SkLearn_titanic.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyM9VIF1Disj6XAtzZUSKPXt",
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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/salim4n/kaggle_competition/blob/main/SkLearn_titanic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Titanic with Scikit Learn"
      ],
      "metadata": {
        "id": "3iSb9krZnjPm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "VIlgjTXSnRLQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = sns.load_dataset('titanic')\n",
        "titanic.shape\n",
        "titanic.head()"
      ],
      "metadata": {
        "id": "UbOnoDX-p8yf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "titanic = titanic[['survived','pclass','sex','age']]\n",
        "titanic.dropna(axis=0, inplace=True)\n",
        "titanic['sex'].replace(['male','female'],[0,1], inplace=True)\n",
        "titanic.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1225
        },
        "id": "aykYC4lvqE6j",
        "outputId": "78fb0b9a-c467-4f6d-da19-b99e428861ae"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   survived  pclass  sex   age\n",
              "0         0       3    0  22.0\n",
              "1         1       1    1  38.0\n",
              "2         1       3    1  26.0\n",
              "3         1       1    1  35.0\n",
              "4         0       3    0  35.0"
            ],
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            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
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              "chart = value_plot(_df_25, *['index'], **{})\n",
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f534bc91-bcff-4c9e-b0f8-0337e7662e6f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f534bc91-bcff-4c9e-b0f8-0337e7662e6f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_26, *['survived'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3d458854-35d3-4a48-b453-927ecb707e0f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3d458854-35d3-4a48-b453-927ecb707e0f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_27, *['pclass'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-c65a2617-a9f7-47cc-ac29-e170eefd7218\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-c65a2617-a9f7-47cc-ac29-e170eefd7218\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def value_plot(df, y, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)\n",
              "  plt.gca().spines[['top', 'right']].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = value_plot(_df_28, *['sex'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3e3a1e19-403d-4a2b-9400-d6de5473203c\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3e3a1e19-403d-4a2b-9400-d6de5473203c\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x784f6d9ed4e0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_29, *['index'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-8af47dbf-a653-4d9b-a1a6-2eb1d7372c92\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-8af47dbf-a653-4d9b-a1a6-2eb1d7372c92\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_30, *['survived'], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-0fcec4a4-2b89-412f-b610-735fb01fb268\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-0fcec4a4-2b89-412f-b610-735fb01fb268\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_31, *['pclass'], **{})\n",
              "chart"
            ],
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              "4P/x9XHKcRE4AAAAAABJRU5ErkJggg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-1bd0cbbc-b4da-4b4f-84cb-86757d33c501\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-1bd0cbbc-b4da-4b4f-84cb-86757d33c501\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def histogram(df, colname, num_bins=20, figscale=1):\n",
              "  from matplotlib import pyplot as plt\n",
              "  df[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))\n",
              "  plt.gca().spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = histogram(_df_32, *['sex'], **{})\n",
              "chart"
            ],
            "text/html": [
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-59477bdd-d34c-4527-b239-ebb37771b269\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-59477bdd-d34c-4527-b239-ebb37771b269\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x784f6d80ab90>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def scatter_plots(df, colname_pairs, figscale=1, alpha=.8):\n",
              "  from matplotlib import pyplot as plt\n",
              "  plt.figure(figsize=(len(colname_pairs) * 10 * figscale, 10 * figscale))\n",
              "  for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):\n",
              "    ax = plt.subplot(1, len(colname_pairs), plot_i)\n",
              "    df.plot(kind='scatter', x=x_colname, y=y_colname, s=(32 * figscale), alpha=alpha, ax=ax)\n",
              "    ax.spines[['top', 'right',]].set_visible(False)\n",
              "  plt.tight_layout()\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = scatter_plots(_df_33, *[[['index', 'survived'], ['survived', 'pclass'], ['pclass', 'sex'], ['sex', 'age']]], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-30e1bf72-67dc-4dcd-81d6-c6da86e76946\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-30e1bf72-67dc-4dcd-81d6-c6da86e76946\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x784f6d7dcf10>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Time series</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_34, *['index', 'survived', None], **{})\n",
              "chart"
            ],
            "text/html": [
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              "HCoKhBA5/w9aDUQreMz6mgAAAABJRU5ErkJggg==\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-52b0b79d-ce32-4cbd-b6ed-7bbf451d98b5\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-52b0b79d-ce32-4cbd-b6ed-7bbf451d98b5\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_35, *['index', 'pclass', None], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-f3a9f069-ee25-43f5-9c19-e86e5ab184de\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-f3a9f069-ee25-43f5-9c19-e86e5ab184de\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_36, *['index', 'sex', None], **{})\n",
              "chart"
            ],
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              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-7abd9afd-88f0-4d29-9a3d-f74cacf2bc3f\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-7abd9afd-88f0-4d29-9a3d-f74cacf2bc3f\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "import numpy as np\n",
              "from google.colab import autoviz\n",
              "\n",
              "def time_series_multiline(df, timelike_colname, value_colname, series_colname, figscale=1, mpl_palette_name='Dark2'):\n",
              "  from matplotlib import pyplot as plt\n",
              "  import seaborn as sns\n",
              "  figsize = (10 * figscale, 5.2 * figscale)\n",
              "  palette = list(sns.palettes.mpl_palette(mpl_palette_name))\n",
              "  def _plot_series(series, series_name, series_index=0):\n",
              "    if value_colname == 'count()':\n",
              "      counted = (series[timelike_colname]\n",
              "                 .value_counts()\n",
              "                 .reset_index(name='counts')\n",
              "                 .rename({'index': timelike_colname}, axis=1)\n",
              "                 .sort_values(timelike_colname, ascending=True))\n",
              "      xs = counted[timelike_colname]\n",
              "      ys = counted['counts']\n",
              "    else:\n",
              "      xs = series[timelike_colname]\n",
              "      ys = series[value_colname]\n",
              "    plt.plot(xs, ys, label=series_name, color=palette[series_index % len(palette)])\n",
              "\n",
              "  fig, ax = plt.subplots(figsize=figsize, layout='constrained')\n",
              "  df = df.sort_values(timelike_colname, ascending=True)\n",
              "  if series_colname:\n",
              "    for i, (series_name, series) in enumerate(df.groupby(series_colname)):\n",
              "      _plot_series(series, series_name, i)\n",
              "    fig.legend(title=series_colname, bbox_to_anchor=(1, 1), loc='upper left')\n",
              "  else:\n",
              "    _plot_series(df, '')\n",
              "  sns.despine(fig=fig, ax=ax)\n",
              "  plt.xlabel(timelike_colname)\n",
              "  plt.ylabel(value_colname)\n",
              "  return autoviz.MplChart.from_current_mpl_state()\n",
              "\n",
              "chart = time_series_multiline(_df_37, *['index', 'age', None], **{})\n",
              "chart"
            ],
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              "ffRR0tPT2b59O7t27eKzzz4jMDCQRYsWMXbsWKqqqmR8JcIdYu+DIAgaxDsFQRA0iFIQBEGDKAVB\n",
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              "TkSuQmCC\n",
              "\">\n",
              "        \n",
              "      </div>\n",
              "      <script></script>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-3deb02c0-5b37-4a03-b69f-86f11bc0979e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-3deb02c0-5b37-4a03-b69f-86f11bc0979e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "model = KNeighborsClassifier()"
      ],
      "metadata": {
        "id": "au_ENw-6qnrB"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = titanic['survived']\n",
        "X = titanic.drop('survived',axis=1)"
      ],
      "metadata": {
        "id": "ShQhcrxgrH3_"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X,y)\n",
        "model.score(X,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92zCjohYrSKQ",
        "outputId": "5a97cd85-d7bf-4ea3-d373-05eacae5fdce"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8305322128851541"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2lH1DIZra4M",
        "outputId": "c79ed198-9d30-4cd2-b999-2f26a0be7df3"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
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              "       0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
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              "       1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0,\n",
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              "       1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
              "       1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
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              "       0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       1, 0, 1, 0, 0, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def survie(model, pclass=3, sex=0, age=33):\n",
        "  x = np.array([pclass,sex,age]).reshape(1,3)\n",
        "  print(model.predict(x))\n",
        "  print(model.predict_proba(x))\n"
      ],
      "metadata": {
        "id": "woNTcn1MrihM"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "survie(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgSL3IlKr6pJ",
        "outputId": "d1339fa2-8b2c-4835-e9de-b539717d3196"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "[[1. 0.]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BmdbAJqor8b9"
      },
      "execution_count": 87,
      "outputs": []
    }
  ]
}