Titanic-Survival-Predication.ipynb

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    "# *Titanic - Machine Learning from Disaster*\n",
    "\n",
    "## Probelem Statement\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",
    "## Statement Analysis\n",
    "* Supervised Machine Learning Problem.\n",
    "* The Target Value is **Survived**.\n",
    "\n",
    "## Goal\n",
    "Using this model, We will try to understand what sort of people were more likely to **survive** than others."
   ]
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    "# *Hypothesis Generation*\n",
    "* Females have more chance of surviving.\n",
    "* Children have more chance of surviving.\n",
    "* Upper Class (VIPS) have more chance of surviving.\n",
    "* People with less siblings have more chance of surviving.\n",
    "* Person with no family/sibings have more chance of surviving.\n",
    "* Swimmers have more chance of surviving.\n"
   ]
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    "tags": []
   },
   "source": [
    "# *Libraries*"
   ]
  },
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import os # Path to file\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot  as plt # Data visulization\n",
    "import seaborn as sns  # Data visulization\n",
    "import warnings # warning filter\n",
    "%matplotlib inline\n",
    "\n",
    "# Import Coounter\n",
    "from collections import Counter\n",
    "\n",
    "# Scaling Libraries\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler\n",
    "\n",
    "#train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Machine Learning Models\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# Score Matrics\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score as CVS, StratifiedKFold, learning_curve\n",
    "\n",
    "## Setting Seaborn\n",
    "sns.set(style='white', context='paper', palette='cividis')\n",
    "\n",
    "# Warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
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    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "# Loading Data.\n",
    "train = pd.read_csv('/kaggle/input/titanic/train.csv')\n",
    "test = pd.read_csv('/kaggle/input/titanic/test.csv')\n"
   ]
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     "text": [
      "Train Dataset (row, col): (891, 12) \n",
      "\n",
      "\n",
      "Train Dataset Information:\n",
      "\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",
      "None\n",
      "\n",
      "Test Dataset (row, col): (418, 11) \n",
      "\n",
      "\n",
      "Test Dataset Information:\n",
      "\n",
      "<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",
      "None\n",
      "\n",
      "Train Dataset Values:\n",
      "\n"
     ]
    },
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     "data": {
      "text/plain": [
       "PassengerId    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...\n",
       "Survived                                                  [0, 1]\n",
       "Pclass                                                 [3, 1, 2]\n",
       "Name           [Braund, Mr. Owen Harris, Cumings, Mrs. John B...\n",
       "Sex                                               [male, female]\n",
       "Age            [22.0, 38.0, 26.0, 35.0, nan, 54.0, 2.0, 27.0,...\n",
       "SibSp                                      [1, 0, 3, 4, 2, 5, 8]\n",
       "Parch                                      [0, 1, 2, 5, 3, 4, 6]\n",
       "Ticket         [A/5 21171, PC 17599, STON/O2. 3101282, 113803...\n",
       "Fare           [7.25, 71.2833, 7.925, 53.1, 8.05, 8.4583, 51....\n",
       "Cabin          [nan, C85, C123, E46, G6, C103, D56, A6, C23 C...\n",
       "Embarked                                          [S, C, Q, nan]\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check data\n",
    "print(f'Train Dataset (row, col): {train.shape} \\n')\n",
    "print('\\nTrain Dataset Information:\\n')\n",
    "print(train.info())\n",
    "\n",
    "print(f'\\nTest Dataset (row, col): {test.shape} \\n')\n",
    "print('\\nTest Dataset Information:\\n')\n",
    "print(test.info())\n",
    "\n",
    "print('\\nTrain Dataset Values:\\n')\n",
    "train.apply(lambda x:x.unique())\n"
   ]
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   "source": [
    "## About Dataset\n",
    "\n",
    "This dataset is named Titanic Survival. The dataset contains a set of **891** records under **12** attributes:\n",
    "\n",
    "## Column Name and their Description\n",
    "\n",
    "* **PassengerId**: Unique passenger ID.\n",
    "* **Name** : name of passenger.\n",
    "* **Pclass** : Ticket class (A proxy for **socio-economic status** (SES)).\n",
    "* **Sex** : gender of passenger.\n",
    "* **Age** : age of passenger.\n",
    "* **SibSp**: number of siblings/spouse of passenger.\n",
    "* **Parch** : number of parents of passenger.\n",
    "* **Ticket** : ticket number.\n",
    "* **Fare** :Passenger fare.\n",
    "* **Cabin** : Cabin number.\n",
    "* **Embarked** : Port of Embarkation.\n",
    "\n",
    "## Variable Notes\n",
    "**Pclass**: A proxy for socio-economic status (SES)\n",
    "* 1st = Upper\n",
    "* 2nd = Middle\n",
    "* 3rd = Lower\n",
    "\n",
    "**Age**: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "**sibsp**: The dataset defines family relations in this way...\n",
    "* **Sibling** = brother, sister, stepbrother, stepsister\n",
    "* **Spouse** = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "**parch**: The dataset defines family relations in this way...\n",
    "* **Parent** = mother, father\n",
    "* **Child** = daughter, son, stepdaughter, stepson\n",
    "* Some children travelled only with a nanny, therefore **parch=0** for them.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502eb707",
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    "tags": []
   },
   "source": [
    "# *Future Analysis*\n",
    "\n"
   ]
  },
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   "source": [
    "# Check Dupicates\n",
    "train.drop_duplicates(inplace=True)"
   ]
  },
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
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       "      <td>891.000000</td>\n",
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       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
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       "      <td>B96 B98</td>\n",
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       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
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       "      <td>7</td>\n",
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       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                      891   891   \n",
       "unique          NaN         NaN         NaN                      891     2   \n",
       "top             NaN         NaN         NaN  Braund, Mr. Owen Harris  male   \n",
       "freq            NaN         NaN         NaN                        1   577   \n",
       "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                      NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                      NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare    Cabin  \\\n",
       "count   714.000000  891.000000  891.000000     891  891.000000      204   \n",
       "unique         NaN         NaN         NaN     681         NaN      147   \n",
       "top            NaN         NaN         NaN  347082         NaN  B96 B98   \n",
       "freq           NaN         NaN         NaN       7         NaN        4   \n",
       "mean     29.699118    0.523008    0.381594     NaN   32.204208      NaN   \n",
       "std      14.526497    1.102743    0.806057     NaN   49.693429      NaN   \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000      NaN   \n",
       "25%      20.125000    0.000000    0.000000     NaN    7.910400      NaN   \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200      NaN   \n",
       "75%      38.000000    1.000000    0.000000     NaN   31.000000      NaN   \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200      NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Summery\n",
    "train.describe(include=[object, np.number])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48940af4",
   "metadata": {
    "papermill": {
     "duration": 0.017333,
     "end_time": "2022-12-21T11:36:26.991167",
     "exception": false,
     "start_time": "2022-12-21T11:36:26.973834",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Observations\n",
    "* Most passengers are **Lower class passengers**.\n",
    "* Most passengers are **male**.\n",
    "* Average Age of passengers is **28** oldest passenger=80, youngest=0.42 (about 5 months).\n",
    "* Most passengers are **alone**.\n",
    "* Average Fare is 14.45 pounds\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44f689ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:27.029363Z",
     "iopub.status.busy": "2022-12-21T11:36:27.028252Z",
     "iopub.status.idle": "2022-12-21T11:36:27.037357Z",
     "shell.execute_reply": "2022-12-21T11:36:27.036646Z"
    },
    "papermill": {
     "duration": 0.029925,
     "end_time": "2022-12-21T11:36:27.039206",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.009281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived        0.000000\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age            19.865320\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "Cabin          77.104377\n",
       "Embarked        0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values\n",
    "(train.isnull().sum()/train.shape[0]) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad1f31b3",
   "metadata": {
    "papermill": {
     "duration": 0.017605,
     "end_time": "2022-12-21T11:36:27.074861",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.057256",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Missing values\n",
    "* Age has **19.8%** missing values.\n",
    "* Cabin has **77%** missing values.\n",
    "* Embarked has **0.22%** missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d919fb98",
   "metadata": {
    "papermill": {
     "duration": 0.017899,
     "end_time": "2022-12-21T11:36:27.111147",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.093248",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* ### Data Exploration\n",
    "Lets combine both datasets since both are similier\n",
    "Below are the steps involved to understand, clean and prepare your data for building your predictive model:\n",
    "\n",
    "* Missing values treatment\n",
    "* Outlier treatment\n",
    "* Variable Identification\n",
    "* Univariate Analysis\n",
    "* Bi-variate Analysis\n",
    "* Variable transformation\n",
    "* Variable creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d222f1cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:27.149441Z",
     "iopub.status.busy": "2022-12-21T11:36:27.148705Z",
     "iopub.status.idle": "2022-12-21T11:36:27.159197Z",
     "shell.execute_reply": "2022-12-21T11:36:27.157707Z"
    },
    "papermill": {
     "duration": 0.033001,
     "end_time": "2022-12-21T11:36:27.162354",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.129353",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After combining the datasets (row,col):  (1309, 13)\n"
     ]
    }
   ],
   "source": [
    "# Combining Datasets\n",
    "train['source'] = 'train'\n",
    "test['source'] = 'test'\n",
    "data = pd.concat([train, test], ignore_index=True)\n",
    "print('After combining the datasets (row,col): ', data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1acd65",
   "metadata": {
    "papermill": {
     "duration": 0.018411,
     "end_time": "2022-12-21T11:36:27.199154",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.180743",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* ### Missing Values\n",
    "1. Numeric Values\n",
    "2. Categorial Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aded95ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:27.244386Z",
     "iopub.status.busy": "2022-12-21T11:36:27.243121Z",
     "iopub.status.idle": "2022-12-21T11:36:27.255263Z",
     "shell.execute_reply": "2022-12-21T11:36:27.254535Z"
    },
    "papermill": {
     "duration": 0.040466,
     "end_time": "2022-12-21T11:36:27.257445",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.216979",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived       31.932773\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age            20.091673\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.076394\n",
       "Cabin          77.463713\n",
       "Embarked        0.152788\n",
       "source          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill empty and NaNs values with NaN\n",
    "# Missing values\n",
    "(data.isnull().sum()/data.shape[0]) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d7080e",
   "metadata": {
    "papermill": {
     "duration": 0.019529,
     "end_time": "2022-12-21T11:36:27.302160",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.282631",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Age here is **continuies value** and also age is corelated with **parch/sibling** and since there is subpopulation in age is **children** having higher chance of **surrvival** we need visulize the age **carefully**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "14eed4d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:27.346588Z",
     "iopub.status.busy": "2022-12-21T11:36:27.345794Z",
     "iopub.status.idle": "2022-12-21T11:36:27.356168Z",
     "shell.execute_reply": "2022-12-21T11:36:27.355163Z"
    },
    "papermill": {
     "duration": 0.03211,
     "end_time": "2022-12-21T11:36:27.358345",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.326235",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1046.000000\n",
       "mean       29.881138\n",
       "std        14.413493\n",
       "min         0.170000\n",
       "25%        21.000000\n",
       "50%        28.000000\n",
       "75%        39.000000\n",
       "max        80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets deal with Age first\n",
    "data.Age.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec9a6206",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:27.397190Z",
     "iopub.status.busy": "2022-12-21T11:36:27.396808Z",
     "iopub.status.idle": "2022-12-21T11:36:27.748843Z",
     "shell.execute_reply": "2022-12-21T11:36:27.747789Z"
    },
    "papermill": {
     "duration": 0.374862,
     "end_time": "2022-12-21T11:36:27.751516",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.376654",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Distribution\n",
    "ax = sns.histplot(x=data['Age'], bins=10, kde=True)\n",
    "ax.set_ylabel('Number of Passengers')\n",
    "ax.set_title('Age Distribution', fontsize=20)\n",
    "sns.despine(offset=5, trim=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63780166",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:27.791729Z",
     "iopub.status.busy": "2022-12-21T11:36:27.791307Z",
     "iopub.status.idle": "2022-12-21T11:36:29.608806Z",
     "shell.execute_reply": "2022-12-21T11:36:29.607780Z"
    },
    "papermill": {
     "duration": 1.84018,
     "end_time": "2022-12-21T11:36:29.611061",
     "exception": false,
     "start_time": "2022-12-21T11:36:27.770881",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb1ec226210>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 397.305x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Age\n",
    "\n",
    "ax = sns.catplot(y=\"Age\",x=\"Sex\",data=data,kind=\"box\")\n",
    "ax.despine(offset=1, trim=True)\n",
    "ax = sns.catplot(y=\"Age\",x=\"Sex\",hue=\"Pclass\", data=data,kind=\"box\")\n",
    "ax.despine(offset=1, trim=True)\n",
    "ax = sns.catplot(y=\"Age\",x=\"Parch\", data=data,kind=\"box\")\n",
    "ax.despine(offset=1, trim=True)\n",
    "ax = sns.catplot(y=\"Age\",x=\"SibSp\", data=data,kind=\"box\")\n",
    "ax.despine(offset=1, trim=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51098fd4",
   "metadata": {
    "papermill": {
     "duration": 0.019304,
     "end_time": "2022-12-21T11:36:29.650344",
     "exception": false,
     "start_time": "2022-12-21T11:36:29.631040",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Observations\n",
    "* Age and **Sex** has almost **same distribution**.\n",
    "* **1st class** passenger are **older** then **2nd class**, and 2nd are **older** then **3rd class**.\n",
    "* Person having more **parents/children** are **older**.\n",
    "* Person having more **siblings/spouse** are **younger**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fba762d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:29.692406Z",
     "iopub.status.busy": "2022-12-21T11:36:29.691971Z",
     "iopub.status.idle": "2022-12-21T11:36:30.053646Z",
     "shell.execute_reply": "2022-12-21T11:36:30.052546Z"
    },
    "papermill": {
     "duration": 0.385341,
     "end_time": "2022-12-21T11:36:30.055854",
     "exception": false,
     "start_time": "2022-12-21T11:36:29.670513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation of Age\\n')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert Sex 0, 1 into male female\n",
    "data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})\n",
    "ax = sns.heatmap(data=data[['Age','Sex','Pclass', 'Parch', 'SibSp']].corr(), cmap='gist_heat' , annot=True)\n",
    "ax.set_title('Correlation of Age\\n', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff5c2d0f",
   "metadata": {
    "papermill": {
     "duration": 0.023103,
     "end_time": "2022-12-21T11:36:30.099724",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.076621",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Observations\n",
    "* Above heatmap **confirms the catplots except Parch**. age is not correlated with sex, But **negativly correlated with Parch, Pclass, SibSp**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a9aea1ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:30.142572Z",
     "iopub.status.busy": "2022-12-21T11:36:30.141908Z",
     "iopub.status.idle": "2022-12-21T11:36:30.172069Z",
     "shell.execute_reply": "2022-12-21T11:36:30.170880Z"
    },
    "papermill": {
     "duration": 0.055041,
     "end_time": "2022-12-21T11:36:30.174877",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.119836",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imputate missing values of age wrt Parch, SibSp, Parch, Pclass\n",
    "data['Age'] = data.groupby(['Parch', 'SibSp', 'Pclass'])['Age'].apply(lambda i:i.fillna(i.median()))\n",
    "data['Age'] = data['Age'].fillna(data['Age'].median())\n",
    "\n",
    "# delete cabin data becuase it contains % missing data\n",
    "data = data.drop('Cabin', axis=1)\n",
    "\n",
    "# fill missing values in Embarked with mode\n",
    "data.Embarked = data.Embarked.fillna('S')\n",
    "\n",
    "# Fill missing values in Fare with median\n",
    "data['Fare'] = data['Fare'].fillna(data['Fare'].median())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "accd493f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:30.218248Z",
     "iopub.status.busy": "2022-12-21T11:36:30.217872Z",
     "iopub.status.idle": "2022-12-21T11:36:30.228431Z",
     "shell.execute_reply": "2022-12-21T11:36:30.227328Z"
    },
    "papermill": {
     "duration": 0.036442,
     "end_time": "2022-12-21T11:36:30.232077",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.195635",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values afer imputation \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived       31.932773\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age             0.000000\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "Embarked        0.000000\n",
       "source          0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Missing Values afer imputation \\n')\n",
    "(data.isnull().sum()/data.shape[0]) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81371e9f",
   "metadata": {
    "papermill": {
     "duration": 0.020804,
     "end_time": "2022-12-21T11:36:30.274508",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.253704",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* ### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ca794ab8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:30.318802Z",
     "iopub.status.busy": "2022-12-21T11:36:30.317529Z",
     "iopub.status.idle": "2022-12-21T11:36:30.349678Z",
     "shell.execute_reply": "2022-12-21T11:36:30.348454Z"
    },
    "papermill": {
     "duration": 0.056739,
     "end_time": "2022-12-21T11:36:30.352062",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.295323",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Mabel Helen</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Master. Thomas Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>181</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Constance Gladys</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>325</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. George John Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Miss. Alice Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.00</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>793</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Stella Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>847</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Mr. Douglas Bullen</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>864</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "27            28         0       1     Fortune, Mr. Charles Alexander    male   \n",
       "88            89         1       1         Fortune, Miss. Mabel Helen  female   \n",
       "159          160         0       3         Sage, Master. Thomas Henry    male   \n",
       "180          181         0       3       Sage, Miss. Constance Gladys  female   \n",
       "201          202         0       3                Sage, Mr. Frederick    male   \n",
       "324          325         0       3           Sage, Mr. George John Jr    male   \n",
       "341          342         1       1     Fortune, Miss. Alice Elizabeth  female   \n",
       "792          793         0       3            Sage, Miss. Stella Anna  female   \n",
       "846          847         0       3           Sage, Mr. Douglas Bullen    male   \n",
       "863          864         0       3  Sage, Miss. Dorothy Edith \"Dolly\"  female   \n",
       "\n",
       "      Age  SibSp  Parch    Ticket    Fare        Cabin Embarked source  \n",
       "27   19.0      3      2     19950  263.00  C23 C25 C27        S  train  \n",
       "88   23.0      3      2     19950  263.00  C23 C25 C27        S  train  \n",
       "159   NaN      8      2  CA. 2343   69.55          NaN        S  train  \n",
       "180   NaN      8      2  CA. 2343   69.55          NaN        S  train  \n",
       "201   NaN      8      2  CA. 2343   69.55          NaN        S  train  \n",
       "324   NaN      8      2  CA. 2343   69.55          NaN        S  train  \n",
       "341  24.0      3      2     19950  263.00  C23 C25 C27        S  train  \n",
       "792   NaN      8      2  CA. 2343   69.55          NaN        S  train  \n",
       "846   NaN      8      2  CA. 2343   69.55          NaN        S  train  \n",
       "863   NaN      8      2  CA. 2343   69.55          NaN        S  train  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlier detection \n",
    "\n",
    "def detect_outliers(df,n,features):\n",
    "    \"\"\"\n",
    "    Takes a dataframe df of features and returns a list of the indices\n",
    "    corresponding to the observations containing more than n outliers according\n",
    "    to the Tukey method.\n",
    "    \"\"\"\n",
    "    outlier_indices = []\n",
    "    \n",
    "    # iterate over features(columns)\n",
    "    for col in features:\n",
    "        # 1st quartile (25%)\n",
    "        Q1 = np.percentile(df[col], 25)\n",
    "        # 3rd quartile (75%)\n",
    "        Q3 = np.percentile(df[col],75)\n",
    "        # Interquartile range (IQR)\n",
    "        IQR = Q3 - Q1\n",
    "        \n",
    "        # outlier step\n",
    "        outlier_step = 1.5 * IQR\n",
    "        \n",
    "        # Determine a list of indices of outliers for feature col\n",
    "        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index\n",
    "        \n",
    "        # append the found outlier indices for col to the list of outlier indices \n",
    "        outlier_indices.extend(outlier_list_col)\n",
    "        \n",
    "    # select observations containing more than 2 outliers\n",
    "    outlier_indices = Counter(outlier_indices)        \n",
    "    multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )\n",
    "    return multiple_outliers\n",
    "\n",
    "# detect outliers from Age, SibSp , Parch and Fare\n",
    "Outliers_to_drop = detect_outliers(train,2,[\"Age\",\"SibSp\",\"Parch\",\"Fare\"])\n",
    "\n",
    "#  Show the outliers rows\n",
    "print(\"Outliers \\n\\n\")\n",
    "train.loc[Outliers_to_drop]\n",
    "#  Show the outliers rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "befca091",
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    "execution": {
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   "outputs": [],
   "source": [
    "# Drop outliers\n",
    "train = train.drop(Outliers_to_drop, axis = 0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fba3ea0",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>train</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Embarked source  \n",
       "0         A/5 21171   7.2500        S  train  \n",
       "1          PC 17599  71.2833        C  train  \n",
       "2  STON/O2. 3101282   7.9250        S  train  \n",
       "3            113803  53.1000        S  train  \n",
       "4            373450   8.0500        S  train  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "146e1913",
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    "tags": []
   },
   "source": [
    "#### Veriable Identification\n",
    "* Numeric Data\n",
    "* Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aa308be6",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # assign male and female to 0,1 in Sex\n",
    "# data['Sex'] = data['Sex'].map({0:'male', 1:'female'})\n",
    "\n",
    "# # assign 1st, 2nd, 3rd class to Pclass\n",
    "# data['Pclass'] = data['Pclass'].map({1:'Upper', 2:'Middle', 3:'Lower'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00abc6a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:30.602426Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Separating Numeric and categorical Data\n",
    "num_df = data.select_dtypes('number')\n",
    "cat_df = data.select_dtypes('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c9183217",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare\n",
       "0            1       0.0       3    0  22.0      1      0   7.2500\n",
       "1            2       1.0       1    1  38.0      1      0  71.2833\n",
       "2            3       1.0       3    1  26.0      0      0   7.9250\n",
       "3            4       1.0       1    1  35.0      1      0  53.1000\n",
       "4            5       0.0       3    0  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Numeric Data\n",
    "num_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3a73d82b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:30.714353Z",
     "iopub.status.busy": "2022-12-21T11:36:30.713971Z",
     "iopub.status.idle": "2022-12-21T11:36:30.725520Z",
     "shell.execute_reply": "2022-12-21T11:36:30.724452Z"
    },
    "papermill": {
     "duration": 0.036337,
     "end_time": "2022-12-21T11:36:30.727612",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.691275",
     "status": "completed"
    },
    "tags": []
   },
   "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>Name</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>C</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>113803</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>373450</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name            Ticket  \\\n",
       "0                            Braund, Mr. Owen Harris         A/5 21171   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...          PC 17599   \n",
       "2                             Heikkinen, Miss. Laina  STON/O2. 3101282   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)            113803   \n",
       "4                           Allen, Mr. William Henry            373450   \n",
       "\n",
       "  Embarked source  \n",
       "0        S  train  \n",
       "1        C  train  \n",
       "2        S  train  \n",
       "3        S  train  \n",
       "4        S  train  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a996ed3",
   "metadata": {
    "papermill": {
     "duration": 0.020952,
     "end_time": "2022-12-21T11:36:30.769604",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.748652",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* # *Data Visualization*\n",
    "* Numeric Data\n",
    "* Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8d2ca0f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:30.813870Z",
     "iopub.status.busy": "2022-12-21T11:36:30.813297Z",
     "iopub.status.idle": "2022-12-21T11:36:31.264072Z",
     "shell.execute_reply": "2022-12-21T11:36:31.263028Z"
    },
    "papermill": {
     "duration": 0.476334,
     "end_time": "2022-12-21T11:36:31.266805",
     "exception": false,
     "start_time": "2022-12-21T11:36:30.790471",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation of Survived with  Numeric Data\\n')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check Correlation\n",
    "ax = sns.heatmap(data=train[num_df.columns[1:]].corr(), cmap='YlOrBr', fmt='.2f', annot=True)\n",
    "ax.set_title('Correlation of Survived with  Numeric Data\\n', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe194fc",
   "metadata": {
    "papermill": {
     "duration": 0.022485,
     "end_time": "2022-12-21T11:36:31.312568",
     "exception": false,
     "start_time": "2022-12-21T11:36:31.290083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* Only **Fare** seems to have a **positive** correlation wirh surviving.\n",
    "* **Pclass** has the **worst** correlation of all with suriving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d80e17f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:31.359590Z",
     "iopub.status.busy": "2022-12-21T11:36:31.359179Z",
     "iopub.status.idle": "2022-12-21T11:36:32.163866Z",
     "shell.execute_reply": "2022-12-21T11:36:32.162629Z"
    },
    "papermill": {
     "duration": 0.83096,
     "end_time": "2022-12-21T11:36:32.166124",
     "exception": false,
     "start_time": "2022-12-21T11:36:31.335164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb1ebc86e50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Age Vs Survived\n",
    "ax = sns.FacetGrid(train, col='Survived', margin_titles=True)\n",
    "ax = ax.map(sns.distplot, 'Age')\n",
    "ax.set_ylabels('Survival Probability')\n",
    "# ax.set_titles(['Age Survived', 'Age Not Survived'])\n",
    "ax.despine(offset=3, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "151ec15a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:32.214040Z",
     "iopub.status.busy": "2022-12-21T11:36:32.213645Z",
     "iopub.status.idle": "2022-12-21T11:36:32.522929Z",
     "shell.execute_reply": "2022-12-21T11:36:32.522030Z"
    },
    "papermill": {
     "duration": 0.335726,
     "end_time": "2022-12-21T11:36:32.525057",
     "exception": false,
     "start_time": "2022-12-21T11:36:32.189331",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb1ec138f90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jR48SERGRaRdaGltbW9q0aaO9z61bt7hy5YrOfSIiIggMDMzwmTh+/LjO6/L8dy7o/31ldt1oaQklfR98ml27djF16lQsLS0pXrx4hpN/iYmJOl/oac3B2bNnZ/iFALzwUOLDhw8DZNvt1LNnT3r27MnTp0/ZvXs38+fPx9HRUeeEXFb07Qp58uRJptelnTxO+7X6/GCIqKioF+o+LF68eIZjqtVqIiMjM+2meBHP7z8+Pp64uDhKlCgBKO+BunXr6nSLnjhxIsN+snv+XVxc8PDw0CapzKR90J88eaJ9H2UWX2aM8TylZ21tzdChQwkICODWrVtUq1YNW1vbTF/3zGT2fnN0dKRVq1YEBgbSt29fgoKC6NixY7bvTZVKRceOHQkKCuL9998nMDCQli1bapNBkSJFUKlUjB07llatWmW4f9prnNnzl/Y+SO/gwYN07949y3gA3n77bTZt2sTmzZsz3GaM16lPnz4sXrxYe6I9Pz6Tvr6+LF68mISEBAIDA6lRo0aO3Vi+vr6MHj2a+/fvExgYiJubG6+99pr2dhcXF3x8fDI955vW05DZdy4onwl9RjCaVcsmLi6O/fv306VLFzZs2KDzN2XKFB4/fsyxY8cAqFWrFn/99ZfOScM///xTZ38VK1bEw8ODe/fuUbt27Qx/L/KF++DBA1avXk3jxo155ZVXctzezc2Nfv360ahRI27cuAGgbbrm9eTupUuXuH//vvbyqVOnePLkiba/P+3cUtqoOVBac8+PMLG2ttYrlrp167J3717UarX2ut27d2u7QvLDkSNHdH6F7tmzB5VKRa1atQBlgMDzwz9///33LPeX2fPfpEkTHj9+jIODQ6bvC4CqVatia2ur855KTU3N8B7LjCGfp9jY2ExPlKdNfE77lenh4cGzZ890Rif+9ddfuTpW586dCQ4OZt++fdy5cyfbX8/p7/P333+zf/9+goODdbrdHBwcqFevHqGhoZk+72nnE2rVqsWRI0eIj4/X3vf5wQoPHz7k0qVLmSat9Nzd3enTpw/fffddhve4Pq9Tbj6rmf0Qefr0qc4oNX0/k9np2LEjiYmJ7Nmzh7179+p1HrFZs2Y4OzsTFBREUFBQhnNvTZo04caNG1SpUiXD65L2PVe7dm0eP36sMzDq/v37XLp0Sa+4zapl8+effxIfH89bb72l0y8IysmqL7/8kh07dtCsWTOGDh3K5s2bGT16NEOHDiU8PJx169Zhb2+v/fVlYWGBn58fH374IbGxsbRs2RJra2vu3LnD3r17WbFihU6/8PPS+pg1Go12PP+PP/6Io6NjtrOCV6xYQVRUFN7e3hQtWpRLly5x4sQJbZ966dKlsbOz45dffqFIkSJYWVlpv+Ryo2jRoowaNYpx48aRmJjI4sWLqVmzpnZwQMmSJalVqxbLly/H3t6e1NRU1q5dq/NLHZSk/Oeff7J37148PDwoUaJEppPh3n33XXr27MmYMWPo378/Dx48YPHixTRv3lznpHde2NraMmrUKIYPH054eDgLFy6kXbt22jkdTZs2ZdasWXz55ZfUrVuXgwcPcvToUZ195PT8N2vWjObNm/P2228zYsQIKleuTGxsLFeuXCExMZFJkyZRtGhR3nzzTVauXImVlRWVK1dm27ZtenWFGvJ5Cg0N5d1336V37940aNAAOzs7Ll++zJo1a6hevbr2S7JFixbY2dkxdepUhg0bxt27d/nxxx9zdaxWrVphZ2fH9OnTtSOhclKrVi0qVKjAJ598gp2dXYbJhR988AFDhw7FwsKCDh064OjoyD///MOBAweYOHEiFStW1H62R40axbBhw3j06BFr167V+fV86NAhKlSooD2Bn5133nmHH3/8kcePH+t8zvR5ndL27+/vT+fOnbGzs8uygkTXrl1p06YNzZo1w93dnXv37vHNN99gZ2dHjx49AP0/k9lxd3fH29ubzz77jOjo6GzP16SxtramXbt2fPvtt4SHh/Ppp5/q3D506FB+++03hgwZwqBBg/Dw8ODx48cEBwfTsGFDunTpQqtWrahWrRoTJkzggw8+wMbGhpUrV2batZYZs0o2O3fuxNPTM0OiAeXJ6tSpEzt27GDmzJl4eHiwdu1a5s6dy9ixY6lUqRLz5s1j2LBhOn24vr6+ODo6snbtWrZv346FhQXlypWjdevW2l8tWbl16xZ9+/bFwsKCIkWK8Morr/D222/Tv39/ihQpkuX9ateuzXfffcfOnTt59uwZpUuXZty4cQwZMgRQvlBnz57NF198weDBg0lOTubq1au5fr4aNGhAkyZNmDdvHk+fPsXb25vZs2frbLN06VI+/vhjJk+ejIeHB5MnT+b777/X2WbAgAFcvnyZqVOnEhUVxdixYxk3blyG41WpUoV169axdOlSxo4di5OTE507d84wSigvOnfujKOjI9OmTSMuLg4fHx+dLrN+/fpx9+5dNmzYQGJiIs2aNWPJkiW8+eab2m1yev5VKhWrVq1izZo1fP/99/zzzz+4uLhQrVo1nbIbH374ISkpKXzxxRdYWFjQrVs3hg0bxoIFC7J9DIZ8nsqXL0+fPn04fPgwP/74IwkJCZQqVYrevXszYsQI7Yx+Nzc3VqxYwcKFCxkzZgw1a9ZkyZIlev0KTmNnZ4ePjw+///47I0eO1Pt+nTp1Ys2aNXTu3DnDj7lGjRrxww8/sGLFCj788ENSU1MpXbo0LVq00P769/Dw4KuvvmLOnDmMGzeOSpUqsWjRIp0ungMHDuTYqklTsmRJevbsydatW3Wu1+d1KlOmDB999BEbN25k06ZNlCxZkn379mV6nDFjxvDnn38yZ84coqKiKF68OPXr1+fzzz+nXLly2u30+UzmpHPnznz88cfUq1dP79MBnTt35qeffqJEiRI0atRI5zY3Nzf8/f1ZtmwZ8+fPJzo6mhIlStCgQQNtclWpVHz55Zd88sknTJ06FXd3d0aNGsWRI0f0GuqtKkzLQp88eZKBAwfy/fff6/RHCiEKj6SkJBo3bsyqVat0RvMJ81agk82iRYuoUaMGxYoVIzQ0lNWrV+Pq6sovv/yChYVZnY4SQoiXmll1o+VWUlISCxcu5MmTJzg6OtKsWTP8/Pwk0QghhJkp0C0bIYQQBYM0AYQQQhicJBshhBAGJ8lGCCGEwUmyEUIIYXCSbIQQQhicJBshhBAGJ8lGCCGEwUmyEUIIYXCSbIQQQhicJBshhBAGJ8lGCCGEwUmyEUIIYXCSbIQQQhicJBshhBAGJ8lGCCGEwUmyEUIIYXCSbIQQQhicJBshhBAGJ8lGCCGEwUmyEUIIYXCSbIQQQhicJBshhBAGJ8lGCCGEwVmZOgAhhHheamoq9+/fJzk52dShFDoajYYSJUrg5ORk1ONKshFCmJ379+/j7OyMs7OzqUMpdJKTk7l//77Rk410owkhzE5ycrIkGgOxtrYmNTXV6MeVZCOEEMLgJNkIIYQwODlnI4Qwe23bwqNHedtHiRKwd2/mt929e5c2bdqwfv16mjdvzt27d5k9ezZr167NdPvLly8THx9PgwYNdK5Xq9V88sknhIaGAtCgQQMmT578wjFfvnyZEydOMGTIkBe6f06Pw5gk2YgCKTkZNm+Gpk2hShVTRyMM7dEjWLEib/sYPz772ytVqsRXX31F8+bNc9zX5cuXiYiIyJBsDh8+jK2tLVu2bAEgMjJSr9jUajWWlpYZrq9evTrVq1fXax/mTpKNKHDCw8HXF6pWhS++gL59YdIkU0clCrpy5crh7OxMcHAwpUqV0l5/6tQpFi5ciEqlomnTpowfP54NGzYQGxvLgQMHWL16NUWKFAHAzs6OW7duERYWhqenJ66urgD4+fkxcOBAateuzfr16ylatCje3t588MEHlClTBjc3N86fP8/XX3+Nk5MTO3bsICwsjFdffZU//viDN998k3Xr1rFkyRIAhg8fzqxZs4iJiWH+/PmkpqZSsWJFZs6cSVxcHO+//z5JSUmUK1fO6M9jVuScjShwPvoIunSBESNg7lz4/nu4etXUUYnCYPTo0Rm6nObPn8+KFSv48ccfuXDhApcvX+att96if//+bNy4UZtoABo3bkznzp2ZOnUq7du3JzAwMNvj3bt3jzlz5jBt2jQ6dOjA7t27AdixYwfdu3fXbletWjXu3btHXFwcjx8/Jjk5mTJlyjB37lwWL17Mxo0bcXBw4PDhw2zbto3GjRvz7bffUr9+/Xx8dvJGko0oUM6ehYsXoWVL5bK1NYwZA+++a9KwRCFRqVIlHB0dCQkJ0V6XmJiIh4cHAPXq1dOej8nKm2++yebNm9myZQuff/45KSkpqFQq7e0ajUb7fy8vL+zt7QHo3LkzO3fuJCIigmfPnmVolbRp04Y9e/YQGBhI586dAbh+/Trvv/8+gwcP5sSJEzx69IjQ0FDq1q0LQJ06dfLwbOQvSTaiQJkzB4YMgXSfXby8lHM40roR+WH06NGsWbNGe9nW1paHDx8CcPbsWSpWrIi1tTVqtTrDfR89ekRsbCwARYoUwdraGgBnZ2ftPi5fvqzdPv15mhIlSqBSqfj+++/p2LFjhn137dqVnTt3snv3bu3tVatWZfny5WzcuJGAgAC6deuGp6cn58+fB9D+aw7knI0oMKKj4coVpSXzvHbtYN06WLzY+HEJwytRIucT/PrsQx/Vq1endOnS2haIn58f48aNQ6VS8dprr1G9enWKFCnCDz/8wIULF5g3b552Nv6DBw+YN28elpaWpKSkMGLECKysrHjjjTeYNGkSAQEB2NnZZXnsLl268Omnn3LgwIEMt5UsWZKUlBSKFi2Ki4sLAFOnTmXSpEmkpKRgYWHBxx9/TJ8+fZg4cSIHDhygcuXKuXuSDEilSd+mE8KMff01nDwJ/fplvC05GcaNg/Pnla41UbCFhoZSsWJFU4dRaJni+ZVuNFFgbNwIr7+e+W3W1lC3LmTyg1AIYQYk2YgCISICYmPh3/O0mWrYEH7/3XgxCSH0J8lGFAj79iktl+xIy0YI8yXJRhQIO3dCTlMGbG3B1RVyGJkqhDABSTbC7Gk0cOwY6FO1o0EDyGEenRDCBGToszB7N24o52qs9Hi3NmgAP/+c+fBoUYAZuhIn+VtEszAV0MwvkmyE2TtwAGrX1m/bcuWUuTgaje7ET1HAGaES54sU0XwZCmjmF4N2o23dupV+/foxePBg7ty5o3NbSEgI/fr1o2/fvuzfv1/ntl9//dWsavoI0/q//4MaNfTbVqWCkiUhLMygIYlCKH0RTQBXV1f8/Py0s/DXr19PQEAAd+/epV+/fkyaNIkFCxbQr18/bdWAHTt2sGrVKo4fP86sWbO4cuUKk9JViR0+fDj37t3jypUrDBkyhMGDBzN9+nQ0Gg3Pnj1j1KhRDBs2jK+++sroj9/QDJZsIiMj2bZtG5s2bWLy5Mksfm5q9/z581m+fDnffPMNy5cv15Z+SE5OZteuXTpVV8XL7exZyM1E6GrVlAQlRG7kpojmy1RAM78YLNmEhITg7e2NlZUVderU0Slel5iYiFqtxsPDA0dHRzw9PbW/Jvz9/enVqxcWFjJ2QcDTp8ooM33O16SpWRMOHjRcTKLwer6IphTQzD8G+0aPiorS1u8B3RcqMjJSpyy3s7MzUVFRxMXF8X//93+0a9cuV8fy8vLKe8DCLOk7Ci29qlWVsjZC5EZmRTSlgGb+MdgAAWdnZ66mK8ObvqXi4uJCTEyM9nJMTAwuLi589913DBw40FAhiQLo0CGlqnNu2NiApSXExEC63zSiIDNCJc7MimjWqlXrpS+gmV8MVogzMjKSUaNGsXnzZq5cucLatWtZkW40Sf/+/Vm+fDlOTk4MGjSIrVu34ufnR0REBACnT5+mY8eOzJ8/P8djeXl56SQ2UXi8/rpSYNPNLXf3W70axo79b90bUbBIIU7DMsXza7CWjaurKz169GDgwIFYWVkxd+5cAgICKFu2LN7e3vj5+TF+/Hg0Gg1jxozByspKZxBBly5d9Eo0ovDSaODhw9wnGoBXXoHgYEk2QpiLQrHEgLRsCqfbt5WF0mbMyP19Q0MhKAi2bs33sIQRSMvGsGSJASHSOX0aKlV6sfuWLw/pzucKIUxMko0wWydOvHiysbRUBgpER+dvTMI4rK2tiZYXzyCSk5NNMrVEytUIs3XyJIwc+eL3r1xZaR21bp1vIQkjKV26NPfv3+fJkyemDqXQ0Wg0lNB3jex8JMlGmCWNBu7ehWLFXnwfFStKsimoLCwsKFu2rKnDEPlIutGEWXrwANzd81ZM85VXlGQjhDA9STbCLJ0/D56eeduHpydcupQf0Qgh8kqSjTBLZ89ChQp524eNDSQnK39CCNOSZCPM0unTSjdYXpUrBzIFSwjTk2QjzNKVK8pcmbzy9IRz5/K+HyFE3kiyEWYnORlSUsDaOu/7qlgRTp3K+36EEHkjyUaYnWvX8qdVA8qk0DNn8mdfQogXJ8lGmJ2QkLwPDkhTtKiyfL0QwrQk2Qizkx8j0dKoVODkpKz4KYQwHUk2wuyEhOR9jk16np5w4UL+7U8IkXuSbITZuXMHPDzyb3/lyysJTAhhOpJshFmJi1NGoeWlTM3zPD2VrjkhhOlIshFm5coVZSJmfpJuNCFMT5KNMCsXL+Z/snF0VNa1Kfhr0gpRcEmyEWYlP0eipVe8uLJkgRDCNCTZCLOSH9WeM1O+vNJqEkKYhiQbYVbu3VNaIfmtXDklkQkhTEOSjTAbcXHKsgD5ORItjRTkFMK0JNkIs3H1av4PDkhTvrwy0k0IYRqSbITZuHQJDLXsvJ2d0nJKTTXM/oUQ2ZNkI8zG+fOGa9kAlCwJt28bbv9CiKxJshFm48IFwwx7TlOunIxIE8JUJNkIs3H7dv7WRHue1EgTwnQk2QizkJSkjEKztDTcMWREmhCmI8lGmIXr16FMGcMeo1w55ThCCOOTZCPMwuXLhhuJlsbGRmlBqdWGPY4QIiMrUwcgBCiDA14k2djfu0H5DXNwCj1PqrUtD9v050Gnt0m1d8x0+1Kl4NYtqFIljwELIXIlx5bNI1nAXRjBxYu5H4nmEfQtNaf3Iq5iTW6NXMCdfpNxvH2JBuOa4fB35jM4pUaaEKaRY7J59913GT58OL/99hsJCQnGiEm8hG7cyN05m5I711N65zpujlpITLVX0VhZk1KkKOGv9+Vur3HU/KQXjrcyFkOTEWlCmEaOyWb79u34+flx5coVevTowZQpUzh69KgxYhMvidRUSExUVujUh+Ot85QNWE7YkBlobOwy3J5QuhJ/D/Sjxqx+2D66o3ObjEgTwjT0GiBQpUoV/ve///Hee+9x+PBhFi5cSJcuXQgMDDR0fOIlcPu2MrtfH6qkRKotGMLdNyaisbbJcrvEEuW53200NWb3R6VO0V5fpgzcvJnXiIUQuZXjAIHjx4/zyy+/EBISQtu2bdmwYQMVK1YkIiKC3r174+vra4w4RSGWm5FoZX5eSUzVRiSUqpjjts9eqc2zG+eo8P0swt6eBYCVlTIaLTlZ/5aUECLvcmzZ+Pv74+vry++//87EiROpWFH5kBctWpRPP/3U4AGKwu/iRf3O11jGRlEqcD3hLXvpve9HbfrjfnSHzvmbcuXg2rUXiVQI8aJyTDYlSpSgRYsWWFj8t+mCBQsAaNWqleEiEy8NfWuild/yGU+adM30PE2WLC251+M9qi4dpS35XL68LKQmhLHlmGyOHz+uc1mj0XD48GGDBSRePleuKAkgO5bxsRQ7/DNPX22f6/0nlKlMgocnJQO/AZTEdvbsCwQqhHhhWSabb775hubNm3P9+nWaN2+u/WvVqpXeLZqtW7fSr18/Bg8ezJ07uqOCQkJC6NevH3379mX//v0AXLlyRbv9W2+9xcOHD/Pw0ERBoNFATAw4OGS/XcnAb4is7wOWLzYP+WHbAZTbtgTLuBgqVpThz0IYm0qj0Wiy2+Dzzz9n4sSJud5xZGQkI0aMYMuWLVy6dIn169ezfPly7e39+/dn2bJlODk5MXDgQLZv305qairW/561DQgI4Pbt23od28vLi6tXr+Y6RmF6Dx9Cz54wb142G6Wm0uiduoSOmI/a3umFj+V2ZAcaaxtuvTOPceOU7jshhHFk2bK5f/8+AD169CA0NDTDX05CQkLw9vbGysqKOnXq6NwnMTERtVqNh4cHjo6OeHp6EhYWpk00ADExMXh5eeXlsYkC4PLlnBdMcwv+g7hy1fKUaACeNu6E+5HfsH18D2trePYsT7sTQuRCln0SX375JbNnz2b69OkZblOpVGzYsCHbHUdFReHi4qK9nL4BFRkZSZEiRbSXnZ2diYqKAuDo0aMsWbKEmJgYvvrqK/0fiSiQLl3KeSRa6d/X8qRJl7wfzNKSRz798PzuUzw9v+bCBWjcOO+7FULkLMtkM3v2bAA2btz4Qjt2dnbW6dpKP5rNxcWFmJgY7eWYmBhtYmrSpAk//fQTf/75J0uXLtXpehOFz4ULULVq1rdbRz3G7kEo8WUq58vxoms1o/j//UzDpjc4d66yJBshjCTLZOPv75/tHfv27Zvt7XXr1mX16tWo1WquXLlChXRjW+3s7LC0tOTRo0c4OTlx+/ZtKlSoQFJSEjY2yqxwZ2dn7OxyMcRVFEgXL0K7dlnfXmL3RiLqvZ5/B1SpeNhuEG8emcpSu635t18hRLayTDbh4eF52rGrqys9evRg4MCBWFlZMXfuXAICAihbtize3t74+fkxfvx4NBoNY8aMwcrKit27d7NhwwZUKhXW1tbMmjUrTzEI8xceDq6uWd9ecs8mwoZk7MrNi9gq9Sn/51Zijl0EaubrvoUQmctxNFpBIKPRCqboaHj9dViyJPPb7e9cw2vxO4QNy/8fHY63zvP3t3vxif4VlSrfdy+EeE6WLZulS5fy/vvv8/7776PK5NO4JKtvCCH0lNNkzhJ/biaydguDHPvZK7UpbuHPg6AzlPKtb5BjCCH+k2Wyad9emandr18/owUjXi6XLmVfgLP44V+49c5cgx3/dPWB+H4yFXyDDHYMIYQiy3k2tWrVAsDb25uKFSsSExNDbGwsFStWxNvb22gBisLr/Pms59g4hF4k2dmdVLvMl3fOD6lVqxP3JA5OnDDYMYQQCr2qPg8aNIi//vqLw4cPM2TIELZt22aM2EQhl91S0MUPbiOqZlODHr9MGdjpOgimTjXocYQQeqxn8+2337J9+3acnJTZ27Gxsbzxxhv06dPH4MGJwu3uXShRIvPbih3dQejQGQY9fvHi8GNUFSgGHD4MzZsb9HhCvMxybNm4ubnpTMi0sLDAzc3NoEGJwi8hQVnILLORYLYPbpNqbUtqHsvT5MTCQllALa7nQJg2zaDHEuJll2XLZsmSJahUKooXL07Xrl1p1aoVKpWKQ4cOUbOmzE0QeXPtWtaDA4od/pno6saZ2l+6NFxLrkg9BwfYuxfatjXKcYV42WSZbF555RUAKlasSOvWrbXXpw0cECIvsquJVvxQAPd6jTNKHKVKwfXrUG/QIKV106ZN5s0tIUSeZJlsevbsacw4xEsmJCTzOTZWsZFYxUWT7FrcKHGUKQNnzgB9yihNrZ9/hl76LzsthNBPjgMEbt26xdKlS7l58yZJSUna6//880+DBiYKtwsXILPyekVP7CK6aiOjxVGyJISF/Xuhf3+YORO6dVNOKAkh8k2OAwSmTp3KyJEjsbGx4aeffmLAgAHS6hF5FhqqfNE/r9jhX4mp/qrR4kgbpJCQABQrBvXqwbffGu34Qrwsckw2SUlJ1KlTh9TUVIoWLcrw4cOlVSPyJK2BbGn53A1qNU43zhBfpopR4ylTRjlvA0CfPrB0KcTFGTUGIQq7HJONnZ0dycnJVK1alS+++IKtW7eSkpJijNhEIXX9euaVA5wvHyeufDVlTLIRlS6trBgKgJOTMiJNav8Jka9y/FR/9tlnpKam8umnn5KamsrNmzdZsWKFMWIThdSlS5knG/cjvxFbtaHR4ylXTolJq2tX2LIFHj0yeixCFFY5Jpty5cqhUqm4f/8+7du3Z9KkSVSsWNEYsYlCKiQk82TjdnIPMVUbGD0eDw+4fTvdFTY28Oab8PHHRo9FiMIqx2Szf/9+fH19+fzzz1m6dCldunThwIEDRghNFFYXLmQc9mz99CEaSyuDFt7MiqWlkl/SrVQOrVrB6dPPNXmEEC8qx/Gdn332GT/88AMeHh4APHz4kKFDh+pM9BQiN27ezDih0y14FzFVjN+qSVOunLK+zqtpA+FUKhg+HCZMgD17TBaXEIVFji2bIkWKULz4fxPs3N3dtUU5hcitxETl3+dHorkf+Z0YL+Ofr0lTtqyy5IGO6tWVsdGBgSaJSYjCJMuWjb+/PwCVK1dmwIABtGvXDpVKxZ49e6hSxbhDU0XhkWlNNLUax9AL3O8xxiQxAVSsqBQPyODtt2HKFGjXTqnaKYR4IVkmm/DwcADKlClDmTJliI+PB6BZs2bGiUwUShcvZjxfU+TaKeLLVjVpTTIXF3j6FNTq51pdxYsrfWsrVsCkSSaLT4iCLstkM3bsWGPGIV4SZ89mTDZuxwOJrVzPFOHoKFVKqWxQufJzN/TpAxMnwltvKclHCJFrOZ6zuX37NsOGDaNVq1a0atWK4cOHc1tnnKgQ+jt/XumySs8t+A9iTDC/5nnlymVy3gbA1lYZCi0regrxwnJMNlOmTGHo0KEcPHiQgwcPMmTIEKZMmWKM2EQhdPu2Mq8ljWVsFJaJ8aidXEwX1L8qVoRz57K4sVUr5cYsNxBCZCfHZBMTE0OrVq20l1u2bElsbKxBgxKFU3y8MrgrfTWaoqf/JMYMutBAKQx66xZoNJncqFLBiBEwfnwWGwghspNjsqlatSpz587l/PnznD9/ns8++0xGo4kXculSFudrTDi/Jj1LS3B3h7t3s9igShVwdc1i2JoQIjs5Jpu5c+fi4eHBmjVrWLNmDe7u7sydO9cYsYlCJiQEKlTQvc7l4lGeedYwTUCZqFhRKRyQpSFDYPr0/yYMCSH0km0FgdTUVHr27ElQUJCx4hGF2Nmz4On532W7ezdJKloCLM1nobLKleHYMejePYsN3NygeXNlKPTkyUaNTYiCLNuWjYWFBV5eXty8edNY8YhCLCREdySaW/AuYivVM1k8mUlbuTPb0zI9eyoLrD15YqywhCjwcvxJGRYWRvfu3alUqRL29vZoNBpUKhU//vijMeIThciDB1C06H+X3Y7v4pFPJmtDm5CFxX8DBSpVymIjW1vo3VtZQlqW2xBCLzkmmy+++MIYcYhC7vFjcHb+r0iASp2C/f2bJBUrk/0dTaBqVTh6NJtkA/D66/D++0ozKH3foBAiU1kmm8ePH7NmzRru3LlD5cqVGT16NEWKFDFmbKIQuXBB9zu5yOUTxJX3MmmJmqx4eYG/PwwalM1GFhYwcCD4+YG08oXIUZbnbCZPnkyRIkUYNGgQSUlJzJw505hxiUImJER32LPb8SBiK9U1XUDZKFJEGWyW43SyV19VZqlmWnZACJFelsnmyZMnTJgwgRYtWjBt2jRu3LhhzLhEIXPqlG63VNFTe8xmfk1mqlSBEyf02HDQIKV1I4TIVpbJJiUlhbCwMEJDQwkNDSU5OVnnshC5cfHif91oVrGRWCQnonZ0NmlM2alZE/RakLZmTaUJpFdmEuLlleU5G3d3dz755BPtZTc3N+1llUrFhg0bDB+dKBTUaqVUjZ2dctn1zH6z7UJLU7q00kOWmKgMPsvWoEHwySfwxx9GiU2IgijLZLNx40ZjxiEKsRs3dBdMczu20yyWFMiOSqUMFDhxAlq0yGHjKlWUrBQcnG5daSFEejmWqxEir86d0y1T43LpGHGeNU0XkJ5q14a9e/XcuH9/pYyNECJTkmyEwZ0+/V/lALt7N0lyKYbGyvyXWC5bFv7+G54902NjLy/l3E1IiMHjEqIgkmQjDO7Mmf9Gormd/MPsu9DSqFRK6+bPP/W8Q58+MHu2QWMSoqDK8pzN4cOHs71j8+bN8z0YUTjdvfvfaspux4IIf/1N0waUC40awZYt0K2bHhvXqQMbN8LNmzmUHxDi5ZNlstm5c2e2d9Qn2WzdupWAgACsra2ZN28e5cqV094WEhLCvHnz0Gg0jB49mtdff529e/eydu1abGxs8PDw4LPPPsPa2vy7W0TWnjwBJyellaCUqLlBYvGyOd/RTDg7K6PR9M4fPXvCZ5/BV18ZPDYhCpIsk838+fPztOPIyEi2bdvGli1buHTpEosXL2b58uU6+1++fDlOTk4MHDiQli1bUr16dTZv3oy1tTWLFy8mKCiIbnr9pBTm6uzZ/76ki1w6Tlz5amZZoiY7zZopFWmmTdNj49deg82blWJwxYoZPDYhCoocC3HGx8fz008/cePGDZKSkrTX55SMQkJC8Pb2xsrKijp16uhMBE1MTEStVuPx72L0np6ehIWFUSndT0dra2usrMxnnRPxYk6d+m8yp9uJoAJzvia9ypWVKTR65Q8LC+jUCVatghkzjBGeEAVCjgMEPvjgAxITEzl69CitW7cmKioKZ+ecZ35HRUXh4uKivaxJt0BIZGSkTlFPZ2dnoqKitJf//vtvDh8+TNu2bfV+IMI8nTqlTEMBcDtp3iVqsqJSQZMmsG2bnndo105pCiUkGDQuIQqSHJPN/fv3eeedd7C1taVDhw6sWrWK09mum6twdnYmOjr6vwNZ/HcoFxcXYmJitJdjYmK0ienJkydMnjyZRYsWYWNjk6sHI8zPlStQrhxYRT9FlZqC2qFgVg6vV0+Z4Pn4sR4b29hA06bKyAIhBKBHskk7QV+sWDFOnjzJ33//TURERI47rlu3LsHBwajVai5evEiFdLP67OzssLS05NGjR8TFxXH79m0qVKjAs2fPGDduHFOmTMFT1ggp8OLjlVaBlRUUPbWXmEr1TR3SC7O0BB8fZYFOvXTurHSlZbvkpxAvjxxPiowcOZKYmBg++ugj5s6dy7Nnz/joo49y3LGrqys9evRg4MCBWFlZMXfuXAICAihbtize3t74+fkxfvx4NBoNY8aMwcrKirVr13L79m2WLFkCQO/evenRo0eeH6QwjZCQ/87XuB/bQXT1xiaNJ69q1YK//oJr15QF1rLl7g4eHnD4sB71boQo/FQaTfY/vZKSksy+O8vLy4urV6+aOgzxnC++UIYMd+uq4dUhNbj+v1VgYWnqsPLkwQP4+WdYvVpp7WTryhVlZMHPPxslNiHMWY7daF26dGHixIns2rWLBDnhKXLhxAllcIBj2EUSPcoX+EQDULKkMpR70yY9Nq5WTSkdfe+eweMSwtzlmGz++OMPBgwYwMmTJ+nWrRvjx48nMDDQGLGJAi4kRPlidjsWSEzlgnu+5nlt2sCRI3qWQevYEb780uAxCWHuckw2KpWKV199lY8//piNGzdibW3NpEmTjBGbKMASEyElRRmY5X48kFivhqYOKd9YWkK/frB4sdKtlq3WrWH7dkg3R02Il1GOyebBgwds2LCBQYMG8e6771K1alWCgoKMEZsowM6fVyo9W8Q/wyr6CcmuxU0dUr4qWhR69YKPP1aKPWfJxkYpsCbnbcRLLsdk895775GQkMC8efMICAhg1KhRMixZ5OjkSaULreiZfQWyaoA+PD2hVSv46CNlmHeWfH2V0RJCvMRyHPocEBBgjDhEIXP8uLJopXvgb8RWLTxdaM+rXVvpLvzoI1iwABwcMtmoVCnl38uXoXp1o8YnhLnIMtn4+fmxYMEC+vbtiypd4USNRoNKpeLHH380SoCiYDp7Fvr30+By4QiPXu9r6nAMqn59ZfJqWsJxdMxko44dlUme0sIRL6ksk837778PwNKlS40WjCgc4uIgNRVcH1wh0b0UWBb+gqr16ik1OCdPVlYYKPJ8VZ7GjWHsWOXJybT5I0ThluU5mxIlSgDw66+/kpCQQJkyZXT+hMjK2bNKpWS3YzsLZOHNF1WnjlKwc/LkTAYNWFpC8+ZKgU4hXkI5DhBwdXVl5syZdO/enZUrV3Lt2jVjxCUKsOPHlcEBxY78Tky1V00djlHVrq2sf/Phh5kMGujQAdauNUlcQphajslmwIABbNiwgW+//ZYSJUowb948fH19jRGbKKCOHYNa5aOxehZV6IY866N2bWjYUBkWnZyc7oZixZRlS8+cMVlsQphKjskGIDU1latXr3L58mX++ecfqsuIGpGNS5egfvhuYgrpkGd9NGwIFSrAwoXPFX5OW1hNiJdMjslm2rRpdOjQgZ9//pnmzZvz+++/a6syC/G8p0/B3h6KH/mFmOrepg7HpFq0UAoH/PBDuisbNICjRyHdek5CvAyyHSaUmppKmTJl2Llzp9lXfhbm4cQJqFY1lSKHTvKg09umDsekVCro1g3WrVNqcjZqhDJkrXVr2LABxowxdYhCGE22LRsLCwv27NkjiUbo7fBhaOV4kvgyVZQv1pecpSUMGAArVyqtPkAZKPD117Kwmnip5Pht0KJFC7Zu3Up0dDRJSUnaPyEy89df0OxRADHVX65RaNlxdlYq1sydq8w/wsUFSpdWniwhXhI5Lp7m4+OT8U4qFX/++afBgsotWTzNPKSmKqtZnkiqx60R80m1tTd1SGZlxw6lO+2NN1BGUezfD9u2mTosIYwix6nd+/btM0YcohC4ehUauYeSElVEEk0mOnRQVvh87TUoW726Mufm4UNl+WghCrkck42/v3+m1/ftW7jrXYnc++sv6MavRFd7uUehZcXaGnr0UMrZLF+uwiJtYbUZM0wdmhAGl+M5m/DwcO3fvXv38Pf3Jzg42BixiQLm4EFo+vAXYmq8ZupQzFaFClC8uNKlho8P+Ps/N/NTiMIpx5bN2LFjM1wePny4wQISBVfoySc4aaJ59BJWDciNtO605s1tcWvUSFnJs18/U4clhEHlemxqREQED3JcC1e8bB4+hA5JvxFTvbGpQzF7trbQrt2/hQS6dIFly0wdkhAGl2PLpnnz5jqXHR0dGT9+vMECEgXTkSPQPXEb0bX6mzqUAqFWLQgOhrP/eFDP2VmpKtCkianDEsJgckw2hw8fNkYcooA7sucZzVP+5l6JcqYOpcDo2lVp3ayZ0AOrhQvh559NHZIQBpNlN9q9e/eISVe/6ejRo8yZM4dvvvlGJnWKDCx27SShViNTh1GgFCsGVatCwOUacPcuhIaaOiQhDCbLZDNhwgTi4uIAuHDhAv/73/8oXbo0169fZ/r06UYLUJi/qCjo+GQzzxq0NHUoBU7r1srItNi2PZQ1pYUopLLsRktMTMTj38lmv/76K2+++SZvv/02Go2Gbt26GS1AYf6O7n1GLdUNHpeqaOpQChwbG2jbFlaeasKU+1tkkqcotLJs2Wg0GlJTUwE4duwYTZs2BZRSNUKk9+CbnTyt/KpS5ljkWu3acPeeBfe8e8DixaYORwiDyDLZdOrUieHDhzNp0iQAGjdWhrTeuXMHR0dH40QnCoSKf/2AplkLU4dRYKlUymCBeUdeRxMYCI8fmzokIfJdtoU4z5w5w+PHj2natKk2wYSGhhIXF0fNmjWNFmROpBCn6UT9HcUjr+Y8m7/S1KEUeL//Dl3s9lC/eqK0cEShk2PV54JAko3pnB77DZG7T+D2nsyAz6v4eFizWs03ThOwPPCnnLsRhYqsbiXyxHH7BhJea23qMAoFe3to2dqSnY594JNPTB2OEPlKko14cffvkxQZR/HaJU0dSaHRoAEExbbk2eHTcPmyqcMRIt9IshEvLGLZ95x3b4W1takjKTxUKujWXcWqZ8PQ/G+iqcMRIt9IshEvRqNB88Nmouq1NnUkhU6pUpBStSZhdy3hl19MHY4Q+UKSjXgxf/3FjZQKvFJbhsEbQrt2sCTqHZI/nAaxsaYOR4g8k2QjXoh69Rr20o4SJUwdSeFkYwMtuhblV3UXNJMnmzocIfJMko3IvadPiT9yluTqdaRogAF5ecHpEh0J338B9uwxdThC5IkkG5F7X3/NCdd2eFWTTGNoXbupmBszgZQxEyA83NThCPHCJNmI3FGr0Xz/Pdsi2lGpkqmDKfzs7aFx52JstByKpm8/UKtNHZIQL8SgyWbr1q3069ePwYMHc+fOHZ3bQkJC6NevH3379mX//v0APHz4kF69elG/fn3Onz9vyNDEi/r9d6LK1MCphIMMeTaSGjXgprs311Mqgpy/EQWUwZJNZGQk27ZtY9OmTUyePJnFz9V6mj9/PsuXL+ebb75h+fLlqNVqXF1dWb9+PR06dDBUWCIvNBpYsIBd9r0wo9J4L4WuXWH+7QHEnbwIK6UOnSh4DJZsQkJC8Pb2xsrKijp16hCabhXCxMRE1Go1Hh4eODo64unpSVhYGLa2thQtWtRQIYm8OnAA3NzYfb4U1aubOpiXi60tdO9pgd+jSaRu+gE2bTJ1SELkisGSTVRUFC4uLtrL6et9RkZGUqRIEe1lZ2dnoqKiDBWKyC+zZnGvWR+cnZUvP2Fcnp7gWdWG9WU+heXLYfNmU4ckhN4MlmycnZ2Jjo7+70AW/x3KxcWFmJgY7eWYmBidxCTM0O7dYGvLjgsVqVfP1MG8vFq3hrNX7QnuOhNWrIBVq0wdkhB6MViyqVu3LsHBwajVai5evEiFChW0t9nZ2WFpacmjR4+Ii4vj9u3bOrcLM6NWg58fqW8N5cgR5YS1MA0LC+jXD1Z+7cDDMbPg119h0iT4d1VdIcyVlaF27OrqSo8ePRg4cCBWVlbMnTuXgIAAypYti7e3N35+fowfPx6NRsOYMWOwsrIiOTmZESNGcOPGDW7evImvry/Dhg0zVIhCX19/DdWrcz68JOXKKbPbhekUKQI9e8Knc21YsWwKNj9uUEYQbN4M0kMgzJQsnlaQ3bsHX30FgYHKylupqcqXTadOMHgwVKyY92PcvQvt28OSJcxdao+XF1SunPfdirz76y+IiYGPPgLV/x2Cn35SEk6tWqYOTYgMZFJnQZSaCvPnQ4cOkJICM2YofferV8PUqZCYCH36KP0tt27l7Thvvw3vvENMij03biATOc1I06YQHQ3btwMtWyrdaf37w3ffmTo0ITKQlk1BExcHAweCoyMMGUKWMys1Gjh9Gr7/Hnx9Yfp0cHDI3bEmT4ZHj2DYMH76Ce7fV77ThPlITlZ6OUeMgEaNUFq4K1YoS0p/+aVSgkAIMyAtm4IkKQm6d4eqVeGdd7JONKCswtWwIXz+OSQkgLc37Nih/7FWrYJTp2DIEFJTISjo3y8zYVasrZXfHitXwt9/oySXDz+E4sWheXO4ft3UIQoBSLIpODQa5VulZk3lnIy+LC2hR4//uto6doTsSgElJysnAX79Ffz8wMKCo0ehbNncN4yEcTg7Kz2mn34KEREoPzQ6d4ZRo5QfJ7/9ZuoQhZBkU2AsXqyMe+3R48XuX6yYkjy6dVO+hNq3B39/ZQBAQoIy2OCbb5TmS2QkTJmiHXa2ZYt0n5m7UqWU3xHTpik9aYDSAl6wABYtUrpRZXi0MCE5Z1MQHD0KY8cqXxz5Vf3y77/h0CG4dk0Z0uToCLVrg4+P0gXzr3Pn4IcfYMCA/DmsMKxTp+DqVWX8iKXlv1eq1fDtt8qKnz/8oLzWQhiZJBtzFxcHjRsrP1lLljTqoTUaGDNG6Ynx8DDqoUUeHDgAz54pjVOL9H0Xe/YolSB+/RVKlzZVeOIlJd1o5s7PTxnibOREA0rDp1gxSTQFTevWyr8rVig/GLTatVPmX7VvDxcumCI08RKTZGPOTp2C48eVoctGFh+vTNdo29bohxb5wNcXnj6FtWufSzh16iij1d58U2kCCWEkkmzMVWoqvPcevPvuc30hxrF+vTJa2tnZ6IcW+UClUsaS/P238lrqJJzy5WH2bJgwQak6IIQRSLIxV+vXK3VhPD2Nfuhz5+DiReVUkSi4LCzgjTeUIhJr1jyXcNzdYe5cZaTa2rUmi1G8PCTZmKPoaFi61CRDwJ4+hSVLoG9fkzSoRD5LSzj//APLlj03+tnJSWnh/PijkngK/lghYcbk68QczZ6tVPE18hDV+Hj4+GNlPqCrq1EPLQzIwkKpEp2QAHPmKPN2tWxslBf9+HH43/9kLo4wGEk25ub2bWV4aocORj1sfLwyurphQ/DyMuqhhRGoVErhiaJFlfEBsbHpbrS0hIkT4ckTZbSaTjYSIn9IsjE3U6bAoEHpZuQZ3tOnSsHgmjWVZCMKrxYtoEEDZWzAnTvpblCplHp7rq5Kq/rZM1OFKAopSTbm5OxZCA01asXL4GDlR23r1pJoXha1a0OvXkrv2eHDz93Yp4/yRmjbFsLDTRKfKJykgoA5adtW+bAboR8rPFwZofTkidKfLws8vnyePVPK41WpopTL01mB9eRJ2LABAgJktTyRL6RlYy7+/BOsrAyeaP75Rxno9tFHykJoQ4dKonlZOToqr79Go5Teu3Ej3Y2NGsH48Urh1gzNHyFyT1o25iA1FV57TRkNZICaVWq18kM1IACioqBZM+X8jAxtFmkePFDeH/Xrw7BhYGf37w3h4cqw6PHjlVVbhXhBkmzMwebN8PPPStXLfPT0qbJe2r59UKGCUhGgTJl8PYQoRNRqOHYMTpxQlk5q2/bfHyQJCcrkKy8vZTG+/Ko8Ll4qkmxMLTFROSE7Z06+TW4JDYVNmyAsTEkw9eqBrW2+7Fq8BGJjlV7du3eVkdDNm4OFSgPbtikL723dqiygI0QuSLIxtSVLlDVl+vfP865u34avvlK6ylq1Us7rqlT5EKN4KUVEKLU6795Vlplo1w7sr55V3mTLlytXCKEnSTam9PSpcgLl88/TdZLnXkwMrFunLJrVoQO88ko+xiheerGxSoGBc+eUVnK35k+o9PNiVM2bK+dzdIaxCZE5STamNGaMMqU7D3X8Dx2Cr79WWjINGkhLRhiOWq00wk+fhqePUxlZbDt1EoOx37oBqlUzdXjCzEmyMZUrV5RCm4sXv9CwsPh4pUGUNk/GyckAMQqRhYQEZf21iOAb9Ph7BTeaD6HMoonUrmshP3hEpiTZmIJGA23aKBM4q1fP9d3//htmzVJO/r/6qrRmhGklxiRhu20TVn/f4sPi31KzRxX69lXGvch7U6SxMnUAL6WfflJWJXuBRHP8OKxerSy0KMOYhTmwLWIDb7+N9d9X8P+5NyfODeDTkA8Iu2tF587KiLbatU0dpTA1adkYW1QUNGkC8+bleqjz9u2wZ49Sp1O6zYQ5UqUkUWKfP463Qrgw4Wv2htfl4EFlLMyAAUriKVnS1FEKU5BkY2wjRoCHR64GBaSmKosp3rqltGhkTp0wd3b/hFLm51VENPAhbNgsIhPtOXAADh5U3v6jR4Ovr1KhSbwcpGCJMe3bpwwMaNNG77ukpCiNoCdPlKk4kmhEQZBQqiI3Ry9ElZJMg3e9KX9zP926KdPK+vSBLVuUrrUPP4SbN00drTAGadkYS0SEMhV75kwoVkyvuyQmwvTpymTt1q0NG54QhmId8ZAyv6wmwaMCN8YuI8XZDYCkJDhyROkatrRUltPp3dvoC9QKI5FkYwwajbKASIMGyupVeoiNVdZRq11bGXUmRIGm0eBy7iAl9vtze9A0HrUdqDNU7cEDJekcOaJ8TIYMAR8f6WYrTCTZGMOiRUp1Qz0LbT5+rCSaVq2gVi0DxyaEEVnGxVAy6Bss42K49v5aEspU0rldo4GLF5XJyiEh0Lix0u3Wpg3Y25soaJEvJNkY2u7dMG2acuJFjxMut2/DjBnQpYuy3owQhZHjrfOU2vEV4a368Hf/j9DYZKwUq1YriefYMSXxlCoFnTpB+/bKEhkyh6dgkWRjSGfOKOOU58xRytLosfmyZdCvnxTVFYWfKiUZ98O/4Bryf9wcvZAI747Zbv/gAZw6pSSe27eVaWpt2yqtHi8vST7mTpKNoYSEKFlj2rQcZ19qNPDrrxAYqKwjIitnipeJdcQjSgV+TaqlNTfHLiO+bJUc76PRKEtonDunrHpw/75SJLRTJ6UYrYeHwcMWuSTJxhCOHVNWNZw6FcqWzXbT+HhlOGhcnFLjTIY2i5eV463zlAz6lujq3oQNnUmym/4ZIzVVWdb69GnlLzVVmcfTqxfUrSutHnMgySa/ffedUiFzypQcp0pfuqTU4WzSRKlxJsRLT6PB5exBShz6iacN23Gn/4ckuee+Tzk2VhmTc/y4sh5Phw5KR4PUEjQdSTb55ckTePdd5V0+bly2Q2eio5VlAW7cUFozxYsbMU4hCoJUNa5nDlDsr1+JqdqQu30m8qxSnRfaVVISBAfDX38pXW/t2yuJp3FjSTzGJMkmr+LjlcqYX3+tFH/KZh5NbKxSg3PfPll/Rgi9aDQ4XTuN+7EdWCbE8aD9W4S37kNy0RIvtLukJDh5UpnPc/OmMlm6Vy9o2VKWTjc0gyabrVu3EhAQgLW1NfPmzaNcuXLa20JCQpg3bx4ajYbRo0fz+uuvA7By5UqOHDmCo6MjCxcuxM3NLcfjmCTZXL4M334Lv/+uvGO7d890tc3UVOUEZlCQcpfXXoNGjeTcjBC5ZRX9FNez+3G+eBSNpTVPvTsQ2aANMV6NSLXN/SSclBRlgMGJE8raPGXKKN1trVop53nkM5q/DJZsIiMjGTFiBFu2bOHSpUusX7+e5cuXa2/v378/y5Ytw8nJiYEDB7J9+3Zu3brFokWL+OqrrwgKCuL8+fN8+OGHOR7L4MkmLk5Zc/ncOTh8WBkAULy4kmSaNNFZFjcmRmmqX7+ubB4aCuXLQ506ULnyC62TJoR4jmVsFEWuncIx7CL2924AEFe2CvFlqxJfuhJJ7qVIdilGiqMLqfaOpFrZoLGwRIUG1GosUpKwSErAIjEey4RnWMbHEn0vhvvXYnh6O4aERzEUtYqmXNFYSjnHUdQhkSI2CdhbJWNJ6n+BWFkpn38bG3BwUGrtFCmiLCHi6qoMLXVx+e//af++hM0ogxWDCAkJwdvbGysrK+rUqUNoaKj2tsTERNRqNR7/jk/09PQkLCyMkydP0vrfImCvv/463333naHCy9z69UqBpiw8sivHZU01rql8iL9qTdKBaOCPDNtZqMDVBRoUg9bFQZUAnIDoE4YLXYiXzRMAPKGoJxapKbj8cw+X6wcoFuePY8JT7BKjlOSSA7WFFclW9iRb2VPTyoGkf/+fqLEn+oEd4bdtuJlgQ5zaBjUOpGKBBhUWaLBEjRUpWBONg0U49hYJOKgScCAOe00cDppn2Gue4ZQak/sHmJbEbGyUZpa1tVJEzspK+VelUn69Wlgo/3d2Vr7DzHSJboMlm6ioKFzSTRhJ34CKjIykSJEi2svOzs5ERUURFRWl7Wqzs7MjLi7OUOFlztNTWQKgSBFlEqa7u/KioiyDu2srREYqr7WDDbjaKuMAnByVHzVy/kUI04miJlEG2K8KcPn3L73ERKXTIyEB4hIhPAmSkyFFDeoUUKeCJhVSNf/962CvzKXTqcWbmqqc+42NVXYYH6/sNCFBOcmUmKj0+SUnK/+q1cp90v5NTVUmHtnbm3XXicGSjbOzs07XlkW6J8HFxYWYmP8yfUxMDC4uLjg7OxMdHQ0orR8HBwe9jpVvXWht2mRZ/t8OeGt8/hxGCCFeNgZLg3Xr1iU4OBi1Ws3FixepUKGC9jY7OzssLS159OgRcXFx3L59mwoVKvDqq69y6NAhAA4ePEiDBg0MFZ4QQggjMuhotC1btvDrr79iZWXF3LlzOXXqFGXLlsXb25tz584xf/58NBoNI0eOpM2/LYrly5dz7NixXI1GK8y8vLxMHYIQIhMmn9tXwBSKeTZCCCHMm/meTRJCCFFoSLIRQghhcJJshBBCGJwkGyGEEAYnyUYIIYTBSbIRQghhcAarICDyJruK2QXZmTNnWLBgAdbW1jg4OLB48WJSUlL48MMPefbsGU2bNmXcuHGmDjPfnDx5koEDB3L06FGAQvc4Q0JCWLZsGcnJybRq1YpevXoVuscIMGvWLC5dukRqaiqTJk2ibt26+Pn58ejRI6pUqcKnn36qUyVFZEIjzE5ERITmjTfe0CQnJ2vOnTunGT9+vKlDyjcPHjzQxMXFaTQajWbz5s2a1atXaxYsWKAJDAzUaDQazYgRIzTXr183ZYj5auzYsZpevXppnjx5UugeZ2JiombEiBHa11Oj0RS6x6jRaDShoaGat956S6PRaDT379/XDBgwQLNp0ybNunXrNBqNRjNjxgzNgQMHTBligSCp2AxlVzG7oPPw8MD+31VMra2tsbS05PTp09r1jFq3bk1wcLApQ8w3+/fvp2HDhtoaf4XtcZ49exY7OzvGjx/P22+/zZUrVwrdYwQoVqwYdnZ2pKSkEB0djZubGydPnix0j9PQJNmYoewqZhcWERERbN68mTfeeIO4uDjs/l14Lq0CeEGXmprK5s2b6d+/v/a6wvY4Hz16xI0bN1i+fDnTpk1j5syZhe4xAjg6OlK6dGk6duzI8OHDGT58OFFRUTg7OwOF53EampyzMUPZVcwuDOLj45kwYQIff/wxbm5u2Nvbk5iYiK2trbYCeEH3+++/4+Pjg226RbIK2+N0dnamQYMGODg4UKlSJWJjYwvdYwT466+/iIyMZPfu3Tx+/JjRo0dTvnx5oqOjKV68eKF5nIZWuL7FConsKmYXdCkpKUycOJHBgwdrq3o3bNiQgwcPAnDo0CEaNWpkyhDzxbVr1/jjjz8YPnw4V69e5YMPPih0j7Nu3bqEhoaSmppKeHg4NjY2he4xgtJKdXFxwcLCAicnJ+Li4nQq1BeWx2loUojTTD1fMbuwJJxffvmFOXPmUL16dYAMI5hee+01JkyYYOIo89fgwYO1S6IXtsf5008/ERAQQEpKCpMnT6ZSpUqF7jGq1Wr8/Py4d+8eiYmJDBkyhHbt2uHn58fjx4+pVKkSM2bMKHQ9EPlNko0QQgiDk1QshBDC4CTZCCGEMDhJNkIIIQxOko0QQgiDk2QjhBDC4CTZCJGPfvjhB+rXr09CQoKpQxHCrEiyESIf7dq1Cy8vLw4cOGDqUIQwK5JshMgn4eHhPH78mPHjx7Nr1y4AHj9+zODBg+nSpQuLFi3Cx8cHUCopzJkzh969e9O9e3f27dtnytCFMDhJNkLkk927d9O+fXu8vb0JCQkhPj6eVatW0aFDB3bs2EGpUqW0227bto2yZcuyfft2fvjhB5YsWUJycrIJoxfCsCTZCJFPgoKC6NSpE1ZWVjRp0oSDBw9y5swZfH19AbT/Ahw5cgR/f3+6d+/OwIEDefbsGY8ePTJV6EIYnFR9FiIfhIeHc+7cOd577z0AEhMTiY2NzXJ5CI1Gw9y5c7XFSIUo7KRlI0Q++OOPPxg0aBD79u1j37592lZNzZo1CQoKAtD+C9CkSRO2bNlCamoqAJcvXzZJ3EIYiyQbIfLBrl27aNu2rfaylZUV3t7eNG7cmKCgILp27crt27dxdHQEoF+/fhQrVozu3bvTuXNnvvjiC1OFLoRRSNVnIQwoMTERKysrLC0t+fXXXzl69CgLFiwwdVhCGJ2csxHCgO7evcukSZNQq9W4urry2WefmTokIUxCWjZCCCEMTs7ZCCGEMDhJNkIIIQxOko0QQgiDk2QjhBDC4CTZCCGEMDhJNkIIIQzu/wHCN+wHVH9lJwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Age Distro\n",
    "ax = sns.kdeplot(num_df[num_df[\"Survived\"] == 0]['Age'], color='b', shade=True)\n",
    "ax = sns.kdeplot(num_df[num_df[\"Survived\"] == 1]['Age'], color='r', shade=True)\n",
    "ax.set_xlabel(\"Age\")\n",
    "ax.set_ylabel(\"Survival Probability\")\n",
    "ax.set_title('Age Distribution based on Survived/Not Survived\\n', fontsize=15)\n",
    "sns.despine(offset=3, trim=True)\n",
    "ax.legend(['Not Survived', 'Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1367005a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:32.574063Z",
     "iopub.status.busy": "2022-12-21T11:36:32.573297Z",
     "iopub.status.idle": "2022-12-21T11:36:32.985064Z",
     "shell.execute_reply": "2022-12-21T11:36:32.984298Z"
    },
    "papermill": {
     "duration": 0.438409,
     "end_time": "2022-12-21T11:36:32.987181",
     "exception": false,
     "start_time": "2022-12-21T11:36:32.548772",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Survived based on SibSp\n",
    "ax = sns.barplot(x='SibSp', y='Survived', data=train)\n",
    "ax.set_title('SibSp Vs Survived', fontsize=15)\n",
    "sns.despine(offset=5, trim=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "db4fd5a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:33.036097Z",
     "iopub.status.busy": "2022-12-21T11:36:33.035546Z",
     "iopub.status.idle": "2022-12-21T11:36:33.425740Z",
     "shell.execute_reply": "2022-12-21T11:36:33.424965Z"
    },
    "papermill": {
     "duration": 0.417155,
     "end_time": "2022-12-21T11:36:33.427693",
     "exception": false,
     "start_time": "2022-12-21T11:36:33.010538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Survived based on parch\n",
    "ax = sns.barplot(x='Parch', y='Survived', data=train)\n",
    "ax.set_title('Parch Vs Survived', fontsize=15)\n",
    "sns.despine(offset=5, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6f51458c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:33.478224Z",
     "iopub.status.busy": "2022-12-21T11:36:33.477611Z",
     "iopub.status.idle": "2022-12-21T11:36:34.051444Z",
     "shell.execute_reply": "2022-12-21T11:36:34.050674Z"
    },
    "papermill": {
     "duration": 0.601453,
     "end_time": "2022-12-21T11:36:34.053510",
     "exception": false,
     "start_time": "2022-12-21T11:36:33.452057",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(num_df['Fare'], color='m', label=\"Skewness : %.2f\"%(num_df[\"Fare\"].skew()), kde=True)\n",
    "ax.set_ylabel('Number of Passeners')\n",
    "ax.set_title('Fare Distribution', fontsize=15)\n",
    "ax = ax.legend(loc=\"best\")\n",
    "sns.despine(offset=5, trim=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bcb1c3c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:34.105414Z",
     "iopub.status.busy": "2022-12-21T11:36:34.104808Z",
     "iopub.status.idle": "2022-12-21T11:36:34.111532Z",
     "shell.execute_reply": "2022-12-21T11:36:34.110733Z"
    },
    "papermill": {
     "duration": 0.035064,
     "end_time": "2022-12-21T11:36:34.113521",
     "exception": false,
     "start_time": "2022-12-21T11:36:34.078457",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Applying Log to reduce skewness\n",
    "data['Fare'] = data['Fare'].apply(lambda i:np.log(i) if i > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad1d3ecf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:34.165475Z",
     "iopub.status.busy": "2022-12-21T11:36:34.164857Z",
     "iopub.status.idle": "2022-12-21T11:36:34.521913Z",
     "shell.execute_reply": "2022-12-21T11:36:34.521090Z"
    },
    "papermill": {
     "duration": 0.38569,
     "end_time": "2022-12-21T11:36:34.523968",
     "exception": false,
     "start_time": "2022-12-21T11:36:34.138278",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(data['Fare'], color='m', label=\"Skewness : %.2f\"%(data[\"Fare\"].skew()), kde=True)\n",
    "ax.set_ylabel('Number of Passeners')\n",
    "ax.set_title('Fare Distribution', fontsize=15)\n",
    "ax = ax.legend(loc=\"best\")\n",
    "sns.despine(offset=5, trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e4f788",
   "metadata": {
    "papermill": {
     "duration": 0.02503,
     "end_time": "2022-12-21T11:36:34.574280",
     "exception": false,
     "start_time": "2022-12-21T11:36:34.549250",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* Seems like there are more chances of **survivial** those who are **younger**, also we can clearly see there a peak from (0-5) **children** in 2nd chart **indecating** more chances of **surviving**.\n",
    "* Passengers with **1 or 2 sibSp** have heigher chance of **surviving**\n",
    "* **Small families** have more chance to **survive**, more than **single (Parch 0)**, medium (Parch 3,4) and large families (Parch 5,6 ). be carefull with 3 Parch as it has **standard deviation** \n",
    "* Fare is largly **skewed** towords **right** we fix that with **np.log** and skewness clearly **reduced after log transformation**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7316a199",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:34.627407Z",
     "iopub.status.busy": "2022-12-21T11:36:34.626790Z",
     "iopub.status.idle": "2022-12-21T11:36:34.887350Z",
     "shell.execute_reply": "2022-12-21T11:36:34.886191Z"
    },
    "papermill": {
     "duration": 0.289894,
     "end_time": "2022-12-21T11:36:34.889850",
     "exception": false,
     "start_time": "2022-12-21T11:36:34.599956",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KICKiJpJtxGAwGODk5CQuC4Igvr5w4QLy8/Px5ptvonPnzggPD0f//v3h7OwsVzlERNRIso0YHB0dUV5e/teOlH/tysnJCV26dEHXrl3RunVr+Pr64vTp03KVQkRETSBbMPj7+yM7OxtGoxG5ubnw8PAQ2+zs7ODq6orS0lIYjUYUFBSgS5cucpVCRERNYPZU0h9//IFOnTo1uWNnZ2eEhIQgIiICKpUKWq0WOp0Obm5u0Gg0+Mc//oHZs2ejtrYWY8aMQYcOHW7qAIiI6PZSCNee/L+O0NBQODs748knn0RgYCDs7e0tVVs93t7eyM/Pt9r+iazh4kU9Yqa/au0yqBnZsDYeLi7OsvVvdsSwfft2FBQUYMeOHVi7di369OmDsWPH4rHHHpOtKCIisp5G3ZXUvXt3vPDCC/Dx8cGbb76J48ePo7a2FtOnT8fo0aPlrpGIiCzIbDAcPnwYO3fuRE5ODoYPH47NmzfD09MTZWVlCA0NZTAQEbUwZoNh27ZtGDduHLRareSW03bt2mHx4sWyFkdERJZn9nbVTp06YeDAgZJQeOONNwAAgwcPlq8yIiKyCrPBcPjwYcmyIAg4ePCgbAUREZF1NXgqaePGjdi4cSP0ej0CAgLE9UqlEmPGjLFIcUREZHkNBkN0dDSio6Px1ltv4cUXX7RkTUREZEUNBsPZs2fRpUsXhISESGZG/ZOnp6eshRERkXU0GAzr1q3DsmXLsGjRonptCoUCmzdvlrUwIiKyjgaDYdmyZQCALVu2WKwYIiKyvgaDYdu2bTfccOLEibe9GCIisr4Gg6GkpMSSdRARUTPRYDA8//zzlqyDiIiaiQaDYdWqVXjppZfw0ksvQaFQ1GtfuXKlrIUREZF1NBgMgYGBAIBJkyZZrBgiIrK+BoOhV69eAACNRoOSkhLk5ORAoVCgd+/e6Nixo8UKJCIiyzI7V9K2bdsQGRmJr7/+GgcPHsTTTz+N1NRUS9RGRERWYHba7U2bNmH79u1o27YtAKCyshJ/+9vfMH78eNmLIyIiyzMbDC4uLpIpt5VKJVxcXBrVeUpKCnQ6HdRqNeLi4uDu7i62RUVFoba2Fmq1GgMHDsS0adNuonwiIrrdGgyGlStXQqFQoGPHjhgzZgwGDx4MhUKBAwcOwNfX12zHer0eqampSE5ORl5eHhITE5GUlCR5z9q1axsdMkREZBkNBkPXrl0BXJ0sb8iQIeL6Py9Km5OTkwONRgOVSgU/P7/rTsT3/PPPo1WrVpgzZw569OjRxNKJiEgODQbDuHHjbqljg8EAJycncVkQBEl7UlISXFxcUFBQgLlz52Lnzp23tD8iIro9zF5jOHHiBFatWoXCwkLU1NSI6/fu3XvD7RwdHZGfny8uX3udAoB4Cql79+5QqVS4cuUK7O3tm1Q8ERHdfmZvV50/fz6mTZsGW1tbfPLJJwgPD2/UaMLf3x/Z2dkwGo3Izc2Fh4eHpL2yshLA1TmZLl++zFAgImomzI4Yampq4OfnB5PJhHbt2iEmJgbjxo0zO5eSs7MzQkJCEBERAZVKBa1WC51OBzc3Nzz88MP4+9//Dnt7exiNRixYsOC2HRAREd0as8Fgb2+P2tpaeHl54d1330XHjh1RV1fXqM7DwsIQFhYmLl87atDpdDdRLhERyc3sqaSEhASYTCYsXrwYJpMJhYWFeOeddyxRGxERWYHZEYO7uztqampw9uxZBAYGwtPTE7a2tpaojYiIrMBsMHz11VfQarXo1q0bAKCoqAjz58+XPNtAREQth9lgSEhIwMcffwxXV1cAwO+//47JkyczGIiIWiiz1xgcHBwk02y3b99enFCPiIhangZHDNu2bQMAPPDAAwgPD8cTTzwBhUKBrKwsdO/e3WIFEhGRZTUYDCUlJQCAe++9F/feey8uX74MABgwYIBlKiMiIqtoMBjMPcBGREQtk9lrDKdOncIzzzyDwYMHY/DgwYiJicGpU6csURsREVmB2WB49dVXMXnyZOzfvx/79+/H008/jVdffdUStRERkRWYDYaKigoMHjxYXB40aJA4AR4REbU8Zp9j8PLyglarxdixYwEA6enpvCuJiKgFMxsMWq0WH330EdavXw8A6NOnD2bPni17YUREZB03DAaTyYRx48YhIyPDUvUQEZGV3fAag1KphLe3NwoLCy1VDxERWZnZU0knT57Ek08+iW7duqFVq1YQBAEKhQJbt261RH1ERGRhZoPh3XfftUQdRETUTDQYDKWlpVi/fj2Ki4vxwAMPIDY2Fg4ODpasjYiIrKDBawxz586Fg4MDIiMjUVNTgyVLljS585SUFEyaNAlRUVEoLi6u115RUYFHHnkEu3fvbnLfREQkjwZHDBcuXBBvSx04cCBCQkKa1LFer0dqaiqSk5ORl5eHxMREJCUlSd6zYcMG+Pv7N71qIiKSTYPBUFdXh5MnT0IQBABAbW2tZNnT0/OGHefk5ECj0UClUsHPzw9FRUWS9tLSUhQXF6N37963egxERHQbNRgM7du3x8KFC8VlFxcXcVmhUGDz5s037NhgMMDJyUlc/jNQ/rR+/XpMnToVWVlZN1U4ERHJo8Fg2LJlyy117OjoiPz8fHFZqfzrckZxcTHKy8vh4+PDYCAiambM3q56s/z9/bF27VoYjUYcP34cHh4eYtuxY8dw+vRpxMTE4PTp02jTpg26devGOZiIiJoB2YLB2dkZISEhiIiIgEqlglarhU6ng5ubGwIDAxEYGAgAWL16Nbp3785QICJqJmQLBgAICwtDWFiYuHztqOFPM2fOlLMEIiJqogaD4eDBgzfcMCAg4LYXQ0RE1tdgMKSlpd1wQwYDEVHL1GAwxMfHW7IOIiJqJsxeY7h8+TI++eQT/Prrr6ipqRHXMziIiFoms9/5PGfOHFRXV+PQoUMYMmQIDAYDHB0dLVEbERFZgdlgOHv2LKZMmQI7OzuMGDECa9aswQ8//GCJ2oiIyArMnkpSq9UAgA4dOuC7775Dhw4dUFZWJnthRERkHWaDYdq0aaioqMC8efOg1Wpx6dIlzJs3zxK1ERGRFZgNhkGDBsHW1hY+Pj63PH8SERE1f2aDITg4GL6+vhgxYgSGDBkCe3t7S9RFRERWYvbi8xdffIHw8HB89913GDt2LGbNmoX09HRL1EZERFZgNhgUCgX69euHBQsWYMuWLVCr1Xj55ZctURsREVmB2VNJ58+fR2ZmJjIzM1FVVYURI0Zw4jsiohbMbDBMnz4dI0eORFxcHO677z5L1ERERFZkNhh0Op0l6iAiomaiwWB45ZVX8MYbb2DixIlQKBTiekEQoFAosHXrVosUSEREltVgMLz00ksAgFWrVlmsGCIisr4Gg6FTp04AgF27dmHEiBHo1q2bxYoiIiLrMXuNwdnZGUuWLIHBYMDw4cMxYsQIeHl5NarzlJQU6HQ6qNVqxMXFwd3dXWybOXMmysrKcPnyZUyZMgWjRo26+aMgIqLbRiEIgtCYN168eBFZWVnIyMjAH3/8YfYhN71ej6lTpyI5ORl5eXnYsGEDkpKSxPaamhrY2tqisrIS48ePR0ZGhtkavL29kZ+f35hyiVqMixf1iJn+qrXLoGZkw9p4uLg4y9a/2QfcAMBkMiE/Px/Hjh3DuXPn0KNHD7Pb5OTkQKPRQKVSwc/PD0VFRZJ2W1tbAEBVVRW6d+9+E6UTEZEczJ5Keu2113DkyBH06dMHgYGBmD9/vvhL/UYMBgOcnJzE5esNTJ555hkcP35cvNBNRETWd8NgMJlMuPfee5GWltaoMLiWo6Oj5LSPUll/cLJp0yaUl5dj/PjxGDlyJBwcHJq0DyIiuv1ueCpJqVQiKyuryaEAAP7+/sjOzobRaERubi48PDzENpPJhNraWgCAvb097OzsYGdn1+R9EBHR7Wf2VNLAgQORkpKCkSNHSqbcNhcWzs7OCAkJQUREBFQqFbRaLXQ6Hdzc3NC7d29MmzYNAFBbW4spU6bcVPgQEdHtZ/aupKFDh9bfSKHA3r17ZSuqIbwrie5GvCuJ/pfcdyWZHTF8+eWXsu2ciIiaH7PBsG3btuuunzhx4m0vhoiIrM9sMJSUlIiva2pqcPDgQXTt2pXBQETUQpkNhueff77eckxMjGwFERGRdTXqyedrlZWV4fz583LUQkREzYDZEUNAQIBkuU2bNpg1a5ZsBRERkXWZDYaDBw9aog4iImomGgyGM2fOwNHRUZym4tChQ9i7dy+6dOmCyMhIPpBGRNRCNXiNYfbs2aiqqgIAHD16FC+88AK6dOmCgoICLFq0yGIFEhGRZTU4YqiuroarqyuAq9/iNmHCBERHR0MQBIwdO9ZiBRIRkWU1OGIQBAEmkwkA8O2336J///4Ark6HQURELVeDI4ZRo0YhJiYGLi4uAIBHHnkEAFBcXIw2bdpYpjoiIrK4BoNhxowZ+PHHH1FaWoqlS5eK36dQV1eHhQsXWqxAIiKyrBvertqnT5966zw9PWUrhoiIrK/JTz5Ty2UymcTrSkR092IwEICroRAbG4vY2FiGA9FdzuyTz3R3qKioQGFhofjaycnJyhURkbVwxEBERBKyjhhSUlKg0+mgVqsRFxcHd3d3AEB5eTlmzpyJ2tpaCIKABQsWwNfXV85SiIiokWQLBr1ej9TUVCQnJyMvLw+JiYlISkoCANja2mLFihVwdXVFYWEhli9fjk2bNslVChERNYFswZCTkwONRgOVSgU/Pz8UFRWJbfb29rC3twdwNSRsbGzkKoOIiJpItmsMBoNBcgFTEIR67xEEAfHx8ZgyZYpcZRARURPJFgyOjo4oLy//a0fK+rvSarXQaDR49NFH5SqDiIiaSLZg8Pf3R3Z2NoxGI3Jzc+Hh4SFpX79+PWxsbDB58mS5SiAiopsg2zUGZ2dnhISEICIiAiqVClqtFjqdDm5ubnB3d0dSUhL69u2LqKgodOrUCStXrpSrFLP05ZdQU1Nrtf03B9eO7kouGFBdW//U393G1lYNZ0dOGEl3H1lvVw0LC0NYWJi4fO2o4dixY3LuuklqamoxNPQf1i7DqhQwotN/fxrGPbMEAnhDwJfbV1i7BCKr4ANuREQkwWAgIiIJBgMREUkwGIiISILBQEREEgwGIiKSYDAQEZEEv6iHAAAClKgVbMXXRHT3YjDQfylw0egmviaiuxeDga7BQCAiXmMgIqL/wWAgIiIJBgMREUkwGIiISILBQEREEgwGIiKSYDAQEZEEg4GIiCRkDYaUlBRMmjQJUVFRKC4ulrQtXboUAQEBWLp0qZwlEBFRE8kWDHq9Hqmpqfjoo48wd+5cJCYmStqfffbZeuuIiMj6ZAuGnJwcaDQaqFQq+Pn5oaioSNLu6uoKhYJTMBARNTeyBYPBYICTk5O4LAiCXLsiIqLbSLZgcHR0RHl5+V87UvI6NxHRnUC239b+/v7Izs6G0WhEbm4uPDw85NoVERHdRrJNu+3s7IyQkBBERERApVJBq9VCp9PBzc0NGo0G69atQ1ZWFi5cuIBTp05hw4YNcpVCRERNIOv3MYSFhSEsLExcvnbU8Nxzz+G5556Tc/dERHQTeOKfiIgkGAxERCTBYCAiIgkGAxERSTAYiIhIgsFAREQSDAYiIpJgMBARkQSDgYiIJBgMREQkwWAgIiIJBgMREUkwGIiISILBQEREEgwGIiKSYDAQEZEEg4GIiCQYDEREJCFrMKSkpGDSpEmIiopCcXGxpC0nJweTJk3CxIkT8dVXX8lZBhERNYFs3/ms1+uRmpqK5ORk5OXlITExEUlJSWJ7fHw8kpKS0LZtW0RERGDQoEGwsbGRqxwiImok2UYMOTk50Gg0UKlU8PPzQ1FRkdhWXV0No9EIV1dXtGnTBvfffz9OnjwpVylERNQEsgWDwWCAk5OTuCwIgvhar9fDwcFBXHZ0dITBYJCrFCIiagLZTiU5OjoiPz9fXFYq/8ogJycnVFRUiMsVFRWSELG0Th2ccXT/P622f6IbcXFxxo6t66xdBt1FZBsx+Pv7Izs7G0ajEbm5ufDw8BDb7O3tYWNjgz/++ANVVVU4deqUpL0h1wYNERHJQyFce47nNktOTsauXbugUqmg1Wrx/fffw83NDRqNBj///DPi4+MhCAKmTZuGYcOGyVUGERE1gazBQEREdx4+4EZERBKyXXymO5O3t7e1SyBqEK8zWgZPJRERkQRPJRERkQSDgYiIJBgMREQkwWAgIiIJBgMREUkwGIiISILBQA3S6XTYsGGDtcugFmjx4sWIjIzEkSNHblufwcHBt62vux0fcCMii/vhhx/w2WefWbsMagCD4S5x+PBhrF+/Hm3btkVhYSFmzpyJXbt24bfffkNCQgLS0tLwyy+/4NKlS5gxY0a9SQ3T0tLw8ccfw2QyITQ0FOPHj7fSkdCdTqvVori4GFFRUQgICMD+/fthNBoRGxuLxx9/HFFRUfDx8UFubi66deuGe+65BwcOHICnpyfi4+Px66+/YsmSJTCZTHB0dERSUhJsbW3F/g0GAxYuXAi9Xg87OzskJCTAxcXFikd8BxLorvDtt98KkyZNEkwmk5CVlSWEhIQIRqNR+Oabb4SFCxcKly5dEgRBEPR6vTBu3DhBEARh+/btwgcffCCUlZUJERERQl1dnWA0GoWwsDChoqLCmodDd7igoCChoKBAmDlzpiAIgnDlyhXx5y4yMlL4z3/+IwiCIISEhAiZmZmCIAhCeHi48PvvvwuXL18WjEajIAiCkJiYKKSlpYl9CoIgrFixQtizZ48gCIKwd+9eITEx0XIH1kJwxHAX8fHxgUKhgKurK7y8vKBUKtGpUyfo9Xps2bIF+/btg0qlwtmzZyXbnT59GqdOncLkyZMBAOXl5fj999/Rtm1bKxwFtRQFBQXIy8tDVFQUAKCqqgqVlZUAgJ49ewIAOnXqBB8fH/G1wWBAZWUl3njjDVRVVaG0tBTt2rWr1+/333+Pf/3rX6irq0P37t0teFQtA4PhLqJQKK77Wq/XY/fu3di+fTvKy8sxevRoyXbu7u7o1q0bNm7cCKVSidraWqjVaovVTS1T165d4e/vj5UrVwIAampqJKeE/nTtz6ogCPj3v/+NCRMmYPjw4UhMTJR8bTAAdOvWDf3798fAgQPFfqlpGAwEJycnODg4ICIiAj179pR8HzcAtGvXDhMmTEBUVBSUSiXs7Ozwz3/+U/J1rURN5e3tDT8/P0RERECpVKJLly5ISEgwu93jjz8OrVaL7du3w8HBod6IITY2FosWLcIHH3wAk8mECRMmYMyYMXIdRovE2VWJiEiCf/IREZEEg4GIiCQYDEREJMFgICIiCQYDERFJ8HZVolu0Zs0aZGRkQKlUQq1WIykpCe7u7tYui+imMRiIbsEPP/yA7Oxs7Ny5E2q1GufPn0erVq2sXRbRLeGpJKJbUFpaivbt24tPgnfu3BlOTk7Yt28fJkyYgCeffBKLFi2CyWRCWloaZs2aBQA4evQoQkNDUVdXZ83yia6LwUB0C/r374+CggIEBQVh+fLl+OWXX3Dx4kV8+OGH2LJlC3bt2gUbGxtkZmYiKCgItbW1+Pzzz7Fo0SIsW7YMKhUH7dT88KeS6Ba0bdsWO3bswJEjR/DNN98gOjoaCQkJyM/Px4QJEwAAV65cQZcuXQAAS5YsQVBQEMLCwsSJ4oiaGwYD0S1SqVTo378/+vfvj3bt2kGr1WLYsGFYtmxZvfeeOXMGtra2KCkpsUKlRI3DU0lEt+DEiRM4ffo0gKszfxYUFGDSpEk4dOgQzp8/DwAoKyvD+fPnUVNTg9dffx2bNm3CuXPncPDgQWuWTtQgTqJHdAuOHj2KZcuWid8j4Ovri6VLl+LIkSN4++23xSnKly1bhszMTKjVajz//PMoLi5GbGwsUlJS0KZNGysfBZEUg4GIiCR4KomIiCQYDEREJMFgICIiCQYDERFJMBiIiEiCwUBERBIMBiIikmAwEBGRxP8DY4yd70654bAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Explore categorical Data\n",
    "\n",
    "#Explore Survived based on Sex\n",
    "ax = sns.barplot(x='Sex', y='Survived',data=data)\n",
    "ax.set_title('Sex Vs Survived', fontsize=15)\n",
    "ax.set_ylabel('Survival Probability')\n",
    "ax.set_xticklabels(['male', 'female'])\n",
    "sns.despine(offset=5, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "db607a79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:34.942524Z",
     "iopub.status.busy": "2022-12-21T11:36:34.942075Z",
     "iopub.status.idle": "2022-12-21T11:36:35.239539Z",
     "shell.execute_reply": "2022-12-21T11:36:35.238489Z"
    },
    "papermill": {
     "duration": 0.326487,
     "end_time": "2022-12-21T11:36:35.241686",
     "exception": false,
     "start_time": "2022-12-21T11:36:34.915199",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Survived with Pclass\n",
    "ax = sns.barplot(x='Pclass', y='Survived', data=train)\n",
    "ax.set_title('Pclass Vs Survived', fontsize=15)\n",
    "ax.set_ylabel('Survival Probability')\n",
    "ax.set_xticklabels(['Upper', 'Middle', 'Lower'])\n",
    "sns.despine(offset=5, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "96424e29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:35.297097Z",
     "iopub.status.busy": "2022-12-21T11:36:35.296644Z",
     "iopub.status.idle": "2022-12-21T11:36:35.588802Z",
     "shell.execute_reply": "2022-12-21T11:36:35.587919Z"
    },
    "papermill": {
     "duration": 0.322081,
     "end_time": "2022-12-21T11:36:35.590848",
     "exception": false,
     "start_time": "2022-12-21T11:36:35.268767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpaVhzZo1aNGiBcaPH49evXrB2tpaV+UQEVEN6azHYGlpicLCwn92ZPTPrqysrODg4IC2bduicePGcHNzw7Vr17Ru08XFRSe1EhHRP3QWDJ6enjh9+jTUajXOnTsHJycnsc3MzAz29vbIycmBWq3GpUuX4ODgoKtSiIioFnQ2lGRtbQ0/Pz8EBARALpcjIiICCQkJcHR0hFKpxDvvvIPQ0FCUlZVh+PDhaN68ua5KISKiWpAJDw/+13MuLi5IS0szdBlEWhUUFGDUqFEAgISEBD4riZ4qvMGNiIgkGAxERCTBYCAiIgkGAxERSTAYiIhIgsFAREQSDAYiIpJgMBARkQSDgYiIJBgMREQkwWAgIiIJnb7ak+hJUqnuoqyszNBl1MjDj5zPyyuAWv10PJLMxMQEFhbmhi6DDIzBQE+NsrIyBIUsMnQZNaLRqMXPoe98ACMjYwNWU3NbNq4wdAlUD3AoiYiIJBgMREQkwWAgIiIJBgMREUkwGIiISILBQEREEgwGIiKSYDAQEZEEg4GIiCQYDEREJMFgICIiCQYDERFJ6PQhert370ZCQgJMTEwQGRmJVq1aiW2BgYEoKyuDiYkJXnrpJUyfPl2XpRARUQ3pLBjy8/MRHx+PuLg4nD9/HlFRUYiOjpYss3HjRtjY2OiqBCIiqgOdDSWlpKRAqVRCLpfDw8MDGRkZlZaZNWsWgoKCcOHCBV2VQUREtaSzHkNBQQGsrKzEaUGQvqgkOjoaNjY2uHTpEsLCwvD111/rqhQiIqoFnfUYLC0tJW+xMjKS7qpiCKlDhw6Qy+W4f/++rkohIqoVjUYDjUZj6DIMRmfB4OnpidOnT0OtVuPcuXNwcnKStBcVFQEAsrOzce/ePSgUCl2VQkRUYxqNBsHBwQgODm6w4aCzoSRra2v4+fkhICAAcrkcERERSEhIgKOjI7p164Y33ngDCoUCarUaixcv1lUZRES1olKpkJ6eLn5+eEi8odDp5ar+/v7w9/cXpx/uNSQkJOhy10REVEe8wY2IiCQYDEREJMFgICIiCQYDERFJMBiIiEiCwUBERBI6vVyVqKGSyYxgZGwmfiZ6mmj9L/b27dv6qIPomSKTyWDetDXMm7aGTCYzdDlEtaK1xzBjxgxYW1vj1VdfhY+PDx9dQVRDDAR6WmkNhr179+LSpUvYt28fNm7cCC8vL4wYMQI9e/bUR31ERKRnNTrH0KFDB7z11ltwdXXFmjVrkJqairKyMoSEhGDIkCG6rpGIiPRIazCcOnUKX3/9NVJSUjBw4EB88cUXaNOmDfLy8jB69GgGAxHRM0ZrMOzatQsjR45ERESE5J0KTZs2xfvvv6/T4oiISP+0XpVkZ2eHl156SRIKK1euBAD07dtXd5UREZFBaA2GU6dOSaYFQcDx48d1VhARERlWtUNJW7duxdatW5Gfnw9vb29xvpGREYYPH66X4oiISP+qDYYpU6ZgypQpWL9+Pd5++2191kRERAZUbTBkZWXBwcEBfn5+yMjIqNTepk0bnRZGRESGUW0wbNq0CcuXL8d7771XqU0mk+GLL77QaWFERGQY1QbD8uXLAQBffvml3oohIiLDqzYYdu3a9cgVx44d+8SLISIiw6s2GLKzs/VZBxER1RPVBsOsWbP0WQcREdUT1QbDunXrMHfuXMydO7fKxwevXbtWp4UREZFhVBsMPj4+AIBx48bprRgiIjK8aoOhc+fOAAClUons7GykpKRAJpPB3d0dtra2eiuQiIj0S+uzknbt2oUJEybgxx9/xPHjxzFx4kTEx8fXaOO7d+/GuHHjEBgYiMzMzErtKpUKPXr0wLffflv7yomISCe0PnZ727Zt2Lt3L5o0aQIAKCoqwmuvvYYxY8Y8cr38/HzEx8cjLi4O58+fR1RUFKKjoyXLbNmyBZ6eno9RPhERPWlaeww2NjaSR24bGRnBxsZG64ZTUlKgVCohl8vh4eFR6bEaOTk5yMzMhLu7ex3KJiIiXam2x7B27VrIZDLY2tpi+PDh6Nu3L2QyGY4dOwY3NzetGy4oKICVlZU4LQiCpD02NhbTpk3DwYMHH6N8IiJ60qoNhrZt2wJ48LC8fv36ifMrTkprY2lpibS0NHH64V5HZmYmCgsL4erqymAgIqpnqg2GkSNHPtaGPT09sXHjRqjVaqSmpsLJyUlsu3DhAq5du4agoCBcu3YN5ubmaNeuHTp06PBY+yQiosen9eTz5cuXsW7dOqSnp6O0tFScf+jQoUeuZ21tDT8/PwQEBEAulyMiIgIJCQlwdHSEj4+PeJ9ETEwMOnTowFAgesbdvXsX5eXlhi5Dq8LCwio/Pw3kcjnMzc0ffzvaFggPD0d4eDiWLFmCHTt2ICEhAffu3avRxv39/eHv7y9OP9xrqDB79uxalEtET6vy8nIseW+pocvQ6uHwWrFyDeRyrT+T9cbyZe8/ke1ovSqptLQUHh4e0Gg0aNq0KYKCgrT2FujJ0Gg00Gg0hi6DiBoYrVGoUChQVlYGZ2dnfPzxx7C1tX0quoNPO41Gg+DgYAAPruB6+OQ9EZEuaf21WbVqFTQaDd5//31oNBqkp6djw4YN+qitQVOpVEhPT0d6ejpUKpWhyyGiBkRrj6FVq1YoLS1FVlYWfHx80KZNG5iamuqjNiIiMgCtwXDkyBFERESgXbt2AICMjAyEh4dL7m0gIqJnh9ZgWLVqFb766ivY29sDAG7duoVJkyYxGIiInlFazzFYWFhIHrPdrFkz8YF6RET07Km2x7Br1y4AQPv27TF+/HgMGjQIMpkMBw8e5M1oRETPsGqDITs7GwDQsmVLtGzZUryprXfv3vqpjIiIDKLaYJg1a5Y+6yAionpC6zmGq1evYvLkyejbty/69u2LoKAgXL16VR+1ERGRAWgNhkWLFmHSpEk4evQojh49iokTJ2LRokX6qI2IiAxAazCoVCr07dtXnO7Tpw+Kiop0WhQRERmO1vsYnJ2dERERgREjRgAAkpKSeFUSEdEzTGswREREYPv27YiNjQUAeHl5ITQ0VOeFERGRYTwyGDQaDUaOHInk5GR91aNz+YV3UVpaZugytHr4BSHZuQUoKRMesXT9YmpqAmvLx39ZCBEZxiODwcjICC4uLkhPTxeflfS0Ky0tQ//R7xi6DK1kUMPuv/86IycvhQBjwxZUC4f3rjZ0CUT0GLQOJV25cgWvvvoq2rVrh0aNGkEQBMhkMuzcuVMf9RERkZ5pDYaPP/5YH3UQEVE9UW0w5OTkIDY2FpmZmWjfvj2Cg4NhYWGhz9qIiMgAqr2PISwsDBYWFpgwYQJKS0uxdGn9f4k3ERE9vmp7DLm5ueJlqS+99BL8/Pz0VRMRERlQtcFQXl6OK1euQBAeXCZZVlYmmW7Tpo1+KiQiIr2qNhiaNWuGJUuWiNM2NjbitEwmwxdffKH76oiISO+qDYYvv/xSn3UQEVE9ofVy1cexe/duJCQkwMTEBJGRkWjVqpXYNnv2bOTl5eHevXuYOnUqBg8erMtSiIiohnQWDPn5+YiPj0dcXBzOnz+PqKgoREdHi+1r166FqakpioqKMGbMGAYDEVE9ofWx23WVkpICpVIJuVwODw8PZGRkSNpNTU0BAMXFxXxaKxFRPVJtj+H48eOPXNHb2/uR7QUFBbCyshKnK65metjkyZORmpqKuXPnaquzwRFghDLBVPxMRKQv1QZDYmLiI1fUFgyWlpZIS0sTp42MKv+4bdu2DYWFhRgzZgxeeeUV3lktIcMdtaP4mYhIX6oNhhUrVjzWhj09PbFx40ao1WqkpqbCyclJbNNoNFCr1TAxMYFCoYCZmRnMzMwea3/PJgYCEemf1pPP9+7dw549e/DXX3+htLRUnK8tOKytreHn54eAgADI5XJEREQgISEBjo6OcHd3x/Tp0wE8uHFu6tSp4jkHIiIyLK3BMH/+fHh5eeHkyZOYN28e9u/fL7ns9FH8/f3h7+8vTj/ca+B9EkRE9ZPWs5pZWVmYOnUqzMzM4Ovri48++gi//fabPmojItI7Y2NjKBQKKBQKGBs/PS/IepK09hhMTEwAAM2bN8cvv/yC5s2bIy8vT+eFEREZgkwmQ9t2zuLnhkhrMEyfPh0qlQoLFixAREQE7t69iwULFuijNiIig2iogVBBazD06dMHpqamcHV15XkBIqIGQGswDBs2DG5ubvD19UW/fv2gUCj0URcRERmI1pPP3333HcaPH49ffvkFI0aMwJw5c5CUlKSP2oiIyAC0BoNMJkP37t2xePFifPnllzAxMcG8efP0URsRERmA1qGkmzdv4sCBAzhw4ACKi4vh6+uL2bNn66M2IiIyAK3BEBISgldeeQWRkZFo3bq1PmoiIiID0hoMCQkJ+qiDiIjqiWqDYeHChVi5ciXGjh0ruaZXEATIZDLs3LlTLwUSEZF+VRsMFe9IWLdund6KISIiw6s2GOzs7AAA+/fvh6+vL9q1a6e3ooiIyHC0nmOwtrbG0qVLUVBQgIEDB8LX1xfOzs76qI2IiAxAazCMHz8e48ePx507d3Dw4EFERkbi9u3bvMmNiOgZVaOXCWs0GqSlpeHChQu4ceMGOnbsqOu6iIjIQLT2GN599138/PPP8PLygo+PD8LDw/m2NSKiZ9gjg0Gj0aBly5ZITExkGBARNRCPHEoyMjLCwYMHGQpERA2I1nMML730Enbv3o3CwkKUlpaKf0RE9GzSeo7hP//5DwAgNjZWnCeTyXDo0CHdVUVERAajNRgOHz6sjzqIiKie0BoMu3btqnL+2LFjn3gxRERkeFqDITs7W/xcWlqK48ePo23btgwGIqJnlNZgmDVrVqXpoKAgnRVERESGpTUY/ldeXh5u3rxZo2V3796NhIQEmJiYIDIyEq1atQIAFBYWYvbs2SgrK4MgCFi8eDHc3NxqWwoREemA1mDw9vaWTJubm2POnDlaN5yfn4/4+HjExcXh/PnziIqKQnR0NADA1NQUq1evhr29PdLT0/HBBx9g27ZtdfwKRET0JGkNhuPHj9dpwykpKVAqlZDL5fDw8EBGRobYplAooFAoADwICWNj4zrtg4iInrxqg+H69euwtLSEhYUFAODkyZM4dOgQHBwcMGHCBK13QxcUFMDKykqcFgSh0jKCIGDFihWYOnVqXesnIqInrNo7n0NDQ1FcXAwA+PPPP/HWW2/BwcEBly5dwnvvvad1w5aWligsLPxnR0aVdxUREQGlUokXX3yxLrUTEZEOVNtjKCkpgb29PYAHb3F7/fXXMWXKFAiCgBEjRmjdsKenJzZu3Ai1Wo3U1FQ4OTlJ2mNjY2FsbIxJkyY93jcgIqInqtpgEAQBGo0GRkZG+OmnnxAeHg7gweMwasLa2hp+fn4ICAiAXC5HREQEEhIS4OjoiFatWiE6OhovvPACAgMDYWdnh7Vr1z6Zb0RERI+l2mAYPHgwgoKCYGNjAwDo0aMHACAzMxPm5uY12ri/vz/8/f3F6Yd7DRcuXKhTwUREpFvVBsPMmTPx+++/IycnB8uWLRPPEZSXl2PJkiV6K5CIiPTrkZerenl5VZrXpk0bnRVDRESGV6N3PhMRUcPBYCAiIgkGAxERSTAYiIhIgsFAREQSDAYiIpJgMBARkQSDgYiIJBgMREQkwWAgIiIJBgMREUkwGIiISILBQEREEgwGIiKSYDAQEZEEg4GIiCQYDEREJMFgICIiCQYDERFJMBiIiEiCwUBERBIMBiIikmAwEBGRhE6DYffu3Rg3bhwCAwORmZkpaVu2bBm8vb2xbNkyXZZARES1pLNgyM/PR3x8PLZv346wsDBERUVJ2t98881K84iIyPB0FgwpKSlQKpWQy+Xw8PBARkaGpN3e3h4ymUxXuyciojrSWTAUFBTAyspKnBYEQVe7IiKiJ0hnwWBpaYnCwsJ/dmTE89xERE8Dnf1ae3p64vTp01Cr1Th37hycnJx0tSsiInqC5LrasLW1Nfz8/BAQEAC5XI6IiAgkJCTA0dERSqUSmzZtwsGDB5Gbm4urV69iy5YtuiqFiIhqQWfBAAD+/v7w9/cXpx/uNcyYMQMzZszQ5e6JiKgOOPBPREQSDAYiIpJgMBARkQSDgYiIJBgMREQkwWAgIiIJBgMREUkwGIiISILBQEREEgwGIiKSYDAQEZEEg4GIiCQYDEREJMFgICIiCQYDERFJMBiIiEiCwUBERBIMBiIikmAwEBGRBIOBiIgkGAxERCTBYCAiIgkGAxERSTAYiIhIgsFAREQSOg2G3bt3Y9y4cQgMDERmZqakLSUlBePGjcPYsWNx5MgRXZZBRES1INfVhvPz8xEfH4+4uDicP38eUVFRiI6OFttXrFiB6OhoNGnSBAEBAejTpw+MjY11VQ4REdWQznoMKSkpUCqVkMvl8PDwQEZGhthWUlICtVoNe3t7mJub4/nnn8eVK1d0VQoREdWCzoKhoKAAVlZW4rQgCOLn/Px8WFhYiNOWlpYoKCjQVSlERFQLOhtKsrS0RFpamjhtZPRPBllZWUGlUonTKpVKEiK6ZNfcGn8e/VQv+6Iny8bGGvt2bjJ0GVRHVlZW2BC9ztBlUA3orMfg6emJ06dPQ61W49y5c3BychLbFAoFjI2Ncfv2bRQXF+Pq1auS9uo8HDRERKQbMuHhMZ4nLC4uDvv374dcLkdERAR+/fVXODo6QqlU4o8//sCKFSsgCAKmT5+OAQMG6KoMIiKqBZ0GAxERPX14gxsREUno7OQzPRkuLi6GLoGowWqo5zU5lERERBIcSiIiIgkGAxERSTAYiIhIgsFAREQSDAYiIpJgMBARkQTvY6in1Go1lixZIj6uvGvXrggLCzNwVVRTZ8+exbp161BWVga1Wo0pU6Zg0KBBhi6LauDMmTNYu3YtBEGAmZkZ3n33XbRt29bQZekVg6GeOn78OMzMzBAXFwfgwaPK6elQWFiI8PBwfPLJJ3BwcEBpaSnOnDlj6LKoBir+7bZs2YLnnnsOqampCA0Nxb59+yCXN5yfSw4l1VMKhQKXL18WX2BkbW1t0Hqo5n744QcMGDAADg4OAABTU1MolUoDV0U1ceTIEQwaNAjPPfccAMDV1RXOzs4NLtgZDPVUjx49MHToUISHh8PHxwdJSUmGLolq6ObNm2jRooWhy6A6uHXrlhgKFZ577jncunXLQBUZBoOhHnv99dexY8cOxMXFYf369SgvLzd0SVQDLVq0wI0bNwxdBtWBvb19pX+7GzduwM7OzkAVGQaDoZ66ffs2ioqKAAAWFhYwMTExcEVUU/369cPhw4eRlZUFACgrK8Pp06cNXBXVRL9+/XDw4EHcvHkTAHDx4kVcunQJXl5eBq5Mv/gQvXoqJSUFkZGRMDY2Rnl5OcaNG4eRI0cauiyqobNnz2Lt2rUoLy/nVUlPmd9++w3r169HSUkJ7ty5gy1bttToDZPPEgYDEVEVysvLMWfOHPj4+MDPz8/Q5egVg4GIiCR4joGIiCQYDEREJMFgICIiCQYDERFJMBioQXNzc8Orr74q/n377bc1XnfhwoU4duxYnfcdExMjPgurtnr37l3n/RJp03CeCkVUBWtra+zfv1/v+1Wr1XrfJ1FNMRiIqtC7d2/4+PjgxIkT8PT0xODBg7Fx40bcv38fmzZtgqOjIwDg8OHD4uO1IyMj4enpid9//x0rV65ESUkJLC0tERUVBTs7O8TExCArKwuXL1+Gl5cXzM3Nxf3FxMQgOzsbS5cuRWxsLL7//nuUlpYiMDAQr7/+OoqLixEWFoaMjAx069bNUIeFGggOJVGDlp+fLxlKOnv2LAAgJycHw4cPx7fffou//voLJ06cQHx8PMaMGYNdu3aJ6+fm5mLfvn1Ys2YNFi9eDADo0KEDduzYga+//hrjxo3DZ599Ji5/7do1bN++HQsXLhTnxcTE4Pbt21i6dCn+7//+D3fu3MHevXuxd+9exMfH4/bt2/jqq6/g6OiIpKQk9OnTBzk5OXo6QtQQscdADVp1Q0lWVlbo2rUrAKB9+/Z48cUXATz40f/jjz/E5QYPHgyZTIZOnTpBrVajsLAQKpUK8+fPx/Xr11FeXi4+fhsABg4cKHnu1Y4dO+Ds7IyoqCjIZDKcOHECR44cwc8//wwAUKlUuHbtGn7//XcEBweL22jUqNGTPxhE/8VgIKrCwz/eRkZG4rSRkRE0Go3YJpPJJJ9lMhk2bNgAHx8fjBo1CmfPnsXq1avFZRQKhWQ/rq6uuHjxIvLy8mBjYwNBEBAaGorhw4dLltu6desT/X5Ej8KhJKLHkJycDABITU2FsbExLCwsUFRUBFtbWwDAvn37Hrl+165d8dZbb2HGjBm4e/cuevXqhT179qCkpAQAcPnyZZSUlMDLy0vc15EjR3Dv3j0dfitq6NhjoAat4hxDhQkTJmDMmDE1Xt/GxgYjR45EaWkpIiMjAQBTpkxBeHg4GjduXKPLSl9++WUUFBRg9uzZiI2NxcWLF/Haa69BEATY2NggNjYWAQEBmD9/PoYOHYpu3bqhefPmtf+yRDXEh+gREZEEh5KIiEiCwUBERBIMBiIikmAwEBGRBIOBiIgkGAxERCTBYCAiIgkGAxERSfw/O9cK2N61R+MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Survived with Embarked\n",
    "ax = sns.barplot(x='Embarked', y='Survived', data=train)\n",
    "ax.set_title('Embarked Vs Survived', fontsize=15)\n",
    "ax.set_ylabel('Survival Probability')\n",
    "sns.despine(offset=5, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e7c4bcd3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:35.645574Z",
     "iopub.status.busy": "2022-12-21T11:36:35.644864Z",
     "iopub.status.idle": "2022-12-21T11:36:36.043400Z",
     "shell.execute_reply": "2022-12-21T11:36:36.042341Z"
    },
    "papermill": {
     "duration": 0.428303,
     "end_time": "2022-12-21T11:36:36.045906",
     "exception": false,
     "start_time": "2022-12-21T11:36:35.617603",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Survived with Pclass and Sex\n",
    "ax = sns.barplot(x='Pclass', y='Survived', hue='Sex', data=train)\n",
    "ax.set_title('Pclass Vs Survived with Sex', fontsize=15)\n",
    "ax.set_ylabel('Survival Probability')\n",
    "ax.set_xticklabels(['Upper', 'Middle', 'Lower'])\n",
    "sns.despine(offset=5, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e25c38a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:36.099997Z",
     "iopub.status.busy": "2022-12-21T11:36:36.099582Z",
     "iopub.status.idle": "2022-12-21T11:36:36.789456Z",
     "shell.execute_reply": "2022-12-21T11:36:36.788335Z"
    },
    "papermill": {
     "duration": 0.72007,
     "end_time": "2022-12-21T11:36:36.791760",
     "exception": false,
     "start_time": "2022-12-21T11:36:36.071690",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore Survived with Pclass and Embarked\n",
    "ax = sns.catplot(data=data, x='Pclass', col='Embarked', kind='count', height=6)\n",
    "ax.despine(offset=3, trim=True)\n",
    "ax = ax.set_ylabels('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d4ccc1",
   "metadata": {
    "papermill": {
     "duration": 0.027677,
     "end_time": "2022-12-21T11:36:36.847076",
     "exception": false,
     "start_time": "2022-12-21T11:36:36.819399",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* **Female passengers** have higher chance of **surviving**, So **Sex**, might play an **important role** in the prediction of the survival.\n",
    "* **Upper Class** passengers have higher chance of **surviving**\n",
    "* **Upper Class Female** passengers have higher chance of **surviving**\n",
    "* Passengers coming from **Cherbourg** (C) have higher chance of **surviving**, because most **Upper class** passengers are coming from **Cherbourg** (C), where as **lower** and **Middle** class are from **Southampton** (S) and **Queenstown** (Q) respectivley."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84206afa",
   "metadata": {
    "papermill": {
     "duration": 0.027478,
     "end_time": "2022-12-21T11:36:36.902302",
     "exception": false,
     "start_time": "2022-12-21T11:36:36.874824",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* ### **Future Engeneering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7aa1e332",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:36.961145Z",
     "iopub.status.busy": "2022-12-21T11:36:36.960749Z",
     "iopub.status.idle": "2022-12-21T11:36:36.976682Z",
     "shell.execute_reply": "2022-12-21T11:36:36.975931Z"
    },
    "papermill": {
     "duration": 0.047848,
     "end_time": "2022-12-21T11:36:36.978744",
     "exception": false,
     "start_time": "2022-12-21T11:36:36.930896",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1.981001</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>C</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>2.070022</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>S</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1       0.0       3   \n",
       "1            2       1.0       1   \n",
       "2            3       1.0       3   \n",
       "3            4       1.0       1   \n",
       "4            5       0.0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket      Fare Embarked source  \n",
       "0         A/5 21171  1.981001        S  train  \n",
       "1          PC 17599  4.266662        C  train  \n",
       "2  STON/O2. 3101282  2.070022        S  train  \n",
       "3            113803  3.972177        S  train  \n",
       "4            373450  2.085672        S  train  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a24d63b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:37.039250Z",
     "iopub.status.busy": "2022-12-21T11:36:37.038555Z",
     "iopub.status.idle": "2022-12-21T11:36:37.046252Z",
     "shell.execute_reply": "2022-12-21T11:36:37.045189Z"
    },
    "papermill": {
     "duration": 0.039166,
     "end_time": "2022-12-21T11:36:37.048692",
     "exception": false,
     "start_time": "2022-12-21T11:36:37.009526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                              Braund, Mr. Owen Harris\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
       "2                               Heikkinen, Miss. Laina\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
       "4                             Allen, Mr. William Henry\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Name'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79eee2a9",
   "metadata": {
    "papermill": {
     "duration": 0.028256,
     "end_time": "2022-12-21T11:36:37.104209",
     "exception": false,
     "start_time": "2022-12-21T11:36:37.075953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Name Title\n",
    "* We have different **titles** with names (**Mr/Miss/Lady**) etc.\n",
    "* Since some passenger with **distingused title** may be preferred during the **evacuation**, it is interesting to add them to the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ff4c1e2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:37.161811Z",
     "iopub.status.busy": "2022-12-21T11:36:37.161356Z",
     "iopub.status.idle": "2022-12-21T11:36:37.171373Z",
     "shell.execute_reply": "2022-12-21T11:36:37.170635Z"
    },
    "papermill": {
     "duration": 0.040906,
     "end_time": "2022-12-21T11:36:37.173421",
     "exception": false,
     "start_time": "2022-12-21T11:36:37.132515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
       "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
       "       'Jonkheer', 'Dona'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Finding Titles\n",
    "data['Title'] = data['Name'].apply(lambda i:i.rsplit(',')[1].split('.')[0].strip())\n",
    "data['Title'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "443a05b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:37.234441Z",
     "iopub.status.busy": "2022-12-21T11:36:37.234021Z",
     "iopub.status.idle": "2022-12-21T11:36:37.551984Z",
     "shell.execute_reply": "2022-12-21T11:36:37.551026Z"
    },
    "papermill": {
     "duration": 0.352672,
     "end_time": "2022-12-21T11:36:37.554411",
     "exception": false,
     "start_time": "2022-12-21T11:36:37.201739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NiQEDBqDVahkzZgzffPMNqampjBkzpihqE0IIUcyeGhK///47V69exdLSEk9PT3788ceiqksIIUQJYHJOYvz48axevZrExEQWLFjAvHnz8vUBERERuLu7k5CQQEJCAn5+fvTo0YP58+cb37Nv3z66d++Oj48PkZGR+focIYQQBc9kT+LkyZNs27YNtVpNRkYGPXv2ZNiwYXn+gFWrVuHh4QHA0qVL6dq1K+3atWPAgAFER0fj5uZGcHAwa9asITU1lc8//5zQ0ND8b5EQQogCY7InYWlpiVp9/2UbGxvycc0d+/bto2HDhtja3r9I7cSJE7Rs2RIALy8vjh8/TkxMDFWqVMHe3h5XV1eys7PJzMzMz7YIIYQoYCZ7EhcuXKBZs2bGnxMTEx/6+cCBA09t2GAwsG7dOkJCQti7dy8AaWlp2NjYAODg4MD169cfu+7CwcGBxMREXF1d87dFQgghCozJkDh79uwzNbx9+3beeecdrK2tjctKlSpFZmYm1tbWaLVaHB0dcXR0RKvVGt+j1WopU6bMM322EEKIgmH2Yrr8unDhArt37+bTTz/l/PnzjBo1ioYNGxIWFgZAeHg4jRo1onLlysTExJCWlkZ8fDwajeahYBFCCFF8Cu0ufaNHjzb+u3fv3syaNQuAgIAAVq5cSZMmTahRowYA/v7+9OvXD5VKxdixYwurJCGEEHlk8i6wORPKJZXcBVYIIQqfyeGmESNGAPefJyGEEOLFZHK4SaPRMG3aNG7cuMGcOXMeez0nRIQQQjy/TIbEkiVLOHToEP/9739xc3MrypqEEEKUECZDomzZsnTo0IHatWtTrVq1oqxJCCFECWH27CYLCwv69evH5cuXAahevTpff/01lStXLvTihBBCFC+z10mMHTuWjz/+mLCwMMLCwujbt6+cpiqEEC8IsyGh1Wrx9PQ0/tyiRQtSUlIKtSghhBAlg9nhppo1a/LNN9/QsWNHAHbu3Gm8CE4IIcTzzWxIfPPNN6xZs4bFixcD0KBBA4YPH17ohQkhhCh+ZkPCxsYGPz+/oqhFCCFECVNoN/gTQgjx7ychIYQQwqSnhoTBYGDUqFFFVYsQQogS5qkhoVariYuLIz09vajqEUIIUYKYnbh2cnKic+fONG/enFKlShmXyw3+hBDi+Wc2JFq2bEnLli2LohYhhBAljNmQ6Ny5M3fu3OHq1au88cYb6HQ6DAZDUdQmhBCimJk9u2njxo0MHjyYgIAAAG7cuMGgQYMKvTAhhBDFz2xIrFmzhrVr12Jvbw+Am5sbd+/eLfTChBBCFD+zw02WlpZYWlqiUqkA0Ol0uWr4zp07+Pv7Y2FhgV6vZ9KkSVSqVInAwEBu375NjRo1mDBhAmq1msjISKZOnYqiKAwaNEjmQIQQooQwGxKtW7dm6tSppKamsmPHDjZu3EinTp3MNuzk5MS6detQq9UcPXqU77//ngYNGuDh4YGfnx+TJk1i//79eHp6Mm3aNIKDg7G3t8fX15cWLVqg0WgKYvuEEEI8A7PDTYMGDaJ58+a0atWKM2fO0K9fPz799FOzDWs0GtTq+81rtVpq1apFRESEsZfg5eXF8ePHyczMRK/X4+rqip2dHVWqVCEmJubZtkoIIUSBMNuTAKhVqxaZmZmo1Wrq1KmT68ajo6MZN24cN2/eZP78+Rw6dAgHBwcAHBwcSEpKIjExkdKlSxvXyVkuhBCi+JntSaxfv55evXpx6NAhDhw4QN++fdm4cWOuGq9evTo//fQTS5YsISgoCAcHB5KTk4H7vQtHR0ccHR3RarXGdXKWCyGEKH5mexIrV65k8+bNxrObUlJS6NatG97e3k9dT6fTYWVlBUDp0qWxsbHhzTffJDw8nGrVqhEeHk6zZs2wsbFBo9Fw+/Zt7O3tuXLlijw/WwghSgizIVG2bFnj3ALcv59T2bJlzTZ89uxZZs+ebTwrKjAwkKpVqxIYGIivry/VqlWjRYsWxteGDRuGoigMGTIEC4tcjYIJIYQoZCpFUZQnvZCzg79y5QpnzpzB09MTlUpFeHg4r732GnPnzi3iUh/m7u7O+fPnH1qmT00jYfXP+WqvbJ8uaOxsC6I0IYR4bpg8ZK9atSpw/+I5Ly8v43IPD49CL0oIIUTJYDIkOnfuXJR1CCGEKIHMDv6HhYWxaNEibt68iV6vNy4/cOBAoRYmhBCi+JkNiSlTphASEkLNmjWNk9BCCCFeDGavk3j11VepWrWqBIQQQryAzPYkRo0aRe/evWnQoAGWlpbG5fJkOiGEeP6ZDYlJkybxxhtvUL169YeulxC5k52hRdHn7s65j1JprLCwKW3+jUIIUUjMhkR2djZjx44tilqeS4peR9Tqfvlat3aflQVcjRBC5I3ZkGjSpAnff/89Xl5eDw03ubm5FWphQgghip/ZkDhz5gwA+/fvNy5TqVSsXr268KoSQghRIpgNiR9//LEo6hBCCFECmQ2JOXPmPHG5nN0khBDPP7Mh8eDcg06nY9++fbi6uhZqUUIIIUoGsyHx6D2cvL296dmzZ6EVJIQQouQwGxI63f+d468oClFRUSQkJBRqUUIIIUoGsyHRtm1bVCoViqKg0Wh49dVXmTx5clHUJoQQopiZDYk//vijKOoQQghRApkMCXO3Am/WrFmBFyOEEKJkMRkSO3bseOLyw4cPExcXR1RUVKEVJYQQomQwGRLTpk0z/ttgMLBjxw5WrFiBh4cHwcHBZhs+efIk06dPx9LSEltbW2bNmkV2djYBAQGkpqby1ltvMXToUAD27dvH4sWLUalUfPnll9StW7cANk0IIcSzeuqcRGZmJhs3bmTNmjU0atSI2bNnG599bc4rr7zCDz/8QKlSpQgNDWXt2rUkJyfTtWtX2rVrx4ABA4iOjsbNzY3g4GDWrFlDamoqn3/+OaGhoQWycUIIIZ6NyZBYsGABW7du5b333mP16tW4uLjkqeEHL7iztLREo9Fw4sQJhg8fDoCXlxfHjx9HpVJRpUoV7O3tsbe3Jzs7m8zMTKytrfO5SUIIIQqKyZCYP38+9vb2/PLLL/zyyy+PvZ7bZ1zfu3ePdevWsWzZMrZv346NjQ0ADg4OXL9+naSkJBwcHIzvd3BwIDExUa7qFkKIEsBkSJw7d+6ZG09PT2f48OGMGzeOsmXLUqpUKWMvQavV4ujoiKOjI1qt1riOVqulTJkyz/zZz8qQkYaiz87XuiqNBWob2wKuSAghip7Z6yTyKzs7my+++ILevXvzxhtvANCwYUPCwsJ47733CA8PZ8SIEVSuXJmYmBjS0tJITU1Fo9GUiKEmRZ9N/KrZ+Vq3XN+RBVyNEEIUj0ILiV9//ZWIiAhSU1NZvXo1np6e9O/fn4CAAFauXEmTJk2oUaMGAP7+/vTr1w+VSiVPwRNCiBKk0EKiU6dOdOrU6bHly5Yte2xZq1ataNWqVWGVIoQQIp/UxV2AEEKIkktCQgghhEkSEkIIIUySkBBCCGGShIQQQgiTJCSEEEKYJCEhhBDCJAkJIYQQJklICCGEMElCQgghhEkSEkIIIUySkBBCCGGShIQQQgiTJCSEEEKYJCEhhBDCJAkJIYQQJklICCGEMElCQgghhEkSEkIIIUySkBBCCGFSoYVEVlYWPj4+NGrUiF27dgGQkJCAn58fPXr0YP78+cb37tu3j+7du+Pj40NkZGRhlSSEECKPCi0kLCwsmDdvHn379jUuW7p0KV27diU0NJTTp08THR2NXq8nODiY5cuXExwczLRp0wqrJCGEEHlUaCGhUqlwcXF5aNmJEydo2bIlAF5eXhw/fpyYmBiqVKmCvb09rq6uZGdnk5mZWVhlCSGEyIMinZNIS0vDxsYGAAcHB5KSkkhKSsLBwcH4HgcHBxITE4uyLCGEECYUaUiUKlXK2EvQarU4Ojri6OiIVqs1vker1VKmTJmiLEsIIYQJRRoSDRs2JCwsDIDw8HAaNWpE5cqViYmJIS0tjfj4eDQaDdbW1kVZlhBCCBMsCrPx4cOHc+bMGWxtbYmMjKR///4EBASwcuVKmjRpQo0aNQDw9/enX79+qFQqxo4dW5glCSGEyINCDYng4ODHli1btuyxZa1ataJVq1aFWYoQQoh8kIvphBBCmCQhIYQQwiQJCSGEECZJSAghhDBJQkIIIYRJhXp2kyhYugwtBr0uX+uqNVZY2ZQu4IqEEM87CYl/EYNeR9hPvfO1rqfPjwVcjRDiRSDDTUIIIUySkBBCCGGShIQQQgiTJCSEEEKYJCEhhBDCJAkJIYQQJklICCGEMElCQgghhEkSEkIIIUySkBBCCGGShIQQQgiTJCSEEEKYVGJu8LdhwwZ+/vlnLC0tmTp1KhUrVizukkQupGcmo9dn5WtdjcaSUtYOxp9TdMlk5bMtS40l9lYO5t8ohMiTEhESiYmJbNy4kdDQUP7++29mzZpFcHBwcZclckGvz2LJLz3zte7AD9c99HOWPosRu3zz1dactmvztZ4Q4ulKREhERkbyn//8BwsLC+rWrcs///xT3CU99zIzktEb8tkDUFtibVNyj9q1ujR0+ux8rWulsaC0le0DbWWg0+vz2ZaG0lY2/9dWZiY6Qz7bUmsobW39/9vRoTMY8tmOmtLWVv9Xky6LLL2Sr7YsNSpKW1kCkKrTk53Pdiw0KuysNMafM3UG9PlsS6NRYW31f6PoWZkGDPlsS61RYWl9vy19ugEln+2oNCo0pf6vJkN69jO1pS51f7dtyNChZOfv70BloUZtY2X+jYBKUZT8VVuAtm/fzs2bNxkwYAAAH3zwAdu3b3/qOu7u7pw/f74oyhNCiBdWiZi4dnBwIDk52fizWm2+LAkIIYQofCUiJOrVq8fx48fR6/WcPXuWypUrF3dJQgghKCFzEmXKlKFTp074+vpiYWHBN998Uyif4+7uXijtCiFESVQQIy4lYk5CCCFEyVQihpuEEEKUTBISQgghTJKQEEIIYZKEhBBCCJMkJIQQQpgkISGEEMIkCQkhhBAmvVAhIZeECPHvl5WVvxtTlnR52T/lvLco9mkvTEgYDAZUKlWBtffof05JD6Dirk9RlGeqwZDPu54WppJYU0Eqidt38uRJQkNDSUpKKtB2dTpdgbaXVzn7p9zWoVKpuHDhAhEREYX+//Tch8S9e/eIiopCrVaTkZHB2LFjiYyMfKY2FUUxBs6RI0e4d+/eMweQPp+3o86NBwMyJSUl1+sVZLCoVCoSExPz1a5erzf+/x08eJCMjIwCqelZfuc5NQHcuHGjQOp5VM6XX6fTkZaWlu92jhw5ws8//5yndR7cvsOHD+f7sx/1rDu0l156icjISA4ePEh6enqB1BQfH8/BgwcBiImJKZbAUKvVnDt3jsDAQK5evWr2/ZGRkYSEhGBlZZWrG6I+y+/9uQ0JRVFISkrC39+fSZMmceLECRYtWkTFihWpW7fuM7Wds8Ndu3Yt8+fPz9OO11StGo2G5ORkli9fzrVr156pvUfl/BFt3LiRkJAQszvpnNdVKhVxcXEP1ZOf4DAYDGRmZjJgwABOnjyZ60DNOVrUaDSkpaUxbtw47t69i42NjZk1zcv5nQMsWbKEsLAwzp49a3a9e/fuce7cOTQaDSkpKYwcOZJTp049cz1PolariY2NZcqUKXkKokd3CFqtluzs7Ce+9qjMzEySkpLQaDRkZmYyY8YMTp8+/Uw7mQf/ZtRqNTExMezcuZOEhARjXebkBHqlSpXo1asXYWFhnDhxokCGniwtLbl06RI+Pj7MmTMHjUZTJD3vBz/jzJkzjBo1io8//phKlSoZ74qd83t/8IAmOTmZ77//HmdnZ2rXrp2rz8nZB/z6669s2bIl1793eI5DQqVS4ejoaHyY0YIFC9i7dy8ODvcflpOfo8gHvyhxcXFs3ryZuXPncufOHbZu3Zrvm2mpVCquX7/Oxx9/jLOzc4E+ujXnD3HmzJn88ccfBAYGmt1JP9hL6t+/P99++y0zZsx46LW8yMrKwtrami5dunDnzp2H6jJl165djB07lnHjxjF06FC+++47MjIyePvtt3O1vjkqlYr09HRGjx5NWloaycnJTJo0yeQwxqMHHRcuXGDhwoU0btyYtm3bEhsby5UrV56pppzPyRETE8OSJUuoUKECNWrUMLtuSkoKO3bsQK1Wk5WVRUxMDFlZWVSsWJHt27eTkZHx1KPOGzdusGfPHnQ6HYqisH37dnbv3s2AAQNQq9X57nk9eGS+d+9exo0bx8WLF5k7dy5HjhzJVRsajYaMjAyuXr1KnTp18PHx4ddff+XChQv5DrCc7SlTpgzp6enodDqaNm2KRqNBpVIValA82LvP6Sm2a9eOW7duMX78eIYMGcL58+dRq9UYDAbjAc2RI0fIzs7ms88+Iy0tjb///tvsDj/ncyZMmEB4eDh//vknX3/9Nbdv385Vrc9lSOzdu5fLly8D0KZNG9q1a0fHjh2xtrbmv//9L2fPnjX+0nPj0SGrMWPGEBcXx7vvvsuSJUs4cOAAV69eZe/evblu89E/7MjISFq3bk2LFi2YPXs2EyZM4Ny5c7lu71E5f+A5n9O4cWOOHDlCbGws8OSQfPBLERYWRnBwMCtWrGDevHmcOnWKDRs2PPY+c9v2+++/s2bNGpKTk3F1dSUiIgJ4etgsWLCA//73vwwdOpQvvviCl19+mbNnz2JlZcWxY8fQarX5CqtH675+/TouLi707duX8PBw3n//fezs7J6403nwoAMgODiYAwcOUK5cOUaOHMnu3buZN2/eM42V6/X6h7YrKyuLrKwsFEV56HkrpqSlpbFy5Ur27t1LREQES5cuZcaMGTg5OfH2229z4MABk+v+9ttvrFixgjp16gD3h5i8vLxwd3cnJCQEIF9H2BEREYwePdq43pkzZwgODqZhw4acOXOGypUrm2zz8uXL/P333wDExsbSq1cvVqxYgY+PDxUrVuSdd95h06ZNxu96Xmk0Gm7evMmff/5Jp06dmDhxIrdu3WL//v0A3L17N1/t5kZOWE+cOJF9+/bh5ORE5cqViYuLIygoiJo1axq/K2q1mpSUFHr37k1YWBhDhgwhOTmZhg0bsm3bNi5evPjEz3jw9/r333/j7u7OzJkzsbOz48iRI/z4448kJCSYr7UAtrdEOXnyJEOGDGH8+PH88ccflCpViqNHj1K3bl26dOlCUlISM2fO5Pr162bbMjVkVaVKFerWrcvAgQMZNGgQQ4cOJSEhgdTU1FzXmfNHsmrVKvbv30+lSpW4efMmS5cupWvXrri4uDzTY1xVKhWRkZEEBASwatUqqlWrxsSJE/nyyy/Jzs5+7Av/6A7KycmJO3fuGIdgpk6dSkhICEePHn3qDjpnLFun06HT6bC1tcXOzo5x48bx8ssvc+rUKeMf/5OcPXuWpUuX0qdPH2rXrs1LL73E6NGjqVWrFvHx8SQkJHD06NE8dZfh4XmknHHn0qVLk5aWxsCBA2nTpg1du3Zl+vTpj/UIHz3o+OCDD2jbti02NjbY2NgwceJEXn/9dePvNb9y1l2+fLlxx9yuXTt0Oh2RkZFPHSs3GAy4uLjQu3dvvv/+exwdHfnmm29o1KgREydOJCIiwnjQ8ehO+YcffmD9+vV069YNNzc39u3bx9GjR7lx4wbjxo3jxo0brF+/HshbT/LGjRvUrFmTChUqMHv2bABu3rzJxIkT2bJlC8uWLePixYvs3r37sZp+++03xowZw+XLl7l8+TI//vgj3t7eTJw4ka5duzJs2DBat25N6dKluXnzZq5retDFixcZO3YsP/zwA1u2bCEmJoaGDRuyc+dOfHx8Cm0YESA7O5uvvvoKZ2dn2rRpwyuvvML7779v/P+7fPky7du3N/5ejh49Stu2bRkzZgzp6encvn0bb29vypcv/1hIPDpcfOvWLerUqUPPnj357rvvqFq1KqtWreLAgQO5CtjnLiQaNGjAt99+i1qt5pdffuHGjRtcv36do0eP0rp1a+rVq4elpSW2trZm23rw6FGj0RiHrOzt7Y2vW1paMmHCBGxtbRk9erTZNm/dumX8T5w2bRpRUVHUq1cPDw8PgoKCCAgI4OLFi+zdu/eZnn9x6tQp5syZQ9++fUlJSWHy5Ml8+OGHuLm5MXjwYGP9gLE7m56ezrfffsvu3buxtLRk2rRpbN68mXPnzuHm5sbcuXOpVq3aEz8vZ5s0Gg2XLl3Cz8+P5cuXc+DAAby9vXnnnXc4efIkUVFRTz1Ce+211xg/fjzr1q0zHuVYWlrSs2dPMjMzuXv3LrGxsXkaYniwa79x40ZGjhzJV199hV6vp3z58rz++uskJyfj7+9P3bp1HxrnfdJBx5EjR/Dw8KB58+YcP36ciIgIFixYwEcffWT828ittLS0h3qMkydP5vz587i6urJo0SIcHByoUaMG//vf/546nKlWq9m6dSv//e9/cXd3Z8SIEVy7do22bdsSGBhIx44d2bJlC9euXXtoR5+amsrp06f56quvqF27NnFxcVSpUgWA6OhotFotvr6+HDlyhKSkpFz1JBITE5k+fTobNmwgKSmJTz75hLt377J9+3aGDx/OyZMn+fDDD9mxYwehoaE0aNDgoZr+97//8dNPPzFjxgw6dOhA1apVcXFxIT4+HgBfX1+qVq1KQkICn3/+Oc2bN8/V7/rB2g8ePMjs2bPx8/Nj/vz5uLu7Exsbi7u7Oz4+PvTp04fWrVvnqt28fjaAhYUFGRkZlCtXjlmzZjF37lxWrFhhPIBasWIFTk5OxjkXe3t7tmzZwscff8zo0aP5z3/+w9KlSxkwYAAdO3Z8qO1Hh4unTp1qHC6+efMm1tbWBAcH89FHH9GoUSOztZeIhw4VtLZt25KSksKhQ4eoVKkSr7/+OgDlypVj8ODBODs7Y2Hx9E3fu3cvbm5uVK1alTZt2uDs7Iy9vT2rV6/m999/p2HDhtSpUwcbGxv8/PzMziMkJiayZs0a3n//feD+ziExMRFvb292795NVFQUb7/9Nq6urvz6668sXLiQ8uXL53qbL126ZNyBJyYmYmNjQ6tWrbh9+zYnT55k9OjRpKSkMGHCBOMRUlJSEo6OjqjVauMRdYMGDbh16xZHjhyhffv2tG3blm+++YZFixbxxhtvPPGzd+7cSWxsLBUqVKBt27bMmDGDwYMHo1ar+fbbb7l48SKdOnUC4NVXX8XT0/Op29KpUyfi4+P57rvvmDx5MiqVCmtra9566y2GDh360EScOQaDwfje/fv3c+vWLVatWsWqVavYsmUL7dq1Iy0tjTNnzjBixIjHTmrIOejYsGEDv/zyC927d+f69escO3aMjz76iJkzZ3Lx4kWCgoKoUKFCrmrKoSgKf//9N/b29sY6k5OTmT59OhYWFqhUKpYvX853332HtbW1cSjoSduWkpLCsWPH+OKLL6hevTqLFy9m5MiRrFy5ksqVK1O5cmVu3LjxWG/E1tYWd3d3FixYQP369Vm2bBlt27Zl69at9OjRA61WS5s2bZg+fTrW1tZmtyc5OZkRI0aQnp5O+fLlOXjwIK1atWLAgAHMmjWLihUrsnTpUs6cOcPNmzdZvHjxY72vO3fu0L17d6pWrYpOp8PKyor69euzf/9+fvjhB5ydnYmKinqs9/s0er3+oc959dVXSUpKMs6RtWjRgh07dtC5c2fq1atHvXr1HvsdP4ucOsPCwoiLi8PV1ZURI0Zw6NAhOnToQFJSEqtXr6ZXr17GHfeKFSu4ePEi5cqV47333qNhw4b8888/lC9fnrFjx/Lhhx8a23+w95DzOYsXL2bFihU4Ozvj4+PDnj178PPz4+eff6Z58+YPrf80z11PAu4feXbs2BFXV1f27NnDpEmT+OCDD1Cr1ZQvX95sQORmyGrGjBlcu3YNa2vrpwZEzpDV559/zsGDB9HpdOzevZvY2FgaN27MsWPHsLe3x9PTk02bNuHh4UFwcHCeAgLuz2lMmTKFGTNmsG7dOu7du8fx48dZtWoVs2fP5urVqwQFBZGUlET9+vWNE8Nff/0148ePJy0tjVq1ajFixAi8vb2pU6cOMTExtGvXjkGDBpk8Ql64cCFbt26lYsWKjB07loMHD+Lp6cnNmzcJCQkhKCiI0qVLG087btGihdlt0Wg09OnTB2tra1asWEFaWhqzZ8/GxsYGlUqVqy/to/NIAQEBrFy5ktOnT2Nra8tnn31GUlIS27Zto06dOvTo0cPkWW9t27alffv2GAwG40GHoii4uLgwevRovvvuuzwHBNz/Qjdo0IBXX32V7777jtTUVFxdXQkKCjJ+rqWlJenp6bRs2fKhIcJHd1729vY4OjqyY8cOAAYNGsTdu3dZu3YtANeuXePkyZOP7ZBVKhUdOnTgrbfeQq/XExISwrhx4+jbty/Z2dk4OTnh6upqNiBy2nJ0dMTDwwNbW1tSUlI4fPgwx48f56WXXuKTTz5h3rx5ZGVl4e3tTUBAwBOH51QqFYcOHTIGBNwPa09PT/755x/+/PNPZs+ejYuLi9mQyM7OJiMjA41Gg8FgICgoiC1btqDX6xk/fjybN29m+/btbN26Fa1W+9i+4VkD4sEexNatW1m9ejWVK1dmyZIlREZG4u3tTUZGBiEhIbRu3dq4vWvWrOHixYuMGzeOf/75h927d+Pv74+npydbt27F39+fzp07G9t+9DqwJw0XT5o0ifT0dAIDA3MdEPCcP5kuPj6eSZMm0bhxY3r37v3QuLQ527dvZ8OGDZQtW5bu3bsza9YsPvroI1q2bMmCBQuIjY1l5syZlC1bNlftzZ07l2PHjhknRn19fWnYsCGOjo7cvXuXSZMm4eHhwYABA/K0jTlHSIqi0LNnT1JTU9m2bRsAy5YtIz09Ha1WaxwLfumll1iwYAGXLl2if//+uLi4MGvWLJKTk1GpVHz99de4uLiwZs0a4uPj+eKLL0x+9tmzZ+nZsycrVqygYcOGLF68GGdnZyIiIoiPj2fw4MFkZWUxf/58JkyYQM2aNfO0bfHx8YwbN46//vqLUaNG0a1bN7Pr5BzNDh48GL1eT0BAAHv37jVOUI8YMYJGjRrRs2dPEhMT2bZtG3369DHbbmpqqjEMPv74Y1JTU7Gzs8vT9uTIzs5+aGeUszO3sLDAx8eHoKAgrKysOHfuHD4+PvTo0eOJ7cTFxfHNN99QvXp1GjVqxEsvvURoaCiOjo5kZmaiVqsZOnQopUqVIjMzE71e/9Rh1pzguXPnDmPGjMHPz4+mTZvmapse7HmfO3eOEydOUKpUKdavX4+bmxvt2rWjYcOGhIWFUbp06acOEV27do0tW7ZQt25dvLy8jLXNnz8fT09P6tatm6ud98WLF1m1ahWtW7fG3d2dr7/+mnr16lGhQgW2b9/O9OnTOXv2LAsWLKBt27b06dMHS0vLPO0nniY+Pp5y5coZf164cCFvvfUWsbGx/Prrr0yYMAFLS0v+/PNPSpUqRbNmzYzv3bZtG2q1mgsXLpCWlsaoUaO4desWVapUMf4/5dSZ83N6ejohISHUrVuXSpUqkZqayurVqxk8eDC1atUiIiKCKlWq4OzsnKfteK5DAu7/oWRmZuLh4ZGn9bKysti0aROHDh1izJgxLF26lNq1a/PRRx9x+/btPA9Z5XxxbG1tWb9+PVWqVKFt27ZUq1aNnTt34uTkhLe3d7628cKFC5w4cYK6desyb9482rZtS6dOnUhMTESn0xEdHc1bb70F3N+x+/r68sMPP1C/fn3jts6cOZM9e/bQtWtXkpOTiYqK4vPPP6dhw4ZP/ezNmzdz6tQpmjZtyuTJk+nQoQPnz5+nTJkyuLi4oNVq8ff3p1KlSvnatvPnz5OQkJDrnVWO4OBgjh07ho2NDbdu3cLX15eePXty7do1Jk2aRIcOHYxDYLn1LAcdT7Jp0yZjr/f27dv89NNPuLi40KNHD6KiojAYDLz22msAj33WjRs3+OKLLxg4cCCXLl0iKiqKjh07UrduXX755Reys7ONBxyPhpIpaWlpnD59mpkzZ9K1a1d69uyZq+04efIkPXr0oGHDhnz66adUq1aN2bNn88UXXxAWFsaePXuoWLEi3t7e1K9fP1c7+B07dhhPb+3cuTOzZ8+mUqVKjBkzJlc17d+/n++//54ePXrQsmVL4uLiOHv2LC1atGDkyJHY2NiQnZ3NzJkz2bRpExcvXuSbb77JVdu5kZCQwPLly2nZsiV//PEHb7/9NtHR0Rw+fBg7Oztmz57Nhg0buHLlCkOGDMHW1pYrV65w9uxZ2rVrx+7du/npp5+oWLEiQUFBTJ06FUdHR4YMGWL8jJzhYuCh4eKXXnqJmJgY2rdvT3x8PKGhoSxatCjP82U5nvuQeBbPcvRo7ouzd+9eKlSogI+PD25ubsbrN/Lqjz/+YMWKFQwfPpw333yTq1evMnbsWLp06cLx48fx8/OjevXqD62zefNmjh49SmBgoLEndOnSJb788kuGDBlCSkoKdevWzdUQSs4QxZo1a1i+fDl169Zl/fr1nD17lrJlyzJ48GBjF7qwPRrKf/75J6VLl+aHH37A3t6eUaNGUbduXSIiIkhLS8vV0Nej8nvQkZ6eTlBQEM2bN6ddu3Z89dVXAFhZWREXF8d3331HTEwMmzZtok6dOsahhJyvZ3h4ODdv3sTHxwe4H1hJSUlYW1szduxYKlasiJWVFR9++OFDc0d5GVM3GAxERESgKAqNGzfO0/Y9rec9d+5cdDodX3/9tXGnlptabty4wYoVK4wHHKZ6VE8yZ84c3nnnHerXr2/sbet0OsaOHUuHDh2oXr0606ZNo2/fvjRu3Jhhw4bRtWtXs/Nl5jwY5hs3bmTKlCn06NGDwMBAIiIiCA0NpXHjxly6dIk7d+5QtmxZrl27RkBAAJMnT8bS0pLy5cvj7+/Pxo0bsbW15eTJk1SvXp3hw4cbP2fXrl1s27YNZ2dnFEVh+PDhLFmyhK+++oq0tDTjsGO3bt04dOiQ8fqi/G6UeIrbt28rQ4YMUVavXq0oiqIYDIZcr7tt2zalV69eyrBhw5SDBw8qnTt3VkJDQ5Vbt24pgYGByogRI5TExMQ81aPX6x/6d1BQkPLbb78pp0+fVhYvXqyEh4crMTExyoIFC5Tff//9iW1kZ2crixYtUsaNG2fcnqtXryozZszIUy050tLSlIkTJyrLly83LktKSspXW/l14sQJxd3dXenZs6eyd+9eJSYmRhk6dKhy+fJlZc2aNUrHjh2V3r17K1evXi3SunL8/fffSrNmzZRevXopCxYsUPbt26coiqJMnjxZefvtt5WJEycq2dnZyl9//aVMmzbtsTr/+usvpX///srBgwcVRbn/f5iZmalMnjxZOXXqlHL06FFlzJgxyoULF4zr5OVv9VnpdDpl3bp1ir+/v3Lt2jXl66+/VkJDQxW9Xq/cvHlTycrKKrJasrKylN69eysHDhxQFOX+9yQ7O1tRFEUJCgpSxo8fr3z66afKX3/9ZVwn5/Vn8eDvOz09Xbly5Yoyb948ZfTo0YpOp1MU5f7f6cmTJ5VVq1YZ3zt+/HilW7duSlRUlKIoivLll18qa9asUe7evaukpKQ8VKder1dCQkKUL774Qvn777+VO3fuKIGBgcrgwYOVIUOGKHFxcYqiKMqPP/6ozJkz55m3SVEU5bmcuC5I5cqVY/jw4TRo0ADI23niT5rwfLDNGTNm5PrICh6+n87du3dRq9VUqFCBGzdu8PPPP9OsWTNmzpyJvb09gwYNMnkKn6mJ4TJlyuS6lgeVKlWKwYMHc+rUKX744QeAfPeM8is3pz5bWVnlex4hv9LT01EUhdq1azNo0CDeeOMNIiMjcXZ2ZuXKlTRo0IBt27axZ88e/v77bypWrEh0dPRj96eqW7cuvXv3ZvXq1URHR6PRaLCwsKBMmTKsWLGCJUuWMGDAgIeuzC7IG1qa86wnixQkCwsLOnbsSHR0NElJSajVauMEuYuLC2XKlDH2KqHgz2DasGEDAQEBbNmyhaFDh1KhQgVmzJjBX3/9xZEjR3j99deN82C//fYbX375JRqNhj/++APAOI+2d+9e7OzsjHUqikJUVNRj1xFNnjyZV155hbNnz7JhwwamTp3K7t2789VTfqICiRphUkpKihIUFKSsXLnS+HNeJSQkGI8ytFqtMnDgQGXUqFHKwoULje/JyspS1qxZowwfPlxJTU3NVbu3b99WBgwYoDRu3FjZuHFjnut61IULF5TTp08/czv5VZKOZhVFUdatW6d07txZ2bBhg6IoirJ161Zly5Ytyv/+9z9l4MCBSs+ePZX169crn3/+ubJ161ZFURQlMzNT2b17t8k2N23apAwdOtTYoxw0aJAycOBA5d69e4qiFG3v4UmepeddkC5duqTMmTNH2bhxo/H7MGXKFGXChAnGo/oHe+UFZd26dcqYMWOU8+fPK/PmzVPGjx+vKIqihISEKCNHjlQmTpyo9OnTR1EURUlMTFQCAgIURVGUa9euKZ07d1b++OMPRVEU5cqVK0p8fPwTP2PTpk3K6NGjlbt37xqXRUdHKx999JESFham7NixQ7l27VqBbZPMSRSB/E54Kg+cqWMwGBg3bhy7d++mYsWKtGzZktatW+Pv74+fnx9r167l/PnzTJgwIU9X/eZ3YrikKsizkJ7ViRMnmD59OikpKQwYMABbW1vWrFnDhAkTiIqKIjg4GE9PT5o3b24cCzd3VKvX61mxYgXnzp3D0tLSeEV6zmvPcsV3QcnvvE1Bi4yM5LfffiMqKgpHR0fKlSvHuHHjCu3zrl+/zuLFizEYDEydOhW4f78kKysrvvzySzIzM7GxseHzzz+nUqVK9OnTh3nz5jF58mQA43Uu69evN84HPunvQa/Xs3TpUm7cuGG8jujatWuEhoYSEBBQ4NslIVFEnuWLExwczNGjR3FycqJXr17Uq1ePIUOG8Oabb7Jp0yYmTZpE7dq183xq2/OqoM9CehYREREsX74cd3d3atasyYIFC5g4cSI1a9Zk06ZNtGvXjldeeQV4/AwmU9LS0ggICMDd3Z2hQ4cCJScgSqJr166RmZlpPIGjoH9X6enprF+/3ng3hy1btvD666/TqVMnUlJSWLFiBYMHDzYOuWVlZdG/f38sLS0pV64cjRo1oly5cjg5OaFSqYxntD1NWloac+bM4eWXX6ZHjx58+eWX1KlTJ8+n0OeGhEQJ9eiZOidPniQ7O5uzZ8/SqFEj7OzsjBe6eXt706pVq+IuuUQpKUeziqLw66+/8vvvvzNz5kwOHDhAjRo1jDe2y7nbaF5DLDMz03iBW0GNqb8ICut3NXfuXGxtbfH09CQpKYmdO3fSrFkzWrVqZfy/3bx5M0lJSbz11ltUqFCBTz75hOrVq9OuXTuuX7+OlZUVXbt2zXWd+bmOKD8kJEogU6fP5txtdOvWrdja2mJhYcGoUaNydf8VUXz0ej3ff/89cXFxTJw4sUDbLs5e0ovowd/3sWPHOHnyJAMHDiQ9PZ2FCxdiZ2dHmzZtuHjxIqVLlzYO427YsIHw8HCaNm3KmTNn6NKlC2XLluXTTz9l5cqVuLm55aueohgulsOPEsjUmTpHjhwxngf/5ptvsnDhQgmIfwGNRmO8zUVun5+QWxIQRefRe0VVr16dQ4cOsX37dkqVKoWfnx+7du0iMjKSpk2bGnfchw4dYteuXXTv3h1fX186duzI5s2bsbCwICgoCEtLy3zX5O7uXujzic/lDf6eB0+6SaGiKJQrV44hQ4ZQrly5Ij2tUDwbW1tbxo8fn6t7IImSKWceY+LEiTg5OVGtWjW+/fZbRo4ciaOjI1evXqVWrVq88cYblC5dmpSUFPR6PTVr1qR58+acOHGC6tWr07RpU27cuGF8oBOU7CFDGW4qwUrSmTqi4MgQ0b9HXFwcWq2W6tWro9frmTJlCjY2Nnh7ezNixAhGjRqFs7Mze/bsISIigm+//ZZy5cpx5MgRVqxYgYWFBe3bt8fW1pabN2+SlZVFt27dsLW1LbGh8Kh/R5UvKDs7OwYOHEhERAQ//vgjdnZ2RfLsXVG4JCD+HQ4fPszQoUM5ffo0165dQ6/Xk5iYSKdOnahatSrTp09nyZIluLm58dlnn+Hh4WG8Bcm6desYMGAAw4YNIyYmBoPBQK1atUhISCA2NtYYEP+G77OMV5RwOVdnZ2ZmArKDEaIoHD58mHnz5jF27FgaNGhg7P395z//ISwsjIoVK1K6dGlcXV2B+0NR/fv3Jzg4mG+//RYbGxvq16+PhYUFsbGx7Nu3j6CgINzc3B66c/S/4fssPYl/gRo1ahT7qZxCvEgSEhLw9fWlQYMGZGdnG3fm9erVQ61WM2LECAICAmjRooVxnsnR0RE/Pz8URWH//v1MmDABuH+RncFgICMjI9ePFihJpCchhBCPSEtL488//6RNmzZYWlo+9AAkb29vmjVrhp2d3WMPHHvllVfw9vYmKSmJw4cPExISwp9//smcOXOwsbEpjk15ZtKTEEKIRzRo0IBXXnmFQ4cOARgDYvfu3Vy7do1atWqZfCJlzZo18fb2xt3dndatW7Ny5UqcnJzQ6/VFVn9Bkp6EEEI8olKlSjg7O3Py5EmioqLw9vZm+vTpWFlZmR36ValUNGrUiGvXrrF3715q1aqFwWD41942RXoSQogXlsFgeOJyKysrOnbsSPv27YmOjmbZsmVUrVo110+vU6vV2NrakpiYaPz530qukxBCvPB++OEHsrOzKVWqFL6+vs/cnqIobN26FRcXl2d7KlwJ8O+NNyGEKACLFi3i/PnzvPvuu2zZsoVt27YZX8vvMbRKpaJTp07/+oAACQkhxAsmOzv7oZ8NBgM+Pj5s376devXq8d577xEfHw8823UM/4ZrIHJDQkII8UK4ceMGcP/xpnFxcZw9exa4/+jVWbNmkZqayvjx45k3bx579+4tzlJLFJmTEEI81xRFwWAw0KNHD7p160bTpk0ZPXo0er2eTz75hIoVK7JhwwZq1qzJ8ePHqVKlCl988UVxl11iSEgIIZ5rWq2W0qVL888//zB69GgqV67MyJEjURSFoKAgBg4ciIuLC1qtlri4OOOjZMV9MtwkhHhuxcXF8ccff3D79m2ys7MZOnQop06dIikpiVdffRUfHx/mzJlDcnIytWrVkoB4AgkJIcRzy8nJiXv37tG7d28WLlyIp6cn/fr1Y8qUKWRmZuLl5YWPjw8uLi7FXWqJJSEhhHhuWVlZ4ezsTIUKFahTpw4AvXr1om7dunzyyScAvP/++7z00kvFWWaJJnMSQojnXmxsLOvXr6dKlSp07NiRhIQEwsLC6NatW3GXVuJJT0II8dx7+eWX8fT0JCIignbt2hEdHS0BkUvSkxBCvDDu3LnDrVu35PkseSAhIYQQwiQZbhJCCGGShIQQQgiTJCSEEEKYJCEhhBDCJAkJIYQQJskzroXIhy5duqDX67lz5w4WFhaUKVOGixcvMmfOHNq2bcvPP/+Ml5cXZcuWBeDtt9/m4MGDxVy1EHknISFEPvz8888AzJ8/H2dnZ3r06PHQ61u2bKFevXrGkBDi30pCQogCkhMYzs7OnDlzBn9/f5ycnFi3bt1D71u0aBF79uxBp9PRu3dvPvroo2KqWAjzJCSEKGDvvvsuHh4eTJw4kWrVqj30Wnh4OAkJCWzevBmdToevry9eXl5yF1JRYklICFGEDh06xL59+zh27Bhw/4E4V69elZAQJZaEhBBFSFEUhg8fzgcffFDcpQiRK3IKrBCFwM7OjtTU1MeWv/XWW2zatInMzEwALl++bPy3ECWR9CSEKASdO3cmICCAsmXLPjRx7enpyYULF+jWrRuKolC2bFkWL15cjJUK8XRyF1ghhBAmyXCTEEIIkyQkhBBCmCQhIYQQwiQJCSGEECZJSAghhDBJQkIIIYRJEhJCCCFM+n/L5+Gy8xl7zgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see Titles\n",
    "ax = sns.countplot(data=data, x='Title')\n",
    "ax.set_title('Titles', fontsize=15)\n",
    "ax.set_ylabel('Number of Passengers')\n",
    "sns.despine(offset=3, trim=True)\n",
    "ax = plt.setp(ax.get_xticklabels(), rotation=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "37285eec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:37.616523Z",
     "iopub.status.busy": "2022-12-21T11:36:37.615146Z",
     "iopub.status.idle": "2022-12-21T11:36:38.215354Z",
     "shell.execute_reply": "2022-12-21T11:36:38.214174Z"
    },
    "papermill": {
     "duration": 0.633546,
     "end_time": "2022-12-21T11:36:38.217928",
     "exception": false,
     "start_time": "2022-12-21T11:36:37.584382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(data=data[data['source'] == 'train'], x='Title', y='Survived')\n",
    "ax.set_title('Title Vs Survived', fontsize=15)\n",
    "ax.set_ylabel('Survival Probability')\n",
    "sns.despine(offset=3, trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5690cd",
   "metadata": {
    "papermill": {
     "duration": 0.03761,
     "end_time": "2022-12-21T11:36:38.286478",
     "exception": false,
     "start_time": "2022-12-21T11:36:38.248868",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* total **17 titles**, but they can be **reduced**, lets convert them into **title categories**.\n",
    "* It looks like the **rare titles** have more chances of **surviving**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "55b55178",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:38.357048Z",
     "iopub.status.busy": "2022-12-21T11:36:38.356240Z",
     "iopub.status.idle": "2022-12-21T11:36:38.367214Z",
     "shell.execute_reply": "2022-12-21T11:36:38.366228Z"
    },
    "papermill": {
     "duration": 0.045821,
     "end_time": "2022-12-21T11:36:38.369839",
     "exception": false,
     "start_time": "2022-12-21T11:36:38.324018",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert Title into categries\n",
    "data['Title'] = data['Title'].replace(['Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
    "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'the Countess', 'Jonkheer',\n",
    "       'Dona'], 'others')\n",
    "data['Title'] = data['Title'].map({'Mr':0, 'Mrs':1, 'Miss':1, 'Master':2, 'others':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ebd9da89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:38.430877Z",
     "iopub.status.busy": "2022-12-21T11:36:38.430481Z",
     "iopub.status.idle": "2022-12-21T11:36:38.662635Z",
     "shell.execute_reply": "2022-12-21T11:36:38.661396Z"
    },
    "papermill": {
     "duration": 0.266235,
     "end_time": "2022-12-21T11:36:38.665634",
     "exception": false,
     "start_time": "2022-12-21T11:36:38.399399",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=data, x='Title')\n",
    "ax.set_ylabel('Number of Passengers')\n",
    "ax.set_title('Title Vs Survived', fontsize=15)\n",
    "ax = ax.set_xticklabels(['Mr', 'Mrs-Miss', 'Master', 'others'])\n",
    "sns.despine(offset=3, trim=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f2fe21fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:38.729036Z",
     "iopub.status.busy": "2022-12-21T11:36:38.728541Z",
     "iopub.status.idle": "2022-12-21T11:36:39.262567Z",
     "shell.execute_reply": "2022-12-21T11:36:39.261316Z"
    },
    "papermill": {
     "duration": 0.568021,
     "end_time": "2022-12-21T11:36:39.265306",
     "exception": false,
     "start_time": "2022-12-21T11:36:38.697285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop Name columns we don't need that now\n",
    "ax = sns.barplot(data=data[data['source'] == 'train'], x='Title', y='Survived')\n",
    "ax.set_title('Title Vs Survived', fontsize=15)\n",
    "ax.set_ylabel('Survival Probability')\n",
    "ax = ax.set_xticklabels(['Mr', 'Mrs-Miss', 'Master', 'others'])\n",
    "sns.despine(offset=3, trim=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aefcfe8",
   "metadata": {
    "papermill": {
     "duration": 0.029893,
     "end_time": "2022-12-21T11:36:39.326010",
     "exception": false,
     "start_time": "2022-12-21T11:36:39.296117",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* Women and Children First"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "08e17a60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:39.388884Z",
     "iopub.status.busy": "2022-12-21T11:36:39.388118Z",
     "iopub.status.idle": "2022-12-21T11:36:39.394339Z",
     "shell.execute_reply": "2022-12-21T11:36:39.393129Z"
    },
    "papermill": {
     "duration": 0.041016,
     "end_time": "2022-12-21T11:36:39.397264",
     "exception": false,
     "start_time": "2022-12-21T11:36:39.356248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lets create new Feature called family size. +1 for passenger himself\n",
    "data['Fsize'] = data['SibSp'] + data['Parch'] + 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "062d888f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:39.460424Z",
     "iopub.status.busy": "2022-12-21T11:36:39.459956Z",
     "iopub.status.idle": "2022-12-21T11:36:39.956972Z",
     "shell.execute_reply": "2022-12-21T11:36:39.956073Z"
    },
    "papermill": {
     "duration": 0.531231,
     "end_time": "2022-12-21T11:36:39.959265",
     "exception": false,
     "start_time": "2022-12-21T11:36:39.428034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.pointplot(x='Fsize', y='Survived', data=data[data['source'] == 'train'])\n",
    "ax.set_title('Fsize Vs Survived', fontsize=15)\n",
    "ax.set_ylabel(\"Survival Probability\")\n",
    "sns.despine(offset=3, trim=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f709fcd",
   "metadata": {
    "papermill": {
     "duration": 0.029601,
     "end_time": "2022-12-21T11:36:40.018946",
     "exception": false,
     "start_time": "2022-12-21T11:36:39.989345",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* Family size seems to be **important**, larger family size has **worst chance of surviving**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fdbe6094",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:40.081397Z",
     "iopub.status.busy": "2022-12-21T11:36:40.080652Z",
     "iopub.status.idle": "2022-12-21T11:36:40.088885Z",
     "shell.execute_reply": "2022-12-21T11:36:40.087947Z"
    },
    "papermill": {
     "duration": 0.04226,
     "end_time": "2022-12-21T11:36:40.091205",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.048945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Lets Create Family Category\n",
    "def f_size(i):\n",
    "    if i ==1:\n",
    "        return 'Single'\n",
    "    if i ==2:\n",
    "        return 'SmallF'\n",
    "    if i==3 or i == 4:\n",
    "        return 'MedF'\n",
    "    if i>4:\n",
    "        return 'LargeF'\n",
    "    else:\n",
    "        return 'nan'\n",
    "data['Fsize'] = data['Fsize'].apply(lambda i:f_size(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a9cf5f1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:40.153287Z",
     "iopub.status.busy": "2022-12-21T11:36:40.152630Z",
     "iopub.status.idle": "2022-12-21T11:36:40.483306Z",
     "shell.execute_reply": "2022-12-21T11:36:40.482182Z"
    },
    "papermill": {
     "duration": 0.364378,
     "end_time": "2022-12-21T11:36:40.485553",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.121175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='Fsize',y=\"Survived\",data=data[data['source'] == 'train'])\n",
    "ax.set_title('Fsize Vs Survived', fontsize=15)\n",
    "ax.set_ylabel(\"Survival Probability\")\n",
    "sns.despine(offset=3, trim=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32c589e",
   "metadata": {
    "papermill": {
     "duration": 0.031755,
     "end_time": "2022-12-21T11:36:40.547102",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.515347",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* It looks like **Med and Small Families** have more chances of **surviving**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "17063004",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:40.609382Z",
     "iopub.status.busy": "2022-12-21T11:36:40.608987Z",
     "iopub.status.idle": "2022-12-21T11:36:40.617359Z",
     "shell.execute_reply": "2022-12-21T11:36:40.616307Z"
    },
    "papermill": {
     "duration": 0.041894,
     "end_time": "2022-12-21T11:36:40.619575",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.577681",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lets Deal with Ticket\n",
    "data['Ticket'] = data.Ticket.apply(lambda i:i.replace('/', '',).replace('.', '').strip().split(' ')[0])\n",
    "data['Ticket'] = data.Ticket.apply(lambda i:'X' if i.isdigit() else i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5df76284",
   "metadata": {
    "papermill": {
     "duration": 0.028797,
     "end_time": "2022-12-21T11:36:40.677836",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.649039",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* ### **Variable transformation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ed391eef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:40.739417Z",
     "iopub.status.busy": "2022-12-21T11:36:40.738995Z",
     "iopub.status.idle": "2022-12-21T11:36:40.752084Z",
     "shell.execute_reply": "2022-12-21T11:36:40.751347Z"
    },
    "papermill": {
     "duration": 0.047262,
     "end_time": "2022-12-21T11:36:40.754643",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.707381",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Label Encoding \n",
    "data=pd.get_dummies(data=data, columns=['Pclass', 'Sex','Ticket', 'Embarked','Title', 'Fsize'],\n",
    "                     prefix=['Pc', 'Sex','Tkt', 'Em','T', 'Fm'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6aed0b7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:40.816189Z",
     "iopub.status.busy": "2022-12-21T11:36:40.815038Z",
     "iopub.status.idle": "2022-12-21T11:36:40.843420Z",
     "shell.execute_reply": "2022-12-21T11:36:40.842254Z"
    },
    "papermill": {
     "duration": 0.061613,
     "end_time": "2022-12-21T11:36:40.845691",
     "exception": false,
     "start_time": "2022-12-21T11:36:40.784078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Pc_1</th>\n",
       "      <th>Pc_2</th>\n",
       "      <th>Pc_3</th>\n",
       "      <th>Sex_0</th>\n",
       "      <th>Sex_1</th>\n",
       "      <th>Tkt_A</th>\n",
       "      <th>Tkt_A4</th>\n",
       "      <th>...</th>\n",
       "      <th>Em_Q</th>\n",
       "      <th>Em_S</th>\n",
       "      <th>T_0.0</th>\n",
       "      <th>T_1.0</th>\n",
       "      <th>T_2.0</th>\n",
       "      <th>T_3.0</th>\n",
       "      <th>Fm_LargeF</th>\n",
       "      <th>Fm_MedF</th>\n",
       "      <th>Fm_Single</th>\n",
       "      <th>Fm_SmallF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.266662</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.070022</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.972177</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2.085672</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age      Fare  Pc_1  Pc_2  Pc_3  Sex_0  Sex_1  Tkt_A  Tkt_A4  \\\n",
       "0       0.0  22.0  1.981001     0     0     1      1      0      0       0   \n",
       "1       1.0  38.0  4.266662     1     0     0      0      1      0       0   \n",
       "2       1.0  26.0  2.070022     0     0     1      0      1      0       0   \n",
       "3       1.0  35.0  3.972177     1     0     0      0      1      0       0   \n",
       "4       0.0  35.0  2.085672     0     0     1      1      0      0       0   \n",
       "\n",
       "   ...  Em_Q  Em_S  T_0.0  T_1.0  T_2.0  T_3.0  Fm_LargeF  Fm_MedF  Fm_Single  \\\n",
       "0  ...     0     1      1      0      0      0          0        0          0   \n",
       "1  ...     0     0      0      1      0      0          0        0          0   \n",
       "2  ...     0     1      0      1      0      0          0        0          1   \n",
       "3  ...     0     1      0      1      0      0          0        0          0   \n",
       "4  ...     0     1      1      0      0      0          0        0          1   \n",
       "\n",
       "   Fm_SmallF  \n",
       "0          1  \n",
       "1          1  \n",
       "2          0  \n",
       "3          1  \n",
       "4          0  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unncessery Columns \n",
    "test_ids = data[data['source'] == 'test']['PassengerId']\n",
    "final_data = data.drop(['PassengerId', 'Name','SibSp','Parch'], axis=1)\n",
    "\n",
    "# Separate Train and test data\n",
    "final_train = final_data[final_data['source'] == 'train']\n",
    "final_test = final_data[final_data['source'] == 'test']\n",
    "final_train = final_train.drop('source', axis=1)\n",
    "final_test = final_test.drop(['source','Survived'], axis=1)\n",
    "final_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b592789e",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# *Machine Learning Models*\n",
    "![Explantion](https://en.proft.me/media/science/ml_types2.png)\n",
    "Let's divide our dataset into two variables.\n",
    "* **X** as the features we defined earlier. \n",
    "* **y** as the **Survived** the target value we want to predict.\n",
    "### Assumptions:\n",
    "* This is a **Classification** problem.\n",
    "* Train test **split 8:2** ratio respectively.\n",
    "\n",
    "### Classification Models\n",
    "* SVC\n",
    "* Decision Tree\n",
    "* AdaBoost\n",
    "* Random Forest\n",
    "* Naïve Bayes\n",
    "* Extra Trees\n",
    "* Gradient Boosting\n",
    "* Multiple layer perceprton (neural network)\n",
    "* KNN\n",
    "* Logistic regression\n",
    "* Linear Discriminant Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e3db07a5",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (668, 55)\n",
      "y_train shape: (668,)\n",
      "X_test shape: (223, 55)\n"
     ]
    }
   ],
   "source": [
    "## Spliting and Scaling\n",
    "features = final_train.columns[1:]\n",
    "X = StandardScaler().fit_transform(final_train.drop('Survived', axis =1))\n",
    "y = final_train['Survived'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 20)\n",
    "\n",
    "print('X_train shape: {}'.format(X_train.shape))\n",
    "print('y_train shape: {}'.format(y_train.shape))\n",
    "print('X_test shape: {}'.format(X_test.shape))\n",
    "\n",
    "def cross_val(model, X, y, cv):\n",
    "    scores = CVS(model, X_train, y_train, cv=cv, n_jobs=4, scoring='accuracy')\n",
    "    return (scores.mean(), scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22acae91",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* ### Models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6bff7a8c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cross validate model with Kfold stratified cross val\n",
    "kfold = StratifiedKFold(n_splits=10)\n",
    "r_s = 2\n",
    "classifiers = []\n",
    "\n",
    "# Algos\n",
    "classifiers.append(KNeighborsClassifier())\n",
    "classifiers.append(LinearDiscriminantAnalysis())\n",
    "classifiers.append(SVC(random_state=r_s))\n",
    "classifiers.append(DecisionTreeClassifier(random_state=r_s))\n",
    "classifiers.append(RandomForestClassifier(random_state=r_s))\n",
    "classifiers.append(ExtraTreesClassifier(random_state=r_s))\n",
    "classifiers.append(LogisticRegression(random_state=r_s))\n",
    "classifiers.append(GradientBoostingClassifier(random_state=r_s))\n",
    "classifiers.append(AdaBoostClassifier(\n",
    "    DecisionTreeClassifier(random_state=r_s),random_state=r_s,learning_rate=0.1))\n",
    "\n",
    "\n",
    "score_results = [cross_val(model,X_train, y_train, kfold) for model in classifiers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f0df7d47",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "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>CrossValMeans</th>\n",
       "      <th>CrossValerrors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNeighboors</th>\n",
       "      <td>79.20</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearDiscriminantAnalysis</th>\n",
       "      <td>79.64</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVC</th>\n",
       "      <td>80.10</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTree</th>\n",
       "      <td>80.69</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest</th>\n",
       "      <td>80.23</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExtraTrees</th>\n",
       "      <td>79.49</td>\n",
       "      <td>3.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>80.09</td>\n",
       "      <td>2.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoosting</th>\n",
       "      <td>82.64</td>\n",
       "      <td>4.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AdaBoost</th>\n",
       "      <td>79.20</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            CrossValMeans  CrossValerrors\n",
       "Algorithm                                                \n",
       "KNeighboors                         79.20            4.77\n",
       "LinearDiscriminantAnalysis          79.64            3.11\n",
       "SVC                                 80.10            3.16\n",
       "DecisionTree                        80.69            4.40\n",
       "RandomForest                        80.23            4.18\n",
       "ExtraTrees                          79.49            3.71\n",
       "LogisticRegression                  80.09            2.85\n",
       "GradientBoosting                    82.64            4.15\n",
       "AdaBoost                            79.20            2.83"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert algo scores into Dataframe\n",
    "algo_df=pd.DataFrame({\"CrossValMeans\":[float(\"{:.2f}\".format(score[0]*100)) for score in score_results],\n",
    "                      \"CrossValerrors\":[float(\"{:.2f}\".format(score[1]*100)) for score in score_results],\n",
    "                      \"Algorithm\":[\"KNeighboors\",\"LinearDiscriminantAnalysis\",\"SVC\",\n",
    "                                  \"DecisionTree\",\"RandomForest\",\"ExtraTrees\",\n",
    "                                   \"LogisticRegression\",\"GradientBoosting\",\"AdaBoost\"]})\n",
    "# Check df\n",
    "algo_df=algo_df.set_index('Algorithm')\n",
    "algo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "08a89772",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cross validation scores')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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icU+a4EhQi1KRdz+96EZCCFFKVBoNajPTohu+BCSoRam4s2ZLWZcghHiF2fTrVdYlPDdyMZkQQghhwCSohRBCCAMmQS2EEEIYMAlqIYQQwoBJUAshhBAGTIK6FCQkJOjfEXvnzh3ef/99vvnmG1xdXUlOTgZgy5YtrFy5stAxtmzZwq+//lro+oULFxIbG/vY8i5dujxb8UIIIQyKBHUpSk5OZtiwYYwfPx5bW1tq1qzJ6tWri9W3V69eNG3atJQrLJyiKMiryoUQouzJfdSlJDU1lWHDhjFy5EjeeusttmzZQteuXYmJiWHw4MH52u7YsYN169ah0+no3bs3ffr0YeHChTg4ONCxY0dmzJjBiRMn0Gq1xMfH62fSe/bsYdu2bdy6dYslS5ZQuXJlsrKyCAoK4uLFi7z33nsMHjyYmzdvEhwcTFZWFjY2Nnz++eeYm5szf/584uPjAZgwYQKurq74+fnRsGFDTp06xbx58xg9erS+zqVLl2JhYfHidqIQ4pWUlplBWmbmM42RmZSExsL8mWuxsrLCysrqmcd5FhLUpeTs2bPUrVuX5s2b65dpNBr69u3L2rVrqVq1KvBg1h0ZGUlERAQqlYoPP/yQ9957T9/n5MmTJCYmEhUVxdWrV9mxY4d+nZ2dHePGjWPVqlXExMTg5+fHtWvX+OSTT7CxscHb25sePXqwdOlSvLy88PT0ZPny5WzcuJE333yT06dPExUVRVJSEgEBAWzatAkAd3d3JkyYwKFDh9BqtXz22WcyuxZCvDCL98QQHr312Qb5NOC51DJlyhRCQ0Ofy1glJUFdStzc3GjQoAEhISHMnj1bv7xXr154eXnRp08fAK5cucLly5cZMGAAAPfu3eP69ev69pcvX8bZ2RmAWrVqUbFiRf06JycnAKpXr865c+f0/65SpQrw4NmxCQkJXLp0CX9/fwBcXV356aefqFKlCo0bN9b3yfzbX68uLi4ANG3alKNHjzJ+/Hjs7OwYNWoUxsbGz20fCSFEQYa3f49+Lds+0xjWfTo9txl1WZOgLkUBAQFMnjyZOXPm4ODgAICJiQk9e/YkKiqKHj16UKtWLerWrcu3336LWq0mJycnXxjWrl2bmJgY4MFFanfv3tWvU6lU+n8/nPFeu3aNW7duYWNjw5kzZ6hRowb29vb8/vvveHp6cvToUezt7alTpw7bt28HICkpCTMzM/1YavWDSxdycnIYPnw4ACEhIcTHx9OyZcvS2FVCCKFnaWaOpdmzhaxN9epoyv0zTtVJUJey0NBQRo8erZ/lAvTt25dly5YBULFiRby8vPDz80OtVmNqaqpfB+Ds7IytrS3e3t44OjrmG6cg1apVY+7cuZw9exZPT08qV66Mv78/wcHBREREYG1tzZw5c7CwsMDBwQFvb28URSE4OPixsU6cOMG8efMwMjLC1NRUPwMXQgjx4qgUOflo8B7Osq9evUpQUBBRUVFlXdITabVaDo6ZVNZlCCFeYTb9er1UM2qtVivvo36ZhYWFceHCBdLT05k4cWJZlyOEEOIFkqB+CUybNq2sSxBCCFFG5IEnQgghhAGToBZCCCEMmAS1EEIIYcDkHLUoFTb9epV1CUKIV5hKoynrEp4bCWpRKl6m2yKEEMKQyaFvIYQQwoBJUAshhBAGTIJaCCGEMGByjlqUipz7t8u6BCGEQKUxwcis7N+A9SwkqEWpOLVmYFmXIIQQOPVbVdYlPDM59C2EEEIYMAlqIYQQwoBJUAshhBAGTIJaCCGEMGClFtQJCQl89NFH+s+nTp1i9erVpbKt9u3b4+fnx4ABAxg6dCgnTpwA4ObNm8ydO7fE427ZsoVff/21WG2XLVvGhQsXSrytv1u3bl2+z3l5ebRo0YLIyMgSjbdw4UJiY2OL3b40f1ZCCCGezgu76tvJyQknJ6fnPq5Op8PMzIyIiAgALl++zMcff8zatWupUqUKgYGBJRo3Ly+PXr2K/7zqoUOHlmg7BYmMjMTX11f/+dChQ3h4eLBr1y58fHye23YKU1o/KyGEEE/vhQV1fHw8O3fuZPLkyXTu3JmWLVty7NgxnJ2dCQkJITs7mylTppCYmAhAWFgYderU4ZNPPiEpKYn09HQmTZqEi4sLwcHBmJmZkZiYyLhx4/Jtp06dOnTo0IF//etfuLu7ExYWxtKlS/nqq684ePAgZmZm+Pr64unpybfffktsbCympqYMGTIEMzMzVq5ciampKQ0aNCAnJwcHBwcaNmzI2LFjqVWrFqdPn2bo0KHs3buXCxcuEBQUROvWrQkODsbX15f09HSWLVtGuXLluHDhApMnT6Zp06asWrWK/fv3k5qaire3N3379mXLli3s37+fvLw8EhISmDdvHmfOnCEhIQE/Pz/at29P//792bFjB0OGDOHLL7/k2rVrVKtWjYULF3L58mXS0tK4desWS5YsoXLlygXur4fWrVuHWq3Gx8eHjIwMfH192bBhAwEBAaSmpqJSqZgxYwZJSUn6n1VISAgXLlzAyMiIMWPG4O7u/qJ+ZYQQokD3M3O5n5lbrLYVkpIwtsgq9thWVlZYWRnWfddlch91RkYG3t7eTJw4kW7dupGWlsb27dtxcnJi1qxZnD59mgULFjBv3jxCQ0OxsLDgwoULzJw5kxUrVgBgb29PaGhogeNXq1aN69ev51u2b98+Nm7ciImJCTqdjjNnznDw4EEiIyPRaDTk5eVx+PBhbt26xaZNm9BoNCxcuFDf//bt26xbt44LFy4wZMgQ9uzZw40bN5g6dSqtW7fOt6309HRWrFjBH3/8wZIlS2jatCl9+/Zl4MCBZGdn06NHD/r06QOAiYkJc+fOZdeuXXz//fcEBwezePFi/RGC7OxsEhMTqVu3Lt26dSM6OppBgwYBYGdnx7hx41i1ahUxMTH4+fkVur8AunbtyogRI/Dx8WH37t14enpy7do1FEVh7dq1wIMjFElJSQDk5OTwxx9/sHnzZtRqNTqdrqQ/ciGEeG7W7DnPkugzxWv8qfNTjT1lypRCs6WslElQm5ub89prrwFQvXp1UlJSOHv2LMeOHWP37t36Nnl5eXz55ZecOHECIyMjkpOT9WO4uroWOv61a9d4/fXX8y0LCgpi0qRJqFQq/P39OXfuHO7u7mj+71VoD//X2dlZ/++/q1evHiYmJlStWhV7e3vMzMywtbXNV9ND9evXR6VSUb16df366Ohotm7dikql4saNG6SkpADoDzE/3A+POnDgADdu3GDw4MHodDoyMjL0Qf33vufOnXvi/gIoX748VatW5fz582zfvp0ZM2Zga2tLq1atCAwMpGLFiowZM0bf3tjYGH9/fyZMmICZmRkjR47E1ta20P0uhBAvQr/2dendsk6x2jr0mYexhU2xxza02TSUUVCrVKp8nxVFoV69emi1Wry9vYEHM8k///yTxMREIiMjOX/+PKNHj9b3UasLvg7u6tWr7N69m7Vr15Kenq5f7ubmRosWLTh8+DCLFy/mo48+YvPmzeh0unyzxYJC+tGa//5vRVGK/H4AS5cuZceOHQB07NhR36+gsf6+7KeffmLZsmXUrl0bgPHjx3Pp0qUC+z5pfz30/vvvs3jxYoyMjLC1tSU7OxsfHx98fX1ZvHgxO3bswN7eHnhwnr59+/Z06tSJH374gXXr1j12qkEIIV60cmZGlDMrXnzZVa+OcblKpVxR6SrVoD527BgDBgwAHsxymzdvXmjbPn36MHXqVKKjo1EUBQ8PD7y8vEhJSaFfv364ubkV2jczMxM/Pz/UajUmJibMmjULGxubfEE9cuRIsrOzyc7OZtSoUTg6OtK8eXO8vb0xMzNj0KBBmJubP7fv/qiWLVvi4+ODo6Mj5cuXf2LbNm3aMHToUFq0aMG5c+f0IQ3QokULfvrppwL7vfbaa0Xur7feeovPPvuMoKAgABITEwkJCUGj0aAoCnPmzOHq1asA3L9/n48//hi1Wk1OTg6TJ08uyVcXQgjxDFRKQVNC8Y+lKAo+Pj6sWbMGExOTUtmGVqtl0xhtqYwthBBPw6nfqpdiRq3Vajl9+nSB6+SBJ6+QhIQE+vXrR9euXUstpIUQQjxf8vasV0jNmjX1V5MLIYR4OciMWgghhDBgEtRCCCGEAZOgFkIIIQyYnKMWpcKp36qyLkEIIVBpXv4LZyWoRal4GW6HEEKIl4Ec+hZCCCEMmAS1EEIIYcAkqIUQQggDJueoRalIT79d1iUIIYSeRm2MqdmT37NgqCSoRanYvPGDsi5BCCH0enutL+sSSkwOfQshhBAGTIJaCCGEMGAS1EIIIYQBk6AWQgghDJhcTPYKycvLY9KkSVy8eBGAypUrc/v2bdav/99FFuHh4dStW5cePXqwatUq9uzZg0qlonz58oSGhmJra1tW5QshxCtJgvoVcvDgQUxNTYmMjAQgOTmZoUOHcu3aNapVqwbA3r17GTZsGD/88ANnzpwhIiICjUbD5cuXyc7OLsvyhRDilSRB/QoxMzPjwoULXLp0CXt7e6ytrenUqRMxMTEMHDiQo0eP4uDggKWlJT/88ANTp05Fo9EAUKdOnTKuXggh/icjI5fMzNxit09KSsLcPKtYba2srLCysippac+dBPUrpFmzZly+fJlPP/2UW7duMWbMGN577z0CAgIYOHAgO3bsoHPnzgD5ZtlCCGFoYnZeYtsPF4rdfvQ452K3nTJlCqGhoSWoqnRIUL9ivLy88PLy4vbt23h7exMTE4OpqSmXL1/mX//6F0FBQQBUq1aNpKQkmUkLIQzSe572tG1ds9jtO3f7GnNzm2K1NaTZNEhQv1Ju3LiBhYUFlpaWWFlZYWxsDEDnzp2ZOnUqLi4umJqaAtC9e3e++eYbZsyYgUaj4erVqwDUqlWrzOoXQoiHzM2NMDcvfoRVr14dC4uX8/W7EtSvkGvXrjFz5kw0Gg25ubn4+/tjZGSEp6cnYWFhLF68WN+2e/fu3Llzhw8//BC1Wo2VlRVTp04tw+qFEOLVpFIURSnrIsQ/i1ar5bOJ9mVdhhBC6PX2Wm/QM2qtVsvp06cLXCcPPBFCCCEMmAS1EEIIYcAkqIUQQggDJkEthBBCGDAJaiGEEMKAye1ZolT09lpfdCMhhHhBNGrjsi6hxCSoRakw5NsghBDiZSKHvoUQQggDJkEthBBCGDAJaiGEEMKAyTlqUSruZtwu6xKEEOIxxhpjLE3Kl3UZT0WCWpSKcbG+ZV2CEEI8Zl7HdWVdwlOTQ99CCCGEAZOgFkIIIQyYBLUQQghhwCSohRBCCAMmF5OVgoSEBHr37o1WqwWgevXqBAYGUrVq1WL1v3nzJmvWrCEwMLDA9RMnTmTWrFnFrmfTpk1s376de/fucefOHezt7alevTpz5swp9hhCCCHKhkpRFKWsi/inSUhIICwsjKVLlwKwd+9eVq1axdq1a8u0rvj4eHbu3MnkyZPzLdfpdKjVz+/gilarpfns157beEII8bzM67iOiuaG94hjrVbL6dOnC1wnM+oXwMPDgxUrVpCUlMSsWbNITk7G1NSUzz//HBsbG7799ltiY2MxNTVlyJAh1K1bVx/0X331FQcPHsTMzAxfX188PT3p0qULP/30E+fPn2fKlCnodDpef/11wsLC+PXXX1m2bBnlypXjwoULTJ48maZNmz5WU3BwMGZmZiQmJjJu3Di2b9/OyZMnyc3NZeLEiTRq1IhDhw6xaNEiFEWhefPmjBw5sgz2nhBCvNokqF+QatWq8cknnzBgwAA8PDzYt28fq1atomvXrhw8eJDIyEg0Gg15eXkkJSXp++3bt4+NGzdiYmKCTqfLN2Z4eDjBwcE0bNiQ0NBQ9u7di5WVFenp6axYsYI//viDJUuWFBjUAPb29oSGhnLgwAF0Oh0RERHcuXOH0aNHs2bNGsLDw4mIiMDCwoKAgAAuXLjA66+/Xqr7SQghniQnI5ecjNwS90/6K4kM86wS97eyssLKyqrE/UtCgvoFuXbtGiYmJixfvpzvvvuO3NxcHBwcOHfuHO7u7mg0GgD9/z4UFBTEpEmTUKlU+Pv7U7duXf26hIQEGjZsCICrqysXL16kUaNG1K9fH5VKRfXq1UlOTi60JldXVwDOnj3LwYMH8fPzA+D+/fvcvXuXhIQEPvroIwDu3btHUlKSBLUQokyd/OESxzaeL3H/jTg/0/anTJlCaGjoM43xtCSoX4C4uDjUajVvvPEGzZs3p1WrVgBkZ2dz6dIlNm/erD9P/Ois2c3NjRYtWnD48GEWL17M3Llz9etq1KjByZMnadiwIUePHqVly5YAqFSqYtX18Lx0vXr1aNeunf7itezsbIyNjbG3t2f58uWYmZmh0+mQyxmEEGWtYXd7tO/WLHH/KW0WYW1uU+L+L3o2DRLUpeb333/Hz88PlUpFtWrVmDdvHmZmZkyePJkVK1ag0+nw8vKia9euNG/eHG9vb8zMzBg0aBD16tXTjzNy5Eiys7PJzs5m1KhR+bYxfvx4/YVh9vb2tGvXjt9+++2pa23Tpg2HDx/Wz6gbNWpEUFAQY8aMwd/fHwAjIyPCw8OpVMnwLsIQQrw6jM2NMDYveXRVt6tukBeTPUmRV32npaURHR3NX3/9lW+2N27cuFIvTryc5KpvIYSh+kde9T1kyBBcXFxwcHB4rrfwCCGEEKJoRQZ1dnY2wcHBL6IWIYQQQjyiyKD+4IMPWLVqFS1atMDY2Fi//LXX5NCmEEIIUdqKDOqEhAS2bt3K3r179VcTq1Qq1qxZU+rFCSGEEK+6IoM6Ojqa3bt3Y2Ji8iLqEUIIIcTfFBnUDRs25ObNm9SoUeNF1CP+IeZ1XFfWJQghxGOMNcZFNzIwRQb1xYsX6dSpE3Xr1sXExARFUVCpVERFRb2I+sRLyhBvfxBCiJdRkUG9aNGiF1GHEEIIIQpQZFDXqFGDzMxMbty4QV5e3ouoSQghhBD/p8igXrJkCVFRUdSqVUu/TK76FkIIIV6MIoN669atxMbGYmZm9iLqEf8QtzPul3UJQgihZ6LRYGXycuZYkUH92muvkZ2dLUEtnkq/mNVlXYIQQuitea9/WZdQYoUG9bhx41CpVOTk5PDee+/h5uaW717qv79uUQghhBClo9Cg9vb2fpF1CCGEEKIAhb4Oq2nTpjRt2pR9+/bp//33ZUIIIYQofUW+tzI+Pv6xZf/+979LpRghhBBC5Ffooe9vv/2Wb7/9luTkZFq2bKlfrlar6dq16wspzpAlJCTQu3dvHB0dycjIoH///iXeL/Hx8ezcuZPJkyeXuB4XFxecnZ0BaNWqFUOHDi3xWI/as2cPzs7O2NraPrcxhRBCFE+hQT1o0CAGDRrE/PnzGTt27Ius6aXh4uLC0qVLycjIoFu3bmX6B0zNmjWJiIgodvu8vDw0Gk2x2u7ZswdbW1sJaiGEKAOFBvVff/2FnZ0dPXr04OLFi4+tl/dR/09aWho5OTmcO3eOqVOnotPpKF++PAsWLMDExIQOHTrQtGlTTp06RefOnRk8eDA3btwgMDAQExMTKlWqhKWlJQC7d+9m2bJlAPTu3Rtvb28WLlzIpUuXSE1N5d69e3h7e/PDDz+QkZHBihUr9H3/Lj09nQkTJnD37l1MTU35/PPPqVy5Mh06dKBZs2ZkZGQwbtw4QkNDycrKwtrami+++IIbN24wfvx4TExMKFeuHJ988gn/+te/OHPmDFqtllmzZr3QfSuEEK+6QoP6m2++ISwsrMDDsfJksgd+//13fH19+eOPPwgNDaVmzZqsXr0atVrN3Llz2bNnD506deLWrVsEBQVhYWFB165dGTx4MMuWLePDDz/E09OTRYsWcefOHfLy8pg/fz6bNm3CxMQEb29v3n33XeDBo1zHjRvHzJkzOXnyJKtWreLLL78kLi6OLl26kJCQgJ+fHwDDhg3j7NmzuLq6MmjQIKKjo1m2bBmffvop169fJyAggKpVqzJmzBgCAwOpX78+ERERbN26FSMjIzp27MjAgQPR6XSo1WpatWqFr6+v/tC6EEKUhdyMTHIzMkvUN+mvJLLMLUq8bSsrK6ysrErc/1kUGtRhYWHodDp8fHzo1KnTi6zppfHw0PehQ4fYsmULzs7OzJ49m/T0dG7dukXFihUBsLOzw9raGgBj4wevWLt06RL+/v4ANGrUiLi4OO7evYutrS3lypUDoH79+iQkJADg5OQEgK2trX4sW1tbUlJSgMcPfe/atYtu3boB4ObmxtatW4EHgV+1alUAzp8/z4wZMwDIysqidevWDBgwgG+++YbAwECcnJwYMmTI899xQghRAhe27+XcxugS9XUm5Jm2PWXKFEJDQ59pjJJ64pPJ1Go1K1eulKAuwttvv82KFSsICwvD19eX9u3bEx4ejqIowIMjEI+yt7fn+PHjdOjQgePHjwNQsWJFrl+/zv379zExMeHUqVPUrFnzsTH+/u+H2yho/GPHjuHu7s6RI0ewt7cHyHde+rXXXmPMmDG8/vrrAGRnZ5OXl8f48eMBGDBgAB4eHhgbG8sLWYQQZe71bh7U7tCiRH0XtPXC5hln1GWlyEeINmnShPnz59OxY8d8jxGVc9T5ffjhh0yePJl58+axefNmrKys9DPqgvj7+xMYGEhUVBTVqlXD1NQUjUbD6NGj6d+/PyqVivfffx8bG5sS1ePl5UVQUBB79+7Vn6N+1IQJEwgLCyM9PR2Ajz/+mLt37xIREYFGo6FKlSrUqlWL1q1bEx4eToMGDZg4cWKJ6hFCiGdlZG6GkXnJHmdd3a46lczLPeeKXgyVUtiU7P88PO+Zr5OcoxZPoNVqqTdrdFmXIYQQemve62/QQa3Vajl9+nSB64qcUT/NLT9CCCGEeL6KDOrMzEy+++47jhw5AoC7uzv9+/eXt2kJIYQQL0CRjxCdOHEiqampBAYGEhgYSFpampynFEIIIV6QImfUFy5cYP78+frPWq2W7t27l2pRQgghhHigyBm1paUle/bs0X+Oi4sr8ElYQgghhHj+irzq+8qVK0yfPp1Tp04B8MYbbxASEkLt2rVfSIHi5aPVavl/vx8p6zKEEELPRKPBysRwr616pqu+a9eurX/2tBDFZci3QQghxMukyKAu6MIxS0tLGjRoQJcuXTAyKnIIIYQQQpRQkeeoTU1NSU9Pp3Xr1rRu3ZrMzEwyMjL4z3/+w6RJk15EjUIIIcQrq8jp8PHjx9myZYv+c8eOHenVqxdbtmyhS5cupVqcEEII8aorMqizs7M5e/YsDg4OAJw9e5asrKwHneWwtyjE7RK+ik4IIZ4nE7UaK1OTsi7jmRSZtGFhYYwbNw548KYmlUpFWFgYGRkZ8gpEUaiBO/YU3UgIIUrZqs7ty7qEZ1ZkULu6uvLjjz+SmpqKoiiUL19ev04OfQshhBClq9Cg3rBhwxM79u3b97kXI4QQQoj8Cg3qmzdvFrg8IyODn3/+WYJaCCGEeAEKDeqRI0fq/52VlUVcXBwxMTEcPXqUZs2avZDihBBCiFddoUGdnZ2tD+fjx4/z9ttvc/jwYQ4cOIBGo3mRNQohhBCvrEKDulmzZtSvX5+AgADCw8PRaDS0a9dOQvo5SkhIoHfv3jg6OgJQuXLlfG8qe2jPnj04Oztja2tb5Jh5eXkMGDAAgFOnTlGvXj2MjY0ZPnw4b7/99nOtXwghROkrNKjHjx9PbGws06dP591336Vjx46oVKoXWdsrwcXFhaVLlz6xzZ49e7C1tX0sqPPy8h77w0mj0RAREQGAn58fCxYswMbG5ol9hBBCGK5Cg9rX1xdfX19u3brFzp07mT59Ojdv3uSLL76gffv2uLq6vsg6XwmKouDv709AQAA1a9Zk2LBhhISE8K9//YszZ86g1Wrp0aMHK1euxNTUlAYNGlCjRg02btxIRkYGrVq1YvTo0Y+Nm5CQwPjx46lRowY2Njb07t2bWbNmodPpeO2115g6dSo5OTlMmTKFxMRE4MH983Z2dgQEBJCamopKpWLGjBny1jQhRJnIzUgnNyPjqfsl/fUXWeZP99YsKysrrKysnnpbpaXI+6grV678WGjPmzdPP2sTz+b333/Hz88PgAYNGjBjxgxGjRqFjY0N48ePp3HjxrRq1QpfX1+cnZ2Jj4/n1q1bbNq0CY1GQ3p6Ol27dkVRFHx8fPjggw+oUqXKY9tJTExk1apVmJub4+fnx7x586hSpQqzZ8/m4MGDXL16FScnJ2bNmsXp06dZsGABY8eORVEU1q5dC4BOp3uh+0YIIR46/+M2zmyKfOp+ziXY1pQpUwgNDS1Bz9LxVM8A/Xtoi+ejoEPfDRs25MyZMzRt2rTAPs7OzvrD17/88gurVq1CURSuXr3K9evXCwxqrVaLubk58OAxsA+fNnf//n0cHBw4e/Ysx44dY/fu3QCYm5tTq1YtWrVqRWBgIBUrVmTMmDFYWlo+t+8uhBDFVbdrD+q093zqfvM8WmFTghm1IZGHdRuYEydOcOXKFV5//XV27tyJp6cnxsbG5OXl6dv8/RzzggULWL16NRUqVMDHxwdFUQoc9+99HB0d+fLLL/XnrnNycsjMzESr1eLt7Q08uOo/OzsbHx8ffH19Wbx4MTt27JD754UQZcLI3AIjc4un7lfdzo5KTxnUhkaCuoz9/dA3PLhnfeHChZQvX56BAwfi4uJC69atCQ8Pp0GDBrRr1y5f/86dO9OvXz/q1auHmVnxfhk//fRTAgMDyc3NRa1W89lnn9GnTx+mTp1KdHQ0iqLg4eFB69atCQkJQaPRoCgKc+bMea7fXQghRNFUSmFTMCFKSKvVop0xt6zLEEIIVnVu/1LMqLVaLadPny5wnfoF1yKEEEKIpyBBLYQQQhgwCWohhBDCgElQCyGEEAZMgloIIYQwYHJ7ligVqzq3L+sShBACE/XLPx+VoBal4mW4HUIIIV4GL/+fGkIIIcQ/mAS1EEIIYcDk0LcoFSkZuWVdghBC6BlpVJQz0RTd0ABJUItSMSk2qaxLEEIIvbCO1cu6hBKTQ99CCCGEAZOgFkIIIQyYBLUQQghhwCSohRBCCAMmQS2EEEIYsFcqqBMSEvjoo49K1Pfnn38mOjq6wHWnTp3iyJEj+s8TJ04sdJyFCxfSuXNn/Pz88PPzIymp7K6OnjFjBqmpqWW2fSGEEEV7pYL6Wbzzzjt06tSpwHWnTp3i6NGj+s+zZs164lijRo0iIiKC3r17s3bt2hLXlJeXV+K+ACEhIVhZWT3TGEIIIUrXK30f9c2bNwkODiYrKwsbGxs+//xzzM3NmTFjBidOnECr1RIfH09sbCxbtmzh7t279OvXj4CAAFJTU1GpVMyYMYM1a9aQlpZGXFwcixcvxsfHh59++onk5GQ+/fRTUlNTUavVrFixIt/27927h06nA+DQoUMsWrQIRVFo3rw5I0eO5Nq1a4wbNw5zc3Ps7OwwNjZm8uTJdO7cmXfeeYdTp06xYMECJk2aRHJyMqampnz++edoNBpGjhyp387SpUv55Zdf+Prrr7GwsMDd3Z0xY8bg5+fHggULMDMzY8KECdy9e1c/RuXKlenQoQNNmzbl1KlTdO7cmcGDB7/Qn48QQjxJdkYa2RlpxWqb9JfCffOiI8/KysrgJjCvdFAvXboULy8vPD09Wb58ORs3bsTd3Z3ExESioqK4evUqO3bsyNfn2rVrKIqinwnrdDr69evH3bt3HwuypUuX8u6779KjRw90Oh3q/3uLy8KFC1myZAn37t0jMjISRVEIDw8nIiICCwsLAgICuHDhAuvWrWPQoEG0b9+eRYsWcefOHQAyMjLo1q0bEyZM4IsvvqB79+54eHiwb98+Vq1aRfPmzdFqtXz22WcoigJAbGws06ZNo0GDBvo/Dh7auHEjrq6uDBo0iOjoaJYtW8ann37KrVu3CAoKwsLCgq5du0pQCyEMytEflvHrxi+L1XZVMcecMmUKoaGhJS2pVLzSQX3p0iX8/f0BcHV15aeffqJy5co4OzsDUKtWLSpWrJivT61atWjVqhWBgYFUrFiRMWPGFDr+uXPn6NevH4A+pOHBoW9PT08mTpzI+fPnMTY2znf+/N69eyQlJXHp0iWGDh0KQKNGjYiLiwPA1NQUJycnAM6ePct//vMfvvvuO3Jzc3FwcKBp06YcPXqU8ePHY2dnx6hRoxgxYgQrV67k/v37dOnShbZt2+rruXjxIt26dQPAzc2NrVu3AmBnZ4e1tTUAxsbGT71/hRCiNLl2H0qDdz8oVttP2thSvpgzakPzSge1vb09v//+O56enhw9ehR7e3tq165NTEwM8ODis7t37+brk52djY+PD76+vixevJgdO3ZgYWFR4PnievXq8euvv9K9e/d8M2oAlUpFQEAA48aNIzIyEnt7e5YvX46ZmRk6nQ5FUdi7dy8nTpzA1taWEydO6PtqNP97Xm3dunVp3rw5rVq10teXk5PD8OHDgQfnoePj43F3d2fatGlkZ2c/FtT29vYcO3YMd3d3jhw5gr29vb5GIYQwVCbmlpiYWxarbXW76lQoRlAbopez6mdw7NgxBgwYADyYHUdFRREREYG1tTVz5szBwsICW1tbvL29cXR0pEqVKvn6JyYmEhISgkajQVEU5syZg06nY926dZw8eZKZM2fq23700UdMnDiRTZs2odFoHjtHbWdnR9WqVfn1118ZM2aMfnZvZGREeHg4Q4YMITAwkMjISKpUqUKFChUe+z7Dhg1j8uTJrFixAp1Oh5eXF9WqVWPevHkYGRlhampK48aN+frrrzl69Ci5ubm8//77+cbw8vIiKCiIvXv36s9RCyGEMAwq5eFJTKGXk5ODsbExV69eJSgoiKioqDKpIzc3F41Gg0qlYtGiRdjY2PDBB8U7zFOWtFotnrP3lHUZQgihF9bRsGfUWq2W06dPF7jOcKsuQ2FhYVy4cIH09PQn3hNd2m7evElgYCCKolChQgXmzp1bZrUIIYQoGxLUBZg2bVpZlwBA9erVWb9+fVmXIYQQogzJA0+EEEIIAyZBLYQQQhgwCWohhBDCgMk5alEqwjpWL+sShBBCz0jz8j4XQoJalApDvg1CCCFeJnLoWwghhDBgEtRCCCGEAZOgFkIIIQyYnEgUpSIr/fGXlAghhCFTa1QYmxre/FWCWpSKw9/fKesShBDiqTR536asSyiQ4f3pIIQQQgg9CWohhBDCgElQCyGEEAZMgloIIYQwYBLUwMmTJxk0aBB+fn74+Pgwf/78px4jPj5e/3rMGTNmkJqa+tT9z507p//s4uKCn58f3t7eTJo06anrKciWLVvIyMgA4NSpU6xevfq5jCuEEKL0vPJXfd+7d4+JEyeyZMkSatSoAcChQ4fytcnLy0Oj0RR7zJCQkKeu49dff8XBwYF69eoBULNmTSIiIgD4+OOP+fPPP6lfv/5Tj/t3W7dupU2bNpibm+Pk5ISTk9MzjSeEEKL0vfJBHRcXh4eHhz6kAd5++20WLlxIYmIiycnJ+Pj4EB8fz4kTJ7h//z4jRozAw8ODGzduEBgYiImJCZUqVcLS0hIAPz8/FixYgKWlJVOmTCExMRGAsLAw6tSpQ4cOHWjatCmnTp2ic+fOeHl5sXXrVszNzfn+++9ZsWKFvhadTkdGRgYWFhYAzJ8/n/j4eAAmTJiAq6sr//nPf5gzZw4qlYrmzZsTEBDAsWPHCAsLw9zcnDp16tCtWzdOnTrF8OHDefPNN2nZsiU7d+5k8uTJdO7cmZYtW3Ls2DGcnZ0JCQkhLS2NMWPGkJeXR7169bhy5QpLly59UT8WIYQokfSMNNIz00rU96+kTEzNiz8p+zsrKyusrKxK1Lcor3xQX7t2jWrVqgFw+fJlPvvsM27evEnLli2xsrJi9uzZALz55ptYWFiQkpLCwIED8fDwYNmyZXz44Yd4enqyaNEi7tzJf+/w999/j5OTE7NmzeL06dMsWLCAefPmcevWLYKCgrCwsKBr164MHjyYnj174uDgQMeOHQFISEjAz8+PmzdvYm9vT61atfjjjz84ffo0UVFRJCUlERAQwKZNm5g1axZff/01tra2DB06lFOnThEXF8fQoUN599130el0qNVqnJycWLBgATY2NvqwB8jIyMDb25uJEyfSrVs30tLS2LhxIy1btmTAgAFs3bqVK1euvKCfiBBClFxk7GJWbQsvWecxJd/ulClTCA0NLfkAT/DKB3W1atU4f/48AHXq1CEiIgI/Pz90Oh1NmjTRt4uIiCAuLg4jIyP++usvAC5duoS/vz8AjRo1Ii4uLt/YZ8+e5dixY+zevRsAc3NzAOzs7LC2tgbA2Ni4wLr+fuh7xowZ7NixA7VaTePGjQGoXr06mZmZAGRlZWFraws8OLd98eJFfH19WbJkCTt37qRFixb06tWr0H1gbm7Oa6+9ph83JSWFS5cu0b17d/13i42NLWpXCiFEmfPpOJzubfqVqG+jLtbPNKMuLa98ULdp04bly5fTp08fatasCUBubi4AavWDa+2Sk5OJjY1l8+bN3Lt3j06dOgFgb2/P8ePH6dChA8ePH39s7Hr16qHVavH29gYgOzsbAJXq8feiGhsbk5dX8GM3K1SowJ07d3B3d2f79u0AJCUlYWZmBoCpqSnXr1/H1taW33//nbZt21KuXDk+++wzFEWhY8eOdOnSpdBtPFqPoijUqVOHkydP4u7uzokTJ4rYi0IIYRgszC2xMLcsUV+76jaYWpQsqEvTKx/U5cuXZ9asWUyaNInc3FyMjY1xc3PDyOh/u6ZChQrY2dnh6+vLG2+8of/Lyd/fn8DAQKKioqhWrRqmpqb5xu7Tpw9Tp04lOjoaRVHw8PBgwIABBdbx9ttvM3v2bOLi4vjiiy/0h74VRcHc3Jy5c+dSvnx5HBwc8Pb2RlEUgoODAQgODmbUqFGoVCreeustnJyc+O6779i1axeKotCqVStMTEzw8PAgKCiI5s2b62fmhfHy8mLMmDHs378fe3v7Qmf+QgghSpdKURSlrIsQhkdRFPLy8jAyMmLr1q2cO3eOoKCgYvXVarV8G3KwlCsUQojnq8n7ZTej1mq1nD59usB1r/yMWhQsNzeX/v37o1KpUKvVhIeX8OIMIYQQz0SCWhTI2NiY9evXl3UZQgjxypMnkwkhhBAGTIJaCCGEMGAS1EIIIYQBk3PUolQ0ed+mrEsQQoinotY8/owLQyBBLUqFIT40QAghXkZy6FsIIYQwYBLUQgghhAGToBZCCCEMmJyjFqUiLy2nrEsQQrzCVBoVavN/RsT9M76FMDi3lp8q6xKEEK+wyv5OZV3CcyOHvoUQQggDJkEthBBCGDAJaiGEEMKASVALIYQQBkyC2gAFBAQwduzYx5YnJCTw0UcfFdovPj6eVq1a4efnR69evdi0adNzqWfdunXPZRwhhBBPT4LawKSlpXHv3j1u3LhBenr6U/fv0KEDERERREVFsWLFiudSU2Rk5HMZRwghxNOT27MMzO7du3n33XfJyclh7969tGvXjnHjxpGdnU2tWrX07VatWsX+/ftJTU3F29ubvn375hsnMzMTU1NTANLT05kwYQJ3797F1NSUzz//nMqVK7Nu3Tp++OEHFEVh2LBheHh4sGHDBjZu3Ei5cuXw9PQkNzeXhIQE/Pz88PHxoVOnTi90fwghxN+lZd4nLet+ke2ykyqgKWdcrDGtrKywsrJ61tJKjQS1gdm5cyezZs1CURQmT57M7du3adasGYMGDWLr1q3ExsYC0LdvXwYOHEh2djY9evSgT58+wIOgP3v2LBcvXmTQoEEAbNy4EVdXVwYNGkR0dDTLli3jo48+YuvWrURFRZGZmYmXlxdt27blxx9/ZPny5djY2KDT6VCr1WzatImIiIgy2ydCCPHQN3FrmLtrSdENpxZ/zClTphAaGlrimkqbBLUBuXPnDidPnmT8+PEAnD17lkqVKtGtWzcAGjVqpA/q6Ohotm7dikql4saNG6SkpAAPDn1PnjyZ7Oxs+vXrx3vvvcfFixf1Y7i5ubF161YSEhKoX78+RkZGWFpaUrVqVe7evcuECRMIDw8nOzsbX19fXF1dy2BPCCFEwT5u0w+/t3sX2c7G1+GpZtSGTILagMTExDB69Gj97HjTpk3cvXuXEydO4O7uzokTJ/Rtly5dyo4dOwDo2LEjiqLkG8vExAQTExNSUlKwt7fn2LFjuLu7c+TIEezt7alZsyZ//vknubm5ZGZmcv36dSpWrIiFhQUzZ87k2rVrjBkzhqioKFQqw3xHqxDi1WNpVg5Ls3JFtqtc3Q6NZfGC2tBJUBuQHTt2EB4erv/cvHlzgoODMTMzIy4ujnr16unXtWzZEh8fHxwdHSlfvrx++cND31lZWbi4uFC/fn1q1apFUFAQe/fu1Z+jrlSpEj169MDHxwdFURg3bhxqtZrQ0FASExPJzs7mgw8+AKB+/fqMGDGC3r17065duxe3Q4QQQqBSHp2KCfGMtFotPw97PreGCSFESVT2d3qpZtRarZbTp08XuE5uzxJCCCEMmAS1EEIIYcAkqIUQQggDJkEthBBCGDAJaiGEEMKAye1ZolRU9ncq6xKEEK8wleaf8/wHCWpRKl6m2yKEEMKQyaFvIYQQwoDJjFqUCq1WW9YlCCHEP4I8mUwIIYQwYHLoWwghhDBgEtRCCCGEAZOgFkIIIQyYBLUQQghhwCSohRBCCAMmQS2EEEIYMLmPWjw3GzduZMuWLRgbGzNz5kxq1apV1iUZhKNHjzJ79myMjY2xsLAgPDyc3NxcPvnkE+7fv0/z5s0ZNWpUWZdpEA4fPoyvry+HDh0CkH1UgOPHj/Pll1+Sk5ND69at6dWrl+ynR0ybNo0//vgDnU5HYGAgjRs3Jjg4mBs3buDg4MCUKVNQq1+ieaoixHNw9+5d5f3331dycnKUY8eOKQEBAWVdksG4du2akp6eriiKoqxfv15ZvHixMnv2bCU6OlpRFEXx9/dXzp49W5YlGoyRI0cqvXr1Um7fvi37qABZWVmKv7+//vdJURTZT4+4ePGi0q9fP0VRFOWvv/5SPvjgA2Xt2rXK8uXLFUVRlNDQUCUuLq4sS3xqL9GfFMKQHT9+nKZNm2JkZESjRo24ePFiWZdkMGxtbTE3NwfA2NgYjUbDkSNHaNu2LQBt2rTht99+K8sSDcL+/ftxd3fHwsICQPZRAX7//XfMzMwICAhg0KBB/Pnnn7KfHlG5cmXMzMzIzc3l3r172NjYcPjw4Zd6H0lQi+ciJSWFChUq6D8r8sC7x9y9e5f169fz/vvvk56ejpmZGQDly5cnJSWljKsrWzqdjvXr1+Pj46NfJvvocTdu3ODcuXMsWLCAkJAQpk6dKvvpEeXKlcPOzo6OHTsyePBgBg8eTEpKCuXLlwdezn0k56jFc1G+fHlOnz6t//xSnf95ATIyMhg9ejSfffYZNjY2mJubk5WVhampKampqfn+yHkV/fjjj7Rr1w5TU1P9MtlHjytfvjxubm5YWFhQt25d0tLSZD894t///jfJycns2rWLW7duMWzYMGrXrs29e/eoUqXKS7mP5P9NxXPRuHFjfvvtN/Ly8vjvf/9LnTp1yrokg5Gbm8vYsWPx8/PDzc0NAHd3dw4cOADAzz//TJMmTcqyxDJ35swZdu7cyeDBgzl9+jTjx4+XfVSAxo0bc/HiRXQ6HTdv3sTExET20yN0Oh0VKlRArVZjaWlJeno6b775Jj///DPwcu4jeSmHeG4iIyP54YcfMDIyYsaMGRLW/2fbtm1Mnz4dJycngMeu1H3rrbcYPXp0GVdpOPz8/FiwYAGA7KMCfP/992zZsoXc3FyCgoKoW7eu7Ke/ycvLIzg4mMTERLKysujfvz8dOnQgODiYW7duUbduXUJDQ1+qo34S1EIIIYQBe3n+pBBCCCFeQRLUQgghhAGToBZCCCEMmAS1EEIIYcAkqIUQQggDJkEthCg1Wq2W6dOn6z9fuHABrVZLZGRkqW733r17NGzYkG3btpXqdoR4ESSohRClxsbGhiNHjugfKRsbG4ujo2Opb3fPnj00bNiQmJiYUttGXl5eqY0txN9JUAshSo1araZRo0YcPXoUePBUqHfeeUe//tKlSwwcOJBevXoxePBgbt26BcBXX31F79696dKlC/PmzdO3b9euHV999RXdunXD19eXtLS0ArcbExNDUFAQFy5cIDU1Vb980aJFdOnSha5duxIVFQXA3r176d69O926dSMkJAR48NCV8+fPAxAfH8/YsWMBCA4OJjQ0lN69e7N69Wr27NlDnz596N69Ox9//DH3798H4Pr16wwdOpRu3brRq1cvkpKS6Nu3L1euXAEePK3O09OTrKysZ9/J4h9PnvUthChVHTt2JDY2looVK1KtWjX9CyTgwXuDp0+fTo0aNYiJiWHx4sVMnjyZfv36ERAQgE6n4+OPP+bPP/+kfv36ANSuXZvt27cTFhZGdHQ0Xl5e+baXkpLCxYsXcXNzo02bNuzZs4eePXuyf/9+jhw5wpYtWzAxMSE5OZlbt24xe/Zs1q1bR9WqVUlOTi7y+6SmpvL999+jUqlISUmhffv2ACxfvpzvv/+e/v37M2PGDDp27EivXr3IzMwEoEePHmzfvp2RI0fy888/8+abb+Z7trkQhZEZtRCiVL355pscOXKEmJgYPD099cvT0tI4cuQIw4cPp3v37ixevJhr164BcOjQIXr37k337t05ceIE586d0/fz8PAAwMnJicTExMe2t2fPHtq1a4dKpcLT05PY2FgAfvnlF3r37o2JiQkA1tbWHDt2jLfffpuqVavqlxXF09MTlUoFQFJSEgMGDKBr165s2LBBPws/evQoPXv2BMDMzAwzMzM6d+7Mzp07gQePle3Ro0ex96F4tcmMWghRqjQaDQ0bNiQqKorY2Fh96CqKgq2tLT/88EO+9llZWcyePZstW7ZQqVIlpk2bRnZ2tn79w6BVq9UFnieOjY3l1KlT7NmzB4A7d+5w7969p6pZrVbrz6v/fdtAviMC06dPZ9SoUTRr1ozY2Fji4uIKHbN8+fI4ODhw4MABzp49i7u7+1PVJF5dMqMWQpQ6Pz8/xo8fj4WFhX6ZlZUV5cuX5+DBgwDk5ORw/vx5srKyUKvVlC9fnuTkZPbv31/s7aSkpHDmzBl+/vln9u3bx759++jVqxd79+6lefPmbN68WR+8ycnJuLi4cOjQIW7cuKFfBmBnZ8epU6cA2LdvX6HbS0tLo0qVKuh0unx/cLi5ubF161bgwR8eDw9/9+zZk5CQEDp27KiflQtRFAlqIUSpq1u3Lt26dXtseXh4OCtXrqRbt2706NGDY8eOUb58eTp37kynTp0YNWoULi4uxd7Onj17aNWqVb43I7Vv356YmBhat26Nm5sbPXv2pFu3buzatYtKlSoxceJEhgwZQrdu3Zg7dy4AAwYM4JtvvqFXr15oNJpCtzd8+HD8/f3p06cPNWvW1C8PCQkhJiaGrl274uPjo/8DoEWLFuTk5NC9e/difych5O1ZQgjxgpw/f55Jkyaxfv36si5FvERkRi2EEC/Apk2bGDJkCAEBAWVdinjJyIxaCCGEMGAyoxZCCCEMmAS1EEIIYcAkqIUQQggDJkEthBBCGDAJaiGEEMKASVALIYQQBuz/A07lSCYba6QoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(\"CrossValMeans\",algo_df.index,data = algo_df,**{'xerr':algo_df['CrossValerrors']})\n",
    "ax.set_xlabel(\"Mean Accuracy\")\n",
    "ax.set_title(\"Cross validation scores\", fontsize=15)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa132bc",
   "metadata": {
    "papermill": {
     "duration": 0.032828,
     "end_time": "2022-12-21T11:36:45.894719",
     "exception": false,
     "start_time": "2022-12-21T11:36:45.861891",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* **GradientBoosting** has the **highest** accuracy.\n",
    "* I chose **GradientBoosting**, **SVC**, **RandomForest** and **LogesticRegression**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c096d177",
   "metadata": {
    "papermill": {
     "duration": 0.030576,
     "end_time": "2022-12-21T11:36:45.958835",
     "exception": false,
     "start_time": "2022-12-21T11:36:45.928259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Random Forest\n",
    "Documentation remaining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "88c9183f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:46.029096Z",
     "iopub.status.busy": "2022-12-21T11:36:46.028149Z",
     "iopub.status.idle": "2022-12-21T11:36:46.671289Z",
     "shell.execute_reply": "2022-12-21T11:36:46.670135Z"
    },
    "papermill": {
     "duration": 0.684349,
     "end_time": "2022-12-21T11:36:46.673830",
     "exception": false,
     "start_time": "2022-12-21T11:36:45.989481",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 0.,\n",
       "       1., 1., 1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 1.,\n",
       "       0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 1., 0.,\n",
       "       1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 1., 1., 1.,\n",
       "       0., 1., 1., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 1., 1., 1., 0., 1., 1., 1., 0., 0., 0., 0., 1.,\n",
       "       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 1., 0., 1., 0., 0., 0.,\n",
       "       0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "       1., 1., 1., 1., 1., 0., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1.,\n",
       "       0., 0., 1., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0.,\n",
       "       0., 1., 1., 1., 1., 0., 1., 0., 1., 0., 1., 1., 1., 1., 0., 0., 0.,\n",
       "       0., 1., 1., 0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1.,\n",
       "       0., 0.])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "## Model\n",
    "model = RandomForestClassifier(n_estimators=300,random_state=10,)\n",
    "\n",
    "# Fit\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_predict = model.predict(X_test)\n",
    "\n",
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b2f81783",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:46.740554Z",
     "iopub.status.busy": "2022-12-21T11:36:46.739673Z",
     "iopub.status.idle": "2022-12-21T11:36:46.747167Z",
     "shell.execute_reply": "2022-12-21T11:36:46.746215Z"
    },
    "papermill": {
     "duration": 0.04307,
     "end_time": "2022-12-21T11:36:46.749259",
     "exception": false,
     "start_time": "2022-12-21T11:36:46.706189",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8251121076233184"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b4d5d277",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:46.812931Z",
     "iopub.status.busy": "2022-12-21T11:36:46.812516Z",
     "iopub.status.idle": "2022-12-21T11:36:47.786594Z",
     "shell.execute_reply": "2022-12-21T11:36:47.785472Z"
    },
    "papermill": {
     "duration": 1.008447,
     "end_time": "2022-12-21T11:36:47.788933",
     "exception": false,
     "start_time": "2022-12-21T11:36:46.780486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.219298            Age\n",
      "0.218597           Fare\n",
      "0.028040           Pc_1\n",
      "0.018673           Pc_2\n",
      "0.045094           Pc_3\n",
      "0.078401          Sex_0\n",
      "0.063008          Sex_1\n",
      "0.000000          Tkt_A\n",
      "0.000274         Tkt_A4\n",
      "0.003965         Tkt_A5\n",
      "0.000000        Tkt_AQ3\n",
      "0.000000        Tkt_AQ4\n",
      "0.000046         Tkt_AS\n",
      "0.000934          Tkt_C\n",
      "0.004911         Tkt_CA\n",
      "0.000070    Tkt_CASOTON\n",
      "0.000000         Tkt_FC\n",
      "0.000485        Tkt_FCC\n",
      "0.000048         Tkt_Fa\n",
      "0.001390       Tkt_LINE\n",
      "0.000000         Tkt_LP\n",
      "0.007007         Tkt_PC\n",
      "0.000058         Tkt_PP\n",
      "0.000136        Tkt_PPP\n",
      "0.000000         Tkt_SC\n",
      "0.000000       Tkt_SCA3\n",
      "0.000046       Tkt_SCA4\n",
      "0.000345       Tkt_SCAH\n",
      "0.000115       Tkt_SCOW\n",
      "0.000734    Tkt_SCPARIS\n",
      "0.000254    Tkt_SCParis\n",
      "0.000871        Tkt_SOC\n",
      "0.000000        Tkt_SOP\n",
      "0.002494       Tkt_SOPP\n",
      "0.000115    Tkt_SOTONO2\n",
      "0.003340    Tkt_SOTONOQ\n",
      "0.000151         Tkt_SP\n",
      "0.007170      Tkt_STONO\n",
      "0.001786     Tkt_STONO2\n",
      "0.000000     Tkt_STONOQ\n",
      "0.003202       Tkt_SWPP\n",
      "0.003809         Tkt_WC\n",
      "0.000708        Tkt_WEP\n",
      "0.012376          Tkt_X\n",
      "0.011640           Em_C\n",
      "0.009621           Em_Q\n",
      "0.014523           Em_S\n",
      "0.087481          T_0.0\n",
      "0.062661          T_1.0\n",
      "0.013855          T_2.0\n",
      "0.003869          T_3.0\n",
      "0.022738      Fm_LargeF\n",
      "0.016360        Fm_MedF\n",
      "0.016324      Fm_Single\n",
      "0.012977      Fm_SmallF\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, ' feature importance')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vasulization of model's perfomance\n",
    "R_FI = pd.Series(features, model.feature_importances_)\n",
    "print(R_FI)\n",
    "\n",
    "plt.figure(figsize = (8,10))\n",
    "ax = sns.barplot(model.feature_importances_, features, )\n",
    "ax.set_xlabel(\"Relative importance\",fontsize=12)\n",
    "ax.set_ylabel(\"Features\",fontsize=15)\n",
    "ax.tick_params(labelsize=5)\n",
    "ax.set_title(\" feature importance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fffd348",
   "metadata": {
    "papermill": {
     "duration": 0.031224,
     "end_time": "2022-12-21T11:36:47.851006",
     "exception": false,
     "start_time": "2022-12-21T11:36:47.819782",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Realizations\n",
    "* **Age, Fare, Family size, Titles** are mostly the **important** features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3093674c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:47.915293Z",
     "iopub.status.busy": "2022-12-21T11:36:47.914923Z",
     "iopub.status.idle": "2022-12-21T11:36:47.992222Z",
     "shell.execute_reply": "2022-12-21T11:36:47.991108Z"
    },
    "papermill": {
     "duration": 0.112022,
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         0\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict Test Data\n",
    "\n",
    "# Scaling Test Data\n",
    "test = StandardScaler().fit_transform(final_test)\n",
    "\n",
    "#Predict data and convert it into series\n",
    "test_Survived = pd.Series(model.predict(test), name=\"Survived\")\n",
    "\n",
    "# Convert it into Dataframe\n",
    "test_ids = test_ids.reset_index(drop=True)\n",
    "results = pd.concat([test_ids,test_Survived],axis=1)\n",
    "\n",
    "# Check Data\n",
    "results['PassengerId']=results['PassengerId'].astype(int)\n",
    "results['Survived']=results['Survived'].astype(int)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "60da2681",
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    "execution": {
     "iopub.execute_input": "2022-12-21T11:36:48.059644Z",
     "iopub.status.busy": "2022-12-21T11:36:48.058737Z",
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   "outputs": [],
   "source": [
    "# Save File\n",
    "results.to_csv('titanic_submission.csv', index=False)"
   ]
  },
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   "execution_count": null,
   "id": "0fc9698d",
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