aish2997/project-logistic-regression-titanic-dataset.ipynb

## project-logistic-regression-titanic-dataset.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Project - Logistic Regression Titanic Dataset",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1hSFgw8-KU9-ljxACmcnvdlwfZoMWQ2Mr",
      "authorship_tag": "ABX9TyNwfAlRCUeK62nFGBvv3MDj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/aish2997/e626b8aa8243810b08dce01809fc5065/project-logistic-regression-titanic-dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_U9yUOdiVVJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yUTRJ3FwmasX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_x = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Project - Logistic Regression/0000000000002429_test_titanic_x_test.csv\")\n",
        "train_xy = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Project - Logistic Regression/0000000000002429_training_titanic_x_y_train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7B6I5-UoHUU",
        "colab_type": "code",
        "outputId": "8dc81269-0b54-439c-d0b1-400be079b0f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "sns.heatmap(train_xy.isnull(), yticklabels= False, cbar = False, cmap = 'viridis')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb735f3128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yIv8atXsofJ7",
        "colab_type": "code",
        "outputId": "9944fdf8-57f2-4765-efac-24548bc821d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Survived', data = train_xy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb7317e6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgKCPDeJo7Op",
        "colab_type": "code",
        "outputId": "4e95db9c-4a62-42bd-f7ff-9332f9c7439d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Survived', hue = \"Sex\", data = train_xy, palette = 'RdBu_r')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb7319f4a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwKBRERMSgURETEoFAQERGDQkFERAwK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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bC4dwXQzpO-V",
        "colab_type": "code",
        "outputId": "93e6fb03-bbc0-4885-93c1-835b9f69a689",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Survived', hue = \"Pclass\", data = train_xy, palette = 'rainbow')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb7323e198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z2mazKTIyUtu2bfNZUQAA//AoFJozPxEAoO3w6JlCRUWF3njjDcXGxio2NlYZGRmPzFUE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\nhgXN9gsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ih-Pwq7lpmFN",
        "colab_type": "code",
        "outputId": "c39b9da8-2ba7-4631-fbf3-1dd7fc626ca2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(train_xy['Age'].dropna(), kde = False, bins = 40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb73147ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0FKWlpTh58qSsIYmIqC+/HyMsLy/HT3/6U/zmN795aPnChQuxePFi2YIREZFvfn8Dnzx5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\ngRMRqRQLnIhIpf4PZOo3odgK6AsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0d9piEFqla7",
        "colab_type": "code",
        "outputId": "a2c22eff-153e-4bf7-e798-e15f6cb77425",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(x= 'SibSp', data= train_xy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb73387978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RAAAYRAEAYBAFfKpC4ZDTE7oVr7uAeJPg9ADcWhITEjVl3RSnZ1zjvUXvOT0B6BM4UgBu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\nAACM/wEkdvNGL7433wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wde5Ev2TqxRH",
        "colab_type": "code",
        "outputId": "de0c897c-1b74-4850-87f8-108200f4c4a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.distplot(train_xy['Fare'], kde = False , bins = 40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb73460eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qABiIcAcAAxHuAGAgwh0ADES4A4CBCHcAMBDhDgAGItwBwEAxrwo5lnVd6VNPsH/YsUn2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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEOEpFMTrJ0f",
        "colab_type": "code",
        "outputId": "ab1878f7-8860-406e-b379-653b4701a040",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        }
      },
      "source": [
        "plt.figure(figsize= (12,7))\n",
        "sns.boxplot(x='Pclass', y= 'Age', data = train_xy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb72b9a2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7s3jx4sydOzeLFy/OAw88UK/DAwBAzdStNL/61a/OZz7zmUfdt3LlysyaNSsb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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "criIKx7ZrjoU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def impute_age(cols):\n",
        "  Age = cols[0]\n",
        "  Pclass = cols[1]\n",
        "\n",
        "  if pd.isnull(Age):\n",
        "    if Pclass == 1:\n",
        "      return 37\n",
        "    elif Pclass == 2:\n",
        "      return 29\n",
        "    else:\n",
        "      return 24\n",
        "  \n",
        "  else:\n",
        "    return Age"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z14GWbfxr4Z4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_xy['Age'] = train_xy[['Age', 'Pclass']].apply(impute_age, axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lna_NjqMuZrB",
        "colab_type": "code",
        "outputId": "e503078b-903b-440f-ffa7-ec463c60ec6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        }
      },
      "source": [
        "sns.heatmap(train_xy.isnull())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb728b5278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//TZ+fn4FGpwQTxpJZViHomFBX9u2bWnbtm2e10aMGPHQa81dqWYyMP/0009U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\np4sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gom2vzwBuevR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_xy.drop('Cabin', axis = 1, inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sEGkXmFu-AI",
        "colab_type": "code",
        "outputId": "93f751e2-652d-4ddc-be53-cd18a486d92c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        }
      },
      "source": [
        "sns.heatmap(train_xy.isnull())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb72c0bdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dm9DQUADc3Nzo1KkTvXv3pl+/fgQFBdG0aVPNBiyEEGVVkgKTiiCbsQohrIoW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\nCAsjgVkIISyMBGYhhLAwEpiFEMLC/D+kU+vx+GKpsgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vBB_8eTu_cs",
        "colab_type": "code",
        "outputId": "089f8ee0-7421-4bee-889f-3507314e1507",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_xy.head()"
      ],
      "execution_count": 0,
      "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>Weisz, Mrs. Leopold (Mathilde Francoise Pede)</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>228414</td>\n",
              "      <td>26.000</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
              "      <td>male</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 2466</td>\n",
              "      <td>8.050</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>250655</td>\n",
              "      <td>26.000</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Mrs. Nils (Alma Cornelia Berglund)</td>\n",
              "      <td>female</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.075</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Sutehall, Mr. Henry Jr</td>\n",
              "      <td>male</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SOTON/OQ 392076</td>\n",
              "      <td>7.050</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass                                            Name  ... Embarked  Survived\n",
              "0       2   Weisz, Mrs. Leopold (Mathilde Francoise Pede)  ...        S         1\n",
              "1       3               Williams, Mr. Howard Hugh \"Harry\"  ...        S         0\n",
              "2       2  Morley, Mr. Henry Samuel (\"Mr Henry Marshall\")  ...        S         0\n",
              "3       3     Palsson, Mrs. Nils (Alma Cornelia Berglund)  ...        S         0\n",
              "4       3                          Sutehall, Mr. Henry Jr  ...        S         0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "52c0aXavxalh",
        "colab_type": "text"
      },
      "source": [
        "Dropping the first row is known as Dummy Variable Trap. Web just took only two columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiD_U7gZvIWp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embark = pd.get_dummies(train_xy['Embarked'], drop_first=True).head()\n",
        "sex = pd.get_dummies(train_xy['Sex'], drop_first=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKhHLqS3xYi5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_xy.drop(['Sex', 'Embarked', 'Name', 'Ticket'], axis = 1, inplace= True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUB662yAyDuN",
        "colab_type": "code",
        "outputId": "9b81005c-d149-4f2c-e1bb-8f9edd7ddcbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_xy.head()"
      ],
      "execution_count": 0,
      "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.050</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>21.075</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.050</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass   Age  SibSp  Parch    Fare  Survived\n",
              "0       2  29.0      1      0  26.000         1\n",
              "1       3  24.0      0      0   8.050         0\n",
              "2       2  39.0      0      0  26.000         0\n",
              "3       3  29.0      0      4  21.075         0\n",
              "4       3  25.0      0      0   7.050         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhyhZLS7yINW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_xy = pd.concat([train_xy, sex, embark], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfjOVwKtySdp",
        "colab_type": "code",
        "outputId": "220e66af-39d4-4358-f049-921e09c93750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "train_xy.head()"
      ],
      "execution_count": 0,
      "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Survived</th>\n",
              "      <th>male</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.050</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>21.075</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.050</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass   Age  SibSp  Parch    Fare  Survived  male    Q    S\n",
              "0       2  29.0      1      0  26.000         1     0  0.0  1.0\n",
              "1       3  24.0      0      0   8.050         0     1  0.0  1.0\n",
              "2       2  39.0      0      0  26.000         0     1  0.0  1.0\n",
              "3       3  29.0      0      4  21.075         0     0  0.0  1.0\n",
              "4       3  25.0      0      0   7.050         0     1  0.0  1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvRNXnS7yUHD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x = train_xy.drop('Survived', axis = 1).head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut9VpN7y1sB5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_y = train_xy['Survived'].head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5aNSSYP1xRN",
        "colab_type": "code",
        "outputId": "b4d73d1c-6d85-44b1-d96c-963ae30fc9a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "alg = LogisticRegression(penalty='l2',solver = 'sag')\n",
        "alg.fit(train_x, train_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='sag', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jCmvEKH2KOA",
        "colab_type": "code",
        "outputId": "a96d3373-40bd-4964-ef41-77696feb47f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "alg.score(train_x, train_y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPX8t8m42qsa",
        "colab_type": "code",
        "outputId": "b44d5402-5612-496f-acba-f519da9c7977",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        }
      },
      "source": [
        "sns.heatmap(test_x.isnull())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb6f85f198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SiCiVUQ8tD8xqpzLS0tLQuXNntGzZknlMLBbjxIkT8PX1Vff0hBDCPn1PZSQlJcHPz0/u\nsfPnz8PBwQG2trbqnp4QoiKdG8VqkEys3SNmtQJzaWkpzp8//9pO2MnJya8Fa0KIZlEqo27anmOm\nkmxCiE5hoyS7cEg/hY+1SkhVuz1lUeUfIXqKRsx10/K9WNULzNu3b0dcXBx4PB6cnZ0RFRWFa9eu\n4dtvv4VUKkXTpk0RHR0Ne3t7tvpLCFGQrgVLjdLywKzyrAyRSISdO3fiwIEDSExMhEQiQVJSEhYv\nXozvvvsOCQkJ8Pf3x4YNG9jsLyGEqE0mVvzWkLS0NPj4+MDb2xubNm2q9Zjk5GT4+vrCz8/vtanF\ntVFrxCyRSPDq1SsYGhri1atXsLGxAQCUlJQw/69+jBCiWZTKqBtbqQyJRILIyEhs27YNAoEAw4YN\ng5eXF9q3b88ck5WVhU2bNmHv3r2wsLBAQUFBg+dVOTALBAKMHz8eH3zwAUxMTNC7d2/06dMHy5Yt\nw6RJk2BiYgIzMzPs379f1SYIIYQTbAXm9PR02Nvbw87ODgDg5+cHoVAoF5j379+PUaNGwcLCAgBg\nbW3d4HlVDszFxcUQCoUQCoVo3rw5ZsyYgYSEBJw4cQKbNm2Cm5sbYmJiEBUVhWXLlqnaDFEAjYwI\nUQ5bgVkkEsnVawgEAqSnp8sdk5WVBQAYMWIEpFIppk6dCk9Pz3rPq3JgPn/+PFq3bs1ssjpw4EBc\nu3YNd+7cYTZo9fX1xcSJE1VtghBCuCFTfLu72NhYxMbGMvdDQkIQEhKi8O9LJBI8ePAAu3btQl5e\nHkaPHo0jR47A3Ny8zt9ROTC/8847uHnzJsrKytCkSRNcuHABLi4uSElJQWZmJtq1a4dz587B0dFR\n1SaIgmgUS4hylBkx1xeIBQIB8vLymPsikQgCgeC1Y9zc3GBkZAQ7Ozu0bdsWWVlZcHV1rbNNlQOz\nm5sbfHx8MHToUBgaGqJTp04ICQmBra0tpk+fDh6PBwsLCyxfvlzVJgghhBNSMTsbRHfp0gVZWVnI\nzs6GQCBAUlISVq1aJXfMhx9+iKSkJAQFBaGwsBBZWVlMTrouVPlHiJ7S12sPbFT+PfbwUvjYVhdO\n1ft8amoqli9fDolEgqCgIEyePBnr1q2Di4sLBgwYAJlMhujoaJw9exZ8Ph9ffPFFg0tWUGDWA/r6\nB0jUo6/vCzYC86P3FA/MrS/VH5i5QCXZeoCCJakNvS/qJpOyk8rgilqBeceOHYiLi4NMJkNwcDDG\njh2LO3fuYNGiRSgtLUWrVq3w3Xff0b5/hDQCfR0xs6Hx8wT1Uzkw3717F3FxcYiLi4ORkREmTpyI\nDz74AAsWLMDcuXPRs2dPxMfHIyYmBqGhoWz2mRCiAF0LlpqktyPme/fuwdXVFaampgAAd3d3HD9+\nHFlZWXB3dwcA9O7dGxMmTKDATEgjoBFz3aQSPQ3Mzs7OWLt2LYqKitCkSROkpaXBxcUFTk5OEAqF\n+PDDD5GSkoLc3Fw2+0sIUZCuBUtN0tsRs6OjIyZOnIgJEybA1NQUHTt2hIGBAZYtW4Zly5bhp59+\ngpeXF4yNjdnsL6kFjYxIbeh9UTeZEpV/jUGti3/BwcEIDg4GAKxevRoCgQCOjo7YunUrACAzMxNn\nzpxRu5OEEMImvV4ov6CgANbW1sjJycHx48exf/9+5jGpVIoNGzZgxIgRbPWV1EHXRitEM+h9UTep\nPo+Yp02bhmfPnsHQ0BCLFi2Cubk5duzYgT179gAAvL29ERQUxEpHCSHKoVRG3aQSlfcI0Qiq/CNE\nT+lrYGaj8u8vJ1+Fj+30T7La7SmLKv8I0VO6NorVJL2dlUEI0W76OmJmg7bnmBtMtISHh8PDwwP+\n/v7MY0ePHoWfnx86duyIW7duyR2/ceNGeHt7w8fHB2fPau6NQQghipLJeArfGkODI+bAwECMHj0a\nc+fOZR5zdnbGDz/8gEWLFskdm5GRgaSkJCQlJUEkEmHcuHE4duwY+Hw++z0nDBoZEaKcxr+yVr8G\nA7O7uzsePXok91hdu5IIhUL4+fnB2NgYdnZ2sLe3R3p6Orp168ZOb0mtKFiS2pi+01ejH9q6RCLV\n7lkZrOaYRSIRs98fULWlikgkYrMJQogS6EO7djo/YiaE6CZKcdVN2y/+sRqYFdmYkBCiGboWLDVJ\nr9fK+C8vLy/Mnj0b48aNg0gkanAnWEIId2jEXDedHzHPmjULly9fRlFRETw9PTFt2jS0aNECS5Ys\nQWFhIT7//HN06tQJW7ZsgZOTEwYNGgRfX1/w+XxERETQjAxCiNbR8hQzlWQToq/0dcTMRkn2Odth\nCh/bOy9e7faURRf/9IC+/gESwhUtX/WTArM+oGBJiHJk0PEcc3h4OM6cOQNra2skJiYCAFasWIHT\np0/DyMgIbdq0QVRUFMzNzVFUVITp06fjjz/+wNChQxEREcH5CyA0YiZEWdJGT+DWT6WS7N69e2P2\n7NkwNDTEypUrsXHjRoSFhcHExAQzZszAP//8g3/++YfTjpN/UbAkRDlSXR8x11aS3adPH+bnrl27\nIiUlBQDQtGlT9OjRAw8fPmS5m6Q+NGImRDkSXQ/MDTlw4AAGDRrERl+IiihYEqIcnc8x12fDhg3g\n8/n4+OOP2eoPIYRwTm9nZRw8eBBnzpzB9u3bweNp96ePvqNUBiHK0cvAnJaWhpiYGOzevRumpqZs\n94koiYIlIcrR+VRGbSXZmzZtQkVFBcaNGwcAcHNzQ2RkJICq9TJKSkpQWVmJkydPYuvWrWjfvj23\nr4IQQpSg5Vv+UUk2IfpKX1NcbJRkH7L9ROFjA/L2qN2esqjyjxDyxtH5HHNtlX9r166FUCiEgYEB\nrK2tERUVBYFAgMOHD2Pz5s0AgGbNmmHx4sXo2LEjt6+A6O3IiKiH/q3qJtXyCQsNpjKuXLmCpk2b\nYu7cuUxgLikpgZmZGQBg586dyMjIQGRkJK5duwZHR0dYWFggNTUV69evR1xcXIOdoFQGIezT1w9s\nNlIZcW+PUvjY4Nxf1G5PWQ3uSOju7g4LCwu5x6qDMgCUlZUx0+W6d+/OHNu1a1e53UwIIURbSJW4\nNSQtLQ0+Pj7w9vbGpk2b6jzu2LFj6NChA27dutXgOVXOMa9ZswaHDh1C8+bNsXPnzteej4+Ph6en\np6qnJ4QQzohZSmVIJBJERkZi27ZtEAgEGDZsGLy8vF6biVZSUoKdO3fKbVZdH5X38J45cyZSU1Mx\nePBg7N69W+65ixcvIj4+Hl999ZWqpyeEEM7IlLjVJz09Hfb29rCzs4OxsTH8/PwgFApfO27dunX4\nv//7P5iYmCjUP5UDc7XBgwfj+PHjzP07d+5g4cKF+Omnn2Bpaanu6QkhhHVSnuK32NhYBAYGMrfY\n2FjmPCKRCLa2tsx9gUAAkUgk19bt27eRl5eH/v37K9w/lVIZWVlZaNu2LQBAKBTCwcEBAJCTk4Np\n06bh22+/Rbt27VQ5NVGBvl7kIYQrykyXCwkJQUhIiGrtSKWIjo5GVFSUUr+nUuVfWloaMjMzwePx\n0KpVK3zzzTcAgB9//BHPnj1j7vP5fBw8eFCFl0MIIdxhq6pOIBDITXIQiUQQCATM/ZcvX+Lu3bv4\n9NNPAQBPnjzB5MmTsWHDBnTp0qXO81Llnx6gETN5k7AxXW5L69EKHzvh0e46nxOLxfDx8cH27duZ\ni3+rVq2Ck5NTrcePGTMGc+bMqTcoA1T5pxcoWJLa0Ad23cQsncfQ0BARERGYOHEiJBIJgoKC4OTk\nhHXr1sHFxQUDBgxQ6bw0YiaE6BQ2Rsw/2yk+Yv4iu+4RM1cUGjHXVpZdbevWrVixYgUuXLgAKysr\nnDx5EuvWrYOBgQH4fD7mz5+PHj16cNJ5UoVGRqQ29L6om86vlQHUviErAOTm5uLcuXN45513mMc8\nPDwwYMAA8Hg83LlzB6GhocyegIQbuvZHQUhj04vAXNuGrAAQFRWFsLAwfPnll8xjzZo1Y36uWa5N\nCNEs+sCuW6Pnbxug8sW/kydPwsbGptbV406cOIFVq1ahsLAQGzduVKuDhBDVUCqjbtq+UL5KlX9l\nZWXYuHEjZsyYUevz3t7eSElJwY8//oh169ap1UFCCGGbWIlbY1ApMD98+BCPHj3CkCFD4OXlhby8\nPAQGBuLJkydyx7m7uyM7OxuFhYWsdJYQQtjA1loZXFEpldGhQwdcuHCBue/l5YX4+HhYWVnhwYMH\naNOmDXg8Hm7fvo2KigpaM4MQolW0PZWhUGCurSw7ODi41mOPHTuGhIQEGBoaokmTJlizZg1dACSE\naBVtn5VBBSZ6gC7ykNro6/uCjQKTKHvFC0zCH2hpgQnRbhQsSW1M3+mr0eCsS8RaPmGOAjMheoqC\nct20OywrMCsjPDwcHh4e8Pf3Zx774Ycf0LdvXwwZMgRDhgxBamqq3O/k5OSgW7du2LJlC/s9JoQQ\nNbG55x8XGhwx11WOPXbsWEyYMKHW34mOjkbfvvT1mhCinXR+VkZd5dh1OXnyJFq1aoWmTZuq1TFC\nCOGKVMuTGSrv+ffLL79g8ODBCA8PR3FxMYCq1fo3b96MqVOnstZBQghhm14WmIwcORJffvkleDwe\n1q1bx+xptX79enz22WdyCxkRQhoHzdapm17OymjZsiXzc3BwML744gsAwM2bN3Hs2DF89913eP78\nOQwMDGBiYoLRoxWfM0gIYYe+zmNmg3aHZRUDc35+PmxsbABU5ZSr97fas2cPc8wPP/yApk2bUlAm\nhGgdba/8U2mX7MuXL+POnTsAgFatWiEyMpLzjhJCCFu0/eIflWTrAfrKSmqjr+8LNkqyZ7YdofCx\na7L2qd2esqjyTw9QsCREOTqfyiCEEH0j0fJURoOBua4dsnft2oVffvkFfD4f/fr1w5w5c/Do0SP4\n+vqiXbt2AAA3NzfKP2uAvn5lJYQr2p5jVqkk++LFixAKhTh8+DCMjY1RUFDAPNemTRskJCRw01tS\nKwqWpDb0vqibdodlFUuy9+7di0mTJsHY2BgAYG1tzU3vCCEqo29SddP5EXNtsrKycPXqVaxZswYm\nJiaYM2cOXF1dAQCPHj1CQEAAzMzMEBoaih49erDaYfI6+gMkRDl6efFPIpGguLgY+/fvx61btxAa\nGgqhUAgbGxucPn0alpaW+OOPPzBlyhQkJSXBzMyM7X4TQojKdP7iX20EAgG8vb3B4/Hg6uoKAwMD\nFBUVwcrKiklvuLi4oE2bNsjMzESXLl1Y7TSRR6NYQpQj08fA/OGHH+LSpUvo1asXMjMzUVlZCUtL\nSxQWFsLCwgJ8Ph/Z2dnIysqCnZ0d230m/0GpDEKUo/OpjNpKsoOCgjB//nz4+/vDyMgI0dHR4PF4\nuHLlCr7//nsYGhrCwMAA33zzDVq0aKGJ1/FGo2BJiHKkjV/wXC8qySZET+nrNyk2SrJH2wcqfOzu\nBwfVbk9ZVPlHCHnj6Px0udoq/0JDQ5GZmQkAePHiBZo3b84Uldy5cweLFi1CSUkJDAwMEB8fDxMT\nEw5fAiGEKEfnZ2XUVvm3du1a5ufo6GhmOpxYLEZYWBhWrlyJjh07oqioCIaGNCgnpDHQtYe66fyI\nub7NWGUyGY4ePYodO3YAAM6dO4cOHTqgY8eOAABLS0sWu0oIUYa+5pjZwOZ0ubS0NCxbtgxSqRTB\nwcGYNGmS3PPbtm1DXFwc+Hw+rKyssHz5crRqVf91NZU3YwWAq1evwtraGm3btgUAZGZmgsfjYcKE\nCRg6dCg2b96szukJIYQTUiVu9ZFIJIiMjERMTAySkpKQmJiIjIwMuWM6deqEAwcO4MiRI/Dx8cHK\nlSsb7J9agTkxMRH+/v5ynfz999+xcuVK7NmzBydPnsSFCxfUaYIQQlgnk8kUvtUnPT0d9vb2sLOz\ng7GxMfz8/CAUCuWO6dWrF0xNTQEAXbt2RV5eXoP9UzkBLBaLceLECRw8+O9UEltbW7i7u8PKygoA\n4Onpidu3b8PDw0PVZogC6CsrIcphK8csEolga2vL3BcIBEhPT6/z+Pj4eHh6ejZ4XpUD8/nz5+Hg\n4CDXqT59+iAmJgZlZWUwMjLClStXMHbsWFWbIAqiYEmIcpSZlREbG4vY2FjmfkhICEJCQpRuMyEh\nAX/88Qd2797d4LEqVf4FBwcjOTkZfn5+csdaWFhg7NixGDZsGHg8Hjw9PdG/f3+lXwAhhHBJmRFz\nfYFYIBDIpSZEIhEEAsFrx/VB4GcAAB0hSURBVJ0/fx4///wzdu/ezawnVB+q/NMDlMogtdHX9wUb\nlX+D7AYpfOzR7KN190Usho+PD7Zv3w6BQIBhw4Zh1apVcHJyYo75888/MX36dMTExDATJRpCk4z1\nAAVLQpTD1iJGhoaGiIiIwMSJEyGRSBAUFAQnJyesW7cOLi4uGDBgAL799luUlpZixowZAIC3334b\nP//8c73npRGzHtDXkRFRj76+L9gYMQ+0+0jhY49np6jdnrJUKsn+66+/sGjRIpSXl4PP52Px4sVw\ndXVFTEwMjhw5AqBq6ty9e/dw4cIFWmGOYxQsCVGORKbdC382OI85MDAQMTExco+tXLkSU6ZMQUJC\nAmbMmMFMmJ44cSISEhKQkJCAWbNmwd3dnYIyIUTrSCFT+NYYGgzM7u7usLCwkHuMx+Ph5cuXAKoW\nMbKxsXnt95KSkuSKTwghRFvIlPivMah08W/+/PmYMGECVqxYAalUin379sk9X1ZWhrNnz+Lrr79m\npZOEEOVRiqtu2r5Qvkol2Xv37kV4eDhSU1MRHh6OBQsWyD1/+vRpdO/endIYhBCtJFPi1hhUGjH/\n+uuvTDAeNGgQFi5cKPd8UlLSa8UnhBDN0tdZGWzQ9mU/VRox29jY4PLlywCAixcvyk2afvHiBa5c\nuYIBAwaw0kFCCGGbRCZV+NYYVCrJXrJkCZYvXw6xWAwTExNERkYyx584cQK9e/dG06ZNOe04IaR+\nujaK1SRtHzFTgYkeoK+spDb6+r5go8DE/Z2GV3irdiUnTe32lEUl2XqAgiUhytGC8Wi9KDATQt44\n2p7KUKkku3on7NLSUrRq1QrfffcdzMzMUFlZiYULF+LPP/+EWCxGQEAAPv/8c85fBCHkdfRNqm7a\nXpKt0i7ZCxYswNy5c9GzZ0/Ex8cjJiYGoaGhSElJQUVFBY4cOYKysjL4+fnBz88PrVu35vRFEEJe\np685ZjY0VkWfolQqyc7KyoK7uzsAoHfv3jh+/DiAqlLtsrIyiMVivHr1CkZGRjAzM+Og24QQojqp\nTKbwrTGolGN2cnKCUCjEhx9+iJSUFOTm5gIAfHx8IBQK0adPH7x69Qrh4eFU/UdII9G1UawmafuI\nWaXAvGzZMixbtgw//fQTvLy8mK1S0tPTYWBggLNnz+L58+f45JNP8P7778POzo7VThN59JWVEOVo\n+1oZKgVmR0dHbN26FQCQmZmJM2fOAAASExPRt29fGBkZwdraGt27d8etW7coMHOMgiUhytHLEXNB\nQQGsra0hlUqxYcMGjBgxAkDVlimXLl1CQEAASktLcfPmTXz22WesdpgQohj6JlU3bZ+V0WDlX82S\nbGtra0ybNg2lpaXYs2cPAMDb2xuzZ89m1mgODw/HvXv3IJPJEBgYiIkTJzbYCar8I4Qoio3KP8eW\n3RU+9t7Ta2q3pywqydYDNDIitdHX9wUbgdmhZTeFj73/9Lra7SmLKv8IIW8cmZanMhoMzLm5uZgz\nZw4KCgrA4/EwfPhwfPbZZ3j27BlmzpyJx48fo1WrVli7di0sLCxQXFyM+fPn4+HDhzAxMcHy5cvh\n7OysidfyxqJRLCHK0faS7AYLTPh8PubNm4fk5GTExsZiz549yMjIwKZNm+Dh4YHjx4/Dw8MDmzZt\nAgD8/PPP6NSpE44cOYIVK1Zg2bJlnL8IQghRhkwmU/jWGBocMdvY2DCbrZqZmcHBwQEikQhCoRC7\ndu0CAAQEBGDMmDEICwvDvXv3MGnSJABV0+oeP36Mp0+fomXLlhy+DELIf2nym5Qm89ls0PZZGUrl\nmB89eoS//voLbm5uKCgoYAL2W2+9hYKCAgBAx44dcfz4cfTo0QPp6enIyclBXl4eBWZCNIwu/tVN\nbwpMXr58ienTp2P+/PmvrX/B4/HA4/EAAJMmTcKyZcswZMgQODs7o1OnTuDz+ez2msjR1z9AQrii\nFwUmlZWVmD59OgYPHoyBAwcCAKytrZGfnw8bGxvk5+fDysoKQFW6IyoqCkBVHmfAgAFU+ccxCpaE\nKEcLZgnXq8GLfzKZDAsWLICDgwPGjRvHPO7l5YVDhw4BAA4dOsRsvvr8+XNUVFQAAOLi4tCjRw9a\nYY4QolWkkCl8awwNFphcvXoVo0aNgrOzMwwMquL4rFmz4OrqitDQUOTm5uKdd97B2rVr0aJFC1y/\nfh3z5s0DULUK3bJly15bNvS/qMCEEKIoNnLMVs2dFD628MU/arenLKr8I0RP6eu1BzYCs6VZe4WP\nLSrJULs9ZVHlnx7Q1z9Aoh76t6qbtheYUGDWA/QHSGpDH9h104JEQb1ULslesWIFTp8+DSMjI7Rp\n0wZRUVEwNzcHAGzcuBHx8fEwMDDAwoUL0bevbv2j6Rr6AyREOdo+j7nBHHN+fj6ePHmCzp07o6Sk\nBEFBQfjxxx+Rl5eHXr16wdDQECtXrgQAhIWFISMjA7NmzUJ8fDxEIhHGjRuHY8eO1TuXmXLMhLBP\nXz+w2cgxN2vaVuFjX5Zmqd2eslQuye7Tpw9zTNeuXZGSkgIAEAqF8PPzg7GxMezs7GBvb4/09HR0\n66b4MnuEEPXRt5u6SaTaXZLd4DzmmmqWZNd04MABeHp6AgBEIhFsbW2Z5wQCAUQiEQtdJYQQdsiU\n+K8haWlp8PHxgbe3N7OYW00VFRUIDQ2Ft7c3goOD8ejRowbPqXZJ9oYNG8Dn8/Hxxx8reipCiAbo\nayqDDWxd/JNIJIiMjMS2bdsgEAgwbNgweHl5oX37f6fjxcXFwdzcHCdOnEBSUhK+++47rF27tt7z\nqlySDQAHDx7EmTNnsH37dmatDIFAgLy8POYYkUgEgUCg1IslhKhP14KlJrEVmNPT02Fvb88sO+Hn\n5wehUCgXmE+dOoWpU6cCAHx8fBAZGQmZTMbEzNo0GJjrKslOS0tDTEwMdu/eDVNTU+ZxLy8vzJ49\nG+PGjYNIJEJWVhZcXV3rbYONZD4hhCiqUomYExsbi9jYWOZ+SEgIQkJCANSeuk1PT5f7fZFIhLff\nfhsAYGhoiObNm6OoqIhZX6g2DQbm33//HQkJCXB2dsaQIUMAVJVkL126FBUVFUywdnNzQ2RkJJyc\nnDBo0CD4+vqCz+cjIiKCVpcjhOismoFYUxoMzD169MDff//92uP9+vWr83cmT56MyZMnq9czQgjR\ncoqkbgUCAXJzc2FrawuxWIwXL17A0tKy3vMqNSuDEELIv7p06YKsrCxkZ2ejoqICSUlJ8PLykjvG\ny8sLv/76KwDg2LFj6NWrV735ZUBLFjEihBBdlZqaiuXLl0MikSAoKAiTJ0/GunXr4OLiggEDBqC8\nvBxhYWH466+/YGFhgTVr1jS4Rj0FZkII0TKUyiCEEC1DgZkQQrQMBWZCCNEyFJgJUdBnn32m0GOE\nqEvnFsp/+PAhbG1tYWxsjEuXLuHvv/9GQEAAsxY0m54+fYrVq1cjPz8fMTExyMjIwPXr1xEcHMxq\nO2vXrsXUqVNhaFj1z1FSUoJly5Yxu42zRVOvp9qTJ0+Qnp4OHo+HLl264K233uKknWoikQiPHz+G\nRCJhHnN3d1f7vOXl5SgrK0NRURGKi4uZct6SkhJOF+iSyWQ4fPgwsrOzMXXqVOTk5ODp06cNVtIq\nq7CwEPv378fjx48hFouZx9l+/33xxRf1Pv/zzz+z2p4u07kR87Rp02BgYIAHDx4gIiICubm5mD17\nNidtzZs3D3369EF+fj4AoG3btti5cyfr7UgkEgwfPhx37tzBuXPnEBQUhM6dO7PejqZeD1C1cEtw\ncDBOnDiBY8eOISQkBPHx8Zy0BQArV67EyJEjsWHDBmzZsoW5sWHfvn0IDAzE/fv3MXToUAQGBiIw\nMBBffvklRo8ezUobtVm8eDFu3LiBpKQkAECzZs3wzTffsN7Ol19+iRcvXsDDwwP9+/dnbmwbP348\nxo8fj9atW6NJkyYYPnw4hg8fjmbNmqFNmzast6fTZDomICBAJpPJZJs3b5bt3LlTJpPJZEOGDOGk\nrcDAwNfO//HHH3PS1vnz52VdunSR9e7dW5aVlcVJG5p8PQMHDpQVFhYy9wsLC2UDBw7kpK3q9srL\nyzk7v0wmY95vmlL9Xq/57zV48GDW2+HqPVCXoUOHKvTYm0znRsyGhoZITEzEoUOHmE/1ml+/2NS0\naVMUFRUxVTo3btxA8+bNWW/nypUrWLp0KaZMmYL33nsPS5Ys4eQrsqZeDwBYWlqiWbNmzP1mzZo1\nWIaqDjs7O1RWVnJ2fgAICgrCTz/9hK+//hoAkJWVhdOnT3PWnqGhISQSCfPvVVhYCAMD9v9k+/fv\nj9TUVNbPW5eysjJkZ2cz97Ozs1FWVqax9nWBzhWYZGRkYN++fejatSv8/f2RnZ2No0ePYtKkSay3\ndfv2bSxZsgT//PMPnJycUFRUhHXr1qFjx46stjNs2DBER0czSwUeP34cq1evZnaFYYumXg8AzJkz\nB3fv3sWAAQPA4/EgFArRoUMHdOjQAQDkVipUx5IlS8Dj8SASiXDnzh14eHjA2NiYeX7hwoWstAMA\noaGh6Ny5MxISEpCYmIiysjKMGDECCQkJrLVR0+HDh5GcnIw///wTQ4cORUpKCkJDQzFo0CBW2+nW\nrRvKyspgbGwMQ0NDZknKa9eusdpOtbS0NERERMDOzg4ymQw5OTn45ptvaG/QGnQuMNdUXFyM3Nxc\nTgJLNbFYjMzMTMhkMrRr1w5GRkastyGRSF5bga+oqIiTEaYmXg8ArF+/vt7nq9enVVf1GgR1GTp0\nKCvtAEBgYCAOHjyIgIAAHDp0CADw8ccf4/Dhw6y18V/37t3DxYsXIZPJ4OHhAUdHR87a0qSKigrc\nv38fAODg4CD3YUp0cFbGmDFjsGHDBojFYgQGBsLa2hrdu3dHeHg4621JJBKkpqYyV/rPnTsHgL3R\nXrWioiKsXr0aIpEIW7Zs4Wy2xPHjx+XuZ2VloXnz5nB2doa1tTWrbdUMvMXFxTA3N29w4RZVVAfe\n0tJSmJiYMB9wEokEFRUVrLZlbGyMV69eMa/j4cOHnAUUiUQCPz8/pKSkcBaM7927B0dHR9y+fbvW\n57m4AA1UpTK2bduGnJwcLF26FFlZWcjMzMQHH3zASXu6SOcC84sXL2BmZoa4uDgEBAQwO6tw4Ysv\nvoCJiQmcnZ05ye1VmzdvHgIDA5npQm3btsXMmTNZD8zx8fG4ceMG3nvvPQDA5cuX0blzZzx69Ahf\nfvklAgIC1G5j/fr1GDRoEBwdHVFRUYGJEyfizp074PP5WLVqFd5//32126jN2LFjsW3bNiav/erV\nK0yYMAH79u1jrY1p06Zh4sSJzEyg69evsz6lrBqfz0e7du2Qk5ODd955h5M2tm/fjiVLliA6Ovq1\n53g8HmczdsLDw9G5c2fcuHEDQNWymDNmzKDAXIPOBWaJRIL8/HwcPXoUoaGhnLaVl5eHI0eOcNoG\nUDVi9vX1ZTZyNDQ05OSDQCKRIDk5GS1btgRQNa957ty52L9/P0aPHs1KYD569CimTJkCoCrNIJPJ\ncOHCBWRlZWHu3LmcBeby8vLXLjayfUGpd+/eePfdd3Hz5k1mZ5/6dqFQ1/Pnz+Hn5wdXV1e5XYLY\nmu+7ZMkSAMCuXbtYOZ+iHj58iLVr1zLTAE1NTVnb6klf6Fxg/vLLLzFhwgT873//g6urK7Kzs9G2\nbVtO2vL09MRvv/2GPn36cHL+apqaLZGbm8sEZQCwtrZGbm4uWrRowRS3qMvIyIh5Hb/99hv8/PzA\n5/Ph6OgoV/jBNlNTU9y+fZv5+v3HH3+gSZMmrLaxbt06zJgxg5kNJJVKMXv2bKxatYrVdqrNmDGD\nk/P+V3l5Ofbs2YPff/8dPB4P//vf/zBy5EiYmJhw0p4mU0K6SucC86BBg+SuStvZ2eGHH37gpK2u\nXbti6tSpkEqlnF6tnjdvHiZPnoyHDx9ixIgRzGwJtvXs2ROff/45PvroIwBVi3b37NkTpaWlrH0Q\nGBsb4+7du2jZsiUuXbqEOXPmMM9xOSVqwYIFmDFjBmxsbCCTyfD06VOsWbOG1Tby8vKwceNGfP75\n56ioqMCMGTPw7rvvstpGTT179uTs3DXNmTMHzZo1Y4plEhMTERYWhu+//56T9jSZEtJVOjcro7y8\nHPHx8fjnn39QXl7OPM7FP6yXlxd++ukndOjQgZMLV+np6Xj77bfx1ltvQSwWIzY2FseOHUP79u0x\nffp0tGjRgtX2ZDIZjh8/jt9//x0AYG5ujoKCAixatIi1Nm7evIm5c+eiqKgIn376KZPWSE1NRUJC\nAlavXs1aW9WkUilu3LiBLl26IDMzEwA4mXEik8nw1VdfwdnZGZcuXYKnpyfGjh3Lahs13bhxA0uW\nLMH9+/dRWVkJiUQCU1NT1gcGvr6+SE5ObvAxNhUVFTEpITc3N05TQrpI5wpMwsLC8OTJE/z222/o\n2bMnRCKRXG6RTW+//TacnZ05CcoAsGjRIiZ4XL9+HRs2bMCoUaNgbm6OiIgI1tvj8Xiws7MDn8/H\nyZMncenSJdav+Lu5uSElJQWXLl1igjJQtUckF0EZAAwMDBAZGQkjIyM4OzvD2dmZ1aB8+/Zt3L59\nG3/++Sc+/fRTJCcnw97eHu7u7nXOaGBDZGQkVq9eDXt7e9y8eRNLly7FqFGjWG/n3XffZS7EAVUf\nri4uLqy3U23dunWwtLRE//798cEHH6BFixacLaugq3QulfHw4UN8//33EAqFGDp0KPz9/Tl5swJV\naZIxY8bA09NTLgfG1nQ5iUTCjIqTk5MREhICHx8f+Pj4MDuSsyEzMxNJSUlITEyEpaUlfH19IZPJ\nOL3oU1RUhB9//JHJW3bv3h1TpkzhrPrPw8MDx44dw8CBA1n/IP3vrAVzc3NkZGQgOjqa09kLAGBv\nb8/Mcw8KCkJAQABrQax6NpNYLMaIESOY2R85OTlwcHBgpY3aaDolpIt0LjBXX6QyNzdncpkFBQWc\ntNW6dWu0bt0alZWVnJT7SqVSiMViGBoa4sKFC8xVcgCsXigbNGgQevTogY0bN8Le3h5A1VQpLs2a\nNQs9evRg8pRHjhzBzJkzOWt337592LZtGwwNDWFsbMzq9QBNz1qoZmpqioqKCnTq1AnffvstbGxs\nIJVKWTt/Y63mtnz5cnz11VfYuHGjRlJCukjncsxxcXEYOHAg/v77b4SHh6O0tBTTp0/HyJEjG7tr\nStuwYQNSU1NhaWmJ3Nxc/Prrr+DxeHjw4AHmzp3L2hzckydPIikpCdeuXUPfvn3h5+eHBQsW4NSp\nU6ycvzb+/v5ITEyUe2zw4MEamX7IldWrV2PixInMErPFxcXYunUrZs6cyUl7jx8/RsuWLVFZWYnt\n27fjxYsX+OSTT5gPV7YVFBTIXbdhe/50zbSPWCxGREQEunfvjmHDhgHgrqBFF+lcYNakwsJCbN68\nGRkZGXJvWDa/ut64cQNPnjxB79690bRpUwBVqYfS0lLW36ilpaUQCoVISkrCxYsXMWTIEHh7e3My\nHTAqKgqurq7MDJqUlBTcunULc+fOZb2tasXFxXjw4IHcvxUb6zFXq1mKXW3o0KENloUri8uiktoI\nhUKsWLEC+fn5sLKyQk5ODhwdHZl5xmwZM2ZMnc9xnRLSNToTmLdt21bv82yXSQNV68cOGjQIW7du\nxTfffINff/0VVlZWCAsLY70tTSsuLkZKSgqSk5OxY8cO1s7brVs38Hg8yGQylJWVyZVIN23alLOF\nceLi4rBz507k5eWhY8eOuHnzJrp27crqH/vgwYNx4MAB5nrDq1evEBQUxHoAqxnsp02bxtl00Gof\nf/wxduzYgXHjxuHQoUO4ePEiDh8+jOXLl7PellQqRUpKCnx9fVk/tz7RmRzzy5cvNd7ms2fPEBwc\njJ07d6Jnz57o2bMngoKCNN4PLlhYWCAkJAQhISGsnvf69eusnk9RO3fuRHx8PIYPH45du3bh3r17\nrM9jHjx4MD777DMEBgYCALOgEdtqjpVqLo/JFUNDQ1haWkIqlUIqlaJXr16cBGWgagZNTEwMBeYG\n6ExgZms1MmVUX2i0sbHBmTNnYGNjg+LiYo33Q5c01sI4xsbGTKVaRUUFHB0dmTnNbJk0aRI6dOiA\nixcvAqiqQuViqcqas0q4mqpZk7m5OV6+fAl3d3d89dVXsLKyYtJqXHj//fexZcsW+Pr6ypWasz1v\nX5fpTCqj2ty5c7FgwQK5CzDR0dGcFJicPn0aPXr0QG5uLpYsWYKXL19iypQpGDBgAOtt6Yuvv/4a\nS5Yskcsn1gwuXOURp0yZgqioKOzYsQMXL16Eubk5xGIxNm/ezEl7XOrUqROzfkR5eTlTWs525emD\nBw/w9OlTdOrUCU2aNIFUKsWRI0fw+PFj9O/fn7O5zF5eXq89Vr1mN6mic4G5tgswtT1GGkfNakag\naiGjY8eOoXXr1pg6dapGRkWXL1/Gixcv0LdvX1bWYBg5ciT27t3L5M+rcb2gPNc+//xzzJo1i9m8\noNrff/+NNWvW0OaojUhnUhnVpFIpiouLYWFhAaAqD8z24jj1LfLO4/HkKtqIvEWLFjEXaq9cuYJV\nq1bh66+/xl9//YWIiAjW118oLy/H3r178fDhQzg7O2PYsGGsrzFRvcZHY+XPufL06dPXgjIAdOjQ\nAY8fP+a07bt37yIjI0NuzWwu8vW6SucC8/jx4zF8+HC5aVgNbYuurNrya6WlpThw4ACePXtGgbke\nmqpmrDZ37lwYGhqiR48eSEtLQ0ZGBqvbSQGayfM2hhcvXtT53KtXrzhrd/369bh06RLu3buHfv36\nIS0tDf/73/8oMNegc4E5ICAALi4uzAWY9evXM3vlsWX8+PHMzyUlJdi5cycOHjwIX19fuefI6zRV\nzVjt3r17TNHKsGHDWN9cAKgqvKhvuiYXUzU1wcXFBfv378fw4cPlHo+Li+O02OPYsWNISEhAQEAA\noqKi8PTpU72YgsomnQnM//3KOmLECNbWEK7Ns2fPsG3bNhw5coSZV1qdPiF18/Pzw+jRo2FpaYkm\nTZqgR48eAKouNJmZmbHeXs33AFfvB6lU2ijTNbk2f/58TJ06FUeOHJFbx7qysrLBPRvVYWJiAgMD\nAxgaGqKkpIRZF5z8S2cu/oWGhsp9ZW3VqhUWLFjASVsrVqzAiRMnMHz4cIwaNYqz1ev0lSarGatn\nMACQm8XA5oU5Lqr7tMnFixfxzz//AADat28PDw8PTttbvHgxZs2ahaSkJGzbtg1NmzZFp06daE3m\nGnQmMNdcZ0EsFiM4OJizP5aOHTvC2NgYfD5fr67CE9XQrB/uPHr0CCUlJZzudK+LdCaVoYmvrNXu\n3LnD6fmJbuF6Jb43UfWGDdVbWVFglqczI2ZNfGUlhHBv8eLFePjwIfz8/ABUzd5p06YNqzvp6Dqd\nCcyEEP3w0Ucf4ejRo0yaUCqVws/PD0ePHm3knmkPndtaihCi2+zt7ZGTk8Pcz83N5WyNaV2lMzlm\nQohuqy4Ee/nyJXx9feHq6gqgqoy/+mdShQIzIUQjqDhLcZRjJoQ0ipKSEojFYuY+Lfv5LxoxE0I0\nKjY2Ft9//z1MTEyY3W5o2U95NGImhGjUwIEDsW/fPlhZWTV2V7QWzcoghGiUnZ2d3M4l5HU0YiaE\naNSff/6J8PBwuLm5yW1kwPZyrbqMcsyEEI2KiIhAr1694OzsDAMD+tJeGwrMhBCNEovFCA8Pb+xu\naDX6uCKEaJSnpydiY2ORn5+PZ8+eMTfyL8oxE0I0inbJbhgFZkII0TKUyiCEaMTmzZuZn/+7ktzq\n1as13R2tRoGZEKIRycnJzM+bNm2Se+7s2bOa7o5Wo8BMCNGImlnT/2ZQKaMqjwIzIUQjau6fWfPn\n2u6/6ejiHyFEI6q3h6u5NRxQNVquqKjA7du3G7mH2oMCMyGEaBlKZRBCiJahwEwIIVqGAjMhhGgZ\nCsyEEKJlKDATQoiW+X8+eyZSnyY1KwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0t3bxvI20Mc",
        "colab_type": "code",
        "outputId": "d6bd6c8f-600d-44f5-90a4-01b090d33080",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "test_x.head()"
      ],
      "execution_count": 0,
      "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>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>2</td>\n",
              "      <td>Davies, Master. John Morgan Jr</td>\n",
              "      <td>male</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>C.A. 33112</td>\n",
              "      <td>36.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Leader, Dr. Alice (Farnham)</td>\n",
              "      <td>female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17465</td>\n",
              "      <td>25.9292</td>\n",
              "      <td>D17</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Kilgannon, Mr. Thomas J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36865</td>\n",
              "      <td>7.7375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>243847</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>McGough, Mr. James Robert</td>\n",
              "      <td>male</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17473</td>\n",
              "      <td>26.2875</td>\n",
              "      <td>E25</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass                                               Name  ... Cabin  Embarked\n",
              "0       2                     Davies, Master. John Morgan Jr  ...   NaN         S\n",
              "1       1                        Leader, Dr. Alice (Farnham)  ...   D17         S\n",
              "2       3                            Kilgannon, Mr. Thomas J  ...   NaN         Q\n",
              "3       2  Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...  ...   NaN         S\n",
              "4       1                          McGough, Mr. James Robert  ...   E25         S\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3H6I9FyR6XSU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_x['Age'] = test[['Age', 'Pclass']].apply(impute_age, axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-26N6YB66tTg",
        "colab_type": "code",
        "outputId": "0c118640-7247-41a6-c495-98a2ec5e1cb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        }
      },
      "source": [
        "sns.heatmap(test_x.isnull())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb6f7bb0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aNWuGP//8E9OnT0dcXBwaNWrEdr8JIURtuv7wT60Rs1AohKurK3g8Hjp37gwj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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQfxol_n636q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embark = pd.get_dummies(test_x['Embarked'], drop_first=True).head()\n",
        "sex = pd.get_dummies(test_x['Sex'], drop_first=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5ciXnBq7Pto",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_x.drop(['Sex', 'Embarked', 'Name', 'Ticket'], axis = 1, inplace= True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVUb1J6M7T-x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_x = pd.concat([test_x, sex, embark], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cME9l9a7kIs",
        "colab_type": "code",
        "outputId": "fb67d184-11fa-4845-94a1-650b63908549",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "test_x = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/Project - Logistic Regression/0000000000002429_test_titanic_x_test.csv\")\n",
        "test_x['Age'] = test[['Age', 'Pclass']].apply(impute_age, axis = 1)\n",
        "embark = pd.get_dummies(test_x['Embarked'], drop_first=True)\n",
        "sex = pd.get_dummies(test_x['Sex'], drop_first=True)\n",
        "test_x.drop(['Sex', 'Embarked', 'Name', 'Ticket', 'Cabin'], axis = 1, inplace= True)\n",
        "test_x = pd.concat([test_x, sex, embark], axis = 1)\n",
        "test_x.head()"
      ],
      "execution_count": 0,
      "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>male</th>\n",
              "      <th>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>36.7500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.9292</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7375</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>26.2875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
              "0       2   8.0      1      1  36.7500     1  0  1\n",
              "1       1  49.0      0      0  25.9292     0  0  1\n",
              "2       3  24.0      0      0   7.7375     1  1  0\n",
              "3       2  24.0      2      1  27.0000     0  0  1\n",
              "4       1  36.0      0      0  26.2875     1  0  1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dd41wBRu-AXO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = alg.predict(test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1N6rTA1EzcM",
        "colab_type": "code",
        "outputId": "4b707ca1-46ee-42df-8450-f76635390bde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "alg.score(test_x, y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEA_-ZyMF3Kv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.savetxt('/content/drive/My Drive/Colab Notebooks/Project - Logistic Regression/pred.csv', y_pred, delimiter=',')  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ek_hED9oGQjf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}