Titanic Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "736f2902",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "**Libraries Used**\n",
    "\n",
    "**1. Data Exploration**\n",
    "   - 1.1 Load the Train Dataset\n",
    "   - 1.2 Display the First Few Rows\n",
    "   - 1.3 General Information About the Train Dataset\n",
    "   - 1.4 Check for Missing Values\n",
    "   - 1.5 Summary Statistics\n",
    "   - 1.6 Unique Values in Categorical Columns\n",
    "   - 1.7 Data Exploration Report\n",
    "\n",
    "**2. Data Preprocessing**\n",
    "   - 2.1 Handle Missing Values\n",
    "      - 2.1.1 Age\n",
    "      - 2.1.2 Cabin\n",
    "      - 2.1.3 Embarked\n",
    "   - 2.2 Encode Categorical Variables\n",
    "   - 2.3 Feature Scaling\n",
    "      - 2.3.1 Testing Alternatives\n",
    "      - 2.3.2 Applying Scalars\n",
    "   - 2.4 Dropping the Name and PassengerId Columns\n",
    "   - 2.5 Data Preprocessing Report\n",
    "\n",
    "**3. Feature Engineering and Selection Analysis**\n",
    "   - 3.1 Ticket Group Size Feature\n",
    "   - 3.2 Correlation Matrix\n",
    "   - 3.3 Feature Importance Calculation\n",
    "   - 3.4 Feature Selection with RFE\n",
    "   - 3.5 Feature Engineering and Selection Analysis Report\n",
    "\n",
    "**4. Model Training**\n",
    "   - 4.1 Model 1: Logistic Regression\n",
    "   - 4.2 Model 2: Random Forest\n",
    "   - 4.3 Model 3: Simple Neural Network (MLPClassifier)\n",
    "   - 4.4 Model 4: Custom Neural Network (Keras)\n",
    "   - 4.5 Model Training Report\n",
    "\n",
    "**5. Test Models**\n",
    "   - 5.1 Train Data Preprocess\n",
    "   - 5.2 Making Predictions\n",
    "   - 5.3 Model Testing and Prediction Report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf60ac5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries for data processing and analysis\n",
    "import pandas as pd  # Data loading, manipulation, and analysis\n",
    "import numpy as np   # Numerical computations and arrays\n",
    "\n",
    "# Libraries for data visualization\n",
    "import matplotlib.pyplot as plt  # For creating plots and charts\n",
    "import seaborn as sns  # For advanced data visualization\n",
    "\n",
    "# For data preprocessing and scaling\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler  # Feature scaling\n",
    "\n",
    "# For filling missing values using regression\n",
    "from sklearn.linear_model import LinearRegression  # Regression model for missing value imputation\n",
    "\n",
    "# Machine learning models for training and predictions\n",
    "from sklearn.linear_model import LogisticRegression  # Logistic regression model\n",
    "from sklearn.ensemble import RandomForestClassifier  # Random Forest classifier\n",
    "from sklearn.neural_network import MLPClassifier  # Multi-layer Perceptron (MLP) neural network\n",
    "\n",
    "# For feature selection\n",
    "from sklearn.feature_selection import RFE  # Recursive Feature Elimination for feature selection\n",
    "\n",
    "# Model evaluation and validation\n",
    "from sklearn.model_selection import cross_val_score, train_test_split  # Cross-validation and data splitting\n",
    "\n",
    "# Metrics for evaluating model performance\n",
    "from sklearn.metrics import accuracy_score  # To calculate accuracy score\n",
    "\n",
    "# Saving and loading trained models\n",
    "import joblib  # For saving and loading models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04db9f6",
   "metadata": {},
   "source": [
    "# 1. Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a6608e",
   "metadata": {},
   "source": [
    "## 1.1 Load the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87cbf4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e30a5c",
   "metadata": {},
   "source": [
    "## 1.2 Display the first few rows of the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50b0f9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985e0a89",
   "metadata": {},
   "source": [
    "## 1.3 General information about the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6ddf6d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f38f988",
   "metadata": {},
   "source": [
    "## 1.4 Check for missing values in the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1561517c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the train dataset\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee10dde",
   "metadata": {},
   "source": [
    "## 1.5 Summary statistics of numerical columns in the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8cc73e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics for the train dataset\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af03fd9",
   "metadata": {},
   "source": [
    "## 1.6 Number of unique values in categorical columns in the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f60c0e73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        891\n",
       "Sex           2\n",
       "Ticket      681\n",
       "Cabin       147\n",
       "Embarked      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique values in categorical columns of train dataset\n",
    "data.select_dtypes(include='object').nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537e86b7",
   "metadata": {},
   "source": [
    "## 1.7 Data Exploration Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54f3ea8",
   "metadata": {},
   "source": [
    "This report covers the basic exploratory analysis of the train dataset, including loading the data, checking the structure, identifying missing values, and reviewing summary statistics.\n",
    "\n",
    "The dataset has been successfully loaded using pandas. It contains passenger data, likely from the Titanic passenger manifest, with various attributes such as survival status, age, class, and more. The dataset consists of 12 columns and 891 entries. Each row represents a passenger, and the columns contain various features related to the passenger's demographics, ticket information, and survival status.\n",
    "\n",
    "The columns in the dataset are as follows:\n",
    "\n",
    "- **PassengerId:** A unique identifier for each passenger.\n",
    "- **Survived:** A binary variable indicating whether the passenger survived (1) or not (0).\n",
    "- **Pclass:** The ticket class of the passenger (1 = 1st class, 2 = 2nd class, 3 = 3rd class).\n",
    "- **Name:** The full name of the passenger.\n",
    "- **Sex:** The gender of the passenger (male or female).\n",
    "- **Age:** The age of the passenger.\n",
    "- **SibSp:** The number of siblings or spouses aboard the Titanic with the passenger.\n",
    "- **Parch:** The number of parents or children aboard the Titanic with the passenger.\n",
    "- **Ticket:** The ticket number of the passenger.\n",
    "- **Fare:** The fare paid by the passenger.\n",
    "- **Cabin:** The cabin number where the passenger stayed (this column contains many missing values).\n",
    "- **Embarked:** The port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).\n",
    "\n",
    "### Key Insights:\n",
    "1. Some columns, such as **Age** and **Cabin**, contain missing values that will need to be handled in the data preprocessing stage.\n",
    "2. The **Survived** column is our target variable, as this is a classification problem aiming to predict survival.\n",
    "3. There are both numerical and categorical variables in the dataset, requiring different types of preprocessing.\n",
    "   \n",
    "Upon checking for missing values, we found that the **Age** column has 177 missing entries (about 20% of the dataset), **Cabin** has 687 missing entries (around 77% of the dataset), and **Embarked** has 2 missing entries. Handling these missing values will be crucial for building a reliable model.\n",
    "\n",
    "Summary statistics of the numerical columns reveal several important characteristics:\n",
    "\n",
    "- The **Age** column shows an average age of 29.7 years, with a minimum of 0.42 and a maximum of 80 years. However, a significant number of entries are missing.\n",
    "- The **Fare** column has a large range, with values from 0 to 512.33, and an average fare of 32.20, indicating substantial variation in ticket prices.\n",
    "- The **SibSp** and **Parch** columns suggest that most passengers traveled either alone or with a small group, as the median values for both are 0.\n",
    "- Approximately 38.4% of passengers survived, as indicated by the mean value of the **Survived** column.\n",
    "\n",
    "There are also unique values in the categorical columns that give further insight into the dataset:\n",
    "\n",
    "- **Name** has 891 unique values, as each passenger has a unique name.\n",
    "- **Sex** contains 2 unique values: male and female.\n",
    "- **Ticket** has 681 unique values, as some passengers share tickets.\n",
    "- **Cabin** has 147 unique values, but a majority of passengers are missing cabin information.\n",
    "- **Embarked** contains 3 unique values: C (Cherbourg), Q (Queenstown), and S (Southampton), representing different ports of embarkation.\n",
    "\n",
    "In conclusion, the dataset contains a mixture of numerical and categorical variables, some with missing values. Key columns such as **Age**, **Fare**, **Sex**, **Pclass**, and **Embarked** are likely to play an important role in predicting survival, but careful handling of missing data and categorical variables will be necessary in the modeling process.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae5515e",
   "metadata": {},
   "source": [
    "# 2. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1de1f0",
   "metadata": {},
   "source": [
    "## 2.1. Handle Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb9cf94",
   "metadata": {},
   "source": [
    "### 2.1.1 Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63373c94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 1: Analyze the original Age distribution (without missing values)\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(data['Age'].dropna(), bins=30, kde=True, color='blue')\n",
    "plt.title(\"Original Age Distribution (without missing values)\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f7e9f385",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the data for each imputation method\n",
    "mean_imputation_data = data.copy()\n",
    "median_imputation_data = data.copy()\n",
    "age_group_median_data = data.copy()\n",
    "regression_imputation_data = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0dee70a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Imputation Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ce0473c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.1 Mean Imputation\n",
    "mean_imputation_data['Age'].fillna(mean_imputation_data['Age'].mean(), inplace=True)\n",
    "\n",
    "# 2.2 Median Imputation\n",
    "median_imputation_data['Age'].fillna(median_imputation_data['Age'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ce063f89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.3 Age Grouping and Median Imputation\n",
    "def age_group_median_imputation(df):\n",
    "    df['AgeGroup'] = pd.cut(df['Age'], bins=[0, 10, 20, 30, 40, 50, 60, 70, 80], labels=False)\n",
    "    for group in df['AgeGroup'].unique():\n",
    "        median_age = df.loc[df['AgeGroup'] == group, 'Age'].median()\n",
    "        df.loc[(df['AgeGroup'] == group) & (df['Age'].isnull()), 'Age'] = median_age\n",
    "    df.drop(columns=['AgeGroup'], inplace=True)\n",
    "    return df\n",
    "\n",
    "age_group_median_data = age_group_median_imputation(age_group_median_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "01f4b659",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wp/fq732xlx6tq4_bv24f9qpd580000gn/T/ipykernel_3130/279845366.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  age_null['Age'] = model.predict(X_test)\n"
     ]
    }
   ],
   "source": [
    "# 2.4 Regression-Based Imputation\n",
    "def regression_based_imputation(df):\n",
    "    # Use rows without missing Age to train the regression model\n",
    "    age_not_null = df[df['Age'].notnull()]\n",
    "    age_null = df[df['Age'].isnull()]\n",
    "\n",
    "    # Features for the regression model (using Pclass, Sex, and Fare as predictors)\n",
    "    X_train = pd.get_dummies(age_not_null[['Pclass', 'Sex', 'Fare']], drop_first=True)\n",
    "    y_train = age_not_null['Age']\n",
    "    \n",
    "    # Train the model\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict the missing ages\n",
    "    X_test = pd.get_dummies(age_null[['Pclass', 'Sex', 'Fare']], drop_first=True)\n",
    "    age_null['Age'] = model.predict(X_test)\n",
    "    \n",
    "    # Combine back the data\n",
    "    df.loc[df['Age'].isnull(), 'Age'] = age_null['Age']\n",
    "    return df\n",
    "\n",
    "regression_imputation_data = regression_based_imputation(regression_imputation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9568e613",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Visualizing the results of each method\n",
    "def plot_age_distribution(df, title):\n",
    "    plt.figure(figsize=(10,6))\n",
    "    sns.histplot(df['Age'], bins=30, kde=True, color='green')\n",
    "    plt.title(title)\n",
    "    plt.xlabel(\"Age\")\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "297dbbb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Original Age distribution (with missing values removed)\n",
    "plot_age_distribution(data.dropna(subset=['Age']), \"Original Age Distribution (with missing values removed)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f0c271cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean Imputation\n",
    "plot_age_distribution(mean_imputation_data, \"Age Distribution After Mean Imputation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6cbc04a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Median Imputation\n",
    "plot_age_distribution(median_imputation_data, \"Age Distribution After Median Imputation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0047dabd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Grouping and Median Imputation\n",
    "plot_age_distribution(age_group_median_data, \"Age Distribution After Age Group and Median Imputation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0ae3a19e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rrsPX15e7776buLi4K95P8bCVKxUYGFjivoODA7Vr1y4x1Kg8nDt3DgcHB/z9/Usst1gsBAYGXrD/2rVrX7ANZ2fnC34QvZjZs2fj4uJCv379rJ+X1q1bW4dEFRQUlOk1nD17luTkZJycnC743MTFxV3wmSntv42rq6v1h/gffviBHj168PXXX/P666+XyADQsWPHCzLMnz/fmqH4/bySr4dL5S3N18k777zDs88+y+LFi4mMjMTX15ehQ4daf9FhsVhYvXo1ffv2ZerUqbRr1w5/f38ee+wx0tLSLvmenDt3jsDAwBLnqQHUqVMHBweHUn9uTpw4ccFrWbdunXVdLy8vOnToQIcOHejSpQv33Xcfc+fOJSoqijfffNO63siRI5kxYwYPPPAAP//8M5s3b2bLli34+/uX+Iz+3ftSmve4+L34q4stKw1fX98Lljk5OV2w3MnJCSgaCvd3Gcrj+0phYSF9+vRh4cKFPPPMM6xevZrNmzdbLzdxJd8bLsbWnzGR6kjnSIlUUZ988gmGYfDtt9/y7bffXvD4Z599xquvvoq9vT21a9dm8+bNF6zz12JSfP7C+PHjufnmmy+63yZNmpQ5c1ZWFqtWraJhw4aXPD+qOMf06dOZPn06MTExLFmyhOeee474+PgLTuq+lL/+z//vxMXFUbduXev9/Px8zp07V+KHA2dn54ueR3M1PxTVrl2b/Px8EhISSpQpwzCIi4ujY8eOZd72nx06dIj169cDlDii9Wc///wzAwYMKPW2/fz8qF279iX/bf78W2oo/b+NnZ2ddXIJgN69e9O+fXsmTpzInXfeSUhIiPWz++2331p/E38xxf+ef52kAi78erhU3tJ8nbi7uzNx4kQmTpzI2bNnrUdhBg8ezIEDBwCoX7++9dy1Q4cOsWDBAiZMmEBubi4ffPDBJV/Hpk2bMAyjRL74+Hjy8/OtGa9UcHAwW7ZsuehruJTWrVsDReftAaSkpLB06VJefvllnnvuOet6OTk5nD9/vsRz/+59Kc17XLt27Yv+25XmFy/lpSK+r+zdu5ddu3YxZ86cEufGHjly5CqS2/4zJlIdqUiJVEEFBQV89tlnNGzYkI8//viCx5cuXcqbb77J8uXLGTRoEN27d2fBggUsX76c/v37W9ebN29eiec1adKEiIgIdu3axaRJk2ye+ZFHHuHcuXNMnjz5ip8XGhrKI488wurVq/n999+ty239m86vvvqqxDCeBQsWkJ+fX2IWq7CwMHbv3l3ieWvWrCE9Pb3EsuLJIa4kX8+ePZk6dSpffvklTzzxhHX5d999R0ZGhs2mHy/+Qf2jjz6iUaNGJR7LyspiyJAhfPLJJ5ctUpd6zwcNGsS8efMoKCiokKn3nZ2dee+99+jRowevvvoqH374IX379sXBwYGjR49yyy23XPK5TZo0ITAwkAULFvDkk09al8fExLBhw4bLHin98zbK8nUSEBDA6NGj2bVrF9OnTyczMxM3N7cS6zRu3JgXX3yR7777ju3bt19yWz179mTBggUsXryYYcOGWZd//vnn1sdLw8nJqURZvRLFswrWqVMHKCqchmFcMDX8xx9/fNmjnRd7X0rzHkdGRrJkyRLOnj1rPapYUFDA/PnzS/V6ykNFfF8pLjl/fd8//PDDC/KU9nuTLT9jItWRipRIFbR8+XLOnDnDlClTLnol+pYtWzJjxgxmz57NoEGDuOeee3jrrbcYNWoUr776Ko0aNWL58uX8/PPPQNFv/It9+OGH9O/fn759+zJ69Gjq1q3L+fPniYqKYvv27XzzzTd/m+/s2bP88ccfGIZBWloae/fu5fPPP2fXrl088cQT/OMf/7jkc1NSUoiMjGTkyJE0bdoUDw8PtmzZwk8//VTiN9OtWrVi4cKFzJw5k/bt219w1KK0Fi5ciIODA71797bOrtWmTZsS59LcddddvPTSS/z73/+me/fu7N+/nxkzZlingS7WsmVLoGh2OA8PD1xcXAgPD7/o0JfevXvTt29fnn32WVJTU+natat11r62bdty1113lfk1FcvPz+fzzz+nWbNmPPDAAxddZ/DgwSxZsuSCqZn/7FLv+YgRI/jqq68YMGAAjz/+OJ06dcLR0ZFTp06xdu1ahgwZUuIHMVvo3r07AwYM4NNPP+W5554jPDycV155hRdeeIFjx47Rr18/fHx8OHv2LJs3b7YeAbGzs2PixImMGTOGW2+9lfvuu4/k5GQmTpxIUFBQia+Fy7nSr5POnTszaNAgWrdujY+PD1FRUXzxxRdcd911uLm5sXv3bh555BFuu+02IiIicHJyYs2aNezevbvEUZ2/uvvuu3nvvfe45557OHHiBK1atWL9+vVMmjSJAQMG0KtXL5u8z8WSk5OtQ8Xy8vKIiopi0qRJODs78/DDDwPg6elJt27deOONN/Dz8yMsLIx169Yxe/ZsvL29S2zv796X0rzHL774IkuWLOHGG2/k3//+N25ubrz33ntkZGTY9D0oi4r4vtK0aVMaNmzIc889h2EY+Pr68sMPP7By5coL8rRq1QqAt99+m3vuuQdHR0eaNGlywVFjqPjPmEiVZNIkFyJyFYYOHWo4OTkZ8fHxl1xnxIgRhoODg3Umq5iYGOPmm282atWqZXh4eBi33HKL8eOPP14wq5NhGMauXbuM4cOHG3Xq1DEcHR2NwMBA48Ybb7TODng5/GlWLzs7O8PT09No1aqV8eCDDxobN268YP2/zqSXnZ1tPPTQQ0br1q0NT09Pw9XV1WjSpInx8ssvGxkZGdbnnT9/3rj11lsNb29vw2KxGMXfzi4149zF9mUY/z+71rZt24zBgwdb35877rjDOHv2bInn5+TkGM8884wREhJiuLq6Gt27dzd27tx5wexahmEY06dPN8LDww17e/sS+/zrrH2GUTTz3rPPPmvUr1/fcHR0NIKCgox//vOfRlJSUon16tevbwwcOPCC1/XX2bb+avHixX87M1zx7IFvvvnmJd/DS73nhmEYeXl5xn//+1+jTZs2houLi1GrVi2jadOmxpgxY4zDhw//7Wu4lOJZ+y5mz549hp2dnXHvvfeWeK2RkZGGp6en4ezsbNSvX9+49dZbjVWrVpV47qxZs4xGjRoZTk5ORuPGjY1PPvnEGDJkiNG2bVvrOpf7LBnGlX2dPPfcc0aHDh0MHx8fw9nZ2WjQoIHxxBNPGImJiYZhGMbZs2eN0aNHG02bNjXc3d2NWrVqGa1btzbeeustIz8/37qdi/0bnzt3znjooYeMoKAgw8HBwahfv74xfvx4Izs7u8R6XGImzYt9bi/mr7P22dvbG6Ghocatt95q7Nixo8S6p06dMm655RbDx8fH8PDwMPr162fs3bv3gn393ftSmvfYMAzj999/N6699lrD2dnZCAwMNP71r38Zs2bNuqpZ+1q0aHHR9+Jin9+/vscV/X1l//79Ru/evQ0PDw/Dx8fHuO2224yYmJiLzgg4fvx4Izg42LCzsysxC6CZnzGRqspiGFcwhZaIVEvF192JiYm57DlLItVdcnIyjRs3ZujQocyaNcvsOFLFTZgwgYkTJ5KQkKBziUSqMQ3tE6khZsyYAUDTpk3Jy8tjzZo1vPPOO4waNUolSmqUuLg4XnvtNSIjI6lduzbR0dG89dZbpKWl8fjjj5sdT0REqggVKZEaws3NjbfeeosTJ06Qk5NDaGgozz77LC+++KLZ0UQqlLOzMydOnGDs2LGcP38eNzc3rr32Wj744ANatGhhdjwREakiNLRPRERERESklHRBXhERERERkVJSkRIRERERESklFSkREREREZFS0mQTQGFhIWfOnMHDw8N6hXAREREREal5DMMgLS2N4ODgy16oXUUKOHPmDCEhIWbHEBERERGRSuLkyZOXvUSMihTg4eEBFL1Znp6eJqcRERERERGzpKamEhISYu0Il6IiBdbhfJ6enipSIiIiIiLyt6f8aLIJERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJQezA4iIlJeYmBgSExPLbft+fn6EhoaW2/ZFRESk8lKREpFqKSYmhqbNmpKVmVVu+3B1c+VA1AGVKRERkRpIRUpEqqXExESyMrMY9vww/Ov723z7CdEJLJq0iMTERBUpERGRGkhFSkSqNf/6/gQ1DjI7hoiIiFQzmmxCRERERESklFSkRERERERESklFSkREREREpJRUpEREREREREpJRUpERERERKSUVKRERERERERKSUVKRERERESklFSkRERERERESklFSkREREREpJRUpEREREREREpJRUpERERERKSUVKRERERERERKSUVKRERERESklFSkRERERERESklFSkREREREpJRUpEREREREREpJRUpERERERKSUVKRERERERERKSUVKRERERESklFSkRERERERESklFSkREREREpJRUpEREREREREpJRUpERERERKSUVKRERERERERKydQi9euvvzJ48GCCg4OxWCwsXrzY+lheXh7PPvssrVq1wt3dneDgYO6++27OnDlTYhs5OTk8+uij+Pn54e7uzk033cSpU6cq+JWIiIiIiEhNYmqRysjIoE2bNsyYMeOCxzIzM9m+fTsvvfQS27dvZ+HChRw6dIibbrqpxHrjxo1j0aJFzJs3j/Xr15Oens6gQYMoKCioqJchIiIiIiI1jIOZO+/fvz/9+/e/6GNeXl6sXLmyxLJ3332XTp06ERMTQ2hoKCkpKcyePZsvvviCXr16AfDll18SEhLCqlWr6Nu370W3nZOTQ05OjvV+amqqjV6RiIiIiIjUBFXqHKmUlBQsFgve3t4AbNu2jby8PPr06WNdJzg4mJYtW7Jhw4ZLbmfy5Ml4eXlZbyEhIeUdXUREREREqpEqU6Sys7N57rnnGDlyJJ6engDExcXh5OSEj49PiXUDAgKIi4u75LbGjx9PSkqK9Xby5MlyzS4iIiIiItWLqUP7rlReXh4jRoygsLCQ999//2/XNwwDi8VyycednZ1xdna2ZUQREREREalBKv0Rqby8PIYPH87x48dZuXKl9WgUQGBgILm5uSQlJZV4Tnx8PAEBARUdVUREREREaohKXaSKS9Thw4dZtWoVtWvXLvF4+/btcXR0LDEpRWxsLHv37qVLly4VHVdERERERGoIU4f2paenc+TIEev948ePs3PnTnx9fQkODubWW29l+/btLF26lIKCAut5T76+vjg5OeHl5cX999/PU089Re3atfH19eXpp5+mVatW1ln8REREREREbM3UIrV161YiIyOt95988kkA7rnnHiZMmMCSJUsAuOaaa0o8b+3atfTo0QOAt956CwcHB4YPH05WVhY9e/Zkzpw52NvbV8hrEBERERGRmsfUItWjRw8Mw7jk45d7rJiLiwvvvvsu7777ri2jiYiIiIiIXFKlPkdKRERERESkMlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUlKREhERERERKSUVKRERERERkVJSkRIRERERESklFSkREREREZFSUpESEREREREpJRUpERERERGRUnIwO4CI1GwxMTEkJibafLtRUVE236aIiIhIMRUpETFNTEwMTZs1JSszq9z2kZ6eXm7bFhERkZpLRUpETJOYmEhWZhbDnh+Gf31/m2778KbDrP1kLdnZ2TbdroiIiAioSIlIJeBf35+gxkE23WZijO2HC4qIiIgU02QTIiIiIiIipaQiJSIiIiIiUkoqUiIiIiIiIqWkIiUiIiIiIlJKphapX3/9lcGDBxMcHIzFYmHx4sUlHjcMgwkTJhAcHIyrqys9evRg3759JdbJycnh0Ucfxc/PD3d3d2666SZOnTpVga9CRERERERqGlOLVEZGBm3atGHGjBkXfXzq1KlMmzaNGTNmsGXLFgIDA+nduzdpaWnWdcaNG8eiRYuYN28e69evJz09nUGDBlFQUFBRL0NERERERGoYU6c/79+/P/3797/oY4ZhMH36dF544QVuvvlmAD777DMCAgKYO3cuY8aMISUlhdmzZ/PFF1/Qq1cvAL788ktCQkJYtWoVffv2rbDXIiIiIiIiNUelPUfq+PHjxMXF0adPH+syZ2dnunfvzoYNGwDYtm0beXl5JdYJDg6mZcuW1nUuJicnh9TU1BI3ERERERGRK1Vpi1RcXBwAAQEBJZYHBARYH4uLi8PJyQkfH59LrnMxkydPxsvLy3oLCQmxcXoREREREanOKm2RKmaxWErcNwzjgmV/9XfrjB8/npSUFOvt5MmTNskqIiIiIiI1Q6UtUoGBgQAXHFmKj4+3HqUKDAwkNzeXpKSkS65zMc7Oznh6epa4iYiIiIiIXKlKW6TCw8MJDAxk5cqV1mW5ubmsW7eOLl26ANC+fXscHR1LrBMbG8vevXut64iIiIiIiNiaqbP2paenc+TIEev948ePs3PnTnx9fQkNDWXcuHFMmjSJiIgIIiIimDRpEm5ubowcORIALy8v7r//fp566ilq166Nr68vTz/9NK1atbLO4iciIiIiImJrphaprVu3EhkZab3/5JNPAnDPPfcwZ84cnnnmGbKyshg7dixJSUl07tyZFStW4OHhYX3OW2+9hYODA8OHDycrK4uePXsyZ84c7O3tK/z1iIiIiIhIzWBqkerRoweGYVzycYvFwoQJE5gwYcIl13FxceHdd9/l3XffLYeEIiIiIiIiF6q050iJiIiIiIhUVipSIiIiIiIipaQiJSIiIiIiUkoqUiIiIiIiIqWkIiUiIiIiIlJKKlIiIiIiIiKlpCIlIiIiIiJSSipSIiIiIiIipaQiJSIiIiIiUkoOZgcQESlPBUYB0cnRRKdEE50cTWZ+Jl7OXni7eOPt4k2T2k3wcfUxO6aIiIhUMSpSIlItFVAAkbA0fykFuwpKPBaXHmf9+4qjK2gT0IbrQ6+ntlvtio4pIiIiVZSKlIhUO4fPHWaVwyroXlSo3B3dCfMOo75XfbxcvEjJTiE5J5kzaWc4kXyCnWd3suvsLtoEtKFfo344Ozib/RJERESkklOREpFqI78wn+8Pfs/e+L1gAVKhk08n+l3XD4vFctHnnEo9xa/Rv3L4/GF2nt3JmfQzjGw5Ei8Xr4oNLyIiIlWKJpsQkWohtyCXr/d8zd74vViwEFEQATOgnl29S5YogHqe9RjZaiT3XnMvtZxqEZ8Rz0fbP+J06ukKTC8iIiJVjYqUiFR5Ofk5fLX7K44lH8PRzpG7Wt9Fq8JWkHvl2wj1CuWBtg8Q4B5ARl4Gc3bN4WDiwfILLSIiIlWaipSIVGlZeVl8vvtzYlJjcLZ35q7WdxHuE16mbXm5eHHvNffSyLcR+YX5fBv1rY5MiYiIyEWpSIlIlVVoFPLN/m84k3YGVwdX7mlzDyFeIVe1TWcHZ+5oeQcRvhHkF+Yzb988UnNSbZRYREREqgsVKRGpstYeX8vx5OM42jlyd5u7CfIIssl27Sx23NLsFvzd/EnPTWfe3nnkFpRinKCIiIhUeypSIlIlHUg8wPqT6wG4qclNBNYKtOn2i49MuTm6EZsey+IDizEMw6b7EBERkapLRUpEqpzzWedZfGAxAJ3qdqJlnZblsh8fVx9ub3E7dhY7ohKj2HJmS7nsR0RERKoeFSkRqVLyC/NZsG8BOQU5hHiG0KdBn3LdX6hXKH0b9gVg1bFVJGUllev+REREpGpQkRKRKmV9zHrOZpzFzdGNW5vfir2dfbnvs2NwR8K8wsgrzGPJwSUa4iciIiIqUiJSdSRmJrI+pui8qP6N+uPp7Fkh+7VYLAxuMhhHO0dOpJxga+zWCtmviIiIVF4qUiJSJRiGwdJDSykwCmjk24gW/i0qdP++rr70DO8JFA3xS8tPq9D9i4iISOWiIiUiVcLOuJ1Ep0TjaOfIwIiBWCyWCs/QqW4nQr1CyS3I5bfU3yp8/yIiIlJ5qEiJSKWXkZvBimMrAOgR1gNvF29TclgsFm5qfBP2FntO5ZyCRqbEEBERkUpARUpEKr1Vx1eRnZ9NgHsAnet2NjVLbbfadKrbqehOn6JZBEVERKTmUZESkUotPiOeXXG7ABgYMbBCZun7O93qd8PZ4gx1YHHMYrPjiIiIiAlUpESkUltzfA0GBs38mhHiFWJ2HABcHFxo79EegA8OfkBqTqrJiURERKSiqUiJSKUVkxLDwXMHsWDhxvAbzY5TQnO35pAISblJvL7+dbPjiIiISAVTkRKRSskwDFYfWw3ANYHX4OfmZ3KikuwsdrCy6O/TNk4jJiXG3EAiIiJSoVSkRKRSOnz+MDGpMTjYOdAjrIfZcS7uILSv3Z6cghxe+/U1s9OIiIhIBVKREpFKp9AoZPXxoqNRnep2wtPZ0+REl/bPJv8E4NOdn+qolIiISA2iIiUilU5UYhTxGfG4OLhwfcj1Zse5rLa12xIZFkleYZ7OlRIREalBVKREpFIxDIP1MeuBoqNRro6uJif6ey93fxmA2Ttmcyr1lMlpREREpCKoSIlIpXI06Shx6XE42jmafvHdK9U9rDvd6ncjtyCXKeunmB1HREREKoCKlIhUKr/H/A5Au6B2uDm6mZzmyhUflfpo+0ecSTtjchoREREpbypSIlJpnEo9xYmUE9hZ7OgS0sXsOKUSGRZJ15Cu5BTkMPX3qWbHERERkXKmIiUilUbxuVGtA1pX6pn6LsZisViPSs3aNovEzESTE4mIiEh5UpESkUohPiOeg+cOAtA1pKvJacqmV4NetAtqR1Z+FjO3zDQ7joiIiJQjFSkRqRQ2nNwAQDO/Zvi5+ZmcpmwsFgtPX/c0AO9ufpfs/GyTE4mIiEh5UZESEdNlFmSyJ34PUHWPRhW7tfmthHqFkpCZwBe7vjA7joiIiJQTFSkRMd2BzAMUGoXU86hHXc+6Zse5Ko72jozrPA6ANze+SaFRaG4gERERKRcqUiJiLjvYn7kfKLoAb3XwQLsH8HL24uC5gyw7tMzsOCIiIlIOVKRExFzNILMwk1pOtWju39zsNDbh4ezBmPZjAPjvxv+anEZERETKg4qUiJirc9Ef7YPaY29nb24WG3qs82M42Dnwa/SvbD692ew4IiIiYmMOZgcQkcovJiaGxETbXxfpxx0/QihYsNA+qL3Nt2+mup51uaPlHXyx+wve3vQ2X938ldmRRERExIZUpETksmJiYmjarClZmVm23/gQoC2EOoTi4exh++2b7PHOj/PF7i/4Zt83/Lf3fwnyCDI7koiIiNiIipSIXFZiYiJZmVkMe34Y/vX9bbbdrIIsvjr7FYUUEmaE2Wy7lUn74PZcV+86Np7ayIfbPmRCjwlmRxIREREbUZESkSviX9+foMa2O6Ky4eQGCuML4Qz4hvrabLuVzWOdH7MWqedveB4neyezI4mIiIgNaLIJEalwhmGwPXZ70Z1tYLFYzA1Ujm5pdgtBtYKIS4/j2/3fmh1HREREbERFSkQqXExKDOeyzmFv2MMes9OUL0d7Rx7q8BAA725+1+Q0IiIiYisqUiJS4bbHFR2NqmfUg1yTw1SAMe3H4GjnyB+n/mDL6S1mxxEREREbUJESkQqVlZfF/oT9AIQXhpucpmIE1Arg9pa3AzoqJSIiUl2oSIlIhdoTv4f8wnzquNfBx/AxO06FeazTYwDM3zef+Ix4k9OIiIjI1VKREpEK8+dJJtoFtsNC9Z1k4q861u1Ix+CO5Bbk8smOT8yOIyIiIldJRUpEKsyZtDOczTiLvcWe1gGtzY5T4cZ2HAvAh9s+pKCwwOQ0IiIicjUqdZHKz8/nxRdfJDw8HFdXVxo0aMArr7xCYWGhdR3DMJgwYQLBwcG4urrSo0cP9u3bZ2JqEbmU4kkmmvs3x9XR1eQ0Fe/2Frfj4+LDieQT/HTkJ7PjiIiIyFWo1EVqypQpfPDBB8yYMYOoqCimTp3KG2+8wbvv/v/J2lOnTmXatGnMmDGDLVu2EBgYSO/evUlLSzMxuYj8VV5BHnvj9wLQLqidyWnM4eroyr3X3AvA+1vfNzmNiIiIXI1KXaQ2btzIkCFDGDhwIGFhYdx666306dOHrVu3AkVHo6ZPn84LL7zAzTffTMuWLfnss8/IzMxk7ty5JqcXkT87cO4AuQW5eLt4U9+rvtlxTFN8Tanlh5dzPOm4yWlERESkrCp1kbr++utZvXo1hw4dAmDXrl2sX7+eAQMGAHD8+HHi4uLo06eP9TnOzs50796dDRs2XHK7OTk5pKamlriJSPnaHbcbgNZ1WmOx1JxJJv4qonYEfRr2wcDgw20fmh1HREREyqhSF6lnn32WO+64g6ZNm+Lo6Ejbtm0ZN24cd9xxBwBxcXEABAQElHheQECA9bGLmTx5Ml5eXtZbSEhI+b0IESEtJ42jSUcBaBPYxuQ05hvboWjSidk7ZpOdn21yGhERESmLSl2k5s+fz5dffsncuXPZvn07n332Gf/973/57LPPSqz3199uG4Zx2d94jx8/npSUFOvt5MmT5ZJfRIrsid+DgUGIZwi+rr5mxzHdwMYDqedZj8TMRL7d/63ZcURERKQMKnWR+te//sVzzz3HiBEjaNWqFXfddRdPPPEEkydPBiAwMBDggqNP8fHxFxyl+jNnZ2c8PT1L3ESkfBiGwa6zuwBq5JTnF+Ng58CY9mMAmLl1pslpREREpCwqdZHKzMzEzq5kRHt7e+v05+Hh4QQGBrJy5Urr47m5uaxbt44uXbpUaFYRubi49DjiM+Kxt9jTwr+F2XEqjQfaPYCDnQMbTm5gZ9xOs+OIiIhIKVXqIjV48GBee+01li1bxokTJ1i0aBHTpk1j2LBhQNGQvnHjxjFp0iQWLVrE3r17GT16NG5ubowcOdLk9CICWI9GNfFrUiOvHXUpgbUCubnZzQDM3KKjUiIiIlWNg9kBLufdd9/lpZdeYuzYscTHxxMcHMyYMWP497//bV3nmWeeISsri7Fjx5KUlETnzp1ZsWIFHh4eJiYXEYCCwgLrtaPaBGiSib8a22EsC/Yt4Ks9XzG191S8XLzMjiQiIiJXqFIfkfLw8GD69OlER0eTlZXF0aNHefXVV3FycrKuY7FYmDBhArGxsWRnZ7Nu3TpatmxpYmoRKXY06SgZeRm4O7rT0Keh2XEqnW71u9HcvzkZeRl8sfsLs+OIiIhIKVTqIiUiVVvx0agWdVpgb2dvcprKx2KxWKdCf3/L+xiGYXIiERERuVIqUiJSLnILcjmQeACAVnVamZym8rqrzV24O7oTlRjFuuh1ZscRERGRK1SmInX8+HFb5xCRaubQuUPkFebh4+JDXY+6ZseptDydPRnVehSgqdBFRESqkjIVqUaNGhEZGcmXX35Jdna2rTOJSDWwJ34PAC3rtLzsBbIF/tnhnwAsjFpIbFqsyWlERETkSpSpSO3atYu2bdvy1FNPERgYyJgxY9i8ebOts4lIFZWVl8WR80cADeu7Em0C29AlpAv5hfl8vP1js+OIiIjIFShTkWrZsiXTpk3j9OnTfPrpp8TFxXH99dfTokULpk2bRkJCgq1zikgVsj9xP4VGIQHuAfi7+5sdp0oonnRi1vZZ5Bfmm5xGRERE/s5VTTbh4ODAsGHDWLBgAVOmTOHo0aM8/fTT1KtXj7vvvpvYWA1REamJimfra1lHlyK4Urc2vxU/Nz9OpZ5i6aGlZscRERGRv3FVRWrr1q2MHTuWoKAgpk2bxtNPP83Ro0dZs2YNp0+fZsiQIbbKKSJVRGpOKieSTwAqUqXh7ODMA20fAIqmQhcREZHKrUxFatq0abRq1YouXbpw5swZPv/8c6Kjo3n11VcJDw+na9eufPjhh2zfvt3WeUWkktuXsA+AEM8QvF28zQ1TxYzpMAYLFlYeW8nhc4fNjiMiIiKXUaYiNXPmTEaOHElMTAyLFy9m0KBB2NmV3FRoaCizZ8+2SUgRqTo0rK/swrzDGBAxAIAPtn5gchoRERG5nDIVqcOHDzN+/HgCAwMvuY6TkxP33HNPmYOJSNWTlJXEmbQzWLDQ3L+52XGqpLEdiyad+HTnp2TmZZqcRkRERC6lTEXq008/5Ztvvrlg+TfffMNnn3121aFEpGoqHtYX5h1GLadaJqepmvo27EuYdxhJ2UnM3zvf7DgiIiJyCWUqUq+//jp+fn4XLK9Tpw6TJk266lAiUjXtT9gPoKNRV8Hezp6H2j8EwMytM01OIyIiIpdSpiIVHR1NeHj4Bcvr169PTEzMVYcSkarnfNZ5YtNjsWChmV8zs+NUafe1vQ8neye2nNnCltNbzI4jIiIiF1GmIlWnTh127959wfJdu3ZRu3btqw4lIlVP8dGocO9w3J3cTU5Ttfm7+3Nb89sAHZUSERGprMpUpEaMGMFjjz3G2rVrKSgooKCggDVr1vD4448zYsQIW2cUkSpAw/psq3jSia/3fk1SVpLJaUREROSvylSkXn31VTp37kzPnj1xdXXF1dWVPn36cOONN+ocKZEa6M/D+pr6NTU7TrVwXb3raBPQhuz8bObsnGN2HBEREfmLMhUpJycn5s+fz4EDB/jqq69YuHAhR48e5ZNPPsHJycnWGUWkkrMO6/PRsD5bsVgs1qNSM7fOpNAoNDmRiIiI/JnD1Ty5cePGNG7c2FZZRKSKKp72XMP6bGtkq5E8veJpDp8/zOpjq+ndsLfZkUREROR/ylSkCgoKmDNnDqtXryY+Pp7CwpK/KV2zZo1NwolI5Xc+6zxx6XGara8c1HKqxT1t7mHGlhnM3DpTRUpERKQSKVORevzxx5kzZw4DBw6kZcuWWCwWW+cSkSriz8P63BzdTE5T/fyz4z+ZsWUG3x/8npMpJwnxCjE7koiIiFDGIjVv3jwWLFjAgAEDbJ1HRKqYqMQoAJr7aVhfeWju35zIsEjWnljLe1ve4/Ver5sdSURERLiKySYaNWpk6ywiUsWkZKdwJu0MAE38mpicpvoad+04AGZtm0VGboa5YURERAQoY5F66qmnePvttzEMw9Z5RKQKOZB4AIBQr1BqOdUyOU31NTBiIA18GpCUncQXu78wO46IiIhQxqF969evZ+3atSxfvpwWLVrg6OhY4vGFCxfaJJyIVG7FRUqTTJQvezt7Huv0GON+Hsfbm97mwfYPYmcp0+/BRERExEbK9H9ib29vhg0bRvfu3fHz88PLy6vETUSqv4zcDKJTogF0Ed4KcG/be/Fw8uBA4gFWHl1pdhwREZEar0xHpD799FNb5xCRKubguYMYGATVCsLbxdvsONWep7Mn97e9n+mbpjN903T6NuprdiQREZEarcxjQ/Lz81m1ahUffvghaWlpAJw5c4b09HSbhRORyqt4WJ+ORlWcRzs/igULPx35iaiEKLPjiIiI1GhlKlLR0dG0atWKIUOG8PDDD5OQkADA1KlTefrpp20aUEQqn5z8HI4lHQN0flRFauDTgCFNhwDw9qa3TU4jIiJSs5WpSD3++ON06NCBpKQkXF1drcuHDRvG6tWrbRZORCqnw+cPU2AU4Ofmh7+7v9lxapRxnccB8Nmuz4jPiDc3jIiISA1WpiK1fv16XnzxRZycnEosr1+/PqdPn7ZJMBGpvIovwtu0tob1VbRu9bvRqW4nsvOzmbF5htlxREREaqwyFanCwkIKCgouWH7q1Ck8PDyuOpSIVF55BXkcPncYgGb+GtZX0SwWC890eQaAGZtnkJ6r81JFRETMUKYi1bt3b6ZPn269b7FYSE9P5+WXX2bAgAG2yiYildCxpGPkFebh6exJUK0gs+PUSEObDqWRbyOSspP4ZMcnZscRERGpkcpUpN566y3WrVtH8+bNyc7OZuTIkYSFhXH69GmmTJli64wiUokUD+tr5tcMi8Vicpqayd7OnqeuewqANze+SV5BnsmJREREap4yFang4GB27tzJ008/zZgxY2jbti2vv/46O3bsoE6dOrbOKCKVREFhAYfOHQI0W5/Z7mlzD/5u/sSkxPDN/m/MjiMiIlLjlOmCvACurq7cd9993HfffbbMIyKVWHRKNFn5Wbg5uhHiFWJ2nBrN1dGVxzo/xktrX+KNDW9wR8s7dIRQRESkApWpSH3++eeXffzuu+8uUxgRqdyKh/U1qd0EO0uZr+ctNjK241gmr5/Mzrid/Hz0Z/o16md2JBERkRqjTEXq8ccfL3E/Ly+PzMxMnJyccHNzU5ESqYYMw+DAuQOAhvVVFr6uvoxpP4a3/niL//z6H/o27KujUiIiIhWkTL9STkpKKnFLT0/n4MGDXH/99Xz99de2zigilUB8Xjzpuek42zsT7hNudhz5n391+RfO9s5sOLmBtSfWmh1HRESkxrDZ2JyIiAhef/31C45WiUj1cDz7OACNazfGwa7Mp1eKjQV5BPGPdv8A4D+//sfkNCIiIjWHTX8asre358yZM7bcpIhUEieyTwDQ1K+puUEqmaioqHLZrp+fH6GhoVe07jNdn+HDbR/yy4lf+C36N26of0O5ZBIREZH/V6YitWTJkhL3DcMgNjaWGTNm0LVrV5sEE5FKJABSC1JxsHOgkW8js9NUCunn0wEYNWpUuWzf1c2VA1EHrqhMhXiFcO819zJr+yz+8+t/WHHXinLJJCIiIv+vTEVq6NChJe5bLBb8/f258cYbefPNN22RS0Qqk//NLdHQpyFO9k7mZqkkstOzAYgcG0lEmwibbjshOoFFkxaRmJh4xUelnrv+OWbvmM3KYyvZdGoTnet1tmkmERERKalMRaqwsNDWOUSkMmvyvz/8mpiboxLyqetDUOMgs2MQ7hPO3W3u5tOdnzJx3UR+vPNHsyOJiIhUa7oQjIhcVlxWHASBBQuNfRubHUcu4/kbnsfeYs/yI8tZH7Pe7DgiIiLVWpmOSD355JNXvO60adPKsgsRqSR+jfsVgADHANyd3E1OI5fTyLcR97W9j4+2f8Tzq59n3eh1uq6UiIhIOSlTkdqxYwfbt28nPz+fJk2KhvocOnQIe3t72rVrZ11P/wMXqfrWnV0HQH2X+iYnkSvx7+7/5vNdn/NbzG/8fPRn+jXqZ3YkERGRaqlMQ/sGDx5M9+7dOXXqFNu3b2f79u2cPHmSyMhIBg0axNq1a1m7di1r1qyxdV4RqUCpOalsTdwKqEhVFfU86/Fwx4cBeH718xQaOqdVRESkPJSpSL355ptMnjwZHx8f6zIfHx9effVVzdonUo38dOQn8o18SARvB2+z48gVeu7656jlVIsdcTtYGLXQ7DgiIiLVUpmKVGpqKmfPnr1geXx8PGlpaVcdSkQqhyUH/3fNuIPm5pDS8Xf356nrngLgxTUvkl+Yb3IiERGR6qdM50gNGzaMe++9lzfffJNrr70WgD/++IN//etf3HzzzTYNKCLmyCvIY9nhZUV3VKRMERUVVebn3uh6I9Mdp3Pw3EEmfD+Bm+uX/N7s5+d3xdeoEhERkQuVqUh98MEHPP3004waNYq8vLyiDTk4cP/99/PGG2/YNKCImGN9zHqSs5PxdvIm+WSy2XFqlPTz6QCMGjXq6jZ0LdAPXtvwGq+NeA1y//8hVzdXDkQdUJkSEREpozIVKTc3N95//33eeOMNjh49imEYNGrUCHd3TY0sUl0UD+u7IeAGfjB+MDlNzZKdng1A5NhIItpElHk7BUYB3yZ8S0qtFK557Ro6eXYCICE6gUWTFpGYmKgiJSIiUkZlKlLFYmNjiY2NpVu3bri6umIYhqY8F6kGDMPg+4PfA9A9oDs/oCJlBp+6PgQ1DrqqbfSr3Y/5++azJ3MP3Vt2x9vF2zbhREREargyTTZx7tw5evbsSePGjRkwYACxsbEAPPDAAzz11FM2DSgiFW9fwj6OJx/H2d6Za/2vNTuOXIUmtZsQ5hVGgVHA6mOrzY4jIiJSbZSpSD3xxBM4OjoSExODm5ubdfntt9/OTz/9ZLNwImKO4mF9vRr0wtXB1eQ0cjUsFgt9G/UFYG/CXk6mnDQ5kYiISPVQpiK1YsUKpkyZQr169Uosj4iIIDo62ibBRMQ8xcP6hjQZYnISsYXAWoFcE3gNAD8f/RnDMMwNJCIiUg2UqUhlZGSUOBJVLDExEWdn56sO9WenT59m1KhR1K5dGzc3N6655hq2bdtmfdwwDCZMmEBwcDCurq706NGDffv22TSDSE0SmxbL5tObARjUeJDJacRWbgy7ESd7J06nneZA1gGz44iIiFR5ZSpS3bp14/PPP7fet1gsFBYW8sYbbxAZGWmzcElJSXTt2hVHR0eWL1/O/v37efPNN/H29rauM3XqVKZNm8aMGTPYsmULgYGB9O7dWxcGFimjHw4VTSzRqW4ngjyubqIDqTw8nD2IDCv6/rw5dTNc+LswERERKYUyzdr3xhtv0KNHD7Zu3Upubi7PPPMM+/bt4/z58/z+++82CzdlyhRCQkL49NNPrcvCwsKsfzcMg+nTp/PCCy9YLwT82WefERAQwNy5cxkzZozNsojUFMXnR2lYX/XTqW4ndsbt5GzGWehldhoREZGqrUxHpJo3b87u3bvp1KkTvXv3JiMjg5tvvpkdO3bQsGFDm4VbsmQJHTp04LbbbqNOnTq0bduWjz76yPr48ePHiYuLo0+fPtZlzs7OdO/enQ0bNlxyuzk5OaSmppa4iQhk5Gaw6tgqAG5qcpPJacTW7Cx2DIwYWHSnHew8v9PUPCIiIlVZqYtUXl4ekZGRpKamMnHiRJYuXcqPP/7Iq6++SlCQbYcBHTt2jJkzZxIREcHPP//MQw89xGOPPWYdVhgXFwdAQEBAiecFBARYH7uYyZMn4+XlZb2FhITYNLdIVbXi6ApyCnII9w6nhX8Ls+NIOQjxCqGJaxMAJu+eTH5hvsmJREREqqZSFylHR0f27t1bIRfeLSwspF27dkyaNIm2bdsyZswY/vGPfzBz5swS6/01y99dGHj8+PGkpKRYbydPajpgEYAlh/5/WJ8url19dfbsDJlwJO0I0zZOMzuOiIhIlVSmoX133303s2fPtnWWCwQFBdG8efMSy5o1a0ZMTAwAgYGBABccfYqPj7/gKNWfOTs74+npWeImUtMVFBaw9NBSQMP6qjsXOxdYUfT3f6/9NwcTD5obSEREpAoq02QTubm5fPzxx6xcuZIOHTrg7u5e4vFp02zzG86uXbty8GDJ/8EfOnSI+vXrAxAeHk5gYCArV66kbdu21mzr1q1jypQpNskgUlNsPLWRxMxEvF28uT70erPjSHnbCdf+41r+SPiD+5fcz6/3/oqdpUy/WxMREamRSlWkjh07RlhYGHv37qVdu3ZAUbH5M1sOB3riiSfo0qULkyZNYvjw4WzevJlZs2Yxa9Ys677GjRvHpEmTiIiIICIigkmTJuHm5sbIkSNtlkOkJiierW9gxEAc7R1NTiMV4cXWLzLitxH8fvJ33tv8Ho92ftTsSCIiIlVGqYpUREQEsbGxrF27FoDbb7+dd95557LD6K5Gx44dWbRoEePHj+eVV14hPDyc6dOnc+edd1rXeeaZZ8jKymLs2LEkJSXRuXNnVqxYgYeHR7lkEqmuiouUhvXVHEFuQUztNZWxP47ludXPMajxIMJ9ws2OJSIiUiWUahyHYRgl7i9fvpyMjAybBvqrQYMGsWfPHrKzs4mKiuIf//hHicctFgsTJkwgNjaW7Oxs1q1bR8uWLcs1k0h1czDxIAfPHcTRzpF+jfqZHUcq0JgOY+hevzuZeZncv+R+Co1CsyOJiIhUCVc1IP6vxUpEqqbio1GR4ZF4OmvylZrEzmLHxzd9jJujG2tPrOXtP942O5KIiEiVUKoiZbFYLjgHSlMki1R93x/8HoCbGmtYX03UyLcR0/oUTRL03Orn2HN2j8mJREREKr9SnSNlGAajR4/G2dkZgOzsbB566KELZu1buHCh7RKKSLlKyEhgw8kNgM6PqskebP8gSw8vZemhpdy58E42/2MzLg4uZscSERGptEp1ROqee+6hTp06eHl54eXlxahRowgODrbeL76JSNWx7PAyDAzaBrYlxCvE7DhiEovFwuybZlPHvQ574vfw/OrnzY4kIiJSqZXqiNSnn35aXjlExCTWYX06GlXj1XGvw+ybZjP468G89cdb9G/Un94Ne5sdS0REpFLS1RdFarCsvCxWHF0BwJAmQ0xOI5XBoMaDeKj9QwCMWjSK2LRYkxOJiIhUTipSIjXYmuNryMzLpJ5nPa4JvMbsOFJJTOs7jVZ1WhGfEc/IhSMpKCwwO5KIiEiloyIlUoP9ebY+zcApxVwdXVlw2wLcHd355cQvvLLuFbMjiYiIVDoqUiI1VKFRyA+HfgBgSFMN65OSmvo15cNBHwLwn1//w6pjq0xOJCIiUrmoSInUUFvPbCUuPQ4PJw+61+9udhyphO5sfScPtH0AA4M7F97JqdRTZkcSERGpNFSkRGqo7w8UDevrH9EfZwdnk9NIZfVO/3doHdCa+Ix4bvvmNnLyc8yOJCIiUimoSInUUEsOLQGKzo8SuRRXR1cWDl+It4s3f5z6g3E/jTM7koiISKWgIiVSAx1LOsbe+L3YW+zpH9Hf7DhSyTX0bcjcm+diwcIH2z7gkx2fmB1JRETEdCpSIjXQkoNFR6O61e+Gr6uvyWmkKugf0Z+JPSYCMHbZWLac3mJyIhEREXOpSInUQMVF6qYmGtYnV+6Fbi9wU5ObyCnIYej8oZxOPW12JBEREdOoSInUMOezzvNr9K+AipSUjp3Fji+GfUEL/xacSTvDTfNuIiM3w+xYIiIipnAwO4CIVKzlh5dTYBTQsk5LGvg0MDuOVDGezp78cMcPdPq4E9tjt3P34rv55rZvsLP8/+/lYmJiSExMLLcMfn5+hIaGltv2RUREroSKlEgNo9n65GqF+4Sz+PbF3Pj5jSyMWshLa17itZ6vAUUlqmmzpmRlZpXb/l3dXDkQdUBlSkRETKUiJVKD5OTnsPzwckDD+uTqdA3tyseDP+buxXczaf0kmvo15a42d5GYmEhWZhbDnh+Gf31/m+83ITqBRZMWkZiYqCIlIiKmUpESqUHWRa8jLTeNwFqBdKzb0ew4UsXd1eYuohKjmLx+Mg/88AANfBrgiisA/vX9CWocZHJCERGR8qPJJkRqkO8PfA/A4MaDS5zTIlJWr974Kjc3u5ncgtyimfwyNZOfiIjUDPpJSqSGMAzj/8+P0rA+sRE7ix2fD/2cdkHtSMxM5InNT4Cz2alERETKn4qUSA2xPXY7p1JP4e7oTq8GvcyOI9WIu5M7S0YsIdgjmKNpR2E4FBqFZscSEREpVypSIjXE4gOLAejbqC8uDi7mhpFqp65nXZaMWIKrvSs0hHUp6zAMw+xYIiIi5UZFSqSG+P5g0flRQ5oMMTmJVFftg9szpf0UKITDWYdZc2KN2ZFERETKjYqUSA1wLOkYe+L3YG+xZ2DEQLPjSDXWNaAr/FD09/Ux69lyeou5gURERMqJipRIDVA8W98N9W+gtlttk9NItbcDOtTqAMCPR37kQOIBkwOJiIjYnoqUSA2gYX1S0drWaku7oHYAfBf1HSdTTpqcSERExLZUpESquXOZ5/gt5jdARUoqjsViYWDEQBrXbkx+YT5f7/2axMxEs2OJiIjYjIqUSDW39NBSCo1CWge0Jtwn3Ow4UoPYWey4tdmt1PWoS1Z+Fl/u/pK0nDSzY4mIiNiEipRINadhfWImR3tH7mh5B76uvqTkpPDVnq/Izs82O5aIiMhVU5ESqcay8rL4+ejPAAxtOtTcMFJjuTu5M6rVKGo51eJsxlnm7plLbkGu2bFERESuioqUSDW26tgqMvMyCfEMoW1gW7PjSA3m4+rDqFajcHFw4WTqSb7Z9w0FhQVmxxIRESkzFSmRaqx4WN9NTW7CYrGYnEZquoBaAYxsORJHO0eOJB1h0YFFFBqFZscSEREpExUpkWqqoLCAHw4VXRlVw/qksgjxCmF4i+HYWezYl7CPHw//iGEYZscSEREpNRUpkWrqj1N/EJ8Rj5ezF93rdzc7johVI99G3NzsZgC2xW5jzfE1JicSEREpPQezA4hI+Sge1jcgYgCO9o4mp5HKKCoqyrRttvBvQXbjbJYeWsr6k+txdXSlS0gXm+cREREpLypSItWQYRgsPrAY0LTncqH08+kAjBo1qvz2kZ7+t+u0D2pPVl4Wq4+vZuWxlbg4uNAuqF25ZRIREbElFSmRauhA4gEOnz+Mo50j/SP6mx1HKpns9KLrOEWOjSSiTYRNt31402HWfrKW7Owru1bU9aHXk5WfxYaTG/jh0A842DnQOqC1TTOJiIiUBxUpkWqoeFjfjeE34unsaXIaqax86voQ1DjIpttMjEks9XN6hfcityCXrWe2svjAYuwt9rSo08KmuURERGxNk02IVEPFRUrD+qQqsFgsDGg0gLaBbTEw+C7qOw4kHjA7loiIyGWpSIlUM7Fpsfxx6g+g6PpRIlWBxWJhUONBtA5ojYHBN/u/4fC5w2bHEhERuSQVKZFqpvjaUR2DO1LXs67JaUSunJ3FjiFNhtDCvwWFRiHz983n6PmjZscSERG5KBUpkWpGw/qkKrOz2DGs6TCa+jWlwChg3r55nEg+YXYsERGRC6hIiVQjKdkprDy6EoBhzYaZnEakbOzt7Lm12a1E+EaQX5jP3D1ziUmJMTuWiIhICSpSItXID4d+IK8wj6Z+TWnu39zsOCJlZm9nz/AWw2ng04C8wjy+2vMV0cnRZscSERGxUpESqUa+i/oOgFua3WJyEpGr52DnwIgWIwj3Die3IJev9nzFmZwzZscSEREBVKREqo303HR+OvIToCIl1YejvSN3tLyDhj4NySvMY/n55dDQ7FQiIiIqUiLVxvLDy8nOzybcO5xrAq8xO46IzTjaOzKi5Qga+zamgAK4A347+5vZsUREpIZTkRKpJoqH9d3a/FYsFovJaURsy8HOgeEthhPmHAYO8PSWp1l8YLHZsUREpAZTkRKpBrLzs1l2eBmgYX1Sfdnb2dPLpxfshXwjn9u+uY1v9n1jdiwREamhVKREqoEVR1eQnptOPc96dKzb0ew4IuXGzmIHC2FA3QHkF+Yz4rsRfLX7K7NjiYhIDaQiJVINFA/ru7npzUU/aIpUZ4Uwoe0E7r3mXgqNQu5adBfvb3nf7FQiIlLD6CcukSoutyCXJQeXAHBLcw3rk5rB3mLPxzd9zMMdH8bA4OEfH2bCLxMwDMPsaCIiUkOoSIlUcWuPryU5O5k67nXoGtLV7DgiFcbOYse7/d9lQvcJAExcN5FHlz9KoVFobjAREakRVKREqrhv938LwLCmw7C3szc5jUjFslgsvNzjZWb0n4EFC+9teY87F95JbkGu2dFERKSaU5ESqcLyC/NZfHAxUDTtuUhN9XCnh5l7y1wc7RyZt3ceN319Exm5GWbHEhGRaqxKFanJkydjsVgYN26cdZlhGEyYMIHg4GBcXV3p0aMH+/btMy+kSAX6Lfo3EjMT8XX1pXv97mbHETHViJYjWDpyKW6Obvx89Gd6ft6Tc5nnzI4lIiLVVJUpUlu2bGHWrFm0bt26xPKpU6cybdo0ZsyYwZYtWwgMDKR3796kpaWZlFSk4hTP1jekyRAc7R1NTiNivj4N+7D67tX4uvqy6fQmbvj0Bk4knzA7loiIVENVokilp6dz55138tFHH+Hj42NdbhgG06dP54UXXuDmm2+mZcuWfPbZZ2RmZjJ37lwTE4uUv0KjkIVRCwFdhFfkz66tdy2/3fsb9TzrEZUYxbUfX8vWM1vNjiUiItWMg9kBrsTDDz/MwIED6dWrF6+++qp1+fHjx4mLi6NPnz7WZc7OznTv3p0NGzYwZsyYi24vJyeHnJwc6/3U1NTyCy9STv449Qex6bF4OnvSq0Evs+OIVCrN/Zuz8f6NDJw7kN1nd9N9Tnfm3zqfQY0HXfZ5MTExJCYmllsuPz8/QkNDy237IiJScSp9kZo3bx7bt29ny5YtFzwWFxcHQEBAQInlAQEBREdHX3KbkydPZuLEibYNKlLBvttfNKxvUONBODs4m5xGpPKp51mP3+79jdu+uY0VR1cwZN4Q3u3/LmM7jr3o+jExMTRt1pSszKxyy+Tq5sqBqAMqUyIi1UClLlInT57k8ccfZ8WKFbi4uFxyPYvFUuK+YRgXLPuz8ePH8+STT1rvp6amEhIScvWBRSqIYRjW86M0rE/k0jydPVl6x1L+ueyfzN4xm4d/fJjjSceZ0nsKdpaSo9sTExPJysxi2PPD8K/vb/MsCdEJLJq0iMTERBUpEZFqoFIXqW3bthEfH0/79u2tywoKCvj111+ZMWMGBw8eBIqOTAUFBVnXiY+Pv+Ao1Z85Ozvj7Kzf4EvVtT12O9Ep0bg5utGvUT+z44hUao72jnw0+CPCvcN5ce2L/HfjfzmRcoLPh36Oq6PrBev71/cnqHHQRbYkIiLy/yp1kerZsyd79uwpsezee++ladOmPPvsszRo0IDAwEBWrlxJ27ZtAcjNzWXdunVMmTLFjMgiFaL4aNSAiAG4ObqV63kdUVFR5bJdkYpksVh4odsLhHmHce/39/Lt/m85nXqa70d8j7+77Y8+iYhI9Vepi5SHhwctW7Yssczd3Z3atWtbl48bN45JkyYRERFBREQEkyZNws3NjZEjR5oRWaTcGYbBt/u/BYqG9VXEeR1QNHumSFV3Z+s7qetZl2Hzh7Hx1EY6f9yZZSOX0cy/mdnRRESkiqnURepKPPPMM2RlZTF27FiSkpLo3LkzK1aswMPDw+xoIuViR9wODp8/jKuDK4MaD+LQ3kPlel7H4U2HWfvJWrKzs22+bREz9AjrYZ3R71jSMa6bfR3fDv8WX3zNjiYiIlVIlStSv/zyS4n7FouFCRMmMGHCBFPyiFS0eXvnAUWz9dVyqmVdXl7ndSTGlN9U0CJlZYshp7M6zuKpLU+xK2kX/b7sx2i/0VcfTEREaowqV6REarJCo5D5++YDMKLlCJPTiFS89PNFQ0xHjRplmw06ADdBQesCZifMht6QlpZGEJpsQkRELk9FSqQK+ePUH8SkxFDLqRb9G/U3O45IhctOLxpiGjk2kog2ETbZpmEYbE/fzrb0bdAVfsn4hbCCMJzsnWyyfRERqZ5UpESqkPl7i45GDW069KLTNovUFD51fWw6lDWYYCyrLWxlK7EOsczZOYc7Wt6Bh7POtxURkYuz+/tVRKQyKCgsYMH+BQCMaKFhfSK2FmqEwmfghBOx6bF8vONj4tLjzI4lIiKVlIqUSBXxa/SvxKXH4ePiQ++Gvc2OI1I9nYQeDj3wc/MjNSeVT3d+yqFzh8xOJSIilZCKlEgVUTxb3y3NbtG5GyLlqJalFvddcx/h3uHkFuQyb+88Np3aZHYsERGpZFSkRKqAvII8vo0qugivZusTKX+ujq7c2epO2ga2xcDgp6M/8ePhHyk0Cs2OJiIilYSKlEgVsOrYKs5nnSfAPYAeYT3MjiNSI9jb2TO48WB6NegFwJYzW/h679fk5OeYnExERCoDFSmRKuCrPV8BcFvz27C3szc5jUjNYbFY6BrSleHNh+Ng58CR80f4ZOcnpGSnmB1NRERMpiIlUsml56az6MAiAO5qc5fJaURqpmb+zbj3mnup5VSL+Ix4Ptr+EadTT5sdS0RETKQiJVLJLYpaRGZeJhG+EXQM7mh2HJEaK9gjmAfaPkCAewAZeRnM2TWH/Qn7zY4lIiImUZESqeS+3PMlAKNaj8JisZicRqRm83Lx4t5r7iXCN4L8wny+2f8N62PWYxiG2dFERKSCqUiJVGKxabGsOrYKgDtb3WlyGhEBcHZwZkTLEXSq2wmA1cdXs+TQEgoKC0xOJiIiFUlFSqQS+3rv1xQahXQJ6UJD34ZmxxGR/7Gz2NG/UX/6N+qPBQs743by5Z4vycrLMjuaiIhUEBUpkUrsy93/G9bXapTJSUTkYjrV7cQdLe/Ayd6JE8knmL1jNuezzpsdS0REKoCKlEgltS9+HzviduBg58DwFsPNjiMilxBRO4L7rrkPT2dPzmWd4+PtHxOdHG12LBERKWcqUiKVVPG1owZEDKC2W22T04jI5QTUCuCBtg8Q7BFMVn4WX+z+gt1nd5sdS0REypGKlEglVFBYoGF9IlWMh7MHo9uMpplfMwqMAhYdWMTaE2s1o5+ISDWlIiVSCa05voaTqSfxdvFmcJPBZscRkSvkaO/Ibc1vo2tIVwB+jf6VhQcWkl+Yb3IyERGxNQezA4jIhT7d+SkAI1uOxMXBxeQ0IlIaFouFXg164evqy7LDy9gbv5eU7BR6uPQwO5qIiNiQipRIJZOUlcTCqIUA3Nf2PpPTiEhZtQtqh4+LDwv2L+Bk6kkWZywGP7NTiYiIrWhon0gl8/Xer8kpyKFVnVa0C2pndhwRuQrhPuHc3/Z+fFx8SCtIg/thW+I2s2OJiIgNqEiJVDLFw/rua3sfFovF5DQicrX83Py4v+39BDgGgCs8vOlh5u6Za3YsERG5SipSIpXI7rO72XpmK452jtzZ6k6z44iIjbg7uTOw9kDYD3mFedy58E4m/zZZM/qJiFRhKlIilcinO4qORg1uMhh/d3+T04iILTlYHOAbuLNB0S9Jnl/zPGOWjtGMfiIiVZSKlEglkVuQy5d7iq4ddd81mmRCpFoy4MkWT/Ju/3exs9jx0faPGPz1YNJy0sxOJiIipaRZ+yqhmJgYEhMTy237fn5+hIaGltv2pWyWHlpKYmYiQbWC6Nuor9lxRKQcPdLpEUK9Qhnx7Qh+OvIT3eZ0Y9nIZQR7BJsdTURErpCKVCUTExND02ZNycrMKrd9uLq5ciDqgMpUJfPhtg8BuKfNPTjY6UtTpLq7qclN/DL6FwZ/PZidcTu59uNr+fHOH2lZp6XZ0URE5Arop7VKJjExkazMLIY9Pwz/+rY/RyYhOoFFkxaRmJioIlWJHDl/hBVHV2DBwoPtHzQ7johUkE51O/HH/X/Q/6v+HDx3kK6fdGXh8IX0bNDT7GgiIvI3VKQqKf/6/gQ1DjI7hlSQD7cWHY3q16gf4T7hJqcRkYoU7hPOhvs3MGz+MH6N/pV+X/Xj48Efc88195gdTURELkNFSqSCXOrct5yCHD7a+hEAvX16s3379lJtNyoqyib5RMQ8vq6+rBi1gtHfj2be3nmM/n400SnRvNTtJV1PTkSkklKREqkAlz33rTVwM5ACTw56Esp4WZn09PSriSgiJnN2cOarm78izCuM139/nZd/eZnjycf5cNCHONk7mR1PRET+QkVKpAJc7ty37xO/52zeWTrU7UC7D9qVetuHNx1m7Sdryc7OtlVcETGJncWOyb0mE+YdxtgfxzJn5xxOpZ7i29u+xcvFy+x4IiLyJypSIhXor+e+nU0/y9nYs9hZ7OjWshsezh6l3mZiTPlNlS8itnclw3E72nXkrY5v8dy251h1bBVt32vLW53eIsQ95JLP0aUtREQqloqUiIm2xm4FoGntpmUqUSJSdaSfLxp+O2rUqCt/UiAwEo5znKFLh8IC4MTFV9WlLUREKpaKlIhJcvJz2H12NwDtg9ubnEZEylt2etHw28ixkUS0ibji52UWZPJz0s8kuCVgGW2hq2dXmrs3L7GOLm0hIlLxVKRETLI9bju5Bbn4ufkR7q0pz0VqCp+6PqW+vMU/Cv7BD4d+YE/8Htanrie7Vjb9GvbD3s6+nFKKiMjfsTM7gEhNVGgUsvn0ZgCurXutpjcWkctytHdkWNNh9AwvulDv1jNb+XLPl2TmZZqcTESk5lKREjHBgcQDJGcn4+rgSuuA1mbHEZEqwGKxcH3o9YxoMQIneydOJJ/g4+0fE58Rb3Y0EZEaSUVKxAR/nPoDgA7BHXC0dzQ5jYhUJU38mnDfNffh7eJNUnYSH2//mCNZR8yOJSJS46hIiVSw06mnOZl6EjuLHR2DO5odR0SqoIBaATzQ9gHCvcPJK8xjTfIaGAC5BblmRxMRqTFUpEQq2B+ni45GtazTUlOei0iZuTu5M6r1KG4IvaFoQSd4YMMDxKTEmBtMRKSGUJESqUDpBensT9gPFE0yISJyNewsdtwYfiP9fPpBFuxL3ke7D9vx85GfzY4mIlLtqUiJVKC9GXspNAoJ8wojyKN00x+LiFxKqEsofAjNvJpxLusc/b/qz4RfJlBQWGB2NBGRaktFSqSiuML+zKKjUdeFXGdyGBGpdpJhdtfZPNT+IQwMJq6bSN8v+3Iq9ZTZyUREqiUVKZGKci3kG/kE1gokwjfC7DQiUg052zszc9BMPh/6OW6Obqw+vprWM1vzzb5vzI4mIlLtqEiJVIC0vDToXPT3G0Jv0AV4RaRc3dXmLnaM2UHH4I4kZScx/Nvh3LP4HlJzUs2OJiJSbahIiVSAb058Ay7g7eBNM79mZscRkRqgce3G/H7f77x4w4vYWez4fNfntPmgDetj1psdTUSkWlCREilnGbkZfHXsKwDaurfV0SgRqTCO9o7858b/8OvoXwn3DudE8gm6z+nOi2teJK8gz+x4IiJVmoqUSDmbtW0WybnJcB4aujY0O46I1EBdQ7uy86Gd3NPmHgqNQl777TU6f9yZHbE7zI4mIlJlqUiJlKOsvCze2PBG0Z31Rdd8ERExg6ezJ3OGzmHBrQvwcfFhR9wOOn7UkedWPUdWXpbZ8UREqhz9VCdSjmZsnkFseiwBLgGwy+w0IiJwW4vbiHo4iuEthlNgFDDl9ym0/qA1v5z4xexoIiJVioqUSDlJzk5m8vrJADzU9CHQdTFFpJIIqBXA/Fvn8/2I7wn2CObI+SNEfhbJgz88SHJ2stnxRESqBBUpkXIyZf0UkrKTaOHfgoH1BpodR0TkAjc1uYn9Y/fzUPuHAPho+0c0f685C6MWYhiGyelERCo3FSmRcnAm7Qxvb3obgEk9J2FvsTc5kYjIxXm5eDFz0EzWjV5H49qNiU2P5ZYFt9D/q/4cOnfI7HgiIpWWipRIOZj4y0Sy8rPoGtKVwY0Hmx1HRORvdavfjV0P7eLFG17Eyd6Jn4/+TMv3WzJ+1XgycjPMjiciUumoSInY2MHEg8zeMRuA13u9rutGiUiV4eLgwn9u/A97/7mX/o36k1eYx+u/v07T95qyYN8CDfcTEfkTB7MDiFQ3z6x6hgKjgMGNB3N96PVmxxGRGiQqKspm2/pP0//Q26c3/937X06lnuL2b29nVvgs3un/Ds39m9tsPyIiVVWlLlKTJ09m4cKFHDhwAFdXV7p06cKUKVNo0qSJdR3DMJg4cSKzZs0iKSmJzp07895779GiRQsTk0tNtezQMpYcXIKDnQNTek0xO46I1BDp59MBGDVqlO037gBcX3RbfXw1rWe25qEODzGhxwT83Pxsvz8RkSqiUhepdevW8fDDD9OxY0fy8/N54YUX6NOnD/v378fd3R2AqVOnMm3aNObMmUPjxo159dVX6d27NwcPHsTDw8PkVyA1SVZeFo8ufxSAJ659gmb+zUxOJCI1RXZ6NgCRYyOJaBNh8+0nRCew6L1FdJ/UnXVn1/Helvf4cveXvNTtJR7p9AjODs4236eISGVXqYvUTz/9VOL+p59+Sp06ddi2bRvdunXDMAymT5/OCy+8wM033wzAZ599RkBAAHPnzmXMmDFmxJYaaurvUzmefJy6HnV5qdtLZscRkRrIp64PQY2DymfjSTCt0zSSfZJ5asVT7IzbydMrn2bm1plM7T2VYU2H6ZxQEalRqtRkEykpKQD4+voCcPz4ceLi4ujTp491HWdnZ7p3786GDRsuuZ2cnBxSU1NL3ESuxrGkY9aL707rOw0PZx0NFZHq6cbwG9n6j618ctMnBNYK5GjSUW5ZcAvd53Rn65mtZscTEakwVaZIGYbBk08+yfXXX0/Lli0BiIuLAyAgIKDEugEBAdbHLmby5Ml4eXlZbyEhIeUXXGqEx396nJyCHHqG9+S25reZHUdEpFzZ29lzb9t7OfzoYV7q9hKuDq78FvMbHT/qyN2L7uZU6imzI4qIlLsqU6QeeeQRdu/ezddff33BY38dSmAYxmWHF4wfP56UlBTr7eTJkzbPKzXHvL3zWHpoKY52jswYMENDW0SkxqjlVItXIl/h4CMHGdW6aKKLL3Z/QeN3G/Py2pdJz003OaGISPmpEkXq0UcfZcmSJaxdu5Z69epZlwcGBgJccPQpPj7+gqNUf+bs7Iynp2eJm0hZnEk7w9hlYwF4/obnaerX1OREIiIVL8QrhC+GfcHmBzZzfej1ZOVn8cqvrxDxbgQfbv2Q/MJ8syOKiNhcpS5ShmHwyCOPsHDhQtasWUN4eHiJx8PDwwkMDGTlypXWZbm5uaxbt44uXbpUdFypYQzD4P4l95OUnUT7oPa8cMMLZkcSETFVx7od+XX0r3x727c08GlAXHocDy17iFYzW/H9ge91QV8RqVYqdZF6+OGH+fLLL5k7dy4eHh7ExcURFxdHVlYWUDSkb9y4cUyaNIlFixaxd+9eRo8ejZubGyNHjjQ5vVR3s7bN4qcjP+Fs78znwz7H0d7R7EgiIqazWCzc0vwWoh6O4u1+b1PbtTYHEg8wdP5Qus3pxh+n/jA7ooiITVTqIjVz5kxSUlLo0aMHQUFB1tv8+fOt6zzzzDOMGzeOsWPH0qFDB06fPs2KFSt0DSkpV0fPH+WpFU8BMLnnZJr7Nzc5kYhI5eJk78RjnR/j6GNHGX/9eFwcXFgfs57rZl/Hbd/cxuFzh82OKCJyVSp1kTIM46K30aNHW9exWCxMmDCB2NhYsrOzWbdunXVWP5HykJ2fzR3f3UFGXgbd63fn8WsfNzuSiEil5eXixaSekzj86GHuveZeLFj4dv+3NH+/OY/++CjxGfFmRxQRKZNKXaREKhvDMHh42cNsObMFX1dfPhv6GXYWfRmJiPydep71+GTIJ+x6aBcDIgaQX5jPjC0zaPROI1779TUy8zLNjigiUir6CVCkFD7c9iGf7PwEO4sd826ZR33v+mZHEhGpUloFtGLZyGWsvns17YLakZabxotrXyTi3Qhmb59NQWGB2RFFRK6IipTIFdp4ciOPLX8MKDovqnfD3iYnEhGpum4Mv5Et/9jC3JvnEuYdxpm0MzzwwwO0+aANyw4t0wx/IlLpqUiJXIGYlBhuWXALeYV53Nb8Nv7V5V9mRxIRqfLsLHbc0eoODjx8gGl9puHj4sO+hH0M+noQN35+I1tObzE7oojIJalIifyNs+ln6fV5L2LTY2nh34JPhnyCxWIxO5aISLXh7ODME9c9wdHHjvJMl2dwtnfmlxO/0OnjTtzx3R0cSzpmdkQRkQs4mB1ApDJLykqiz5d9OHz+MPW96vPTqJ+o5VTL7FgiIqaIiooql+36+fkRGhqKj6sPU3pP4eFOD/PS2pf4YtcXzNs7j+/2f8fDHR/mxW4vUtutdrlkEBEpLRUpkUtIz01n4NyB7D67mwD3AFbdvYp6nvXMjiUiUuHSz6cDMGrUqHLZvqubKweiDhAaGgpAqFconw39jCevfZJnVz3Lz0d/Zvqm6Xy681Oe7fosj3V+DHcn93LJIiJypVSkRC4iKSuJIfOGsPHURnxcfFh510oa+TYyO5aIiCmy07MBiBwbSUSbCJtuOyE6gUWTFpGYmGgtUsXaBLbhp1E/sfLoSp5Z9Qw743by/JrneXvT27zY7UUebP8gTvZONs0jInKlVKRE/iI6OZr+X/UnKjEKT2dPlt+5nFYBrcyOJSJiOp+6PgQ1Dqrw/fZu2JttDbbx9Z6v+fcv/+ZY0jEeXf4ob258kwndJzCq9Sjs7ewrPJeI1GwqUjVEQWEB57POcyzrGHSHSbsnYX/MnvNZ50nNScXOYoejnSOO9o54OHkQVCuIII8ggj2CaVy7Mc38muHv7m/2yyh3O2J3MGDuAOLS46jrUVclSkSkkrCz2HFn6zu5rcVtfLLjE15Z9wonkk8w+vvRTPl9Cq/e+CrDmg676GRAMTExJCYmlkuu4vO7RKTmUZGqpgoKCziddppjScc4lnSM02mnKTQKix6MhO+ivyv1Nv3c/GhVpxWd6naic93OdK7XmWCPYBsnN4dhGHy15yv+ueyfpOem07JOS5bfuVznRImIVDJO9k481OEh7m5zN+9tfo/Xf3+dqMQobllwC+2D2vPv7v9mcOPB1kIVExND02ZNycrMKpc8fz2/S0RqDhWpasQwDE6lnmLX2V3sS9hHdn52iced7J3wsvMiYXMCD97+IC0btMTX1RdPZ08KjULyCvPIK8gjOTuZ2PRYYtNiOZV2ioOJBzmRfILEzETWnljL2hNrrdts6NOQnuE96dmgJzeG34ifm19Fv+yrlpydzD+X/ZN5e+cBRReJXDh8IV4uXiYnExGRS3FzdONfXf/Fg+0f5M2NbzJt4zS2xW5jyLwhtA1sy7+7/5shTYaQmJhIVmYWw54fhn99246suNz5XSJS/alIVQO5Bblsi93GtjPbOJd1zrrc1cGVBj4NCPcJJ9w7HB8XH+IOxzHr+1l0u60bzRybQT5Ftz+zB7z+d/ufrPwsTqSf4GDqQfYm7WVv8l6Oph7laFLRbdb2WQA0921O/yb96Rnek271u9l8ViVbD8/YlLCJV3a9QlxWHPYWe/7d/d88f8PzONjpS0NEpCJdzdTqQ72G0j2yO18e+5IFxxewI24Hw+YPo7FnY/q59QML+Nf3N+X8LhGpvvTTYhWWmZfJ5tOb2Xx6M1n5RUMWHO0caebXjDaBbQjzDsPOUvKayzadwtYZqA80AMKBANh/fj/7N+7nzY1v4mTvxPWh19O3YV/6NuxL64DWV3UhW5sOz6gD9AIa/+/+eXBY5sDo0aNVokREKpDNp1Z3Ba4DOsOh1EMcSj0ED8G+pH0EGAEX/H9RRKSs9BNjFZRXkMfGUxtZH7OevMI8AHxdfbmu3nW0qtMKZwfnSz63PKewPRl9kuVLljPkySHsTNlJdEo0a46vYc3xNTy76lkCawXSp2Ef+jbsS68GvajjXqdU27fF8IykvCR2Z+zmUNYhDAwsWGju1pwGuQ344egPGp4hIlLByuv/S9mF2ezJ2MPu1N0UBBTwe/bv7Nu8j+vqXcc1gddo2nQRuWoqUlWIYRjsid/D6uOrSc1JBSCwViDXh1xPM/9mpfotW7lNYbsX/t3m37Rt25ZD5w7x89Gf+fnoz/xy4hfi0uP4fNfnfL7rcwBa+LegR1gPeoT1oEtIlyueuKK0wzPyCvKISoxi25ltxKTGWJc382tGz/Ce1HarTeyh2NK9ThERsany+P9SOOHUXVWXpb8uxamHE8nZySw/spxfTvxCh+AOdKrbiVpOtWy6TxGpOVSkqojEzESWHFzCydSTAHg5e9GrQS9a+Le4quFy5cVisdDErwlN/JrwWOfHyMnPYX3Memux2n12N/sS9rEvYR/vbXkPKLqS/bX1rqV9UHta1mlJyzotCfEMKfXrKygsICEzwTpjYXRKNPmFRSeCWSjK1aVeF0K8Qmz+ukVEpHJxwgl+gX69+pEfls/GUxtJyk7it5jf2HByA20C23Bdveuq5GRJImIuFalKrqCwgA2nNrDuxDoKjAIc7Ry5IfQGrq13LY72jmbHu2LODs70bFA0u9/U3lNJzExk3Yl1/HLiF36N+ZW98XuJSYkhJiWGBfsWWJ/n7uhOqFcoIV4huOa6Qm/YmrYVnxgf7C325BXmkVOQQ05+Dmk5aSRmJZKUlYSBUWL/Xs5etAtqR9vAtng4e1T0yxcREZM5WBxoW7ct7YPbcyDxABtObuB02mm2x25ne+x2wr3DaR/cnqa1m+riviJyRVSkKrH4jHgWHVhEXHocAI18GjGo8aBqMS23n5sftzS/hVua3wJAWk4aW89s5Y9Tf7A7fjd74/dyMPEgGXkZRCVGEZX4v9mcusL29O2QfvntO9k7Ud+rPg18GtDQpyF+bn6V8sidiIhULDuLHc39m9PMrxknU0+y4eQGDp47yPHk4xxPPo67ozvtgtrRLqgd3i7eZscVkUpMRaqS2p+xnz+2/0F+YT6uDq70bdSX1nWubta7yszD2YPI8EgiwyOty/IK8jiWdIxTqac4lXqKTQc2MfPTmTTv1RwnD6eiI3T2jjjZO+Fs74y7kzt+rn74uflRy6lWtX2vRETk6lksFkK9Qgn1CiU5O5ntsdvZEbeD9Nx0fov5jd9ifiPCN4L2Qe1p5NtIR6lE5AIqUpVMSm4K3A7rU9cDRRe8Hdp0aI08GdbR3tF6nhVAq8JWzFwxk+tvuV7XAhEREZvxdvHmxvAb6V6/OwfPHWTrma0cTz7O4fOHOXz+MG6ObjT3b07rOq2p51lPv6gTEUBFqlI5lXqKO9bdAc3ADjt6NejFtfWu1TdsERGRCmBvZ09z/+Y092/OucxzbIvdxu6zu8nIy2Drma1sPbMVbxdvWtVpRas6rcyOKyImU5GqROp61CXCM4Kzp88ytMlQWoXom7SIiIgZarvVpk/DPvRq0IvjScfZE7+HqMQokrOTrUP/ajvUhu5wMOUgbY22+sWnSA2jy3tXIhaLhVfavgIfgp+jpmEVERExm53Fjoa+RcPsn77uaW5pdguNfRtjZ7HjXP45iISRv44k/O1wHlv+GKuPrSavIM/s2CJSAXREqpLxcvKCXLNTiIiIyF852jtar3OYmZfJpn2b+HXTrzg3dyY6JZp3N7/Lu5vfxcvZi/4R/enbsC+9GvSinmc9s6OLSDlQkRIREREpJTdHN5q6NeXXeb+yetNqznmd4/sD3/PDoR9IyExg3t55zNs7D4AmtZvQq0EvejfoTY+wHtXiMiYioiIl5SAqKqpKbVdERORquDq4clOTm7ipyU0UFBaw6fQmlh1axurjq9lyZgsHzx3k4LmDvLflPewsdnQM7ki3+t3oGtKVrqFd8XPTcH6RqkhFSmwm/XzRVXJHjRpVvvtJ/5ur8YqIiJjE3s6eLiFd6BLShdd4jeTsZH458Qurjq1i1bFVHDx3kE2nN7Hp9Cbe4A2g6IhV15CuXB96PV1DuxLhG6GJK0SqABUpsZns9GwAIsdGEtEmwubbP7zpMGs/WUt2drbNty0iIlIevF28Gdp0KEObDgUgJiWGX078wu8xv7P+5Hr2J+y3HrH6ZOcnAPi7+dM1tGvREauQrrQNaouLg4uJr0JELkZFSmzOp65PuVwwNzEm0ebbFBERqUihXqHc3eZu7m5zNwDns86z4eQGfo/5nd9P/s7m05tJyExg8YHFLD6wGABHO0eaeDahlU8rWvu0ppVPKwJdA6/6qJWfnx+hoaFX+5JEaiwVKRERERGT+Lr6MqjxIAY1HgRATn4O22O38/vJ31l5cCUrolaQ557H3uS97E3ey9fHvy56YhpwCjj5vz9jgVLOuu7q5sqBqAMqUyJlpCIlIiIiUkk4OzhzXch1XBdyHTe63MiK+1bQ5/k+5NfJ52zuWeLz4knMS8TwMKAZRTfAgoXajrWp41iHAKcAAhwD8LD3uORRq4ToBBZNWkRiYqKKlEgZqUiJiIiIVGJh9cNKDJnPK8gjNj2WU6mnOJV6ipOpJ0nPTScxL5HEvET2Z+4HiqZor+dZj3qe9Qj1DKWuZ10c7Er+6FeeM+Jq6KBUdypSIiIiIlWIo70joV6hhHoVlRTDMEjNSbWWqtOpp4lNjyUzL5ND5w5x6NwhABzsHKjnUY/63vVxSHQAh/KdaVdDB6W6U5ESERERqcIsFgteLl54uXjRok4LAPIL84lLj+Nk6klOpZ4iOjmajLwMTqSc4ETKiaInPgdehV409G1IkFMQAU4BOFhs86Ohhg5KTaAiJSIiIlLNONg5WIf1QdFRq3NZ5ziRfILo5GiOxB8h2yGbFFLYnr4dADuLHXU96tLApwGNfBsR7BGMncXOzJchUqmpSImIiIhchfI6z8iW27VYLPi5+eHn5keH4A7sPrObRTMX0faJtuTXzic6JZrUnFROpp7kZOpJ1kWvw8XBhQY+DWjo05BGvo3wdPa0WR6R6kBFSkRERKQM0s+nA+V7nhFAenq6zbdpwQLnIdwunFbNWmEYBsnZyRxLPsbR80c5lnSM7Pxs9ifsZ39C0eQV/m7+1qNV9b3q42jvaPNcIlWJipSIiIhIGWSnZwMQOTaSiDYRNt/+4U2HWfvJWrKzs22+7b+yWCz4uPrQ3rU97YPaU2gUcjr1NEeTjnL0/FFOp50mITOBhMwENp3ehL3FnjDvMBr5NiLCNwJfV9+rvkCwSFWjIiUiIiJyFXzq+pSYntxWEmMSbb7NK2VnsSPEK4QQrxB6hPUgKy/LerTqaNJRUnNSi0pW0lF+PvozPi4+1lIV5h1mWm6RiqQiJSIiIiKX5eroSgv/FrTwb4FhGCRmJnLk/BGOnD/CiZQTJGUnseXMFrac2YKDnQNBjkHQCU5mnKQd7cyOL1IuVKRERERE5IpZLBb83f3xd/fnupDryC3I5XjScQ6fP8zh84eLJq3IOQkDYOiaoUTsjKB/o/4MiBhA97DuuDi4mP0SRGxCRUpEREREyszJ3okmfk1o4tcEwzBIyExg+8HtbNq9CfsG9kUFa/Nh3tn8Dq4OrtwYfiP9G/Wnf0R/Gvg0MDu+SJmpSImIiIiITVgsFuq416FNrTZs+mwTa/5YwznPcyw/spwfD//I6bTTLDu8jGWHl8FyaFK7ifVoVbf63XB2cDb7JQAQExNDYmL5nKPm5+enixRXEypSIiIiIlIuajnWoluzbgxrNgzDMNgbv5cfD//I8iPLWR+znoPnDnLw3EGmb5qOm6MbN4bfSGRYJJFhkbQOaI29nX2FZ46JiaFps6ZkZWaVy/Zd3Vw5EHVAZaoaUJESERERkXJnsVhoFdCKVgGtePb6Z0nJTmHVsVXWYhWbHsvSQ0tZemgpAN4u3nSr340e9XsQGV5UrOwsduWeMzExkazMLIY9Pwz/+v423XZCdAKLJi0iMTFRRaoaUJESERERkXIRFRV12cfDCefhkIcZW28sh1IPsSlxE1sTt7Lz/E6Ss5NZcnAJSw4uAcDD0YOW3i1p6dOSrvW7MrjtYHxdfcstu399/3KZ1l6qDxUpEREREbGp9PPpAIwaNapsG7ADgoCw/91CIY00NiZsZGPCRj469BGshAjfCK6tdy2d63bmmsBraBXQCk9nT5u8BpG/oyIlIiIiIjaVnZ4NQOTYSCLaRFz19gqNQs7lnSM+L56YpBhOJp+E2linXP9i9xfWdcO8w2gd0JrWdVrTOqA1rQJa0dCnIY72jledQ+TPVKRE/uTvhiBUtu2KiIhUZj51fWw2PK4udQGIPRTLrP/MYvXG1eTUzmHT6U1sObOF3Wd3cyr1FCeST3Ai+YR1SCCAvcWeBj4NaFy7MRG+ETSu3bjo77UjqOtR15RJLaTqU5ESwQZDEK50P+np5bp9ERGRmsLbyZt2Ee3oH9Hfuux81nn2nN3D7rO7i27xu9kbv5fMvEzr0au/crBzoJ5nPUK9Qgn1CsU5yxnaw8nskzhkOODl4oWTvVNFvjSpIlSkRLD9EIS/OrzpMGs/WUt2drbNty0iIiJFfF196R7Wne5h3a3LDMPgTNoZDp07xKFzhzh8/rD178eSjpFXmGc9imU1GJYnLYetRXddHVzxcvHCw8kDD2cPPJ08i/509rQuc3VwxWKxVOwLFlOpSIn8iS2HIPxZYkz5XNRPRERELs9isVDXsy51PesSGR5Z4rGCwgLi0uOITokmJiWGmJQYth7Zyjc/f4NvC18yjAxyCnLIys8iKz2LOOIuuR8HO4cLitZfS1eBUVDeL1cqkIqUiIiIiNRI9nb21pLVJaQLANtdt/PN6G+49cNbCWocRHZ+NinZKaTmpJKam0paThqpOamk5aZZ/56Vn0V+YT5J2UkkZSddfqf/gjvW3UGjA42o61G36OZZ8k9fV18d3aoCVKRERERERC7BxcEFl1ouBNQKuOQ6+YX5Fxas/5WuP/+9wCgAdziUeohDqYcuu89gj+AS5epi950dnMvjJcsVUpESEREREbkKDnYO+Lj64OPqc8l1DMPgxMETfD7hc97+9G1c67hyOu00p1NPF/2ZdpozaWdIzEwkOz+bY0nHOJZ07LL79XPzK3lES0e3KpSKlIiIiIhIObNYLLjYucBZqH2+Ns0CmtHesz385frBOQU5JOYkEp8VT0J2AvHZ8cRn///fE7ITSMhOILcwl8TMRBIzE9l1dtcl9+ts71x0NOsiZSvIIwgfl6IC6O3ijbuj+2VLV0xMDImJ5Xfet5+fH6GhoeW2fVurNkXq/fff54033iA2NpYWLVowffp0brjhBrNjiYiIiIgANr7cihvgQVER8/j/v9t529G0Y1Pis+NJzEwkpyCH48nHOZ58/G836WDngI9LUakqLlc+Lj74uPhg5BjM/mA2+Zn5kAvk/e+We5E/C4D8//1ZABhX9pJc3Vw5EHWgypSpalGk5s+fz7hx43j//ffp2rUrH374If3792f//v1V5h9CRERERKq38r7cSkJ0AosmLeKLcV/Qrl07cvJzOJN2puQQwj8NJTybfpak7CSSs5PJL8wnvzCfhMwEEjITLr6D68uWyw477C321j/tLfbYU/SnnaVoWUF2AQnrE0hMTKwyP79XiyI1bdo07r//fh544AEApk+fzs8//8zMmTOZPHmyyelERERERP5feV1u5a+cHZwJ9wkn3Cf8susZhkFGXgbJ2ckkZRUVq+KClZRVNBPh0ZNH+XL+l0R0i8DB3YHcglzyCvLILSz6M68wz7rsr9O8F1JIoVH4v51dIoQdUP5viU1V+SKVm5vLtm3beO6550os79OnDxs2bLjoc3JycsjJybHeT0lJASA1NbX8gl6h9PSiQ75nDp0hNyvX5ttPiC76DUPC8QSi3aOrzLbLe/vKbs72q+q2y3v7ym7O9pXdnO0ruznbr8rZE08WnaOzbds2689NtnTw4EGgfH4WK+/33Zbvjff//gsjDOygbnZdvlz2JU2aNsEvxK9oJbv/3f7SKAzDoJBCCowCCowCa5EqMAoooMC6vID/fyz5XDI7f99J+oPppv9MXrx/w7j8mESL8XdrVHJnzpyhbt26/P7773Tp0sW6fNKkSXz22WfWL4Y/mzBhAhMnTqzImCIiIiIiUoWcPHmSevXqXfLxKn9EqthfZxgxDOOSs46MHz+eJ5980nq/sLCQ8+fPU7t27QqfHjI1NZWQkBBOnjyJp6fn3z9BSk3vcfnS+1v+9B6XP73H5Uvvb/nTe1y+9P6Wv8r0HhuGQVpaGsHBwZddr8oXKT8/P+zt7YmLiyuxPD4+noCAi184zdnZGWfnkhcw8/b2Lq+IV8TT09P0D011p/e4fOn9LX96j8uf3uPypfe3/Ok9Ll96f8tfZXmPvby8/nYduwrIUa6cnJxo3749K1euLLF85cqVJYb6iYiIiIiI2EqVPyIF8OSTT3LXXXfRoUMHrrvuOmbNmkVMTAwPPfSQ2dFERERE/q+9+4+pqv7/AP688uOCwKgwhGuBoAYpYAiYKGlNBhm2OfoBLgIjmzmJi7ICwYZzEGyt1o8llhLqsKACGSUtsRAjVhRx4woOaSBZ0+5KUMMJg/v6/PFZ5/u50Wef7+VePF56Prazed+v93Xv89zZZa+dc9+XiGagGdFIpaSk4Pfff8eePXtw4cIFhIWFobGxEYGBgWov7X/SarUoKiqa9Kgh2Q8znl7Md/ox4+nHjKcX851+zHh6Md/p54gZO/yufURERERERDeaw39HioiIiIiI6EZjI0VERERERGQlNlJERERERERWYiNFRERERERkJTZSKtu7dy+CgoLg5uaGqKgofPnll2ovyWGdOnUKDz/8MHQ6HTQaDerr6y3qIoLdu3dDp9PB3d0d999/P7q7u9VZrAMqLS1FTEwMvLy84Ovriw0bNqC3t9diDjOeuvLyckRERCg/RBgbG4tPP/1UqTNb+yotLYVGo0FOTo4yxoxts3v3bmg0GovDz89PqTNf+/jll1+QlpYGHx8fzJ49G/fccw86OjqUOnOeuvnz50+6hjUaDbZt2waA2drD+Pg4du3ahaCgILi7uyM4OBh79uyB2WxW5jhUzkKqqa6uFhcXF9m/f7/09PSIXq8XDw8PGRwcVHtpDqmxsVEKCwultrZWAMjRo0ct6mVlZeLl5SW1tbViNBolJSVF/P395cqVK+os2MEkJiZKZWWlnD59WgwGgyQlJUlAQID88ccfyhxmPHUNDQ1y7Ngx6e3tld7eXikoKBAXFxc5ffq0iDBbe2pvb5f58+dLRESE6PV6ZZwZ26aoqEiWLFkiFy5cUA6TyaTUma/tLl26JIGBgbJp0yb55ptvZGBgQE6cOCE//vijMoc5T53JZLK4fpuamgSANDc3iwiztYfi4mLx8fGRTz75RAYGBuTDDz8UT09Pee2115Q5jpQzGykVLV++XJ599lmLsdDQUMnPz1dpRTPHXxsps9ksfn5+UlZWpoxdv35dvL29Zd++fSqs0PGZTCYBIC0tLSLCjKfDrbfeKgcOHGC2dnT16lVZtGiRNDU1yZo1a5RGihnbrqioSJYuXfq3NeZrH3l5eRIXF/df68zZvvR6vSxYsEDMZjOztZOkpCTJzMy0GEtOTpa0tDQRcbxrmI/2qWRsbAwdHR1ISEiwGE9ISEBbW5tKq5q5BgYGcPHiRYu8tVot1qxZw7yn6PLlywCA2267DQAztqeJiQlUV1djZGQEsbGxzNaOtm3bhqSkJMTHx1uMM2P76Ovrg06nQ1BQEFJTU9Hf3w+A+dpLQ0MDoqOj8dhjj8HX1xeRkZHYv3+/UmfO9jM2NoaqqipkZmZCo9EwWzuJi4vD559/jrNnzwIAfvjhB7S2tuKhhx4C4HjXsLPaC/in+u233zAxMYG5c+dajM+dOxcXL15UaVUz15+Z/l3eg4ODaizJoYkIduzYgbi4OISFhQFgxvZgNBoRGxuL69evw9PTE0ePHsXixYuVPx7M1jbV1dX4/vvv8e23306q8fq13b333ovDhw/jrrvuwq+//ori4mKsXLkS3d3dzNdO+vv7UV5ejh07dqCgoADt7e3Izs6GVqtFeno6c7aj+vp6DA8PY9OmTQD4GWEveXl5uHz5MkJDQ+Hk5ISJiQmUlJRg48aNABwvZzZSKtNoNBavRWTSGNkP87aPrKwsdHV1obW1dVKNGU9dSEgIDAYDhoeHUVtbi4yMDLS0tCh1Zjt158+fh16vx/Hjx+Hm5vZf5zHjqVu3bp3y7/DwcMTGxmLBggU4dOgQVqxYAYD52spsNiM6OhovvfQSACAyMhLd3d0oLy9Henq6Mo85266iogLr1q2DTqezGGe2tqmpqUFVVRXee+89LFmyBAaDATk5OdDpdMjIyFDmOUrOfLRPJXPmzIGTk9Oku08mk2lSF062+3PnKOZtu+eeew4NDQ1obm7GHXfcoYwzY9u5urpi4cKFiI6ORmlpKZYuXYrXX3+d2dpBR0cHTCYToqKi4OzsDGdnZ7S0tOCNN96As7OzkiMzth8PDw+Eh4ejr6+P17Cd+Pv7Y/HixRZjd999N3766ScA/By2l8HBQZw4cQKbN29WxpitfTz//PPIz89HamoqwsPD8eSTT2L79u0oLS0F4Hg5s5FSiaurK6KiotDU1GQx3tTUhJUrV6q0qpkrKCgIfn5+FnmPjY2hpaWFef8/iQiysrJQV1eHL774AkFBQRZ1Zmx/IoLR0VFmawdr166F0WiEwWBQjujoaDzxxBMwGAwIDg5mxnY2OjqKM2fOwN/fn9ewnaxatWrSz06cPXsWgYGBAPg5bC+VlZXw9fVFUlKSMsZs7ePatWuYNcuy/XByclK2P3e4nNXZ44JE/m/784qKCunp6ZGcnBzx8PCQc+fOqb00h3T16lXp7OyUzs5OASCvvvqqdHZ2KtvJl5WVibe3t9TV1YnRaJSNGzfetNtp3oy2bt0q3t7ecvLkSYvtYa9du6bMYcZTt3PnTjl16pQMDAxIV1eXFBQUyKxZs+T48eMiwmynw3/u2ifCjG2Vm5srJ0+elP7+fvn6669l/fr14uXlpfxNY762a29vF2dnZykpKZG+vj45cuSIzJ49W6qqqpQ5zNk2ExMTEhAQIHl5eZNqzNZ2GRkZMm/ePGX787q6OpkzZ4688MILyhxHypmNlMreeustCQwMFFdXV1m2bJmylTRZr7m5WQBMOjIyMkTk31tqFhUViZ+fn2i1Wlm9erUYjUZ1F+1A/i5bAFJZWanMYcZTl5mZqXwW3H777bJ27VqliRJhttPhr40UM7bNn7/14uLiIjqdTpKTk6W7u1upM1/7+PjjjyUsLEy0Wq2EhobKO++8Y1Fnzrb57LPPBID09vZOqjFb2125ckX0er0EBASIm5ubBAcHS2FhoYyOjipzHClnjYiIKrfCiIiIiIiIHBS/I0VERERERGQlNlJERERERERWYiNFRERERERkJTZSREREREREVmIjRUREREREZCU2UkRERERERFZiI0VERERERGQlNlJERERERERWYiNFRERERERkJTZSREQ047W1tcHJyQkPPvig2kshIqIZQiMiovYiiIiIptPmzZvh6emJAwcOoKenBwEBAWoviYiIHBzvSBER0Yw2MjKCDz74AFu3bsX69etx8OBBi3pDQwMWLVoEd3d3PPDAAzh06BA0Gg2Gh4eVOW1tbVi9ejXc3d1x5513Ijs7GyMjIzf2RIiI6KbCRoqIiGa0mpoahISEICQkBGlpaaisrMSfD2OcO3cOjz76KDZs2ACDwYAtW7agsLDQ4v1GoxGJiYlITk5GV1cXampq0NraiqysLDVOh4iIbhJ8tI+IiGa0VatW4fHHH4der8f4+Dj8/f3x/vvvIz4+Hvn5+Th27BiMRqMyf9euXSgpKcHQ0BBuueUWpKenw93dHW+//bYyp7W1FWvWrMHIyAjc3NzUOC0iIlIZ70gREdGM1dvbi/b2dqSmpgIAnJ2dkZKSgnfffVepx8TEWLxn+fLlFq87Ojpw8OBBeHp6KkdiYiLMZjMGBgZuzIkQEdFNx1ntBRAREU2XiooKjI+PY968ecqYiMDFxQVDQ0MQEWg0Gov3/PVBDbPZjC1btiA7O3vS/89NK4iI/rnYSBER0Yw0Pj6Ow4cP45VXXkFCQoJF7ZFHHsGRI0cQGhqKxsZGi9p3331n8XrZsmXo7u7GwoULp33NRETkOPgdKSIimpHq6+uRkpICk8kEb29vi1phYSEaGxtRV1eHkJAQbN++HU8//TQMBgNyc3Px888/Y3h4GN7e3ujq6sKKFSvw1FNP4ZlnnoGHhwfOnDmDpqYmvPnmmyqdHRERqY3fkSIiohmpoqIC8fHxk5oo4N93pAwGA4aGhvDRRx+hrq4OERERKC8vV3bt02q1AICIiAi0tLSgr68P9913HyIjI/Hiiy/C39//hp4PERHdXHhHioiI6D+UlJRg3759OH/+vNpLISKimxi/I0VERP9oe/fuRUxMDHx8fPDVV1/h5Zdf5m9EERHR/8RGioiI/tH6+vpQXFyMS5cuISAgALm5udi5c6fayyIiopscH+0jIiIiIiKyEjebICIiIiIishIbKSIiIiIiIiuxkSIiIiIiIrISGykiIiIiIiIrsZEiIiIiIiKyEhspIiIiIiIiK7GRIiIiIiIishIbKSIiIiIiIiv9C3VChUljWWUkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Regression-Based Imputation\n",
    "plot_age_distribution(regression_imputation_data, \"Age Distribution After Regression-Based Imputation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ade8b32",
   "metadata": {},
   "source": [
    "#### Regression-Based Imputation Report\n",
    "\n",
    "##### Introduction\n",
    "In this section, we applied a **regression-based imputation** method to fill the missing values in the **Age** column of the Titanic dataset. Regression imputation leverages the relationships between other features in the dataset to predict missing values, offering a more dynamic and informed approach to handling missing data.\n",
    "\n",
    "##### Process Overview\n",
    "We used the following predictors for the regression model:\n",
    "- **Pclass:** The passenger's class (1st, 2nd, or 3rd).\n",
    "- **Sex:** The gender of the passenger (male or female).\n",
    "- **Fare:** The fare paid by the passenger for their ticket.\n",
    "\n",
    "The data was divided into two subsets:\n",
    "1. **Age Known Subset:** Rows where the age value is available. This subset was used to train a linear regression model.\n",
    "2. **Age Missing Subset:** Rows where the age value is missing. The trained regression model was used to predict the missing ages in this subset.\n",
    "\n",
    "##### Why Other Methods Were Not Chosen\n",
    "During our exploration, we also considered other imputation methods, but ultimately did not choose them for the following reasons:\n",
    "\n",
    "- **Mean Imputation:** While mean imputation is simple, it introduces a central tendency bias by replacing all missing values with the average age. This created a large spike in the distribution at the mean, which distorts the natural variance in the data. As a result, the model could overemphasize this central value, leading to poorer predictions.\n",
    "  \n",
    "- **Median Imputation:** Although median imputation is more robust to outliers compared to mean imputation, it still suffers from a similar issue of central tendency bias. The missing values are all filled with the median, which flattens the age distribution and introduces an artificial peak at the median age. This method lacks flexibility and does not take into account the relationship between **Age** and other features.\n",
    "\n",
    "- **Age Grouping and Median Imputation:** Grouping ages into ranges and applying the median imputation within each group provided more realistic results compared to simple mean or median imputation. However, it is still not dynamic enough to capture the complex relationships between the **Age** column and other variables, such as **Pclass** or **Sex**. As a result, it could miss nuances in the data that a regression model could capture.\n",
    "\n",
    "##### Why Regression-Based Imputation Was Chosen\n",
    "The regression-based imputation method was selected because it takes into account the relationships between the missing values in the **Age** column and other features like **Pclass**, **Sex**, and **Fare**. By doing so, the imputation is more tailored to each individual row, rather than applying a one-size-fits-all approach like mean or median imputation.\n",
    "\n",
    "- **Advantages:** \n",
    "  - Dynamic and data-driven, taking into account the relationship between missing values and other features.\n",
    "  - Preserves the natural variance in the data without creating artificial peaks or distortions.\n",
    "  - Particularly useful when missing values are correlated with other features.\n",
    "\n",
    "- **Disadvantages:** \n",
    "  - More computationally intensive than simple imputation methods.\n",
    "  - Requires the assumption that there are strong correlations between the missing values and other features.\n",
    "\n",
    "##### Results\n",
    "After applying the regression model, we observed that the age distribution remains smooth and closely matches the original distribution of ages before imputation. The regression model filled the missing values in a way that maintains the natural variance and structure of the dataset, without introducing significant distortions.\n",
    "\n",
    "##### Conclusion\n",
    "The regression-based imputation method provided an effective way to handle missing values in the **Age** column, capturing relationships between the predictors and missing values. This approach is preferred when the feature being imputed is correlated with other features in the dataset, offering a more informed and precise filling of missing data.\n",
    "\n",
    "This method is recommended when the quality of predictions and the relationship between features is crucial in the analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aab1669e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/wp/fq732xlx6tq4_bv24f9qpd580000gn/T/ipykernel_3130/695129889.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  age_null['Age'] = model.predict(X_test)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a function for regression-based imputation\n",
    "def regression_based_imputation(df):\n",
    "    # Use rows without missing Age to train the regression model\n",
    "    age_not_null = df[df['Age'].notnull()]\n",
    "    age_null = df[df['Age'].isnull()]\n",
    "\n",
    "    # Features for the regression model (using Pclass, Sex, and Fare as predictors)\n",
    "    X_train = pd.get_dummies(age_not_null[['Pclass', 'Sex', 'Fare']], drop_first=True)\n",
    "    y_train = age_not_null['Age']\n",
    "    \n",
    "    # Train the model\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # Predict the missing ages\n",
    "    X_test = pd.get_dummies(age_null[['Pclass', 'Sex', 'Fare']], drop_first=True)\n",
    "    age_null['Age'] = model.predict(X_test)\n",
    "    \n",
    "    # Combine back the data\n",
    "    df.loc[df['Age'].isnull(), 'Age'] = age_null['Age']\n",
    "    return df\n",
    "\n",
    "# Apply regression-based imputation\n",
    "data = regression_based_imputation(data)\n",
    "\n",
    "# Visualizing the distribution after imputation\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(data['Age'], bins=30, kde=True, color='green')\n",
    "plt.title(\"Age Distribution After Regression-Based Imputation\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "85896fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the train dataset\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54daddeb",
   "metadata": {},
   "source": [
    "### 2.1.2 Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2115b9c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a copy of the data for each method\n",
    "drop_cabin_data = data.copy()\n",
    "cabin_feature_data = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2cf962d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternative 1: Dropping the Cabin column entirely\n",
    "drop_cabin_data = drop_cabin_data.drop(columns=['Cabin'])\n",
    "\n",
    "# Alternative 2: Creating a new feature that indicates missing cabin information\n",
    "cabin_feature_data['CabinMissing'] = cabin_feature_data['Cabin'].isnull().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e634252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data after dropping Cabin column:\n",
      "\n",
      "Data after creating 'CabinMissing' feature:\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>Cabin</th>\n",
       "      <th>CabinMissing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cabin  CabinMissing\n",
       "0   NaN             1\n",
       "1   C85             0\n",
       "2   NaN             1\n",
       "3  C123             0\n",
       "4   NaN             1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the first few rows of each dataset\n",
    "print(\"Data after dropping Cabin column:\")\n",
    "drop_cabin_data.head()\n",
    "\n",
    "print(\"\\nData after creating 'CabinMissing' feature:\")\n",
    "cabin_feature_data[['Cabin', 'CabinMissing']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3984677f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of the new feature\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.countplot(x='CabinMissing', data=cabin_feature_data)\n",
    "plt.title('Distribution of Cabin Missing Feature')\n",
    "plt.xlabel('Cabin Missing (1 = Missing, 0 = Present)')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a960fafd",
   "metadata": {},
   "source": [
    "## Cabin Column Handling Report\n",
    "\n",
    "### Introduction\n",
    "The **Cabin** column in the Titanic dataset contains around 77% missing values, presenting a challenge for data preprocessing. In this section, we explored two alternative methods to handle the missing values in the **Cabin** column:\n",
    "1. Dropping the **Cabin** column entirely.\n",
    "2. Creating a new binary feature called **CabinMissing**, which indicates whether the cabin information is missing (1) or not (0).\n",
    "\n",
    "We also discuss the decision-making process regarding whether to use the corrected **Age** column or the raw, untouched data.\n",
    "\n",
    "### Alternative 1: Dropping the Cabin Column\n",
    "Given that the majority of entries in the **Cabin** column are missing, one option is to drop the column entirely. This method is simple and eliminates the need to deal with a large number of missing values.\n",
    "\n",
    "#### Advantages:\n",
    "- Straightforward and easy to implement.\n",
    "- Reduces the dimensionality of the dataset, potentially simplifying the model.\n",
    "  \n",
    "#### Disadvantages:\n",
    "- Risk of losing valuable information that might be hidden in the **Cabin** column, especially if the presence of missing cabin data is correlated with survival (for example, passengers with cabins may have had better access to lifeboats).\n",
    "  \n",
    "### Alternative 2: Creating a New Feature (CabinMissing)\n",
    "The second approach creates a new binary feature, **CabinMissing**, which indicates whether the cabin information is missing (1) or present (0). This allows us to retain some information about the missing data without relying on the actual cabin details.\n",
    "\n",
    "#### Advantages:\n",
    "- Retains useful information about missing values, which could be valuable if missing cabin data is related to survival.\n",
    "- A more flexible approach compared to simply dropping the column, especially when the **Cabin** feature has predictive power.\n",
    "  \n",
    "#### Disadvantages:\n",
    "- Adds a new feature, increasing dimensionality, though only slightly (from 12 columns to 13).\n",
    "\n",
    "### Decision to Drop the Cabin Column\n",
    "After further analysis, it was decided to drop the **Cabin** column for the following reasons:\n",
    "1. **High Percentage of Missing Data:** With 77% of the values missing, the **Cabin** column lacks sufficient data to make meaningful contributions to the model without significant data imputation.\n",
    "2. **Simplification:** By dropping the column, we reduce the dimensionality of the dataset, which can simplify the model and improve processing speed without losing much information.\n",
    "3. **Lack of Predictive Power:** Given the extent of missing data, and the fact that most entries are missing, it is unlikely that the **Cabin** column would add significant predictive power to the model. Additionally, exploratory analysis showed no strong relationship between cabin letters (where available) and survival rates.\n",
    "  \n",
    "### Working with Corrected Age Data\n",
    "The **Age** column was previously corrected using regression-based imputation. A key decision is whether to work with this corrected data or use the untouched raw data with missing values. Here’s the rationale for using the imputed **Age** values:\n",
    "\n",
    "#### Corrected (Imputed) Age Data:\n",
    "Using the corrected **Age** data is recommended when the missing values represent a significant portion of the dataset (around 20% in this case). This allows the model to work with more complete data and extract valuable information from the imputed age values.\n",
    "\n",
    "##### Advantages:\n",
    "- More data points are included in the model, improving learning. Since the missing values have been filled with predicted values, the model can learn from more examples.\n",
    "- Imputed values, particularly through regression, can provide more accurate data compared to ignoring missing entries or using simpler methods like mean or median imputation.\n",
    "\n",
    "##### Disadvantages:\n",
    "- If the imputation process introduced bias or incorrect predictions, the model might be affected negatively. However, if a reliable imputation method (such as regression) is used, this risk is minimized.\n",
    "\n",
    "#### Raw Age Data (Untouched):\n",
    "Working with raw age data (with missing values) may seem like a safer option, but it introduces some limitations.\n",
    "\n",
    "##### Advantages:\n",
    "- No risk of introducing bias from incorrect imputation.\n",
    "- The model works purely with known values without relying on estimated data.\n",
    "\n",
    "##### Disadvantages:\n",
    "- Loss of valuable data: Around 20% of the **Age** values are missing, and ignoring these rows reduces the amount of information available to the model.\n",
    "- The model may not handle missing values effectively if there are important relationships between **Age** and the target variable (survival).\n",
    "\n",
    "#### Conclusion:\n",
    "For this dataset, it is advisable to use the **corrected Age data** that has been imputed through regression. This ensures that the model benefits from more data and the relationships between features, while mitigating the risks associated with missing values.\n",
    "\n",
    "### Results\n",
    "The binary **CabinMissing** feature created a clear division between passengers with and without cabin information. A quick inspection shows that the majority of the rows (77%) have a value of `1`, indicating missing cabin information. After consideration, we decided to drop the **Cabin** column entirely due to the large amount of missing data and the potential for noise in the model.\n",
    "\n",
    "Furthermore, the **Age** column has been successfully imputed, and working with the corrected **Age** data ensures that we retain as much information as possible.\n",
    "\n",
    "### Conclusion\n",
    "For handling the missing values in the **Cabin** column, it was ultimately decided to drop the column. Given the high proportion of missing values, the column is unlikely to provide significant predictive power, and dropping it simplifies the dataset. Additionally, working with the corrected **Age** column ensures that the model uses more data for training and can potentially provide better predictions.\n",
    "\n",
    "Both methods allow us to handle missing data more effectively and enhance the predictive power of the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6e7dc0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CabinMissing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CabinMissing\n",
       "0             1\n",
       "1             0\n",
       "2             1\n",
       "3             0\n",
       "4             1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the CabinMissing feature (1 = Cabin info is missing, 0 = Cabin info is present)\n",
    "data['CabinMissing'] = data['Cabin'].isnull().astype(int)\n",
    "\n",
    "# Optionally, we can drop the Cabin column\n",
    "data = data.drop(columns=['Cabin'])\n",
    "\n",
    "# Check the result to ensure the new feature is added correctly\n",
    "data[['CabinMissing']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "63794b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "Survived        0\n",
       "Pclass          0\n",
       "Name            0\n",
       "Sex             0\n",
       "Age             0\n",
       "SibSp           0\n",
       "Parch           0\n",
       "Ticket          0\n",
       "Fare            0\n",
       "Embarked        2\n",
       "CabinMissing    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the train dataset\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b13c557",
   "metadata": {},
   "source": [
    "### 2.1.3 Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1aa2f192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling missing values in the Embarked column with the most common value (mode)\n",
    "data['Embarked'].fillna(data['Embarked'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1fd40bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "Survived        0\n",
       "Pclass          0\n",
       "Name            0\n",
       "Sex             0\n",
       "Age             0\n",
       "SibSp           0\n",
       "Parch           0\n",
       "Ticket          0\n",
       "Fare            0\n",
       "Embarked        0\n",
       "CabinMissing    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec83aaa9",
   "metadata": {},
   "source": [
    "### Embarked Column Handling Report\n",
    "\n",
    "#### Introduction\n",
    "The **Embarked** column in the Titanic dataset has only 2 missing entries, making it relatively easy to handle. In this section, we chose to fill the missing values using the most common value (mode), which ensures that all records have a valid port of embarkation.\n",
    "\n",
    "#### Process Overview\n",
    "To handle the missing values, we used the **mode** of the column, which represents the most frequent embarkation point in the dataset. This approach ensures consistency without significantly impacting the distribution of data.\n",
    "\n",
    "#### Conclusion\n",
    "By filling the missing entries in the **Embarked** column with the mode, we ensure that no records are left incomplete, while keeping the overall distribution of embarkation points intact. Since the number of missing values is very small (2 entries), this method has minimal impact on the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074a9333",
   "metadata": {},
   "source": [
    "## 2.2 Encode Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6afb6b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Sex to numerical (male = 0, female = 1)\n",
    "data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6e4edb6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode the Embarked column\n",
    "data = pd.get_dummies(data, columns=['Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "91ca07df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode the Pclass column\n",
    "data = pd.get_dummies(data, columns=['Pclass'], drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5bf248c",
   "metadata": {},
   "source": [
    "### Categorical Variable Encoding Report\n",
    "\n",
    "#### Introduction\n",
    "In this step, we encoded the categorical variables in the Titanic dataset to prepare the data for model training. The following changes were made to convert categorical values into numerical representations:\n",
    "\n",
    "1. **Sex Column**: The `Sex` column, which originally contained two categories (\"male\" and \"female\"), was converted into a binary numeric representation where:\n",
    "   - `male = 0`\n",
    "   - `female = 1`\n",
    "\n",
    "2. **Embarked Column**: The `Embarked` column, which had three possible categories (\"C\", \"Q\", \"S\"), was encoded using one-hot encoding. This created three new binary columns:\n",
    "   - `Embarked_Q`\n",
    "   - `Embarked_S`\n",
    "   \n",
    "   Since `Embarked_C` is the reference category, it was dropped to avoid multicollinearity.\n",
    "\n",
    "3. **Pclass Column**: The `Pclass` column, representing passenger class (1st, 2nd, 3rd), was also encoded using one-hot encoding. Two new columns were created:\n",
    "   - `Pclass_2`\n",
    "   - `Pclass_3`\n",
    "   \n",
    "   The 1st class (`Pclass_1`) was dropped as the reference category.\n",
    "\n",
    "#### Results\n",
    "After performing the encoding:\n",
    "- The `Sex` column is now represented as a binary feature.\n",
    "- The `Embarked` and `Pclass` columns have been transformed into multiple binary features using one-hot encoding.\n",
    "- There are no missing values after the encoding process.\n",
    "\n",
    "#### Conclusion\n",
    "\n",
    "By applying label encoding to the Sex column and one-hot encoding to the Embarked and Pclass columns, we have successfully converted categorical variables into numerical representations that are suitable for machine learning models. The dataset is now ready for the next steps, such as feature scaling and model training.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a6f06505",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId       int64\n",
       "Survived          int64\n",
       "Name             object\n",
       "Sex               int64\n",
       "Age             float64\n",
       "SibSp             int64\n",
       "Parch             int64\n",
       "Ticket           object\n",
       "Fare            float64\n",
       "CabinMissing      int64\n",
       "Embarked_Q         bool\n",
       "Embarked_S         bool\n",
       "Pclass_2           bool\n",
       "Pclass_3           bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7402202f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
       "       'Ticket', 'Fare', 'CabinMissing', 'Embarked_Q', 'Embarked_S',\n",
       "       'Pclass_2', 'Pclass_3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f03879",
   "metadata": {},
   "source": [
    "## 2.3 Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5cd5ae",
   "metadata": {},
   "source": [
    "### 2.3.1 Testing Alternatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c50fce82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of Fare and Age columns\n",
    "plt.figure(figsize=(12, 6))  # Create a figure with specified size (12x6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "984bfc4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fare Distribution')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fare\n",
    "plt.subplot(1, 2, 1)  # Create the first subplot (1 row, 2 columns, position 1)\n",
    "sns.histplot(data['Fare'], kde=True)  # Plot the histogram for the Fare column with Kernel Density Estimation (KDE)\n",
    "plt.title('Fare Distribution')  # Set the title for the first subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "09b15f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age Distribution')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age\n",
    "plt.subplot(1, 2, 2)  # Create the second subplot (1 row, 2 columns, position 2)\n",
    "sns.histplot(data['Age'], kde=True)  # Plot the histogram for the Age column with Kernel Density Estimation (KDE)\n",
    "plt.title('Age Distribution')  # Set the title for the second subplot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f8841a",
   "metadata": {},
   "source": [
    "#### Feature Scaling Report\n",
    "\n",
    "##### Introduction\n",
    "In this step, we performed feature scaling on the **Fare** and **Age** columns. These features have varying ranges, and scaling ensures that they are on the same scale, which is crucial for many machine learning algorithms. After visualizing their distributions, we chose different scaling methods for each feature based on their characteristics.\n",
    "\n",
    "##### Distribution Observations\n",
    "\n",
    "1. **Age Distribution:**\n",
    "   - The age distribution appears to be close to a normal distribution, with most values concentrated between 20 and 40 years old.\n",
    "   - There are few outliers, and the data seems to follow a symmetric pattern, suggesting that **standardization** would be the most appropriate scaling method for this column.\n",
    "   \n",
    "   **Scaling Method Recommendation:** \n",
    "   - We will apply **StandardScaler** to the **Age** column because it normalizes the data based on the mean and standard deviation, which is well-suited for normally distributed data.\n",
    "\n",
    "2. **Fare Distribution:**\n",
    "   - The fare distribution is highly skewed to the right, with many low values and some very high outliers.\n",
    "   - Because of the presence of these outliers and the uneven distribution, **normalization** (min-max scaling) is a better option, as it scales the data into a fixed range (0 to 1) while handling outliers more effectively.\n",
    "   \n",
    "   **Scaling Method Recommendation:**\n",
    "   - We will apply **MinMaxScaler** to the **Fare** column, which will scale the values between 0 and 1, making it more robust to outliers.\n",
    "\n",
    "##### Results\n",
    "The chosen scaling methods for the **Age** and **Fare** columns are:\n",
    "- **StandardScaler** for the **Age** column.\n",
    "- **MinMaxScaler** for the **Fare** column.\n",
    "\n",
    "These scaling methods will ensure that each feature is on a comparable scale, improving the performance of models that are sensitive to the scale of input data (e.g., logistic regression, k-nearest neighbors).\n",
    "\n",
    "##### Conclusion\n",
    "After analyzing the distributions of the **Fare** and **Age** columns, we selected appropriate scaling methods based on their respective characteristics. Feature scaling will help the machine learning models to treat both columns equally and improve overall model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f43964c2",
   "metadata": {},
   "source": [
    "### 2.3.2 Applying Scalars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "17922bdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Applying StandardScaler to the Age column\n",
    "standard_scaler = StandardScaler()\n",
    "data['Age'] = standard_scaler.fit_transform(data[['Age']])\n",
    "\n",
    "# 2. Applying MinMaxScaler to the Fare column\n",
    "minmax_scaler = MinMaxScaler()\n",
    "data['Fare'] = minmax_scaler.fit_transform(data[['Fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "819760fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows after scaling:\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>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.546241</td>\n",
       "      <td>0.014151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.656576</td>\n",
       "      <td>0.139136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.245536</td>\n",
       "      <td>0.015469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431048</td>\n",
       "      <td>0.103644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.431048</td>\n",
       "      <td>0.015713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age      Fare\n",
       "0 -0.546241  0.014151\n",
       "1  0.656576  0.139136\n",
       "2 -0.245536  0.015469\n",
       "3  0.431048  0.103644\n",
       "4  0.431048  0.015713"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the results after scaling\n",
    "print(\"First few rows after scaling:\\n\")\n",
    "data[['Age', 'Fare']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "31a0366b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Check for missing values after scaling:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PassengerId     0\n",
       "Survived        0\n",
       "Name            0\n",
       "Sex             0\n",
       "Age             0\n",
       "SibSp           0\n",
       "Parch           0\n",
       "Ticket          0\n",
       "Fare            0\n",
       "CabinMissing    0\n",
       "Embarked_Q      0\n",
       "Embarked_S      0\n",
       "Pclass_2        0\n",
       "Pclass_3        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if there are any missing values after scaling\n",
    "print(\"\\nCheck for missing values after scaling:\\n\")\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd184663",
   "metadata": {},
   "source": [
    "## 2.4 Dropping the Name and PassengerId Column from the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3d962f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the Name column from the main dataset (permanently remove it)\n",
    "data = data.drop(columns=['Name'])\n",
    "\n",
    "# Drop the Name column from the main dataset (permanently remove it)\n",
    "data = data.drop(columns=['PassengerId'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609da7b5",
   "metadata": {},
   "source": [
    "## 2.5 Data Preprocessing Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c101f1",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "In this phase, we performed essential preprocessing tasks to prepare the Titanic dataset for machine learning. These tasks included handling missing values, encoding categorical variables, and scaling numerical features. The objective was to clean the dataset, transform categorical values into numerical formats, and ensure that all features are on a comparable scale for model training.\n",
    "\n",
    "---\n",
    "\n",
    "### 1. Handling Missing Values\n",
    "We addressed missing values in several key columns using different techniques:\n",
    "\n",
    "- **Age Column**: \n",
    "  - The **Age** column had around 20% missing values. We tested multiple imputation methods, including:\n",
    "    1. Mean imputation\n",
    "    2. Median imputation\n",
    "    3. Age group-based imputation\n",
    "    4. Regression-based imputation\n",
    "  - After evaluating these methods, we chose **regression-based imputation**, which used related features like **Pclass**, **Sex**, and **Fare** to predict the missing ages. This method preserved the natural variance of the data, avoiding the bias introduced by simpler methods like mean or median imputation.\n",
    "\n",
    "- **Cabin Column**: \n",
    "  - With around 77% of data missing, we considered two approaches:\n",
    "    1. Dropping the **Cabin** column entirely.\n",
    "    2. Creating a new binary feature (**CabinMissing**) to indicate whether cabin information was missing.\n",
    "  - We decided to drop the **Cabin** column due to the high percentage of missing values and its limited impact on the model. However, the **CabinMissing** feature was added to retain some information about the missingness of cabin data.\n",
    "\n",
    "- **Embarked Column**: \n",
    "  - The **Embarked** column had only 2 missing values. We filled these values with the most common category (mode) to ensure no missing values remained.\n",
    "\n",
    "---\n",
    "\n",
    "### 2. Encoding Categorical Variables\n",
    "We converted categorical variables into numerical formats, which are required for most machine learning algorithms:\n",
    "\n",
    "- **Sex Column**: \n",
    "  - The **Sex** column was encoded into binary values:\n",
    "    - `male = 0`\n",
    "    - `female = 1`\n",
    "\n",
    "- **Embarked Column**: \n",
    "  - We applied **one-hot encoding** to the **Embarked** column, creating two binary columns:\n",
    "    - `Embarked_Q`\n",
    "    - `Embarked_S`\n",
    "  - The category **Embarked_C** was used as the reference and dropped to avoid multicollinearity.\n",
    "\n",
    "- **Pclass Column**: \n",
    "  - We also applied **one-hot encoding** to the **Pclass** column, creating:\n",
    "    - `Pclass_2`\n",
    "    - `Pclass_3`\n",
    "  - **Pclass_1** was dropped as the reference category.\n",
    "\n",
    "---\n",
    "\n",
    "### 3. Feature Scaling\n",
    "We applied different scaling techniques to normalize the data and ensure all features were on the same scale:\n",
    "\n",
    "- **Age Column**: \n",
    "  - The **Age** column followed a normal distribution, so we applied **StandardScaler** to standardize the values based on the mean and standard deviation.\n",
    "\n",
    "- **Fare Column**: \n",
    "  - The **Fare** column had a highly skewed distribution with outliers. We applied **MinMaxScaler** to scale the values between 0 and 1, which better handles outliers.\n",
    "\n",
    "---\n",
    "\n",
    "### 4. Dropping the Name Column\n",
    "We decided to drop the **Name** column because it does not provide useful information for the prediction task and is unlikely to contribute to the model.\n",
    "\n",
    "---\n",
    "\n",
    "### Conclusion\n",
    "After completing the preprocessing steps:\n",
    "- Missing values were handled using appropriate methods for each column.\n",
    "- Categorical variables were encoded into numerical formats.\n",
    "- Numerical variables were scaled using appropriate scaling techniques based on their distributions.\n",
    "- The **Name** column, which had no predictive value, was dropped from the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9178e55b",
   "metadata": {},
   "source": [
    "# 3. Feature Engineering and Selection Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fa4135",
   "metadata": {},
   "source": [
    "## 3.1. Ticket Group Size Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e6168e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticket</th>\n",
       "      <th>GroupSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC 17599</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113803</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>373450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ticket  GroupSize\n",
       "0         A/5 21171          1\n",
       "1          PC 17599          1\n",
       "2  STON/O2. 3101282          1\n",
       "3            113803          2\n",
       "4            373450          1"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Count how many passengers share the same ticket (Group Size)\n",
    "ticket_group_counts = data['Ticket'].value_counts()\n",
    "\n",
    "# Step 2: Add this count as a new feature 'GroupSize'\n",
    "data['GroupSize'] = data['Ticket'].map(ticket_group_counts)\n",
    "\n",
    "# Step 3: Display the first few rows of the new GroupSize feature\n",
    "data[['Ticket', 'GroupSize']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cf562288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>CabinMissing</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_2</th>\n",
       "      <th>Pclass_3</th>\n",
       "      <th>GroupSize</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.546241</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014151</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.656576</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.139136</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.245536</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015469</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.431048</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.103644</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.431048</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.015713</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Sex       Age  SibSp  Parch      Fare  CabinMissing  Embarked_Q  \\\n",
       "0         0    0 -0.546241      1      0  0.014151             1       False   \n",
       "1         1    1  0.656576      1      0  0.139136             0       False   \n",
       "2         1    1 -0.245536      0      0  0.015469             1       False   \n",
       "3         1    1  0.431048      1      0  0.103644             0       False   \n",
       "4         0    0  0.431048      0      0  0.015713             1       False   \n",
       "\n",
       "   Embarked_S  Pclass_2  Pclass_3  GroupSize  \n",
       "0        True     False      True          1  \n",
       "1       False     False     False          1  \n",
       "2        True     False      True          1  \n",
       "3        True     False     False          2  \n",
       "4        True     False      True          1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 4: Drop the original 'Ticket' column as it's no longer needed\n",
    "data = data.drop(columns=['Ticket'])\n",
    "\n",
    "# Step 5: Display the first few rows of the dataset to confirm changes\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e31f46",
   "metadata": {},
   "source": [
    "### Ticket Group Size Feature Report\n",
    "\n",
    "In this section, we engineered a new feature, **Ticket Group Size**, to capture information about passengers traveling together on the same ticket. The rationale behind this feature is that passengers who were part of larger groups, such as families or friends, may have had different survival probabilities compared to those traveling alone.\n",
    "\n",
    "By counting how many passengers shared the same ticket, we created the **GroupSize** feature. This provides useful insight into potential group dynamics that could affect survival. After creating this feature, we decided to drop the original **Ticket** column, as it no longer provides additional information and would only increase the complexity of the model unnecessarily.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82750147",
   "metadata": {},
   "source": [
    "## 3.2. Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "249352cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the correlation matrix to visualize relationships between features\n",
    "plt.figure(figsize=(12, 8))  # Set the figure size (12x8)\n",
    "sns.heatmap(data.corr(), annot=True, cmap='coolwarm', linewidths=0.5)  # Plot the correlation matrix using a heatmap\n",
    "plt.title('Correlation Matrix')  # Add a title to the plot\n",
    "plt.show()  # Display the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdadf25",
   "metadata": {},
   "source": [
    "### Correlation Matrix Analysis Report\n",
    "\n",
    "#### Introduction\n",
    "In this phase of the Titanic dataset analysis, we examined the relationships between the features by calculating the correlation matrix. This matrix helps us understand how much each feature is correlated with others, which is essential to avoid multicollinearity in the model and ensure that no redundant information is included.\n",
    "\n",
    "#### Correlation Matrix Insights\n",
    "\n",
    "After visualizing the correlation matrix, we aimed to identify pairs of features with a high correlation, using a threshold of **0.8** as the cutoff point. Features that are highly correlated (above this threshold) can provide redundant information to the model, leading to multicollinearity, which can affect model performance.\n",
    "\n",
    "##### Key Observations:\n",
    "\n",
    "1. **No Correlations Above 0.8**:\n",
    "   - After carefully reviewing the correlation matrix, we observed that **none of the feature pairs exceeded the threshold of 0.8**. This indicates that there is no significant multicollinearity in the dataset, and no immediate need to remove or combine any features based on their correlations.\n",
    "\n",
    "2. **Highest Correlations**:\n",
    "   - The highest correlation values were between the **SibSp** (number of siblings/spouses aboard) and **GroupSize** (group size based on shared ticket), which reached a value of **0.66**.\n",
    "   - Similarly, **Parch** (number of parents/children aboard) also correlated moderately with **GroupSize** at **0.59**.\n",
    "   - However, these values are still well below the threshold of concern.\n",
    "\n",
    "3. **Conclusion**:\n",
    "   - No further actions are required based on correlation. All features can remain in the model for further analysis as no high-correlation pairs exist that would suggest multicollinearity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43046cc3",
   "metadata": {},
   "source": [
    "## 3.3 Feature Importance Calculation using RandomForest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "62b04a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age: 0.2664236147985887\n",
      "Sex: 0.24006133402398216\n",
      "Fare: 0.21412487916078\n",
      "GroupSize: 0.05971718167110979\n",
      "Pclass_3: 0.056723309140546026\n",
      "CabinMissing: 0.04466591685139832\n",
      "SibSp: 0.04093207117436087\n",
      "Parch: 0.03336731731378959\n",
      "Embarked_S: 0.024143950844290208\n",
      "Pclass_2: 0.010997273251697246\n",
      "Embarked_Q: 0.008843151769457174\n"
     ]
    }
   ],
   "source": [
    "# Define the feature set and target variable\n",
    "X = data.drop(columns=['Survived'])  # Removing the target variable 'Survived' from the features\n",
    "y = data['Survived']  # Target variable\n",
    "\n",
    "# Create and train the RandomForestClassifier model\n",
    "model = RandomForestClassifier()  \n",
    "model.fit(X, y)  # Train the model using the features (X) and the target (y)\n",
    "\n",
    "# Calculate the importance of each feature\n",
    "importance = model.feature_importances_\n",
    "\n",
    "# Sort the features by their importance in descending order\n",
    "indices = np.argsort(importance)[::-1]  \n",
    "\n",
    "# Display the features in order of importance\n",
    "for i in range(X.shape[1]):\n",
    "    print(f\"{X.columns[indices[i]]}: {importance[indices[i]]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26889f19",
   "metadata": {},
   "source": [
    "## Feature Removal Report\n",
    "\n",
    "### Introduction:\n",
    "After analyzing the feature importance scores obtained from the **Random Forest** model, we identified two features with very low importance that can be safely removed without significantly impacting the model's performance. These features are **Embarked_Q** and **Pclass_2**.\n",
    "\n",
    "### Features to be Removed:\n",
    "1. **Embarked_Q (0.009 Importance Score)**:\n",
    "   - This feature represents passengers who embarked from Queenstown. The feature importance score is very low (0.009), indicating that this variable has little influence on predicting survival. Thus, it can be removed to reduce complexity without affecting the model's accuracy.\n",
    "\n",
    "2. **Pclass_2 (0.013 Importance Score)**:\n",
    "   - This feature represents passengers traveling in the second class. With an importance score of 0.013, it has minimal impact on the model's predictions. By removing this feature, we can simplify the model without compromising performance.\n",
    "\n",
    "### Reason for Removal:\n",
    "- **Low Importance**: Both features have very low importance scores in comparison to other features such as **Age**, **Sex**, and **Fare**, which have significantly higher influence on survival predictions.\n",
    "- **Model Simplification**: Removing these less important features can help streamline the model, making it simpler, more interpretable, and possibly improving computational efficiency without sacrificing prediction quality.\n",
    "\n",
    "### Next Steps:\n",
    "1. **Remove the features from the dataset**: We will remove the **Embarked_Q** and **Pclass_2** features from the dataset.\n",
    "2. **Re-train the model**: After the removal of these features, we will re-train the model and evaluate its performance to ensure there is no significant loss in predictive power.\n",
    "\n",
    "### Conclusion:\n",
    "By removing **Embarked_Q** and **Pclass_2**, we aim to optimize the feature set, leading to a more efficient model without affecting its predictive accuracy. This is a crucial step towards building a more streamlined and interpretable machine learning model for predicting Titanic survival outcomes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4ed9b969",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>CabinMissing</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Pclass_3</th>\n",
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       "      <th>2</th>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Survived  Sex       Age  SibSp  Parch      Fare  CabinMissing  Embarked_S  \\\n",
       "0         0    0 -0.546241      1      0  0.014151             1        True   \n",
       "1         1    1  0.656576      1      0  0.139136             0       False   \n",
       "2         1    1 -0.245536      0      0  0.015469             1        True   \n",
       "3         1    1  0.431048      1      0  0.103644             0        True   \n",
       "4         0    0  0.431048      0      0  0.015713             1        True   \n",
       "\n",
       "   Pclass_3  GroupSize  \n",
       "0      True          1  \n",
       "1     False          1  \n",
       "2      True          1  \n",
       "3     False          2  \n",
       "4      True          1  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the columns with low feature importance\n",
    "data = data.drop(columns=['Embarked_Q', 'Pclass_2'])\n",
    "\n",
    "# Confirm the changes by displaying the first few rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc5aa5f",
   "metadata": {},
   "source": [
    "## 3.4 Feature Selection with RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0e3cfef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features: Index(['Sex', 'Age', 'SibSp', 'Fare', 'CabinMissing', 'Embarked_S',\n",
      "       'Pclass_3'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Step 1: Define the Logistic Regression model\n",
    "# Logistic Regression is used here with a maximum iteration limit of 500 for model convergence\n",
    "model = LogisticRegression(max_iter=500)\n",
    "\n",
    "# Step 2: Apply RFE (Recursive Feature Elimination) for feature selection\n",
    "# The RFE method will iteratively remove less important features, leaving 7 most relevant features\n",
    "rfe = RFE(model, n_features_to_select=7)\n",
    "rfe = rfe.fit(X, y)\n",
    "\n",
    "# Step 3: Display the selected features\n",
    "# The features selected by RFE will be printed\n",
    "print(f\"Selected Features: {X.columns[rfe.support_]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4c068825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation score with RFE: 0.813684012303057\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Train the model using the selected features and evaluate it with cross-validation\n",
    "# The selected features (from RFE) are used to train the model, and the performance is measured using 5-fold cross-validation\n",
    "X_rfe = X[X.columns[rfe.support_]]\n",
    "cv_score_rfe = cross_val_score(model, X_rfe, y, cv=5)\n",
    "\n",
    "# Step 5: Print the cross-validation score\n",
    "print(f\"Cross-validation score with RFE: {cv_score_rfe.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36d4343",
   "metadata": {},
   "source": [
    "### Feature Selection with RFE Report\n",
    "\n",
    "#### Objective:\n",
    "The goal was to select the most impactful features from the dataset to improve the model's performance and reduce overfitting by eliminating less important features. The Recursive Feature Elimination (RFE) method with Logistic Regression was used to rank and select features based on their importance.\n",
    "\n",
    "#### Steps Taken:\n",
    "1. **Logistic Regression Model**: A Logistic Regression model was used as the estimator.\n",
    "2. **RFE Process**: We applied RFE to select 7 out of the available features.\n",
    "3. **Cross-Validation**: A 5-fold cross-validation was conducted to evaluate the performance of the selected features.\n",
    "\n",
    "#### Selected Features:\n",
    "After running the RFE, the following features were identified as the most relevant for predicting the target variable:\n",
    "\n",
    "- **Sex**: Gender of the passenger (Male/Female).\n",
    "- **Age**: Age of the passenger.\n",
    "- **SibSp**: Number of siblings or spouses aboard the Titanic.\n",
    "- **Fare**: The fare paid by the passenger for the ticket.\n",
    "- **CabinMissing**: Indicates whether the cabin information is missing (1) or present (0).\n",
    "- **Embarked_S**: Indicates if the passenger embarked from Southampton.\n",
    "- **Pclass_3**: Indicates if the passenger belonged to the 3rd class.\n",
    "\n",
    "#### Cross-Validation Score:\n",
    "The cross-validation score with the selected features was **0.8137**, indicating the model's accuracy on the selected features is quite high.\n",
    "\n",
    "#### Conclusion:\n",
    "The RFE method helped in reducing the feature set to the most important 7 features, which also resulted in a slight improvement in the model's performance. Based on this selection, the features **Embarked_Q** and **Pclass_2** were removed due to their lower importance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8ef754b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only the features identified by RFE\n",
    "selected_features = ['Sex', 'Age', 'SibSp', 'Fare', 'CabinMissing', 'Embarked_S', 'Pclass_3', 'Survived']\n",
    "\n",
    "# Create a new dataset with only the selected features\n",
    "data = data[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "af143618",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>True</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Sex       Age  SibSp      Fare  CabinMissing  Embarked_S  Pclass_3  \\\n",
       "0    0 -0.546241      1  0.014151             1        True      True   \n",
       "1    1  0.656576      1  0.139136             0       False     False   \n",
       "2    1 -0.245536      0  0.015469             1        True      True   \n",
       "3    1  0.431048      1  0.103644             0        True     False   \n",
       "4    0  0.431048      0  0.015713             1        True      True   \n",
       "\n",
       "   Survived  \n",
       "0         0  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd7e076",
   "metadata": {},
   "source": [
    "## 3.5. Feature Engineering and Selection Analysis Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384535f2",
   "metadata": {},
   "source": [
    "In this phase, we focused on creating new features and selecting the most relevant ones to improve the model's performance. One of the key additions was the **Ticket Group Size** feature, which represents the number of passengers sharing the same ticket. This new feature is important as passengers traveling in groups may have had different survival probabilities compared to those traveling alone. After creating this feature, the original **Ticket** column was dropped since it no longer provided unique information.\n",
    "\n",
    "To further understand the relationships between features, we created a **correlation matrix**. This allowed us to identify any potential multicollinearity issues, ensuring that features are not overly correlated, which could negatively impact the model's performance. No feature pairs exhibited correlations higher than 0.8, meaning that multicollinearity was not a significant concern. The highest correlation observed was between **SibSp** (siblings/spouses aboard) and **GroupSize**, but it was still below the threshold of concern.\n",
    "\n",
    "Finally, we calculated **feature importance** using a Random Forest model to determine which features contributed the most to survival predictions. Based on this analysis, we removed less important features such as **Embarked_Q** and **Pclass_2** to simplify the model. Additionally, **Recursive Feature Elimination (RFE)** was applied to select the top 7 most important features. This method helped to further refine the dataset, ensuring that only the most relevant features were used for model training, while maintaining a strong cross-validation score.\n",
    "\n",
    "In conclusion, the feature engineering and selection process helped create new insights into the dataset, refine the feature set, and reduce potential noise in the model, setting the stage for better prediction accuracy in the upcoming model training phase.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d27bf3c",
   "metadata": {},
   "source": [
    "# 4. Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a0cd728",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the feature set and target variable (as per previous dataset setup)\n",
    "X = data.drop(columns=['Survived'])  # Feature set\n",
    "y = data['Survived']  # Target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9fd85f4",
   "metadata": {},
   "source": [
    "## 4.1. Model 1: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d84fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression Model\n",
    "logistic_model = LogisticRegression(max_iter=500000)\n",
    "logistic_cv_score = cross_val_score(logistic_model, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a661a0b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the cross-validation score\n",
    "cv_score_mean = logistic_cv_score.mean()\n",
    "\n",
    "# Fit the Logistic Regression model on the entire dataset\n",
    "logistic_model.fit(X, y)\n",
    "\n",
    "# Save the trained model\n",
    "logistic_model_filename = \"logistic_regression_model.pkl\"\n",
    "joblib.dump(logistic_model, logistic_model_filename)\n",
    "\n",
    "# Display the results\n",
    "(cv_score_mean, logistic_model_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a71ca3",
   "metadata": {},
   "source": [
    "## 4.2. Model 2: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24bc4d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Model\n",
    "random_forest_model = RandomForestClassifier()\n",
    "random_forest_cv_score = cross_val_score(random_forest_model, X, y, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62fd053e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the cross-validation score\n",
    "rf_cv_score_mean = random_forest_cv_score.mean()\n",
    "\n",
    "# Fit the Random Forest model on the entire dataset\n",
    "random_forest_model.fit(X, y)\n",
    "\n",
    "# Save the trained model\n",
    "random_forest_model_filename = \"random_forest_model.pkl\"\n",
    "joblib.dump(random_forest_model, random_forest_model_filename)\n",
    "\n",
    "# Display the results\n",
    "(rf_cv_score_mean, random_forest_model_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5437a0",
   "metadata": {},
   "source": [
    "## 4.3 Model 3: Simple Neural Network (MLPClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a01f134",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Initialize the MLPClassifier\n",
    "mlp_model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=5000)\n",
    "\n",
    "# Perform cross-validation\n",
    "mlp_cv_score = cross_val_score(mlp_model, X, y, cv=5)\n",
    "\n",
    "# Display the cross-validation score\n",
    "mlp_cv_score_mean = mlp_cv_score.mean()\n",
    "\n",
    "# Fit the MLP model on the entire dataset\n",
    "mlp_model.fit(X, y)\n",
    "\n",
    "# Save the trained MLP model\n",
    "mlp_model_filename = \"mlp_model.pkl\"\n",
    "joblib.dump(mlp_model, mlp_model_filename)\n",
    "\n",
    "# Display the results\n",
    "(mlp_cv_score_mean, mlp_model_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e757b336",
   "metadata": {},
   "source": [
    "## 4.4 Model 4: Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d09bf3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the feature set and target variable\n",
    "X = data.drop(columns=['Survived'])  # Features\n",
    "y = data['Survived']  # Target variable\n",
    "\n",
    "# Split the data into training and validation sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_val = scaler.transform(X_val)\n",
    "\n",
    "# Define the Keras model\n",
    "def create_model():\n",
    "    model = Sequential()\n",
    "    model.add(Input(shape=(X_train.shape[1],)))  # Using Input layer instead of input_dim\n",
    "    model.add(Dense(128, activation='relu'))  # First hidden layer with 128 neurons and ReLU activation\n",
    "    model.add(Dense(64, activation='relu'))   # Second hidden layer with 64 neurons and ReLU activation\n",
    "    model.add(Dense(32, activation='relu'))   # Third hidden layer with 32 neurons and ReLU activation\n",
    "    model.add(Dense(1, activation='sigmoid')) # Output layer with 1 neuron for binary classification\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])  # Compile the model\n",
    "    return model\n",
    "\n",
    "# Build and train the model\n",
    "model = create_model()\n",
    "model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=1)  # Train the model for 50 epochs\n",
    "\n",
    "# Evaluate the model on the validation set\n",
    "accuracy = model.evaluate(X_val, y_val, verbose=0)\n",
    "print(f\"Validation accuracy: {accuracy[1] * 100:.2f}%\")  # Print the validation accuracy\n",
    "\n",
    "# Save the model\n",
    "model.save(\"single_cv_model.h5\")\n",
    "print(\"Model saved as single_cv_model.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59106f63",
   "metadata": {},
   "source": [
    "## 4.5.  Model Training Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa3491e",
   "metadata": {},
   "source": [
    "In this section, we applied several machine learning algorithms to the Titanic dataset to predict survival outcomes. We used four different models: **Logistic Regression**, **Random Forest**, **Multilayer Perceptron (MLP)**, and a **Custom Neural Network**. Each model was evaluated using cross-validation, and the best-performing models were saved for future use.\n",
    "\n",
    "---\n",
    "\n",
    "### 1. Logistic Regression\n",
    "\n",
    "**Model**: Logistic Regression  \n",
    "**Cross-Validation Score**: `0.8137`  \n",
    "**Saved Model**: `logistic_regression_model.pkl`\n",
    "\n",
    "- **Logistic Regression** is a baseline linear model often used in classification tasks. It performed well with a cross-validation score of 81.37%, demonstrating that it is capable of handling the Titanic dataset effectively.\n",
    "- The model was trained using 5-fold cross-validation, and after training, the final model was saved as `logistic_regression_model.pkl`.\n",
    "\n",
    "---\n",
    "\n",
    "### 2. Random Forest\n",
    "\n",
    "**Model**: Random Forest  \n",
    "**Cross-Validation Score**: `0.8126`  \n",
    "**Saved Model**: `random_forest_model.pkl`\n",
    "\n",
    "- **Random Forest** is an ensemble model that builds multiple decision trees and combines their predictions. It performed comparably to Logistic Regression with a score of 81.26%.\n",
    "- The model was trained with 5-fold cross-validation, and the trained model was saved as `random_forest_model.pkl`.\n",
    "\n",
    "---\n",
    "\n",
    "### 3. Multilayer Perceptron (MLP)\n",
    "\n",
    "**Model**: MLPClassifier  \n",
    "**Cross-Validation Score**: `0.8137`  \n",
    "**Saved Model**: `mlp_model.pkl`\n",
    "\n",
    "- **MLPClassifier** is a simple neural network model that uses backpropagation to learn the relationships between features. This model achieved a cross-validation score of 81.37%, which is comparable to Logistic Regression.\n",
    "- The model was saved as `mlp_model.pkl`.\n",
    "\n",
    "---\n",
    "\n",
    "### 4. Custom Neural Network (Keras)\n",
    "\n",
    "**Model**: Custom Neural Network (Keras)  \n",
    "**Validation Accuracy**: `81.01%`  \n",
    "**Saved Model**: `single_cv_model.h5`\n",
    "\n",
    "- A **Custom Neural Network** was built using the Keras library, with three hidden layers (128, 64, 32 neurons respectively) and a binary classification output layer. After 50 epochs of training, the model achieved a validation accuracy of 81.01%.\n",
    "- The model was saved as `single_cv_model.h5`.\n",
    "\n",
    "---\n",
    "\n",
    "### Summary\n",
    "\n",
    "The following models were evaluated and saved:\n",
    "\n",
    "- Logistic Regression: `0.8137`\n",
    "- Random Forest: `0.8126`\n",
    "- MLPClassifier: `0.8137`\n",
    "- Custom Neural Network: `81.01% validation accuracy`\n",
    "\n",
    "All models performed within a similar accuracy range, with Logistic Regression and MLP showing the best performance during cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72590c79",
   "metadata": {},
   "source": [
    "# 5. Test Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d84c32e",
   "metadata": {},
   "source": [
    "## 5.1. Train Data Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8313f91d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Load the test dataset\n",
    "test_data = pd.read_csv('data/test.csv')\n",
    "\n",
    "# Save the PassengerId column (for use in the submission file)\n",
    "passenger_ids = test_data['PassengerId']\n",
    "\n",
    "# 1. Data Exploration\n",
    "# Display general information about the test dataset\n",
    "test_data.info()  # Provides an overview of the dataset including data types and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "316371f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill missing values in the 'Fare' column with the median value\n",
    "test_data['Fare'].fillna(test_data['Fare'].median(), inplace=True)\n",
    "\n",
    "# After filling missing 'Fare' values, proceed with handling the missing 'Age' values using regression-based imputation\n",
    "test_data = regression_based_imputation(test_data)\n",
    "\n",
    "# Drop the 'Cabin' column since it has too many missing values\n",
    "test_data = test_data.drop(columns=['Cabin'])\n",
    "\n",
    "# Fill missing values in the 'Embarked' column with the most frequent value (mode)\n",
    "test_data['Embarked'].fillna(test_data['Embarked'].mode()[0], inplace=True)\n",
    "\n",
    "# Convert the 'Sex' column to numerical format (male = 0, female = 1)\n",
    "test_data['Sex'] = test_data['Sex'].map({'male': 0, 'female': 1})\n",
    "\n",
    "# Apply one-hot encoding to the 'Embarked' and 'Pclass' columns\n",
    "test_data = pd.get_dummies(test_data, columns=['Embarked', 'Pclass'], drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8498c147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply StandardScaler to the 'Age' column\n",
    "# This standardizes the 'Age' values by removing the mean and scaling to unit variance\n",
    "standard_scaler = StandardScaler()\n",
    "test_data['Age'] = standard_scaler.fit_transform(test_data[['Age']])\n",
    "\n",
    "# Apply MinMaxScaler to the 'Fare' column\n",
    "# This normalizes the 'Fare' values to a range between 0 and 1\n",
    "minmax_scaler = MinMaxScaler()\n",
    "test_data['Fare'] = minmax_scaler.fit_transform(test_data[['Fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "814ed72a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the 'Name' and 'Ticket' columns\n",
    "# These columns are not useful for prediction, so we remove them to simplify the dataset\n",
    "test_data = test_data.drop(columns=['Name', 'Ticket'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faad7308",
   "metadata": {},
   "source": [
    "## 5.2. Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ca75ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the trained model to get the feature order used during training\n",
    "model = joblib.load('models/logistic_regression_model.pkl')\n",
    "\n",
    "# Retrieve the feature names used during training\n",
    "feature_names = model.feature_names_in_\n",
    "\n",
    "# To ensure the test data matches the features used during training, add missing features and fill them with 0\n",
    "for feature in feature_names:\n",
    "    if feature not in test_data.columns:\n",
    "        test_data[feature] = 0  # Fill missing columns with 0\n",
    "\n",
    "# Reorder the test data to match the order of features used in the training phase\n",
    "X_test = test_data[feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9dfe7a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, make predictions using the loaded model\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Combine the predictions with PassengerId to create the submission DataFrame\n",
    "submission = pd.DataFrame({\n",
    "    'PassengerId': passenger_ids,\n",
    "    'Survived': predictions\n",
    "})\n",
    "\n",
    "# Save the submission DataFrame to a CSV file\n",
    "submission.to_csv('data/submission.csv', index=False)\n",
    "\n",
    "print(\"submission.csv file successfully created.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ce2d73",
   "metadata": {},
   "source": [
    "## 5.3. Model Testing and Prediction Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfbb3f5f",
   "metadata": {},
   "source": [
    "In this section, we tested the trained models on the **Titanic test dataset** to generate predictions for submission. The test data required preprocessing, which included handling missing values, encoding categorical variables, and scaling numerical features.\n",
    "\n",
    "First, we loaded the test dataset and saved the **PassengerId** column separately, as it is needed for the final submission file. We addressed missing values by filling the **Fare** column's single missing entry with the median fare value. For the **Age** column, we applied regression-based imputation to predict missing values based on related features such as **Pclass**, **Sex**, and **Fare**. The **Cabin** column was dropped entirely due to its high proportion of missing data (over 77%).\n",
    "\n",
    "Next, we filled the missing values in the **Embarked** column with the mode (most frequent value) to ensure consistency. The **Sex** column was converted into numerical format (male = 0, female = 1) to be compatible with machine learning models. We applied one-hot encoding to the **Embarked** and **Pclass** columns, creating binary columns for each category.\n",
    "\n",
    "We used **StandardScaler** to standardize the **Age** values and **MinMaxScaler** to normalize the **Fare** values between 0 and 1, ensuring both features were on comparable scales. Afterward, the **Name** and **Ticket** columns were dropped, as they were not relevant for prediction.\n",
    "\n",
    "Finally, we ensured that the test data had the same structure as the training data by checking for any missing features and filling them with 0 if necessary. We re-ordered the test data's columns to match the feature order used during the training phase. With the test data preprocessed, we used the trained logistic regression model to make predictions on the test set.\n",
    "\n",
    "The results were combined with the **PassengerId** column to create the submission DataFrame. The predictions were saved in a `submission.csv` file, ready for competition submission.\n",
    "\n",
    "The preprocessing steps ensured that the test data was in the same format as the training data, allowing for accurate predictions.\n"
   ]
  }
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