Binary_Classification.ipynb

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      "source": [
        "# CS-6600 Assignment #4 - Binary Classification\n",
        "\n",
        "**YOUR NAME HERE**\n",
        "\n",
        "*Weber State University*"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f71e8288",
      "metadata": {
        "id": "f71e8288"
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      "source": [
        "This assignment is a bit of a right of passage in the machine learning community. It's a binary classification problem based upon a famous historic event - [the sinking of the Titanic](https://en.wikipedia.org/wiki/Titanic).\n",
        "\n",
        "For reference, the RMS Titanic was a British passenger liner which sank in the North Atlantic Ocean on April 15th, 1912 after striking an iceberg during its maiden voyage from Southampton, England to New York City, United States. Of the estimated 2,224 passengers and crew aboard, more than 1,500 died, making it the deadliest sinking of a single ship up to that time, and it remains the deadliest sinking of an ocean liner or cruise ship.\n",
        "\n",
        "It was also notable because there were quite a few hours between when the ship struck the iceberg and when it went under, and most of those who survived did so by boarding lifeboats. In other words, there was a (noisy and nonuniform) process that determined who would be on the lifeboats and who would not, and this process mostly determined who survived.\n",
        "\n",
        "For this assignment, we're going to try to predict who survives based upon known passenger information.\n",
        "\n",
        "*Note* - Whether a passenger was on a floating door isn't one of the variables."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "25646506",
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        "id": "25646506"
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      "source": [
        "<center>\n",
        "  <img src=\"https://drive.google.com/uc?export=view&id=1Tb7l6nDAQog3imqv24znRQiMcUkm_dUY\" alt=\"Titanic Door\">\n",
        "</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97c41e48",
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        "id": "97c41e48"
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      "source": [
        "The variables we will have are:\n",
        "\n",
        "|*Variable* |*Description* |*Details* |\n",
        "|:----|:----|:----|\n",
        "|survival|Whether the passenger survived|0 = No, 1 = Yes|\n",
        "|pclass|The passenger class of the ticket|1 = 1st, 2 = 2nd, 3 = 3rd|\n",
        "|name| First and last name of the passenger||\n",
        "|sex|The gender of the passenger||\n",
        "|age|The age of the passenger||\n",
        "|sibsp|The number of siblings / spouses on the ship with the passenger||\n",
        "|parch|The number of parents / children on the ship with the passenger||\n",
        "|ticket|The ticket number||\n",
        "|fare|The cost of the ticket||\n",
        "|embarked|The port of embarcation for the passenger|C = Cherbourg, Q = Queenstown, S = Southampton|"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "abdbe859",
      "metadata": {
        "id": "abdbe859"
      },
      "source": [
        "Note that we're using a slightly modified and cleaned version of the commonly available Titanic dataset. Before we import the data, we'll first import our favorite libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "fd11543a",
      "metadata": {
        "id": "fd11543a"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And then, we'll grab some more."
      ],
      "metadata": {
        "id": "w1eId-UOGB7S"
      },
      "id": "w1eId-UOGB7S"
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns #Data visualization library based on matplotlib\n",
        "\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.tree import export_graphviz\n",
        "from graphviz import Source\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import roc_auc_score"
      ],
      "metadata": {
        "id": "PvcHqYy_GHBb"
      },
      "id": "PvcHqYy_GHBb",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's get the dataset. The code below should let you import if from my (Dylan's) Google Drive."
      ],
      "metadata": {
        "id": "dRSHGnrZGPo9"
      },
      "id": "dRSHGnrZGPo9"
    },
    {
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      "execution_count": 5,
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          "height": 357
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        "outputId": "3c1a9074-06f1-499b-c6ce-6580bbff1e4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerID  Survived  Pclass  \\\n",
              "0          159         0       3   \n",
              "1          344         0       2   \n",
              "2          224         0       3   \n",
              "3          532         0       3   \n",
              "4          760         1       1   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                                Smiljanic, Mr. Mile    male  25.0      0   \n",
              "1         Sedgwick, Mr. Charles Frederick Waddington    male  25.0      0   \n",
              "2                               Nenkoff, Mr. Christo    male  25.0      0   \n",
              "3                                  Toufik, Mr. Nakli    male  25.0      0   \n",
              "4  Rothes, the Countess. of (Lucy Noel Martha Dye...  female  33.0      0   \n",
              "\n",
              "   Parch  Ticket     Fare Embarked  \n",
              "0      0  315037   8.6625        S  \n",
              "1      0  244361  13.0000        S  \n",
              "2      0  349234   7.8958        S  \n",
              "3      0    2641   7.2292        C  \n",
              "4      0  110152  86.5000        S  "
            ],
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              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 711,\n  \"fields\": [\n    {\n      \"column\": \"PassengerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 258,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 711,\n        \"samples\": [\n          729,\n          886,\n          690\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 711,\n        \"samples\": [\n          \"Bryhl, Mr. Kurt Arnold Gottfrid\",\n          \"Rice, Mrs. William (Margaret Norton)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.144695148979931,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          30.5,\n          25.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 567,\n        \"samples\": [\n          \"112277\",\n          \"C.A. 29178\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.61630945049645,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 228,\n        \"samples\": [\n          23.25,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#Import the Titanic data\n",
        "url = 'https://drive.google.com/uc?export=download&id=1Oytm0kGCmWsydZrRvCyK_cOE2WfnaVIA'\n",
        "titanic_col_names = ['PassengerID','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Embarked']\n",
        "titanic = pd.read_csv(url,header=0,names=titanic_col_names)\n",
        "titanic.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "165cfd21",
      "metadata": {
        "id": "165cfd21"
      },
      "source": [
        "I'll also import the seaborn data visualization library based on matplotlib, set a couple style parameters, and get rid of some annoying warnings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "edcc5172",
      "metadata": {
        "id": "edcc5172"
      },
      "outputs": [],
      "source": [
        "#import seaborn as sns #Data visualization library based on matplotlib\n",
        "\n",
        "#Set some style parameters for seaborn\n",
        "#sns.set(style=\"white\") #white background style for seaborn plots\n",
        "#sns.set(style=\"whitegrid\", color_codes=True)\n",
        "#Get rid of some annying warnings\n",
        "#import warnings\n",
        "#warnings.simplefilter(action='ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "deb4b8e0",
      "metadata": {
        "id": "deb4b8e0"
      },
      "source": [
        "Have you heard the phrase \"women and children first?\" Well, it relates to the [code of conduct](https://en.wikipedia.org/wiki/Women_and_children_first) that is supposed to be practiced in a life-threatening situation. Let's see if it's a decent predictor on the Titanic. In particulary, let's look at how survival percentage broke down by gender."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "80070e73",
      "metadata": {
        "id": "80070e73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "bfad0130-92a2-45c9-9139-76b8f6d31095"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Examine survival by gender\n",
        "sns.barplot(x='Sex',y='Survived', data=titanic)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c394bfc4",
      "metadata": {
        "id": "c394bfc4"
      },
      "source": [
        "Whoa! Looks like many more men went down with the ship. Now, let's take a look at survival rates based on age, and see how well the \"children\" aspect of that dictum holds up."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "d4c32bee",
      "metadata": {
        "id": "d4c32bee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "outputId": "14296d3d-8243-4683-f4e1-b027b09055b9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Survival by age histogram\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.histplot(titanic[\"Age\"][titanic.Survived == 1], label='Survived', color='blue', bins=17, kde=False)\n",
        "sns.histplot(titanic[\"Age\"][titanic.Survived == 0], label='Died', color='red', bins=17, kde=False)\n",
        "plt.legend(['Survived', 'Died'])\n",
        "plt.title('Histogram of Age for Surviving Population and Deceased Population')\n",
        "plt.xlabel('Age')\n",
        "plt.xlim(0,85)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d2863119",
      "metadata": {
        "id": "d2863119"
      },
      "source": [
        "OK, it looks like being a child did make one more likely to survive as well.\n",
        "\n",
        "Let's use these two characteristics - age and gender - to build a decision tree predictive model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "31ed9c0f",
      "metadata": {
        "id": "31ed9c0f"
      },
      "source": [
        "First, we're going to want to convert our gender data column into a numeric one. So, we'll create a dummy \"male\" variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "c194b973",
      "metadata": {
        "id": "c194b973",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "eab651ed-983c-4a50-c49c-ff884076e937"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerID  Survived  Pclass  \\\n",
              "0          159         0       3   \n",
              "1          344         0       2   \n",
              "2          224         0       3   \n",
              "3          532         0       3   \n",
              "4          760         1       1   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                                Smiljanic, Mr. Mile    male  25.0      0   \n",
              "1         Sedgwick, Mr. Charles Frederick Waddington    male  25.0      0   \n",
              "2                               Nenkoff, Mr. Christo    male  25.0      0   \n",
              "3                                  Toufik, Mr. Nakli    male  25.0      0   \n",
              "4  Rothes, the Countess. of (Lucy Noel Martha Dye...  female  33.0      0   \n",
              "\n",
              "   Parch  Ticket     Fare Embarked   male  \n",
              "0      0  315037   8.6625        S   True  \n",
              "1      0  244361  13.0000        S   True  \n",
              "2      0  349234   7.8958        S   True  \n",
              "3      0    2641   7.2292        C   True  \n",
              "4      0  110152  86.5000        S  False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a910889-77c3-405b-b7fc-6588cfb3fb26\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerID</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>159</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Smiljanic, Mr. Mile</td>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315037</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>344</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Sedgwick, Mr. Charles Frederick Waddington</td>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>244361</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>224</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Nenkoff, Mr. Christo</td>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>349234</td>\n",
              "      <td>7.8958</td>\n",
              "      <td>S</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>532</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Toufik, Mr. Nakli</td>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2641</td>\n",
              "      <td>7.2292</td>\n",
              "      <td>C</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>760</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Rothes, the Countess. of (Lucy Noel Martha Dye...</td>\n",
              "      <td>female</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>110152</td>\n",
              "      <td>86.5000</td>\n",
              "      <td>S</td>\n",
              "      <td>False</td>\n",
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              "        document.querySelector('#' + key + ' button');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "titanic",
              "summary": "{\n  \"name\": \"titanic\",\n  \"rows\": 711,\n  \"fields\": [\n    {\n      \"column\": \"PassengerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 258,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 711,\n        \"samples\": [\n          729,\n          886,\n          690\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 711,\n        \"samples\": [\n          \"Bryhl, Mr. Kurt Arnold Gottfrid\",\n          \"Rice, Mrs. William (Margaret Norton)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.144695148979931,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          30.5,\n          25.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 567,\n        \"samples\": [\n          \"112277\",\n          \"C.A. 29178\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48.61630945049645,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 228,\n        \"samples\": [\n          23.25,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#Create a dummy \"male\" variable\n",
        "#WARNING - If you run this twice, you'll create two distinct dummy variable columns\n",
        "gender = pd.get_dummies(titanic[\"Sex\"],drop_first=True)\n",
        "titanic = pd.concat([titanic,gender], axis='columns')\n",
        "titanic.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ad92def",
      "metadata": {
        "id": "7ad92def"
      },
      "source": [
        "Then we'll create a dependent and independent variable for the logistic regression model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "cf069732",
      "metadata": {
        "id": "cf069732"
      },
      "outputs": [],
      "source": [
        "#Create the dependent and independent variable for the logistic model\n",
        "X = titanic[[\"Age\",\"male\"]]\n",
        "y = titanic[\"Survived\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5c291ef",
      "metadata": {
        "id": "d5c291ef"
      },
      "source": [
        "We'll split our data into a training dataset and a test dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "f7edec8a",
      "metadata": {
        "id": "f7edec8a"
      },
      "outputs": [],
      "source": [
        "#Split the data into training data and test data, 75/25\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = .25,random_state = 1912)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f5c1d2df",
      "metadata": {
        "id": "f5c1d2df"
      },
      "source": [
        "Then, we'll build our decision tree predictive model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "2e904b4e",
      "metadata": {
        "id": "2e904b4e"
      },
      "outputs": [],
      "source": [
        "#Build the logistic model using the Fare variable\n",
        "tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
        "tree_clf.fit(X_train, y_train);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can graph visualize this decision tree with the code below."
      ],
      "metadata": {
        "id": "aKihZGQzHomX"
      },
      "id": "aKihZGQzHomX"
    },
    {
      "cell_type": "code",
      "source": [
        "export_graphviz(\n",
        "        tree_clf,\n",
        "        out_file=str(\"titanic_decision_tree.dot\"),\n",
        "        feature_names=[\"Age\", \"Gender\"],\n",
        "        rounded=True,\n",
        "        filled=True\n",
        "    )\n",
        "Source.from_file(\"titanic_decision_tree.dot\")"
      ],
      "metadata": {
        "id": "xXsyc3xF-IeL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "07cc43f8-8e13-46db-e5e8-a19b07b3709b"
      },
      "id": "xXsyc3xF-IeL",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<graphviz.sources.Source at 0x7e38100f5930>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "583e0577",
      "metadata": {
        "id": "583e0577"
      },
      "source": [
        "It looks like it first splits on gender, and then on age, but the age split is very different for the men and women."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we'll use this model to predict survival in the test dataset."
      ],
      "metadata": {
        "id": "E3jCuSCEH2df"
      },
      "id": "E3jCuSCEH2df"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "d8bd2ba3",
      "metadata": {
        "id": "d8bd2ba3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8bd925c-0c10-4948-f822-9de8f3d33ab8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[89, 17],\n",
              "       [18, 54]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "#Use the model to predict the y values in the test set.\n",
        "y_pred = tree_clf.predict(X_test[[\"Age\",\"male\"]])\n",
        "#Create the confusion matrix\n",
        "confusion_matrix(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a589505b",
      "metadata": {
        "id": "a589505b"
      },
      "source": [
        "We can check our accuracy, precision, and recall:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "bb0ecdfe",
      "metadata": {
        "id": "bb0ecdfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39228ae9-fce9-47d5-cf36-8e7298228b6f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8033707865168539\n",
            "Precision: 0.7605633802816901\n",
            "Recall: 0.75\n"
          ]
        }
      ],
      "source": [
        "#Check the accuracy, precision, and recall\n",
        "print(\"Accuracy:\",accuracy_score(y_test,y_pred))\n",
        "print(\"Precision:\",precision_score(y_test,y_pred))\n",
        "print(\"Recall:\",recall_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed142797",
      "metadata": {
        "id": "ed142797"
      },
      "source": [
        "Grab the probabilities for the ROC curve:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "6cba12f3",
      "metadata": {
        "id": "6cba12f3"
      },
      "outputs": [],
      "source": [
        "#Get the probabilities for the ROC curve\n",
        "pred_prob = tree_clf.predict_proba(X_test[[\"Age\",\"male\"]])\n",
        "fpr, tpr, thresh = roc_curve(y_test, pred_prob[:,1],pos_label=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f077da1c",
      "metadata": {
        "id": "f077da1c"
      },
      "source": [
        "Finally, graph the ROC curve and get the AUC score:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "bc89df53",
      "metadata": {
        "id": "bc89df53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "8da78649-f2b2-4cb0-c7eb-1e2b13a356d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8099449685534591"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#Graph the ROC curve and display the AUC score\n",
        "plt.plot(fpr,tpr)\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.show()\n",
        "roc_auc_score(y_test,pred_prob[:,1])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2df938a2",
      "metadata": {
        "id": "2df938a2"
      },
      "source": [
        "An AUC of .81 isn't great, but it's not terrible. For this assignment, you're going to see if you can do better.\n",
        "\n",
        "The rules are:\n",
        "\n",
        "- You need to use a logistic regression model.\n",
        "- You can only use tha data provided in the dataset, and obviously you can't use \"survival\" (the variable you want to predict) as a predictor.\n",
        "- You should build your model on the training data produced above, and test it (ROC curve and AUC score) on the test data produced above.\n",
        "\n",
        "Do some data exploration, try to figure out a good model, and prove the ROC curve and AUC score for the one you find. Please chart the ROC curve and provide the AUC score as above. Full credit will be awarded if you can find an AUC above .8, and a bonus will go to whoever in the class can produce the best model as measured by AUC score on the test dataset.\n",
        "\n",
        "*Hint* - It helps to be rich."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The assignment is this - do some data exploration and try to figure out a good model. Use random forest as your predictor, and do some data exploration to figure out which variables you want to use, and why.\n",
        "\n",
        "Once you've got a good model, please chart the ROC curve and provide the AUC score as above.\n",
        "\n",
        "I'll run your model on a test dataset, with extra credit and glory to whoever gets the highest AUC."
      ],
      "metadata": {
        "id": "2YusQ5a-IFXw"
      },
      "id": "2YusQ5a-IFXw"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Notes\n",
        "\n",
        "*  [The Titanic competition on Kaggle](https://www.kaggle.com/competitions/titanic).\n",
        "*  [Soundtrack](https://www.youtube.com/watch?v=9bFHsd3o1w0) (Could there by any other option?)\n",
        "\n"
      ],
      "metadata": {
        "id": "sOx7Yt85IvFt"
      },
      "id": "sOx7Yt85IvFt"
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "88ed7988",
      "metadata": {
        "id": "88ed7988",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "34efc0ad-9ad4-4485-8a4b-6ee9d72ecb50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC Score: 0.8485324947589098\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Your work here.\n",
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression  # Import LogisticRegression\n",
        "from sklearn.metrics import roc_auc_score, roc_curve\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Your work here.\n",
        "\n",
        "# Example: Create a 'FamilySize' feature\n",
        "titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch'] + 1\n",
        "\n",
        "# Select features for the model\n",
        "features = ['Pclass', 'Age', 'Fare', 'FamilySize', 'male']\n",
        "# 'male' is the dummy variable created earlier\n",
        "X = titanic[features]\n",
        "y = titanic['Survived']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1912)\n",
        "\n",
        "# Handle missing values (e.g., impute with the mean for 'Age')\n",
        "X_train['Age'].fillna(X_train['Age'].mean(), inplace=True)\n",
        "X_test['Age'].fillna(X_test['Age'].mean(), inplace=True)\n",
        "\n",
        "\n",
        "# Create and train the logistic regression model\n",
        "model = LogisticRegression(max_iter=1000)  # Increase max_iter if needed\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict probabilities on the test set\n",
        "y_pred_prob = model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Calculate the AUC score\n",
        "auc_score = roc_auc_score(y_test, y_pred_prob)\n",
        "print(f\"AUC Score: {auc_score}\")\n",
        "\n",
        "# Plot the ROC curve\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
        "plt.plot(fpr, tpr, label=f'AUC = {auc_score:.2f}')\n",
        "plt.plot([0, 1], [0, 1], 'k--')  # Diagonal line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    }
  ],
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