Titanic Survival.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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    {
     "data": {
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       "<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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "ds_train_titanic = pd.read_csv(r'data\\titanic\\train.csv')\n",
    "ds_test_titanic = pd.read_csv(r'data\\titanic\\test.csv')\n",
    "ds_train_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_train_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Dataset:  PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "Test Dataset:  PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Checking any null values in the data set\n",
    "print('Train Dataset: ',ds_train_titanic.isnull().sum())\n",
    "print('Test Dataset: ',ds_test_titanic.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We need to clean the data in both Train and Test datasets.  Let's concat both datasets and clean them.  Once it is\n",
    "# done, we can separate them into train and test datasets to test with different models.\n",
    "survived = ds_train_titanic.Survived\n",
    "ds_train_titanic.drop(['Survived'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <td>1</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",
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       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</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",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
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       "      <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>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>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  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_titanic = pd.concat([ds_train_titanic, ds_test_titanic])\n",
    "full_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1046 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 122.7+ KB\n"
     ]
    }
   ],
   "source": [
    "full_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "263\n",
      "% of missing data:  20.09\n"
     ]
    }
   ],
   "source": [
    "#null values in Age, cabin, Fare & Embarked.  We need to check percentage of data missing to evaluate their influence in the data\n",
    "print(full_titanic['Age'].isnull().sum())\n",
    "print('% of missing data: ', round(full_titanic['Age'].isnull().sum()/(full_titanic['Age'].isnull().count())*100,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We need to replace null values with mean, mode(for categorical) values. We don't need to use \"axis\" parameter as below equation\n",
    "# in Series data\n",
    "full_titanic['Age'].fillna(value=full_titanic['Age'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAErBJREFUeJzt3X+s3XV9x/Hne1QRe7Xl501tmxVj4zR2/OgN1rEs94LTgsbyhywYosV06T/M4eyiZUtcTLaIyRA1WUgaUapxXBjCaApTSend4hJQCkjByijYwKVdK1rqLqCz23t/nE/lrNz2nHvPOfecfng+kpt7vp/v55zzuv1+++Lbzz3nEJmJJKlev9PvAJKk3rLoJalyFr0kVc6il6TKWfSSVDmLXpIqZ9FLUuUsekmqnEUvSZWb1+8AAGeccUYuW7ZsVvd98cUXmT9/fncDdYG5ZsZcM2OumRvUbJ3k2rFjx/OZeWbLiZnZ96+VK1fmbG3fvn3W9+0lc82MuWbGXDM3qNk6yQU8mG10rEs3klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXIWvSRVzqKXpMpZ9JJUuYH4CASdOJZtvLsnj7thxWGuOs5j77nuAz15Xum1oK0r+ojYExE7I+KRiHiwjJ0WEfdGxJPl+6llPCLiKxGxOyIejYjze/kDSJKObyZLN2OZeW5mjpTtjcC2zFwObCvbAJcAy8vXeuDGboWVJM1cJ2v0a4DN5fZm4LKm8W+Uz9y5H1gYEYs6eB5JUgfaLfoEvhcROyJifRkbzsx9AOX7WWV8MfBs030ny5gkqQ+i8UmXLSZFvCUz90bEWcC9wCeALZm5sGnOwcw8NSLuBj6fmd8v49uAT2fmjqMecz2NpR2Gh4dXjo+Pz+oHmJqaYmhoaFb37aVac+187lAX07xi+BTY//Kx969YvKAnz9tKrcexVwY1Fwxutk5yjY2N7WhaTj+mtl51k5l7y/cDEXEncAGwPyIWZea+sjRzoEyfBJY23X0JsHeax9wEbAIYGRnJ0dHRdqK8ysTEBLO9by/Vmut4r4zpxIYVh7l+57FPxz1XjvbkeVup9Tj2yqDmgsHNNhe5Wi7dRMT8iHjTkdvA+4DHgC3A2jJtLXBXub0F+Fh59c0q4NCRJR5J0txr54p+GLgzIo7M/8fM/E5E/BC4LSLWAc8Al5f59wCXAruBl4CPdz21JKltLYs+M58Gzplm/OfAxdOMJ3B1V9JJkjrmRyBIUuUsekmqnEUvSZWz6CWpcha9JFXOopekyln0klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXIWvSRVzqKXpMpZ9JJUOYtekipn0UtS5Sx6SaqcRS9JlbPoJalyFr0kVc6il6TKWfSSVDmLXpIqZ9FLUuUsekmqnEUvSZWz6CWpcha9JFXOopekyln0klQ5i16SKtd20UfESRHxcERsLdtnR8QDEfFkRNwaEa8v4yeX7d1l/7LeRJcktWMmV/TXALuatr8A3JCZy4GDwLoyvg44mJlvA24o8yRJfdJW0UfEEuADwFfLdgAXAbeXKZuBy8rtNWWbsv/iMl+S1AeRma0nRdwOfB54E/CXwFXA/eWqnYhYCvxLZr4rIh4DVmfmZNn3FPDuzHz+qMdcD6wHGB4eXjk+Pj6rH2BqaoqhoaFZ3beXas2187lDXUzziuFTYP/Lx96/YvGCnjxvK7Uex14Z1FwwuNk6yTU2NrYjM0dazZvXakJEfBA4kJk7ImL0yPA0U7ONfa8MZG4CNgGMjIzk6Ojo0VPaMjExwWzv20u15rpq493dC9Nkw4rDXL/z2KfjnitHe/K8rdR6HHtlUHPB4Gabi1wtix64EPhQRFwKvAF4M/AlYGFEzMvMw8ASYG+ZPwksBSYjYh6wAPhF15NLktrSco0+M6/NzCWZuQy4ArgvM68EtgMfLtPWAneV21vKNmX/fdnO+pAkqSc6eR39Z4BPRcRu4HTgpjJ+E3B6Gf8UsLGziJKkTrSzdPNbmTkBTJTbTwMXTDPnV8DlXcgmSeoC3xkrSZWz6CWpcha9JFXOopekyln0klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXIWvSRVzqKXpMpZ9JJUOYtekipn0UtS5Sx6SaqcRS9JlbPoJalyFr0kVc6il6TKWfSSVDmLXpIqZ9FLUuUsekmqnEUvSZWz6CWpcha9JFXOopekyln0klQ5i16SKmfRS1LlWhZ9RLwhIn4QET+KiMcj4nNl/OyIeCAinoyIWyPi9WX85LK9u+xf1tsfQZJ0PO1c0f8auCgzzwHOBVZHxCrgC8ANmbkcOAisK/PXAQcz823ADWWeJKlPWhZ9NkyVzdeVrwQuAm4v45uBy8rtNWWbsv/iiIiuJZYkzUhba/QRcVJEPAIcAO4FngJeyMzDZcoksLjcXgw8C1D2HwJO72ZoSVL7IjPbnxyxELgT+Czw9bI8Q0QsBe7JzBUR8Tjw/sycLPueAi7IzJ8f9VjrgfUAw8PDK8fHx2f1A0xNTTE0NDSr+/ZSrbl2Pneoi2leMXwK7H/52PtXLF7Qk+dtpdbj2CuDmgsGN1snucbGxnZk5kirefNm8qCZ+UJETACrgIURMa9ctS8B9pZpk8BSYDIi5gELgF9M81ibgE0AIyMjOTo6OpMovzUxMcFs79tLtea6auPd3QvTZMOKw1y/89in454rR3vyvK3Uehx7ZVBzweBmm4tc7bzq5sxyJU9EnAK8F9gFbAc+XKatBe4qt7eUbcr++3Im/2yQJHVVO1f0i4DNEXESjf8w3JaZWyPix8B4RPwt8DBwU5l/E/DNiNhN40r+ih7kliS1qWXRZ+ajwHnTjD8NXDDN+K+Ay7uSTpLUMd8ZK0mVs+glqXIWvSRVzqKXpMpZ9JJUuRm9YUqDYVkHb1rasOJwz970JGkweUUvSZWz6CWpcha9JFXOopekyln0klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXIWvSRVzqKXpMpZ9JJUOYtekipn0UtS5Sx6SaqcRS9JlbPoJalyFr0kVc6il6TKWfSSVDmLXpIqZ9FLUuUsekmqnEUvSZVrWfQRsTQitkfEroh4PCKuKeOnRcS9EfFk+X5qGY+I+EpE7I6IRyPi/F7/EJKkY2vniv4wsCEz3wGsAq6OiHcCG4Ftmbkc2Fa2AS4Blpev9cCNXU8tSWpby6LPzH2Z+VC5/V/ALmAxsAbYXKZtBi4rt9cA38iG+4GFEbGo68klSW2Z0Rp9RCwDzgMeAIYzcx80/mMAnFWmLQaebbrbZBmTJPVBZGZ7EyOGgH8F/i4z74iIFzJzYdP+g5l5akTcDXw+M79fxrcBn87MHUc93noaSzsMDw+vHB8fn9UPMDU1xdDQ0Kzu20u9zLXzuUOzvu/wKbD/5S6G6ZJWuVYsXjB3YZq8Fs+vTgxqLhjcbJ3kGhsb25GZI63mzWvnwSLidcC3gW9l5h1leH9ELMrMfWVp5kAZnwSWNt19CbD36MfMzE3AJoCRkZEcHR1tJ8qrTExMMNv79lIvc1218e5Z33fDisNcv7Otwz6nWuXac+Xo3IVp8lo8vzoxqLlgcLPNRa52XnUTwE3Arsz8YtOuLcDacnstcFfT+MfKq29WAYeOLPFIkuZeO5d2FwIfBXZGxCNl7K+A64DbImId8Axwedl3D3ApsBt4Cfh4VxNLkmakZdGXtfY4xu6Lp5mfwNUd5pIkdYnvjJWkyln0klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXIWvSRVzqKXpMpZ9JJUOYtekio3eJ9XK01jWQcfzdyJm1fP78vzSt3kFb0kVc6il6TKWfSSVDmLXpIqZ9FLUuUsekmqnEUvSZWz6CWpcha9JFXOopekyln0klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXIWvSRVzqKXpMpZ9JJUOYtekirX8n8OHhFfAz4IHMjMd5Wx04BbgWXAHuBPMvNgRATwZeBS4CXgqsx8qDfRpd7b+dwhrurT/5h8z3Uf6Mvzqj7tXNHfDKw+amwjsC0zlwPbyjbAJcDy8rUeuLE7MSVJs9Wy6DPz34BfHDW8Bthcbm8GLmsa/0Y23A8sjIhF3QorSZq5yMzWkyKWAVublm5eyMyFTfsPZuapEbEVuC4zv1/GtwGfycwHp3nM9TSu+hkeHl45Pj4+qx9gamqKoaGhWd23l3qZa+dzh2Z93+FTYP/LXQzTJeZ6tRWLFxxz32vxvO/UoGbrJNfY2NiOzBxpNa/lGv0MxTRj0/6XJDM3AZsARkZGcnR0dFZPODExwWzv20u9zNXJmvGGFYe5fme3D3vnzPVqe64cPea+1+J536lBzTYXuWb7qpv9R5ZkyvcDZXwSWNo0bwmwd/bxJEmdmm3RbwHWlttrgbuaxj8WDauAQ5m5r8OMkqQOtPPyyluAUeCMiJgE/ga4DrgtItYBzwCXl+n30Hhp5W4aL6/8eA8yS5JmoGXRZ+ZHjrHr4mnmJnB1p6EkSd3jO2MlqXIWvSRVzqKXpMpZ9JJUOYtekipn0UtS5QbvPecz5MfIStLxeUUvSZWz6CWpcif80o1Uq2XHWZLcsOJwz5YsXZKsj0XfgX79RZSkmXDpRpIqZ9FLUuUsekmqnEUvSZWz6CWpcha9JFXOopekyvk6ekn/z/HeH9JKp+8f8c1aveEVvSRVzqKXpMpZ9JJUOYtekipn0UtS5Sx6SaqcRS9JlbPoJalyFr0kVc6il6TKWfSSVDmLXpIq15MPNYuI1cCXgZOAr2bmdb14Hkl16eQD1Vo53geu1f5hal2/oo+Ik4B/AC4B3gl8JCLe2e3nkSS1pxdLNxcAuzPz6cz8b2AcWNOD55EktaEXSzeLgWebtieBd/fgeSSpK3q5ZNTKzavn9/w5IjO7+4ARlwPvz8w/LdsfBS7IzE8cNW89sL5svh14YpZPeQbw/Czv20vmmhlzzYy5Zm5Qs3WS63cz88xWk3pxRT8JLG3aXgLsPXpSZm4CNnX6ZBHxYGaOdPo43WaumTHXzJhr5gY121zk6sUa/Q+B5RFxdkS8HrgC2NKD55EktaHrV/SZeTgi/gz4Lo2XV34tMx/v9vNIktrTk9fRZ+Y9wD29eOxpdLz80yPmmhlzzYy5Zm5Qs/U8V9d/GStJGix+BIIkVe6ELfqIWB0RT0TE7ojY2OcsX4uIAxHxWNPYaRFxb0Q8Wb6fOseZlkbE9ojYFRGPR8Q1g5CrZHhDRPwgIn5Usn2ujJ8dEQ+UbLeWX+bPdbaTIuLhiNg6KJlKjj0RsTMiHomIB8vYIBzLhRFxe0T8pJxr7+l3roh4e/lzOvL1y4j4ZL9zlWx/Uc75xyLilvJ3oefn2AlZ9AP4MQs3A6uPGtsIbMvM5cC2sj2XDgMbMvMdwCrg6vJn1O9cAL8GLsrMc4BzgdURsQr4AnBDyXYQWNeHbNcAu5q2ByHTEWOZeW7TS/EG4Vh+GfhOZv4ecA6NP7u+5srMJ8qf07nASuAl4M5+54qIxcCfAyOZ+S4aL1a5grk4xzLzhPsC3gN8t2n7WuDaPmdaBjzWtP0EsKjcXgQ80ed8dwF/PIC53gg8ROPd088D86Y7xnOUZQmNArgI2ApEvzM1ZdsDnHHUWF+PJfBm4KeU3/UNSq6jsrwP+PdByMUrnxpwGo0XwmwF3j8X59gJeUXP9B+zsLhPWY5lODP3AZTvZ/UrSEQsA84DHhiUXGWJ5BHgAHAv8BTwQmYeLlP6cUy/BHwa+N+yffoAZDoige9FxI7yrnLo/7F8K/Az4OtlueurETF/AHI1uwK4pdzua67MfA74e+AZYB9wCNjBHJxjJ2rRxzRjvnxoGhExBHwb+GRm/rLfeY7IzP/Jxj+tl9D4ILx3TDdtrvJExAeBA5m5o3l4mqn9Os8uzMzzaSxXXh0Rf9SnHM3mAecDN2bmecCL9Gf5aFplrftDwD/1OwtA+Z3AGuBs4C3AfBrH82hdP8dO1KJv62MW+mx/RCwCKN8PzHWAiHgdjZL/VmbeMSi5mmXmC8AEjd8jLIyII+/tmOtjeiHwoYjYQ+MTVy+icYXfz0y/lZl7y/cDNNabL6D/x3ISmMzMB8r27TSKv9+5jrgEeCgz95ftfud6L/DTzPxZZv4GuAP4A+bgHDtRi/5E+JiFLcDacnstjTXyORMRAdwE7MrMLw5KrpLtzIhYWG6fQuMvwC5gO/DhfmTLzGszc0lmLqNxPt2XmVf2M9MRETE/It505DaNdefH6POxzMz/BJ6NiLeXoYuBH/c7V5OP8MqyDfQ/1zPAqoh4Y/n7eeTPq/fnWL9+SdKFX2xcCvwHjbXdv+5zlltorLn9hsZVzjoa67vbgCfL99PmONMf0vgn4KPAI+Xr0n7nKtl+H3i4ZHsM+GwZfyvwA2A3jX9un9yn4zkKbB2UTCXDj8rX40fO9wE5lucCD5Zj+c/AqQOS643Az4EFTWODkOtzwE/Kef9N4OS5OMd8Z6wkVe5EXbqRJLXJopekyln0klQ5i16SKmfRS1LlLHpJqpxFL0mVs+glqXL/BwmM8xLki4tpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_titanic.Age.hist()\n",
    "plt.show()\n",
    "#Distribution has shifted to right after replacing with mean value and this looks good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1014\n",
      "% of missing data:  77.46\n"
     ]
    }
   ],
   "source": [
    "#Checking Cabin feature values.\n",
    "print(full_titanic['Cabin'].isnull().sum())\n",
    "print('% of missing data: ', round(full_titanic['Cabin'].isnull().sum()/(full_titanic['Cabin'].isnull().count())*100,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#77% of Cabin data is null values and we should better to drop such columns.\n",
    "full_titanic.drop(['Cabin'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 10 columns):\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Name           1309 non-null object\n",
      "Sex            1309 non-null object\n",
      "Age            1309 non-null float64\n",
      "SibSp          1309 non-null int64\n",
      "Parch          1309 non-null int64\n",
      "Ticket         1309 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Embarked       1307 non-null object\n",
      "dtypes: float64(2), int64(4), object(4)\n",
      "memory usage: 112.5+ KB\n"
     ]
    }
   ],
   "source": [
    "full_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can add Fare null value with average as it is only one value.\n",
    "full_titanic.Fare.fillna(full_titanic.Fare.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    914\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_titanic.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_titanic.Embarked.fillna(value='S', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "5            6       3                                   Moran, Mr. James   \n",
       "6            7       1                            McCarthy, Mr. Timothy J   \n",
       "7            8       3                     Palsson, Master. Gosta Leonard   \n",
       "8            9       3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
       "9           10       2                Nasser, Mrs. Nicholas (Adele Achem)   \n",
       "\n",
       "      Sex        Age  SibSp  Parch            Ticket     Fare Embarked  \n",
       "0    male  22.000000      1      0         A/5 21171   7.2500        S  \n",
       "1  female  38.000000      1      0          PC 17599  71.2833        C  \n",
       "2  female  26.000000      0      0  STON/O2. 3101282   7.9250        S  \n",
       "3  female  35.000000      1      0            113803  53.1000        S  \n",
       "4    male  35.000000      0      0            373450   8.0500        S  \n",
       "5    male  29.881138      0      0            330877   8.4583        Q  \n",
       "6    male  54.000000      0      0             17463  51.8625        S  \n",
       "7    male   2.000000      3      1            349909  21.0750        S  \n",
       "8  female  27.000000      0      2            347742  11.1333        S  \n",
       "9  female  14.000000      1      0            237736  30.0708        C  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_titanic.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name and Ticket features does not have any value in the dataset.  We need to drop them\n",
    "full_titanic.drop(['Name','Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>398</th>\n",
       "      <td>1290</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>1291</td>\n",
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7.7333</td>\n",
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       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>1292</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164.8667</td>\n",
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       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>1293</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>1294</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.4000</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>1295</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>1296</td>\n",
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       "      <td>male</td>\n",
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       "      <td>27.7208</td>\n",
       "      <td>C</td>\n",
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       "      <th>405</th>\n",
       "      <td>1297</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>1298</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
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       "      <th>407</th>\n",
       "      <td>1299</td>\n",
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       "      <td>male</td>\n",
       "      <td>50.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>211.5000</td>\n",
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       "      <th>408</th>\n",
       "      <td>1300</td>\n",
       "      <td>3</td>\n",
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       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7208</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1301</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.7750</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1302</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0000</td>\n",
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       "      <th>412</th>\n",
       "      <td>1304</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
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       "      <td>39.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>29.881138</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass     Sex        Age  SibSp  Parch      Fare Embarked\n",
       "398         1290       3    male  22.000000      0      0    7.7750        S\n",
       "399         1291       3    male  31.000000      0      0    7.7333        Q\n",
       "400         1292       1  female  30.000000      0      0  164.8667        S\n",
       "401         1293       2    male  38.000000      1      0   21.0000        S\n",
       "402         1294       1  female  22.000000      0      1   59.4000        C\n",
       "403         1295       1    male  17.000000      0      0   47.1000        S\n",
       "404         1296       1    male  43.000000      1      0   27.7208        C\n",
       "405         1297       2    male  20.000000      0      0   13.8625        C\n",
       "406         1298       2    male  23.000000      1      0   10.5000        S\n",
       "407         1299       1    male  50.000000      1      1  211.5000        C\n",
       "408         1300       3  female  29.881138      0      0    7.7208        Q\n",
       "409         1301       3  female   3.000000      1      1   13.7750        S\n",
       "410         1302       3  female  29.881138      0      0    7.7500        Q\n",
       "411         1303       1  female  37.000000      1      0   90.0000        Q\n",
       "412         1304       3  female  28.000000      0      0    7.7750        S\n",
       "413         1305       3    male  29.881138      0      0    8.0500        S\n",
       "414         1306       1  female  39.000000      0      0  108.9000        C\n",
       "415         1307       3    male  38.500000      0      0    7.2500        S\n",
       "416         1308       3    male  29.881138      0      0    8.0500        S\n",
       "417         1309       3    male  29.881138      1      1   22.3583        C"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_titanic.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>Family</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass     Sex   Age     Fare Embarked  Family\n",
       "0            1       3    male  22.0   7.2500        S       1\n",
       "1            2       1  female  38.0  71.2833        C       1\n",
       "2            3       3  female  26.0   7.9250        S       0\n",
       "3            4       1  female  35.0  53.1000        S       1\n",
       "4            5       3    male  35.0   8.0500        S       0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace Siblings and Parents with Family by adding them\n",
    "full_titanic['Family'] = full_titanic['SibSp'] + full_titanic['Parch']\n",
    "full_titanic.drop(['SibSp', 'Parch'], axis=1, inplace=True)\n",
    "full_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass   Age     Fare  Family  Sex_female  Sex_male  \\\n",
       "0            1       3  22.0   7.2500       1           0         1   \n",
       "1            2       1  38.0  71.2833       1           1         0   \n",
       "2            3       3  26.0   7.9250       0           1         0   \n",
       "3            4       1  35.0  53.1000       1           1         0   \n",
       "4            5       3  35.0   8.0500       0           0         1   \n",
       "\n",
       "   Embarked_C  Embarked_Q  Embarked_S  \n",
       "0           0           0           1  \n",
       "1           1           0           0  \n",
       "2           0           0           1  \n",
       "3           0           0           1  \n",
       "4           0           0           1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We need to convert Sex and Embarked category values to Numeric values using pd.get_dummies\n",
    "full_titanic = pd.get_dummies(full_titanic, columns = ['Sex', 'Embarked'])\n",
    "full_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "   PassengerId  Pclass   Age     Fare  Family  Sex_male  Embarked_C  \\\n",
       "0            1       3  22.0   7.2500       1         1           0   \n",
       "1            2       1  38.0  71.2833       1         0           1   \n",
       "2            3       3  26.0   7.9250       0         0           0   \n",
       "3            4       1  35.0  53.1000       1         0           0   \n",
       "4            5       3  35.0   8.0500       0         1           0   \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  "
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     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "#We can drop any Sex attribute.  Because any one column can represent both male and female.  \n",
    "#This is not possible in the case of Embarked feature.\n",
    "full_titanic.drop(['Sex_female'], axis=1, inplace=True)\n",
    "full_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset size\n",
      "-----------------------\n",
      "Train size:  (891, 11)\n",
      "Test size:  (418, 11)\n",
      "Final dataset size\n",
      "--------------------\n",
      "x_train:  (891, 9)\n",
      "x_test:  (418, 9)\n"
     ]
    },
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      "text/plain": [
       "   PassengerId  Pclass   Age     Fare  Family  Sex_male  Embarked_C  \\\n",
       "0            1       3  22.0   7.2500       1         1           0   \n",
       "1            2       1  38.0  71.2833       1         0           1   \n",
       "2            3       3  26.0   7.9250       0         0           0   \n",
       "3            4       1  35.0  53.1000       1         0           0   \n",
       "4            5       3  35.0   8.0500       0         1           0   \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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data looks good.  Now we need to split full_titanic into original train and test data sets. Add the survived featue\n",
    "#to x_train dataset\n",
    "print(\"Original dataset size\\n-----------------------\")\n",
    "print('Train size: ',ds_train_titanic.shape)\n",
    "print('Test size: ',ds_test_titanic.shape)\n",
    "\n",
    "print(\"Final dataset size\\n--------------------\")\n",
    "x_train = full_titanic.iloc[:891]\n",
    "y_train = survived\n",
    "x_test = full_titanic.iloc[891:]\n",
    "print(\"x_train: \", x_train.shape)\n",
    "print(\"x_test: \", x_test.shape)\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.800225 (0.019960)\n",
      "LDA: 0.799101 (0.029492)\n",
      "KNN: 0.601610 (0.088589)\n",
      "CART: 0.764395 (0.055698)\n",
      "NB: 0.787978 (0.044396)\n",
      "SVM: 0.616217 (0.070492)\n"
     ]
    }
   ],
   "source": [
    "# Spot Check Algorithms\n",
    "models = []\n",
    "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC(gamma='auto')))\n",
    "# evaluate each model in turn\n",
    "kfold=10\n",
    "scoring='accuracy'\n",
    "seed = 1\n",
    "results = []\n",
    "names = []\n",
    "for name, model in models:\n",
    "\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
    "\tcv_results = model_selection.cross_val_score(model, x_train, y_train, cv=kfold, scoring=scoring)\n",
    "\tresults.append(cv_results)\n",
    "\tnames.append(name)\n",
    "\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "\tprint(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:  0.9449760765550239\n",
      "Confusion matrix:\n",
      " [[250  16]\n",
      " [  7 145]]\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.96       266\n",
      "           1       0.90      0.95      0.93       152\n",
      "\n",
      "   micro avg       0.94      0.94      0.94       418\n",
      "   macro avg       0.94      0.95      0.94       418\n",
      "weighted avg       0.95      0.94      0.95       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Based on above accurcy, LR is giving more predectivity values. Let's try with test data to check its accuracy.\n",
    "lr_model = LogisticRegression()\n",
    "lr_model.fit(x_train, y_train)\n",
    "lr_model.score(x_train, y_train)\n",
    "y_pred = lr_model.predict(x_test)\n",
    "y_test = pd.read_csv(r'data\\titanic\\gender_submission.csv')\n",
    "y_test = y_test.Survived\n",
    "print('Accuracy score: ',accuracy_score(y_test, y_pred))\n",
    "print('Confusion matrix:\\n',confusion_matrix(y_test, y_pred))\n",
    "print('Classification report:\\n',classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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