Final notebook.ipynb

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      "source": [
        "# Titanic Analysis\n",
        "BABD - Cloud & Cognitive Services Project\n",
        "\n",
        "Made by Jacopo Bulgarelli, Sara Canu, Gianmarco Fistani & Jade Meneghini."
      ]
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      "source": [
        "## Task\n",
        "Create a model that predicts which passengers survived the Titanic shipwreck <br>\n",
        "Steps:\n",
        "- Conduct Exploratory Data Analysis to gain insights into the dataset.\n",
        "- Clean the data by addressing missing or incorrect values.\n",
        "- Consider at classification algorithms and perform hyperparameter tuning for each algorithm.\n",
        "- Compare the performance of the best models.\n",
        "- Predict survivial of the passenger based on the best model."
      ]
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      },
      "source": [
        "## Colab Env\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive/MyDrive/CCS\n",
        "#%cd /gdrive/MyDrive/BABD/CCS"
      ],
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          "text": [
            "Mounted at /gdrive\n",
            "/gdrive/MyDrive/BABD/CCS\n"
          ]
        }
      ]
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            "\u001b[33m\r0% [Working]\u001b[0m\r            \rGet:1 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,626 B]\n",
            "Get:2 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease [1,581 B]\n",
            "Hit:3 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Get:4 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB]\n",
            "Get:6 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  Packages [830 kB]\n",
            "Hit:7 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
            "Hit:8 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n",
            "Hit:9 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2,037 kB]\n",
            "Hit:11 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:12 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,371 kB]\n",
            "Get:14 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [1,756 kB]\n",
            "Get:15 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,077 kB]\n",
            "Fetched 7,306 kB in 2s (3,360 kB/s)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "46 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "tar: spark-3.2.1-bin-hadoop3.2.tgz: Cannot open: No such file or directory\n",
            "tar: Error is not recoverable: exiting now\n",
            "Collecting pyspark\n",
            "  Downloading pyspark-3.5.1.tar.gz (317.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.0/317.0 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n",
            "Building wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.5.1-py2.py3-none-any.whl size=317488491 sha256=c2ad87bb08107a09a44eff08cd05d4a73eb0bf521733499e18c25a09455e38c0\n",
            "  Stored in directory: /root/.cache/pip/wheels/80/1d/60/2c256ed38dddce2fdd93be545214a63e02fbd8d74fb0b7f3a6\n",
            "Successfully built pyspark\n",
            "Installing collected packages: pyspark\n",
            "Successfully installed pyspark-3.5.1\n",
            "Requirement already satisfied: py4j in /usr/local/lib/python3.10/dist-packages (0.10.9.7)\n"
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              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://f3e850694f1c:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.5.1</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local[*]</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>Our First Spark Example</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
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      "source": [
        "!sudo apt update\n",
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "#Check this site for the latest download link https://www.apache.org/dyn/closer.lua/spark/spark-3.2.1/spark-3.2.1-bin-hadoop3.2.tgz\n",
        "!wget -q https://dlcdn.apache.org/spark/spark-3.2.1/spark-3.2.1-bin-hadoop3.2.tgz\n",
        "!tar xf spark-3.2.1-bin-hadoop3.2.tgz\n",
        "!pip install -q findspark\n",
        "!pip install pyspark\n",
        "!pip install py4j\n",
        "\n",
        "import os\n",
        "import sys\n",
        "# os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "# os.environ[\"SPARK_HOME\"] = \"/content/spark-3.2.1-bin-hadoop3.2\"\n",
        "\n",
        "\n",
        "import findspark\n",
        "findspark.init()\n",
        "findspark.find()\n",
        "\n",
        "import pyspark\n",
        "\n",
        "from pyspark.sql import DataFrame, SparkSession\n",
        "from typing import List\n",
        "import pyspark.sql.types as T\n",
        "import pyspark.sql.functions as F\n",
        "\n",
        "spark= SparkSession \\\n",
        "       .builder \\\n",
        "       .appName(\"Our First Spark Example\") \\\n",
        "       .getOrCreate()\n",
        "\n",
        "spark"
      ]
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              "<pyspark.sql.session.SparkSession at 0x78bffbf37850>"
            ],
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              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://f3e850694f1c:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.5.1</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local[*]</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>Our First Spark Example</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
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          "execution_count": 3
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      "source": [
        "## Import libraries"
      ]
    },
    {
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      "source": [
        "from pyspark.sql.functions import col, isnan, when, count, desc, udf, regexp_extract, expr, sum as spark_sum\n",
        "from pyspark.sql.types import IntegerType, DoubleType\n",
        "from pyspark.sql import functions as F\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import pyspark"
      ]
    },
    {
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        "id": "gMuHAM79JR5K"
      },
      "source": [
        "## Import data\n",
        "\n",
        "Data from the 'train.csv' in the [Titanic - Machine Learning from Disaster](https://www.kaggle.com/c/titanic/overview) Kaggle competition."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/jademene/Cloud-Cognitive-Services.git\n",
        "\n",
        "df = spark.read.csv('Cloud-Cognitive-Services/train.csv', header = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeXtMfMvuDxF",
        "outputId": "dc924964-ca9c-4ad9-fce5-855bd75f12fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path 'Cloud-Cognitive-Services' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
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            "text/plain": [
              "DataFrame[PassengerId: string, Survived: string, Pclass: string, Name: string, Sex: string, Age: string, SibSp: string, Parch: string, Ticket: string, Fare: string, Cabin: string, Embarked: string]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "#df = spark.read.csv('titanic-train.csv', header = True)\n",
        "df = spark.read.csv('train.csv', header = True)\n",
        "df.cache() #makes its read faster"
      ]
    },
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          "text": [
            "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
            "|PassengerId|Survived|Pclass|                Name|   Sex| Age|SibSp|Parch|          Ticket|   Fare|Cabin|Embarked|\n",
            "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
            "|          1|       0|     3|Braund, Mr. Owen ...|  male|  22|    1|    0|       A/5 21171|   7.25| NULL|       S|\n",
            "|          2|       1|     1|Cumings, Mrs. Joh...|female|  38|    1|    0|        PC 17599|71.2833|  C85|       C|\n",
            "|          3|       1|     3|Heikkinen, Miss. ...|female|  26|    0|    0|STON/O2. 3101282|  7.925| NULL|       S|\n",
            "|          4|       1|     1|Futrelle, Mrs. Ja...|female|  35|    1|    0|          113803|   53.1| C123|       S|\n",
            "|          5|       0|     3|Allen, Mr. Willia...|  male|  35|    0|    0|          373450|   8.05| NULL|       S|\n",
            "|          6|       0|     3|    Moran, Mr. James|  male|NULL|    0|    0|          330877| 8.4583| NULL|       Q|\n",
            "|          7|       0|     1|McCarthy, Mr. Tim...|  male|  54|    0|    0|           17463|51.8625|  E46|       S|\n",
            "|          8|       0|     3|Palsson, Master. ...|  male|   2|    3|    1|          349909| 21.075| NULL|       S|\n",
            "|          9|       1|     3|Johnson, Mrs. Osc...|female|  27|    0|    2|          347742|11.1333| NULL|       S|\n",
            "|         10|       1|     2|Nasser, Mrs. Nich...|female|  14|    1|    0|          237736|30.0708| NULL|       C|\n",
            "|         11|       1|     3|Sandstrom, Miss. ...|female|   4|    1|    1|         PP 9549|   16.7|   G6|       S|\n",
            "|         12|       1|     1|Bonnell, Miss. El...|female|  58|    0|    0|          113783|  26.55| C103|       S|\n",
            "|         13|       0|     3|Saundercock, Mr. ...|  male|  20|    0|    0|       A/5. 2151|   8.05| NULL|       S|\n",
            "|         14|       0|     3|Andersson, Mr. An...|  male|  39|    1|    5|          347082| 31.275| NULL|       S|\n",
            "|         15|       0|     3|Vestrom, Miss. Hu...|female|  14|    0|    0|          350406| 7.8542| NULL|       S|\n",
            "|         16|       1|     2|Hewlett, Mrs. (Ma...|female|  55|    0|    0|          248706|     16| NULL|       S|\n",
            "|         17|       0|     3|Rice, Master. Eugene|  male|   2|    4|    1|          382652| 29.125| NULL|       Q|\n",
            "|         18|       1|     2|Williams, Mr. Cha...|  male|NULL|    0|    0|          244373|     13| NULL|       S|\n",
            "|         19|       0|     3|Vander Planke, Mr...|female|  31|    1|    0|          345763|     18| NULL|       S|\n",
            "|         20|       1|     3|Masselmani, Mrs. ...|female|NULL|    0|    0|            2649|  7.225| NULL|       C|\n",
            "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.show(20)"
      ]
    },
    {
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      "source": [
        "## 1) EDA"
      ]
    },
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      "source": [
        "### 1.1) Data overview"
      ]
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            "root\n",
            " |-- PassengerId: string (nullable = true)\n",
            " |-- Survived: string (nullable = true)\n",
            " |-- Pclass: string (nullable = true)\n",
            " |-- Name: string (nullable = true)\n",
            " |-- Sex: string (nullable = true)\n",
            " |-- Age: string (nullable = true)\n",
            " |-- SibSp: string (nullable = true)\n",
            " |-- Parch: string (nullable = true)\n",
            " |-- Ticket: string (nullable = true)\n",
            " |-- Fare: string (nullable = true)\n",
            " |-- Cabin: string (nullable = true)\n",
            " |-- Embarked: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.printSchema()"
      ]
    },
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        "id": "mvNmcGqTJR5K"
      },
      "source": [
        "For the sake of simplicity, numerical columns are converted from string to integer/double format."
      ]
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      "outputs": [],
      "source": [
        "df = df\\\n",
        "    .withColumn(\"PassengerId\", col(\"PassengerId\").cast(IntegerType()))\\\n",
        "    .withColumn(\"Survived\", col(\"Survived\").cast(IntegerType()))\\\n",
        "    .withColumn(\"Pclass\", col(\"Pclass\").cast(IntegerType()))\\\n",
        "    .withColumn(\"Age\", col(\"Age\").cast(IntegerType()))\\\n",
        "    .withColumn(\"SibSp\", col(\"SibSp\").cast(IntegerType()))\\\n",
        "    .withColumn(\"Parch\", col(\"Parch\").cast(IntegerType()))\\\n",
        "    .withColumn(\"Fare\", col(\"Fare\").cast(DoubleType()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "buSIfFDZJR5K",
        "outputId": "9ffdba72-6a8f-4574-f515-fdd32110fc22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- PassengerId: integer (nullable = true)\n",
            " |-- Survived: integer (nullable = true)\n",
            " |-- Pclass: integer (nullable = true)\n",
            " |-- Name: string (nullable = true)\n",
            " |-- Sex: string (nullable = true)\n",
            " |-- Age: integer (nullable = true)\n",
            " |-- SibSp: integer (nullable = true)\n",
            " |-- Parch: integer (nullable = true)\n",
            " |-- Ticket: string (nullable = true)\n",
            " |-- Fare: double (nullable = true)\n",
            " |-- Cabin: string (nullable = true)\n",
            " |-- Embarked: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.printSchema()"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "8TObV4mzJR5L"
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      "source": [
        "**Variables Dictionary:**\n",
        "\n",
        "- PassengerId: Identifier for each passenger.\n",
        "- Survived: Survival status of the passenger.\n",
        "  - 1 = Yes\n",
        "  - 0 = No\n",
        "- Pclass: Ticket class, serving as a proxy for socio-economic status.\n",
        "  - 1 = 1st cabin\n",
        "  - 2 = 2nd cabin\n",
        "  - 3 = 3rd cabin\n",
        "- Name: Full name of the passenger.\n",
        "- Sex: Gender of the passenger.\n",
        "- Age: Age of the passenger in years.\n",
        "- SibSp: Number of siblings/spouses aboard.\n",
        "- Parch: Number of parents/children aboard.\n",
        "- Ticket: Ticket number.\n",
        "- Fare: Fare paid by the passenger.\n",
        "- Cabin: Cabin number.\n",
        "- Embarked: Port of embarkation.\n",
        "  - C = Cherbourg port\n",
        "  - Q = Queenstown port\n",
        "  - S = Southampton port"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "showTitle": false,
          "title": ""
        },
        "id": "GGFcg3L6JR5L",
        "outputId": "919dd7d7-e6f5-4065-f283-18ea84d67423",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rows: 891\n",
            "columns: 12\n"
          ]
        }
      ],
      "source": [
        "print(\"rows:\", df.count())\n",
        "print(\"columns:\", len(df.columns))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
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          "showTitle": false,
          "title": ""
        },
        "id": "k_Aswg1DJR5L"
      },
      "source": [
        "### 1.3) Duplicate values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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          "title": ""
        },
        "id": "F8bVjrjUJR5L",
        "outputId": "6114efbb-be0a-42b9-9267-138ae21c0fbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 0\n"
          ]
        }
      ],
      "source": [
        "duplicates = df.count() - (df.dropDuplicates()).count()\n",
        "print(f\"Number of duplicate rows: {duplicates}\")\n",
        "\n",
        "# display the duplicate rows\n",
        "if duplicates > 0:\n",
        "    from pyspark.sql.functions import col\n",
        "    # Columns to check for duplicates, if you want to specify: ['column1', 'column2']\n",
        "    df.groupBy(df.columns).count().filter(col('count') > 1).drop('count').show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "showTitle": false,
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        },
        "id": "D-wvCBAVJR5L"
      },
      "source": [
        "### 1.4) Missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "Denzd4kWJR5L",
        "outputId": "a5fc016d-1001-490c-f02b-4541a124c213",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "|PassengerId|Survived|Pclass|Name|Sex|Age|SibSp|Parch|Ticket|Fare|Cabin|Embarked|\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "|          0|       0|     0|   0|  0|177|    0|    0|     0|   0|  687|       2|\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "missing_data = df.select([count(when(col(c).isNull(), c)).alias(c) for c in df.columns])\n",
        "missing_data.show()"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "4_RidgYTJR5L"
      },
      "source": [
        "Missing values for:\n",
        "- Age (177 missing values)\n",
        "- Cabin (687 missing values)\n",
        "- Embarked (2 missing values)"
      ]
    },
    {
      "cell_type": "markdown",
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        },
        "id": "D7cy_oB7JR5a"
      },
      "source": [
        "### 1.5) Statistical summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "_BQTjMBGJR5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "017291fb-643b-4f09-b26e-8e8e151d7d0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------------+-------------------+------------------+--------------------+------+------------------+------------------+-------------------+------------------+-----------------+-----+--------+\n",
            "|summary|      PassengerId|           Survived|            Pclass|                Name|   Sex|               Age|             SibSp|              Parch|            Ticket|             Fare|Cabin|Embarked|\n",
            "+-------+-----------------+-------------------+------------------+--------------------+------+------------------+------------------+-------------------+------------------+-----------------+-----+--------+\n",
            "|  count|              891|                891|               891|                 891|   891|               714|               891|                891|               891|              891|  204|     889|\n",
            "|   mean|            446.0| 0.3838383838383838| 2.308641975308642|                NULL|  NULL|29.679271708683473|0.5230078563411896|0.38159371492704824|260318.54916792738| 32.2042079685746| NULL|    NULL|\n",
            "| stddev|257.3538420152301|0.48659245426485753|0.8360712409770491|                NULL|  NULL|14.536482769437564|1.1027434322934315| 0.8060572211299488|471609.26868834975|49.69342859718089| NULL|    NULL|\n",
            "|    min|                1|                  0|                 1|\"Andersson, Mr. A...|female|                 0|                 0|                  0|            110152|              0.0|  A10|       C|\n",
            "|    max|              891|                  1|                 3|van Melkebeke, Mr...|  male|                80|                 8|                  6|         WE/P 5735|         512.3292|    T|       S|\n",
            "+-------+-----------------+-------------------+------------------+--------------------+------+------------------+------------------+-------------------+------------------+-----------------+-----+--------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.describe().show()"
      ]
    },
    {
      "cell_type": "markdown",
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        },
        "id": "bw0_Cq3EJR5a"
      },
      "source": [
        "**Analysis:**\n",
        "- The average age of passengers is approximately 30 years old, with a standard deviation of approximately 14.54 years. However, there are 177 missing values for age, which may affect the analysis.\n",
        "- The average fare paid by passengers is around 32.20, with a standard deviation of approximately 49.69, indicating a wide range of fares paid by passengers.\n",
        "- Passengers' ages range from 0 to 80 years.\n",
        "- SibSp (number of siblings/spouses aboard) ranges from 0 to 8. The average of approximately 0.52 and standard deviation 1.10 suggests less than half of the passengers yravelled with more siblings or spouses as some passengers had several family members aboard.\n",
        "- Parch (number of parents/children aboard) ranges from 0 to 6, with an average of approximately 0.38, indicating that, on average, passengers had less than one parent or child aboard.\n",
        "\n",
        "Ticket classes, fares, and family sizes (siblings, spouses, parents, children) vary significantly, indicating diverse socio-economic backgrounds and family travelling situations. It's also important to note the missing values for age, cabin, and embarked, which may affect the analysis."
      ]
    },
    {
      "cell_type": "markdown",
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        },
        "id": "5npR_1jrJR5a"
      },
      "source": [
        "### 1.6) Data visualization"
      ]
    },
    {
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      "execution_count": null,
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          "height": 203
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        "outputId": "af87aa99-3625-441a-aeee-298d7444f1e2"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
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              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "dfp = df.toPandas()\n",
        "type(dfp)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def makeBarchart2(df, target, hue=None, color_palette=\"viridis\", title=\"\", legend=True, label_position=\"top\", xlabel=\"\", ylabel='Number of passengers'):\n",
        "    \"\"\"\n",
        "    Create a bar chart with optional hue differentiation.\n",
        "\n",
        "    Parameters:\n",
        "        df (DataFrame): The DataFrame containing the data.\n",
        "        target (str): The column name to be used as the x-axis.\n",
        "        hue (str, optional): The column name to be used for hue differentiation.\n",
        "        color_palette (str, optional): The color palette to use.\n",
        "        title (str, optional): The title of the plot.\n",
        "        legend (bool, optional): Whether to display the legend.\n",
        "        label_position (str, optional): Position of the labels ('top' or 'bottom').\n",
        "        xlabel (str, optional): Label for the x-axis.\n",
        "        ylabel (str, optional): Label for the y-axis.\n",
        "    \"\"\"\n",
        "    ax = sns.countplot(x=target, hue=hue, data=df, palette=color_palette) if hue else sns.countplot(x=target, data=df, palette=color_palette)\n",
        "    plt.title(title)\n",
        "    plt.xlabel(xlabel)\n",
        "    plt.ylabel(ylabel)\n",
        "\n",
        "    for p in ax.patches:\n",
        "        count = int(p.get_height())\n",
        "        if count > 0:  # Only annotate bars that have a height greater than zero\n",
        "            percentage = f'{100 * count / len(df):.1f}%'\n",
        "            annotation_text = f'{count}\\n({percentage})'\n",
        "            x_pos = p.get_x() + p.get_width() / 2\n",
        "            y_pos = p.get_height() if label_position == \"top\" else 0\n",
        "            text_color = 'black' if label_position == \"top\" else 'white'\n",
        "            ax.annotate(annotation_text, (x_pos, y_pos), ha='center', va='bottom', fontsize=10, color=text_color, xytext=(0, 10), textcoords='offset points')\n",
        "\n",
        "    if legend and hue:\n",
        "        plt.legend(title=hue)\n",
        "\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "id": "vrEbPSP2SUJ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "81ed7ddb-8638-478d-857a-52b6cf93d418",
          "showTitle": false,
          "title": ""
        },
        "id": "qtOZBOIkJR5a"
      },
      "source": [
        "#### 1.6.1) Survived VS Not Survived"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "d77c3bd7-eb0a-4e6a-a5ea-11cb3d33b9a3",
          "showTitle": false,
          "title": ""
        },
        "id": "lpi16ofIJR5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "outputId": "85e462fb-f4d3-481b-ad40-67fb306abba1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-78-8fffab59b8e8>:19: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  ax = sns.countplot(x=target, hue=hue, data=df, palette=color_palette) if hue else sns.countplot(x=target, data=df, palette=color_palette)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "title = f'1.6.1) Survived VS Not Survived (Total Passengers: {len(dfp)})'\n",
        "makeBarchart2(dfp,'Survived', None, ['#d93725', '#21913c'], title, True, None, 'Status')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The plot above showcase *Not Survived passengers* as 0 and *Survived passengers* as 1. Only 38.4% of the passengers aboard the Titanic survived."
      ],
      "metadata": {
        "id": "lZvokidtwatm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "1cc454b3-6634-4a29-a953-476019ada3b4",
          "showTitle": false,
          "title": ""
        },
        "id": "uB-CiL2oJR5a"
      },
      "source": [
        "#### 1.6.2) Survived VS Not Survived Split by Gender"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "91117170-ab8b-43b1-95c1-15a826058753",
          "showTitle": false,
          "title": ""
        },
        "id": "nxLYlyUtJR5a"
      },
      "outputs": [],
      "source": [
        "#makeBarchart2(dfp,'Sex',None,['blue','pink'],'Distribution of Passengers by Sex', None, None, 'Sex')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "2bf66a3a-4e02-4d91-9193-4c85999cd149",
          "showTitle": false,
          "title": ""
        },
        "id": "eOMUXYKFJR5b"
      },
      "outputs": [],
      "source": [
        "#makeBarchart2(dfp[dfp['Survived'] == 1],'Sex',None,['pink','blue'],'Survived Passengers by Sex', None, None, 'Sex')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "27047d3f-d148-49cc-80a8-97e0775820d1",
          "showTitle": false,
          "title": ""
        },
        "id": "JMtoDanSJR5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "8840a8ce-ff78-41df-e7ed-c5d282720ae6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "makeBarchart2(dfp,'Sex','Survived', ['#d93725', '#21913c'],'1.6.2 Survived VS Not Survived Passengers by Sex', 'Survived', 'centre', 'Sex')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In green are the surviving passengers, while in red are the casualties.\n",
        "\n",
        "The number of male passengers is almost double the number of female passengers (577 vs 314). Despite this, we can clearly see that the number of surviving female passengers is more than twice the number of male passengers. The survival rate of women far exceeds that of male passengers."
      ],
      "metadata": {
        "id": "K8F0yeM0oHnK"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "fa2666a2-9cc2-4948-86c9-c589561b9c70",
          "showTitle": false,
          "title": ""
        },
        "id": "TX0J-YiXJR5b"
      },
      "source": [
        "#### 1.6.3) Passangers Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "f44663d9-1732-4e39-9b64-9eda1eebfc63",
          "showTitle": false,
          "title": ""
        },
        "id": "K8Ul6Me9JR5b"
      },
      "outputs": [],
      "source": [
        "#makeBarchart2(dfp,'Pclass',None,'mako','1.6.3 Total Number of Passengers by Class', None, None, 'Class')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "c1e571ee-57fe-4694-a1af-a16c129fd8bc",
          "showTitle": false,
          "title": ""
        },
        "id": "dciUBOYuJR5b"
      },
      "outputs": [],
      "source": [
        "#makeBarchart2(dfp[dfp['Survived'] == 1],'Pclass',None,'mako','Survived by Class', None, None, 'Class')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "makeBarchart2(dfp,'Pclass','Survived', ['#d93725', '#21913c'],'1.6.3 Survived VS Not Survived Passengers by Class', 'Survived', 'centre', 'Sex')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "4XEiYF8RzUeW",
        "outputId": "db2bdd8e-4f65-49c4-b859-a4595d9a752c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Over half of passengers were in third class (55%).\n",
        "\n",
        "First-class passengers had a 62% survival rate, while second-class passengers had slightly lower odds, with about a 50/50 chance of survival. Conversely, for third-class passengers, survival was very unlikely.\n",
        "\n",
        "The unfortunate fate that third-class passengers met is somewhat expected, as lower-class passengers likely had cabins situated below deck and possibly in very cramped areas. Consequently, they would have been the first ones to face danger and possibly had a harder time reaching the upper decks due to overcrowding of the spaces."
      ],
      "metadata": {
        "id": "h-K3cVhyz1--"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "abbd5ef3-cf99-4bb9-911f-e98501bffa85",
          "showTitle": false,
          "title": ""
        },
        "id": "mrjhcrx5JR5b"
      },
      "source": [
        "#### 1.6.4) Passangers Age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "fe66bc16-cf6a-489c-9137-52eab7af3488",
          "showTitle": false,
          "title": ""
        },
        "id": "vRPJYCKPJR5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "2a72a28a-246a-4a3e-c13f-46034a91ac8d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "survived_passengers = dfp[dfp['Survived'] == 1]\n",
        "not_survived_passengers = dfp[dfp['Survived'] == 0]\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(15, 6))\n",
        "\n",
        "# Survived plot\n",
        "sns.histplot(survived_passengers['Age'], bins=20, color='#21913c', kde=True, ax=axs[0])\n",
        "axs[0].set_title('1.6.4A Age Distribution of Survived')\n",
        "axs[0].set_xlabel('Age')\n",
        "axs[0].set_ylabel('Frequency')\n",
        "\n",
        "# Not Survived plot\n",
        "sns.histplot(not_survived_passengers['Age'], bins=20, color='#d93725', kde=True, ax=axs[1])\n",
        "axs[1].set_title('1.6.4B Age Distribution of Not Survived')\n",
        "axs[1].set_xlabel('Age')\n",
        "axs[1].set_ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph on the left shows that the majority of survivors were under 30 years old. There is a peak in frequency around age 20-25. The frequency gradually decreases as age increases.\n",
        "\n",
        "The graph on the right shows that the majority of those who did not survive were over 30 years old. There is a peak in frequency around age 40-45. Similar to the survival graph, the frequency gradually decreases as age decreases."
      ],
      "metadata": {
        "id": "8fWl8BK87LhX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data for survivors and casualties\n",
        "survivors_age = df.filter(col(\"Survived\") == \"1\").select(\"Age\").collect()\n",
        "casualties_age = df.filter(col(\"Survived\") == \"0\").select(\"Age\").collect()\n",
        "\n",
        "# Extract ages from Row objects and convert to float\n",
        "survivors_age = [float(row.Age) for row in survivors_age if row.Age is not None]\n",
        "casualties_age = [float(row.Age) for row in casualties_age if row.Age is not None]\n",
        "\n",
        "# Prepare data for box plots\n",
        "data = [survivors_age, casualties_age]\n",
        "labels = ['Survived', 'Not Survived']\n",
        "\n",
        "# Create box plots\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(data, labels=labels, patch_artist=True, showmeans=True)\n",
        "plt.xlabel('Passenger Status')\n",
        "plt.ylabel('Age')\n",
        "plt.title('1.6.4.1 Box Plot of Age for Survived vs Not Survived')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "sq885138zsix",
        "outputId": "6f7bfa6f-dc50-4400-ec67-2747ab0f5e83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Among the passengerd that didn't survive, there are more outliers, and the boxplot is longer. The age of the casualties tends to be between 1 and 66, while survivors are between 0 and 60."
      ],
      "metadata": {
        "id": "p5ZcmR3V1Rpg"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "b631cc3f-27be-48d2-b4af-700fce1b1109",
          "showTitle": false,
          "title": ""
        },
        "id": "1GiH0C5BJR5b"
      },
      "source": [
        "#### 1.6.5) Port of embarkation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "e651a170-494f-469f-8acd-80cdac82ad58",
          "showTitle": false,
          "title": ""
        },
        "id": "X0Y6MNUSJR5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "421964c5-b9a4-482b-c4c6-6cbc08f021de"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#makeBarchart2(dfp[dfp['Survived'] == 1],'Embarked',None,'mako','',None,'top','Port of Embarkation')\n",
        "makeBarchart2(dfp,'Embarked','Survived','mako','1.6.5 Survival Rate by Port of Embarkation',None,'top','Port of Embarkation')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "886d3204-2a96-4b5d-9bcf-0809fe37a17b",
          "showTitle": false,
          "title": ""
        },
        "id": "cr0oD1glJR5b"
      },
      "source": [
        "Passengers who embarked from Southampton had the highest number of survivors, followed by Cherbourg, and then Queenstown. However, it's important to consider the total number of people who boarded the Titanic in those ports. This suggests that while more passengers survived from Southampton overall, the proportion of survivors compared to the total number who embarked from that port show that Cherbourg passengers had a higher survival rate."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "1fb2330b-a755-430f-890c-54e4b3044cb3",
          "showTitle": false,
          "title": ""
        },
        "id": "wTlFbclsJR5b"
      },
      "source": [
        "#### 1.6.6) Number of siblings/spouses aboard (SibSp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "81f004ac-f51e-4af7-8093-0a3052f5f230",
          "showTitle": false,
          "title": ""
        },
        "id": "_cs9J8vcJR5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "dc67b7c4-7e36-49ce-a84d-b55ed0301c9e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-78-8fffab59b8e8>:19: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  ax = sns.countplot(x=target, hue=hue, data=df, palette=color_palette) if hue else sns.countplot(x=target, data=df, palette=color_palette)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#makeBarchart2(dfp,'SibSp',None,'mako','Passangers by SibSp',None,'top','SibSp') #all\n",
        "makeBarchart2(dfp[dfp['Survived'] == 1],'SibSp',None,'mako','1.6.6 Survived Passengers by SibSp',None,'top','SibSp') #survived"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "9d9400fd-0ae7-42c7-b1e6-d0b8f019863e",
          "showTitle": false,
          "title": ""
        },
        "id": "1qTbHRZOJR5b"
      },
      "source": [
        "It is evident that passengers with fewer siblings/spouses (SibSp) had a higher survival rate compared to those with more siblings/spouses. This observation suggests that having a smaller family size might have increased the chances of survival."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "e419fd5f-90b8-4cc1-9c4c-3fd9bd66b4b7",
          "showTitle": false,
          "title": ""
        },
        "id": "zmJMyadTJR5c"
      },
      "source": [
        "#### 1.6.7) Number of parents/children aboard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "3c08d9ea-a2d2-43e1-9bf5-b44fd0ee0193",
          "showTitle": false,
          "title": ""
        },
        "id": "hsULo7QAJR5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "d0c00b38-cc17-4f03-ca50-a6e3ed764216"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-78-8fffab59b8e8>:19: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  ax = sns.countplot(x=target, hue=hue, data=df, palette=color_palette) if hue else sns.countplot(x=target, data=df, palette=color_palette)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "makeBarchart2(dfp[dfp['Survived'] == 1],'Parch',None,'mako','1.6.7 Survived Passengers by ParCh',None,'top','Number of parents/children aboard')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The survival trend observed in plot 1.6.6 is also reflected in the 1.6.7 Parch plot, where passengers not having children or parents dependent on them ahd a better chance at surviving."
      ],
      "metadata": {
        "id": "NjagQF4_9nH1"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "70ef91b5-f1c8-46c2-bebe-0a237f931ce2",
          "showTitle": false,
          "title": ""
        },
        "id": "hHp2zmOoJR5c"
      },
      "source": [
        "#### 1.6.8) Ticket Fare"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "2f3990bf-5b6c-435d-9947-cdbf5aaf5717",
          "showTitle": false,
          "title": ""
        },
        "id": "LspT1OC0JR5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "48463469-b084-4502-cbc4-665ba897a5c4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Filter the DataFrame for survived and not survived passengers separately\n",
        "survived_fare = dfp[dfp['Survived'] == 1]['Fare']\n",
        "not_survived_fare = dfp[dfp['Survived'] == 0]['Fare']\n",
        "\n",
        "# Setting up the figure and axes for the subplots\n",
        "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n",
        "\n",
        "# Plotting the distribution of fares for survivors\n",
        "sns.histplot(survived_fare, kde=True, color='green', bins=30, ax=axes[0])\n",
        "axes[0].set_title('Distribution of Fare for Survived Passengers')\n",
        "axes[0].set_xlabel('Fare')\n",
        "axes[0].set_ylabel('Frequency')\n",
        "axes[0].grid(True)\n",
        "\n",
        "# Plotting the distribution of fares for non-survivors\n",
        "sns.histplot(not_survived_fare, kde=True, color='red', bins=30, ax=axes[1])\n",
        "axes[1].set_title('Distribution of Fare for Not Survived Passengers')\n",
        "axes[1].set_xlabel('Fare')\n",
        "axes[1].set_ylabel('Frequency')\n",
        "axes[1].grid(True)\n",
        "\n",
        "# Displaying the plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "d0b56fef-6559-4d58-be3e-5bb2ee0084eb",
          "showTitle": false,
          "title": ""
        },
        "id": "52sElNguJR5c"
      },
      "source": [
        "Distribution of fares for passengers by whether they survived or not.\n",
        "\n",
        "The distribution for survived passengers (left graph) has a peak around 100 fare units, and trails off to zero around 300 fare units.\n",
        "\n",
        "The distribution for not-survived passengers (right graph) has a peak closer to 150 fare units, and also trails off to zero around 300 fare units.\n",
        "\n",
        "However, it is interesting to note that the distribution of fares for the two groups is very similar (both right-skewed). This suggests that the fare variable alone may not be the major factor in whether or not a passenger survived."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data for survivors and casualties\n",
        "survivors_fare = df.filter(col(\"Survived\") == \"1\").select(\"Fare\").collect()\n",
        "casualties_fare = df.filter(col(\"Survived\") == \"0\").select(\"Fare\").collect()\n",
        "\n",
        "# Extract fares from Row objects\n",
        "survivors_fare = [row.Fare for row in survivors_fare if row.Fare is not None]\n",
        "casualties_fare = [row.Fare for row in casualties_fare if row.Fare is not None]\n",
        "\n",
        "# Prepare data for box plots\n",
        "data = [survivors_fare, casualties_fare]\n",
        "labels = ['Survivors', 'Casualties']\n",
        "\n",
        "# Create box plots\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(data, labels=labels, patch_artist=True, showmeans=True)\n",
        "plt.xlabel('Passenger Status')\n",
        "plt.ylabel('Fare')\n",
        "plt.title('Box Plot of Fare for Survivors and Casualties')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "5Az6A_qn046u",
        "outputId": "ed6d9eb9-0dd7-4900-8133-925eb8b5e3af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The casualties payed a lower fare then casualties, with some outliers in both distributions. In the survivors especially there is an extremely high 500+ fare."
      ],
      "metadata": {
        "id": "_jHb9n4I1ODc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2) Data Cleaning"
      ],
      "metadata": {
        "id": "2zigSL7Jw7iO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1) Managing \"Age\" missing values\n",
        "Strategy to handle missing values:\n",
        "- 1) Extract the titles from the \"Name\" column\n",
        "- 2) Replace less common titles with more common ones\n",
        "- 3) Group by gender, class, and title and calculate the avg. age for each group\n",
        "- 4) Fill missing age values with the corresponding median age (drops the temporary column used for calculating the avg age)"
      ],
      "metadata": {
        "id": "PXqWmisgkvem"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "5733f0da-a7f2-4783-b826-5c935cec07c3",
          "showTitle": false,
          "title": ""
        },
        "id": "ukIHGDwHJR5M"
      },
      "outputs": [],
      "source": [
        "# 1) get the title from 'Name' column\n",
        "df = df.withColumn(\"Title\", regexp_extract(\"Name\", \"([A-Za-z]+)\\.\", 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "4fcf28cc-a0a5-4fb8-a832-711d68afae19",
          "showTitle": false,
          "title": ""
        },
        "id": "BWmn7cTEJR5M",
        "outputId": "6429f669-4bc4-4384-a22a-e5175765a01c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+-----+\n",
            "|   Title|count|\n",
            "+--------+-----+\n",
            "|     Don|    1|\n",
            "|Countess|    1|\n",
            "|    Lady|    1|\n",
            "|     Mme|    1|\n",
            "|    Capt|    1|\n",
            "|     Sir|    1|\n",
            "|Jonkheer|    1|\n",
            "|      Ms|    1|\n",
            "|     Col|    2|\n",
            "|    Mlle|    2|\n",
            "|   Major|    2|\n",
            "|     Rev|    6|\n",
            "|      Dr|    7|\n",
            "|  Master|   40|\n",
            "|     Mrs|  125|\n",
            "|    Miss|  182|\n",
            "|      Mr|  517|\n",
            "+--------+-----+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "(df.groupBy(\"Title\").count()).orderBy('count').show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "ea1b490f-13f2-410b-af3c-15699a1cd425",
          "showTitle": false,
          "title": ""
        },
        "id": "su-RU_i1JR5R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b1e86da-69de-4f70-9a2a-7234d509cbb7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+\n",
            "|Title   |\n",
            "+--------+\n",
            "|Don     |\n",
            "|Miss    |\n",
            "|Countess|\n",
            "|Officer |\n",
            "|Lady    |\n",
            "|Master  |\n",
            "|Mme     |\n",
            "|Mr      |\n",
            "|Mrs     |\n",
            "|Sir     |\n",
            "|Jonkheer|\n",
            "|Mlle    |\n",
            "|Ms      |\n",
            "+--------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from pyspark.sql.functions import regexp_replace\n",
        "\n",
        "# Define a regular expression pattern to match the titles to be replaced\n",
        "titles_to_replace = [\"Capt\", \"Col\", \"Major\", \"Dr\", \"Rev\"]\n",
        "pattern = \"|\".join(titles_to_replace)\n",
        "\n",
        "# Replace titles in the \"Title\" column with \"Officer\"\n",
        "df = df.withColumn(\"Title\", regexp_replace(col(\"Title\"), pattern, \"Officer\"))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Title\").distinct().show(truncate=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import regexp_replace\n",
        "\n",
        "# Define a regular expression pattern to match the additional titles to be replaced\n",
        "additional_titles_to_replace = [\"Jonkheer\", \"Don\", \"Sir\", \"Countess\", \"Dona\", \"Lady\"]\n",
        "additional_pattern = \"|\".join(additional_titles_to_replace)\n",
        "\n",
        "# Replace additional titles in the \"Title\" column with \"Noble\"\n",
        "df = df.withColumn(\"Title\", regexp_replace(col(\"Title\"), additional_pattern, \"Noble\"))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Title\").distinct().show(truncate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhEFwMsHx5oW",
        "outputId": "13fb1d8f-6801-4812-8210-35ee1836398b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+\n",
            "|Title  |\n",
            "+-------+\n",
            "|Miss   |\n",
            "|Officer|\n",
            "|Master |\n",
            "|Mme    |\n",
            "|Noble  |\n",
            "|Mr     |\n",
            "|Mrs    |\n",
            "|Mlle   |\n",
            "|Ms     |\n",
            "+-------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import regexp_replace\n",
        "\n",
        "# Define a dictionary to map titles to their replacements\n",
        "title_replacements = {\n",
        "    \"Mme\": \"Mrs\",\n",
        "    \"Ms\": \"Mrs\",\n",
        "    \"Mlle\": \"Miss\"\n",
        "}\n",
        "\n",
        "# Iterate over the dictionary and replace titles in the \"Title\" column\n",
        "for old_title, new_title in title_replacements.items():\n",
        "    df = df.withColumn(\"Title\", regexp_replace(col(\"Title\"), old_title, new_title))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Title\").distinct().show(truncate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1CvuzToDWZJ",
        "outputId": "2d7890ae-fca9-4ddf-88d8-1e57606c1991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+\n",
            "|Title  |\n",
            "+-------+\n",
            "|Miss   |\n",
            "|Officer|\n",
            "|Master |\n",
            "|Noble  |\n",
            "|Mr     |\n",
            "|Mrs    |\n",
            "+-------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3) group by Sex, Class, and Title, and calculate the avg age for each group\n",
        "avg_age_by_group = df\\\n",
        "    .groupBy(\"Sex\", \"Pclass\", \"Title\") \\\n",
        "    .agg(F.mean(\"Age\").cast('int').alias(\"AvgAge\"))\n",
        "\n",
        "avg_age_by_group.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyJqEa7UyW3d",
        "outputId": "fdafc323-f5e9-479a-9b5c-bd8a730e85ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------+------+-------+------+\n",
            "|   Sex|Pclass|  Title|AvgAge|\n",
            "+------+------+-------+------+\n",
            "|  male|     1| Master|     5|\n",
            "|  male|     1|     Mr|    41|\n",
            "|  male|     2|Officer|    42|\n",
            "|  male|     2|     Mr|    32|\n",
            "|  male|     1|Officer|    51|\n",
            "|female|     3|    Mrs|    33|\n",
            "|female|     1|   Miss|    29|\n",
            "|female|     1|Officer|    49|\n",
            "|female|     2|   Miss|    22|\n",
            "|  male|     1|  Noble|    42|\n",
            "|  male|     3| Master|     5|\n",
            "|female|     3|   Miss|    16|\n",
            "|  male|     3|     Mr|    28|\n",
            "|  male|     2| Master|     2|\n",
            "|female|     1|    Mrs|    40|\n",
            "|female|     1|  Noble|    40|\n",
            "|female|     2|    Mrs|    33|\n",
            "+------+------+-------+------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4) fill missing values with corresponding AvgAge\n",
        "df_with_avg = df.join(avg_age_by_group, [\"Sex\", \"Pclass\", \"Title\"], \"left\")\n",
        "\n",
        "#Replace missing values in the 'Age' column with the AvgAge where missing\n",
        "df = df_with_avg.withColumn(\"Age\", when(col(\"Age\").isNull(), col(\"AvgAge\")).otherwise(col(\"Age\")))\n",
        "\n",
        "df = df.withColumn(\"Age\", col(\"Age\").cast(IntegerType()))\n",
        "\n",
        "df = df.drop(\"AvgAge\")\n",
        "\n",
        "#df.select(\"Age\").show()"
      ],
      "metadata": {
        "id": "40jbwKJzziQh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.filter(col('Age').isNull()).count()"
      ],
      "metadata": {
        "id": "U_-2fTmozi_o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a131447-2a04-4ab5-87b3-916af98f3b11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "6607098f-f9d5-46d8-ac5d-25c16904b9a7",
          "showTitle": false,
          "title": ""
        },
        "id": "zQk9JKIhJR5S"
      },
      "source": [
        "### 2.2) Managing \"Embarked\" missing values\n",
        "OPTIONS:\n",
        "- 1) Drop rows\n",
        "- 2) Impute values with most frequent embarkation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Option 1\n",
        "df = df.filter(col(\"Embarked\").isNotNull())"
      ],
      "metadata": {
        "id": "jO1s_CAG9-gd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "cf827282-43d8-48b3-97f8-576240f556c7",
          "showTitle": false,
          "title": ""
        },
        "id": "iAc5HHLEJR5a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14b8d8da-116e-44a5-e169-22fa2eafdaaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ],
      "source": [
        "(df.filter(col(\"Embarked\").isNull())).count()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "4dc4724e-7649-4680-ba56-e69b8bd56ba8",
          "showTitle": false,
          "title": ""
        },
        "id": "CJvCfhOaJR5Z"
      },
      "source": [
        "### 2.3) Managing \"Cabin\" missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "c291dbcf-175a-48dd-b733-b0013e24789e",
          "showTitle": false,
          "title": ""
        },
        "id": "YuM-d3jQJR5S"
      },
      "source": [
        "OPTIONS:\n",
        "- 1) convert 'Cabin' column into binary indicator\n",
        "    - 0 => passenger doesn't have a cabin\n",
        "    - 1 => passenger has a cabin\n",
        "- 2) cut off rows with missing values and then analise the deck (problem: too many missing values)\n",
        "- 3) Create new category (Not Available)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "0b35fb25-c9b3-4a17-8a77-39b61c56797a",
          "showTitle": false,
          "title": ""
        },
        "id": "WnuPCYGfJR5S"
      },
      "outputs": [],
      "source": [
        "# Option 1\n",
        "df = df.withColumn('Cabin', when(col('Cabin').isNull(), 0).otherwise(1))\n",
        "#df.select(\"Cabin\").show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "9597945c-463e-4d4c-a947-fd86e1af6865",
          "showTitle": false,
          "title": ""
        },
        "id": "8h6_HmWLJR5S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd1b3d98-57a7-4185-bf93-07b5ba9268a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 153
        }
      ],
      "source": [
        "(df.filter(col(\"Cabin\").isNull())).count()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.4) Fare modeling\n",
        "\n",
        "To treat the outliers in Fare i decided to replace values above 500 (definetly too high) with the next highest values, in order to have really high fare, which is still telling us that those passegers were wealthy and were probably in first class"
      ],
      "metadata": {
        "id": "APJDVaCsoY6x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace values above 200 in the \"Fare\" column with 150\n",
        "df = df.withColumn(\"Fare\", when(col(\"Fare\") > 200, 150).otherwise(col(\"Fare\")))\n",
        "\n",
        "#df.select(\"Fare\").show()"
      ],
      "metadata": {
        "id": "veORx2LCpDxu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data for survivors and casualties\n",
        "survivors_fare = df.filter(col(\"Survived\") == \"1\").select(\"Fare\").collect()\n",
        "casualties_fare = df.filter(col(\"Survived\") == \"0\").select(\"Fare\").collect()\n",
        "\n",
        "# Extract fares from Row objects\n",
        "survivors_fare = [row.Fare for row in survivors_fare if row.Fare is not None]\n",
        "casualties_fare = [row.Fare for row in casualties_fare if row.Fare is not None]\n",
        "\n",
        "# Prepare data for box plots\n",
        "data = [survivors_fare, casualties_fare]\n",
        "labels = ['Survivors', 'Casualties']\n",
        "\n",
        "# Create box plots\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(data, labels=labels, patch_artist=True, showmeans=True)\n",
        "plt.xlabel('Passenger Status')\n",
        "plt.ylabel('Fare')\n",
        "plt.title('Box Plot of Fare for Survivors and Casualties')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "asXOjbMIo945",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "e09cb716-f976-43c9-a7f9-6dc972804b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract fares from the DataFrame\n",
        "fares = df.select(\"Fare\").rdd.flatMap(lambda x: x).collect()\n",
        "\n",
        "# Plot histogram\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(fares, bins=20, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Fare')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "y6lVGB5Zo57x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "bf8ba211-08a8-4e85-a2d0-81d3cb19c01f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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xcYqLi9P27duVm5tr1zt27KioqCht2bJFx44ds+vdunVTWFiYUlJSfD6sXr16yePxaMOGDT5z6Nevn4qLi7V582a75na71b9/f+Xm5mrbtm12PSgoSL1791Z2drZ2795t10NDQ5WYmKiMjAzt3bvXrtMTPdETPdETPdETPTXVnlJSUlQdfj8tobi4WOnp6crNzdWbb76pv//971qzZo02bdqk8ePH+xxhlaQBAwbo0ksv1Zw5czRx4kTt2bNHy5Yts18vKChQcHCwPvroI/vCtJOdfOS2/DD3wYMH7dMS/P3txInfuOiJnuiJnuiJnuiJnmrb0+HDhxUREaHc3NxTnkbq9yO3Ho9HnTt3liT17dtXX3/9tZ599lndeOONKi4uVk5Ojs/R23379ikmJkaSFBMTo6+++spnefv27bNfq0pgYKACAwMr1AMCAircZaH8gzlZ+Yaubr2quzfUpG5ZVqX1quZY0zo90VNVdXqiJ4meqppjTev0RE8SPVU1x9rUK8yvWqMakNfrVVFRkfr27atmzZpp5cqV9mupqalKT09XUlKSJCkpKUnffvut9u/fb49Zvny5QkJC1L179wafOwAAAPzLr0dup0+frmHDhql9+/Y6evSoFi9erE8//VTLli1TaGioJkyYoKlTpyo8PFwhISH6/e9/r6SkJJ133nmSpMGDB6t79+665ZZb9MQTTygrK0sPPvigJk+eXOmRWQAAADibX8Pt/v37NWbMGGVmZio0NFS9evXSsmXLdPnll0uSnn76ablcLo0cOdLnRxzKud1uffDBB5o0aZKSkpIUHByssWPHatasWf5qCQAAAH7k9wvKGoMjR44oNDT0tCcoAwAAwD+qm9ca3Tm3AAAAQG0RbgEAAOAYhFsAAAA4BuEWAAAAjkG4BQAAgGMQbgEAAOAYhFsAAAA4BuEWAAAAjkG4BQAAgGMQbgEAAOAYhFsAAAA4RoC/J/BLlZ6eruzs7AZbX2RkpNq3b99g6wMAAPAHwq0fpKenq1tioo4VFDTYOoNatNC2rVsJuAAAwNEIt36QnZ2tYwUFuuGxeYpK6FLv69uftkNvPDhJ2dnZhFsAAOBohFs/ikroorMSe/t7GgAAAI7BBWUAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMfwa7idPXu2+vfvr1atWikqKkrXXnutUlNTfcZccsklsizL53H77bf7jElPT9fw4cPVokULRUVFadq0aSotLW3IVgAAANAIBPhz5WvWrNHkyZPVv39/lZaW6oEHHtDgwYP1/fffKzg42B536623atasWfbzFi1a2P9dVlam4cOHKyYmRp9//rkyMzM1ZswYNWvWTI8//niD9gMAAAD/8mu4Xbp0qc/zhQsXKioqShs3btRFF11k11u0aKGYmJhKl/HJJ5/o+++/14oVKxQdHa0+ffro0Ucf1f33369HHnlEHo+nXnsAAABA4+HXcHuy3NxcSVJ4eLhP/dVXX9WiRYsUExOjq666Sg899JB99Hb9+vXq2bOnoqOj7fFDhgzRpEmT9N133+ncc8+tsJ6ioiIVFRXZz48cOSJJKi0ttU9ncLlccrlc8nq98nq99tjyellZmYwxp6273W5ZluVzmoTX65VlWZKMLG+Zz9yMdfxMEct4fesut2SMb92yjo+vsu6VZYxcMvJ4PPa86qOn8rp0/Gh6deoBAQEyxvjULcuS2+2uMMeq6vX5OdETPdETPdETPdFT4+mpuqecNppw6/V6ddddd+n8889Xjx497PpvfvMbxcfHKzY2Vps3b9b999+v1NRUvfXWW5KkrKwsn2AryX6elZVV6bpmz56tmTNnVqinpKTYp0O0adNGnTp1Ulpamg4cOGCPiYuLU1xcnLZv326HcUnq2LGjoqKitGXLFh07dsyud+vWTWFhYUpJSbE/rNzcXEVERMgl6axs33OMf4rsKre3VDGHdtk143Lpp8hual6Sr8icdLteGhCorPBOCi7MUeujmXa90BOs7LB4hRQcVEj+AUUEFmratGnKy8uTpHrpSZJ69eolj8ejDRs2+PTUr18/FRcXa/PmzXbN7Xarf//+ys3N1bZt2+x6UFCQevfurezsbO3evduuh4aGKjExURkZGdq7d69dr8/PiZ7oiZ7oiZ7oiZ4aT08pKSmqDsucGKX9aNKkSfr444/12WefKS4urspxq1at0qBBg7Rz50516tRJEydO1J49e7Rs2TJ7TEFBgYKDg/XRRx9p2LBhFZZR2ZHbdu3a6eDBgwoJCZFUv99ONm3apAEDBmjyok8U17Wnz9zq48htRuq3mj9+uD7//HP17duXb5H0RE/0RE/0RE/01OR6Onz4sCIiIpSbm2vntco0iiO3U6ZM0QcffKC1a9eeMthK0sCBAyXJDrcxMTH66quvfMbs27dPkqo8TzcwMFCBgYEV6gEBAQoI8N0k5R/Myco3dHXrJy7X5XL9/CFbx0NrJYxVSd2yalh3yViSV5aKi4t/PhWifnqqbd2yrErrVc2xpnV6oqeq6vRETxI9VTXHmtbpiZ4k//RUGb/eCswYoylTpujtt9/WqlWrlJCQcNr3bNq0SZLUtm1bSVJSUpK+/fZb7d+/3x6zfPlyhYSEqHv37vUybwAAADROfj1yO3nyZC1evFjvvvuuWrVqZZ8jGxoaqqCgIO3atUuLFy/WFVdcoYiICG3evFl33323LrroIvXq1UuSNHjwYHXv3l233HKLnnjiCWVlZenBBx/U5MmTKz06CwAAAOfy65HbefPmKTc3V5dcconatm1rP15//XVJksfj0YoVKzR48GB169ZN99xzj0aOHKn333/fXobb7dYHH3wgt9utpKQkjR49WmPGjPG5Ly4AAAB+Gfx65PZ017K1a9dOa9asOe1y4uPj9dFHH9XVtAAAANBE+fXILQAAAFCXCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHINwCwAAAMcg3AIAAMAxCLcAAABwDMItAAAAHCPA3xNAw9m6dWuDrSsyMlLt27dvsPUBAABIhNtfhKPZ+2S5XBo9enSDrTOoRQtt27qVgAsAABoU4fYX4NjRIzJer254bJ6iErrU+/r2p+3QGw9OUnZ2NuEWAAA0KMLtL0hUQhedldjb39MAAACoN1xQBgAAAMcg3AIAAMAx/BpuZ8+erf79+6tVq1aKiorStddeq9TUVJ8xhYWFmjx5siIiItSyZUuNHDlS+/bt8xmTnp6u4cOHq0WLFoqKitK0adNUWlrakK0AAACgEfBruF2zZo0mT56sL774QsuXL1dJSYkGDx6s/Px8e8zdd9+t999/X0uWLNGaNWuUkZGhESNG2K+XlZVp+PDhKi4u1ueff66XX35ZCxcu1MMPP+yPlgAAAOBHfr2gbOnSpT7PFy5cqKioKG3cuFEXXXSRcnNz9dJLL2nx4sW67LLLJEkLFixQYmKivvjiC5133nn65JNP9P3332vFihWKjo5Wnz599Oijj+r+++/XI488Io/H44/WAAAA4AeN6m4Jubm5kqTw8HBJ0saNG1VSUqLk5GR7TLdu3dS+fXutX79e5513ntavX6+ePXsqOjraHjNkyBBNmjRJ3333nc4999wK6ykqKlJRUZH9/MiRI5Kk0tJS+3QGl8sll8slr9crr9drjy2vl5WVyRhz2rrb7ZZlWT6nSXi9XlmWJcnI8pb5zM1Yxw+mW8brW3e5JWN865Z1fHyVda8sY+S2JI/HI0s/z+vn+n/XaUmW6/gyfOouybKqrlcxd5eMPB6PvF6vSktL5Xa7JR0/yn6igIAAGWN86pZlye12V9juVdXr83Mqr1c2d3qiJ3qiJ3qiJ3pq2J6qe8ppowm3Xq9Xd911l84//3z16NFDkpSVlSWPx6OwsDCfsdHR0crKyrLHnBhsy18vf60ys2fP1syZMyvUU1JSFBwcLElq06aNOnXqpLS0NB04cMAeExcXp7i4OG3fvt0O45LUsWNHRUVFacuWLTp27Jhd79atm8LCwpSSkmJ/WLm5uYqIiJBL0lnZvucY/xTZVW5vqWIO7bJrxuXST5Hd1LwkX5E56Xa9NCBQWeGdFFyYo9ZHM+16oSdY2WHxCik4qJD8A2rVrrU6TpumkIASeSW1zstS8LEce/yR4DY6EtxGEbk/qnnxf08JOdyqrfKDWiv6cJoCSv/7ZSA7rL0KPS0Ve2iHrBN27qzwTipzBahnYKGmTZumgwcPasOGDerXr5+Ki4u1efNme6zb7Vb//v2Vm5urbdu22fWgoCD17t1b2dnZ2r17t10PDQ1VYmKiMjIytHfvXrten5+TJPXq1Usej0cbNmzw+ZzoiZ7oiZ7oiZ7oqWF7SklJUXVY5sQo7UeTJk3Sxx9/rM8++0xxcXGSpMWLF2v8+PE+R1klacCAAbr00ks1Z84cTZw4UXv27NGyZcvs1wsKChQcHKyPPvpIw4YNq7Cuyo7ctmvXTgcPHlRISIik+v12smnTJg0YMECTF32iuK49feZWH0duv1n2tv49807dtuBDxSb2qfcjt5nbvtH88cO1bt069enTx9HfIumJnuiJnuiJnuipYXo6fPiwIiIilJuba+e1yjSKI7dTpkzRBx98oLVr19rBVpJiYmJUXFysnJwcn6O3+/btU0xMjD3mq6++8lle+d0UysecLDAwUIGBgRXqAQEBCgjw3STlH8zJyjd0desnLtflcv38IVvHQ2sljFVJ3bJqWHfJWFKZkYqLi2Vk+dQrrtMl1aRexdy9slRcXCyXy+XT98nb9vjUrUrrVW33mtbP5HOqbZ2e6Emip6rmWNM6PdGTRE9VzbGmdSf2VBm/3i3BGKMpU6bo7bff1qpVq5SQkODzet++fdWsWTOtXLnSrqWmpio9PV1JSUmSpKSkJH377bfav3+/PWb58uUKCQlR9+7dG6YRAAAANAp+PXI7efJkLV68WO+++65atWplnyMbGhqqoKAghYaGasKECZo6darCw8MVEhKi3//+90pKStJ5550nSRo8eLC6d++uW265RU888YSysrL04IMPavLkyZUenQUAAIBz+TXczps3T5J0ySWX+NQXLFigcePGSZKefvppuVwujRw5UkVFRRoyZIhefPFFe6zb7dYHH3ygSZMmKSkpScHBwRo7dqxmzZrVUG0AAACgkfBruK3OtWzNmzfXCy+8oBdeeKHKMfHx8froo4/qcmoAAABogvx6zi0AAABQlwi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByjVuF29+7ddT0PAAAA4IzVKtx27txZl156qRYtWqTCwsK6nhMAAABQK7UKt//5z3/Uq1cvTZ06VTExMbrtttv01Vdf1fXcAAAAgBqpVbjt06ePnn32WWVkZOgf//iHMjMzdcEFF6hHjx6aO3euDhw4UNfzBAAAAE7rjC4oCwgI0IgRI7RkyRLNmTNHO3fu1L333qt27dppzJgxyszMrKt5AgAAAKd1RuF2w4YNuuOOO9S2bVvNnTtX9957r3bt2qXly5crIyND11xzTV3NEwAAADitgNq8ae7cuVqwYIFSU1N1xRVX6JVXXtEVV1whl+t4Vk5ISNDChQvVoUOHupwrAAAAcEq1Crfz5s3Tb3/7W40bN05t27atdExUVJReeumlM5ocAAAAUBO1Crc7duw47RiPx6OxY8fWZvEAAABArdTqnNsFCxZoyZIlFepLlizRyy+/fMaTAgAAAGqjVuF29uzZioyMrFCPiorS448/fsaTAgAAAGqjVuE2PT1dCQkJFerx8fFKT08/40kBAAAAtVGrcBsVFaXNmzdXqH/zzTeKiIg440kBAAAAtVGrcHvzzTfrD3/4g1avXq2ysjKVlZVp1apVuvPOO3XTTTfV9RwBAACAaqnV3RIeffRR/fDDDxo0aJACAo4vwuv1asyYMZxzCwAAAL+pVbj1eDx6/fXX9eijj+qbb75RUFCQevbsqfj4+LqeHwAAAFBttQq35c4++2ydffbZdTUXAAAA4IzUKtyWlZVp4cKFWrlypfbv3y+v1+vz+qpVq+pkcgAAAEBN1Crc3nnnnVq4cKGGDx+uHj16yLKsup4XAAAAUGO1Crevvfaa3njjDV1xxRV1PR8AAACg1mp1KzCPx6POnTvX9VwAAACAM1KrcHvPPffo2WeflTGmrucDAAAA1FqtTkv47LPPtHr1an388cc655xz1KxZM5/X33rrrTqZHAAAAFATtQq3YWFhuu666+p6LgAAAMAZqVW4XbBgQV3PAwAAADhjtTrnVpJKS0u1YsUK/e1vf9PRo0clSRkZGcrLy6uzyQEAAAA1Uasjt3v27NHQoUOVnp6uoqIiXX755WrVqpXmzJmjoqIizZ8/v67nCQAAAJxWrY7c3nnnnerXr58OHz6soKAgu37ddddp5cqVdTY5AAAAoCZqdeT2//2//6fPP/9cHo/Hp96hQwf99NNPdTIxAAAAoKZqdeTW6/WqrKysQn3v3r1q1arVGU8KAAAAqI1ahdvBgwfrmWeesZ9blqW8vDzNmDGDn+QFAACA39Qq3D711FNat26dunfvrsLCQv3mN7+xT0mYM2dOtZezdu1aXXXVVYqNjZVlWXrnnXd8Xh83bpwsy/J5DB061GfMoUOHNGrUKIWEhCgsLEwTJkzgjg0AAAC/ULU65zYuLk7ffPONXnvtNW3evFl5eXmaMGGCRo0a5XOB2enk5+erd+/e+u1vf6sRI0ZUOmbo0KE+99UNDAz0eX3UqFHKzMzU8uXLVVJSovHjx2vixIlavHhxbVoDAABAE1arcCtJAQEBGj169BmtfNiwYRo2bNgpxwQGBiomJqbS17Zu3aqlS5fq66+/Vr9+/SRJzz//vK644gr99a9/VWxs7BnNDwAAAE1LrcLtK6+8csrXx4wZU6vJVObTTz9VVFSUWrdurcsuu0yPPfaYIiIiJEnr169XWFiYHWwlKTk5WS6XS19++WWVPxFcVFSkoqIi+/mRI0ckHf9hitLSUkmSy+WSy+WS1+uV1+u1x5bXy8rKZIw5bd3tdsuyLHu50vEL8izLkmRkeX0vzDPW8TNFLOP1rbvckjG+dcs6Pr7KuleWMXJbksfjkaWf5/Vz/b/rtCTLdXwZPnWXZFlV16uYu0tGHo9HXq9XpaWlcrvdklThIsSAgAAZY3zqlmXJ7XZX2O5V1evzcyqvVzZ3eqIneqIneqInemrYnk4eX5Vahds777zT53lJSYkKCgrk8XjUokWLOgu3Q4cO1YgRI5SQkKBdu3bpgQce0LBhw7R+/Xq53W5lZWUpKirK5z0BAQEKDw9XVlZWlcudPXu2Zs6cWaGekpKi4OBgSVKbNm3UqVMnpaWl6cCBA/aYuLg4xcXFafv27crNzbXrHTt2VFRUlLZs2aJjx47Z9W7duiksLEwpKSn2h5Wbm6uIiAi5JJ2Vneozh58iu8rtLVXMoV12zbhc+imym5qX5CsyJ92ulwYEKiu8k4ILc9T6aKZdL/QEKzssXiEFBxWSf0Ct2rVWx2nTFBJQIq+k1nlZCj6WY48/EtxGR4LbKCL3RzUvzrfrh1u1VX5Qa0UfTlNA6X+/DGSHtVehp6ViD+2QdcLOnRXeSWWuAPUMLNS0adN08OBBbdiwQf369VNxcbE2b95sj3W73erfv79yc3O1bds2ux4UFKTevXsrOztbu3fvtuuhoaFKTExURkaG9u7da9fr83OSpF69esnj8WjDhg0+nxM90RM90RM90RM9NWxPKSkpqg7LnBilz8COHTs0adIkTZs2TUOGDKnx+y3L0ttvv61rr722yjG7d+9Wp06dtGLFCg0aNEiPP/64Xn75ZaWm+gbEqKgozZw5U5MmTap0OZUduW3Xrp0OHjyokJAQSfX77WTTpk0aMGCAJi/6RHFde/rMrT6O3H6z7G39e+adum3Bh4pN7FPvR24zt32j+eOHa926derTp4+jv0XSEz3REz3REz3RU8P0dPjwYUVERCg3N9fOa5Wp9Tm3J+vSpYv+8pe/aPTo0T7fCupSx44dFRkZqZ07d2rQoEGKiYnR/v37fcaUlpbq0KFDVZ6nKx0/j/fkC9Ok4x98QIDvJin/YE5WvqGrWz9xuS6X6+cP2ToeWithrErqllXDukvGksqMVFxcLCPLp15xnS6pJvUq5u6VpeLiYrlcLp++T962x6duVVqvarvXtH4mn1Nt6/RETxI9VTXHmtbpiZ4keqpqjjWtO7GnytTqVmBVCQgIUEZGRl0u0sfevXt18OBBtW3bVpKUlJSknJwcbdy40R6zatUqeb1eDRw4sN7mAQAAgMapVkdu33vvPZ/nxhhlZmbqf//3f3X++edXezl5eXnauXOn/TwtLU2bNm1SeHi4wsPDNXPmTI0cOVIxMTHatWuX7rvvPnXu3Nk+7SExMVFDhw7Vrbfeqvnz56ukpERTpkzRTTfdxJ0SAAAAfoFqFW5PPi/Wsiy1adNGl112mZ566qlqL2fDhg269NJL7edTp06VJI0dO1bz5s3T5s2b9fLLLysnJ0exsbEaPHiwHn30UZ9TCl599VVNmTJFgwYNksvl0siRI/Xcc8/Vpi0AAAA0cbUKtyeeRHwmLrnkEp8TjE+2bNmy0y4jPDycH2wAAACApDo+5xYAAADwp1oduS0/faA65s6dW5tVAAAAADVWq3CbkpKilJQUlZSUqGvXrpKk7du3y+1261e/+pU97vivcAEAAAANo1bh9qqrrlKrVq308ssvq3Xr1pKO31h3/PjxuvDCC3XPPffU6SQBAACA6qjVObdPPfWUZs+ebQdbSWrdurUee+yxGt0tAQAAAKhLtQq3R44c8flN4XIHDhzQ0aNHz3hSAAAAQG3UKtxed911Gj9+vN566y3t3btXe/fu1b///W9NmDBBI0aMqOs5AgAAANVSq3Nu58+fr3vvvVe/+c1vVFJScnxBAQGaMGGCnnzyyTqdIAAAAFBdtQq3LVq00Isvvqgnn3xSu3btkiR16tRJwcHBdTo5AAAAoCbO6EccMjMzlZmZqS5duig4OPiUvzYGAAAA1LdahduDBw9q0KBBOvvss3XFFVcoMzNTkjRhwgRuAwYAAAC/qVW4vfvuu9WsWTOlp6erRYsWdv3GG2/U0qVL62xyAAAAQE3U6pzbTz75RMuWLVNcXJxPvUuXLtqzZ0+dTAwAAACoqVoduc3Pz/c5Ylvu0KFDCgwMPONJAQAAALVRq3B74YUX6pVXXrGfW5Ylr9erJ554QpdeemmdTQ4AAACoiVqdlvDEE09o0KBB2rBhg4qLi3Xffffpu+++06FDh7Ru3bq6niMAAABQLbU6ctujRw9t375dF1xwga655hrl5+drxIgRSklJUadOnep6jgAAAEC11PjIbUlJiYYOHar58+frT3/6U33MCQAAAKiVGh+5bdasmTZv3lwfcwEAAADOSK1OSxg9erReeumlup4LAAAAcEZqdUFZaWmp/vGPf2jFihXq27evgoODfV6fO3dunUwOAAAAqIkahdvdu3erQ4cO2rJli371q19JkrZv3+4zxrKsupsdAAAAUAM1CrddunRRZmamVq9eLen4z+0+99xzio6OrpfJAQAAADVRo3NujTE+zz/++GPl5+fX6YQAAACA2qrVBWXlTg67AAAAgD/VKNxallXhnFrOsQUAAEBjUaNzbo0xGjdunAIDAyVJhYWFuv322yvcLeGtt96quxkCAAAA1VSjcDt27Fif56NHj67TyQAAAABnokbhdsGCBfU1DwAAAOCMndEFZQAAAEBjUqtfKAMam/T0dGVnZzfoOiMjI9W+ffsGXScAADg1wi2avPT0dHVLTNSxgoIGXW9QixbatnUrARcAgEaEcIsmLzs7W8cKCnTDY/MUldClQda5P22H3nhwkrKzswm3AAA0IoRbOEZUQhedldjb39MAAAB+xAVlAAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADH8Gu4Xbt2ra666irFxsbKsiy98847Pq8bY/Twww+rbdu2CgoKUnJysnbs2OEz5tChQxo1apRCQkIUFhamCRMmKC8vrwG7AAAAQGPh13Cbn5+v3r1764UXXqj09SeeeELPPfec5s+fry+//FLBwcEaMmSICgsL7TGjRo3Sd999p+XLl+uDDz7Q2rVrNXHixIZqAQAAAI1IgD9XPmzYMA0bNqzS14wxeuaZZ/Tggw/qmmuukSS98sorio6O1jvvvKObbrpJW7du1dKlS/X111+rX79+kqTnn39eV1xxhf76178qNja2wXoBAACA//k13J5KWlqasrKylJycbNdCQ0M1cOBArV+/XjfddJPWr1+vsLAwO9hKUnJyslwul7788ktdd911lS67qKhIRUVF9vMjR45IkkpLS1VaWipJcrlccrlc8nq98nq99tjyellZmYwxp6273W5ZlmUvV5K8Xq8sy5JkZHnLfOZmrOMH0y3j9a273JIxvnXLOj6+yrpXljFyW5LH45Gln+f1c/2/67Qky3V8GT51l2RZVdermLtLRh6PR16vV6WlpXK73ZKksjLf8QEBATLG+NQty5Lb7a6w3auqu1wue1muE7ZnXfd08uchGVmWZfdY1z3V175XXpcqfh71/TnREz3REz3REz2dSU8nj69Kow23WVlZkqTo6GifenR0tP1aVlaWoqKifF4PCAhQeHi4PaYys2fP1syZMyvUU1JSFBwcLElq06aNOnXqpLS0NB04cMAeExcXp7i4OG3fvl25ubl2vWPHjoqKitKWLVt07Ngxu96tWzeFhYUpJSXF/rByc3MVEREhl6SzslN95vBTZFe5vaWKObTLrhmXSz9FdlPzknxF5qTb9dKAQGWFd1JwYY5aH82064WeYGWHxSuk4KBC8g+oVbvW6jhtmkICSuSV1DovS8HHcuzxR4Lb6EhwG0Xk/qjmxfl2/XCrtsoPaq3ow2kKKP3vl4HssPYq9LRU7KEdsk7YubPCO6nMFaCegYWaNm2aDh48qA0bNqhfv34qLi7W5s2b7bFut1v9+/dXbm6utm3bZteDgoLUu3dvZWdna/fu3XY9NDRUiYmJysjI0N69e+16mzZtJElDhgxRz8BCNf95e9Z1Tyd/ThmSIiIi7B7ruqf62vckqVevXvJ4PPa8y9X350RP9ERP9ERP9HQmPaWkpKg6LHNilPYjy7L09ttv69prr5Ukff755zr//POVkZGhtm3b2uNuuOEGWZal119/XY8//rhefvllpab6Bo+oqCjNnDlTkyZNqnRdlR25bdeunQ4ePKiQkBBJ9fvtZNOmTRowYIAmL/pEcV17+sytPo7cfrPsbf175p26bcGHik3sU+9HbjO3faP544dr3bp16tOnT71/i9y0aZMGDhyoO17+WLE/b8/6PnK7N/VbvTB6sL766iv16dOnznvi2z490RM90RM90ZNv/fDhw4qIiFBubq6d1yrTaI/cxsTESJL27dvnE2737dtnh4mYmBjt37/f532lpaU6dOiQ/f7KBAYGKjAwsEI9ICBAAQG+m6T8gzlZ+Yaubv3E5bpcrp8/ZOt4aK2EsSqpW1YN6y4ZSyozUnFxsYwsn3rFdbqkmtSrmLtXloqLi+VyuXz6PnnbHp+6VWm9qu1eVb20tFTeSrZnXfVUcftaMsZU6FGqu57qY9+rbZ2e6Emip6rmWNM6PdGTRE9VzbE29Qrzq9YoP0hISFBMTIxWrlxp144cOaIvv/xSSUlJkqSkpCTl5ORo48aN9phVq1bJ6/Vq4MCBDT5nAAAA+Jdfj9zm5eVp586d9vO0tDRt2rRJ4eHhat++ve666y499thj6tKlixISEvTQQw8pNjbWPnUhMTFRQ4cO1a233qr58+erpKREU6ZM0U033cSdEgAAAH6B/BpuN2zYoEsvvdR+PnXqVEnS2LFjtXDhQt13333Kz8/XxIkTlZOTowsuuEBLly5V8+bN7fe8+uqrmjJligYNGiSXy6WRI0fqueeea/BeAAAA4H9+DbeXXHKJzwnGJ7MsS7NmzdKsWbOqHBMeHq7FixfXx/QAAADQxDTac24BAACAmiLcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcI8DfE4Bzbd261VHrAQAAjR/hFnXuaPY+WS6XRo8e7e+pAACAXxjCLercsaNHZLxe3fDYPEUldKn39aWuW6nlL86u9/UAAIDGj3CLehOV0EVnJfau9/XsT9tR7+sAAABNAxeUAQAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEadbh95JFHZFmWz6Nbt27264WFhZo8ebIiIiLUsmVLjRw5Uvv27fPjjAEAAOBPjTrcStI555yjzMxM+/HZZ5/Zr9199916//33tWTJEq1Zs0YZGRkaMWKEH2cLAAAAfwrw9wROJyAgQDExMRXqubm5eumll7R48WJddtllkqQFCxYoMTFRX3zxhc4777yGnioAAAD8rNGH2x07dig2NlbNmzdXUlKSZs+erfbt22vjxo0qKSlRcnKyPbZbt25q37691q9ff8pwW1RUpKKiIvv5kSNHJEmlpaUqLS2VJLlcLrlcLnm9Xnm9Xntseb2srEzGmNPW3W63LMuylytJXq9XlmVJMrK8ZT5zM9bxg+mW8frWXW7JGN+6ZR0fX2XdK8sYuS3J4/HI0s/z+rn+33VakuU6vgyfukuyrKrrVcy9fH2un/urj5585q7jX4JcJ2zPuu7p5LlLRpZlyev1+ny2AQEBMsaorOy/y7EsS263u8K+VFW9Pve98roknzmeqk5P9ERP9ERP9NQYejp5fFUadbgdOHCgFi5cqK5duyozM1MzZ87UhRdeqC1btigrK0sej0dhYWE+74mOjlZWVtYplzt79mzNnDmzQj0lJUXBwcGSpDZt2qhTp05KS0vTgQMH7DFxcXGKi4vT9u3blZuba9c7duyoqKgobdmyRceOHbPr3bp1U1hYmFJSUuwPKzc3VxEREXJJOis71WcOP0V2ldtbqphDu+yacbn0U2Q3NS/JV2ROul0vDQhUVngnBRfmqPXRTLte6AlWdli8QgoOKiT/gFq1a62O06YpJKBEXkmt87IUfCzHHn8kuI2OBLdRRO6Pal6cb9cPt2qr/KDWij6cpoDS/34ZyA5rr0JPS8Ue2iHrhJ07K7yTylwBuuDn9XUILFTz7NR66alcflCYJGnIkCHq+fP66qOnkz+nDEkRERE6ePCgNmzYIOn4H77+/fsrNzdX27Zts8cGBQWpd+/eys7O1u7du+16aGioEhMTlZGRob1799r1+tz3JKlXr17yeDz2vMv169dPxcXF2rx5s12jJ3qiJ3qiJ3pqLD2lpKSoOixzYpRu5HJychQfH6+5c+cqKChI48eP9zkCK0kDBgzQpZdeqjlz5lS5nMqO3LZr104HDx5USEiIpPr9drJp0yYNGDBAkxd9oriuPX3mVh9HOb9Z9rb+PfNO3bbgQ8Um9qn3I7ebl/5b/555p25f8KFiu/as9yO3KR+/pbce+b3uePljxf68Pev7yO3e1G/1wujB+uqrr9SnTx+77oRvxk78tk9P9ERP9ERPTb+nw4cPKyIiQrm5uXZeq0yjPnJ7srCwMJ199tnauXOnLr/8chUXFysnJ8fn6O2+ffsqPUf3RIGBgQoMDKxQDwgIUECA7yYp/2BOVr6hq1s/cbkul+vnD9k6HvAqYaxK6pZVw7pLxpLKjFRcXCwjy6decZ0uqSb1KuZevj7vSf3VZU8nKy0trbC+U869hj1VnIslY4xcLleFfcayrAo1qep9qab1M9n3alunJ3qS6KmqOda0Tk/0JNFTVXOsTb3C/Ko1qpHIy8vTrl271LZtW/Xt21fNmjXTypUr7ddTU1OVnp6upKQkP84SAAAA/tKoj9zee++9uuqqqxQfH6+MjAzNmDFDbrdbN998s0JDQzVhwgRNnTpV4eHhCgkJ0e9//3slJSVxpwQAAIBfqEYdbvfu3aubb75ZBw8eVJs2bXTBBRfoiy++UJs2bSRJTz/9tFwul0aOHKmioiINGTJEL774op9nDQAAAH9p1OH2tddeO+XrzZs31wsvvKAXXnihgWYEAACAxqxJnXMLAAAAnArhFgAAAI5BuAUAAIBjEG4BAADgGIRbAAAAOAbhFgAAAI5BuAUAAIBjEG4BAADgGIRbAAAAOEaj/oUyAP6Tnp6u7OzsBltfZGSk2rdv32DrAwA4E+EWQAXp6enqlpioYwUFDbbOoBYttG3rVgIuAOCMEG4BVJCdna1jBQW64bF5ikroUu/r25+2Q288OEnZ2dmEWwDAGSHcAqhSVEIXnZXY29/TAACg2rigDAAAAI5BuAUAAIBjEG4BAADgGIRbAAAAOAYXlAFnYOvWrQ22Lu4DCwDA6RFugVo4mr1Plsul0aNHN9g6uQ8sAACnR7gFauHY0SMyXi/3gQUAoJEh3AJngPvAAgDQuHBBGQAAAByDcAsAAADHINwCAADAMQi3AAAAcAzCLQAAAByDcAsAAADH4FZgAAAA9SA9PV3Z2dkNtj5+yfI4wi0AAEAdS09PV7fERB0rKGiwdfJLlscRbgEAAOpYdna2jhUU8EuWfkC4BQAAqCf8kmXD44IyAAAAOAZHbgGgATT0hSUSF5cA+GUi3AJAPfPHhSUSF5cA+GUi3AJAPWvoC0skLi4B8MtFuAWABsKFJQBQ/7igDAAAAI5BuAUAAIBjEG4BAADgGIRbAAAAOAYXlAH4RWrI+85u3bq1QdYDACDcAvgF8td9ZwEA9Y9wC+AXp6HvO5u6bqWWvzi73tcDACDcAvgFa6j7zu5P21Hv6wAAHMcFZQAAAHAMwi0AAAAcg3ALAAAAx+CcWwBwsIa8DVlkZKTat2/fYOsDgMoQbgHAgY5m75Plcmn06NENts6gFi20betWAi4AvyLcAoADHTt6RMbrbbDbne1P26E3Hpyk7Oxswi0AvyLcAoCDNdTtzgCgseCCMgAAADgGR26BJqShLg5qyIuQAACoS4RboAnwx8VBAAA0RYRboAlo6IuDUtet1PIXZ9f7egAAqGuEW6AJaaiLg/an7aj3dVSG0y7QmKWnpys7O7vB1sd9g4HaIdwC8DtOu0Bjl56erm6JiTpWUNBg6+S+wUDtEG4B+B2nXaCxy87O1rGCAu4bDDQBhFsAjYbTT7tA08d9g+sWp3qgPhBuAQBAg+NUD9QXwi0AAGhwnOqB+kK4BQAAfsOpHqhr/PwuAAAAHIMjtwCAOtOQ9xD+JVwc1ND3ZP4lbFM4H+EWAHDG/HGvYidfHOSvez87eZvil4NwCwA4Yw19r2KnXxzU0NtTcv42xS+HY8LtCy+8oCeffFJZWVnq3bu3nn/+eQ0YMMDf0wKAX5SGvjjI6T/ZzMVWdc/p+wynBjkk3L7++uuaOnWq5s+fr4EDB+qZZ57RkCFDlJqaqqioKH9PDwBQx/jJZtSU0/cZTg36L0eE27lz5+rWW2/V+PHjJUnz58/Xhx9+qH/84x/64x//6OfZAQDqGj/ZjJpy+j7DqUH/1eTDbXFxsTZu3Kjp06fbNZfLpeTkZK1fv77S9xQVFamoqMh+npubK0k6dOiQSktL7WW4XC55vV55vV6fZbtcLpWVlckYc9q62+2WZVn2ciXpyJEjkqSftm5WSUGez9zK32mdNGcjy/7fk+uWjE52Yv1Q+i41a9ZMmds2q7gg/7TjT1c3P8+w8vp/15e17Xh/9dHTiQ78sENut9teX330dPIcD/xw/OdbK1tnXfR0cv1Q+i65XK5K95m66unE+omfYXFBfr30dGL9UPpOn32mPno60YEfdsiyrDPaZ2pSP5S+S5ZlneLP/Jn3dPK+dyh9lwICAmq0z5xJ3XefyauXnk6sZ+/ZoWbNmslbdOyU+0xd1cuKj/+bUXGfqbueTt5n6vvzO3nfO5i+W82aNdPGjRt19OhRn3/LJMmyjs/65LrL5ZIxxqduWZYsyzplffv27Sf9W1H3PZ1YP/BDZfvMz/OqZNln+veet6RYkpS5rbI/93X/71P5n8GSwmMqLsirt39zy+veomNq1qyZjhw5opycnDPORuV1SSorK6u0fvjw4eNzMBXn5TvJJu6nn34yksznn3/uU582bZoZMGBApe+ZMWOG0fH9ggcPHjx48ODBg0cTevz444+nzIZN/shtbUyfPl1Tp061n3u9Xh06dEgRERH2N9P6dOTIEbVr104//vijQkJC6n19vwRs0/rBdq17bNP6wXate2zT+sF2rT1jjI4eParY2NhTjmvy4TYyMlJut1v79u3zqe/bt08xMTGVvicwMFCBgYE+tbCwsPqaYpVCQkLYsesY27R+sF3rHtu0frBd6x7btH6wXWsnNDT0tGOa/M/vejwe9e3bVytXrrRrXq9XK1euVFJSkh9nBgAAgIbW5I/cStLUqVM1duxY9evXTwMGDNAzzzyj/Px8++4JAAAA+GVwRLi98cYbdeDAAT388MPKyspSnz59tHTpUkVHR/t7apUKDAzUjBkzKpwagdpjm9YPtmvdY5vWD7Zr3WOb1g+2a/2zjDnd/RQAAACApqHJn3MLAAAAlCPcAgAAwDEItwAAAHAMwi0AAAAcg3DbwF544QV16NBBzZs318CBA/XVV1/5e0pNyuzZs9W/f3+1atVKUVFRuvbaa5Wamuoz5pJLLrF/v7z8cfvtt/tpxo3fI488UmF7devWzX69sLBQkydPVkREhFq2bKmRI0dW+NEUVNShQ4cK29WyLE2ePFkS+2l1rF27VldddZViY2NlWZbeeecdn9eNMXr44YfVtm1bBQUFKTk5WTt27PAZc+jQIY0aNUohISEKCwvThAkTlJeX14BdND6n2q4lJSW6//771bNnTwUHBys2NlZjxoxRRkaGzzIq27//8pe/NHAnjcfp9tVx48ZV2F5Dhw71GcO+WncItw3o9ddf19SpUzVjxgz95z//Ue/evTVkyBDt37/f31NrMtasWaPJkyfriy++0PLly1VSUqLBgwcrPz/fZ9ytt96qzMxM+/HEE0/4acZNwznnnOOzvT777DP7tbvvvlvvv/++lixZojVr1igjI0MjRozw42ybhq+//tpnmy5fvlyS9D//8z/2GPbTU8vPz1fv3r31wgsvVPr6E088oeeee07z58/Xl19+qeDgYA0ZMkSFhYX2mFGjRum7777T8uXL9cEHH2jt2rWaOHFiQ7XQKJ1quxYUFOg///mPHnroIf3nP//RW2+9pdTUVF199dUVxs6aNctn//3973/fENNvlE63r0rS0KFDfbbXv/71L5/X2VfrkEGDGTBggJk8ebL9vKyszMTGxprZs2f7cVZN2/79+40ks2bNGrt28cUXmzvvvNN/k2piZsyYYXr37l3pazk5OaZZs2ZmyZIldm3r1q1Gklm/fn0DzdAZ7rzzTtOpUyfj9XqNMeynNSXJvP322/Zzr9drYmJizJNPPmnXcnJyTGBgoPnXv/5ljDHm+++/N5LM119/bY/5+OOPjWVZ5qeffmqwuTdmJ2/Xynz11VdGktmzZ49di4+PN08//XT9Tq6Jqmybjh071lxzzTVVvod9tW5x5LaBFBcXa+PGjUpOTrZrLpdLycnJWr9+vR9n1rTl5uZKksLDw33qr776qiIjI9WjRw9Nnz5dBQUF/phek7Fjxw7FxsaqY8eOGjVqlNLT0yVJGzduVElJic9+261bN7Vv3579tgaKi4u1aNEi/fa3v5VlWXad/bT20tLSlJWV5bNvhoaGauDAgfa+uX79eoWFhalfv372mOTkZLlcLn355ZcNPuemKjc3V5ZlKSwszKf+l7/8RRERETr33HP15JNPqrS01D8TbCI+/fRTRUVFqWvXrpo0aZIOHjxov8a+Wrcc8QtlTUF2drbKysoq/GpadHS0tm3b5qdZNW1er1d33XWXzj//fPXo0cOu/+Y3v1F8fLxiY2O1efNm3X///UpNTdVbb73lx9k2XgMHDtTChQvVtWtXZWZmaubMmbrwwgu1ZcsWZWVlyePxVPhHLTo6WllZWf6ZcBP0zjvvKCcnR+PGjbNr7Kdnpnz/q+zv1PLXsrKyFBUV5fN6QECAwsPD2X+rqbCwUPfff79uvvlmhYSE2PU//OEP+tWvfqXw8HB9/vnnmj59ujIzMzV37lw/zrbxGjp0qEaMGKGEhATt2rVLDzzwgIYNG6b169fL7Xazr9Yxwi2arMmTJ2vLli0+54dK8jlHqWfPnmrbtq0GDRqkXbt2qVOnTg09zUZv2LBh9n/36tVLAwcOVHx8vN544w0FBQX5cWbO8dJLL2nYsGGKjY21a+ynaOxKSkp0ww03yBijefPm+bw2depU+7979eolj8ej2267TbNnz+ZnZStx00032f/ds2dP9erVS506ddKnn36qQYMG+XFmzsRpCQ0kMjJSbre7wlXm+/btU0xMjJ9m1XRNmTJFH3zwgVavXq24uLhTjh04cKAkaefOnQ0xtSYvLCxMZ599tnbu3KmYmBgVFxcrJyfHZwz7bfXt2bNHK1as0O9+97tTjmM/rZny/e9Uf6fGxMRUuGC3tLRUhw4dYv89jfJgu2fPHi1fvtznqG1lBg4cqNLSUv3www8NM8EmrmPHjoqMjLT/vLOv1i3CbQPxeDzq27evVq5cade8Xq9WrlyppKQkP86saTHGaMqUKXr77be1atUqJSQknPY9mzZtkiS1bdu2nmfnDHl5edq1a5fatm2rvn37qlmzZj77bWpqqtLT09lvq2nBggWKiorS8OHDTzmO/bRmEhISFBMT47NvHjlyRF9++aW9byYlJSknJ0cbN260x6xatUper9f+MoGKyoPtjh07tGLFCkVERJz2PZs2bZLL5arwf62jcnv37tXBgwftP+/sq3XM31e0/ZK89tprJjAw0CxcuNB8//33ZuLEiSYsLMxkZWX5e2pNxqRJk0xoaKj59NNPTWZmpv0oKCgwxhizc+dOM2vWLLNhwwaTlpZm3n33XdOxY0dz0UUX+Xnmjdc999xjPv30U5OWlmbWrVtnkpOTTWRkpNm/f78xxpjbb7/dtG/f3qxatcps2LDBJCUlmaSkJD/PumkoKysz7du3N/fff79Pnf20eo4ePWpSUlJMSkqKkWTmzp1rUlJS7Kv2//KXv5iwsDDz7rvvms2bN5trrrnGJCQkmGPHjtnLGDp0qDn33HPNl19+aT777DPTpUsXc/PNN/urpUbhVNu1uLjYXH311SYuLs5s2rTJ5+/ZoqIiY4wxn3/+uXn66afNpk2bzK5du8yiRYtMmzZtzJgxY/zcmf+capsePXrU3HvvvWb9+vUmLS3NrFixwvzqV78yXbp0MYWFhfYy2FfrDuG2gT3//POmffv2xuPxmAEDBpgvvvjC31NqUiRV+liwYIExxpj09HRz0UUXmfDwcBMYGGg6d+5spk2bZnJzc/078UbsxhtvNG3btjUej8ecddZZ5sYbbzQ7d+60Xz927Ji54447TOvWrU2LFi3MddddZzIzM/0446Zj2bJlRpJJTU31qbOfVs/q1asr/fM+duxYY8zx24E99NBDJjo62gQGBppBgwZV2NYHDx40N998s2nZsqUJCQkx48ePN0ePHvVDN43HqbZrWlpalX/Prl692hhjzMaNG83AgQNNaGioad68uUlMTDSPP/64T1D7pTnVNi0oKDCDBw82bdq0Mc2aNTPx8fHm1ltvrXBgi3217ljGGNMAB4gBAACAesc5twAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0AAAAcg3ALAAAAxyDcAgAAwDEItwAAAHAMwi0ANHLjxo2TZVkVHjt37vT31ACg0Qnw9wQAAKc3dOhQLViwwKfWpk2bGi2jrKxMlmXJ5eK4BgDn4m84AGgCAgMDFRMT4/N49tln1bNnTwUHB6tdu3a64447lJeXZ79n4cKFCgsL03vvvafu3bsrMDBQ6enpKioq0r333quzzjpLwcHBGjhwoD799FP/NQcAdYhwCwBNlMvl0nPPPafvvvtOL7/8slatWqX77rvPZ0xBQYHmzJmjv//97/ruu+8UFRWlKVOmaP369Xrttde0efNm/c///I+GDh2qHTt2+KkTAKg7ljHG+HsSAICqjRs3TosWLVLz5s3t2rBhw7RkyRKfcW+++aZuv/12ZWdnSzp+5Hb8+PHatGmTevfuLUlKT09Xx44dlZ6ertjYWPu9ycnJGjBggB5//PEG6AgA6g/n3AJAE3DppZdq3rx59vPg4GCtWLFCs2fP1rZt23TkyBGVlpaqsLBQBQUFatGihSTJ4/GoV69e9vu+/fZblZWV6eyzz/ZZflFRkSIiIhqmGQCoR4RbAGgCgoOD1blzZ/v5Dz/8oCuvvFKTJk3Sn//8Z4WHh+uzzz7ThAkTVFxcbIfboKAgWZZlvy8vL09ut1sbN26U2+32WUfLli0bphkAqEeEWwBogjZu3Civ16unnnrKvvvBG2+8cdr3nXvuuSorK9P+/ft14YUX1vc0AaDBcUEZADRBnTt3VklJiZ5//nnt3r1b//znPzV//vzTvu/ss8/WqFGjNGbMGL311ltKS0vTV199pdmzZ+vDDz9sgJkDQP0i3AJAE9S7d2/NnTtXc+bMUY8ePfTqq69q9uzZ1XrvggULNGbMGN1zzz3q2rWrrr32Wn399ddq3759Pc8aAOofd0sAAACAY3DkFgAAAI5BuAUAAIBjEG4BAADgGIRbAAAAOAbhFgAAAI5BuAUAAIBjEG4BAADgGIRbAAAAOAbhFgAAAI5BuAUAAIBjEG4BAADgGP8fcUoi5Ycu5UkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import format_number\n",
        "\n",
        "# Clean the \"Fare\" column to have two decimal places\n",
        "df = df.withColumn(\"Fare\", format_number(col(\"Fare\"), 2))\n",
        "\n",
        "# Show the cleaned \"Fare\" column\n",
        "df.select(\"Fare\").show()"
      ],
      "metadata": {
        "id": "iVvJms5RogqX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47fc53f1-a2e9-49dc-ec74-464461ff1cdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+\n",
            "| Fare|\n",
            "+-----+\n",
            "| 7.25|\n",
            "|71.28|\n",
            "| 7.92|\n",
            "|53.10|\n",
            "| 8.05|\n",
            "| 8.46|\n",
            "|51.86|\n",
            "|21.07|\n",
            "|11.13|\n",
            "|30.07|\n",
            "|16.70|\n",
            "|26.55|\n",
            "| 8.05|\n",
            "|31.27|\n",
            "| 7.85|\n",
            "|16.00|\n",
            "|29.12|\n",
            "|13.00|\n",
            "|18.00|\n",
            "| 7.22|\n",
            "+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import col\n",
        "\n",
        "# Convert \"Fare\" column from string to float\n",
        "df = df.withColumn(\"Fare\", col(\"Fare\").cast(\"float\"))"
      ],
      "metadata": {
        "id": "o_GzXUqUFMNA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "931155b5-942f-48db-bdb7-8ea7f008b5e1",
          "showTitle": false,
          "title": ""
        },
        "id": "osUgXu9KJR5d"
      },
      "source": [
        "## 3) Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "fcca8305-54d3-405a-a073-6d67c61bb036",
          "showTitle": false,
          "title": ""
        },
        "id": "SJFlR3pEJR5d"
      },
      "source": [
        "### 3.1) Feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "9b25976d-8c08-4fc6-8749-608dad848de0",
          "showTitle": false,
          "title": ""
        },
        "id": "YzbnayQoJR5d"
      },
      "source": [
        "#### 3.1.1) AgeCategory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "828d390f-d1c5-4b8a-a3da-a58cb7d40ace",
          "showTitle": false,
          "title": ""
        },
        "id": "oJDAdD0DJR5d"
      },
      "outputs": [],
      "source": [
        "df = df.withColumn(\"AgeCategory\",\n",
        "    F.when(df['Age'] <= 17, 0)\n",
        "    .when((df['Age'] > 17) & (df['Age'] <= 32), 1)\n",
        "    .when((df['Age'] > 32) & (df['Age'] <= 48), 2)\n",
        "    .when((df['Age'] > 48) & (df['Age'] <= 64), 3)\n",
        "    .otherwise(4)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "45a56e25-c0ee-4e97-9f0c-1561328362bd",
          "showTitle": false,
          "title": ""
        },
        "id": "EZSFQPzTJR5d"
      },
      "source": [
        "#### 3.1.2) Sex\n",
        "Convert categorical features into numerical\n",
        "- Sex:\n",
        "  - male = 1,\n",
        "  - female = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "9d3a272e-9f4d-412e-b2fb-28967969bcf5",
          "showTitle": false,
          "title": ""
        },
        "id": "oRKpv7a9JR5d"
      },
      "outputs": [],
      "source": [
        "df = df.withColumn(\"Sex\", when(df[\"Sex\"] == \"male\", 1).otherwise(2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "b5ffe7da-c350-47de-b92b-618e550917ca",
          "showTitle": false,
          "title": ""
        },
        "id": "IO9romL6JR5d"
      },
      "source": [
        "#### 3.1.3) Embarked\n",
        "Convert categorical features into numerical\n",
        "- Embarked:\n",
        "  - C = 1,\n",
        "  - Q = 2,\n",
        "  - S = 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "d2c1257d-90b4-4783-b4fe-816238148fb8",
          "showTitle": false,
          "title": ""
        },
        "id": "5ePCPhihJR5e"
      },
      "outputs": [],
      "source": [
        "df = df.withColumn(\"Embarked\", when(df[\"Embarked\"] == \"C\", 1).when(df[\"Embarked\"] == \"Q\", 2).otherwise(3))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1.4) Final dataset"
      ],
      "metadata": {
        "id": "75-vEWdZFVe1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "cc86d6e7-6722-4115-9ded-09007b3e68ec",
          "showTitle": false,
          "title": ""
        },
        "id": "r0WALEwVJR5e"
      },
      "outputs": [],
      "source": [
        "df=df.drop('PassengerId','Name','Ticket','Title','Age')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5XU61Zk_sEe",
        "outputId": "f7106879-2e18-43cd-f6a1-823dc6032c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[Sex: int, Pclass: int, Survived: int, SibSp: int, Parch: int, Fare: float, Cabin: int, Embarked: int, AgeCategory: int]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "beec9ca7-a272-4104-853b-a50535c474c4",
          "showTitle": false,
          "title": ""
        },
        "id": "ZfYKDU9IJR5e"
      },
      "source": [
        "Let's see how it looks!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "d68776fa-816b-4534-a474-acde4da1ad38",
          "showTitle": false,
          "title": ""
        },
        "id": "fd9JBChwJR5e",
        "outputId": "5c380707-fb91-4dc6-e7aa-7285510d8e7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- Sex: integer (nullable = false)\n",
            " |-- Pclass: integer (nullable = true)\n",
            " |-- Survived: integer (nullable = true)\n",
            " |-- SibSp: integer (nullable = true)\n",
            " |-- Parch: integer (nullable = true)\n",
            " |-- Fare: float (nullable = true)\n",
            " |-- Cabin: integer (nullable = false)\n",
            " |-- Embarked: integer (nullable = false)\n",
            " |-- AgeCategory: integer (nullable = false)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.printSchema()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "f63e6f08-0a03-4129-b1cb-1254a1c638d2",
          "showTitle": false,
          "title": ""
        },
        "id": "BQLCw2DmJR5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c47f69c-2a30-4326-ed1f-42dc1c8b539f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+------+--------+-----+-----+-----+-----+--------+-----------+\n",
            "|Sex|Pclass|Survived|SibSp|Parch| Fare|Cabin|Embarked|AgeCategory|\n",
            "+---+------+--------+-----+-----+-----+-----+--------+-----------+\n",
            "|  1|     3|       0|    1|    0| 7.25|    0|       3|          1|\n",
            "|  2|     1|       1|    1|    0|71.28|    1|       1|          2|\n",
            "|  2|     3|       1|    0|    0| 7.92|    0|       3|          1|\n",
            "|  2|     1|       1|    1|    0| 53.1|    1|       3|          2|\n",
            "|  1|     3|       0|    0|    0| 8.05|    0|       3|          2|\n",
            "|  1|     3|       0|    0|    0| 8.46|    0|       2|          1|\n",
            "|  1|     1|       0|    0|    0|51.86|    1|       3|          3|\n",
            "|  1|     3|       0|    3|    1|21.07|    0|       3|          0|\n",
            "|  2|     3|       1|    0|    2|11.13|    0|       3|          1|\n",
            "|  2|     2|       1|    1|    0|30.07|    0|       1|          0|\n",
            "|  2|     3|       1|    1|    1| 16.7|    1|       3|          0|\n",
            "|  2|     1|       1|    0|    0|26.55|    1|       3|          3|\n",
            "|  1|     3|       0|    0|    0| 8.05|    0|       3|          1|\n",
            "|  1|     3|       0|    1|    5|31.27|    0|       3|          2|\n",
            "|  2|     3|       0|    0|    0| 7.85|    0|       3|          0|\n",
            "|  2|     2|       1|    0|    0| 16.0|    0|       3|          3|\n",
            "|  1|     3|       0|    4|    1|29.12|    0|       2|          0|\n",
            "|  1|     2|       1|    0|    0| 13.0|    0|       3|          1|\n",
            "|  2|     3|       0|    1|    0| 18.0|    0|       3|          1|\n",
            "|  2|     3|       1|    0|    0| 7.22|    0|       1|          2|\n",
            "+---+------+--------+-----+-----+-----+-----+--------+-----------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "df.show(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "cfee7e5c-46cc-4b90-b80b-f866276f7da2",
          "showTitle": false,
          "title": ""
        },
        "id": "hGUTiZiQJR5e"
      },
      "source": [
        "### 3.2) Models\n",
        "Let's transform the features into a vector format using PySpark's VectorAssembler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "09846497-b0a2-4e95-a3b9-622b2421d595",
          "showTitle": false,
          "title": ""
        },
        "id": "oclllYTXJR5e"
      },
      "outputs": [],
      "source": [
        "# PySPark Libraries\n",
        "from pyspark.mllib.linalg import Vectors\n",
        "from pyspark.ml.feature import VectorAssembler\n",
        "from pyspark.ml.feature import StringIndexer\n",
        "from pyspark.ml import Pipeline\n",
        "from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
        "from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
        "from pyspark.ml.classification import LogisticRegression\n",
        "from pyspark.ml.classification import LinearSVC\n",
        "from pyspark.ml.classification import DecisionTreeClassifier\n",
        "from pyspark.ml.classification import RandomForestClassifier\n",
        "from pyspark.ml.classification import MultilayerPerceptronClassifier\n",
        "from pyspark.ml.evaluation import MulticlassClassificationEvaluator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "f3d26830-ce04-4419-8474-8a0ad4684d26",
          "showTitle": false,
          "title": ""
        },
        "id": "wdjaXcfzJR5e"
      },
      "outputs": [],
      "source": [
        "assembler = VectorAssembler(\n",
        "  inputCols = ['Pclass', 'Sex','SibSp','Fare', 'Parch' ,'Embarked', 'Cabin','AgeCategory'],\n",
        "  outputCol=\"features\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "83e90d10-6307-4dec-9d4d-d59011e2b0f7",
          "showTitle": false,
          "title": ""
        },
        "id": "T2Esj-NMJR5e"
      },
      "outputs": [],
      "source": [
        "test_size = 0.2\n",
        "test_seed = 2024\n",
        "train_data, test_data = df.randomSplit([1-test_size,test_size],seed=test_seed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "6ecab4d5-7290-4320-acac-a649e5871db0",
          "showTitle": false,
          "title": ""
        },
        "id": "SWUa7xQDJR5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4dbb09f0-4e98-4e6d-ea5d-85885ea433ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\ntrain_data.printSchema()\\ntrain_data.show()\\ntrain_data.describe().show()\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 177
        }
      ],
      "source": [
        "'''\n",
        "train_data.printSchema()\n",
        "train_data.show()\n",
        "train_data.describe().show()\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "bfbcaa6a-8f21-4782-9971-8eacb3ce2497",
          "showTitle": false,
          "title": ""
        },
        "id": "VRuAHGNGJR5e"
      },
      "outputs": [],
      "source": [
        "pipeline1 = Pipeline(stages=[assembler])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "c36d3b5e-9b0c-4027-a129-54b9172bfbdc",
          "showTitle": false,
          "title": ""
        },
        "id": "u22Gxd6uJR5e"
      },
      "outputs": [],
      "source": [
        "evaluator = BinaryClassificationEvaluator(labelCol=\"Survived\")\n",
        "\n",
        "def getCrossValidator(estimator, params, folds):\n",
        "  return CrossValidator(\n",
        "      estimator=estimator,\n",
        "      estimatorParamMaps=params,\n",
        "      evaluator=evaluator,\n",
        "      numFolds=folds,\n",
        "      seed=101\n",
        "  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "bd13ad50-c1a3-4cb0-909f-245da6308bf0",
          "showTitle": false,
          "title": ""
        },
        "id": "azoALtZEJR5f"
      },
      "source": [
        "#### 3.3.1) Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "e734d1eb-1526-4ae0-9ba3-a2f10b3bf192",
          "showTitle": false,
          "title": ""
        },
        "id": "18hFUarAJR5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cce1cde-0e6a-4d38-f409-4fb8814d32eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best regularization parameter (regParam): 0.1\n",
            "Best elastic net parameter (elasticNetParam): 0.01\n"
          ]
        }
      ],
      "source": [
        "lr = LogisticRegression(\n",
        "    featuresCol=\"features\",\n",
        "    labelCol=\"Survived\"\n",
        ")\n",
        "\n",
        "lr_hp_grid = ParamGridBuilder()\\\n",
        "    .addGrid(lr.regParam, [0.1, 0.01])\\\n",
        "    .addGrid(lr.elasticNetParam, [0.01 ,0.1, 0.5])\\\n",
        "    .build()\n",
        "\n",
        "pipeline_lr = Pipeline(stages=[pipeline1, getCrossValidator(lr, lr_hp_grid, 3)])\n",
        "\n",
        "model_lr = pipeline_lr.fit(train_data)\n",
        "\n",
        "# best model\n",
        "best_lr_model = model_lr.stages[-1].bestModel\n",
        "print(\"Best regularization parameter (regParam):\", best_lr_model._java_obj.getRegParam())\n",
        "print(\"Best elastic net parameter (elasticNetParam):\", best_lr_model._java_obj.getElasticNetParam())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "58cb316b-0999-40ee-9707-1be886f05b47",
          "showTitle": false,
          "title": ""
        },
        "id": "I5ENGxKZJR5f"
      },
      "outputs": [],
      "source": [
        "# lr with best parameters\n",
        "best_lr_model = LogisticRegression(\n",
        "    featuresCol=\"features\",\n",
        "    labelCol=\"Survived\",\n",
        "    regParam=best_lr_model._java_obj.getRegParam(),\n",
        "    elasticNetParam=best_lr_model._java_obj.getElasticNetParam()\n",
        ")\n",
        "\n",
        "pipeline_best_lr = Pipeline(stages=[pipeline1, best_lr_model])\n",
        "\n",
        "model1 = pipeline_best_lr.fit(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "bd13ad50-c1a3-4cb0-909f-245da6308bf0",
          "showTitle": false,
          "title": ""
        },
        "id": "kEQu1NcuRCS7"
      },
      "source": [
        "#### 3.3.2) Linear SVC"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "# increments spark memory\n",
        "from pyspark.sql import SparkSession\n",
        "\n",
        "# Imposta la configurazione Spark per la memoria del driver\n",
        "spark = SparkSession.builder \\\n",
        "    .appName(\"MyApp\") \\\n",
        "    .config(\"spark.driver.memory\", \"4g\") \\\n",
        "    .getOrCreate()\n",
        "'''"
      ],
      "metadata": {
        "id": "gb4l6c8wD8G2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "e734d1eb-1526-4ae0-9ba3-a2f10b3bf192",
          "showTitle": false,
          "title": ""
        },
        "id": "FyA5bXlNRCTI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "outputId": "f54a9e92-1250-4518-e43b-71a1951fccff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best max iteration: 5\n",
            "Best regularization parameter: 0.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nbest_params = best_lsvc_model.stages[-1].extractParamMap()\\nprint(\"Best Parameters:\")\\nfor param, value in best_params.items():\\n    print(param.name, \":\", value)\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 199
        }
      ],
      "source": [
        "lsvc = LinearSVC(\n",
        "    featuresCol=\"features\",\n",
        "    labelCol=\"Survived\"\n",
        ")\n",
        "\n",
        "lsvc_hp_grid = ParamGridBuilder()\\\n",
        "    .addGrid(lsvc.maxIter, [5, 25, 50])\\\n",
        "    .addGrid(lsvc.regParam, [0.1, 0.3, 1.6])\\\n",
        "    .addGrid(lsvc.fitIntercept, [True, False])\\\n",
        "    .build()\n",
        "\n",
        "pipeline_lsvc = Pipeline(stages=[pipeline1, getCrossValidator(lsvc, lsvc_hp_grid, 3)])\n",
        "\n",
        "model_lsvc = pipeline_lsvc.fit(train_data)\n",
        "\n",
        "# best model\n",
        "best_lsvc_model = model_lsvc.stages[-1].bestModel\n",
        "print(\"Best max iteration:\", best_lsvc_model.getMaxIter())\n",
        "print(\"Best regularization parameter:\", best_lsvc_model.getRegParam())\n",
        "\n",
        "'''\n",
        "best_params = best_lsvc_model.stages[-1].extractParamMap()\n",
        "print(\"Best Parameters:\")\n",
        "for param, value in best_params.items():\n",
        "    print(param.name, \":\", value)\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lsvc with best parameters\n",
        "best_pipeline_lsvc = Pipeline(stages=[pipeline1, best_lsvc_model])\n",
        "model2 = best_pipeline_lsvc.fit(train_data)"
      ],
      "metadata": {
        "id": "CmglOeYsaS4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "58cb316b-0999-40ee-9707-1be886f05b47",
          "showTitle": false,
          "title": ""
        },
        "id": "mk-9ZPhHRCTI"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "best_lsvc_model = LinearSVC(\n",
        "    featuresCol=\"features\",\n",
        "    labelCol=\"Survived\",\n",
        "    maxIter=best_lsvc_model._java_obj.getMaxIterParam()\n",
        "    regParam=best_lsvc_model._java_obj.getRegParam()\n",
        ")\n",
        "\n",
        "pipeline_best_lsvc = Pipeline(stages=[pipeline1, best_lsvc_model])\n",
        "\n",
        "model2 = pipeline_best_lsvc.fit(train_data)\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "07da538c-d4ce-468f-b493-fe3555eb277b",
          "showTitle": false,
          "title": ""
        },
        "id": "ZkFRykixJR5f"
      },
      "source": [
        "#### 3.3.3) Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "ec9c32d2-abb3-4462-b4d1-050a5da23ad2",
          "showTitle": false,
          "title": ""
        },
        "id": "-IFAUKc6JR5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b2a20ae-bdaa-4b10-a17b-4e6c056d7bd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best max depth: 15\n",
            "Best max bins: 64\n",
            "Best min instances per node: 1\n",
            "Best impurity: gini\n"
          ]
        }
      ],
      "source": [
        "dt = DecisionTreeClassifier(labelCol=\"Survived\")\n",
        "\n",
        "dt_hp_grid = ParamGridBuilder() \\\n",
        "    .addGrid(dt.maxDepth, [5, 10, 15]) \\\n",
        "    .addGrid(dt.maxBins, [32, 64]) \\\n",
        "    .addGrid(dt.minInstancesPerNode, [1, 5, 10]) \\\n",
        "    .addGrid(dt.impurity, ['gini', 'entropy']) \\\n",
        "    .build()\n",
        "\n",
        "pipeline_dt = Pipeline(stages=[pipeline1, getCrossValidator(dt, dt_hp_grid, 3)])\n",
        "\n",
        "model_dt = pipeline_dt.fit(train_data)\n",
        "\n",
        "# best model\n",
        "best_dt_model = model_dt.stages[-1].bestModel\n",
        "print(\"Best max depth:\", best_dt_model.getMaxDepth())\n",
        "print(\"Best max bins:\", best_dt_model.getMaxBins())\n",
        "print(\"Best min instances per node:\", best_dt_model.getMinInstancesPerNode())\n",
        "print(\"Best impurity:\", best_dt_model.getImpurity())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "ca452de8-59a9-42ed-9dc3-feb44475b742",
          "showTitle": false,
          "title": ""
        },
        "id": "zY1s277tJR5f"
      },
      "outputs": [],
      "source": [
        "best_pipeline_dt = Pipeline(stages=[pipeline1, best_dt_model])\n",
        "model3 = best_pipeline_dt.fit(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "07da538c-d4ce-468f-b493-fe3555eb277b",
          "showTitle": false,
          "title": ""
        },
        "id": "YPCqmd_-Rjxb"
      },
      "source": [
        "#### 3.3.4) Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "ec9c32d2-abb3-4462-b4d1-050a5da23ad2",
          "showTitle": false,
          "title": ""
        },
        "id": "q03HL-BGRjxk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "738a3f8f-f0f0-460c-cc07-f84d4b6b64dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best number of trees: 50\n",
            "Best max depth: 30\n"
          ]
        }
      ],
      "source": [
        "rf = RandomForestClassifier(\n",
        "    labelCol=\"Survived\"\n",
        ")\n",
        "\n",
        "rf_hp_grid = ParamGridBuilder() \\\n",
        "    .addGrid(rf.numTrees, [50]) \\\n",
        "    .addGrid(rf.maxDepth, [30]) \\\n",
        "    .addGrid(rf.minInstancesPerNode, [1, 5]) \\\n",
        "    .addGrid(rf.maxBins, [64]) \\\n",
        "    .addGrid(rf.minInfoGain, [0.0, 0.1]) \\\n",
        "    .build()\n",
        "\n",
        "pipeline_rf = Pipeline(stages=[pipeline1, getCrossValidator(rf, rf_hp_grid, 5)])\n",
        "\n",
        "model_rf = pipeline_rf.fit(train_data)\n",
        "\n",
        "# best model\n",
        "best_rf_model = model_rf.stages[-1].bestModel\n",
        "print(\"Best number of trees:\", best_rf_model.getNumTrees)\n",
        "print(\"Best max depth:\", best_rf_model.getMaxDepth())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "ca452de8-59a9-42ed-9dc3-feb44475b742",
          "showTitle": false,
          "title": ""
        },
        "id": "wSCC1k2pRjxl"
      },
      "outputs": [],
      "source": [
        "best_pipeline_rf = Pipeline(stages=[pipeline1, best_rf_model])\n",
        "model4 = best_pipeline_rf.fit(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "09e0c00c-cfa2-4021-9e18-7f5e2d74bdb3",
          "showTitle": false,
          "title": ""
        },
        "id": "xmBF99YKJR5f"
      },
      "source": [
        "#### 3.3.5) Multilayer Perceptron Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "5b4aec6e-1d14-4836-afcb-45606d692766",
          "showTitle": false,
          "title": ""
        },
        "id": "ZtJwujbPJR5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "462d9ed6-1766-4d2d-f402-61c3520e3c78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best layers: [8, 5, 5, 5, 2]\n",
            "Best max iteration: 200\n",
            "Best step size: 0.02\n",
            "Best blockSize: 128\n",
            "Best tol: 0.0001\n"
          ]
        }
      ],
      "source": [
        "mlp = MultilayerPerceptronClassifier(\n",
        "  labelCol=\"Survived\"\n",
        ")\n",
        "\n",
        "test_layer1 = [[8, 5, 2], [8, 5, 5, 2]]\n",
        "test_layer2 = [[8, 5, 5, 2], [8, 5, 5, 5, 2]]\n",
        "test_layer3 = [[8, 5, 5, 5, 2],[8, 6, 5, 4, 3, 2]]\n",
        "\n",
        "test_stepSize1 = [0.001, 0.03]\n",
        "test_stepSize2 = [0.02, 0.04]\n",
        "test_stepSize3 = [0.02, 0.025, 0.03, 0.035, 0.04]\n",
        "\n",
        "mlp_hp_grid = ParamGridBuilder() \\\n",
        "    .addGrid(mlp.layers, test_layer2) \\\n",
        "    .addGrid(mlp.stepSize, test_stepSize2) \\\n",
        "    .addGrid(mlp.maxIter, [100, 150, 200]) \\\n",
        "    .addGrid(mlp.blockSize, [64, 128]) \\\n",
        "    .addGrid(mlp.tol, [1e-4, 1e-6]) \\\n",
        "    .build()\n",
        "\n",
        "pipeline_mlp = Pipeline(stages=[pipeline1, getCrossValidator(mlp, mlp_hp_grid, 3)])\n",
        "\n",
        "model_mlp = pipeline_mlp.fit(train_data)\n",
        "\n",
        "# best model\n",
        "best_perc_model = model_mlp.stages[-1].bestModel\n",
        "\n",
        "print(\"Best layers:\", best_perc_model.getLayers())\n",
        "print(\"Best max iteration:\", best_perc_model.getMaxIter())\n",
        "print(\"Best step size:\", best_perc_model.getStepSize())\n",
        "print(\"Best blockSize:\", best_perc_model.getBlockSize())\n",
        "print(\"Best tol:\", best_perc_model.getTol())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "3fb996f7-bfc5-4032-973c-920a44f452d2",
          "showTitle": false,
          "title": ""
        },
        "id": "5qyFBIN2JR5f"
      },
      "outputs": [],
      "source": [
        "best_pipeline_perc = Pipeline(stages=[pipeline1, best_perc_model])\n",
        "model5 = best_pipeline_perc.fit(train_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "31da129e-6270-4dc0-ae88-3a53f1fda1da",
          "showTitle": false,
          "title": ""
        },
        "id": "RdfM0PykJR5g"
      },
      "source": [
        "### 3.4) Models comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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          },
          "inputWidgets": {},
          "nuid": "022b9073-8836-424f-acaa-1a675a1e41a7",
          "showTitle": false,
          "title": ""
        },
        "id": "DqGibDf4JR5g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8be9775c-b808-49df-8efd-937cb7d28640"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LR\n",
            "AUC (train set):  0.8583866441731108\n",
            "AUC (test set):  0.8582927489177488\n",
            "Accuracy:  0.8258426966292135\n",
            "Precision:  0.8244424786546178\n",
            "-----\n",
            "LSVC\n",
            "AUC (train set):  0.8591716915265002\n",
            "AUC (test set):  0.8532873376623373\n",
            "Accuracy:  0.8033707865168539\n",
            "Precision:  0.80126512729202\n",
            "-----\n",
            "RF\n",
            "AUC (train set):  0.921608010823632\n",
            "AUC (test set):  0.8602543290043294\n",
            "Accuracy:  0.8033707865168539\n",
            "Precision:  0.8010322025697543\n",
            "-----\n",
            "MLP\n",
            "AUC (train set):  0.8699034558786681\n",
            "AUC (test set):  0.8393533549783552\n",
            "Accuracy:  0.797752808988764\n",
            "Precision:  0.7966409176029963\n",
            "-----\n"
          ]
        }
      ],
      "source": [
        "evaluator = BinaryClassificationEvaluator(labelCol=\"Survived\")\n",
        "\n",
        "models = [model1, model2, model4, model5]\n",
        "model_names = [\"LR\", \"LSVC\", \"RF\", \"MLP\"]\n",
        "\n",
        "for i in range(len(models)):\n",
        "    predictionsTrain = models[i].transform(train_data)\n",
        "    auc_train = evaluator.evaluate(predictionsTrain)\n",
        "    print(model_names[i])\n",
        "    print(\"AUC (train set): \", auc_train)\n",
        "\n",
        "    predictionsTest = models[i].transform(test_data)\n",
        "    auc_test = evaluator.evaluate(predictionsTest)\n",
        "    print(\"AUC (test set): \", auc_test)\n",
        "\n",
        "    accuracy = MulticlassClassificationEvaluator(labelCol='Survived', metricName='accuracy').evaluate(predictionsTest)\n",
        "    precision = MulticlassClassificationEvaluator(labelCol='Survived', metricName='weightedPrecision').evaluate(predictionsTest)\n",
        "    print('Accuracy: ', accuracy)\n",
        "    print('Precision: ', precision)\n",
        "    print('-----')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "4097146a-bf2b-46c6-8124-d25df8dfc131",
          "showTitle": false,
          "title": ""
        },
        "id": "TOKw6PwWJR5g"
      },
      "source": [
        "Validate this = Random Forest resulted the best model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "fe00fd4e-1290-46ce-8909-4cf11ffff37a",
          "showTitle": false,
          "title": ""
        },
        "id": "MTUKvuqYJR5g"
      },
      "source": [
        "### 3.5) Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
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          "showTitle": false,
          "title": ""
        },
        "id": "grB7EBC8JR5g"
      },
      "outputs": [],
      "source": [
        "import itertools\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
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          },
          "inputWidgets": {},
          "nuid": "d5bd8a9b-b8fa-4697-b2d6-ab810f8cbca9",
          "showTitle": false,
          "title": ""
        },
        "id": "RV_7QMSNJR5g"
      },
      "outputs": [],
      "source": [
        "predictions = model4.transform(test_data) #JM FIX is the random forest still the best model?\n",
        "class_temp = predictions\\\n",
        "    .select(\"Survived\")\\\n",
        "    .groupBy(\"Survived\")\\\n",
        "    .count()\\\n",
        "    .sort('count', ascending=False)\\\n",
        "    .toPandas()\n",
        "\n",
        "class_temp = class_temp[\"Survived\"].values.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "721ddb12-0f03-45b8-b2bd-f60756329cd2",
          "showTitle": false,
          "title": ""
        },
        "id": "CzPy8LGYJR5g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e53632d4-c560-4516-bf94-0e33a0c5b21e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- Sex: integer (nullable = false)\n",
            " |-- Pclass: integer (nullable = true)\n",
            " |-- Survived: integer (nullable = true)\n",
            " |-- SibSp: integer (nullable = true)\n",
            " |-- Parch: integer (nullable = true)\n",
            " |-- Fare: float (nullable = true)\n",
            " |-- Cabin: integer (nullable = false)\n",
            " |-- Embarked: integer (nullable = false)\n",
            " |-- AgeCategory: integer (nullable = false)\n",
            " |-- features: vector (nullable = true)\n",
            " |-- rawPrediction: vector (nullable = true)\n",
            " |-- probability: vector (nullable = true)\n",
            " |-- prediction: double (nullable = false)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "predictions.printSchema()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluation for predictions"
      ],
      "metadata": {
        "id": "HR6EZeVpIX2F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: add code for evaluation"
      ],
      "metadata": {
        "id": "1inp6BwpIRYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
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          },
          "inputWidgets": {},
          "nuid": "8836e378-0d50-4ca2-a407-97dcba2b5958",
          "showTitle": false,
          "title": ""
        },
        "id": "jlDpgUszJR5g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "085e563f-8478-4104-d7cb-d86911d7ef60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[99, 13],\n",
              "       [22, 44]])"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_true = predictions.select(\"Survived\")\n",
        "y_true = y_true.toPandas()\n",
        "\n",
        "y_pred = predictions.select(\"prediction\")\n",
        "y_pred = y_pred.toPandas()\n",
        "\n",
        "cnf_matrix = confusion_matrix(y_true, y_pred)\n",
        "cnf_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
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          },
          "inputWidgets": {},
          "nuid": "e65dbd07-060e-415e-a947-abd043f7f049",
          "showTitle": false,
          "title": ""
        },
        "id": "t6zh8hNqJR5g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "0e770fd4-0768-4a71-9574-1e3eb7ce8d6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[99 13]\n",
            " [22 44]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(\n",
        "    cnf_matrix,\n",
        "    classes=[\"0\", \"1\"],\n",
        "    title='Confusion matrix, without normalization'\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "3b863c85-ee2b-4d65-872f-6e2d082db229",
          "showTitle": false,
          "title": ""
        },
        "id": "YMJD7Y05JR5g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "0177bced-f5a9-4687-b472-d89f37f463bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normalized confusion matrix\n",
            "[[0.88392857 0.11607143]\n",
            " [0.33333333 0.66666667]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(\n",
        "    cnf_matrix,\n",
        "    classes=[\"0\", \"1\"],\n",
        "    normalize=True,\n",
        "    title='Normalized confusion matrix'\n",
        ")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "cellMetadata": {
            "byteLimit": 2048000,
            "rowLimit": 10000
          },
          "inputWidgets": {},
          "nuid": "9179630a-f205-427c-8acf-1563e99f841b",
          "showTitle": false,
          "title": ""
        },
        "id": "yR56g13xJR5g"
      },
      "source": [
        "## 4) Conclusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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