From 519bd04d524415aff03680a3e473708e4efa585b Mon Sep 17 00:00:00 2001
From: emmanuelakwasi <opokuakwasi239@gmail.com>
Date: Fri, 5 Jun 2026 13:43:48 -0400
Subject: [PATCH] Add Titanic solution by emmanuelakwasi

---
 001/solution/emmanuelakwasi/titanic.ipynb | 2015 +++++++++++++++++++++
 1 file changed, 2015 insertions(+)
 create mode 100644 001/solution/emmanuelakwasi/titanic.ipynb

diff --git a/001/solution/emmanuelakwasi/titanic.ipynb b/001/solution/emmanuelakwasi/titanic.ipynb
new file mode 100644
index 00000000..debc7bf1
--- /dev/null
+++ b/001/solution/emmanuelakwasi/titanic.ipynb
@@ -0,0 +1,2015 @@
+{
+  "metadata": {
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.2-final"
+    },
+    "orig_nbformat": 2,
+    "kernelspec": {
+      "name": "python38264bitpandassconda285c25d0d8784f5bba9542830bc5427b",
+      "display_name": "Python 3.8.2 64-bit ('pandass': conda)"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jx4Lb0ck9TGL"
+      },
+      "source": [
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/gimseng/99-ML-Learning-Projects/blob/master/001/exercise/starter-notebook.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9KbFV6FM9TGU"
+      },
+      "source": [
+        "## A starter notebook with tasks listed to help you get started on your first machine learning project"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LwxsGkZS9TGV"
+      },
+      "source": [
+        "### First phase: Inspecting the data\n",
+        "(this process is where you familiarize yourself with the data)\n",
+        "\n",
+        "Tasks:\n",
+        "- Inspect the data\n",
+        "- Check for null/missing values\n",
+        "- View statistical details using ``describe()``\n",
+        "\n",
+        "Step one has been done for you\n",
+        "After finishing the tasks think of the following:\n",
+        "\n",
+        "What can you infer from the statistical measures? like possible outliers?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X4nCQ2_m9TGW"
+      },
+      "source": [
+        "**Important note:**\n",
+        "\n",
+        "The data is already split into train.csv and test.csv, you generally would build your model using the train.csv **without** using any test.csv data as that would lead to overfitting your model\n",
+        "\n",
+        "#### Reminder: you do not have to stick to the tasks listed word for word, the tasks are listed as a guide to guide you.\n",
+        "#### Feel free to explore more and do more on your own!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "emFjeXEE9TGX"
+      },
+      "outputs": [],
+      "source": [
+        "# imports\n",
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "vBgmfk_P9TGZ",
+        "outputId": "6fa75f93-b076-49f3-b4eb-bf055485f7d2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 246
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-49f1ac8f-1553-49a0-bee7-cafd117556df\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </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>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-49f1ac8f-1553-49a0-bee7-cafd117556df')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-49f1ac8f-1553-49a0-bee7-cafd117556df button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-49f1ac8f-1553-49a0-bee7-cafd117556df');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "train",
+              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "project_url = 'https://raw.githubusercontent.com/gimseng/99-ML-Learning-Projects/'\n",
+        "data_path = 'master/001/data/'\n",
+        "train = pd.read_csv(project_url+data_path+'train.csv')\n",
+        "test = pd.read_csv(project_url+data_path+'test.csv')\n",
+        "\n",
+        "train.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "K9YlWmES9TGb",
+        "outputId": "7fe18307-62a9-4c50-dd7f-cd5210f37034",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   PassengerId  Pclass                                          Name     Sex  \\\n",
+              "0          892       3                              Kelly, Mr. James    male   \n",
+              "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+              "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+              "3          895       3                              Wirz, Mr. Albert    male   \n",
+              "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+              "\n",
+              "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
+              "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
+              "1  47.0      1      0   363272   7.0000   NaN        S  \n",
+              "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
+              "3  27.0      0      0   315154   8.6625   NaN        S  \n",
+              "4  22.0      1      1  3101298  12.2875   NaN        S  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-95ac040c-f34f-41e7-a479-9a472ba128fb\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>SibSp</th>\n",
+              "      <th>Parch</th>\n",
+              "      <th>Ticket</th>\n",
+              "      <th>Fare</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Embarked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>892</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Kelly, Mr. James</td>\n",
+              "      <td>male</td>\n",
+              "      <td>34.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>330911</td>\n",
+              "      <td>7.8292</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>893</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>363272</td>\n",
+              "      <td>7.0000</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>894</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Myles, Mr. Thomas Francis</td>\n",
+              "      <td>male</td>\n",
+              "      <td>62.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>240276</td>\n",
+              "      <td>9.6875</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Q</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>895</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Wirz, Mr. Albert</td>\n",
+              "      <td>male</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>315154</td>\n",
+              "      <td>8.6625</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>896</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3101298</td>\n",
+              "      <td>12.2875</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95ac040c-f34f-41e7-a479-9a472ba128fb')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-95ac040c-f34f-41e7-a479-9a472ba128fb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-95ac040c-f34f-41e7-a479-9a472ba128fb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "test",
+              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "test.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "vaKpymD39TGc",
+        "outputId": "7f5531bb-d763-4415-ddb0-bd4049bf62e2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape: (891, 12)\n",
+            "\n",
+            "Missing values:\n",
+            "PassengerId      0\n",
+            "Survived         0\n",
+            "Pclass           0\n",
+            "Name             0\n",
+            "Sex              0\n",
+            "Age            177\n",
+            "SibSp            0\n",
+            "Parch            0\n",
+            "Ticket           0\n",
+            "Fare             0\n",
+            "Cabin          687\n",
+            "Embarked         2\n",
+            "dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Shape:\", train.shape)\n",
+        "print(\"\\nMissing values:\")\n",
+        "print(train.isnull().sum())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train.describe()"
+      ],
+      "metadata": {
+        "id": "W8QYa86gZEG2",
+        "outputId": "d7f29dad-fb51-49b3-a022-f0c1ac7f146b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        }
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "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  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-93e454d4-fefd-4436-91b0-c627ffc351da\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>PassengerId</th>\n",
+              "      <th>Survived</th>\n",
+              "      <th>Pclass</th>\n",
+              "      <th>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>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93e454d4-fefd-4436-91b0-c627ffc351da')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-93e454d4-fefd-4436-91b0-c627ffc351da button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-93e454d4-fefd-4436-91b0-c627ffc351da');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429856,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079693,\n        \"min\": 0.8360712409770513,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299559\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228577,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Survival counts:\")\n",
+        "print(train['Survived'].value_counts())\n",
+        "\n",
+        "print(\"\\nSurvival percentage:\")\n",
+        "print(train['Survived'].value_counts(normalize=True) * 100)"
+      ],
+      "metadata": {
+        "id": "EWAHNdpFav_0",
+        "outputId": "eb24a6b2-c7d4-401c-9b8b-f922e4c1c281",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Survival counts:\n",
+            "Survived\n",
+            "0    549\n",
+            "1    342\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Survival percentage:\n",
+            "Survived\n",
+            "0    61.616162\n",
+            "1    38.383838\n",
+            "Name: proportion, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lAVpF5pT9TGc"
+      },
+      "outputs": [],
+      "source": [
+        "\"\"\"\n",
+        "1. Survival Rate is Low\n",
+        "\n",
+        "Mean of Survived = 0.38\n",
+        "the dataset is imbalanced — more 0s than 1s. Our model needs to learn from an uneven playing field.\n",
+        "\n",
+        "2. Fare is Heavily Skewed\n",
+        "\n",
+        "Mean fare = £32 but 50% of passengers paid £14.45 or less\n",
+        "Someone paid £512 — that's 35x the median\n",
+        "This means a small group of very wealthy passengers skew the average significantly\n",
+        "\n",
+        "3. Most People Traveled Alone\n",
+        "\n",
+        "50% of passengers had 0 siblings/spouses and 0 parents/children\n",
+        "Yet someone had 8 siblings/spouses aboard — likely a large immigrant family\n",
+        "\n",
+        "4. Age Has Significant Spread\n",
+        "\n",
+        "Range is 0.42 to 80 years — very wide\n",
+        "Standard deviation = 14.5\n",
+        "ages are spread out, not clustered tightly around the mean\n",
+        "The 177 missing ages could bias our model if not handled carefully\n",
+        "\n",
+        "5. Class is Skewed Toward 3rd Class\n",
+        "\n",
+        "Mean Pclass = 2.3, median = 3\n",
+        "majority were 3rd class passengers who paid low fares and had the worst survival odds\n",
+        "\n",
+        "\"\"\"\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HhKXT2Sw9TGd"
+      },
+      "source": [
+        "### Second phase: Data Analysis and Visualization (this process is where you explore the data, clean it and infer some insights from it)\n",
+        "Tasks:\n",
+        "- Plot the gender distribution of passengers on board\n",
+        "- Plot the rate of men and women who survived and who didnt survive\n",
+        "- Plot the survival rate by \"Pclass\" (male and female counted together in each Pclass)\n",
+        "- Plot the survival rate by males only in each \"Pclass\"\n",
+        "- Plot the survival rate by females only in each \"Pclass\"\n",
+        "\n",
+        "Think about the plots that you just did, what can you infer from them?\n",
+        "Now lets have a look at the columns, there are always unnecessary columns that do not contribute to the prediction.\n",
+        "\n",
+        "- Remove unnecessary columns from both train and test datasets\n",
+        "- Handle categorical text values and turn them into numerical\n",
+        "- Plot number of people who survived over age and passenger class\n",
+        "\n",
+        "- Deal with null values in the Age column\n",
+        "- Group the data in the Age column into groups for better prediction.\n",
+        "- Get survival rates by age groups\n",
+        "- Plot the correlation between features and label\n",
+        "\n",
+        "\n",
+        "What do you infer from the data? What can you conclude from it?who is most likely to survive, based on the data?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "6ETbdvHY9TGd",
+        "outputId": "44340592-ed70-47ed-efd7-79535bfe709a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "train['Survived'].value_counts().plot(kind='bar', color=['#e74c3c','#2ecc71'],\n",
+        "                                      edgecolor='white')\n",
+        "plt.title('How many survived vs died?')\n",
+        "plt.xticks([0, 1], ['Died', 'Survived'], rotation=0)\n",
+        "plt.ylabel('Number of passengers')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "rozr8yCc9TGf",
+        "outputId": "403f2466-0428-4054-80e6-a34ac3ed64ab",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 655
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sex\n",
+            "male      577\n",
+            "female    314\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Percentage:\n",
+            "Sex\n",
+            "male      64.758698\n",
+            "female    35.241302\n",
+            "Name: proportion, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "train['Sex'].value_counts().plot(kind='bar',\n",
+        "                                  color=['#3498db', '#e91e8c'],\n",
+        "                                  edgecolor='white')\n",
+        "plt.title('Gender Distribution of Passengers')\n",
+        "plt.xticks(rotation=0)\n",
+        "plt.ylabel('Number of Passengers')\n",
+        "plt.show()\n",
+        "\n",
+        "# Also print the exact numbers\n",
+        "print(train['Sex'].value_counts())\n",
+        "print(\"\\nPercentage:\")\n",
+        "print(train['Sex'].value_counts(normalize=True) * 100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "p4ZtBMYt9TGg",
+        "outputId": "f15b8e47-381a-402a-debe-5818e638f094",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 691
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sex     Survived\n",
+            "female  1           233\n",
+            "        0            81\n",
+            "male    0           468\n",
+            "        1           109\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Survival rate by gender:\n",
+            "Sex\n",
+            "female    74.203822\n",
+            "male      18.890815\n",
+            "Name: Survived, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "sns.countplot(data=train, x='Sex', hue='Survived',\n",
+        "              palette={0: '#e74c3c', 1: '#2ecc71'})\n",
+        "\n",
+        "plt.title('Survival Count by Gender')\n",
+        "plt.xlabel('Gender')\n",
+        "plt.ylabel('Number of Passengers')\n",
+        "plt.legend(labels=['Did Not Survive', 'Survived'])\n",
+        "plt.show()\n",
+        "\n",
+        "# Print the exact numbers\n",
+        "print(train.groupby('Sex')['Survived'].value_counts())\n",
+        "print(\"\\nSurvival rate by gender:\")\n",
+        "print(train.groupby('Sex')['Survived'].mean() * 100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "80wHKNsO9TGh",
+        "outputId": "0d6c4f10-a475-4ab2-a146-24ab7fa0e8a8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 746
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Pclass  Survived\n",
+            "1       1           136\n",
+            "        0            80\n",
+            "2       0            97\n",
+            "        1            87\n",
+            "3       0           372\n",
+            "        1           119\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Survival rate by class:\n",
+            "Pclass\n",
+            "1    62.962963\n",
+            "2    47.282609\n",
+            "3    24.236253\n",
+            "Name: Survived, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "sns.countplot(data=train, x='Pclass', hue='Survived',\n",
+        "              palette={0: '#e74c3c', 1: '#2ecc71'})\n",
+        "\n",
+        "plt.title('Survival Rate by Passenger Class')\n",
+        "plt.xlabel('Passenger Class')\n",
+        "plt.ylabel('Number of Passengers')\n",
+        "plt.xticks([0, 1, 2], ['1st Class', '2nd Class', '3rd Class'])\n",
+        "plt.legend(labels=['Did Not Survive', 'Survived'])\n",
+        "plt.show()\n",
+        "\n",
+        "# Print exact numbers\n",
+        "print(train.groupby('Pclass')['Survived'].value_counts())\n",
+        "print(\"\\nSurvival rate by class:\")\n",
+        "print(train.groupby('Pclass')['Survived'].mean() * 100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "rE9pfiG29TGh",
+        "outputId": "556730ec-e480-4c79-ac5d-53ec2383f70a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 746
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Pclass  Survived\n",
+            "1       0            77\n",
+            "        1            45\n",
+            "2       0            91\n",
+            "        1            17\n",
+            "3       0           300\n",
+            "        1            47\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Male survival rate by class:\n",
+            "Pclass\n",
+            "1    36.885246\n",
+            "2    15.740741\n",
+            "3    13.544669\n",
+            "Name: Survived, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "males = train[train['Sex'] == 'male']\n",
+        "\n",
+        "sns.countplot(data=males, x='Pclass', hue='Survived',\n",
+        "              palette={0: '#e74c3c', 1: '#2ecc71'})\n",
+        "\n",
+        "plt.title('Survival Rate by Passenger Class (Males Only)')\n",
+        "plt.xlabel('Passenger Class')\n",
+        "plt.ylabel('Number of Passengers')\n",
+        "plt.xticks([0, 1, 2], ['1st Class', '2nd Class', '3rd Class'])\n",
+        "plt.legend(labels=['Did Not Survive', 'Survived'])\n",
+        "plt.show()\n",
+        "\n",
+        "# Print exact numbers\n",
+        "print(males.groupby('Pclass')['Survived'].value_counts())\n",
+        "print(\"\\nMale survival rate by class:\")\n",
+        "print(males.groupby('Pclass')['Survived'].mean() * 100)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "lriJ5l4f9TGh",
+        "outputId": "3f75a85a-e26f-4324-e1cb-80dd094146ba",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 746
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Pclass  Survived\n",
+            "1       1           91\n",
+            "        0            3\n",
+            "2       1           70\n",
+            "        0            6\n",
+            "3       0           72\n",
+            "        1           72\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Female survival rate by class:\n",
+            "Pclass\n",
+            "1    96.808511\n",
+            "2    92.105263\n",
+            "3    50.000000\n",
+            "Name: Survived, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "females = train[train['Sex'] == 'female']\n",
+        "\n",
+        "sns.countplot(data=females, x='Pclass', hue='Survived',\n",
+        "              palette={0: '#e74c3c', 1: '#2ecc71'})\n",
+        "\n",
+        "plt.title('Survival Rate by Passenger Class (Females Only)')\n",
+        "plt.xlabel('Passenger Class')\n",
+        "plt.ylabel('Number of Passengers')\n",
+        "plt.xticks([0, 1, 2], ['1st Class', '2nd Class', '3rd Class'])\n",
+        "plt.legend(labels=['Did Not Survive', 'Survived'])\n",
+        "plt.show()\n",
+        "\n",
+        "# Print exact numbers\n",
+        "print(females.groupby('Pclass')['Survived'].value_counts())\n",
+        "print(\"\\nFemale survival rate by class:\")\n",
+        "print(females.groupby('Pclass')['Survived'].mean() * 100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wfEjiLLl9TGi"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load test data too since we clean both together\n",
+        "#test = pd.read_csv('test.csv')\n",
+        "\n",
+        "print(\"Before dropping:\")\n",
+        "print(\"Train columns:\", list(train.columns))\n",
+        "print(\"Test columns:\", list(test.columns))"
+      ],
+      "metadata": {
+        "id": "2Iko-gmDdQjK",
+        "outputId": "5b624131-76ec-4a7b-ea79-89536b9ff37a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Before dropping:\n",
+            "Train columns: ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n",
+            "Test columns: ['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train = train.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin'])\n",
+        "test = test.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin'])\n",
+        "\n",
+        "print(\"Train columns after dropping:\", list(train.columns))\n",
+        "print(\"Test columns after dropping:\", list(test.columns))"
+      ],
+      "metadata": {
+        "id": "k6rS52hFd2An",
+        "outputId": "b1ca4ac4-e3a6-49cb-d18d-5270cf19172c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train columns after dropping: ['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
+            "Test columns after dropping: ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert Sex\n",
+        "train['Sex'] = train['Sex'].map({'male': 0, 'female': 1})\n",
+        "test['Sex'] = test['Sex'].map({'male': 0, 'female': 1})\n",
+        "\n",
+        "# Convert Embarked\n",
+        "train['Embarked'] = train['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})\n",
+        "test['Embarked'] = test['Embarked'].map({'S': 0, 'C': 1, 'Q': 2})\n",
+        "\n",
+        "# Confirm\n",
+        "print(\"Train sample:\")\n",
+        "print(train.head())\n",
+        "print(\"\\nAny remaining text columns?\")\n",
+        "print(train.dtypes)"
+      ],
+      "metadata": {
+        "id": "AvXKViw7kdPW",
+        "outputId": "afb94f4e-2a4f-4769-8709-42705e81e875",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train sample:\n",
+            "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
+            "0         0       3    0  22.0      1      0   7.2500       0.0\n",
+            "1         1       1    1  38.0      1      0  71.2833       1.0\n",
+            "2         1       3    1  26.0      0      0   7.9250       0.0\n",
+            "3         1       1    1  35.0      1      0  53.1000       0.0\n",
+            "4         0       3    0  35.0      0      0   8.0500       0.0\n",
+            "\n",
+            "Any remaining text columns?\n",
+            "Survived      int64\n",
+            "Pclass        int64\n",
+            "Sex           int64\n",
+            "Age         float64\n",
+            "SibSp         int64\n",
+            "Parch         int64\n",
+            "Fare        float64\n",
+            "Embarked    float64\n",
+            "dtype: object\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.scatterplot(data=train, x='Age', y='Survived',\n",
+        "                hue='Pclass', palette={1: '#f39c12', 2: '#95a5a6', 3: '#e74c3c'},\n",
+        "                alpha=0.6)\n",
+        "\n",
+        "plt.title('Survival Over Age and Passenger Class')\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Survived (0 = No, 1 = Yes)')\n",
+        "plt.legend(title='Pclass', labels=['1st Class', '2nd Class', '3rd Class'])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "li-EPrlDoKll",
+        "outputId": "c9a681a7-d3c8-47f7-8931-9b305ccc5c6c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.scatterplot(data=train, x='Age', y='Survived',\n",
+        "                hue='Pclass', palette={1: '#f39c12', 2: '#95a5a6', 3: '#e74c3c'},\n",
+        "                alpha=0.6)\n",
+        "\n",
+        "plt.title('Survival Over Age and Passenger Class')\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Survived (0 = No, 1 = Yes)')\n",
+        "plt.legend(title='Pclass', labels=['1st Class', '2nd Class', '3rd Class'])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "y0jmTlH4pGrz",
+        "outputId": "d80cf19d-37ad-41e3-88c7-51124dd954c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check how many are missing\n",
+        "print(\"Missing ages:\", train['Age'].isnull().sum())\n",
+        "\n",
+        "# Fill with median age\n",
+        "median_age = train['Age'].median()\n",
+        "print(\"Median age:\", median_age)\n",
+        "\n",
+        "train['Age'] = train['Age'].fillna(median_age)\n",
+        "test['Age'] = test['Age'].fillna(median_age)\n",
+        "\n",
+        "# Confirm no more missing\n",
+        "print(\"Missing ages after fix:\", train['Age'].isnull().sum())"
+      ],
+      "metadata": {
+        "id": "tcgbCvn0p1ge",
+        "outputId": "b66389b1-7817-4e6f-8990-73155230b43a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing ages: 177\n",
+            "Median age: 28.0\n",
+            "Missing ages after fix: 0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create age groups for train\n",
+        "train['AgeGroup'] = pd.cut(train['Age'],\n",
+        "                            bins=[0, 12, 18, 35, 60, 100],\n",
+        "                            labels=['Child', 'Teenager', 'YoungAdult', 'Adult', 'Senior'])\n",
+        "\n",
+        "# Create age groups for test\n",
+        "test['AgeGroup'] = pd.cut(test['Age'],\n",
+        "                           bins=[0, 12, 18, 35, 60, 100],\n",
+        "                           labels=['Child', 'Teenager', 'YoungAdult', 'Adult', 'Senior'])\n",
+        "\n",
+        "# Check the distribution\n",
+        "print(\"Age group distribution:\")\n",
+        "print(train['AgeGroup'].value_counts())"
+      ],
+      "metadata": {
+        "id": "iLV_tZ1OqK_e",
+        "outputId": "585a0140-8cc2-4f01-bd56-7335db2a1ad0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Age group distribution:\n",
+            "AgeGroup\n",
+            "YoungAdult    535\n",
+            "Adult         195\n",
+            "Teenager       70\n",
+            "Child          69\n",
+            "Senior         22\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Survival rate by age group\n",
+        "print(\"Survival rate by age group:\")\n",
+        "print(train.groupby('AgeGroup', observed=True)['Survived'].mean() * 100)\n",
+        "\n",
+        "# Plot it\n",
+        "train.groupby('AgeGroup', observed=True)['Survived'].mean().plot(\n",
+        "    kind='bar',\n",
+        "    color=['#3498db', '#2ecc71', '#e74c3c', '#f39c12', '#9b59b6'],\n",
+        "    edgecolor='white'\n",
+        ")\n",
+        "\n",
+        "plt.title('Survival Rate by Age Group')\n",
+        "plt.xlabel('Age Group')\n",
+        "plt.ylabel('Survival Rate (%)')\n",
+        "plt.xticks(rotation=0)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "-uTBiKSmqdch",
+        "outputId": "0db40532-e164-4ef5-b4ac-635125f5e06d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 618
+        }
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Survival rate by age group:\n",
+            "AgeGroup\n",
+            "Child         57.971014\n",
+            "Teenager      42.857143\n",
+            "YoungAdult    35.327103\n",
+            "Adult         40.000000\n",
+            "Senior        22.727273\n",
+            "Name: Survived, dtype: float64\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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Iv6/Iadq0qbCxsRETJkwQCxYsEK1atRJKpVLs3LlTq//hw4cFAAFAzJ49+4nrfrTPSqVSfPjhh+X26d+/v6hbt650xd3HH38sAIg+ffqIhQsXivfee084OzsLW1tb8fbbb0vLVXbfS0pKxJAhQwQA4eHhIT777DMRHx8v5s2bJ9566y2hUqmEvb29uHPnjrQMADFy5Eitdf3xxx+iVq1awsnJSXz++edi9uzZomHDhkKtVotff/1V6ldUVCQaNGggbG1txezZs8XcuXNF8+bNpd+Lf14tpctnQ6QLhhuix/z444/inXfeEW5ubsLCwkKoVCrRuHFjMXr0aJGTk6PR9969e2Lo0KHC2tpaWFpaioEDB4pr167pFW6WLFkihZT79+9rzS8v3JSWlgpnZ2cBQMyYMaPM+TNnzhQuLi5CrVaLNm3aiO3bt5e5vsqGGyGEePvtt4WJiYk4d+6cEEKIrVu3itatWwszMzPh6uoqZs+eLQWux7/osrOzRa9evaSA9/hl4Xfu3BGRkZGicePGQqVSCVtbW/Hqq6+KuXPnal2i/08uLi6iV69eYteuXaJ169ZCrVYLNzc3rUvWH9eiRQuhVCrFn3/++cR1CyHEvHnzBAApjJUlISFBABBbtmwRQvx9OfbkyZOFo6OjqFmzpujSpYtIT08XdevWFe+//77GspXZ90c2bdokevbsKezs7ISpqamwsbERr732mpgzZ464ffu2Rt/ywo0QQhw5ckQEBgYKCwsLYW5uLjp37iwOHjyo1S81NVX4+voKlUolGjRoIObPn1/upeC6fjZEFaUQooLHJomIXgBt2rRBnTp1nukzl27fvo3atWtjxowZWuOm5MrV1RUtW7bE9u3bjV0KyRDH3BAR/X+HDx/GsWPHEBISUmXbuH//vlZbTEwMAGg8doKI9MerpYjohffHH38gNTUV8+bNQ7169bQGZhtSYmIiEhIS0LNnT1hYWCA5ORlr165F9+7d0b59+yrbLtGLhOGGiF54GzZswGeffYZmzZph7dq1VfoU7NatW8PU1BRffPEF8vPz4eDggDFjxmDGjBlVtk2iFw3H3BAREZGscMwNERERyQrDDREREcnKCzfmprS0FFeuXIGlpaXOz3ohIiIi4xBC4M6dO6hfv/5THynywoWbK1euSM9BISIioufL5cuX8dJLLz2xj9HDzcKFCzFnzhxkZ2fDw8MD//3vf9GuXbty+9++fRsTJ07Exo0bcfPmTbi4uCAmJgY9e/as0PYsLS0B/P3mPHoeChEREVVv+fn5cHZ2lr7Hn8So4SYxMRERERGIi4uDr68vYmJiEBgYiIyMDNjb22v1LyoqQrdu3WBvb48NGzbAyckJly5d0nj67NM8OhVlZWXFcENERPScqciQEqNeCu7r6wsfHx/pibilpaVwdnbG6NGjMWHCBK3+cXFxmDNnDk6fPo0aNWrotc38/HxYW1sjLy+P4YaIiOg5ocv3t9GulioqKkJqaioCAgL+V4xSiYCAAKSkpJS5zNatW+Hn54eRI0fCwcEBLVu2xMyZM1FSUlLudgoLC5Gfn6/xIiIiIvkyWri5fv06SkpK4ODgoNHu4OCA7OzsMpe5cOECNmzYgJKSEuzYsQOTJ0/GvHnznnhnz+joaFhbW0svDiYmIiKSt+fqPjelpaWwt7fHN998Ay8vLwQHB2PixImIi4srd5nIyEjk5eVJr8uXLz/DiomIiOhZM9qAYltbW5iYmCAnJ0ejPScnB46OjmUuU69ePdSoUQMmJiZSm7u7O7Kzs1FUVASVSqW1jFqthlqtNmzxREREVG0Z7ciNSqWCl5cXkpKSpLbS0lIkJSXBz8+vzGXat2+Pc+fOobS0VGo7c+YM6tWrV2awISIiohePUU9LRUREYMmSJVi+fDnS09MxfPhwFBQUICwsDAAQEhKCyMhIqf/w4cNx8+ZNjBkzBmfOnMEPP/yAmTNnYuTIkcbaBSIiIqpmjHqfm+DgYOTm5mLKlCnIzs6Gp6cndu7cKQ0yzsrK0rjFsrOzM3bt2oWPPvoIrVu3hpOTE8aMGYPx48cbaxeIiIiomjHqfW6Mgfe5ISIiev48F/e5ISIiIqoKDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN1WkpFQeV9jLZT+IiOjFYdSb+MmZiVKByUnZyLz90Nil6O1lmxqY3rXs53wRERFVVww3VSjz9kNkXC80dhlEREQvFJ6WIiIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZqRbhZuHChXB1dYWZmRl8fX1x6NChcvsmJCRAoVBovMzMzJ5htURERFSdGT3cJCYmIiIiAlFRUThy5Ag8PDwQGBiIa9eulbuMlZUVrl69Kr0uXbr0DCsmIiKi6szo4Wb+/PkIDw9HWFgYmjdvjri4OJibmyM+Pr7cZRQKBRwdHaWXg4PDM6yYiIiIqjOjhpuioiKkpqYiICBAalMqlQgICEBKSkq5y929excuLi5wdnZG3759cfLkyXL7FhYWIj8/X+NFRERE8mXUcHP9+nWUlJRoHXlxcHBAdnZ2mcs0a9YM8fHx2LJlC1atWoXS0lK8+uqr+PPPP8vsHx0dDWtra+nl7Oxs8P0gIiKi6sPop6V05efnh5CQEHh6esLf3x8bN26EnZ0dFi9eXGb/yMhI5OXlSa/Lly8/44qJiIjoWTI15sZtbW1hYmKCnJwcjfacnBw4OjpWaB01atRAmzZtcO7cuTLnq9VqqNXqStdKREREzwejHrlRqVTw8vJCUlKS1FZaWoqkpCT4+flVaB0lJSU4ceIE6tWrV1VlEhER0XPEqEduACAiIgKhoaHw9vZGu3btEBMTg4KCAoSFhQEAQkJC4OTkhOjoaADAZ599hldeeQWNGzfG7du3MWfOHFy6dAnvvvuuMXeDiIiIqgmjh5vg4GDk5uZiypQpyM7OhqenJ3bu3CkNMs7KyoJS+b8DTLdu3UJ4eDiys7NRu3ZteHl54eDBg2jevLmxdoGIiIiqEYUQQhi7iGcpPz8f1tbWyMvLg5WVVZVua8j3l5FxvbBKt1GVmtmqsao/ry4jIiLj0+X7+7m7WoqIiIjoSRhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG5I9kpEqbFLMAi57AcRUVUzNXYBRFXNRKFEZOYqXLifY+xS9NawpgOiXx5i7DKIiJ4LDDf0QrhwPwen7/9l7DKIiOgZ4GkpIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpKVahFuFi5cCFdXV5iZmcHX1xeHDh2q0HLfffcdFAoFgoKCqrZAIiIiem4YPdwkJiYiIiICUVFROHLkCDw8PBAYGIhr1649cbmLFy9i7Nix6NChwzOqlIiIiJ4HRg838+fPR3h4OMLCwtC8eXPExcXB3Nwc8fHx5S5TUlKCt956C9OmTUPDhg2fYbVERERU3Rk13BQVFSE1NRUBAQFSm1KpREBAAFJSUspd7rPPPoO9vT2GDh361G0UFhYiPz9f40VERETyZarrAqWlpfj555+xf/9+XLp0Cffu3YOdnR3atGmDgIAAODs7V3hd169fR0lJCRwcHDTaHRwccPr06TKXSU5OxtKlS3Hs2LEKbSM6OhrTpk2rcE1ERC8KUVoChdLE2GVUihz2gQyvwuHm/v37mDdvHmJjY3Hz5k14enqifv36qFmzJs6dO4fNmzcjPDwc3bt3x5QpU/DKK68YvNg7d+7gP//5D5YsWQJbW9sKLRMZGYmIiAhpOj8/X6cARkQkVwqlCR7sG4bS22eMXYpelDZNYdZpsbHLoGqowuGmadOm8PPzw5IlS9CtWzfUqFFDq8+lS5ewZs0aDBo0CBMnTkR4ePgT12lrawsTExPk5ORotOfk5MDR0VGr//nz53Hx4kX06dNHaistLf17R0xNkZGRgUaNGmkso1aroVarK7qbREQvlNLbZ1B647ixyyAyqAqHm927d8Pd3f2JfVxcXBAZGYmxY8ciKyvrqetUqVTw8vJCUlKSdDl3aWkpkpKSMGrUKK3+bm5uOHHihEbbpEmTcOfOHXz55Zc8IkNEREQVDzdPCzaPq1GjhtYRlPJEREQgNDQU3t7eaNeuHWJiYlBQUICwsDAAQEhICJycnBAdHQ0zMzO0bNlSY3kbGxsA0GonIiKiF5POA4ofV1xcjMWLF2Pfvn0oKSlB+/btMXLkSJiZmVV4HcHBwcjNzcWUKVOQnZ0NT09P7Ny5UxpknJWVBaXS6FesExER0XOiUuHmgw8+wJkzZ/Dvf/8bDx8+xIoVK3D48GGsXbtWp/WMGjWqzNNQALBv374nLpuQkKDTtoiIiEjedAo3mzZtQr9+/aTp3bt3IyMjAyYmf1+GFxgYWCVXSRERERFVlE7ne+Lj4xEUFIQrV64AANq2bYv3338fO3fuxLZt2zBu3Dj4+PhUSaFEREREFaFTuNm2bRsGDx6MTp064b///S+++eYbWFlZYeLEiZg8eTKcnZ2xZs2aqqqViIiI6Kl0HnMTHByMwMBAjBs3DoGBgYiLi8O8efOqojYiIiIinel1GZKNjQ2++eYbzJkzByEhIfjkk0/w4MEDQ9dGREREpDOdwk1WVhYGDhyIVq1a4a233kKTJk2QmpoKc3NzeHh44Mcff6yqOomIiIgqRKdwExISAqVSiTlz5sDe3h7Dhg2DSqXCtGnTsHnzZkRHR2PgwIFVVSsRERHRU+k05ubw4cNIS0tDo0aNEBgYiJdfflma5+7ujl9++QXffPONwYskIiIiqiidwo2XlxemTJmC0NBQ/N///R9atWql1ee9994zWHFEREREutLptNSKFStQWFiIjz76CH/99RcWL+aj5omIiKh60enIjYuLCzZs2FBVtRARERFVWoWP3BQUFOi0Yl37ExERERlChcNN48aNMWvWLFy9erXcPkII7NmzBz169MBXX31lkAKJiIiIdFHh01L79u3Dp59+iqlTp8LDwwPe3t6oX78+zMzMcOvWLZw6dQopKSkwNTVFZGQkhg0bVpV1ExEREZWpwuGmWbNm+P7775GVlYX169dj//79OHjwIO7fvw9bW1u0adMGS5YsQY8ePaSnhBMRERE9azo/W6pBgwb4+OOP8fHHH1dFPURERESVotezpYiIiIiqK4YbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhW9w83+/fsxZMgQ+Pn54a+//gIArFy5EsnJyQYrjojkRZSWGLsEg5DLfhDJlc6XggPA999/j//85z946623cPToURQWFgIA8vLyMHPmTOzYscOgRRKRPCiUJrg99zMU/3nJ2KXozfQlF9iMnWLsMojoCfQKNzNmzEBcXBxCQkLw3XffSe3t27fHjBkzDFYcEclP8Z+XUHz+jLHLICIZ0+u0VEZGBjp27KjVbm1tjdu3b1e2JiIiIiK96RVuHB0dce7cOa325ORkNGzYsNJFEREREelLr3ATHh6OMWPG4LfffoNCocCVK1ewevVqjB07FsOHDzd0jUREREQVpteYmwkTJqC0tBRdu3bFvXv30LFjR6jVaowdOxajR482dI1EREREFaZXuFEoFJg4cSI++eQTnDt3Dnfv3kXz5s1hYWFh6PqIiIiIdKLXaal33nkHd+7cgUqlQvPmzdGuXTtYWFigoKAA77zzjqFrJCIiIqowvcLN8uXLcf/+fa32+/fvY8WKFZUuioiIiEhfOp2Wys/PhxACQgjcuXMHZmZm0rySkhLs2LED9vb2Bi+SiIiIqKJ0Cjc2NjZQKBRQKBRo2rSp1nyFQoFp06YZrDgiIiIiXekUbvbu3QshBLp06YLvv/8ederUkeapVCq4uLigfv36Bi+SiIiIqKJ0Cjf+/v4AgMzMTDg7O0Op5EPFiYiIqHrR61JwFxcXAMC9e/eQlZWFoqIijfmtW7eufGVEREREetAr3OTm5iIsLAw//vhjmfNLSkoqVRQRERGRvvQ6r/Thhx/i9u3b+O2331CzZk3s3LkTy5cvR5MmTbB161ZD10hERERUYXodufnpp5+wZcsWeHt7Q6lUwsXFBd26dYOVlRWio6PRq1cvQ9dJREREVCF6HbkpKCiQ7mdTu3Zt5ObmAgBatWqFI0eOGK46IiIiIh3pFW6aNWuGjIwMAICHhwcWL16Mv/76C3FxcahXr55BCyQiIiLShV6npcaMGYOrV68CAKKiovD6669j9erVUKlUSEhIMGR9RERERDrRK9wMGTJE+reXlxcuXbqE06dPo0GDBrC1tTVYcURERES6Mshd+MzNzdG2bVtYWFhg7ty5hlglERERkV50Dje5ubnYvn07du/eLd3P5uHDh/jyyy/h6uqKWbNmGbxIIiIioorS6bRUcnIyevfujfz8fCgUCnh7e2PZsmUICgqCqakppk6ditDQ0KqqlYiIiOipdDpyM2nSJPTs2RPHjx9HREQEfv/9d/Tr1w8zZ87EqVOn8P7776NmzZpVVSsRERHRU+kUbk6cOIFJkyahZcuW+Oyzz6BQKPDFF19gwIABVVUfERERkU50Cje3bt2SroaqWbMmzM3N0bJlyyopjIiIiEgfOg8oPnXqFI4fP47jx49DCIGMjAxp+tFLVwsXLoSrqyvMzMzg6+uLQ4cOldt348aN8Pb2ho2NDWrVqgVPT0+sXLlS520SERGRPOl8n5uuXbtCCCFN9+7dGwCgUCgghIBCodDpqeCJiYmIiIhAXFwcfH19ERMTg8DAQGRkZEiPeHhcnTp1MHHiRLi5uUGlUmH79u0ICwuDvb09AgMDdd0dIiIikhmdwk1mZqbBC5g/fz7Cw8MRFhYGAIiLi8MPP/yA+Ph4TJgwQat/p06dNKbHjBmD5cuXIzk5meGGiIiIdAs3Li4uBt14UVERUlNTERkZKbUplUoEBAQgJSXlqcsLIfDTTz8hIyMDs2fPNmhtRERE9HzS6/ELhnL9+nWUlJTAwcFBo93BwQGnT58ud7m8vDw4OTmhsLAQJiYmWLRoEbp161Zm38LCQhQWFkrT+fn5himeiIiIqiWjhht9WVpa4tixY7h79y6SkpIQERGBhg0bap2yAoDo6GhMmzbt2RdJRERERmHUcGNrawsTExPk5ORotOfk5MDR0bHc5ZRKJRo3bgwA8PT0RHp6OqKjo8sMN5GRkYiIiJCm8/Pz4ezsbJgdICIiomrHIA/O1JdKpYKXlxeSkpKkttLSUiQlJcHPz6/C6yktLdU49fQ4tVoNKysrjRcRERHJl9FPS0VERCA0NBTe3t5o164dYmJiUFBQIF09FRISAicnJ0RHRwP4+zSTt7c3GjVqhMLCQuzYsQMrV65EbGysMXeDiIiIqokKh5s2bdpAoVBUqO+RI0cqXEBwcDByc3MxZcoUZGdnw9PTEzt37pQGGWdlZUGp/N8BpoKCAowYMQJ//vknatasCTc3N6xatQrBwcEV3iYRERHJV4XDTVBQUJUVMWrUKIwaNarMefv27dOYnjFjBmbMmFFltRAREdHzrcLhJioqqirrICIiIjIIow4oJiIiIjI0vQYUl5SUYMGCBVi3bh2ysrJQVFSkMf/mzZsGKY6IiIhIV3oduZk2bRrmz5+P4OBg5OXlISIiAv/+97+hVCoxdepUA5dIREREVHF6hZvVq1djyZIl+Pjjj2FqaorBgwfj22+/xZQpU/Drr78aukYiIiKiCtMr3GRnZ6NVq1YAAAsLC+Tl5QEAevfujR9++MFw1RERERHpSK9w89JLL+Hq1asAgEaNGmH37t0AgN9//x1qtdpw1RERERHpSK9w069fP+mRCaNHj8bkyZPRpEkThISE4J133jFogURERES60OtqqVmzZkn/Dg4OhouLCw4ePIgmTZqgT58+BiuOiIiISFd6hZsHDx7AzMxMmn7llVfwyiuvGKwoIiIiIn3pdVrK3t4eoaGh2LNnD0pLSw1dExEREZHe9Ao3y5cvx71799C3b184OTnhww8/xOHDhw1dGxEREZHO9B5QvH79euTk5GDmzJk4deoUXnnlFTRt2hSfffaZoWskIiIiqrBKPVvK0tISYWFh2L17N44fP45atWph2rRphqqNiIiISGeVCjcPHjzAunXrEBQUhLZt2+LmzZv45JNPDFUbERERkc70ulpq165dWLNmDTZv3gxTU1MMGDAAu3fvRseOHQ1dHxEREZFO9Ao3/fr1Q+/evbFixQr07NkTNWrUMHRdRERERHrRK9zk5OTA0tLS0LUQERERVVqFw01+fj6srKwAAEII5Ofnl9v3UT8iIiKiZ63C4aZ27dq4evUq7O3tYWNjA4VCodVHCAGFQoGSkhKDFklERERUURUONz/99BPq1Kkj/buscENERERkbBUON/7+/tK/O3XqVBW1EBEREVWaXve5adKkCaZOnYqzZ88auh4iIiKiStEr3IwYMQI//PAD3Nzc4OPjgy+//BLZ2dmGro2IiIhIZ3qFm48++gi///470tPT0bNnTyxcuBDOzs7o3r07VqxYYegaiYiIiCqsUo9faNq0KaZNm4YzZ85g//79yM3NRVhYmKFqIyIiItKZXjfxe9yhQ4ewZs0aJCYmIj8/H2+88YYh6iIiIiLSi17h5syZM1i9ejXWrl2LzMxMdOnSBbNnz8a///1vWFhYGLpGIiIiogrTK9w8Gkg8cuRIDBo0CA4ODoaui4iIiEgvOoebkpISLF68GAMGDEDt2rWroiYiIiIivek8oNjExASjR4/G7du3q6AcIiIiosrR62qpli1b4sKFC4auhYiIiKjS9Ao3M2bMwNixY7F9+3ZcvXoV+fn5Gi8iIiIiY9FrQHHPnj0BAP/61780HqDJp4ITERGRsekVbvbu3WvoOoiIiIgMQq9w8/gTwomIiIiqE73CzS+//PLE+R07dtSrGCIiIqLK0ivcdOrUSavt8bE3HHNDRERExqLX1VK3bt3SeF27dg07d+6Ej48Pdu/ebegaiYiIZK20VBi7hEqrTvug15Eba2trrbZu3bpBpVIhIiICqamplS6MiIjoRaFUKrB3YRpu/3XX2KXoxcbJAp1Hehi7DEmlnwr+OAcHB2RkZBhylURERC+E23/dxY2LvFecIegVbo4fP64xLYTA1atXMWvWLHh6ehqiLiIiIiK96BVuPD09oVAoIITm+bVXXnkF8fHxBimMiIiISB96hZvMzEyNaaVSCTs7O5iZmRmkKCIiIiJ96RVuXFxcDF0HERERkUHodCl4SkoKtm/frtG2YsUKvPzyy7C3t8d7772HwsJCgxZIREREpAudws1nn32GkydPStMnTpzA0KFDERAQgAkTJmDbtm2Ijo42eJFEREREFaVTuDl27Bi6du0qTX/33Xfw9fXFkiVLEBERga+++grr1q0zeJFEREREFaVTuLl16xYcHByk6Z9//hk9evSQpn18fHD58mXDVUdERESkI53CjYODg3SlVFFREY4cOYJXXnlFmn/nzh3UqFHDsBUSERER6UCncNOzZ09MmDAB+/fvR2RkJMzNzdGhQwdp/vHjx9GoUSOdi1i4cCFcXV1hZmYGX19fHDp0qNy+S5YsQYcOHVC7dm3Url0bAQEBT+xPRERELxadws306dNhamoKf39/LFmyBEuWLIFKpZLmx8fHo3v37joVkJiYiIiICERFReHIkSPw8PBAYGAgrl27Vmb/ffv2YfDgwdi7dy9SUlLg7OyM7t2746+//tJpu0RERCRPOt3nxtbWFr/88gvy8vJgYWEBExMTjfnr16+HhYWFTgXMnz8f4eHhCAsLAwDExcXhhx9+QHx8PCZMmKDVf/Xq1RrT3377Lb7//nskJSUhJCREp20TERGR/Oh05OYRa2trrWADAHXq1NE4kvM0RUVFSE1NRUBAwP8KUioREBCAlJSUCq3j3r17ePjwIerUqVPm/MLCQuTn52u8iIiISL70CjeGcv36dZSUlGhcgQX8PXA5Ozu7QusYP3486tevrxGQHhcdHQ1ra2vp5ezsXOm6iYiIqPoyariprFmzZuG7777Dpk2byn2uVWRkJPLy8qQXL1UnIiKSN72eLWUotra2MDExQU5OjkZ7Tk4OHB0dn7js3LlzMWvWLPzf//0fWrduXW4/tVoNtVptkHqJiIio+jPqkRuVSgUvLy8kJSVJbaWlpUhKSoKfn1+5y33xxReYPn06du7cCW9v72dRKhERET0njHrkBgAiIiIQGhoKb29vtGvXDjExMSgoKJCungoJCYGTk5P0zKrZs2djypQpWLNmDVxdXaWxORYWFjpfqUVERETyY/RwExwcjNzcXEyZMgXZ2dnw9PTEzp07pUHGWVlZUCr/d4ApNjYWRUVFGDBggMZ6oqKiMHXq1GdZOhEREVVDRg83ADBq1CiMGjWqzHn79u3TmL548WLVF0RERETPref6aikiIiKif2K4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIllhuCEiIiJZYbghIiIiWWG4ISIiIlkxerhZuHAhXF1dYWZmBl9fXxw6dKjcvidPnkT//v3h6uoKhUKBmJiYZ1coERERPReMGm4SExMRERGBqKgoHDlyBB4eHggMDMS1a9fK7H/v3j00bNgQs2bNgqOj4zOuloiIiJ4HRg038+fPR3h4OMLCwtC8eXPExcXB3Nwc8fHxZfb38fHBnDlzMGjQIKjV6mdcLRERET0PjBZuioqKkJqaioCAgP8Vo1QiICAAKSkpBttOYWEh8vPzNV5EREQkX0YLN9evX0dJSQkcHBw02h0cHJCdnW2w7URHR8Pa2lp6OTs7G2zdREREVP0YfUBxVYuMjEReXp70unz5srFLIiIioipkaqwN29rawsTEBDk5ORrtOTk5Bh0srFarOT6HiIjoBWK0IzcqlQpeXl5ISkqS2kpLS5GUlAQ/Pz9jlUVERETPOaMduQGAiIgIhIaGwtvbG+3atUNMTAwKCgoQFhYGAAgJCYGTkxOio6MB/D0I+dSpU9K///rrLxw7dgwWFhZo3Lix0faDiIiIqg+jhpvg4GDk5uZiypQpyM7OhqenJ3bu3CkNMs7KyoJS+b+DS1euXEGbNm2k6blz52Lu3Lnw9/fHvn37nnX5REREVA0ZNdwAwKhRozBq1Kgy5/0zsLi6ukII8QyqIiIioueV7K+WIiIiohcLww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREclKtQg3CxcuhKurK8zMzODr64tDhw49sf/69evh5uYGMzMztGrVCjt27HhGlRIREVF1Z/Rwk5iYiIiICERFReHIkSPw8PBAYGAgrl27Vmb/gwcPYvDgwRg6dCiOHj2KoKAgBAUF4Y8//njGlRMREVF1ZPRwM3/+fISHhyMsLAzNmzdHXFwczM3NER8fX2b/L7/8Eq+//jo++eQTuLu7Y/r06Wjbti2+/vrrZ1w5ERERVUdGDTdFRUVITU1FQECA1KZUKhEQEICUlJQyl0lJSdHoDwCBgYHl9iciIqIXi6kxN379+nWUlJTAwcFBo93BwQGnT58uc5ns7Owy+2dnZ5fZv7CwEIWFhdJ0Xl4eACA/P78ypVdI/RoP8ND8YZVvp6rUryGeyfv0LDgVW+FhyQNjl6E3p2Ir2XwW92wdUFJYZOwy9GZi6yCbzwIAHtRwRWnNYmOXoRdlDVcUy+izqFEHqFloYuwy9FKjTtV/rz5avxDiqX2NGm6ehejoaEybNk2r3dnZ2QjVPH/mGLsAkizAe8YugR6ZHG3sCggAkAwgwdhF0COfPJvN3LlzB9bW1k/sY9RwY2trCxMTE+Tk5Gi05+TkwNHRscxlHB0ddeofGRmJiIgIabq0tBQ3b95E3bp1oVAoKrkHxpOfnw9nZ2dcvnwZVlZWxi7nhcbPovrgZ1F98LOoXuTweQghcOfOHdSvX/+pfY0ablQqFby8vJCUlISgoCAAf4ePpKQkjBo1qsxl/Pz8kJSUhA8//FBq27NnD/z8/Mrsr1aroVarNdpsbGwMUX61YGVl9dz+oMoNP4vqg59F9cHPonp53j+Ppx2xecTop6UiIiIQGhoKb29vtGvXDjExMSgoKEBYWBgAICQkBE5OToiO/vsw8JgxY+Dv74958+ahV69e+O6773D48GF88803xtwNIiIiqiaMHm6Cg4ORm5uLKVOmIDs7G56enti5c6c0aDgrKwtK5f8u6nr11VexZs0aTJo0CZ9++imaNGmCzZs3o2XLlsbaBSIiIqpGjB5uAGDUqFHlnobat2+fVtsbb7yBN954o4qrqt7UajWioqK0TrnRs8fPovrgZ1F98LOoXl60z0MhKnJNFREREdFzwuh3KCYiIiIyJIYbIiIikhWGGyIiIpIVhptqTqFQYPPmzeXO37dvHxQKBW7fvg0ASEhIeOp9fKZOnQpPT0+D1UgkNxcvXoRCocCxY8cqvAx/r/R7D1xdXRETE1Ml9dCTderUSeOecXLCcGNk2dnZGD16NBo2bAi1Wg1nZ2f06dMHSUlJFVr+1VdfxdWrVyt8YyO5UigUT3xNnTrV2CXKihACAQEBCAwM1Jq3aNEi2NjY4M8//zRCZeVzc3ODWq0u9zl0hvb2229LNyd9nqWkpMDExAS9evV6Jtt72n/oXhS5ubkYPnw4GjRoALVaDUdHRwQGBuLAgQMG28bGjRsxffp0g62vOqkWl4K/qC5evIj27dvDxsYGc+bMQatWrfDw4UPs2rULI0eOLPfhoY9TqVTlPnriRXL16lXp34mJiZgyZQoyMjKkNgsLC2OU9UwUFRVBpVI9020qFAosW7YMrVq1wuLFizFs2DAAQGZmJsaNG4fY2Fi89NJLz7SmJ0lOTsb9+/cxYMAALF++HOPHjzd2Sc+NpUuXYvTo0Vi6dCmuXLlSoVvfU+X1798fRUVFWL58ORo2bIicnBwkJSXhxo0bBttGnTp1KrV8SUkJFAqFxr3oqovqV9ELZMSIEVAoFDh06BD69++Ppk2bokWLFoiIiMCvv/4q9bt+/Tr69esHc3NzNGnSBFu3bpXm/fO0VFlmzZoFBwcHWFpaYujQoXjw4Pl9OnZ5HB0dpZe1tTUUCoVG23fffQd3d3eYmZnBzc0NixYt0lj+8uXLGDhwIGxsbFCnTh307dsXFy9elOY/+l/43LlzUa9ePdStWxcjR47Ew4f/e+r7ypUr4e3tDUtLSzg6OuLNN9/EtWvXNLazdetWNGnSBGZmZujcuTOWL1+u9fklJyejQ4cOqFmzJpydnfHBBx+goKBAmu/q6orp06cjJCQEVlZWeO894zxQ09nZGV9++SXGjh2LzMxMCCEwdOhQdO/eHQ0aNEC7du2gVqtRr149TJgwAcXF/3vydFmnIjw9PTWOsCkUCnz77bfl/uwDFXs/gb+/oN9880385z//QXx8vNa+HDp0CG3atIGZmRm8vb1x9OhRjfllne7dvHlzuc+nmzp1KpYvX44tW7ZIRw/LumdXdXf37l0kJiZi+PDh6NWrFxISEjTmP+1vS1mnPYKCgvD222+XuT1XV1cAQL9+/aBQKKTpF83t27exf/9+zJ49G507d4aLiwvatWuHyMhI/Otf/5L6vPvuu7Czs4OVlRW6dOmCtLQ0aR2PThGuXLkSrq6usLa2xqBBg3Dnzh2pzz8/n1u3biEkJAS1a9eGubk5evTogbNnz0rzH/0ebN26Fc2bN4darUZWVlbVvyF6YLgxkps3b2Lnzp0YOXIkatWqpTX/8T+k06ZNw8CBA3H8+HH07NkTb731Fm7evFmh7axbtw5Tp07FzJkzcfjwYdSrV0/ri13uVq9ejSlTpuDzzz9Heno6Zs6cicmTJ2P58uUAgIcPHyIwMBCWlpbYv38/Dhw4AAsLC7z++usoKiqS1rN3716cP38ee/fuxfLly5GQkKDxx/7hw4eYPn060tLSsHnzZly8eFHjj3hmZiYGDBiAoKAgpKWlYdiwYZg4caJGrefPn8frr7+O/v374/jx40hMTERycrLWTS7nzp0LDw8PHD16FJMnTzb8m1ZBoaGh6Nq1K9555x18/fXX+OOPP7BgwQL07NkTPj4+SEtLQ2xsLJYuXYoZM2bovP4n/exX5P0E/n6C8Pr16zFkyBB069YNeXl52L9/vzT/7t276N27N5o3b47U1FRMnToVY8eO1f9NATB27FgMHDgQr7/+Oq5evYqrV6/i1VdfrdQ6jWHdunVwc3NDs2bNMGTIEMTHx+PRrdGq4m/L77//DgBYtmwZrl69Kk2/aCwsLGBhYYHNmzejsLCwzD5vvPEGrl27hh9//BGpqalo27YtunbtqvHdcP78eWzevBnbt2/H9u3b8fPPP2PWrFnlbvftt9/G4cOHsXXrVqSkpEAIgZ49e2r8J+7evXuYPXs2vv32W5w8eRL29vaG23FDEmQUv/32mwAgNm7c+MR+AMSkSZOk6bt37woA4scffxRCCLF3714BQNy6dUsIIcSyZcuEtbW11N/Pz0+MGDFCY52+vr7Cw8PDIPtRHf3zPWjUqJFYs2aNRp/p06cLPz8/IYQQK1euFM2aNROlpaXS/MLCQlGzZk2xa9cuIYQQoaGhwsXFRRQXF0t93njjDREcHFxuHb///rsAIO7cuSOEEGL8+PGiZcuWGn0mTpyo8fkNHTpUvPfeexp99u/fL5RKpbh//74QQggXFxcRFBRUkbfimcjJyRG2trZCqVSKTZs2iU8//VTr/Vy4cKGwsLAQJSUlQoi/92HBggUa6/Hw8BBRUVHS9NN+9ivyfgohxDfffCM8PT2l6TFjxojQ0FBpevHixaJu3brS+yuEELGxsQKAOHr0qBBC+2dKCCE2bdokHv8TGhUVpfF7FRoaKvr27av1fj1PXn31VRETEyOEEOLhw4fC1tZW7N27VwhRsb8t/v7+YsyYMRp9+vbtq/H+//NnAYDYtGmTAffi+bRhwwZRu3ZtYWZmJl599VURGRkp0tLShBB//02wsrISDx480FimUaNGYvHixUKIv38ezc3NRX5+vjT/k08+Eb6+vtL045/PmTNnBABx4MABaf7169dFzZo1xbp164QQf/8eABDHjh2rkn02JB65MRKhw42hW7duLf27Vq1asLKy0jrdUZ709HT4+vpqtJX3BHU5KigowPnz5zF06FDpf0MWFhaYMWMGzp8/DwBIS0vDuXPnYGlpKc2vU6cOHjx4IPUBgBYtWsDExESarlevnsbnkJqaij59+qBBgwawtLSEv78/AEiHbTMyMuDj46NRX7t27TSm09LSkJCQoFFrYGAgSktLkZmZKfXz9vY20DtUefb29hg2bBjc3d0RFBSE9PR0+Pn5aZyyad++Pe7evavzIOMn/exX5P0EgPj4eAwZMkSaHjJkCNavXy8dnk9PT0fr1q1hZmYm9XmRfkfKk5GRgUOHDmHw4MEAAFNTUwQHB2Pp0qUA+LelqvXv3x9XrlzB1q1b8frrr2Pfvn1o27YtEhISkJaWhrt376Ju3boafysyMzM1/ma5urrC0tJSmv7n36zHpaenw9TUVOMzrVu3Lpo1a4b09HSpTaVSafxeVlccUGwkTZo0gUKhqNCg4Ro1amhMKxQKlJaWVlVpsnL37l0AwJIlS7T+ED8KKnfv3oWXlxdWr16ttbydnZ307yd9DgUFBQgMDERgYCBWr14NOzs7ZGVlITAwUOPUVkXqHTZsGD744AOteQ0aNJD+XdapTGMyNTWFqWnF/5wolUqtgP/4oe9HKvuzf+rUKfz66684dOiQxiDikpISfPfddwgPDzdovXKydOlSFBcXawwgFkJArVbj66+/rtA6XsT3zZDMzMzQrVs3dOvWDZMnT8a7776LqKgojBgxAvXq1StzHNfjQxqq4rujZs2a5Y41q0545MZI6tSpg8DAQCxcuFBjsOgjTxogrAt3d3f89ttvGm2PD1aWOwcHB9SvXx8XLlxA48aNNV4vv/wyAKBt27Y4e/Ys7O3ttfpU9BL706dP48aNG5g1axY6dOgANzc3rf8hNWvWDIcPH9Zo++eYgrZt2+LUqVNadTRu3PiZXxGlL3d3d+l8/SMHDhyApaWldAWVnZ2dxhVu+fn5GkemKqIi7+fSpUvRsWNHpKWl4dixY9IrIiJCOgLh7u6O48ePawyG/efviJ2dHe7cuaPxu/q0e+CoVCqUlJTotE/VRXFxMVasWIF58+ZpvG9paWmoX78+1q5dW6G/Lf/8nEtKSvDHH388cds1atR4bt+3qta8eXMUFBSgbdu2yM7OhqmpqdbfCVtbW73W7e7ujuLiYo3P9MaNG8jIyEDz5s0NtQvPDMONES1cuBAlJSVo164dvv/+e5w9exbp6en46quvDHZ4d8yYMYiPj8eyZctw5swZREVF4eTJkwZZ9/Ni2rRpiI6OxldffYUzZ87gxIkTWLZsGebPnw8AeOutt2Bra4u+ffti//79yMzMxL59+/DBBx9U+DRKgwYNoFKp8N///hcXLlzA1q1bte4fMWzYMJw+fRrjx4/HmTNnsG7dOmlA8qP/CY0fPx4HDx7EqFGjcOzYMZw9exZbtmzRGlBcnY0YMQKXL1/G6NGjcfr0aWzZsgVRUVGIiIiQLhnt0qULVq5cif379+PEiRMIDQ3VOOVXEU97Px8+fIiVK1di8ODBaNmypcbr3XffxW+//YaTJ0/izTffhEKhQHh4OE6dOoUdO3Zg7ty5Gtvy9fWFubk5Pv30U5w/fx5r1qzRunLon1xdXXH8+HFkZGTg+vXrz9URi+3bt+PWrVsYOnSo1nvXv39/LF26tEJ/W7p06YIffvgBP/zwA06fPo3hw4c/9T9urq6uSEpKQnZ2Nm7dulWFe1l93bhxA126dMGqVatw/PhxZGZmYv369fjiiy/Qt29fBAQEwM/PD0FBQdi9ezcuXryIgwcPYuLEiVqBv6KaNGmCvn37Ijw8HMnJyUhLS8OQIUPg5OSEvn37GngPqx7DjRE1bNgQR44cQefOnfHxxx+jZcuW6NatG5KSkhAbG2uQbQQHB2Py5MkYN24cvLy8cOnSJQwfPtwg635evPvuu/j222+l+7L4+/sjISFBOnJjbm6OX375BQ0aNMC///1vuLu7S5e1WllZVWgbdnZ2SEhIwPr169G8eXPMmjVL6wvy5ZdfxoYNG7Bx40a0bt0asbGx0tU9arUawN9jTH7++WecOXMGHTp0QJs2bTBlypTn6t4iTk5O2LFjBw4dOgQPDw+8//77GDp0KCZNmiT1iYyMhL+/P3r37o1evXohKCgIjRo10mk7T3s/t27dihs3bqBfv35ay7q7u8Pd3R1Lly6FhYUFtm3bhhMnTqBNmzaYOHEiZs+erdG/Tp06WLVqFXbs2IFWrVph7dq1T70xZHh4OJo1awZvb2/Y2dkZ9OZrVW3p0qUICAgo88hl//79cfjwYbi7uz/1b8s777yD0NBQhISEwN/fHw0bNkTnzp2fuO158+Zhz549cHZ2Rps2bQy6X88LCwsL+Pr6YsGCBejYsSNatmyJyZMnIzw8HF9//TUUCgV27NiBjh07IiwsDE2bNsWgQYNw6dIlODg46L3dZcuWwcvLC71794afnx+EENixY4fW6a3ngULoMrKViAzq888/R1xcHC5fvmzsUmSB7ycRARxQTPRMLVq0CD4+Pqhbty4OHDiAOXPmPFennKobvp9EVBaGG6Jn6OzZs5gxYwZu3ryJBg0a4OOPP0ZkZKSxy3pu8f0korLwtBQRERHJCgcUExERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQkUGkpKTAxMQEvXr1eqbbLSoqwpw5c9C2bVvUqlUL1tbW8PDwwKRJk3DlypVnWgsRVQ+8FJyIDOLdd9+FhYUFli5dioyMjGfyyIjCwkJ0794dx48fx7Rp09C+fXvY2dkhMzMTa9euRe3atREdHV3mskVFRc/Nw0iJSDc8ckNElXb37l0kJiZi+PDh6NWrV5kPldy6dSuaNGkCMzMzdO7cGcuXL4dCodB4kGJycjI6dOiAmjVrwtnZGR988IHGk7j/acGCBUhOTsZPP/2EDz74AF5eXmjQoAH8/f0RFxeHmTNnSn07deqEUaNG4cMPP4StrS0CAwMBAD///DPatWsHtVqNevXqYcKECSguLpaWc3V1RUxMjMZ2PT09NZ4tpVAoEBsbix49eqBmzZpo2LAhNmzYoNubSEQGw3BDRJW2bt06uLm5oVmzZhgyZAji4+Px+EHhzMxMDBgwAEFBQUhLS8OwYcOkh1w+cv78ebz++uvo378/jh8/jsTERCQnJz/xcQpr165Ft27dyn3A4qOnrT+yfPlyqFQqHDhwAHFxcfjrr7/Qs2dP+Pj4IC0tDbGxsVi6dClmzJih83swefJk9O/fH2lpaXjrrbcwaNAgpKen67weIjIAQURUSa+++qqIiYkRQgjx8OFDYWtrK/bu3SvNHz9+vGjZsqXGMhMnThQAxK1bt4QQQgwdOlS89957Gn32798vlEqluH//fpnbNTMzEx988IFGW1BQkKhVq5aoVauW8PPzk9r9/f1FmzZtNPp++umnolmzZqK0tFRqW7hwobCwsBAlJSVCCCFcXFzEggULNJbz8PAQUVFR0jQA8f7772v08fX1FcOHDy+zbiKqWjxyQ0SVkpGRgUOHDmHw4MEAAFNTUwQHB2Pp0qUafXx8fDSWa9euncZ0WloaEhISYGFhIb0CAwNRWlqKzMzMCtezaNEiHDt2DO+88w7u3bunMc/Ly0tjOj09HX5+fhpHeNq3b4+7d+/izz//rPA2AcDPz09rmkduiIyDD84kokpZunQpiouLNQYQCyGgVqvx9ddfw9raukLruXv3LoYNG4YPPvhAa16DBg3KXKZJkybIyMjQaKtXrx4AoE6dOlr9a9WqVaFaHqdUKjVOsQHAw4cPdV4PET07PHJDRHorLi7GihUrMG/ePBw7dkx6paWloX79+li7di0AoFmzZjh8+LDGsr///rvGdNu2bXHq1Ck0btxY61XeVU2DBw/Gnj17cPToUb3qd3d3R0pKikZ4OXDgACwtLfHSSy8BAOzs7HD16lVpfn5+fplHkn799VetaXd3d73qIqJKMvJpMSJ6jm3atEmoVCpx+/ZtrXnjxo0T3t7eQgghLly4IGrUqCHGjRsnMjIyRGJionjppZcEAGnZtLQ0UbNmTTFy5Ehx9OhRcebMGbF582YxcuTIcrd///590b59e1G7dm0RExMjUlNTxYULF8TOnTtFu3btRNu2baW+/v7+YsyYMRrL//nnn8Lc3FyMHDlSpKeni82bNwtbW1uN8TQTJkwQjo6O4pdffhHHjx8XQUFBwsLCQmvMja2trVi6dKnIyMgQU6ZMEUqlUpw8eVKPd5WIKovhhoj01rt3b9GzZ88y5/32228CgEhLSxNCCLFlyxbRuHFjoVarRadOnURsbKwAoDFY+NChQ6Jbt27CwsJC1KpVS7Ru3Vp8/vnnT6zhwYMHYtasWcLDw0PUrFlTqNVq4ebmJj766CORlZUl9Ssr3AghxL59+4SPj49QqVTC0dFRjB8/Xjx8+FCan5eXJ4KDg4WVlZVwdnYWCQkJZQ4oXrhwoejWrZtQq9XC1dVVJCYmVuQtJKIqwJv4EZFRfP7554iLi8Ply5eNXUqlKRQKbNq0CUFBQcYuhYjAAcVE9IwsWrQIPj4+qFu3Lg4cOIA5c+Y88R42RET6Yrghomfi7NmzmDFjBm7evIkGDRrg448/RmRkpLHLIiIZ4mkpIiIikhVeCk5ERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLy/wAmiJlydd0nqgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert AgeGroup to numbers\n",
+        "train['AgeGroup'] = train['AgeGroup'].map({\n",
+        "    'Child': 0, 'Teenager': 1, 'YoungAdult': 2, 'Adult': 3, 'Senior': 4\n",
+        "})\n",
+        "\n",
+        "test['AgeGroup'] = test['AgeGroup'].map({\n",
+        "    'Child': 0, 'Teenager': 1, 'YoungAdult': 2, 'Adult': 3, 'Senior': 4\n",
+        "})\n",
+        "\n",
+        "print(train.head())"
+      ],
+      "metadata": {
+        "id": "IK7Z7TTxqxLW",
+        "outputId": "b47ab8f6-954e-4e80-c3ad-004e2c680d60",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked AgeGroup\n",
+            "0         0       3    0  22.0      1      0   7.2500       0.0        2\n",
+            "1         1       1    1  38.0      1      0  71.2833       1.0        3\n",
+            "2         1       3    1  26.0      0      0   7.9250       0.0        2\n",
+            "3         1       1    1  35.0      1      0  53.1000       0.0        2\n",
+            "4         0       3    0  35.0      0      0   8.0500       0.0        2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "# Calculate correlation\n",
+        "correlation = train.corr()\n",
+        "\n",
+        "# Plot heatmap\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.heatmap(correlation,\n",
+        "            annot=True,\n",
+        "            fmt='.2f',\n",
+        "            cmap='coolwarm',\n",
+        "            center=0,\n",
+        "            linewidths=0.5)\n",
+        "\n",
+        "plt.title('Correlation Between Features and Survival')\n",
+        "plt.show()\n",
+        "\n",
+        "# Print just the correlation with Survived specifically\n",
+        "print(\"Correlation with Survived:\")\n",
+        "print(correlation['Survived'].sort_values(ascending=False))"
+      ],
+      "metadata": {
+        "id": "y2uBHT5nrFI1",
+        "outputId": "10de6dfd-6a95-45c8-f90d-1dc49f78b8a1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 802
+        }
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Correlation with Survived:\n",
+            "Survived    1.000000\n",
+            "Sex         0.543351\n",
+            "Fare        0.257307\n",
+            "Embarked    0.108669\n",
+            "Parch       0.081629\n",
+            "SibSp      -0.035322\n",
+            "Age        -0.064910\n",
+            "AgeGroup   -0.093191\n",
+            "Pclass     -0.338481\n",
+            "Name: Survived, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Drop Age since AgeGroup captures the same info\n",
+        "train = train.drop(columns=['Age'])\n",
+        "test = test.drop(columns=['Age'])\n",
+        "\n",
+        "# Fix missing Embarked (2 missing) with most common value\n",
+        "train['Embarked'] = train['Embarked'].fillna(train['Embarked'].mode()[0])\n",
+        "test['Embarked'] = test['Embarked'].fillna(test['Embarked'].mode()[0])\n",
+        "\n",
+        "# Fix missing Fare in test (1 missing)\n",
+        "test['Fare'] = test['Fare'].fillna(test['Fare'].median())\n",
+        "\n",
+        "# Confirm no missing values remain\n",
+        "print(\"Train missing values:\")\n",
+        "print(train.isnull().sum())\n",
+        "print(\"\\nTest missing values:\")\n",
+        "print(test.isnull().sum())"
+      ],
+      "metadata": {
+        "id": "7Smd4-q2roYT",
+        "outputId": "561a3d82-dabf-4bbc-af87-89fa541436bf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Train missing values:\n",
+            "Survived    0\n",
+            "Pclass      0\n",
+            "Sex         0\n",
+            "SibSp       0\n",
+            "Parch       0\n",
+            "Fare        0\n",
+            "Embarked    0\n",
+            "AgeGroup    0\n",
+            "dtype: int64\n",
+            "\n",
+            "Test missing values:\n",
+            "Pclass      0\n",
+            "Sex         0\n",
+            "SibSp       0\n",
+            "Parch       0\n",
+            "Fare        0\n",
+            "Embarked    0\n",
+            "AgeGroup    0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lNxXkYRB9TGg"
+      },
+      "source": [
+        "### Third phase:\n",
+        "### Building the model and testing the accuracy (this process is where you start building models and choose a model based on accuracy metrics)\n",
+        "- Choose a model suitable for classification\n",
+        "- Fit the data\n",
+        "- Find out how \"well\" the model performs using some metrics\n",
+        "- Use cross validation to get the avg accuracy on the model you chose\n",
+        "- **Bonus 1**\n",
+        "    - Try other classification algorithms\n",
+        "    - Compare the accuracy metrics (including cross-validation) for the classification algorithms used by presenting them in a readable, easy to compare format\n",
+        "    - Choose a model with the best cross validation accuracy metric\n",
+        "- **Bonus 2**: Get the feature importance of your features using random forests\n",
+        "    (if you are not sure what that is or how to do it, google it!)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# X = everything except Survived\n",
+        "# y = Survived column only\n",
+        "X = train.drop(columns=['Survived'])\n",
+        "y = train['Survived']\n",
+        "\n",
+        "print(\"X shape:\", X.shape)\n",
+        "print(\"y shape:\", y.shape)\n",
+        "print(\"\\nFeatures we are using:\")\n",
+        "print(list(X.columns))"
+      ],
+      "metadata": {
+        "id": "xijYsDWiuVE0",
+        "outputId": "c8d41e97-a232-461f-a897-438b7fdec991",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X shape: (891, 7)\n",
+            "y shape: (891,)\n",
+            "\n",
+            "Features we are using:\n",
+            "['Pclass', 'Sex', 'SibSp', 'Parch', 'Fare', 'Embarked', 'AgeGroup']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "\n",
+        "# Create the model\n",
+        "model = LogisticRegression(max_iter=500)\n",
+        "\n",
+        "# Fit the model on training data\n",
+        "model.fit(X, y)\n",
+        "\n",
+        "# Check accuracy on training data\n",
+        "train_accuracy = model.score(X, y)\n",
+        "print(f\"Training Accuracy: {train_accuracy * 100:.2f}%\")\n",
+        "\n",
+        "# Use cross validation to get a more reliable accuracy\n",
+        "cv_scores = cross_val_score(model, X, y, cv=5)\n",
+        "print(f\"\\nCross Validation Scores: {cv_scores}\")\n",
+        "print(f\"Average CV Accuracy: {cv_scores.mean() * 100:.2f}%\")\n",
+        "print(f\"Standard Deviation: {cv_scores.std() * 100:.2f}%\")"
+      ],
+      "metadata": {
+        "id": "XSisI3LCuiDd",
+        "outputId": "1150bf39-cb2b-4021-8311-275bbb084ef0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training Accuracy: 80.13%\n",
+            "\n",
+            "Cross Validation Scores: [0.7877095  0.78651685 0.79213483 0.78089888 0.8258427 ]\n",
+            "Average CV Accuracy: 79.46%\n",
+            "Standard Deviation: 1.60%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Define all models\n",
+        "models = {\n",
+        "    'Logistic Regression':    LogisticRegression(max_iter=500),\n",
+        "    'Decision Tree':          DecisionTreeClassifier(random_state=42),\n",
+        "    'Random Forest':          RandomForestClassifier(n_estimators=100, random_state=42),\n",
+        "    'Gradient Boosting':      GradientBoostingClassifier(n_estimators=100, random_state=42),\n",
+        "    'K-Nearest Neighbors':    KNeighborsClassifier(n_neighbors=5),\n",
+        "    'Support Vector Machine': SVC(kernel='rbf', random_state=42),\n",
+        "}\n",
+        "\n",
+        "# Train and evaluate each model\n",
+        "results = []\n",
+        "for name, m in models.items():\n",
+        "    m.fit(X, y)\n",
+        "    train_acc = m.score(X, y)\n",
+        "    cv_scores = cross_val_score(m, X, y, cv=5)\n",
+        "    results.append({\n",
+        "        'Model': name,\n",
+        "        'Training Accuracy': f\"{train_acc * 100:.2f}%\",\n",
+        "        'CV Average': f\"{cv_scores.mean() * 100:.2f}%\",\n",
+        "        'CV Std Dev': f\"{cv_scores.std() * 100:.2f}%\"\n",
+        "    })\n",
+        "\n",
+        "# Display as a neat table\n",
+        "results_df = pd.DataFrame(results)\n",
+        "print(results_df.to_string(index=False))"
+      ],
+      "metadata": {
+        "id": "9bOXaILpuw6k",
+        "outputId": "ecc6fa0e-4ffa-4244-afed-26732decfc70",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                 Model Training Accuracy CV Average CV Std Dev\n",
+            "   Logistic Regression            80.13%     79.46%      1.60%\n",
+            "         Decision Tree            93.94%     79.91%      2.89%\n",
+            "         Random Forest            93.94%     81.04%      2.65%\n",
+            "     Gradient Boosting            88.66%     82.04%      2.91%\n",
+            "   K-Nearest Neighbors            84.40%     75.43%      2.80%\n",
+            "Support Vector Machine            67.79%     66.90%      4.56%\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Get feature importances from Random Forest\n",
+        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "rf_model.fit(X, y)\n",
+        "\n",
+        "# Plot feature importances\n",
+        "importances = pd.Series(rf_model.feature_importances_, index=X.columns)\n",
+        "importances = importances.sort_values(ascending=True)\n",
+        "\n",
+        "importances.plot(kind='barh',\n",
+        "                 color=['#e74c3c' if v == importances.max()\n",
+        "                        else '#5c6bc0' for v in importances.values],\n",
+        "                 edgecolor='white')\n",
+        "\n",
+        "plt.title('Feature Importance (Random Forest)')\n",
+        "plt.xlabel('Importance Score')\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"\\nFeature Importance Ranking:\")\n",
+        "for feat, imp in importances.sort_values(ascending=False).items():\n",
+        "    bar = '█' * int(imp * 100)\n",
+        "    print(f\"  {feat:<12} {imp:.4f}  {bar}\")"
+      ],
+      "metadata": {
+        "id": "Yk3zx_LdvMME",
+        "outputId": "d5ffefe9-d30b-4c58-98e3-1caf77d43c16",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 636
+        }
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Feature Importance Ranking:\n",
+            "  Fare         0.3511  ███████████████████████████████████\n",
+            "  Sex          0.3037  ██████████████████████████████\n",
+            "  Pclass       0.1032  ██████████\n",
+            "  AgeGroup     0.0889  ████████\n",
+            "  SibSp        0.0638  ██████\n",
+            "  Parch        0.0467  ████\n",
+            "  Embarked     0.0426  ████\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file