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      "source": ["# Titanic Data EDA and Modeling Example"]
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      "source": [
        "### Required Python Packages\n",
        "- `numpy`\n",
        "- `pandas`\n",
        "- `scikit-learn`\n",
        "- `seaborn`\n",
        "- `catboost`\n",
        "\n",
        "Run the following cell to install the packages."
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      "source": [
        "#\n",
        "# Required Packages\n",
        "#   Run this cell to install required packages.\n",
        "#\n",
        "%pip install \"catboost>=1.0\" \"matplotlib>=2.0\" \"numpy>=1.19\" \"pandas>=1.1\" \"scikit-learn>=0.22.2\" \"seaborn>=0.11\""
      ]
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      },
      "source": ["# Import Packages"]
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      "source": [
        "import random as rnd\n",
        "import warnings\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# data analysis and wrangling\n",
        "import pandas as pd\n",
        "\n",
        "# visualization\n",
        "import seaborn as sns\n",
        "\n",
        "# load dataset\n",
        "from catboost.datasets import titanic\n",
        "from sklearn import metrics\n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Model traning & selection\n",
        "from sklearn.model_selection import (\n",
        "    KFold,\n",
        "    cross_val_predict,\n",
        "    cross_val_score,\n",
        "    train_test_split,\n",
        ")\n",
        "\n",
        "# ML models\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
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      },
      "source": ["# 1. EDA"]
    },
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      "source": [
        "We start by acquiring datasets using catboost library.  \n",
        "We also combine these datasets into `dataset` to run certain operations on both datasets together."
      ]
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          "isComponent": true,
          "name": "Acquire Data",
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      "source": [
        "train_df, test_df = titanic()\n",
        "dataset = {\n",
        "    \"train\": train_df,\n",
        "    \"test\": test_df,\n",
        "}\n",
        "# We need `PassengerId` feature to make submit file.\n",
        "passenger_id = test_df[\"PassengerId\"]"
      ]
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      },
      "source": ["## Describing data"]
    },
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      "source": [
        "### Types of Features\n",
        "- **Categorical**: `Survived`, `Sex`, and `Embarked`.\n",
        "- **Ordinal**: `Pclass`.\n",
        "- **Continous**: `Age`, `Fare`.\n",
        "- **Discrete**: `SibSp`, `Parch`.\n",
        "- **Mixed data type**: `Ticket`, `Cabin`.\n",
        "\n",
        "### Features contraining missing values\n",
        "- Train set: `Cabin` > `Age` > `Embarked`\n",
        "- Test set: `Cabin` > `Age` >> `Fare`\n",
        "\n",
        "### Features to be dropped\n",
        "- `Ticket` : It contains high ratio of duplicates.\n",
        "- `Cabin` : It has many null values(77%, 38% for train and test set).\n",
        "- `PassengerId` : It's not contribute to survival.\n",
        "- `Name` : Title(like 'Mr' or 'Mrs' ...) may contribute to survival. "
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          "name": "Describe Data",
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          "output_type": "stream",
          "text": ["Preview the data\n"]
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>22.0</td>\n",
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              "      <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",
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              "      <td>3</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>26.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
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              "   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  "
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            "\n",
            "========================================\n",
            "Check blank, null or empty values\n",
            "\n",
            "\tTrain\n"
          ]
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              "PassengerId    0.000000\n",
              "Survived       0.000000\n",
              "Pclass         0.000000\n",
              "Name           0.000000\n",
              "Sex            0.000000\n",
              "Age            0.198653\n",
              "SibSp          0.000000\n",
              "Parch          0.000000\n",
              "Ticket         0.000000\n",
              "Fare           0.000000\n",
              "Cabin          0.771044\n",
              "Embarked       0.002245\n",
              "dtype: float64"
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          "text": ["________________________________________\n", "\tTest\n"]
        },
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              "PassengerId    0.000000\n",
              "Pclass         0.000000\n",
              "Name           0.000000\n",
              "Sex            0.000000\n",
              "Age            0.096521\n",
              "SibSp          0.000000\n",
              "Parch          0.000000\n",
              "Ticket         0.000000\n",
              "Fare           0.001122\n",
              "Cabin          0.367003\n",
              "Embarked       0.000000\n",
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            "\n",
            "========================================\n",
            "Check the distribution of numerical features\n"
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              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
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              "    <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",
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              "    <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",
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              "    <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",
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              "  </tbody>\n",
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              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
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            "\n",
            "========================================\n",
            "Check the distribution of categorical features\n"
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>891</td>\n",
              "      <td>204</td>\n",
              "      <td>889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>891</td>\n",
              "      <td>2</td>\n",
              "      <td>681</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>347082</td>\n",
              "      <td>B96 B98</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>577</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Name   Sex  Ticket    Cabin Embarked\n",
              "count                       891   891     891      204      889\n",
              "unique                      891     2     681      147        3\n",
              "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
              "freq                          1   577       7        4      644"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "========================================\n",
            "Check the duplication rate of `Ticket` feature\n"
          ]
        },
        {
          "data": {
            "text/plain": ["0.2356902356902357"]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Preview the data\")\n",
        "display(train_df.head())\n",
        "\n",
        "print(\"\\n\" + \"=\" * 40)\n",
        "print(\"Check blank, null or empty values\\n\")\n",
        "print(\"\\tTrain\")\n",
        "display(train_df.isnull().sum() / len(train_df))\n",
        "print(\"_\" * 40)\n",
        "print(\"\\tTest\")\n",
        "display(test_df.isnull().sum() / len(train_df))\n",
        "\n",
        "print(\"\\n\" + \"=\" * 40)\n",
        "print(\"Check the distribution of numerical features\")\n",
        "display(train_df.describe())\n",
        "\n",
        "print(\"\\n\" + \"=\" * 40)\n",
        "print(\"Check the distribution of categorical features\")\n",
        "display(train_df.describe(include=[\"O\"]))\n",
        "\n",
        "print(\"\\n\" + \"=\" * 40)\n",
        "print(\"Check the duplication rate of `Ticket` feature\")\n",
        "train_df.Ticket.duplicated().sum() / len(train_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "6db63a30-1d86-266e-2799-dded03c45816",
        "_uuid": "946ee6ca01a3e4eecfa373ca00f88042b683e2ad",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "a1c5bc49-0e5d-4d8e-9905-e1cde23b58cf",
          "diskcache": false,
          "headerColor": "none",
          "id": "63a48514-04ab-41d5-9b6f-263f875b4a0d",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## Analyzing the features"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "60f9e2f3-1072-4f97-b551-f6bd11b2ead7",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": [
        "### **Categorical**, **ordinal** or **discrete feature** and **target**\n",
        "- **`Pclass`** : Passengers with `Pclass` 1 has a higher rate of surviving than other Pclasses.\n",
        "- **`Sex`** : `Female` passengers are more likely to survive than male passengers\n",
        "- **`SibSp` and `Parch`** : Survival rate differs depending on whether you are alone or when you have one other family member. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "7838c88c-5e5b-45e2-a1d4-3a444d920446",
          "isComponent": true,
          "name": "Analze by pivoting features",
          "parents": [
            {
              "id": "aff8727c-eb19-46d4-997a-7d77acc41f3f",
              "name": "Acquire Data"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": ["<Figure size 1152x720 with 8 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pivot_features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
        "f, ax = plt.subplots(2, 4, figsize=(16, 10))\n",
        "\n",
        "for i, pivot_feature in enumerate(pivot_features):\n",
        "    train_df[pivot_feature].value_counts().sort_index().plot.bar(x=pivot_feature, y=\"Survived\", ax=ax[0, i])\n",
        "    sns.countplot(pivot_feature, hue=\"Survived\", data=train_df, ax=ax[1, i])\n",
        "    ax[0, i].set_title(pivot_feature)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "0d43550e-9eff-3859-3568-8856570eff76",
        "_uuid": "5c6204d01f5a9040cf0bb7c678686ae48daa201f",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "92b17c0b-def7-4348-b908-c1877b085ca6",
          "diskcache": false,
          "headerColor": "none",
          "id": "b9d9eacc-6c55-4999-a427-68f0dd6d36d3",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["### Correlation between features"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "7f5a5c28-e299-4137-91ee-a193370d342c",
          "diskcache": false,
          "headerColor": "inherit",
          "id": "fcef4d8d-ddc9-4746-8ec1-5b38d351558d",
          "isComponent": false,
          "name": "",
          "parents": []
        },
        "tags": []
      },
      "source": [
        "#### Fig 1 :  Histogram of `Age` by `Survived`\n",
        "- Infants (Age <=4) had high survival rate.\n",
        "- Large number of 15-25 year olds did not survive.\n",
        "- Most passengers are in 15-35 age range.\n",
        "\n",
        "\n",
        "#### Fig 2 : Distribution of `Age` by `Pclass` x `Survived`\n",
        "- The number of children increases with `Pclass`\n",
        "- The survival rate for passengers below `Age` 10 looks to be good irrespective of the `Pclass`.\n",
        "\n",
        "\n",
        "#### Fig 3 : `Survived` rate by `Embarked` x `Pclass` x `Sex`\n",
        "- The survival chances are almost 1 for `Pclass` 1 & 2 irrespecitive of the `Pclass`.\n",
        "- Port S looks to be very unlucky for `Pclass` 3 Passenegers as the survival rate for both men and women is very low.\n",
        "- Port Q looks to be unlukiest for Men, as almost all were from Pclass 3.\n",
        "\n",
        "\n",
        "#### Fig 4 : `Fare` by `Embarked` x `Survived`\n",
        "- The survival rate is high for passengers paying higher fare had better survival.\n",
        "- Port of embarkation correlates with survival rates.\n",
        "\n",
        "#### Decisions\n",
        "- We should use `Age`, `Pclass`, `Embarked`, `Fare` feature to model training.\n",
        "- Consider banding `Age`, `Fare` feature.\n",
        "- Need to complete the `Age`, `Embarked`, `Fare` feature for missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "33159720-a7a4-403f-83f0-3e65e90c3b0c",
          "isComponent": true,
          "name": "Analyze by visualizing data",
          "parents": [
            {
              "id": "aff8727c-eb19-46d4-997a-7d77acc41f3f",
              "name": "Acquire Data"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 1296x576 with 2 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 1150.5x360 with 3 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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IDAHt0bOWm8R7eQvdffoYsJ6a5Jyqelx3f7y7P11Vv5ih5/j5VXVGkv/KMDbvPTL0vN2UZL9xO11VRyQ5K8k/VdU/J7k4Q6/MA5OckWG81i0ae3O/NcM4u0/u7ivGfbyjqv4yyW8n+Yskv76IzT0ryZ9V1acz9Di9MkPoe98kj81w/v1Wd89+Pf82w9i9v19V98/w2b53htf+1Ax/P7bWiRkudvans54vSXefW1UXJ3lqhvfjbfOFvt19dlUdPe7roqp6e5LPZRiz9ocy9KR9f8b3pbu/XlVHZhjn99+r6qTc/Bn90Qw9nR+11HoBgO2XHrYAwJKNV1F/eIaQ5cczXKhprwxjg85cbOm6+dZdjbr7xiRPzjCEwN2S/EaGsOVvkvxEhnB0qdv8TpJDM/Tk+0yGXq6/lSHk2SHJH2X+3n2b87QMQeBPZeh9uUOGHnw/u9BFm5KcnmEc0+TWDYdwSIaLMM13O/xWbHexjk3yaxmCzOdnCDxPSPJjs0O3SbyXC+nudyZ5YpLbJXlPVT14nP73SR6U4f29X4b36ucz9Fo/ZTyO2ds5N8N59q4M4eavZxhr94AkH1pCSS9N8uAkr+rut82Z9wcZesA+t6p+ehHbenaGz+iVYx0vyNDjfu8kb0ny49398jnHcXWGMPMdGQLKX83Qu/TxSf51CcdxC939/gxB9m2SfPlWbO/E3HxhuwVD3+5+WYZj+LcMX648P0PQu0eS4zNcdG328qdkOLc/kuE8/ZWxzodnCHsBAG5SC//bHQBg6arqzRnGf9yvuz+z0vWweeNP5C9Ocm53L3ZcVQAAYEL0sAUAlqyqdqiqW1zZfByL8ulJLhDWrhq/neHn669Z6UIAAAA9bAGArTBezOprSd6T5NMZLs5z3ww/bf52koO6+5wVK5DNqqp7ZOgFvU+Gn7Z/IskDu3ulxh0GAABGAlsAYMmqasckr8hwcaE9k3xfhnFQ35fkpd39sZWrji2pqgMyhO3/k2HM4V/t7ktWsiYAAGAgsAUAAAAAmBLGsAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsIUVUFU3VtV5s25HL2HdA6rqX2/l/s+pqg1bue4JVfWUW7n/HarqVVX1qar6ZFV9uKr2vjXbBGB6rfV2b9zOvavq7VV1UVV9tKpOrqrdbu12AZgu2rykqu5bVe+uqs9U1Wer6sVVJX+CJVi30gXAGvWN7t5/JXZcVTuuxH7neHqSuyW5X3d/t6r2THL9CtcEwOSs6Xavqm6X5N+S/GZ3v22cdkCS9UmuWrnKAJiAtd7m3T7J6Ul+tbvPrKrvS/JPSZ6X5NgVLQ5WEd9wwBSpqkur6k/Hb2I3VtUDq+qd47eSvzJr0TtW1b+N31j+9cy3lVX1unG986vqxXO2+7Kq+miSp86avsP4LepLqmrHqvrzsbfrJ6rqqHGZqqrXjPt6V5K7boND3T3Jld393STp7su7+yvbYLsArCJrqN37uSQfmAlrk6S7z+nuT22DbQOwCqyxNu/c7j4zSbr7f5I8N8nvbINtw5qhhy2sjNtX1Xmznv9pd580Pv58d+9fVccmOSHJI5LcLsmnkvz1uMxDktwnyWVJzkjyM0lOSfLC7v7y+M3q2VV1v+7+xLjOl7r7gUky/oNgXZI3J/lUd/9JVR2Z5Kvd/eCqum2Sc6vqzCQPSLLvuL/dklyQ5I1zD6iqfifJs+Y51vd192/MmXZykvdX1Y8nOTvJ33f3x7bwmgGweq31du9Hk3xki68SANuDtd7m3Tdz2rzu/mxV3b6qdu3ua+d/2YDZBLawMjb3M5nTx/tPJrlDd38tydeq6ltVtes47z+7+5Ikqaq3JHlkhkb8aWNjvC5DL9b7JJlpxGf+kTDjuCQnd/efjM+fkOR+dfOYRbsk2SfJo5K8pbtvTPLfVfXu+Yru7j9P8udbPPJh2curat8kjx1vZ1fVU7v77MWsD8Cqs6bbPQDWFG0ecKsJbGH6fGu8/+6sxzPPZ87ZnrNO13DRrt9O8uDu/kpVnZDh29oZc8eI/Y8kj6mqv+zubyapJL/e3e+cvVBVPXExRS/xW9d097eSvCPJO6rqqiSHZuhtC8DashbavfOTPHox2wVgu7YW2rwLMgTBs9e/Z4ZewNcuZn+AMWxhtXpIVe09jmf09CTvT3LHDA31V2u46vTBW9jGG5K8PcnJVbUuyTuT/GpV3Sa56WrWOyd5X5Knj+Me7Z7kMfNtrLv/vLv3n+d2i7B2HK/pbuPjHZLcL8NPfgBgPqu63UvyD0l+rKp+cmZCVT2qqn50KS8CAGvCam/z3pzkkVX1uHFft0/yqiQvWtrLAGubHrawMuaOa3RGdx+9hPU/nOQ1Se6V5D1JTu3u71bVx5J8OskXkpy7pY1098urapckb8rwjeleST5aVZVkU4Zer6dmGLbggiSfT/KBJdS5kLsmef04flKS/Od4PABsn9Z0u9fd36iqJyV5RVW9Isl3MvyM9Xm3dtsATB1tXtWTk7y6ql6bZI8kL+nuN9/abcNaUt1ze9sDAAAAwK1TVYcmeXmSx3S3X1XCIglsAQAAAACmhDFsAQAAAACmhMAWAAAAAGBKCGwBAAAAAKbEupUuYDEOOuigPuOMM1a6DABYqtqalbR7AKxS2j0A1pKtavcWY1X0sL3mmmtWugQAWDbaPQDWEu0eAHyvVRHYAgAAAACsBQJbAAAAAIApIbAFAAAAAJgSEw1sq+oFVXV+VX2qqt5SVberqr2r6kNVdXFVnVRVO02yBgAAAACA1WJigW1V7ZHkN5Js6O4fTbJjkmckeVmSY7v7Xkm+kuSISdUAAAAAALCaTHpIhHVJbl9V65J8X5Irkzw2ySnj/BOTHDrhGgAAAAAAVoWJBbbdfUWSv0jy+QxB7VeTfCTJtd19w7jY5Un2mFQNAAAAAACrybpJbbiq7pTkkCR7J7k2yT8mOWgJ6x+Z5Mgkucc97jGBCgFgemj3YPU67szzVroEWJKjnrD/Speg3QOAzZjkkAiPS/K57t7U3d9J8s9JHpFk13GIhCTZM8kV863c3cd394bu3rB+/foJlgkAK0+7B8Baot0DgIVNMrD9fJKHVdX3VVUlOTDJBUnek+Qp4zKHJTltgjUAAAAAAKwakxzD9kMZLi720SSfHPd1fJLfS/KbVXVxkrskecOkagAAAAAAWE0mNoZtknT3i5K8aM7kS5I8ZJL7BQAAAABYjSY5JAIAAAAAAEsgsAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsAUAAAAAmBITC2yrat+qOm/W7bqqen5V3bmqzqqqi8b7O02qBgAAAACA1WRigW13f6a79+/u/ZM8KMn/JDk1ydFJzu7ufZKcPT4HAAAAAFjzlmtIhAOTfLa7L0tySJITx+knJjl0mWoAAAAAAJhqyxXYPiPJW8bHu3X3lePjLybZbb4VqurIqtpYVRs3bdq0HDUCwIrR7gGwlmj3AGBhEw9sq2qnJE9O8o9z53V3J+n51uvu47t7Q3dvWL9+/YSrBICVpd0DYC3R7gHAwpajh+3BST7a3VeNz6+qqt2TZLy/ehlqAAAAAACYessR2D4zNw+HkCSnJzlsfHxYktOWoQYAAAAAgKk30cC2qnZO8vgk/zxr8kuTPL6qLkryuPE5AAAAAMCat26SG+/u65PcZc60LyU5cJL7BQAAAABYjZZjSAQAAAAAABZBYAsAAAAAMCUEtgAAAAAAU0JgCwAAAAAwJQS2AAAAAABTQmALAAAAADAlBLYAAAAAAFNCYAsAAAAAMCUEtgAAAAAAU0JgCwAAAAAwJQS2AAAAAABTQmALAAAAADAlBLYAAAAAAFNiooFtVe1aVadU1aer6sKqenhV3bmqzqqqi8b7O02yBgAAAACA1WLSPWxfmeSM7t4vyf2TXJjk6CRnd/c+Sc4enwMAAAAArHkTC2yrapckj0ryhiTp7m9397VJDkly4rjYiUkOnVQNAAAAAACrySR72O6dZFOSv62qj1XV31TVzkl26+4rx2W+mGS3CdYAAAAAALBqTDKwXZfkgUle190PSHJ95gx/0N2dpOdbuaqOrKqNVbVx06ZNEywTAFaedg+AtUS7BwALm2Rge3mSy7v7Q+PzUzIEuFdV1e5JMt5fPd/K3X18d2/o7g3r16+fYJkAsPK0ewCsJdo9AFjYxALb7v5iki9U1b7jpAOTXJDk9CSHjdMOS3LapGoAAAAAAFhN1k14+7+e5M1VtVOSS5I8O0NIfHJVHZHksiRPm3ANAAAAAACrwkQD2+4+L8mGeWYdOMn9AgAAAACsRpMcwxYAAAAAgCUQ2AIAAAAATAmBLQAAAADAlBDYAgAAAABMCYEtAAAAAMCUENgCAAAAAEwJgS0AAAAAwJQQ2AIAAAAATAmBLQAAAADAlBDYAgAAAABMCYEtAAAAAMCUENgCAAAAAEwJgS0AAAAAwJQQ2AIAAAAATIl1k9x4VV2a5GtJbkxyQ3dvqKo7JzkpyV5JLk3ytO7+yiTrAAAAAABYDZajh+1junv/7t4wPj86ydndvU+Ss8fnAAAAAABr3koMiXBIkhPHxycmOXQFagAAAAAAmDqTDmw7yZlV9ZGqOnKctlt3Xzk+/mKS3eZbsaqOrKqNVbVx06ZNEy4TAFaWdg+AtUS7BwALm3Rg+8jufmCSg5M8p6oeNXtmd3eGUPcWuvv47t7Q3RvWr18/4TIBYGVp9wBYS7R7ALCwiQa23X3FeH91klOTPCTJVVW1e5KM91dPsgYAAAAAgNViYoFtVe1cVd8/8zjJE5J8KsnpSQ4bFzssyWmTqgEAAAAAYDVZN8Ft75bk1Kqa2c8/dPcZVfXhJCdX1RFJLkvytAnWAAAAAACwakwssO3uS5Lcf57pX0py4KT2CwAAAACwWk36omMAAAAAACySwBYAAAAAYEoIbAEAAAAApoTAFgAAAABgSghsAQAAAACmxKID26p6ZFU9e3y8vqr2nlxZAAAAAABrz6IC26p6UZLfS/L746TbJPn7SRUFAAAAALAWLbaH7U8neXKS65Oku/87yfdPqigAAAAAgLVosYHtt7u7k3SSVNXOkysJAAAAAGBtWmxge3JVHZdk16r65STvSvL6yZUFAAAAALD2rNvSAlVVSU5Ksl+S65Lsm+T/dPdZE64NAAAAAGBN2WJg291dVW/v7v+VREgLAAAAADAhix0S4aNV9eCJVgIAAAAAsMZtsYft6KFJnlVVlyW5Pkll6Hx7v4lVBgAAAACwxiw2sP2Jrd1BVe2YZGOSK7r7SVW1d5K3JrlLko8k+YXu/vbWbh8AAAAAYHuxqCERuvuy7r4syTeS9KzbYjwvyYWznr8sybHdfa8kX0lyxOLLBQAAAADYfi0qsK2qJ1fVRUk+l+S9SS5N8o5FrLdnkp9M8jfj80ry2CSnjIucmOTQpRYNAAAAALA9WuxFx/44ycOS/Fd3753kwCQfXMR6r0jyu0m+Oz6/S5Jru/uG8fnlSfaYb8WqOrKqNlbVxk2bNi2yTABYnbR7AKwl2j0AWNhix7D9Tnd/qap2qKoduvs9VfWKza1QVU9KcnV3f6SqDlhqYd19fJLjk2TDhg2LHX4BmALHnXneSpcAS3LUE/Zf6RK0ewCsKdo9AFjYYgPba6vqDknel+TNVXV1kuu3sM4jkjy5qp6Y5HZJ7pjklUl2rap1Yy/bPZNcsXWlAwAAAABsXzY7JEJV3WN8eEiS/0nygiRnJPlskp/a3Lrd/fvdvWd375XkGUne3d3PSvKeJE8ZFzssyWlbXT0AAAAAwHZkS2PY/kuSdPf1Sf6xu2/o7hO7+1Xd/aWt3OfvJfnNqro4w5i2b9jK7QAAAAAAbFe2NCRCzXp8z63dSXefk+Sc8fElSR6ytdsCAAAAANhebamHbS/wGAAAAACAbWxLPWzvX1XXZehpe/vxccbn3d13nGh1AAAAAABryGYD2+7ecbkKAQAAAABY67Y0JAIAAAAAAMtkS0MiAADbgePOPG+lS4AlOeoJ+690CQAAsCL0sAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsAUAAAAAmBICWwAAAACAKTGxwLaqbldV/1lVH6+q86vqxeP0vavqQ1V1cVWdVFU7TaoGAAAAAIDVZJI9bL+V5LHdff8k+yc5qKoeluRlSY7t7nsl+UqSIyZYAwAAAADAqjGxwLYHXx+f3ma8dZLHJjllnH5ikkMnVQMAAAAAwGoy0TFsq2rHqjovydVJzkry2STXdvcN4yKXJ9ljgXWPrKqNVbVx06ZNkywTAFacdg+AtUS7BwALm2hg2903dvf+SfZM8pAk+y1h3eO7e0N3b1i/fv2kSgSAqaDdA2At0e4BwMImGtjO6O5rk7wnycOT7FpV68ZZeya5YjlqAAAAAACYdhMLbKtqfVXtOj6+fZLHJ7kwQ3D7lHGxw5KcNqkaAAAAAABWk3VbXmSr7Z7kxKraMUMwfHJ3/2tVXZDkrVX1kiQfS/KGCdYAAAAAALBqTCyw7e5PJHnAPNMvyTCeLQAAAAAAsyzLGLYAAAAAAGyZwBYAAAAAYEoIbAEAAAAApoTAFgAAAABgSghsAQAAAACmhMAWAAAAAGBKCGwBAAAAAKaEwBYAAAAAYEoIbAEAAAAApoTAFgAAAABgSghsAQAAAACmhMAWAAAAAGBKCGwBAAAAAKaEwBYAAAAAYEpMLLCtqrtX1Xuq6oKqOr+qnjdOv3NVnVVVF433d5pUDQAAAAAAq8m6CW77hiS/1d0frarvT/KRqjoryeFJzu7ul1bV0UmOTvJ7E6xjXseded5y7xJulaOesP9KlwAAAADAhE2sh213X9ndHx0ffy3JhUn2SHJIkhPHxU5McuikagAAAAAAWE2WZQzbqtoryQOSfCjJbt195Tjri0l2W2CdI6tqY1Vt3LRp03KUCQArRrsHwFqi3QOAhU08sK2qOyT5pyTP7+7rZs/r7k7S863X3cd394bu3rB+/fpJlwkAK0q7B8Baot0DgIVNNLCtqttkCGvf3N3/PE6+qqp2H+fvnuTqSdYAAAAAALBaTCywrapK8oYkF3b3y2fNOj3JYePjw5KcNqkaAAAAAABWk3UT3PYjkvxCkk9W1XnjtD9I8tIkJ1fVEUkuS/K0CdYAAAAAALBqTCyw7e73J6kFZh84qf0CAAAAAKxWE7/oGAAAAAAAiyOwBQAAAACYEgJbAAAAAIApIbAFAAAAAJgSAlsAAAAAgCkhsAUAAAAAmBICWwAAAACAKSGwBQAAAACYEgJbAAAAAIApsW6lCwAAAIBt6bgzz1vpEmBJjnrC/itdwqI5v1htVtP5NUMPWwAAAACAKSGwBQAAAACYEgJbAAAAAIApMbHAtqreWFVXV9WnZk27c1WdVVUXjfd3mtT+AQAAAABWm0n2sD0hyUFzph2d5Ozu3ifJ2eNzAAAAAAAywcC2u9+X5MtzJh+S5MTx8YlJDp3U/gEAAAAAVpvlHsN2t+6+cnz8xSS7LbRgVR1ZVRurauOmTZuWpzoAWCHaPQDWEu0eACxsxS461t2dpDcz//ju3tDdG9avX7+MlQHA8tPuAbCWaPcAYGHLHdheVVW7J8l4f/Uy7x8AAAAAYGotd2B7epLDxseHJTltmfcPAAAAADC1JhbYVtVbknwgyb5VdXlVHZHkpUkeX1UXJXnc+BwAAAAAgCTrJrXh7n7mArMOnNQ+AQAAAABWsxW76BgAAAAAAN9LYAsAAAAAMCUEtgAAAAAAU0JgCwAAAAAwJQS2AAAAAABTQmALAAAAADAlBLYAAAAAAFNCYAsAAAAAMCUEtgAAAAAAU0JgCwAAAAAwJQS2AAAAAABTQmALAAAAADAlBLYAAAAAAFNCYAsAAAAAMCVWJLCtqoOq6jNVdXFVHb0SNQAAAAAATJtlD2yrasckf5Xk4CT3SfLMqrrPctcBAAAAADBtVqKH7UOSXNzdl3T3t5O8NckhK1AHAAAAAMBUqe5e3h1WPSXJQd39S+PzX0jy0O5+7pzljkxy5Ph03ySfWdZCuTV+IMk1K10EbKecX6vLNd190GIW1O6tas5LmBzn1+qi3VsbnJcwOc6v1WXR7d5STW1gy+pVVRu7e8NK1wHbI+cXTB/nJUyO8wumj/MSJsf5xYyVGBLhiiR3n/V8z3EaAAAAAMCathKB7YeT7FNVe1fVTkmekeT0FagDAAAAAGCqrFvuHXb3DVX13CTvTLJjkjd29/nLXQcTdfxKFwDbMecXTB/nJUyO8wumj/MSJsf5RZIVGMMWAAAAAID5rcSQCAAAAAAAzENgCwAAAAAwJQS2bDNVdVBVfaaqLq6qo1e6HtieVNUbq+rqqvrUStcCDLR7MDnaPZg+2j2YHO0ecwls2Saqasckf5Xk4CT3SfLMqrrPylYF25UTkhy00kUAA+0eTNwJ0e7B1NDuwcSdEO0eswhs2VYekuTi7r6ku7+d5K1JDlnhmmC70d3vS/Llla4DuIl2DyZIuwdTR7sHE6TdYy6BLdvKHkm+MOv55eM0ANgeafcAWEu0ewDLSGALAAAAADAlBLZsK1ckufus53uO0wBge6TdA2At0e4BLCOBLdvKh5PsU1V7V9VOSZ6R5PQVrgkAJkW7B8Baot0DWEYCW7aJ7r4hyXOTvDPJhUlO7u7zV7Yq2H5U1VuSfCDJvlV1eVUdsdI1wVqm3YPJ0u7BdNHuwWRp95irunulawAAAAAAIHrYAgAAAABMDYEtAAAAAMCUENgCAAAAAEwJgS0AAAAAwJQQ2AIAAAAATAmBLawiVfXCqjq/qj5RVedV1UO3wTafXFVHb6P6vr4ttgMAiXYPgLVFuwfMqO5e6RqARaiqhyd5eZIDuvtbVfUDSXbq7v9exLrruvuGZajx6919h0nvB4Dtn3YPgLVEuwfMpoctrB67J7mmu7+VJN19TXf/d1VdOjbmqaoNVXXO+PiYqnpTVZ2b5E1V9cGquu/MxqrqnHH5w6vqNVW1S1VdVlU7jPN3rqovVNVtquqHq+qMqvpIVf17Ve03LrN3VX2gqj5ZVS9Z5tcDgO2bdg+AtUS7B9xEYAurx5lJ7l5V/1VVr62qRy9infskeVx3PzPJSUmeliRVtXuS3bt748yC3f3VJOclmdnuk5K8s7u/k+T4JL/e3Q9K8ttJXjsu88okr+vu/5Xkylt7gAAwi3YPgLVEuwfcRGALq0R3fz3Jg5IcmWRTkpOq6vAtrHZ6d39jfHxykqeMj5+W5JR5lj8pydPHx88Y93GHJD+W5B+r6rwkx2X49jdJHpHkLePjNy3leABgc7R7AKwl2j1gtnUrXQCweN19Y5JzkpxTVZ9McliSG3Lzly+3m7PK9bPWvaKqvlRV98vQSP/KPLs4Pcn/q6o7Z/jHwruT7Jzk2u7ef6Gytu5oAGDztHsArCXaPWCGHrawSlTVvlW1z6xJ+ye5LMmlGRrbJPnZLWzmpCS/m2SX7v7E3Jnjt7ofzvDTl3/t7hu7+7okn6uqp451VFXdf1zl3AzfzCbJs5Z8UACwAO0eAGuJdg+YTWALq8cdkpxYVRdU1ScyjFd0TJIXJ3llVW1McuMWtnFKhgb35M0sc1KSnx/vZzwryRFV9fEk5yc5ZJz+vCTPGb/93WNphwMAm6XdA2At0e4BN6luvdsBAAAAAKaBHrYAAAAAAFNCYAsAAAAAMCUEtgAAAAAAU0JgCwAAAAAwJQS2AAAAAABTQmALAAAAADAlBLYAAAAAAFPi/wNPI0xe7E5rugAAAABJRU5ErkJggg==",
            "text/plain": ["<Figure size 1390.4x288 with 3 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig12, ax = plt.subplots(1, 2, figsize=(18, 8))\n",
        "sns.histplot(data=train_df, y=\"Age\", hue=\"Survived\", ax=ax[0])\n",
        "ax[0].set_title(\"Fig 1 : Histogram of Age by Survived\", size=20)\n",
        "sns.violinplot(data=train_df, x=\"Pclass\", y=\"Age\", hue=\"Survived\", split=True, ax=ax[1])\n",
        "ax[1].set_title(\"Fig 2 : Distribution of Age by Pclass x Survived\", size=20)\n",
        "ax[1].set_yticks(range(0, 110, 10))\n",
        "plt.show()\n",
        "\n",
        "fig3 = sns.factorplot(\"Pclass\", \"Survived\", data=train_df, hue=\"Sex\", col=\"Embarked\", palette=\"deep\")\n",
        "fig3.fig.subplots_adjust(top=0.8)\n",
        "fig3.fig.suptitle(\"Fig 3 : Survived rate by Embarked x Pclass x Sex\", size=20)\n",
        "plt.show()\n",
        "\n",
        "fig4 = sns.FacetGrid(train_df, col=\"Embarked\", size=4, aspect=1.6)\n",
        "fig4.map(sns.barplot, \"Survived\", \"Fare\", alpha=0.5, ci=None)\n",
        "fig4.add_legend()\n",
        "fig4.fig.subplots_adjust(top=0.8)\n",
        "fig4.fig.suptitle(\"Fig 4 : Fare by Embarked x Survived\", size=20)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "a21f66ac-c30d-f429-cc64-1da5460d16a9",
        "_uuid": "c8fd535ac1bc90127369027c2101dbc939db118e",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "64638ae6-961b-4d84-a020-b02bae5f3ec4",
          "isComponent": false,
          "name": "",
          "parents": []
        },
        "tags": []
      },
      "source": ["---"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "cfac6291-33cc-506e-e548-6cad9408623d",
        "_uuid": "73a9111a8dc2a6b8b6c78ef628b6cae2a63fc33f",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "9b66de62-8891-420f-99dd-8ae565a87f41",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["# 2. Wrangling data"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
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        }
      },
      "source": ["## Dropping features\n", "- `Ticket`, `Cabin`, `PassengerId`"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "id": "2e24e473-0ce0-4489-bcce-ad8f7f7f8aba",
          "isComponent": true,
          "name": "Drop Features",
          "parents": [
            {
              "id": "aff8727c-eb19-46d4-997a-7d77acc41f3f",
              "name": "Acquire Data"
            }
          ]
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "for ds_name, ds in dataset.items():\n",
        "    ds.drop([\"Ticket\", \"Cabin\", \"PassengerId\"], axis=1, inplace=True)\n",
        "    dataset[ds_name] = ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": ["## Creating feature : `Title`"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "- Extract `Title` and test correlation between `Title` and `Survived`.\n",
        "- By RegEx pattern, we can find the first word which ends with a dot(`.`) within `Name` feature.\n",
        "\n",
        "### the number of samples by Title x Sex\n",
        "- Most titles are gender-specific. For example: None of the samples with `Master` or `Mr` titles were `female`.\n",
        "\n",
        "### the survival rate by Title\n",
        "- Certain titles mostly survived (`Miss`, `Mrs`) or did\n",
        "\n",
        "### the mean value of Age by Title\n",
        "- Most titles band Age groups accurately. For example: Master title has Age mean of 5 years.\n",
        "\n",
        "### Decision\n",
        "- We decide to retain the new `Title` feature for model training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "id": "29103ea8-fc78-4a97-aa9f-284e62eb1a63",
          "isComponent": true,
          "name": "Create Feature - Title",
          "parents": [
            {
              "id": "2e24e473-0ce0-4489-bcce-ad8f7f7f8aba",
              "name": "Drop Features"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked Title\n",
              "0         0       3    male  22.0      1      0   7.2500        S    Mr\n",
              "1         1       1  female  38.0      1      0  71.2833        C   Mrs\n",
              "2         1       3  female  26.0      0      0   7.9250        S  Miss\n",
              "3         1       1  female  35.0      1      0  53.1000        S   Mrs\n",
              "4         0       3    male  35.0      0      0   8.0500        S    Mr"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for ds_name, ds in dataset.items():\n",
        "    ds[\"Title\"] = ds.Name.str.extract(\n",
        "        \" ([A-Za-z]+)\\.\",\n",
        "        expand=False,\n",
        "    )\n",
        "    ds[\"Title\"] = ds[\"Title\"].replace(\n",
        "        [\"Lady\", \"Countess\", \"Capt\", \"Col\", \"Don\", \"Dr\", \"Major\", \"Rev\", \"Sir\", \"Jonkheer\", \"Dona\"],\n",
        "        \"Rare\",\n",
        "    )\n",
        "    ds[\"Title\"] = ds[\"Title\"].replace(\"Mlle\", \"Miss\")\n",
        "    ds[\"Title\"] = ds[\"Title\"].replace(\"Ms\", \"Miss\")\n",
        "    ds[\"Title\"] = ds[\"Title\"].replace(\"Mme\", \"Mrs\")\n",
        "    ds[\"Title\"] = ds[\"Title\"].fillna(0)\n",
        "    ds = ds.drop([\"Name\"], axis=1)\n",
        "    dataset[ds_name] = ds\n",
        "\n",
        "pd.crosstab(dataset[\"train\"][\"Title\"], dataset[\"train\"][\"Sex\"]).plot.bar()\n",
        "plt.title(\"the number of samples by Title x Sex\", size=15)\n",
        "plt.show()\n",
        "dataset[\"train\"][[\"Title\", \"Survived\"]].groupby([\"Title\"], as_index=False).mean().plot.bar(x=\"Title\")\n",
        "plt.title(\"the survival rate by Title\", size=15)\n",
        "plt.show()\n",
        "dataset[\"train\"][[\"Title\", \"Age\"]].groupby([\"Title\"], as_index=False).mean().plot.bar(x=\"Title\")\n",
        "plt.title(\"the mean value of Age by Title\", size=15)\n",
        "plt.show()\n",
        "display(dataset[\"train\"].head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "77fadd88-a37c-4b3a-9cdb-17564a291b5a",
          "diskcache": false,
          "headerColor": "inherit",
          "id": "057a44f9-1716-4c66-a4f0-3dcf9ae058b6",
          "isComponent": false,
          "name": "",
          "parents": []
        },
        "tags": []
      },
      "source": ["## Completing features\n", "- `Age`, `Embarked`, `Fare`"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "d662fa8c-9068-42fd-9053-ff4decf538f1",
          "isComponent": true,
          "name": "Complete features",
          "parents": [
            {
              "id": "29103ea8-fc78-4a97-aa9f-284e62eb1a63",
              "name": "Create Feature - Title"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": ["<Figure size 514.88x475.2 with 6 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Cross tab Pclass x Sex(X ~ P(Age | Sex, Pclass))\n",
        "grid = sns.FacetGrid(dataset[\"train\"], row=\"Pclass\", col=\"Sex\", size=2.2, aspect=1.6)\n",
        "grid.map(plt.hist, \"Age\", alpha=0.5, bins=20)\n",
        "grid.add_legend()\n",
        "grid.fig.subplots_adjust(top=0.9)\n",
        "grid.fig.suptitle(\"Histogram of P(Age | Pclass, Sex)\", size=15)\n",
        "plt.show()\n",
        "\n",
        "freq_port = dataset[\"train\"].Embarked.dropna().mode()[0]\n",
        "categ_sex = dataset[\"train\"].Sex.unique()\n",
        "\n",
        "for ds_name, ds in dataset.items():\n",
        "    # Complete Age feature with the median of X ~ P(Age | Sex, Pclass)\n",
        "    for i in range(2):\n",
        "        for j in range(3):\n",
        "            guess_df = ds[(ds[\"Sex\"] == categ_sex[i]) & (ds[\"Pclass\"] == j + 1)][\"Age\"].dropna()\n",
        "\n",
        "            ds.loc[(ds.Age.isnull()) & (ds.Sex == categ_sex[i]) & (ds.Pclass == j + 1), \"Age\"] = guess_df.median()\n",
        "\n",
        "    ds[\"Embarked\"].fillna(freq_port, inplace=True)\n",
        "    ds[\"Fare\"].fillna(ds[\"Fare\"].dropna().median(), inplace=True)\n",
        "    dataset[ds_name] = ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "77fadd88-a37c-4b3a-9cdb-17564a291b5a",
          "diskcache": false,
          "headerColor": "inherit",
          "id": "89e422f3-daa2-472d-b298-28cd0880cd33",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": [
        "## Converting features\n",
        "- `Age`, `Fare`\n",
        "- Convert Numeric feature to Ordinal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "5d772a3c-80be-4c78-aec2-d417fd8d6efe",
          "isComponent": true,
          "name": "Convert Feature - num to ord",
          "parents": [
            {
              "id": "d662fa8c-9068-42fd-9053-ff4decf538f1",
              "name": "Complete features"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  Sex  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris    0   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
              "2         1       3                             Heikkinen, Miss. Laina    1   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
              "4         0       3                           Allen, Mr. William Henry    0   \n",
              "\n",
              "   Age  SibSp  Parch  Fare  Embarked  Title  \n",
              "0    1      1      0     0         0      1  \n",
              "1    2      1      0     3         1      3  \n",
              "2    1      0      0     1         0      2  \n",
              "3    2      1      0     3         0      3  \n",
              "4    2      0      0     1         0      1  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Banding `Age`, `Fare` feature\n",
        "dataset[\"train\"][\"AgeBand\"] = pd.cut(dataset[\"train\"][\"Age\"], 5)\n",
        "dataset[\"train\"][[\"AgeBand\", \"Survived\"]].groupby([\"AgeBand\"], as_index=False,).mean().sort_values(\n",
        "    by=\"AgeBand\",\n",
        "    ascending=True,\n",
        ").plot.bar(x=\"AgeBand\")\n",
        "dataset[\"train\"] = dataset[\"train\"].drop([\"AgeBand\"], axis=1)\n",
        "\n",
        "dataset[\"train\"][\"FareBand\"] = pd.qcut(dataset[\"train\"][\"Fare\"], 4)\n",
        "dataset[\"train\"][[\"FareBand\", \"Survived\"]].groupby([\"FareBand\"], as_index=False,).mean().sort_values(\n",
        "    by=\"FareBand\",\n",
        "    ascending=True,\n",
        ").plot.bar(x=\"FareBand\")\n",
        "dataset[\"train\"] = dataset[\"train\"].drop([\"FareBand\"], axis=1)\n",
        "\n",
        "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
        "sex_mapping = {\"female\": 1, \"male\": 0}\n",
        "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
        "\n",
        "for ds_name, ds in dataset.items():\n",
        "    # Convert Numeric feature to Ordinal : `Age`, `Fare`\n",
        "    ds.loc[ds[\"Age\"] <= 16, \"Age\"] = 0\n",
        "    ds.loc[(ds[\"Age\"] > 16) & (ds[\"Age\"] <= 32), \"Age\"] = 1\n",
        "    ds.loc[(ds[\"Age\"] > 32) & (ds[\"Age\"] <= 48), \"Age\"] = 2\n",
        "    ds.loc[(ds[\"Age\"] > 48) & (ds[\"Age\"] <= 64), \"Age\"] = 3\n",
        "    ds.loc[ds[\"Age\"] > 64, \"Age\"]\n",
        "    ds[\"Age\"] = ds[\"Age\"].astype(int)\n",
        "\n",
        "    ds.loc[ds[\"Fare\"] <= 7.91, \"Fare\"] = 0\n",
        "    ds.loc[(ds[\"Fare\"] > 7.91) & (ds[\"Fare\"] <= 14.454), \"Fare\"] = 1\n",
        "    ds.loc[(ds[\"Fare\"] > 14.454) & (ds[\"Fare\"] <= 31), \"Fare\"] = 2\n",
        "    ds.loc[ds[\"Fare\"] > 31, \"Fare\"] = 3\n",
        "    ds[\"Fare\"] = ds[\"Fare\"].astype(int)\n",
        "\n",
        "    # Convert Categorical feature to numeric : `Title`, `Sex`\n",
        "    ds[\"Title\"] = ds[\"Title\"].map(title_mapping)\n",
        "    ds[\"Title\"] = ds[\"Title\"].fillna(0)\n",
        "    ds[\"Sex\"] = ds[\"Sex\"].map(sex_mapping).astype(int)\n",
        "    ds[\"Embarked\"] = ds[\"Embarked\"].map(embarked_mapping).astype(int)\n",
        "\n",
        "    dataset[ds_name] = ds\n",
        "\n",
        "display(dataset[\"train\"].head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "edcfda1e-e0b1-4ef2-92e1-292d3ca3eb93",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## Creating features : `IsAlone`, `Age*Class`"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "4b8d01d3-d638-4aa4-be2b-d4f87fef2110",
          "isComponent": true,
          "name": "Create 2 Features",
          "parents": [
            {
              "id": "5d772a3c-80be-4c78-aec2-d417fd8d6efe",
              "name": "Convert Feature - num to ord"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 432x288 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for ds_name, ds in dataset.items():\n",
        "    # We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets.\n",
        "    ds[\"FamilySize\"] = ds[\"SibSp\"] + ds[\"Parch\"] + 1\n",
        "\n",
        "    # We can create another feature called IsAlone.\n",
        "    ds[\"IsAlone\"] = 0\n",
        "    ds.loc[ds[\"FamilySize\"] == 1, \"IsAlone\"] = 1\n",
        "\n",
        "    # Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone.\n",
        "    ds = ds.drop([\"Parch\", \"SibSp\"], axis=1)\n",
        "\n",
        "    # We can also create an artificial feature combining Age and Pclass.\n",
        "    ds[\"Age*Class\"] = ds.Age * ds.Pclass\n",
        "    dataset[ds_name] = ds\n",
        "\n",
        "dataset[\"train\"][[\"FamilySize\", \"Survived\"]].groupby(\n",
        "    [\"FamilySize\"],\n",
        "    as_index=False,\n",
        ").mean().plot.bar(x=\"FamilySize\")\n",
        "dataset[\"train\"][[\"IsAlone\", \"Survived\"]].groupby(\n",
        "    [\"IsAlone\"],\n",
        "    as_index=False,\n",
        ").mean().plot.bar(x=\"IsAlone\")\n",
        "dataset[\"train\"][[\"Age*Class\", \"Survived\"]].groupby(\n",
        "    [\"Age*Class\"],\n",
        "    as_index=False,\n",
        ").mean().plot.bar(x=\"Age*Class\")\n",
        "\n",
        "for ds_name, ds in dataset.items():\n",
        "    ds = ds.drop([\"FamilySize\"], axis=1)\n",
        "    dataset[ds_name] = ds"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "0d5b953d-6635-46b4-8f58-392e188dc556",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["---"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "69783c08-c8cc-a6ca-2a9a-5e75581c6d31",
        "_uuid": "a55f20dd6654610ff2d66c1bf3e4c6c73dcef9e5",
        "canvas": {
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        }
      },
      "source": [
        "# 3. Model, predict and solve\n",
        "1. Gaussian Naive Bayes\n",
        "2. KNN or k-Nearest Neighbors\n",
        "3. Logistic Regression\n",
        "4. Random Forrest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
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          "parents": []
        }
      },
      "source": ["## Prepare Train & Val set"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "0acf54f9-6cf5-24b5-72d9-29b30052823a",
        "_uuid": "04d2235855f40cffd81f76b977a500fceaae87ad",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "7bf7bd54-532f-451f-89e0-a9eaed8c6b23",
          "isComponent": true,
          "name": "Prepare Train Val Sets",
          "parents": [
            {
              "id": "4b8d01d3-d638-4aa4-be2b-d4f87fef2110",
              "name": "Create 2 Features"
            }
          ]
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "train, val = train_test_split(dataset[\"train\"], test_size=0.3, random_state=0, stratify=dataset[\"train\"][\"Survived\"])\n",
        "\n",
        "X_train = train.iloc[:, 1:]\n",
        "Y_train = train.iloc[:, :1]\n",
        "X_val = val.iloc[:, 1:]\n",
        "Y_val = val.iloc[:, :1]\n",
        "\n",
        "X = dataset[\"train\"][dataset[\"train\"].columns[1:]]\n",
        "Y = dataset[\"train\"][\"Survived\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": "83c8c16c-3ce6-4ed7-ac9e-c73be08eaf40",
          "diskcache": false,
          "headerColor": "inherit",
          "id": "e43b9d48-0d08-4db5-9342-1b178a0814c2",
          "isComponent": false,
          "name": "",
          "parents": []
        },
        "tags": []
      },
      "source": ["## Gaussian Naive Bayes"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "50378071-7043-ed8d-a782-70c947520dae",
        "_uuid": "723c835c29e8727bc9bad4b564731f2ca98025d0",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "570723e4-55c0-4246-878e-ca5e5031f25b",
          "isComponent": true,
          "name": "Model - GaussianNB",
          "parents": [
            {
              "id": "7bf7bd54-532f-451f-89e0-a9eaed8c6b23",
              "name": "Prepare Train Val Sets"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": ["The accuracy of the Gaussian NB is 0.7574626865671642\n"]
        }
      ],
      "source": [
        "clf_gsnb = GaussianNB()\n",
        "clf_gsnb.fit(X_train, Y_train)\n",
        "pred_gsnb = clf_gsnb.predict(X_val)\n",
        "acc_gsnb = metrics.accuracy_score(pred_gsnb, Y_val)\n",
        "print(f\"The accuracy of the Gaussian NB is {acc_gsnb}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "f6d4f01c-38e6-43c7-ab70-bb6990b4763a",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## K-Nearest Neighbours(KNN)"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "ca14ae53-f05e-eb73-201c-064d7c3ed610",
        "_uuid": "54d86cd45703d459d452f89572771deaa8877999",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "2369de54-1b36-4e72-965b-436e0635f4e7",
          "isComponent": true,
          "name": "Model - KNN",
          "parents": [
            {
              "id": "7bf7bd54-532f-451f-89e0-a9eaed8c6b23",
              "name": "Prepare Train Val Sets"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": ["The accuracy of the KNN is 0.7985074626865671\n"]
        },
        {
          "data": {
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",
            "text/plain": ["<Figure size 864x432 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracies for different values of n are: [0.75       0.76119403 0.79104478 0.76492537 0.79850746 0.76865672\n",
            " 0.79477612 0.78358209 0.7761194  0.79477612] with the max value as  0.7985074626865671\n"
          ]
        }
      ],
      "source": [
        "clf_knn = KNeighborsClassifier(n_neighbors=5)\n",
        "clf_knn.fit(X_train, Y_train)\n",
        "pred_knn = clf_knn.predict(X_val)\n",
        "acc_knn = metrics.accuracy_score(pred_knn, Y_val)\n",
        "print(f\"The accuracy of the KNN is {acc_knn}\")\n",
        "\n",
        "search_range = list(range(1, 11))\n",
        "a = pd.Series()\n",
        "for i in search_range:\n",
        "    model = KNeighborsClassifier(n_neighbors=i)\n",
        "    model.fit(X_train, Y_train)\n",
        "    prediction = model.predict(X_val)\n",
        "    a = a.append(pd.Series(metrics.accuracy_score(prediction, Y_val)))\n",
        "\n",
        "plt.plot(search_range, a)\n",
        "plt.xticks(search_range)\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(12, 6)\n",
        "plt.show()\n",
        "print(\"Accuracies for different values of n are:\", a.values, \"with the max value as \", a.values.max())\n",
        "del a, i, search_range, model, prediction, fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "f0a13d35-2c9f-4ebc-9b1d-79341713103e",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## Logistic Regression"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "0edd9322-db0b-9c37-172d-a3a4f8dec229",
        "_uuid": "a649b9c53f4c7b40694f60f5c8dc14ec5ef519ec",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "4a08f1a8-1091-46ea-8791-103535428af3",
          "isComponent": true,
          "name": "Model - Logistic Regression",
          "parents": [
            {
              "id": "7bf7bd54-532f-451f-89e0-a9eaed8c6b23",
              "name": "Prepare Train Val Sets"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy of the Logistic Regression is 0.8246268656716418\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Correlation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sex</td>\n",
              "      <td>2.173073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Title</td>\n",
              "      <td>0.373390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>IsAlone</td>\n",
              "      <td>0.255659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Age</td>\n",
              "      <td>0.231659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Embarked</td>\n",
              "      <td>0.157717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fare</td>\n",
              "      <td>0.093260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Age*Class</td>\n",
              "      <td>-0.249048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pclass</td>\n",
              "      <td>-0.676265</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Feature  Correlation\n",
              "1        Sex     2.173073\n",
              "5      Title     0.373390\n",
              "6    IsAlone     0.255659\n",
              "2        Age     0.231659\n",
              "4   Embarked     0.157717\n",
              "3       Fare     0.093260\n",
              "7  Age*Class    -0.249048\n",
              "0     Pclass    -0.676265"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Logistic Regression\n",
        "clf_lg = LogisticRegression()\n",
        "clf_lg.fit(X_train, Y_train)\n",
        "pred_lg = clf_lg.predict(X_val)\n",
        "acc_lg = metrics.accuracy_score(pred_lg, Y_val)\n",
        "print(f\"The accuracy of the Logistic Regression is {acc_lg}\")\n",
        "\n",
        "# Check Coefficients of the features\n",
        "coeff_df = pd.DataFrame(dataset[\"train\"].columns.delete(0))\n",
        "coeff_df.columns = [\"Feature\"]\n",
        "coeff_df[\"Correlation\"] = pd.Series(clf_lg.coef_[0])\n",
        "display(coeff_df.sort_values(by=\"Correlation\", ascending=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "01fc2f22-4c56-4e91-82b9-913f68c6c3d7",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## Random Forest"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f0694a8e-b618-8ed9-6f0d-8c6fba2c4567",
        "_uuid": "483c647d2759a2703d20785a44f51b6dee47d0db",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "d994faf3-afb7-401e-a32f-0dff430ff43f",
          "isComponent": true,
          "name": "Model - Random Forest",
          "parents": [
            {
              "id": "7bf7bd54-532f-451f-89e0-a9eaed8c6b23",
              "name": "Prepare Train Val Sets"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": ["The accuracy of the Random Forest is 0.8134328358208955\n"]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 864x432 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracies for different values of n are: [0.80223881 0.81343284 0.81343284 0.81343284] with the max value as  0.8134328358208955\n"
          ]
        }
      ],
      "source": [
        "clf_rf = RandomForestClassifier(n_estimators=150, random_state=42)\n",
        "clf_rf.fit(X_train, Y_train)\n",
        "pred_rf = clf_rf.predict(X_val)\n",
        "acc_rf = metrics.accuracy_score(pred_rf, Y_val)\n",
        "print(f\"The accuracy of the Random Forest is {acc_rf}\")\n",
        "\n",
        "search_range = list(range(100, 300, 50))\n",
        "a = pd.Series()\n",
        "for i in search_range:\n",
        "    model = RandomForestClassifier(n_estimators=i, random_state=42)\n",
        "    model.fit(X_train, Y_train)\n",
        "    prediction = model.predict(X_val)\n",
        "    a = a.append(pd.Series(metrics.accuracy_score(prediction, Y_val)))\n",
        "plt.plot(search_range, a)\n",
        "plt.xticks(search_range)\n",
        "fig = plt.gcf()\n",
        "fig.set_size_inches(12, 6)\n",
        "plt.show()\n",
        "print(\"Accuracies for different values of n are:\", a.values, \"with the max value as \", a.values.max())\n",
        "del a, i, search_range, model, prediction, fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "4e4e6407-6d6b-450a-8a92-49de717f76ce",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["---"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "f6c9eef8-83dd-581c-2d8e-ce932fe3a44d",
        "_uuid": "2c1428d022430ea594af983a433757e11b47c50c",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "def8811e-36e2-4859-be80-5a3bee115d40",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["# 4. Model evaluation"]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "47fdf7a6-f8de-4f12-97e5-95b9f3ce6399",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## K-Fold Cross Validation"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6",
        "_uuid": "06a52babe50e0dd837b553c78fc73872168e1c7d",
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "none",
          "id": "34f9d569-5b1d-4941-96d0-88db968756dd",
          "isComponent": true,
          "name": "K-Fold Cross Validation",
          "parents": [
            {
              "id": "570723e4-55c0-4246-878e-ca5e5031f25b",
              "name": "Model - GaussianNB"
            },
            {
              "id": "2369de54-1b36-4e72-965b-436e0635f4e7",
              "name": "Model - KNN"
            },
            {
              "id": "4a08f1a8-1091-46ea-8791-103535428af3",
              "name": "Model - Logistic Regression"
            },
            {
              "id": "d994faf3-afb7-401e-a32f-0dff430ff43f",
              "name": "Model - Random Forest"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CV Mean</th>\n",
              "      <th>Std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Gaussian Naive Bayes</th>\n",
              "      <td>0.711573</td>\n",
              "      <td>0.071717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>0.801411</td>\n",
              "      <td>0.039478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logistic Regression</th>\n",
              "      <td>0.804707</td>\n",
              "      <td>0.038364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest</th>\n",
              "      <td>0.800287</td>\n",
              "      <td>0.038103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       CV Mean       Std\n",
              "Gaussian Naive Bayes  0.711573  0.071717\n",
              "KNN                   0.801411  0.039478\n",
              "Logistic Regression   0.804707  0.038364\n",
              "Random Forest         0.800287  0.038103"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": ["<Figure size 864x432 with 1 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "kfold = KFold(n_splits=10)  # k=10, split the data into 10 equal parts\n",
        "cv_mean = []\n",
        "accuracy = []\n",
        "std = []\n",
        "classifiers = [\"Gaussian Naive Bayes\", \"KNN\", \"Logistic Regression\", \"Random Forest\"]\n",
        "model_list = [\n",
        "    GaussianNB(),\n",
        "    KNeighborsClassifier(n_neighbors=5),\n",
        "    LogisticRegression(),\n",
        "    RandomForestClassifier(n_estimators=150, random_state=42),\n",
        "]\n",
        "for model in model_list:\n",
        "    cv_result = cross_val_score(model, X, Y, cv=kfold, scoring=\"accuracy\")\n",
        "    cv_mean.append(cv_result.mean())\n",
        "    std.append(cv_result.std())\n",
        "    accuracy.append(cv_result)\n",
        "\n",
        "df_kfold_result = pd.DataFrame(\n",
        "    {\"CV Mean\": cv_mean, \"Std\": std},\n",
        "    index=classifiers,\n",
        ")\n",
        "display(df_kfold_result)\n",
        "\n",
        "plt.subplots(figsize=(12, 6))\n",
        "box = pd.DataFrame(accuracy, index=classifiers)\n",
        "box.T.boxplot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "09d5b571-d663-4522-98b5-76da99826b60",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["## Confusion Matrix"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "2e5331e9-c53d-4c99-97af-e51d5b4dd5f7",
          "isComponent": true,
          "name": "Confusion Matrix",
          "parents": [
            {
              "id": "34f9d569-5b1d-4941-96d0-88db968756dd",
              "name": "K-Fold Cross Validation"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": ["<Figure size 720x720 with 8 Axes>"]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "f, ax = plt.subplots(2, 2, figsize=(10, 10))\n",
        "y_pred = cross_val_predict(GaussianNB(), X, Y, cv=10)\n",
        "sns.heatmap(confusion_matrix(Y, y_pred), ax=ax[0, 0], annot=True, fmt=\"2.0f\")\n",
        "ax[0, 0].set_title(\"Matrix for Gaussian NB\")\n",
        "\n",
        "y_pred = cross_val_predict(KNeighborsClassifier(n_neighbors=9), X, Y, cv=10)\n",
        "sns.heatmap(confusion_matrix(Y, y_pred), ax=ax[0, 1], annot=True, fmt=\"2.0f\")\n",
        "ax[0, 1].set_title(\"Matrix for KNN\")\n",
        "\n",
        "y_pred = cross_val_predict(LogisticRegression(), X, Y, cv=10)\n",
        "sns.heatmap(confusion_matrix(Y, y_pred), ax=ax[1, 0], annot=True, fmt=\"2.0f\")\n",
        "ax[1, 0].set_title(\"Matrix for Logistic Regression\")\n",
        "\n",
        "y_pred = cross_val_predict(RandomForestClassifier(n_estimators=100), X, Y, cv=10)\n",
        "sns.heatmap(confusion_matrix(Y, y_pred), ax=ax[1, 1], annot=True, fmt=\"2.0f\")\n",
        "ax[1, 1].set_title(\"Matrix for Random-Forests\")\n",
        "\n",
        "plt.subplots_adjust(hspace=0.2, wspace=0.2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "1cc7453e-91ad-4a75-a9c7-c33a59e9500a",
          "isComponent": false,
          "name": "",
          "parents": []
        }
      },
      "source": ["# 5. Ensemble & Submission"]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "canvas": {
          "comments": [],
          "componentType": "CodeCell",
          "copiedOriginId": null,
          "diskcache": false,
          "headerColor": "inherit",
          "id": "0f6985a3-5fbf-4013-9c96-e15c725a0c92",
          "isComponent": true,
          "name": "Voting Classifier",
          "parents": [
            {
              "id": "2e5331e9-c53d-4c99-97af-e51d5b4dd5f7",
              "name": "Confusion Matrix"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The accuracy for ensembled model is: 0.8283582089552238\n",
            "The cross validated score is 0.8092759051186018\n"
          ]
        }
      ],
      "source": [
        "ensemble_clf = VotingClassifier(\n",
        "    estimators=list(zip(classifiers, model_list)),\n",
        "    voting=\"hard\",\n",
        ").fit(X_train, Y_train)\n",
        "print(\"The accuracy for ensembled model is:\", ensemble_clf.score(X_val, Y_val))\n",
        "cross = cross_val_score(ensemble_clf, X, Y, cv=10, scoring=\"accuracy\")\n",
        "print(\"The cross validated score is\", cross.mean())"
      ]
    },
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            {
              "id": "0f6985a3-5fbf-4013-9c96-e15c725a0c92",
              "name": "Voting Classifier"
            }
          ]
        },
        "tags": []
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         0\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         1"
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      ],
      "source": [
        "Y_pred = ensemble_clf.predict(dataset[\"test\"])\n",
        "Y_pred\n",
        "df_submission = pd.DataFrame({\"PassengerId\": passenger_id, \"Survived\": Y_pred})\n",
        "display(df_submission.head())\n",
        "df_submission.to_csv(\"submission.csv\", index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "Our submission to the competition site Kaggle results in scoring 3,199 of 13,802 competition entries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "aeec9210-f9d8-cd7c-c4cf-a87376d5f693",
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      "source": [
        "## References\n",
        "- [A journey through Titanic](https://www.kaggle.com/omarelgabry/titanic/a-journey-through-titanic)\n",
        "- [Getting Started with Pandas: Kaggle's Titanic Competition](https://www.kaggle.com/c/titanic/details/getting-started-with-random-forests)\n",
        "- [Titanic Best Working Classifier](https://www.kaggle.com/sinakhorami/titanic/titanic-best-working-classifier)\n",
        "- [Titanic Data Science Solutions](https://www.kaggle.com/code/startupsci/titanic-data-science-solutions/notebook)\n",
        "- [EDA To Prediction(DieTanic)](https://www.kaggle.com/code/ash316/eda-to-prediction-dietanic)"
      ]
    }
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