000   \n",
       "872                           Carlsson, Mr. Frans Olof    male  33.000000   \n",
       "873                        Vander Cruyssen, Mr. Victor    male  47.000000   \n",
       "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.000000   \n",
       "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.000000   \n",
       "876                      Gustafsson, Mr. Alfred Ossian    male  20.000000   \n",
       "877                               Petroff, Mr. Nedelio    male  19.000000   \n",
       "878                                 Laleff, Mr. Kristo    male  32.368090   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.000000   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.000000   \n",
       "881                                 Markun, Mr. Johann    male  33.000000   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.000000   \n",
       "883                      Banfield, Mr. Frederick James    male  28.000000   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.000000   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.000000   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  21.773973   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket      Fare Embarked   Title  Imputed  \n",
       "0        1      0         A/5 21171    7.2500        S      Mr        0  \n",
       "1        1      0          PC 17599   71.2833        C     Mrs        0  \n",
       "2        0      0  STON/O2. 3101282    7.9250        S    Miss        0  \n",
       "3        1      0            113803   53.1000        S     Mrs        0  \n",
       "4        0      0            373450    8.0500        S      Mr        0  \n",
       "5        0      0            330877    8.4583        Q      Mr        1  \n",
       "6        0      0             17463   51.8625        S      Mr        0  \n",
       "7        3      1            349909   21.0750        S  Master        0  \n",
       "8        0      2            347742   11.1333        S     Mrs        0  \n",
       "9        1      0            237736   30.0708        C     Mrs        0  \n",
       "10       1      1           PP 9549   16.7000        S    Miss        0  \n",
       "11       0      0            113783   26.5500        S    Miss        0  \n",
       "12       0      0         A/5. 2151    8.0500        S      Mr        0  \n",
       "13       1      5            347082   31.2750        S      Mr        0  \n",
       "14       0      0            350406    7.8542        S    Miss        0  \n",
       "15       0      0            248706   16.0000        S     Mrs        0  \n",
       "16       4      1            382652   29.1250        Q  Master        0  \n",
       "17       0      0            244373   13.0000        S      Mr        1  \n",
       "18       1      0            345763   18.0000        S     Mrs        0  \n",
       "19       0      0              2649    7.2250        C     Mrs        1  \n",
       "20       0      0            239865   26.0000        S      Mr        0  \n",
       "21       0      0            248698   13.0000        S      Mr        0  \n",
       "22       0      0            330923    8.0292        Q    Miss        0  \n",
       "23       0      0            113788   35.5000        S      Mr        0  \n",
       "24       3      1            349909   21.0750        S    Miss        0  \n",
       "25       1      5            347077   31.3875        S     Mrs        0  \n",
       "26       0      0              2631    7.2250        C      Mr        1  \n",
       "27       3      2             19950  263.0000        S      Mr        0  \n",
       "28       0      0            330959    7.8792        Q    Miss        1  \n",
       "29       0      0            349216    7.8958        S      Mr        1  \n",
       "..     ...    ...               ...       ...      ...     ...      ...  \n",
       "861      1      0             28134   11.5000        S      Mr        0  \n",
       "862      0      0             17466   25.9292        S     Mrs        0  \n",
       "863      8      2          CA. 2343   69.5500        S    Miss        1  \n",
       "864      0      0            233866   13.0000        S      Mr        0  \n",
       "865      0      0            236852   13.0000        S     Mrs        0  \n",
       "866      1      0     SC/PARIS 2149   13.8583        C    Miss        0  \n",
       "867      0      0          PC 17590   50.4958        S      Mr        0  \n",
       "868      0      0            345777    9.5000        S      Mr        1  \n",
       "869      1      1            347742   11.1333        S  Master        0  \n",
       "870      0      0            349248    7.8958        S      Mr        0  \n",
       "871      1      1             11751   52.5542        S     Mrs        0  \n",
       "872      0      0               695    5.0000        S      Mr        0  \n",
       "873      0      0            345765    9.0000        S      Mr        0  \n",
       "874      1      0         P/PP 3381   24.0000        C     Mrs        0  \n",
       "875      0      0              2667    7.2250        C    Miss        0  \n",
       "876      0      0              7534    9.8458        S      Mr        0  \n",
       "877      0      0            349212    7.8958        S      Mr        0  \n",
       "878      0      0            349217    7.8958        S      Mr        1  \n",
       "879      0      1             11767   83.1583        C     Mrs        0  \n",
       "880      0      1            230433   26.0000        S     Mrs        0  \n",
       "881      0      0            349257    7.8958        S      Mr        0  \n",
       "882      0      0              7552   10.5167        S    Miss        0  \n",
       "883      0      0  C.A./SOTON 34068   10.5000        S      Mr        0  \n",
       "884      0      0   SOTON/OQ 392076    7.0500        S      Mr        0  \n",
       "885      0      5            382652   29.1250        Q     Mrs        0  \n",
       "886      0      0            211536   13.0000        S     Rev        0  \n",
       "887      0      0            112053   30.0000        S    Miss        0  \n",
       "888      1      2        W./C. 6607   23.4500        S    Miss        1  \n",
       "889      0      0            111369   30.0000        C      Mr        0  \n",
       "890      0      0            370376    7.7500        Q      Mr        0  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delete observations for missing values in Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[pd.isnull(df[\"Embarked\"])].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Embarked\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Plan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Select a performance metric and a target value for this metric. This metric will guide your work and allow you to know how well you're performing. In our case, our performance metric will be 'accuracy' because it is the one defined by Kaggle.\n",
    "\n",
    ">Quickly set up a working end-to-end pipeline. This should allow you to estimate the selected performance metric.\n",
    "\n",
    ">Monitor the system to understand its behaviour, in particular to understand whether its poor performance is related to underfitting, overfitting or defects.\n",
    "\n",
    ">Improve the system by iteration. Here we can apply feature engineering, tune hyperparameters or even change the algorithm, according to the outputs of our monitoring system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    889 non-null int64\n",
      "Survived       889 non-null int64\n",
      "Pclass         889 non-null int64\n",
      "Name           889 non-null object\n",
      "Sex            889 non-null object\n",
      "Age            889 non-null float64\n",
      "SibSp          889 non-null int64\n",
      "Parch          889 non-null int64\n",
      "Ticket         889 non-null object\n",
      "Fare           889 non-null float64\n",
      "Embarked       889 non-null object\n",
      "Title          889 non-null object\n",
      "Imputed        889 non-null int64\n",
      "dtypes: float64(2), int64(6), object(5)\n",
      "memory usage: 97.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">We don't need 'PassengerId' for prediction purposes, so we will exclude it.\n",
    "\n",
    ">'Sex', 'Embarked', and 'Pclass' should be categorical. I'll not consider 'Survived' as categorical because it's the output variable.\n",
    "\n",
    ">We need to parse 'Name' and 'Ticket'. For now, I'll ignore these features.\n",
    "\n",
    ">'SibSp' could be grouped with 'Parch' to create a 'Family' feature. For now, I'll just identify if the passenger is travelling alone or with family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <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",
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       "      <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>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass                                               Name  \\\n",
       "0         0       3                            Braund, Mr. Owen Harris   \n",
       "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2         1       3                             Heikkinen, Miss. Laina   \n",
       "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4         0       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Embarked Title  \\\n",
       "0    male  22.0      1      0         A/5 21171   7.2500        S    Mr   \n",
       "1  female  38.0      1      0          PC 17599  71.2833        C   Mrs   \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250        S  Miss   \n",
       "3  female  35.0      1      0            113803  53.1000        S   Mrs   \n",
       "4    male  35.0      0      0            373450   8.0500        S    Mr   \n",
       "\n",
       "   Imputed  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"PassengerId\",axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Defiine Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Sex\"]=pd.Categorical(df[\"Sex\"])\n",
    "df[\"Embarked\"]=pd.Categorical(df[\"Embarked\"])\n",
    "df[\"Pclass\"]=pd.Categorical(df[\"Pclass\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Family features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\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>C</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>S</td>\n",
       "      <td>Miss</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>S</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>S</td>\n",
       "      <td>Mr</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Pclass                                               Name     Sex  \\\n",
       "0         0      3                            Braund, Mr. Owen Harris    male   \n",
       "1         1      1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "2         1      3                             Heikkinen, Miss. Laina  female   \n",
       "3         1      1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "4         0      3                           Allen, Mr. William Henry    male   \n",
       "\n",
       "    Age  SibSp  Parch            Ticket     Fare Embarked Title  Imputed  \\\n",
       "0  22.0      1      0         A/5 21171   7.2500        S    Mr        0   \n",
       "1  38.0      1      0          PC 17599  71.2833        C   Mrs        0   \n",
       "2  26.0      0      0  STON/O2. 3101282   7.9250        S  Miss        0   \n",
       "3  35.0      1      0            113803  53.1000        S   Mrs        0   \n",
       "4  35.0      0      0            373450   8.0500        S    Mr        0   \n",
       "\n",
       "   Family_Size  \n",
       "0            1  \n",
       "1            1  \n",
       "2            0  \n",
       "3            1  \n",
       "4            0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Family_Size\"]=df[\"SibSp\"]+df[\"Parch\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived Pclass     Sex   Age     Fare Embarked  Family_Size\n",
       "0         0      3    male  22.0   7.2500        S            1\n",
       "1         1      1  female  38.0  71.2833        C            1\n",
       "2         1      3  female  26.0   7.9250        S            0\n",
       "3         1      1  female  35.0  53.1000        S            1\n",
       "4         0      3    male  35.0   8.0500        S            0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop columns of no use\n",
    "df.drop([\"SibSp\",\"Parch\",\"Name\",\"Ticket\",\"Title\",\"Imputed\"],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Family_Size</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Family_Size  Pclass_2  Pclass_3  Sex_male  \\\n",
       "0         0  22.0   7.2500            1         0         1         1   \n",
       "1         1  38.0  71.2833            1         0         0         0   \n",
       "2         1  26.0   7.9250            0         0         1         0   \n",
       "3         1  35.0  53.1000            1         0         0         0   \n",
       "4         0  35.0   8.0500            0         0         1         1   \n",
       "\n",
       "   Embarked_Q  Embarked_S  \n",
       "0           0           1  \n",
       "1           0           0  \n",
       "2           0           1  \n",
       "3           0           1  \n",
       "4           0           1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.get_dummies(df,drop_first=True)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[df.loc[:,df.columns!=\"Survived\"].columns]\n",
    "y=df[\"Survived\"]\n",
    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Train Data: \n",
      "           Age      Fare  Family_Size  Pclass_2  Pclass_3  Sex_male  \\\n",
      "121  32.36809    8.0500            0         0         1         1   \n",
      "687  19.00000   10.1708            0         0         1         1   \n",
      "790  32.36809    7.7500            0         0         1         1   \n",
      "837  32.36809    8.0500            0         0         1         1   \n",
      "659  58.00000  113.2750            2         0         0         1   \n",
      "\n",
      "     Embarked_Q  Embarked_S  \n",
      "121           0           1  \n",
      "687           0           1  \n",
      "790           1           0  \n",
      "837           0           1  \n",
      "659           0           0  \n",
      "Input Test Data: \n",
      " 121    0\n",
      "687    0\n",
      "790    0\n",
      "837    0\n",
      "659    0\n",
      "Name: Survived, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Input Train Data: \\n\",x_train.head())\n",
    "print(\"Input Test Data: \\n\",y_train.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression()\n",
    "logreg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV accuracy: 0.796 +/- 0.023\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "scores=cross_val_score(logreg,x_train,y_train,cv=10)\n",
    "print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "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": [
    "title = \"Learning Curves (Logistic Regression)\"\n",
    "cv = 10\n",
    "plot_learning_curve(logreg, title, x_train, y_train, ylim=(0.7, 1.01), cv=cv, n_jobs=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning curves in a nutshell:\n",
    "\n",
    ">Learning curves allow us to diagnose if the is overfitting or underfitting.\n",
    "\n",
    ">When the model overfits, it means that it performs well on the training set, but not not on the validation set. Accordingly, the model is not able to generalize to unseen data. If the model is overfitting, the learning curve will present a gap between the training and validation scores. Two common solutions for overfitting are reducing the complexity of the model and/or collect more data.\n",
    "\n",
    ">On the other hand, underfitting means that the model is not able to perform well in either training or validations sets. In those cases, the learning curves will converge to a low score value. When the model underfits, gathering more data is not helpful because the model is already not being able to learn the training data. Therefore, the best approaches for these cases are to improve the model (e.g., tuning the hyperparameters) or to improve the quality of the data (e.g., collecting a different set of features).\n",
    "\n",
    "#### Discussion of our results:\n",
    "\n",
    ">The model doesn't overfit. As we can see, the curves converge and no gap between the training and the validation score exists in the last points of the curve.\n",
    "\n",
    ">The model underfits. Our final score is about 0.786. Although our model does better predictions than a flip-a-coin strategy, it is still far from being an 'intelligent' model. For now, it's just an 'artificial' model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\AjayKumar\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot validation curve\n",
    "title = 'Validation Curve (Logistic Regression)'\n",
    "param_name = 'C'\n",
    "param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0] \n",
    "cv = 10\n",
    "plot_validation_curve(estimator=logreg, title=title, X=x_train, y=y_train, param_name=param_name,\n",
    "                      ylim=(0.5, 1.01), param_range=param_range);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "### Validation curves in a nutshell:\n",
    "\n",
    ">Validation curves are a tool that we can use to improve the performance of our model. It counts as a way of tuning our hyperparameters.\n",
    "\n",
    ">They are different from the learning curves. Here, the goal is to see how the model parameter impacts the training and validation scores. This allow us to choose a different value for the parameter, to improve the model.\n",
    "\n",
    ">Once again, if there is a gap between the training and the validation score, the model is probably overfitting. In contrast, if there is no gap but the score value is low, we can say that the model underfits.\n",
    "Discussion of our results:\n",
    "\n",
    "The figure shows that there is no huge difference in model's performance as far as we choose a C value of  10−1  or higher. Note that in a logistic regression, C is the only model parameter that we can change (see scikit-learn documentation)."
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