Martin_Lukan_Quest_Logistic_regression_Titanic.ipynb

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  "metadata": {
    "colab": {
      "name": "Martin Lukan Quest Logistic regression - Titanic",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlukan/GDA3B2021/blob/main/Martin_Lukan_Quest_Logistic_regression_Titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Ap1mtkdiXwS"
      },
      "source": [
        "# Execute the code below\n",
        "You will get a passenger list of the titanic."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53zP-4uyicEu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f98ff865-c36e-4833-a79f-c36f6a7d1634"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "link = \"https://raw.githubusercontent.com/murpi/wilddata/master/quests/titanic.csv\"\n",
        "df_titanic = pd.read_csv(link)\n",
        "df_titanic['Survived'] = df_titanic['Survived'].apply(lambda x: \"Survived\" if x == 1 else \"Dead\")\n",
        "df_titanic.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Siblings/Spouses Aboard</th>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr. Owen Harris Braund</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Survived</td>\n",
              "      <td>1</td>\n",
              "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Survived</td>\n",
              "      <td>3</td>\n",
              "      <td>Miss. Laina Heikkinen</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Survived</td>\n",
              "      <td>1</td>\n",
              "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr. William Henry Allen</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  ... Parents/Children Aboard     Fare\n",
              "0      Dead       3  ...                       0   7.2500\n",
              "1  Survived       1  ...                       0  71.2833\n",
              "2  Survived       3  ...                       0   7.9250\n",
              "3  Survived       1  ...                       0  53.1000\n",
              "4      Dead       3  ...                       0   8.0500\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPKc4UTfjM7t"
      },
      "source": [
        "# Data preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ew_BixpIjRl1"
      },
      "source": [
        "What are the \"type\" of each column? Are there non-numeric columns? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LCDhqijixTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "c3e4fe8a-0c11-4fdb-d64d-a56aa890d63e"
      },
      "source": [
        "# What are the \"type\" of each column? \n",
        "df_titanic.dtypes\n",
        "df_titanic=pd.get_dummies(df_titanic, columns=[\"Sex\"],drop_first=True)\n",
        "df_titanic.rename(columns={\"Sex_male\": \"Sex\"},inplace=True)\n",
        "df_titanic"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Siblings/Spouses Aboard</th>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr. Owen Harris Braund</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Survived</td>\n",
              "      <td>1</td>\n",
              "      <td>Mrs. John Bradley (Florence Briggs Thayer) Cum...</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>Survived</td>\n",
              "      <td>3</td>\n",
              "      <td>Miss. Laina Heikkinen</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Survived</td>\n",
              "      <td>1</td>\n",
              "      <td>Mrs. Jacques Heath (Lily May Peel) Futrelle</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr. William Henry Allen</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>882</th>\n",
              "      <td>Dead</td>\n",
              "      <td>2</td>\n",
              "      <td>Rev. Juozas Montvila</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>883</th>\n",
              "      <td>Survived</td>\n",
              "      <td>1</td>\n",
              "      <td>Miss. Margaret Edith Graham</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>884</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Miss. Catherine Helen Johnston</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>Survived</td>\n",
              "      <td>1</td>\n",
              "      <td>Mr. Karl Howell Behr</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Mr. Patrick Dooley</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>887 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Survived  Pclass  ...     Fare  Sex\n",
              "0        Dead       3  ...   7.2500    1\n",
              "1    Survived       1  ...  71.2833    0\n",
              "2    Survived       3  ...   7.9250    0\n",
              "3    Survived       1  ...  53.1000    0\n",
              "4        Dead       3  ...   8.0500    1\n",
              "..        ...     ...  ...      ...  ...\n",
              "882      Dead       2  ...  13.0000    1\n",
              "883  Survived       1  ...  30.0000    0\n",
              "884      Dead       3  ...  23.4500    0\n",
              "885  Survived       1  ...  30.0000    1\n",
              "886      Dead       3  ...   7.7500    1\n",
              "\n",
              "[887 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHR5fxjtmcWN"
      },
      "source": [
        "Then, make a first exploration (pairplots, correlation heatmaps, etc...) of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q07wQW6PVQyW",
        "outputId": "72247d4f-d705-4f95-b4d8-04acd91ba76c"
      },
      "source": [
        "sns.pairplot(df_titanic[df_titanic.Survived.eq('Dead')])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f90385e0b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 42 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "6MJ0SV8TS4pc",
        "outputId": "bb38cf42-5ddb-423e-ab37-7b6c086f259a"
      },
      "source": [
        "\n",
        "def spearmanrho(df):\n",
        "  rhovalues = df.corr(method='spearman')\n",
        "  return rhovalues\n",
        "\n",
        "spearmanrho(df_titanic)\n",
        "\n"
      ],
      "execution_count": 12,
      "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>Siblings/Spouses Aboard</th>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.387982</td>\n",
              "      <td>-0.040348</td>\n",
              "      <td>-0.020617</td>\n",
              "      <td>-0.688234</td>\n",
              "      <td>0.133258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.387982</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.199269</td>\n",
              "      <td>-0.254234</td>\n",
              "      <td>0.156062</td>\n",
              "      <td>0.081088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Siblings/Spouses Aboard</th>\n",
              "      <td>-0.040348</td>\n",
              "      <td>-0.199269</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.449198</td>\n",
              "      <td>0.445980</td>\n",
              "      <td>-0.193354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <td>-0.020617</td>\n",
              "      <td>-0.254234</td>\n",
              "      <td>0.449198</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.409202</td>\n",
              "      <td>-0.253172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>-0.688234</td>\n",
              "      <td>0.156062</td>\n",
              "      <td>0.445980</td>\n",
              "      <td>0.409202</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.257965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>0.133258</td>\n",
              "      <td>0.081088</td>\n",
              "      <td>-0.193354</td>\n",
              "      <td>-0.253172</td>\n",
              "      <td>-0.257965</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Pclass       Age  ...      Fare       Sex\n",
              "Pclass                   1.000000 -0.387982  ... -0.688234  0.133258\n",
              "Age                     -0.387982  1.000000  ...  0.156062  0.081088\n",
              "Siblings/Spouses Aboard -0.040348 -0.199269  ...  0.445980 -0.193354\n",
              "Parents/Children Aboard -0.020617 -0.254234  ...  0.409202 -0.253172\n",
              "Fare                    -0.688234  0.156062  ...  1.000000 -0.257965\n",
              "Sex                      0.133258  0.081088  ... -0.257965  1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sdzi-h3un-XT"
      },
      "source": [
        "You are looking for Jack. How many people named Jack on board?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDGn6qWIn7Ql",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b024df9f-8892-45fd-9b67-e17211c21ad0"
      },
      "source": [
        "# How many people named Jack on board?\n",
        "Jacks = df_titanic[df_titanic['Name'].eq('Jack')]\n",
        "len(Jacks)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "XwQV4eBIdQLf",
        "outputId": "9c68aab8-59f0-4e79-8cdb-838761be8820"
      },
      "source": [
        "Jacks"
      ],
      "execution_count": 14,
      "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Siblings/Spouses Aboard</th>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Survived, Pclass, Name, Age, Siblings/Spouses Aboard, Parents/Children Aboard, Fare, Sex]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmBcXYnYdIXs"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vpruBrCOnOw8"
      },
      "source": [
        "# Logistic regression\n",
        "\n",
        "Today, in this quest, you have an extraordinary power: you can travel in time to try to save some passengers. \n",
        "You obviously wanted to save Jack. But you didn't find his name on the list. He probably travels under a false name...\n",
        "Too bad, thanks to this trip, you will still try to save as many people as possible. To do this, you have to identify the people who are most probably going to die. \n",
        "\n",
        "- Select features (X) with only numeric values, and without \"Survived\" column\n",
        "- Select \"Survived\" column as target (y)\n",
        "- Split your data with **random_state = 36**\n",
        "- Train a logistic regression\n",
        "- Print the accuracy score on the train set and on the test set. Is there overfitting?\n",
        "- Print the Confusion Matrix on the test set\n",
        "- How many iterations were needed to train this model?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpXqf6uNq3b6"
      },
      "source": [
        "# It's up to you:\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "X = df_titanic.select_dtypes(exclude='object')\n",
        "y = df_titanic['Survived']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,random_state=36)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hPwsCpt-f0sN"
      },
      "source": [
        "model = LogisticRegression().fit(X_train,y_train)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzMa-sl2f9aD",
        "outputId": "660ebc58-370d-4c52-e2f2-5de98f0a760c"
      },
      "source": [
        "print(\"accuracy score on train set:\",model.score(X_train, y_train))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy score on train set: 0.8165413533834587\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2SDyAetgpxp",
        "outputId": "33e4be67-9701-400c-d9f4-b2a578c66fb9"
      },
      "source": [
        "print(\"accuracy score on test set:\",model.score(X_test, y_test))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy score on test set: 0.7882882882882883\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "77DavEY0hKH-",
        "outputId": "38b0933a-57fe-43de-848d-d51acd2b042c"
      },
      "source": [
        "# Print the Confusion Matrix on the test set\n",
        "predictions = model.predict(X_test)\n",
        "cm = confusion_matrix(y_test, predictions, labels=model.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
        "disp.plot(values_format = '')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f902d3c3910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ra4PDTkkhJ1r",
        "outputId": "9e200e46-a9c7-43d1-86a9-1aadd0921a7a"
      },
      "source": [
        "#How many iterations were needed to train this model?\n",
        "model.n_iter_"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([70], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJ4z3yi9w5Ja"
      },
      "source": [
        "# Model improvement\n",
        "\n",
        "You can save all the people that the model will predict as dead. Change the weight of the classes to save all the people at risk.\n",
        "- Change the weight of the classes\n",
        "- Fit the model on train set\n",
        "- Print the accuracy score on the train set and on the test set\n",
        "- Print the Confusion Matrix on the test set, you must have no deaths that have been predicted as \"Survived\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UrojSLL8whJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5447c1c2-d4d2-48c9-f80e-0978b958abfc"
      },
      "source": [
        "# Change the weight of the classes\n",
        "#Fit the model on train set\n",
        "\n",
        "model1 = LogisticRegression(class_weight = {'Dead' : 6})\n",
        "model1.fit(X_train,y_train)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight={'Dead': 6}, dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "vKSjD66sR7jp",
        "outputId": "1e410213-8ca0-498d-8458-f2d2243b3e0c"
      },
      "source": [
        "# Print the accuracy score on the train set and on the test set\n",
        "# Print the Confusion Matrix on the test set, you must have no deaths that have been predicted as \"Survived\"\n",
        "\n",
        "print(\"accuracy score on train set:\",model1.score(X_train, y_train))\n",
        "print(\"accuracy score on test set:\",model1.score(X_test, y_test))\n",
        "predictions = model1.predict(X_test)\n",
        "cm = confusion_matrix(y_test, predictions, labels=model1.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model1.classes_)\n",
        "disp.plot(values_format = '')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy score on train set: 0.7578947368421053\n",
            "accuracy score on test set: 0.6756756756756757\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f90284b3e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "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": "hZzv1nztzdbf"
      },
      "source": [
        "# People most at risk\n",
        "\n",
        "You are looking for people most at risk.\n",
        "- Compute the prediction probabilities **on your test set**\n",
        "- Which column is about \"survived\" probability?\n",
        "- Among the previous prediction probability array, select only the column corresponding to the \"Survived\" probability\n",
        "- Display the passengers by sorting  most likely to survive first (`sorted_values()` method?)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEj-ZIQp0WUn"
      },
      "source": [
        "# Compute the prediction probabilities on your test set\n",
        "model1.predict_proba(X_test)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmoTagRISp8N",
        "outputId": "b674c03f-b5ff-4b9a-9290-43d5817133f4"
      },
      "source": [
        "#Which column is about \"survived\" probability?\n",
        "model1.classes_\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Dead', 'Survived'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPUev5aaUs4N",
        "outputId": "b8e48e73-9bd8-4e10-8e5c-512a18405333",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "#Among the previous prediction probability array, select only the column corresponding to the \"Survived\" probability\n",
        "predictions=model1.predict_proba(X_test)[:,1]\n",
        "dfpredtest = X_test.copy()\n",
        "dfpredtest['pred_survived']=predictions.tolist()\n",
        "# Display the passengers by sorting most likely to survive first (sorted_values() method?\n",
        "dfpredtest.sort_values(by='pred_survived',ascending=False)\n"
      ],
      "execution_count": 35,
      "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>Siblings/Spouses Aboard</th>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex</th>\n",
              "      <th>pred_survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>883</th>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.871853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>777</th>\n",
              "      <td>1</td>\n",
              "      <td>17.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>57.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.833445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534</th>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>106.4250</td>\n",
              "      <td>0</td>\n",
              "      <td>0.818208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>1</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>76.2917</td>\n",
              "      <td>0</td>\n",
              "      <td>0.798633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>1</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>86.5000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.793149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>3</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>39.6875</td>\n",
              "      <td>1</td>\n",
              "      <td>0.004475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>3</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>1</td>\n",
              "      <td>0.004269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>508</th>\n",
              "      <td>3</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.002637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>535</th>\n",
              "      <td>3</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14.5000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.002318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>3</td>\n",
              "      <td>20.0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>69.5500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000647</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>222 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pclass   Age  Siblings/Spouses Aboard  ...      Fare  Sex  pred_survived\n",
              "883       1  19.0                        0  ...   30.0000    0       0.871853\n",
              "777       1  17.0                        1  ...   57.0000    0       0.833445\n",
              "534       1  30.0                        0  ...  106.4250    0       0.818208\n",
              "217       1  32.0                        0  ...   76.2917    0       0.798633\n",
              "755       1  33.0                        0  ...   86.5000    0       0.793149\n",
              "..      ...   ...                      ...  ...       ...  ...            ...\n",
              "264       3  16.0                        4  ...   39.6875    1       0.004475\n",
              "13        3  39.0                        1  ...   31.2750    1       0.004269\n",
              "508       3  66.0                        0  ...    8.0500    1       0.002637\n",
              "535       3  69.0                        0  ...   14.5000    1       0.002318\n",
              "322       3  20.0                        8  ...   69.5500    1       0.000647\n",
              "\n",
              "[222 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LYgaeda4B2dS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DeDfBz285C_"
      },
      "source": [
        "# Bonus - Model improvement, under constraint\n",
        "\n",
        "Your time travel boss tells you that there's a budget cut. You now can only save 120 people max. Not one more.\n",
        "\n",
        "If your model predicts as \"dead\" someone who would have survived in reality. You then \"save\" that person, who would have survived even without your time-traveling help. And you take the place of someone who could have been saved. That's not optimal.\n",
        "\n",
        "Select the 120 people with the highest probability of dying. Of these, how many actually survived?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nGp-3qmzlHt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "560bdf4b-d909-4b8c-c647-9e820fbb9c0d"
      },
      "source": [
        "# Select the 120 people with the highest probability of dying. Of these, how many actually survived?\n",
        "predictions_all = model1.predict_proba(X)[:,1]\n",
        "df_titanic_out = df_titanic.copy()\n",
        "df_titanic_out['pred_survived']=predictions_all.tolist()\n",
        "df_titanic_out.sort_values(by='pred_survived',ascending=False)\n",
        "pred_dead120 = df_titanic_out.sort_values(by='pred_survived').iloc[:120:,]\n",
        "len(pred_dead120[pred_dead120.Survived.eq('Survived')])\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wg1tAdXXGpZ"
      },
      "source": [
        "# Bonus - More predictions\n",
        "\n",
        "Does the Reverend \"Rev. Juozas Montvila\" have a better chance of survival than \"Mrs. William (Margaret Norton) Rice\"?\n",
        "- Filter the initial DataFrame to get only the 2 rows with the 2 persons above, and only columns present in your variables (X)\n",
        "- Make a prediction with probability for this 2 people\n",
        "- Which one has a better chance to survive?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NMoGFW8DioeP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "da79f44c-91e0-49da-c5e1-edf0e4217ef4"
      },
      "source": [
        "#Filter the initial DataFrame to get only the 2 rows with the 2 persons above, and only columns present in your variables (X)\n",
        "montvila_vs_rice = df_titanic_out[df_titanic_out.Name.isin(['Rev. Juozas Montvila','Mrs. William (Margaret Norton) Rice'])]\n",
        "montvila_vs_rice\n"
      ],
      "execution_count": 39,
      "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>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>Siblings/Spouses Aboard</th>\n",
              "      <th>Parents/Children Aboard</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Sex</th>\n",
              "      <th>pred_survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>881</th>\n",
              "      <td>Dead</td>\n",
              "      <td>3</td>\n",
              "      <td>Mrs. William (Margaret Norton) Rice</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>29.125</td>\n",
              "      <td>0</td>\n",
              "      <td>0.105971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>882</th>\n",
              "      <td>Dead</td>\n",
              "      <td>2</td>\n",
              "      <td>Rev. Juozas Montvila</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.000</td>\n",
              "      <td>1</td>\n",
              "      <td>0.059846</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Survived  Pclass  ... Sex  pred_survived\n",
              "881     Dead       3  ...   0       0.105971\n",
              "882     Dead       2  ...   1       0.059846\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1aG4yH-mtNH"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "Make a prediction with probability for this 2 people?\n",
        "Column pred_survived\n",
        "\n",
        "Which one has a better chance to survive?\n",
        "Mrs. William (Margaret Norton) Rice\n",
        "```\n",
        "\n"
      ]
    }
  ]
}