Titanic-Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CS3753-Project_fcy527",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ea9Dg91MUTCS"
      },
      "source": [
        "**CS3753-Data Science Project by Fabian Aguilar Gomez**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yO12UHBqUdEN"
      },
      "source": [
        "#imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "#ML model imports\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore') "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tICx7vaS7a7S"
      },
      "source": [
        "**Load and Prepare data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "tpwinbiRVnPH",
        "outputId": "8b38adf0-c328-4937-fca6-28d3a056a102"
      },
      "source": [
        "#Load the data\n",
        "data = pd.read_csv('train.csv')\n",
        "#Print some rows\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5QUjRsirEgV",
        "outputId": "5b9eec40-0dcd-480d-85e9-729dccda573f"
      },
      "source": [
        "#get the original shape\n",
        "print('Shape of the original data (rows,columns) = ' + str(np.array(data).shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the original data (rows,columns) = (891, 12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDNNC3ks70JE"
      },
      "source": [
        "**Data pre-processing to clean**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rkdsur8_8DOq",
        "outputId": "4f3dbcb8-7749-42d1-dc90-4397eeae7616"
      },
      "source": [
        "#Get empty values\n",
        "data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4HDxzoZ48wcZ"
      },
      "source": [
        "#drop empty vaues\n",
        "data = data.drop([\"Cabin\", \"Name\",\"Ticket\",\"PassengerId\"], axis=1)\n",
        "data = data.dropna(subset=['Embarked', 'Age'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dsk9Zlej9Wc9",
        "outputId": "2df45235-7a06-4c69-c616-d5a3a5b09093"
      },
      "source": [
        "#check the data for no empety values\n",
        "data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0\n",
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "Embarked    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "k025AY1apKIt",
        "outputId": "2dc50b3c-4789-4a33-e3e4-b2e806315e41"
      },
      "source": [
        "#Print the table after preprocesisng\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0         0       3    male  22.0      1      0   7.2500        S\n",
              "1         1       1  female  38.0      1      0  71.2833        C\n",
              "2         1       3  female  26.0      0      0   7.9250        S\n",
              "3         1       1  female  35.0      1      0  53.1000        S\n",
              "4         0       3    male  35.0      0      0   8.0500        S"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dqcbzI_WF9M",
        "outputId": "bb120b20-8f86-4a37-c07f-998bd60273eb"
      },
      "source": [
        "#Get the shape of the data\n",
        "print('Shape of the data (rows,columns) = ' + str(np.array(data).shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the data (rows,columns) = (712, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uc3fYHMF75QT"
      },
      "source": [
        "**Data analysis and plots**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "9Acnh23RWKmb",
        "outputId": "3d449b12-88b1-458f-e960-51311c8c9fa0"
      },
      "source": [
        "#Summary Statistics\n",
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.404494</td>\n",
              "      <td>2.240169</td>\n",
              "      <td>29.642093</td>\n",
              "      <td>0.514045</td>\n",
              "      <td>0.432584</td>\n",
              "      <td>34.567251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.491139</td>\n",
              "      <td>0.836854</td>\n",
              "      <td>14.492933</td>\n",
              "      <td>0.930692</td>\n",
              "      <td>0.854181</td>\n",
              "      <td>52.938648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.645850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>33.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
              "count  712.000000  712.000000  712.000000  712.000000  712.000000  712.000000\n",
              "mean     0.404494    2.240169   29.642093    0.514045    0.432584   34.567251\n",
              "std      0.491139    0.836854   14.492933    0.930692    0.854181   52.938648\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    1.000000   20.000000    0.000000    0.000000    8.050000\n",
              "50%      0.000000    2.000000   28.000000    0.000000    0.000000   15.645850\n",
              "75%      1.000000    3.000000   38.000000    1.000000    1.000000   33.000000\n",
              "max      1.000000    3.000000   80.000000    5.000000    6.000000  512.329200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "zGzaXAx1Wjge",
        "outputId": "79deb3ac-eb32-4f8d-87a1-48e3dc23c05f"
      },
      "source": [
        "#Count number of survivors and casualties\n",
        "survive = data['Survived'].value_counts()\n",
        "print('Number of people that did not survive = ' + str(survive[0]))\n",
        "print('Number of people that survived = ' + str(survive[1]))\n",
        "plt.figure()\n",
        "bars = plt.bar([0,1],survive)\n",
        "bars[0].set_color('orange')\n",
        "plt.xticks([0,1], ['Died', 'Survived'])\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Casualties and Survivor amount in Titanic\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of people that did not survive = 424\n",
            "Number of people that survived = 288\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "xawlscQwXI_M",
        "outputId": "bfc081bc-15e7-4335-a775-c098095ff5dd"
      },
      "source": [
        "#Plot of survivors for columns sex, pclass, sibsp, parch, and embarked\n",
        "cols = ['Sex', 'Pclass','SibSp','Parch','Embarked']\n",
        "plt.figure(figsize = (15,30))\n",
        "for i in enumerate(cols):\n",
        "    plt.subplot(6,3,i[0]+1)\n",
        "    sns.countplot(x=i[1], hue='Survived',data=data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x2160 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "1sTSecr248gm",
        "outputId": "9f32ca87-a510-4aa0-a808-6658c64b940b"
      },
      "source": [
        "#Survival rate by Sex\n",
        "data.groupby('Sex')[['Survived']].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.752896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.205298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Sex             \n",
              "female  0.752896\n",
              "male    0.205298"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "UoOgaEQN5ppV",
        "outputId": "768a277c-c32b-45dd-d434-2ae13306ce48"
      },
      "source": [
        "#Survival rate by Pclass\n",
        "data.groupby('Pclass')[['Survived']].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.652174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.479769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.239437</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Survived\n",
              "Pclass          \n",
              "1       0.652174\n",
              "2       0.479769\n",
              "3       0.239437"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "21PsNOVE5vhT",
        "outputId": "74cac98e-d305-4090-c93d-dc09f8f52d79"
      },
      "source": [
        "#Survival rate by SibSp\n",
        "data.groupby('SibSp')[['Survived']].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.368870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.530055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "SibSp          \n",
              "0      0.368870\n",
              "1      0.530055\n",
              "2      0.440000\n",
              "3      0.333333\n",
              "4      0.166667\n",
              "5      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "c5QkdFr_5zZa",
        "outputId": "a2f300e6-467d-419f-9c8e-e750353b5d38"
      },
      "source": [
        "#Survival rate by Parch\n",
        "data.groupby('Parch')[['Survived']].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.354528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.554545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.573529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Survived\n",
              "Parch          \n",
              "0      0.354528\n",
              "1      0.554545\n",
              "2      0.573529\n",
              "3      0.600000\n",
              "4      0.000000\n",
              "5      0.200000\n",
              "6      0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "nxdpTC-A56nN",
        "outputId": "99791387-e9cd-4701-a452-3e5e3f2bb8b0"
      },
      "source": [
        "#Survival rate by Embarked\n",
        "data.groupby('Embarked')[['Survived']].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.607692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Q</th>\n",
              "      <td>0.285714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>0.362816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Survived\n",
              "Embarked          \n",
              "C         0.607692\n",
              "Q         0.285714\n",
              "S         0.362816"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "g4uODo8q5HaW",
        "outputId": "7a8073dc-257a-4963-cd75-4c2025dd675b"
      },
      "source": [
        "#Survival rate by Sex and Class\n",
        "data.pivot_table('Survived', index='Sex', columns='Pclass')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Pclass</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.963855</td>\n",
              "      <td>0.918919</td>\n",
              "      <td>0.460784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.396040</td>\n",
              "      <td>0.151515</td>\n",
              "      <td>0.150198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Pclass         1         2         3\n",
              "Sex                                 \n",
              "female  0.963855  0.918919  0.460784\n",
              "male    0.396040  0.151515  0.150198"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "xSGQU3DT6D13",
        "outputId": "a67ca84a-4946-4449-91e2-aa7742486c35"
      },
      "source": [
        "#Survival rate by Sex and Port\n",
        "data.pivot_table('Survived', index='Sex', columns='Embarked')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Embarked</th>\n",
              "      <th>C</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.901639</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.715054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.347826</td>\n",
              "      <td>0.062500</td>\n",
              "      <td>0.184783</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Embarked         C         Q         S\n",
              "Sex                                   \n",
              "female    0.901639  0.583333  0.715054\n",
              "male      0.347826  0.062500  0.184783"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "h6_o4Xho54fo",
        "outputId": "19ca2c94-aad1-420c-ed09-81fea37206b0"
      },
      "source": [
        "#Survival rate of each class\n",
        "sns.barplot(x='Pclass', y='Survived',data=data);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "zrIwiSRz6WOk",
        "outputId": "3e0c38d7-26a9-48a2-d649-c9b8288469c5"
      },
      "source": [
        "#Survival rate by sex,age,and class\n",
        "age = pd.cut(data['Age'], [0,18,80])\n",
        "data.pivot_table('Survived',['Sex',age],'Pclass')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>0.909091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 80]</th>\n",
              "      <td>0.972222</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.423729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.215686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 80]</th>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.133663</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Pclass                  1         2         3\n",
              "Sex    Age                                   \n",
              "female (0, 18]   0.909091  1.000000  0.511628\n",
              "       (18, 80]  0.972222  0.900000  0.423729\n",
              "male   (0, 18]   0.800000  0.600000  0.215686\n",
              "       (18, 80]  0.375000  0.071429  0.133663"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "1B4vnRWK690m",
        "outputId": "bceeb000-67fa-4a72-c422-b606d3840702"
      },
      "source": [
        "#plot the prices paid by each class\n",
        "plt.scatter(data['Fare'],data['Pclass'],color='purple',label='Passenger Paid')\n",
        "plt.ylabel(\"Class\")\n",
        "plt.xlabel(\"Price (Fare)\")\n",
        "plt.title(\"Price by Class\")\n",
        "plt.yticks([1,2,3],['First','Second','Third'])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "_Vt1-y2b9xJD",
        "outputId": "2d8347c1-ff40-418f-9b5a-813d388ad8c7"
      },
      "source": [
        "#plot correlations\n",
        "cols2 = ['Survived','Age','SibSp','Parch','Fare']\n",
        "df_num = data[cols2]\n",
        "print(df_num.corr())\n",
        "sns.heatmap(df_num.corr(),annot=True,square=True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          Survived       Age     SibSp     Parch      Fare\n",
            "Survived  1.000000 -0.082446 -0.015523  0.095265  0.266100\n",
            "Age      -0.082446  1.000000 -0.307351 -0.187896  0.093143\n",
            "SibSp    -0.015523 -0.307351  1.000000  0.383338  0.139860\n",
            "Parch     0.095265 -0.187896  0.383338  1.000000  0.206624\n",
            "Fare      0.266100  0.093143  0.139860  0.206624  1.000000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMCG3m4l7-PN"
      },
      "source": [
        "**Prepare data for ML**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LM48P45w_0i2",
        "outputId": "67a2c941-7058-4c8b-904c-8a84f3582337"
      },
      "source": [
        "#check datatypes\n",
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      int64\n",
              "Pclass        int64\n",
              "Sex          object\n",
              "Age         float64\n",
              "SibSp         int64\n",
              "Parch         int64\n",
              "Fare        float64\n",
              "Embarked     object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iT0sxBij_oQT",
        "outputId": "824c01fc-c837-4812-b34d-96cc50968726"
      },
      "source": [
        "#Unique values in columns\n",
        "print(data['Sex'].unique())\n",
        "print(data['Embarked'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['male' 'female']\n",
            "['S' 'C' 'Q']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIJzpRYoASV6"
      },
      "source": [
        "#Encode columns\n",
        "label = LabelEncoder()\n",
        "#sex column\n",
        "data.iloc[:, 2] = label.fit_transform(data.iloc[:, 2].values)\n",
        "#embarked column\n",
        "data.iloc[:, 7] = label.fit_transform(data.iloc[:, 7].values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRE4E6Ng8sJS",
        "outputId": "83d298d0-6400-429f-e949-7767be17c087"
      },
      "source": [
        "#Unique values in columns after encoding\n",
        "print(data['Sex'].unique())\n",
        "print(data['Embarked'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0]\n",
            "[2 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "10jX1tGq_3aT",
        "outputId": "56c5f80d-b29f-460f-d94f-4b27c63f9264"
      },
      "source": [
        "#check data types after encoding\n",
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      int64\n",
              "Pclass        int64\n",
              "Sex           int64\n",
              "Age         float64\n",
              "SibSp         int64\n",
              "Parch         int64\n",
              "Fare        float64\n",
              "Embarked      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKiLNAQyxl3l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "15db2ac9-c88a-4c16-81de-b98768b66000"
      },
      "source": [
        "#Final dataframe used in ML\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0         0       3    1  22.0      1      0   7.2500         2\n",
              "1         1       1    0  38.0      1      0  71.2833         0\n",
              "2         1       3    0  26.0      0      0   7.9250         2\n",
              "3         1       1    0  35.0      1      0  53.1000         2\n",
              "4         0       3    1  35.0      0      0   8.0500         2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tYhQhh98C2V"
      },
      "source": [
        "**Create ML models and train them with training data and target**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTo9t3p-AmFI"
      },
      "source": [
        "#Split the data into independent 'X' & dependent 'Y' variables\n",
        "x = data.iloc[:, 1:8].values\n",
        "#target data\n",
        "y = data.iloc[:, 0].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WNC6rujBeXx"
      },
      "source": [
        "#Get our test data from the training data\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x,y,test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WK06lqN6AxJ_"
      },
      "source": [
        "#Create function with ML models\n",
        "def models(xTrain, yTrain):\n",
        "    #logisitic regression\n",
        "    log = LogisticRegression(random_state=0)\n",
        "    log.fit(xTrain,yTrain)\n",
        "\n",
        "    #Kneighbors\n",
        "    knn = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)\n",
        "    knn.fit(xTrain,yTrain)\n",
        "\n",
        "    #SVC linear kernel\n",
        "    svc_linear = SVC(kernel='linear', random_state=0)\n",
        "    svc_linear.fit(xTrain,yTrain)\n",
        "\n",
        "    #SVC RBF kernel\n",
        "    svc_rbf = SVC(kernel='rbf', random_state=0)\n",
        "    svc_rbf.fit(xTrain,yTrain)\n",
        "\n",
        "    #GaussianNB\n",
        "    gauss = GaussianNB()\n",
        "    gauss.fit(xTrain,yTrain)\n",
        "\n",
        "    #Decision Tree\n",
        "    tree = DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
        "    tree.fit(xTrain,yTrain)\n",
        "\n",
        "    #Random Forest Classifier\n",
        "    forest = RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0)\n",
        "    forest.fit(xTrain,yTrain)\n",
        "\n",
        "    #Print accuracy of each model\n",
        "    print('Logisitic Regression Training Accuracy: ', log.score(xTrain,yTrain))\n",
        "    print('K Neighbors Training Accuracy: ', knn.score(xTrain,yTrain))\n",
        "    print('SVC linear Training Accuracy: ', svc_linear.score(xTrain,yTrain))\n",
        "    print('SVC RBF Training Accuracy: ', svc_rbf.score(xTrain,yTrain))\n",
        "    print('Gaussian NB Training Accuracy: ', gauss.score(xTrain,yTrain))\n",
        "    print('Decision Tree Training Accuracy: ', tree.score(xTrain,yTrain))\n",
        "    print('Random Forest Training Accuracy: ', forest.score(xTrain,yTrain))\n",
        "\n",
        "    return log,knn,svc_linear,svc_rbf,gauss,tree,forest"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfRGbnq3D_2w",
        "outputId": "fd87fd26-c4c3-40e0-9d41-f6a1c470ce60"
      },
      "source": [
        "#Train the models\n",
        "model = models(xTrain, yTrain)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logisitic Regression Training Accuracy:  0.8031634446397188\n",
            "K Neighbors Training Accuracy:  0.789103690685413\n",
            "SVC linear Training Accuracy:  0.7768014059753954\n",
            "SVC RBF Training Accuracy:  0.6854130052724078\n",
            "Gaussian NB Training Accuracy:  0.8031634446397188\n",
            "Decision Tree Training Accuracy:  0.9929701230228472\n",
            "Random Forest Training Accuracy:  0.9753954305799648\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGXj-w8HE8pO",
        "outputId": "a6c642eb-bcdb-46d7-c35e-f37b32b74df8"
      },
      "source": [
        "#Confusion matrix & accuracy for models in test data\n",
        "modelNames = ['Logistic Regression' , 'K Neighbors', 'SVC Linear', 'SVC RBF', \n",
        "              'Gaussian NB', 'Decision Tree', 'Random Forest']\n",
        "for i in range(len(model)):\n",
        "    matrix  = confusion_matrix(yTest,model[i].predict(xTest))\n",
        "\n",
        "    #Get the true negative, false positive, false negative, & true positive\n",
        "    TN, FP, FN, TP = confusion_matrix(yTest,model[i].predict(xTest)).ravel()\n",
        "\n",
        "    #Accuracy\n",
        "    acc = (TP + TN) / (TP + TN + FN + FP)\n",
        "\n",
        "    #Print the info\n",
        "    print(matrix)\n",
        "    print('{} Testing Accuracy = {}'.format(modelNames[i],acc))\n",
        "    print()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[74  8]\n",
            " [18 43]]\n",
            "Logistic Regression Testing Accuracy = 0.8181818181818182\n",
            "\n",
            "[[64 18]\n",
            " [30 31]]\n",
            "K Neighbors Testing Accuracy = 0.6643356643356644\n",
            "\n",
            "[[70 12]\n",
            " [18 43]]\n",
            "SVC Linear Testing Accuracy = 0.7902097902097902\n",
            "\n",
            "[[75  7]\n",
            " [45 16]]\n",
            "SVC RBF Testing Accuracy = 0.6363636363636364\n",
            "\n",
            "[[69 13]\n",
            " [23 38]]\n",
            "Gaussian NB Testing Accuracy = 0.7482517482517482\n",
            "\n",
            "[[60 22]\n",
            " [10 51]]\n",
            "Decision Tree Testing Accuracy = 0.7762237762237763\n",
            "\n",
            "[[67 15]\n",
            " [13 48]]\n",
            "Random Forest Testing Accuracy = 0.8041958041958042\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "K6EN7SgcGKU_",
        "outputId": "1adbbaf4-bf2f-40ec-f58f-c6ef28ad01ed"
      },
      "source": [
        "#Get priority of features\n",
        "forest = model[6]\n",
        "priorities = pd.DataFrame({'Feature':data.iloc[:, 1:8].columns, 'Priority': np.round(forest.feature_importances_,3)})\n",
        "priorities = priorities.sort_values('Priority', ascending=False).set_index('Feature')\n",
        "priorities"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Priority</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feature</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>0.098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>0.050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0.044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Priority\n",
              "Feature           \n",
              "Age          0.300\n",
              "Fare         0.296\n",
              "Sex          0.183\n",
              "Pclass       0.098\n",
              "SibSp        0.050\n",
              "Parch        0.044\n",
              "Embarked     0.030"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "IYl1FE2BIDNY",
        "outputId": "c5c47ca3-8301-4688-97bb-dabc036ade0b"
      },
      "source": [
        "#plot the features\n",
        "priorities.plot.bar()\n",
        "plt.ylabel(\"Value\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEvCAYAAABfWlZwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfcElEQVR4nO3de7xddX3m8c/DSTDcolxSLyQxUWMlXBIkhIqCCCKhtokoGYK0AsVm7Eh1hpFpGDswDUOLomLHFxawRsERA4qtEUMpFVGRIgl3AwRDRDgoEgJWFAIEnvljrQObk3VO9kmy9to7PO/X67yy7vt7Ts7Zz17r91u/JdtEREQMtk3TBURERHdKQERERKUEREREVEpAREREpQRERERUSkBERESlUU0XsKXstttunjRpUtNlRET0lJtuuukR2+Oq1m01ATFp0iSWL1/edBkRET1F0s+HWpdLTBERUSkBERERlRIQERFRaatpg4iIAHjmmWfo7+9n3bp1TZfSVcaMGcP48eMZPXp02/skICJiq9Lf389OO+3EpEmTkNR0OV3BNmvXrqW/v5/Jkye3vV+tl5gkzZK0UtIqSQsq1n9I0h2SbpV0naSpLetOK/dbKemIOuuMiK3HunXr2HXXXRMOLSSx6667jvisqraAkNQHnAccCUwFjm0NgNIltve2PR34JPCZct+pwDxgT2AW8PnyeBERG5Vw2NCm/EzqPIOYCayyvdr208BiYE7rBrZ/0zK7AzDwcIo5wGLbT9n+GbCqPF5ERNfr6+tj+vTp7LXXXsydO5cnnniicrsDDzxwxMce2Oe+++7jkksu2aw6N6bONojdgQda5vuBAwZvJOnDwCnAtsChLfveMGjf3Sv2nQ/MB5g4ceKIipu04Dsj2n6k7jv73bUePyLas6X/1tv5295uu+249dZbATjuuOM4//zzOeWUU55fv379ekaNGsX111/f9usO3mcgIN7//veP8DtoX+PdXG2fZ/v1wF8Bfz3CfS+0PcP2jHHjKu8Uj4ho1EEHHcSqVau49tprOeigg5g9ezZTpxZX23fccUegaEQ+9dRT2Wuvvdh777259NJLAYbdZ8GCBfzwhz9k+vTpnHvuuRx88MHPhxLA2972Nm677bbNqr3OM4gHgQkt8+PLZUNZDPzDJu4bEdF11q9fz5VXXsmsWbMAuPnmm/nJT36yQU+ib37zm9x6663cdtttPPLII+y///4cfPDBw+5z9tln86lPfYorrrgCgF122YUvf/nLfPazn+Wee+5h3bp1TJs2bbPqrzMglgFTJE2meHOfB7zoXEjSFNs/LWffDQxMLwEukfQZ4DXAFODGGmvtOblEFtG9nnzySaZPnw4UZxAnnXQS119/PTNnzqzsZnrddddx7LHH0tfXxytf+Ure/va3s2zZMsaOHTvkPoPNnTuXM888k3POOYdFixZxwgknbPb3UVtA2F4v6WTgKqAPWGR7haSFwHLbS4CTJb0TeAZ4DDi+3HeFpMuAO4H1wIdtP1tXrRERW1JrG0SrHXbYYcTHanef7bffnsMPP5xvfetbXHbZZdx0000jfq3Bam2DsL3U9httv972WeWy08twwPZHbe9pe7rtd9he0bLvWeV+v2/7yjrrjIho0kEHHcSll17Ks88+y5o1a/jBD37AzJnDd9zcaaedePzxx1+07IMf/CAf+chH2H///dl55503u67GG6kjIl7qjjrqKPbZZx+mTZvGoYceyic/+Ule9apXDbvPPvvsQ19fH9OmTePcc88FYL/99mPs2LGceOKJW6Qu2d74Vj1gxowZHsnzIHr9Gn6v1x9Rl7vuuos99tij6TIa8Ytf/IJDDjmEu+++m2222fDzf9XPRtJNtmdUHS9nEBERW4GLL76YAw44gLPOOqsyHDZFBuuLiNgKfOADH+ADH/jAFj1mziAiIqJSAiIitjpbS9vqlrQpP5MERERsVcaMGcPatWsTEi0GngcxZsyYEe2XNoiI2KqMHz+e/v5+1qxZ03QpXWXgiXIjkYCIiK3K6NGjR/TUtBhaLjFFRESlBERERFRKQERERKUEREREVEpAREREpQRERERUSkBERESlBERERFRKQERERKUEREREVEpAREREpQRERERUSkBERESlBERERFRKQERERKUEREREVKo1ICTNkrRS0ipJCyrWnyLpTkm3S/qupNe2rHtW0q3l15I664yIiA3V9kQ5SX3AecDhQD+wTNIS23e2bHYLMMP2E5L+AvgkcEy57knb0+uqLyIihlfnGcRMYJXt1bafBhYDc1o3sP0920+UszcAI3tgakRE1KbOgNgdeKBlvr9cNpSTgCtb5sdIWi7pBknvqaPAiIgYWm2XmEZC0p8AM4C3tyx+re0HJb0OuEbSHbbvHbTffGA+wMSJEztWb0TES0GdZxAPAhNa5seXy15E0juBjwOzbT81sNz2g+W/q4FrgX0H72v7QtszbM8YN27clq0+IuIlrs6AWAZMkTRZ0rbAPOBFvZEk7QtcQBEOD7cs31nSy8rp3YC3Aq2N2xERUbPaLjHZXi/pZOAqoA9YZHuFpIXActtLgHOAHYGvSwK43/ZsYA/gAknPUYTY2YN6P0VERM1qbYOwvRRYOmjZ6S3T7xxiv+uBveusLSIihpc7qSMiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKtUaEJJmSVopaZWkBRXrT5F0p6TbJX1X0mtb1h0v6afl1/F11hkRERuqLSAk9QHnAUcCU4FjJU0dtNktwAzb+wDfAD5Z7rsLcAZwADATOEPSznXVGhERG6rzDGImsMr2attPA4uBOa0b2P6e7SfK2RuA8eX0EcDVth+1/RhwNTCrxlojImKQOgNid+CBlvn+ctlQTgKuHMm+kuZLWi5p+Zo1azaz3IiIaNUVjdSS/gSYAZwzkv1sX2h7hu0Z48aNq6e4iIiXqDoD4kFgQsv8+HLZi0h6J/BxYLbtp0ayb0RE1KfOgFgGTJE0WdK2wDxgSesGkvYFLqAIh4dbVl0FvEvSzmXj9LvKZRER0SGj6jqw7fWSTqZ4Y+8DFtleIWkhsNz2EopLSjsCX5cEcL/t2bYflXQmRcgALLT9aF21RkTEhmoLCADbS4Glg5ad3jL9zmH2XQQsqq+6iIgYTlc0UkdERPdJQERERKUEREREVEpAREREpQRERERUSkBERESlWru5Rgxl0oLv1Hr8+85+d63Hj3gpyBlERERUSkBERESlBERERFRKQERERKUEREREVGo7ICRtX2chERHRXTYaEJIOlHQncHc5P03S52uvLCIiGtXOGcS5wBHAWgDbtwEH11lUREQ0r61LTLYfGLTo2RpqiYiILtLOndQPSDoQsKTRwEeBu+otKyIimtbOGcSHgA8DuwMPAtPL+YiI2Ipt9AzC9iPAcR2oJSIiushGA0LSlwAPXm77z2qpKCIiukI7bRBXtEyPAY4CflFPORER0S3aucR0eeu8pK8B19VWUUREdIVNGWpjCvB7W7qQiIjoLu20QTxO0Qah8t+HgL+qua6IiGhYO5eYdupEIRER0V2GDAhJbx5uR9s3b+zgkmYBfw/0Af9o++xB6w8GPgvsA8yz/Y2Wdc8Cd5Sz99uevbHXi4iILWe4M4hPD7POwKHDHVhSH3AecDjQDyyTtMT2nS2b3Q+cAHys4hBP2p4+3GtERER9hgwI2+/YzGPPBFbZXg0gaTEwB3g+IGzfV657bjNfKyIitrB27oNA0l7AVIr7IACwffFGdtsdaB3krx84YAS1jZG0HFgPnG37n0ewb0REbKZ2ejGdARxCERBLgSMp7oPYWEBsrtfaflDS64BrJN1h+95Btc0H5gNMnDix5nIiIl5a2rkP4mjgMOAh2ycC04CXt7Hfg8CElvnx5bK22H6w/Hc1cC2wb8U2F9qeYXvGuHHj2j10RES0oZ2AWGf7OWC9pLHAw7z4jX8oy4ApkiZL2haYByxppyhJO0t6WTm9G/BWWtouIiKifkMGhKTzJL0NuFHSK4AvADcBNwP/vrED214PnAxcRfH8iMtsr5C0UNLs8jX2l9QPzAUukLSi3H0PYLmk24DvUbRBJCAiIjpouDaIe4BzgNcAvwO+RtFldazt29s5uO2lFO0WrctOb5leRnHpafB+1wN7t/MaERFRjyHPIGz/ve23UDx/ei2wCPgX4ChJUzpUX0RENGSjbRC2f277E7b3BY4F3gPcXXtlERHRqI0GhKRRkv5Y0leBK4GVwHtrrywiIho13FhMh1OcMfwhcCOwGJhv+3cdqi0iIho0XCP1acAlwH+3/ViH6omIiC4x3FhMww7GFxERW7dNeaJcRES8BCQgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUq0BIWmWpJWSVklaULH+YEk3S1ov6ehB646X9NPy6/g664yIiA3VFhCS+oDzgCOBqcCxkqYO2ux+4ATgkkH77gKcARwAzATOkLRzXbVGRMSG6jyDmAmssr3a9tPAYmBO6wa277N9O/DcoH2PAK62/ajtx4CrgVk11hoREYPUGRC7Aw+0zPeXy+reNyIitoCebqSWNF/ScknL16xZ03Q5ERFblVE1HvtBYELL/PhyWbv7HjJo32sHb2T7QuBCgBkzZnhTiozYFJMWfKfW49939rtrPX5EO+o8g1gGTJE0WdK2wDxgSZv7XgW8S9LOZeP0u8plERHRIbUFhO31wMkUb+x3AZfZXiFpoaTZAJL2l9QPzAUukLSi3PdR4EyKkFkGLCyXRUREh9R5iQnbS4Glg5ad3jK9jOLyUdW+i4BFddYXERFD6+lG6oiIqE8CIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolICIiIiKiUgIiKiUgIiIiIqJSAiIqJSAiIiIiolICIiolKtASFplqSVklZJWlCx/mWSLi3X/1jSpHL5JElPSrq1/Dq/zjojImJDo+o6sKQ+4DzgcKAfWCZpie07WzY7CXjM9hskzQM+ARxTrrvX9vS66ouIiOHVeQYxE1hle7Xtp4HFwJxB28wBLiqnvwEcJkk11hQREW2qMyB2Bx5ome8vl1VuY3s98B/AruW6yZJukfR9SQfVWGdERFSo7RLTZvolMNH2Wkn7Af8saU/bv2ndSNJ8YD7AxIkTGygzojdNWvCdWo9/39nvrvX40Rl1nkE8CExomR9fLqvcRtIo4OXAWttP2V4LYPsm4F7gjYNfwPaFtmfYnjFu3LgavoWIiJeuOgNiGTBF0mRJ2wLzgCWDtlkCHF9OHw1cY9uSxpWN3Eh6HTAFWF1jrRERMUhtl5hsr5d0MnAV0Acssr1C0kJgue0lwBeBr0haBTxKESIABwMLJT0DPAd8yPajddUaEREbqrUNwvZSYOmgZae3TK8D5lbsdzlweZ21RUTvqrMNJe0nL8id1BERUSkBERERlRIQERFRKQERERGVEhAREVEpAREREZUSEBERUSkBERERlRIQERFRqVtHc42I2Cr10ki6OYOIiIhKCYiIiKiUgIiIiEoJiIiIqJSAiIiISgmIiIiolICIiIhKCYiIiKiUgIiIiEoJiIiIqJSAiIiISgmIiIiolICIiIhKCYiIiKiUgIiIiEoJiIiIqFRrQEiaJWmlpFWSFlSsf5mkS8v1P5Y0qWXdaeXylZKOqLPOiIjYUG0BIakPOA84EpgKHCtp6qDNTgIes/0G4FzgE+W+U4F5wJ7ALODz5fEiIqJD6jyDmAmssr3a9tPAYmDOoG3mABeV098ADpOkcvli20/Z/hmwqjxeRER0SJ0BsTvwQMt8f7mschvb64H/AHZtc9+IiKjRqKYL2ByS5gPzy9nfSlpZ48vtBjzS7sb6RI2VbJrU36zU36y26+/l2mGT6n/tUCvqDIgHgQkt8+PLZVXb9EsaBbwcWNvmvti+ELhwC9Y8JEnLbc/oxGvVIfU3K/U3q5frb7L2Oi8xLQOmSJosaVuKRuclg7ZZAhxfTh8NXGPb5fJ5ZS+nycAU4MYaa42IiEFqO4OwvV7SycBVQB+wyPYKSQuB5baXAF8EviJpFfAoRYhQbncZcCewHviw7WfrqjUiIjZUaxuE7aXA0kHLTm+ZXgfMHWLfs4Cz6qxvhDpyKatGqb9Zqb9ZvVx/Y7WruKITERHxYhlqIyIiKiUgIiKiUgIiIiIq9fSNcnWS9Ergb4HX2D6yHB/qLba/2HBpIyLpbcAU21+SNA7YsRy+pOtJOqn1512Ox/XXtv+mwbLaJun1QL/tpyQdAuwDXGz7181W1h5Jr6IY4sbAMtsPNVzSiEh6GfA+YBIt73W2FzZV08ZIeu9w621/s1O1QM4ghvNlii66rynn7wH+a2PVbAJJZwB/BZxWLhoN/L/mKhqxwyQtlfRqSXsCNwA7NV3UCFwOPCvpDRQ9USYAlzRbUnskfZDi3qP3UtyjdIOkP2u2qhH7FsW4buuB37V8dbM/Lr9OorgN4Ljy6x+Bjv/8cwYxtN1sXybpNHj+vo5euxfjKGBf4GYA27+Q1DNvsLbfL+kY4A6KP+z32/5Rw2WNxHPl781RwOdsf07SLU0X1aZTgX1trwWQtCtwPbCo0apGZrztWU0XMRK2TwSQ9K/AVNu/LOdfTfGhtaNyBjG035V/FAaQ9AcUgwn2kqfLO9MHvocdGq5nRCRNAT5K8Un858CfStq+2apG5BlJx1KMFnBFuWx0g/WMxFrg8Zb5x8tlveR6SXs3XcQmmjAQDqVfARM7XUTOIIZ2CsWQH6+X9CNgHMWpdi+5TNIFwCsk/TnFKeoXGq5pJL4NnGz738ph4E+hGMJlz2bLatuJwIeAs2z/rBw25isN19SuVcCPJX2L4gPGHOB2SacA2P5Mk8UNR9IdFDWPAk6UtBp4ChBg2/s0WV+bvivpKuBr5fwxwL91uojcKDeMcgDB36f4xVpp+5mGS2pb+YY6HngT8C6K7+Eq21c3WtgISBpr+zeDlr3R9j1N1bSpJO1M8anw9qZraUfZfjWkbu4oIGnI0UkBbP+8U7VsjvLS5MHl7A9s/1PHa0hAVBuiN8F/AHfYfrjT9WwKSXfY7tVT7NaeZLvbntVrPckkXQvMpvgkexPwMPAj26c0WddIleH2a/fYm0V5WXiF7cfL+bHAHrZ/3Gxl7SmDbkp5Br090DfwvXRK2iCGdhJFz4GBXgRfoOgR9CNJf9pkYSNws6T9my5iM3yZoifZq8v5XutJ9vLyDOi9FN1bDwDe2XBNw5J0uqQ3ldMvk3QNcC/wK0ldXXuFfwB+2zL/23JZ1ysvCX8DuKBctDvwz52uIwExtFEUnzbeZ/t9FM/VNnAARVD0ggOAf5d0r6TbJd0hqScucZR2s30Z8Bw8/9TBXupJNqrsffKfeKGRutsdAww8eOt4iveIccDbKc7meolaz3psP0fvtLt+GHgr8BsA2z8Ffq/TRfTKD6sJE2z/qmX+4XLZo5J6pS3iiKYL2Ey93pNsIcUZ0HW2l0l6HfDThmvamKdb3lSPAL5WDrV/V9km10tWS/oIL5w1/BdgdYP1jMRTtp8umhKfbw/t+CW+XvsP76RrJV0BfL2cf1+5bAegJ+6EHWiMk/R7wJiGy9kUPd2TzPbXeeH3B9urKX6PutlTkvai6Fb5DuBjLet6qYsxFD3I/i/w1xRvrt/lhUcUd7vvS/qfwHaSDqcIt293uog0Ug+h7AX0XuBt5aLHgFfa/nBzVY2MpNnApynuBn+Y4tmzd9nu6m6iZbvJA7YfKj85/WeKN9Y7gdNtP9pogW2SNIaiLWtPWgLadtfekSzpAOAiijD+rO0zy+V/CPyp7WObrK9d5bAsF9s+rulaNoWkbSh+d1p7IHa8i3raIIZQnmavprhN/yiKT1N3NVrUyJ0J/AFwj+3JwGEUw1V0uwuAp8vpA4GPA+dRhHQvPfjlK8CrKC7VfJ+i23FHe6GMlO0f236T7V0HwqFcvrRXwgGgvCz22vJxx73of9v+gu25to8GFkn6aqeLyCWmQSS9ETi2/HoEuJTiTOsdjRa2aZ6xvVbSNpK2sf09SZ9tuqg29LWcJRwDXGj7cuBySbc2WNdIvcH2XElzbF8k6RLgh00X1Y6y7ecMijNoA9cBCweG3ugRqyl6HS6hZQymbr7Jr8UESafZ/rsy5C4DOv67n4DY0N0Uf8R/ZHsVgKT/1mxJm+zXknYEfgB8VdLDdP9gZQB9kkaVvZYO48XXjXvpd3agM8Ovy+v6D9FAT5RNtJji92agzeQ4ig9LvdTV9d7yaxt6a5BHKEY9+Go5Ftw7gCttn9vpItIGMYik9wDzKLqY/QvFH8o/lpdoeoKkibbvLxvUn6T4AzkOeDnw1W7/FCjp48AfUpzBTQTebNvlqKgX2X5rowW2qRwR9XKKYb6/BOxI0YZyfqOFtUHST2zvNWhZT9942QskvblldjTF5dYfUYzsiu2bO1pPAqJa+eY6h+JS06HAxcA/2f7XRgtrg6Sbbb+5nL68vI+jp5RdWl8N/Kvt35XL3kjxPIuO/pG8FEn6DMVw35eVi44GZtr+2NB7dRcVzz/5H2zYSeDQxoraCEnfG2a1O117AqIN5VADc4FjbB/WdD0bI+kW2/sOno7OGBjQbijdfA1c0uMUbQ4CduCFGxP7gN/aHttUbSNVDpl9KUVX3Q9R3Pi3xnZX3+ha9mCaa/vSpmtJL6Y22H7M9oW9EA4lDzEdnbHTRr66lu2dbI8t/93G9ujya5teCofSruW4Xc/Y/n7Zvbhrzx4GlHd8n9p0HdBbDX7RvmmSfkPxKXC7chpeGO641/7Qe0o3j3S6MZLeZPvuQdfCn9djl/cGOgn8UtK7gV8AuzRYz0j8m6SPUZwBtfbA6ug9QLnEFFETSRcBH3X5DOryUuWnu/xGuQttzx90Lbx1PKOu/wQ+QNIfUfRInAB8DhgL/I3tJY0W1gZJVc+Nt+3XdbSOBEREParaf7q9TUjSTOB+2w+V88dTdHW9j+Lmra6/i728g/1DwBsoHlf7xbLLdIxQLjFF1GcbSTvbfgxA0i50/9/c+ZT3Okg6GPg74C+B6RR3sffCWFgXUVxe+iFwJMVIzB9ttKJNUN47M5UX98C6uJM1dPsva0Qv+zRwg6SBrqJzgbMarKcdW8Nd7FMH7teQ9EWK7ro9pXyi3yEUAbGUIuiuo+hu3zHpxRRRk/LT3lEUI6P+Cniv7W5/JnVfy7DehwHXtKzrlQ+Uzw/H38OXlo6m+Pk/ZPtEYBrFja4d1Sv/4RE9o+Ia+Pk99Eb1NYqhph+huAv/hwDlXey98iyOaYN67m3X0quvV3rxPWn7OUnry0elPkzR2N5RCYiILW/wNfA96JFHpdo+S9J3eeEu9oFeLNtQtEV0Pdt9TdewBSyX9AqKRx3fRPG41H/vdBHpxRSxhbWOWVRerrlxYOiTiJGSNAkYa7vjjwvOGUTElveia+ADj42MGAlJAw8sGxhuveMBkTOIiC1M0rO8cPergO2AJ+ita+DRIEmfp2jD+lq56Bjg3k4/0TIBERHRZSTdDewx0AZUDuC3wvYenawj3VwjIrrPKopnoQyYUC7rqLRBRER0CUnfpmhz2Am4S9KN5fwBNHDDXwIiIqJ7fKrpAlqlDSIiokuVN8k9/0G+04Ml5gwiIqLLSJoPLATWAc9R9oADMtx3RMRLmaSfAm+x/UiTdaQXU0RE97mX4t6ZRuUMIiKiy0jaF/gS8GPgqYHltj/SyTrSBhER0X0uoBhq/Q6KNohG5AwiIqLLdMujaRMQERFdRtLfUjwH/Nu8+BJTR7u5JiAiIrqMpJ9VLLbtdHONiIjmpZtrRESXkPQ/WqbnDlr3t52uJwEREdE95rVMnzZo3axOFgIJiIiIbqIhpqvma5eAiIjoHh5iumq+dmmkjojoEi2Pq219VC3l/BjboztaTwIiIiKq5BJTRERUSkBERESlBETEMCQ9K+nWlq9Jm3CM90iauuWri6hXRnONGN6Ttqdv5jHeA1wB3NnuDpJG2V6/ma8bsVlyBhExQpL2k/R9STdJukrSq8vlfy5pmaTbJF0uaXtJBwKzgXPKM5DXS7pW0oxyn90k3VdOnyBpiaRrgO9K2kHSIkk3SrpF0pymvud4aUpARAxvu5bLS/8kaTTwOeBo2/sBi4Czym2/aXt/29OAu4CTbF8PLAFOtT3d9r0beb03l8d+O/Bx4BrbM4F3UITMDjV8jxGVcokpYngvusQkaS9gL+BqSQB9wC/L1XtJ+j/AK4Adgas24fWubhnS+V3AbEkfK+fHABMpwieidgmIiJERsML2WyrWfRl4j+3bJJ0AHDLEMdbzwtn7mEHrfjfotd5ne+UmVxuxGXKJKWJkVgLjJL0FQNJoSXuW63YCfllehjquZZ/Hy3UD7gP2K6ePHua1rgL+UuWpSvmc4oiOSUBEjIDtpyne1D8h6TbgVuDAcvX/onjI/I+Au1t2WwycWjY0vx74FPAXkm4Bdhvm5c4ERgO3S1pRzkd0TIbaiIiISjmDiIiISgmIiIiolICIiIhKCYiIiKiUgIiIiEoJiIiIqJSAiIiISgmIiIio9P8BgT8h0ae9vFEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMNzOYuy8Kz4"
      },
      "source": [
        "**ML model of choice results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_57RUQmWJNPh",
        "outputId": "552b74b1-89b1-40de-ad01-1e74d8c24072"
      },
      "source": [
        "#Predict using Random Forest Classifier\n",
        "pred = model[6].predict(xTest)\n",
        "print(pred)\n",
        "print()\n",
        "#Print raw values\n",
        "print(yTest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 0 0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1\n",
            " 1 1 0 0 0 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 1 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1\n",
            " 0 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1\n",
            " 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 0 0 0 0 1]\n",
            "\n",
            "[0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1\n",
            " 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 0 1 1 1\n",
            " 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0\n",
            " 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjyekmtpLILZ"
      },
      "source": [
        "#Test the test file\n",
        "test = pd.read_csv('test.csv')\n",
        "#Drop same columns\n",
        "test = test.drop([\"Cabin\", \"Name\",\"Ticket\",\"PassengerId\"], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0rDolhwVNWz",
        "outputId": "dd84618c-f048-4174-efb0-ccbe0e9ba53e"
      },
      "source": [
        "#Check for Nanvalues\n",
        "test.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass       0\n",
              "Sex          0\n",
              "Age         86\n",
              "SibSp        0\n",
              "Parch        0\n",
              "Fare         1\n",
              "Embarked     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "4Zu5XywmVVSf",
        "outputId": "e54f7786-57c2-491b-aa0e-df8810c4ae54"
      },
      "source": [
        "#Drop na values\n",
        "test = test.dropna(subset=['Age','Fare'])\n",
        "#Encode columns\n",
        "#sex column\n",
        "test.iloc[:, 1] = label.fit_transform(test.iloc[:, 1].values)\n",
        "#embarked column\n",
        "test.iloc[:, 6] = label.fit_transform(test.iloc[:, 6].values)\n",
        "test.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0       3    1  34.5      0      0   7.8292         1\n",
              "1       3    0  47.0      1      0   7.0000         2\n",
              "2       2    1  62.0      0      0   9.6875         1\n",
              "3       3    1  27.0      0      0   8.6625         2\n",
              "4       3    0  22.0      1      1  12.2875         2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHHQ-iWyOljE"
      },
      "source": [
        "#Convert to numpy array\n",
        "testArray = np.array(test)\n",
        "#Create empty array to store survival\n",
        "Survival = []\n",
        "#Test the survival of each person\n",
        "for person in testArray:\n",
        "    person = person.reshape(1,-1)\n",
        "    pred = model[6].predict(person)\n",
        "    Survival.append(pred[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dzq3cqQTSHTo"
      },
      "source": [
        "#Read the data again and add the surival column\n",
        "testResult = pd.read_csv('test.csv')\n",
        "testResult = testResult.dropna(subset=['Age','Fare'])\n",
        "testResult['Survived'] = Survival"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "id": "YSFpUjl5SW3n",
        "outputId": "e315e3d3-5691-4423-ff79-9a34472a6c07"
      },
      "source": [
        "#print a couple rows from the new table\n",
        "testResult.head(20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>897</td>\n",
              "      <td>3</td>\n",
              "      <td>Svensson, Mr. Johan Cervin</td>\n",
              "      <td>male</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7538</td>\n",
              "      <td>9.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>898</td>\n",
              "      <td>3</td>\n",
              "      <td>Connolly, Miss. Kate</td>\n",
              "      <td>female</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330972</td>\n",
              "      <td>7.6292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>899</td>\n",
              "      <td>2</td>\n",
              "      <td>Caldwell, Mr. Albert Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>248738</td>\n",
              "      <td>29.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>900</td>\n",
              "      <td>3</td>\n",
              "      <td>Abrahim, Mrs. Joseph (Sophie Halaut Easu)</td>\n",
              "      <td>female</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2657</td>\n",
              "      <td>7.2292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>901</td>\n",
              "      <td>3</td>\n",
              "      <td>Davies, Mr. John Samuel</td>\n",
              "      <td>male</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>A/4 48871</td>\n",
              "      <td>24.1500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>903</td>\n",
              "      <td>1</td>\n",
              "      <td>Jones, Mr. Charles Cresson</td>\n",
              "      <td>male</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>694</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>904</td>\n",
              "      <td>1</td>\n",
              "      <td>Snyder, Mrs. John Pillsbury (Nelle Stevenson)</td>\n",
              "      <td>female</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>21228</td>\n",
              "      <td>82.2667</td>\n",
              "      <td>B45</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>905</td>\n",
              "      <td>2</td>\n",
              "      <td>Howard, Mr. Benjamin</td>\n",
              "      <td>male</td>\n",
              "      <td>63.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>24065</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>906</td>\n",
              "      <td>1</td>\n",
              "      <td>Chaffee, Mrs. Herbert Fuller (Carrie Constance...</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>W.E.P. 5734</td>\n",
              "      <td>61.1750</td>\n",
              "      <td>E31</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>907</td>\n",
              "      <td>2</td>\n",
              "      <td>del Carlo, Mrs. Sebastiano (Argenia Genovesi)</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>SC/PARIS 2167</td>\n",
              "      <td>27.7208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>908</td>\n",
              "      <td>2</td>\n",
              "      <td>Keane, Mr. Daniel</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>233734</td>\n",
              "      <td>12.3500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>909</td>\n",
              "      <td>3</td>\n",
              "      <td>Assaf, Mr. Gerios</td>\n",
              "      <td>male</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2692</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>910</td>\n",
              "      <td>3</td>\n",
              "      <td>Ilmakangas, Miss. Ida Livija</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101270</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>911</td>\n",
              "      <td>3</td>\n",
              "      <td>Assaf Khalil, Mrs. Mariana (Miriam\")\"</td>\n",
              "      <td>female</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2696</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>912</td>\n",
              "      <td>1</td>\n",
              "      <td>Rothschild, Mr. Martin</td>\n",
              "      <td>male</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17603</td>\n",
              "      <td>59.4000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Pclass  ... Embarked Survived\n",
              "0           892       3  ...        Q        0\n",
              "1           893       3  ...        S        0\n",
              "2           894       2  ...        Q        0\n",
              "3           895       3  ...        S        1\n",
              "4           896       3  ...        S        1\n",
              "5           897       3  ...        S        0\n",
              "6           898       3  ...        Q        0\n",
              "7           899       2  ...        S        0\n",
              "8           900       3  ...        C        0\n",
              "9           901       3  ...        S        0\n",
              "11          903       1  ...        S        0\n",
              "12          904       1  ...        S        1\n",
              "13          905       2  ...        S        0\n",
              "14          906       1  ...        S        1\n",
              "15          907       2  ...        C        1\n",
              "16          908       2  ...        Q        0\n",
              "17          909       3  ...        C        1\n",
              "18          910       3  ...        S        0\n",
              "19          911       3  ...        C        0\n",
              "20          912       1  ...        C        1\n",
              "\n",
              "[20 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0yjRt1Y8RpJ"
      },
      "source": [
        "**Custom test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDiIpxP0UENd",
        "outputId": "f8bede2c-6708-40df-95b4-0c9d135b4481"
      },
      "source": [
        "#Test my own survival\n",
        "mySurival = [[1,0,16,0,0,100,2]]\n",
        "\n",
        "#Print my prediction using Random Forest Classifier\n",
        "pred = model[6].predict(mySurival)\n",
        "print(\"My prediction: \" + str(pred[0]))\n",
        "\n",
        "if pred[0] == 0:\n",
        "    print(\"I did not survive!\")\n",
        "else:\n",
        "    print(\"I survived the Titanic!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "My prediction: 1\n",
            "I survived the Titanic!\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}