Case Study 2 MLP-18BCS6201 (AI-ML-2).ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Problem Statement </b>\n",
    "\n",
    "Titanic: Machine Learning from Disaster\n",
    "\n",
    "\n",
    "The sinking of the Titanic is one of the most infamous shipwrecks in history.\n",
    "\n",
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n",
    "\n",
    "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
    "\n",
    "In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc).\n",
    "\n",
    "<b>Download the dataset from: </b>\n",
    "\n",
    "https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Importing necessary libraries </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                    #Importing numpy library as 'np'.\n",
    "import seaborn as sns                                 #Importing seaborn library as 'sns'.\n",
    "import pandas as pd                                   #Importing pandas library as 'pd'.\n",
    "from matplotlib import pyplot as plt                  #Importing matplotlib.pyplot as'plt'.\n",
    "import re                                             #Importing Regular Expressions.\n",
    "\n",
    "#Setting the backend of matplotlib to the 'inline' backend.\n",
    "%matplotlib inline                                    \n",
    "from matplotlib import style\n",
    "\n",
    "#Importing libraries for algorithms.\n",
    "from sklearn import linear_model\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Reading data from the system </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing 'csv' file as dataframe using 'pd.read_csv()' function.\n",
    "Test = pd.read_csv(\"test.csv\")\n",
    "Train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Returning basic information about the dataframes </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Returning basic information about the created dataframes using '.info()' function.\n",
    "Test.info()\n",
    "Train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</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>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</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>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retruning basic statistical data about the dataframe 'Test'.\n",
    "Test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <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>"
      ],
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       "       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  "
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     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Retruning basic statistical data about the dataframe 'Train'.\n",
    "Train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
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       "      <td>22.0</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
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       "      <td>54.0</td>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  "
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning first 9 tuples from the dataframe using '.head()' function.\n",
    "Train.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "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>Total</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total     %\n",
       "Cabin          687  77.1\n",
       "Age            177  19.9\n",
       "Embarked         2   0.2\n",
       "Fare             0   0.0\n",
       "Ticket           0   0.0\n",
       "Parch            0   0.0\n",
       "SibSp            0   0.0\n",
       "Sex              0   0.0\n",
       "Name             0   0.0\n",
       "Pclass           0   0.0\n",
       "Survived         0   0.0\n",
       "PassengerId      0   0.0"
      ]
     },
     "execution_count": 667,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning total missing values in the dataframe 'Train'\n",
    "Total_missing = Train.isnull().sum().sort_values(ascending=False)\n",
    "Percent1 = Train.isnull().sum()/Train.isnull().count()*100\n",
    "Percent2 = (round(Percent1, 1)).sort_values(ascending=False)\n",
    "Data_missing = pd.concat([Total_missing, Percent2], axis=1, keys=['Total', '%'])\n",
    "\n",
    "#Returning the first 13 tuples using '.head()' function.\n",
    "Data_missing.head(12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Data Visualisation </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Determinng the attributes which influence the 'Survival' of a person </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Embarked, Pclass and Sex </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22b8e2d7508>"
      ]
     },
     "execution_count": 668,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 790.5x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Returning 'FacetGrid' plots to evaluate their relationships.\n",
    "#Embarked: Port of Embarkation, C = Cherbourg, Q = Queenstown, S = Southampton represented as C,Q and S respecitvely.\n",
    "FacetGrid = sns.FacetGrid(Train, row='Embarked', height=5, aspect=2)\n",
    "FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette=None,  order=None, hue_order=None )\n",
    "\n",
    "#Adding legend to showcase the parameters.\n",
    "FacetGrid.add_legend(title='Parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Women on port Q and on port S have a higher chance of survival but if they are at port C. Men have a high survival probability if they are on port C, but a low probability if they are on port Q or S. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <b> Age and Sex </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Survived = 'Survived'\n",
    "#Not_survived = 'Not Survived'\n",
    "#Returning plots using seaborn library.\n",
    "Figure, axes = plt.subplots(nrows=1, ncols=2,figsize=(10, 5))\n",
    "\n",
    "#Returning required parameters.\n",
    "Women = Train[Train['Sex']=='female']\n",
    "Men = Train[Train['Sex']=='male']\n",
    "\n",
    "#Returning plot,dropping the 'Nan' values and labelling plots.\n",
    "ax = sns.distplot(Women[Women['Survived']==1].Age.dropna(), bins=20, label = Survived, ax = axes[0], kde =False)\n",
    "ax = sns.distplot(Women[Women['Survived']==0].Age.dropna(), bins=40, label = Not_survived, ax = axes[0], kde =False)\n",
    "\n",
    "#Adding legend to showcase the parameters.\n",
    "ax.legend()\n",
    "\n",
    "#Returning plot dropping the 'Nan' values and labelling plots.\n",
    "ax.set_title('Female')\n",
    "ax = sns.distplot(Men[Men['Survived']==1].Age.dropna(), bins=20, label = Survived, ax = axes[1], kde = False)\n",
    "ax = sns.distplot(Men[Men['Survived']==0].Age.dropna(), bins=40, label = Not_survived, ax = axes[1], kde = False)\n",
    "\n",
    "#Adding legend to showcase the parameters.\n",
    "ax.legend()\n",
    "_ = ax.set_title('Male')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> The chances of a female surviving are more than a male. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Pclass and Survived </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b89a4ebc8>"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Returning barplot of 'Pclass' vs 'Survived'.\n",
    "sns.barplot(x='Pclass', y='Survived', data=Train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> The chances of a Class-1 person surviving are more than those of Class-2 and Class-3. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> SibSp and Parch </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    537\n",
       "0    354\n",
       "Name: Surrounded, dtype: int64"
      ]
     },
     "execution_count": 671,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    x['Relatives'] = x['SibSp'] + x['Parch']\n",
    "    x.loc[x['Relatives'] > 0, 'Surrounded'] = 0\n",
    "    x.loc[x['Relatives'] == 0, 'Surrounded'] = 1\n",
    "    \n",
    "    #Setting datatype as 'int' to make data homogeneous.\n",
    "    x['Surrounded'] = x['Surrounded'].astype(int)\n",
    "    \n",
    "#Returning number of unique values.\n",
    "Train['Surrounded'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Helenasec\\anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3669: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Returning factorplot for 'Relatives' vs 'Survived'.\n",
    "xy = sns.factorplot('Relatives','Survived', data=Train, aspect = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> The chances of survival with 1 to 3 realitves are high, but a lower one if you had less than 1 or more than 3 (except for some cases with 6 relatives). </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b8d799548>"
      ]
     },
     "execution_count": 673,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fkpox/JLUzP8DerorirED3roAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Returning barplot of 'Survived' vs 'Fare'.\n",
    "sns.barplot(Train['Survived'], Train['Fare'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> The chances of person paying more fare is more than the person paying less fare. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22b8e3c84c8>"
      ]
     },
     "execution_count": 674,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Returning barplot of 'Survived' vs 'Age'.\n",
    "sns.barplot(Train['Survived'], Train['Age'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> The attribute Age does not really influence Survival chances. </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Data Preprocessing</b>\n",
    "\n",
    "Handling missing values and dropping columns which are not needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 675,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping column 'PassengerId' since it does not influence 'Survival' of a person.\n",
    "Train = Train.drop(['PassengerId'], axis=1)\n",
    "\n",
    "#Dropping column 'Ticket' since it does not influence 'Survival' of a person and is difficult to make homogeneous.\n",
    "Train = Train.drop(['Ticket'], axis=1)\n",
    "Test = Test.drop(['Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although 'Cabin' does not seem to affect a person's 'Survival' but the 'Cabin' consists of the 'Deck' number so we will extract that value and create a new attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Cabin </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining 'Deck'\n",
    "Deck = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"U\": 8}\n",
    "\n",
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in data:\n",
    "    x['Cabin'] = x['Cabin'].fillna(\"U0\")\n",
    "    x['Deck'] = x['Cabin'].map(lambda x: re.compile(\"([a-zA-Z]+)\").search(x).group())\n",
    "    x['Deck'] = x['Deck'].map(deck)\n",
    "    #Substituting 'NA' values with zero.\n",
    "    x['Deck'] = x['Deck'].fillna(0)\n",
    "    #Converting 'Deck' to 'int'.\n",
    "    x['Deck'] = x['Deck'].astype(int)\n",
    "    \n",
    "#Dropping 'Cabin'.\n",
    "Train = Train.drop(['Cabin'], axis=1)\n",
    "Test = Test.drop(['Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Age </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 677,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Computing Mean and Std.\n",
    "    Mean = Train[\"Age\"].mean()\n",
    "    Std = Test[\"Age\"].std()\n",
    "    #Summing Null values.\n",
    "    Is_null = x[\"Age\"].isnull().sum()\n",
    "    \n",
    "    #Computing random numbers between the Mean, Std and Is_null.\n",
    "    Age_rand = np.random.randint(Mean - Std, Mean + Std, size = Is_null)\n",
    "    \n",
    "    #Substituing 'NaN' values in 'Age' with 'Age_rand'.\n",
    "    Age_new = x[\"Age\"].copy()\n",
    "    Age_new[np.isnan(Age_new)] = Age_rand\n",
    "    x[\"Age\"] = Age_new\n",
    "    #Converting 'Age' to 'int'.\n",
    "    x[\"Age\"] = Train[\"Age\"].astype(int)\n",
    "    \n",
    "#Returning number of null values.\n",
    "Train[\"Age\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Embarked </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     889\n",
       "unique      3\n",
       "top         S\n",
       "freq      644\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 678,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluating the most common value to fill the 2 missing value.\n",
    "Train['Embarked'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining common value.\n",
    "Common_value = 'S'\n",
    "\n",
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Substituing 'NA' values with \n",
    "    x['Embarked'] = x['Embarked'].fillna(Common_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      891\n",
       "unique       2\n",
       "top       male\n",
       "freq       577\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 680,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Substituing 'NA' values with \n",
    "    x['Sex'] = x['Sex'].fillna('male')\n",
    "Train['Sex'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Making data homogenous </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Fare </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Subsituting missing valyes with 0.\n",
    "    x['Fare'] = x['Fare'].fillna(0)\n",
    "    #Converting 'Fare' to 'int'.\n",
    "    x['Fare'] = x['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Name </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Declaring variable 'Titles'.\n",
    "Titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Extracting titles.\n",
    "    x['Title'] = x.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "    \n",
    "    #Replacing 'Titles' with a more common title or Rare.\n",
    "    x['Title'] = x['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr',\\\n",
    "                                            'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    x['Title'] = x['Title'].replace('Mlle', 'Miss')\n",
    "    x['Title'] = x['Title'].replace('Ms', 'Miss')\n",
    "    x['Title'] = x['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "    #Converting 'Titles' into numbers.\n",
    "    x['Title'] = x['Title'].map(titles)\n",
    "    \n",
    "    # Substituting 'NaN' with 0.\n",
    "    x['Title'] = x['Title'].fillna(0)\n",
    "\n",
    "#Dropping column 'Name'.\n",
    "Train = Train.drop(['Name'], axis=1)\n",
    "Test = Test.drop(['Name'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Gender </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Declaring variable 'Gender'.\n",
    "Gender = {\"male\": 0, \"female\": 1}\n",
    "\n",
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    x['Sex'] = x['Sex'].map(Gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Embarked </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Declaring variable 'Ports'.\n",
    "Ports = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
    "\n",
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    x['Embarked'] = x['Embarked'].map(Ports)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Grouping Data </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Age </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    178\n",
       "6    156\n",
       "5    142\n",
       "3    142\n",
       "2    112\n",
       "1     93\n",
       "0     68\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 685,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Converting 'Age' to 'int'.\n",
    "    x['Age'] = x['Age'].astype(int)\n",
    "    x.loc[ x['Age'] <= 11, 'Age'] = 0\n",
    "    x.loc[(x['Age'] > 11) & (x['Age'] <= 18), 'Age'] = 1\n",
    "    x.loc[(x['Age'] > 18) & (x['Age'] <= 22), 'Age'] = 2\n",
    "    x.loc[(x['Age'] > 22) & (x['Age'] <= 27), 'Age'] = 3\n",
    "    x.loc[(x['Age'] > 27) & (x['Age'] <= 33), 'Age'] = 4\n",
    "    x.loc[(x['Age'] > 33) & (x['Age'] <= 40), 'Age'] = 5\n",
    "    x.loc[(x['Age'] > 40) & (x['Age'] <= 66), 'Age'] = 6\n",
    "    x.loc[ x['Age'] > 66, 'Age'] = 6\n",
    "    \n",
    "#Returning count.\n",
    "Train['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Fare </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Relatives</th>\n",
       "      <th>Surrounded</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Relatives  \\\n",
       "0          0       3    0    2      1      0     7         0          1   \n",
       "1          1       1    1    5      1      0    71         1          1   \n",
       "2          1       3    1    3      0      0     7         0          0   \n",
       "3          1       1    1    5      1      0    53         0          1   \n",
       "4          0       3    0    5      0      0     8         0          0   \n",
       "5          0       3    0    4      0      0     8         2          0   \n",
       "6          0       1    0    6      0      0    51         0          0   \n",
       "7          0       3    0    0      3      1    21         0          4   \n",
       "8          1       3    1    3      0      2    11         0          2   \n",
       "9          1       2    1    1      1      0    30         1          1   \n",
       "10         1       3    1    0      1      1    16         0          2   \n",
       "11         1       1    1    6      0      0    26         0          0   \n",
       "12         0       3    0    2      0      0     8         0          0   \n",
       "\n",
       "    Surrounded  Title  \n",
       "0            0      1  \n",
       "1            0      3  \n",
       "2            1      2  \n",
       "3            0      3  \n",
       "4            1      1  \n",
       "5            1      1  \n",
       "6            1      1  \n",
       "7            0      4  \n",
       "8            0      3  \n",
       "9            0      3  \n",
       "10           0      2  \n",
       "11           1      2  \n",
       "12           1      1  "
      ]
     },
     "execution_count": 686,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    x.loc[ x['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    x.loc[(x['Fare'] > 7.91) & (x['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    x.loc[(x['Fare'] > 14.454) & (x['Fare'] <= 31), 'Fare']   = 2\n",
    "    x.loc[(x['Fare'] > 31) & (x['Fare'] <= 99), 'Fare']   = 3\n",
    "    x.loc[(x['Fare'] > 99) & (x['Fare'] <= 250), 'Fare']   = 4\n",
    "    x.loc[ x['Fare'] > 250, 'Fare'] = 5\n",
    "    x['Fare'] = x['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Adding new features </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Relatives  \\\n",
       "0          0       3    0    2      1      0     0         0          1   \n",
       "1          1       1    1    5      1      0     3         1          1   \n",
       "2          1       3    1    3      0      0     0         0          0   \n",
       "3          1       1    1    5      1      0     3         0          1   \n",
       "4          0       3    0    5      0      0     1         0          0   \n",
       "5          0       3    0    4      0      0     1         2          0   \n",
       "6          0       1    0    6      0      0     3         0          0   \n",
       "7          0       3    0    0      3      1     2         0          4   \n",
       "8          1       3    1    3      0      2     1         0          2   \n",
       "9          1       2    1    1      1      0     2         1          1   \n",
       "10         1       3    1    0      1      1     2         0          2   \n",
       "11         1       1    1    6      0      0     2         0          0   \n",
       "12         0       3    0    2      0      0     1         0          0   \n",
       "\n",
       "    Surrounded  Title  Fare_per_head  \n",
       "0            0      1              0  \n",
       "1            0      3              1  \n",
       "2            1      2              0  \n",
       "3            0      3              1  \n",
       "4            1      1              1  \n",
       "5            1      1              1  \n",
       "6            1      1              3  \n",
       "7            0      4              0  \n",
       "8            0      3              0  \n",
       "9            0      3              1  \n",
       "10           0      2              0  \n",
       "11           1      2              2  \n",
       "12           1      1              1  "
      ]
     },
     "execution_count": 688,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Traversing through 'Data'.\n",
    "for x in Data:\n",
    "    #Declaring new attribute 'Fare_per_head'\n",
    "    x['Fare_per_head'] = x['Fare']/(x['Relatives']+1)\n",
    "    x['Fare_per_head'] = x['Fare_per_head'].astype(int)\n",
    "\n",
    "#Returning 13 tuples from the top.\n",
    "Train.head(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Age times Class </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating variable 'Data'.\n",
    "Data = [Train, Test]\n",
    "\n",
    "#Traversing through 'Data'.\n",
    "for x in data:\n",
    "    x['Age_Pclass']= x['Age']* x['Pclass']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Building Models </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {},
   "outputs": [],
   "source": [
    "XTrain = Train.drop(\"Survived\", axis=1)\n",
    "YTrain = Train[\"Survived\"]\n",
    "XTest  = Test.drop(\"PassengerId\", axis=1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 1. Stochastic Gradient Descent </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'SGD'.\n",
    "Sgd = linear_model.SGDClassifier(max_iter=5, tol=None)\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Sgd.fit(XTrain, YTrain)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Sgd.predict(XTest)\n",
    "\n",
    "#Calculating score using '.score()' function.\n",
    "Sgd.score(XTrain, YTrain)\n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_sgd = round(Sgd.score(XTrain, YTrain) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 2. Logistic Regression </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'LR'.\n",
    "Lr = LogisticRegression()\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Lr.fit(XTrain, YTrain)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Lr.predict(XTest)\n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_lr = round(Lr.score(XTrain, YTrain) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 3. Random Forest </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'RF'.\n",
    "Rf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Rf.fit(XTrain, YTrain)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPrediction = Rf.predict(XTest)\n",
    "\n",
    "#Calculating score using '.score()' function.\n",
    "Rf.score(XTrain, YTrain)\n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_rf = round(Rf.score(XTrain, YTrain) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 4. Gaussian Naive Bayes </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'GNB'.\n",
    "Gaussian = GaussianNB() \n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Gaussian.fit(XTrain, YTrain)  \n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Gaussian.predict(XTest) \n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_gaussian = round(Gaussian.score(XTrain, YTrain) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 5. Decision Tree </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training using 'DT'.\n",
    "Dt = DecisionTreeClassifier() \n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Dt.fit(XTrain, YTrain)  \n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPred = Dt.predict(XTest)  \n",
    "\n",
    "#Rouding up the score.\n",
    "Acc_dt = round(Dt.score(XTrain, YTrain) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Selecting the best model </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "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>Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91.13</th>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91.13</th>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81.59</th>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.23</th>\n",
       "      <td>Gaussian Naive Bayes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.29</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model\n",
       "Score                            \n",
       "91.13               Random Forest\n",
       "91.13               Decision Tree\n",
       "81.59         Logistic Regression\n",
       "78.23        Gaussian Naive Bayes\n",
       "73.29  Stochastic Gradient Decent"
      ]
     },
     "execution_count": 696,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe 'Models' to select best model.\n",
    "Models = pd.DataFrame({\n",
    "    'Model': ['Stochastic Gradient Decent','Logistic Regression','Random Forest',\n",
    "              'Gaussian Naive Bayes','Decision Tree'],\n",
    "    'Score': [Acc_sgd,Acc_lr,Acc_rf,Acc_gaussian,Acc_dt]})\n",
    "\n",
    "#Sorting 'Models' in descending order.\n",
    "Models_df = Models.sort_values(by='Score', ascending=False)\n",
    "Models_df = Models_df.set_index('Score')\n",
    "Models_df.head(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> K-Fold Cross Validation on RF model </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.73333333 0.80898876 0.76404494 0.84269663 0.85393258 0.85393258\n",
      " 0.86516854 0.78651685 0.84269663 0.83146067]\n",
      "Mean: 0.8182771535580524\n",
      "Standard Deviation: 0.04166464246229857\n"
     ]
    }
   ],
   "source": [
    "#Returning the 'Scores','Mean' and 'Standard Deviaition'\n",
    "Rf = RandomForestClassifier(n_estimators=100)\n",
    "Scores = cross_val_score(Rf, XTrain, YTrain, cv=10, scoring = \"accuracy\")\n",
    "print(\"Scores:\", Scores)\n",
    "print(\"Mean:\", Scores.mean())\n",
    "print(\"Standard Deviation:\", Scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy of our RF model is: 0.8205118601747815\n"
     ]
    }
   ],
   "source": [
    "print(\"The average accuracy of our RF model is: 0.8205118601747815\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Clearly RANDOM FOREST is the best choice as a model. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <td>0.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relatives</th>\n",
       "      <td>0.072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_per_head</th>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Surrounded</th>\n",
       "      <td>0.013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Importance\n",
       "feature                  \n",
       "Title               0.274\n",
       "Sex                 0.216\n",
       "Pclass              0.119\n",
       "Age                 0.095\n",
       "Fare                0.074\n",
       "Relatives           0.072\n",
       "Fare_per_head       0.044\n",
       "SibSp               0.039\n",
       "Embarked            0.034\n",
       "Parch               0.021\n",
       "Surrounded          0.013"
      ]
     },
     "execution_count": 712,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning dataframe 'Importance' in descending order according to their importance.\n",
    "Importance = pd.DataFrame({'feature':XTrain.columns,'Importance':np.round(Rf.feature_importances_,3)})\n",
    "Importance = Importance.sort_values('Importance',ascending=False).set_index('feature')\n",
    "Importance.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#returning plot featuring 'Importance'\n",
    "Importance.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping attributes which don't influence 'Survival' of a person.\n",
    "Train  = Train.drop(\"Surrounded\", axis=1)\n",
    "Test  = Test.drop(\"Surrounded\", axis=1)\n",
    "\n",
    "Train  = Train.drop(\"Parch\", axis=1)\n",
    "Test  = Test.drop(\"Parch\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Training Random Forest further </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91.13 %\n"
     ]
    }
   ],
   "source": [
    "#Improving accuracy.\n",
    "Rf = RandomForestClassifier(n_estimators=100, oob_score = True)\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Rf.fit(XTrain, YTrain)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "Y_prediction = Rf.predict(XTest)\n",
    "\n",
    "#Rouding up the score.\n",
    "Rf.score(XTrain, YTrain)\n",
    "\n",
    "Acc_Rf = round(Rf.score(XTrain, YTrain) * 100, 2)\n",
    "print(round(Acc_Rf,2,), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The OOB Score is: 82.27 %\n"
     ]
    }
   ],
   "source": [
    "#Returning 'out-of-bag' error estimating and removing the need for a set aside test set.\n",
    "#Generalization accuracy using 'OOB'\n",
    "print(\"The OOB Score is:\", round(Rf.oob_score_, 4)*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Testing new parameters </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The OOB Score is: 83.73 %\n"
     ]
    }
   ],
   "source": [
    "Rf = RandomForestClassifier(criterion = \"gini\", \n",
    "                                       min_samples_leaf = 1, min_samples_split = 10,n_estimators=100, \n",
    "                                       max_features='auto',oob_score=True,random_state=1, n_jobs=-1)\n",
    "\n",
    "#Adjusting weights of the data.\n",
    "Rf.fit(XTrain, YTrain)\n",
    "\n",
    "#Returning a label for each object using .'predict()' function.\n",
    "YPrediction = Rf.predict(XTest)\n",
    "\n",
    "#Rouding up the score.\n",
    "Rf.score(XTrain, YTrain)\n",
    "\n",
    "#Returning 'out-of-bag' error estimating and removing the need for a set aside test set.\n",
    "print(\"The OOB Score is:\", round(Rf.oob_score_, 4)*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Confusion Matrix </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The confusion matrix is:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[490,  59],\n",
       "       [ 90, 252]], dtype=int64)"
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Returning a confusion matrix.\n",
    "Predictions = cross_val_predict(Rf, XTrain, YTrain, cv=3)\n",
    "print(\"The confusion matrix is:\")\n",
    "confusion_matrix(YTrain, Predictions)\n",
    "\n",
    "#First row indicates 'not-survived' and second row indicates 'survived' cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Precision is:\n",
      " 0.8102893890675241\n",
      "The Recall is:\n",
      " 0.7368421052631579\n",
      "The F1 Score is\n",
      ": 0.7718223583460949\n"
     ]
    }
   ],
   "source": [
    "#Returning 'Precision','Recall' and 'F1-Score' calculated from the confusion matrix.\n",
    "print(\"The Precision is:\\n\", precision_score(YTrain, Predictions))\n",
    "print(\"The Recall is:\\n\",recall_score(YTrain, Predictions))\n",
    "print(\"The F1 Score is\\n:\",f1_score(YTrain, Predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Precision Recall Curve </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creatinng function 'Precision_vs_Recall'.\n",
    "def Precision_vs_Recall(Precision, Recall):\n",
    "    plt.plot(Recall, Precision, \"m--\", linewidth=3)\n",
    "    plt.ylabel(\"Recall\", fontsize=20)\n",
    "    plt.xlabel(\"Precision\", fontsize=20)\n",
    "    plt.axis([0, 1.5, 0, 1.5])\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "Precision_vs_Recall(Precision, Recall)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "YScores = Rf.predict_proba(XTrain)\n",
    "YScores = YScores[:,1]\n",
    "\n",
    "Precision, Recall, Threshold = precision_recall_curve(YTrain, YScores)\n",
    "\n",
    "#Creatinng function 'Precision_vs_Recall'.\n",
    "def Precision_and_Recall(Precision, Recall, Threshold):\n",
    "    plt.plot(Threshold, Precision[:-1], \"r-\", label=\"Precision\", linewidth=5)\n",
    "    plt.plot(Threshold, Recall[:-1], \"m\", label=\"Recall\", linewidth=5)\n",
    "    plt.xlabel(\"Threshold\", fontsize=20)\n",
    "    plt.legend(loc=\"upper right\", fontsize=20)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "Precision_and_Recall(Precision, Recall, Threshold)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> ROC AUC Curve </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "FPR, TPR, Thresholds = roc_curve(YTrain, YScores)\n",
    "#Plotting them against each other.\n",
    "#Creatinng function 'Precision_vs_Recall'.\n",
    "def Plot_Roc_Curve(FPR, TPR, label=None):\n",
    "    plt.plot(FPR,TPR, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'm', linewidth=4)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "Plot_Roc_Curve(FPR, TPR)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ROC-AUC-Score is:\n",
      " 0.9385592091948146\n"
     ]
    }
   ],
   "source": [
    "#Returning thr ROC-AUC-Score.\n",
    "#Returning the area under the curve for above plot.\n",
    "R_A_score = roc_auc_score(YTrain, YScores)\n",
    "print(\"The ROC-AUC-Score is:\\n\", R_A_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>Surrounded</th>\n",
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       "<p>891 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "     Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Relatives  \\\n",
       "0           0       3    0    2      1      0     0         0          1   \n",
       "1           1       1    1    5      1      0     3         1          1   \n",
       "2           1       3    1    3      0      0     0         0          0   \n",
       "3           1       1    1    5      1      0     3         0          1   \n",
       "4           0       3    0    5      0      0     1         0          0   \n",
       "..        ...     ...  ...  ...    ...    ...   ...       ...        ...   \n",
       "886         0       2    0    3      0      0     1         0          0   \n",
       "887         1       1    1    2      0      0     2         0          0   \n",
       "888         0       3    1    4      1      2     2         0          3   \n",
       "889         1       1    0    3      0      0     2         1          0   \n",
       "890         0       3    0    4      0      0     0         2          0   \n",
       "\n",
       "     Surrounded  Title  Fare_per_head  \n",
       "0             0      1              0  \n",
       "1             0      3              1  \n",
       "2             1      2              0  \n",
       "3             0      3              1  \n",
       "4             1      1              1  \n",
       "..          ...    ...            ...  \n",
       "886           1      5              1  \n",
       "887           1      2              2  \n",
       "888           0      2              0  \n",
       "889           1      1              2  \n",
       "890           1      1              0  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 709,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thank You!\n"
     ]
    }
   ],
   "source": [
    "print(\"Thank You!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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