example using titanic.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_9IavYxfqKF"
      },
      "source": [
        "# 12. Machine learning techniques"
      ]
    },
    {
      "cell_type": "code",
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        "id": "W1qnFjBIftmC",
        "outputId": "dee40201-49cf-4db1-89f2-e679ff0ea08b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!git clone https://github.com/s7s/machine_learning_1.git\n",
        "%cd  machine_learning_1/ML_in_practice"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'machine_learning_1'...\n",
            "remote: Enumerating objects: 211, done.\u001b[K\n",
            "remote: Counting objects: 100% (211/211), done.\u001b[K\n",
            "remote: Compressing objects: 100% (153/153), done.\u001b[K\n",
            "remote: Total 211 (delta 100), reused 166 (delta 55), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (211/211), 34.87 MiB | 21.04 MiB/s, done.\n",
            "Resolving deltas: 100% (100/100), done.\n",
            "/content/machine_learning_1/ML_in_practice\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3nP5yDNfqKI"
      },
      "source": [
        "import random as rd\n",
        "rd.seed(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bAbJWz2MfqKH"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8GM6HeSfqKI"
      },
      "source": [
        "## 12.1 Loading and exploring the dataset\n",
        "\n",
        "First, we use pandas to load the dataset from a csv file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "ac40d0a8-cc4e-4dfb-ab6e-55f872856311",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "# use pabdas to read './titanic.csv'\n",
        "raw_data = pd.read_csv(r\"./titanic.csv\")\n",
        "raw_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500   NaN        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500   NaN        S  \n",
              "..     ...               ...      ...   ...      ...  \n",
              "886      0            211536  13.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
              "890      0            370376   7.7500   NaN        Q  \n",
              "\n",
              "[891 rows x 12 columns]"
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              "<p>891 rows × 12 columns</p>\n",
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          "metadata": {},
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      "metadata": {
        "id": "P8cSfkeYfqKL"
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      "source": [
        "Next, we can explore the dataset."
      ]
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      "metadata": {
        "id": "B9A_Cb6afqKL",
        "outputId": "717e78d5-bae6-4236-a383-142a6ce9be7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Use pandas to examine some info of the dataset\n",
        "\n",
        "raw_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_data.isnull().sum()"
      ],
      "metadata": {
        "id": "BXbkRD7gnu21",
        "outputId": "0ce297ab-bb16-42d0-f50c-254300bc6ad9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
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          "metadata": {},
          "execution_count": 10
        }
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    {
      "cell_type": "code",
      "source": [
        "# Use pandas to examine the description of the dataset\n",
        "raw_data.describe().T"
      ],
      "metadata": {
        "id": "ipnuOTVtK5mI",
        "outputId": "602f5260-e93e-4e3b-fb4c-9737c89e25c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             count        mean         std   min       25%       50%    75%  \\\n",
              "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
              "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
              "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
              "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
              "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
              "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
              "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
              "\n",
              "                  max  \n",
              "PassengerId  891.0000  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use pandas to examine the histograms of the dataset columns\n",
        "raw_data.hist(figsize=(20,15))"
      ],
      "metadata": {
        "id": "F5x-1ojDLTVt",
        "outputId": "58554b22-36c6-4083-aa68-d1155d8989bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f2737f92c10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f27370a5c10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f27378d09d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f273797a650>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f27377d72d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f27370a8dd0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f2736fb0590>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2736f65ad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f2736f65b10>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pwL3iZNfqKN",
        "outputId": "adc69e67-1fa9-4510-b842-9dcfe027419d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Use pandas to examine \"survived\" column (labels)\n",
        "sum(raw_data[\"Survived\"])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "342"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT6BD83VfqKO",
        "outputId": "635ad879-654b-4e26-e669-b2e786d5d0bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "# Use pandas to exanine more than one column at the same time [\"Name\", \"Age\"]\n",
        "\n",
        "raw_data[['Name','Age']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Name   Age\n",
              "0                              Braund, Mr. Owen Harris  22.0\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0\n",
              "2                               Heikkinen, Miss. Laina  26.0\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0\n",
              "4                             Allen, Mr. William Henry  35.0\n",
              "..                                                 ...   ...\n",
              "886                              Montvila, Rev. Juozas  27.0\n",
              "887                       Graham, Miss. Margaret Edith  19.0\n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"   NaN\n",
              "889                              Behr, Mr. Karl Howell  26.0\n",
              "890                                Dooley, Mr. Patrick  32.0\n",
              "\n",
              "[891 rows x 2 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
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              "      <th>0</th>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <th>1</th>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>35.0</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>27.0</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dba0ae20-ac0a-405d-a33d-add309b0e383')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dba0ae20-ac0a-405d-a33d-add309b0e383 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dba0ae20-ac0a-405d-a33d-add309b0e383');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87L5P5_GfqKN",
        "outputId": "429b315b-c6de-46d2-e95d-17f43b02161a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Use pandas to check how many passengers survived\n",
        "\n",
        "print(sum(raw_data[\"Survived\"]))\n",
        "print(len(raw_data))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "342\n",
            "891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visulaization \n",
        "a=raw_data.where(raw_data[\"Survived\"]==0)[[\"Age\",\"Survived\"]]\n",
        "b=raw_data.where(raw_data[\"Survived\"]==1)[[\"Age\",\"Survived\"]]\n",
        "v1,b1,_=plt.hist(a[\"Age\"],bins=[0,10, 20, 30, 40,50,60,70,80]);\n",
        "v2,b2,_=plt.hist(b[\"Age\"],bins=[0,10, 20, 30, 40,50,60,70,80]);\n",
        "values=np.stack((v1,v2))\n",
        "stacked_data = 100*values /values.sum(axis=0)\n",
        "\n",
        "N=10\n",
        "ind = np.arange(N) # the x locations for the groups\n",
        "width = 7\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "print(stacked_data[0,:])\n",
        "ax.bar(b1[:-1], stacked_data[0,:], width, color='r')\n",
        "ax.bar(b1[:-1], stacked_data[1,:], width,bottom=stacked_data[0,:], color='b')\n",
        "ax.set_title('All Data')\n",
        "ax.set_xlabel('Age')\n",
        "ax.set_xticks(b1[:-1])\n",
        "ax.set_yticks(np.arange(0, 81, 10))\n",
        "ax.legend(labels=['Not_Survived', 'Survived'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "a=raw_data.loc[(raw_data[\"Survived\"]==0) & (raw_data[\"Sex\"]==\"female\"),[\"Age\",\"Survived\"]]\n",
        "b=raw_data.loc[(raw_data[\"Survived\"]==1) & (raw_data[\"Sex\"]==\"female\"),[\"Age\",\"Survived\"]]\n",
        "v1,b1,_=plt.hist(a[\"Age\"],bins=[0,10, 20, 30, 40,50,60,70,80]);\n",
        "v2,b2,_=plt.hist(b[\"Age\"],bins=[0,10, 20, 30, 40,50,60,70,80]);\n",
        "values=np.stack((v1,v2))\n",
        "stacked_data = 100*values /values.sum(axis=0)\n",
        "\n",
        "N=10\n",
        "ind = np.arange(N) # the x locations for the groups\n",
        "width = 7\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "print(stacked_data[0,:])\n",
        "ax.bar(b1[:-1], stacked_data[0,:], width, color='r')\n",
        "ax.bar(b1[:-1], stacked_data[1,:], width,bottom=stacked_data[0,:], color='b')\n",
        "ax.set_title('Females Only')\n",
        "ax.set_xlabel('Age')\n",
        "ax.set_xticks(b1[:-1])\n",
        "ax.set_yticks(np.arange(0, 81, 10))\n",
        "ax.legend(labels=['Not_Survived', 'Survived'])\n",
        "plt.show()\n",
        "\n",
        "a=raw_data.loc[(raw_data[\"Survived\"]==0) & (raw_data[\"Sex\"]==\"male\"),[\"Age\",\"Survived\"]]\n",
        "b=raw_data.loc[(raw_data[\"Survived\"]==1) & (raw_data[\"Sex\"]==\"male\"),[\"Age\",\"Survived\"]]\n",
        "v1,b1,_=plt.hist(a[\"Age\"],bins=[0,10, 20, 30, 40,50,60,70,80]);\n",
        "v2,b2,_=plt.hist(b[\"Age\"],bins=[0,10, 20, 30, 40,50,60,70,80]);\n",
        "values=np.stack((v1,v2))\n",
        "stacked_data = 100*values /values.sum(axis=0)\n",
        "\n",
        "N=10\n",
        "ind = np.arange(N) # the x locations for the groups\n",
        "width = 7\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "print(stacked_data[0,:])\n",
        "ax.bar(b1[:-1], stacked_data[0,:], width, color='r')\n",
        "ax.bar(b1[:-1], stacked_data[1,:], width,bottom=stacked_data[0,:], color='b')\n",
        "ax.set_title('Males only')\n",
        "ax.set_xlabel('Age')\n",
        "ax.set_xticks(b1[:-1])\n",
        "ax.set_yticks(np.arange(0, 81, 10))\n",
        "ax.legend(labels=['Not_Survived', 'Survived'])\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "4lVlKAHLLuiq",
        "outputId": "0dc0a50f-1de3-4a88-a0ed-c45b792450f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[38.70967742 59.80392157 65.         56.28742515 61.79775281 58.33333333\n",
            " 68.42105263 85.71428571]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[36.66666667 24.44444444 27.77777778 16.66666667 31.25       11.11111111\n",
            "  0.                 nan]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:30: RuntimeWarning: invalid value encountered in true_divide\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[40.625      87.71929825 83.10810811 78.5046729  78.94736842 86.66666667\n",
            " 86.66666667 85.71428571]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IKYLTXINfqKO"
      },
      "source": [
        "## 12.2. Cleaning up the data\n",
        "\n",
        "Now, let's look at how many columns have missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9azVLEVtfqKO",
        "outputId": "f7d8aa50-a4a5-4b1b-e5a3-cd6e2e07721b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# use pandas to check missing data (NA(not available) values) for all the columns\n",
        "raw_data.isnull().sum()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uXFTGpN5fqKP"
      },
      "source": [
        "The Cabin column is missing too many values to be useful. Let's drop it altogether."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A49SL-0NfqKQ"
      },
      "source": [
        "# Use pandas to drop \"Cabin\" column\n",
        "clean_data = raw_data.drop('Cabin',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2227vwAofqKQ",
        "outputId": "1929e20e-5e52-44d8-db51-4cb8a9078fde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "clean_data"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
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              "..           ...       ...     ...   \n",
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              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
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              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
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              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
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              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Embarked  \n",
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              "1        0          PC 17599  71.2833        C  \n",
              "2        0  STON/O2. 3101282   7.9250        S  \n",
              "3        0            113803  53.1000        S  \n",
              "4        0            373450   8.0500        S  \n",
              "..     ...               ...      ...      ...  \n",
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              "887      0            112053  30.0000        S  \n",
              "888      2        W./C. 6607  23.4500        S  \n",
              "889      0            111369  30.0000        C  \n",
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              "\n",
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              "<p>891 rows × 11 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDdfjznYfqKQ"
      },
      "source": [
        "Other columns such as Age or Embarked are missing some values, but they can still be useful.\n",
        "\n",
        "For the age column, let's fill in the missing values with the median of all ages.\n",
        "\n",
        "For the Embarked column, let's make a new category called 'U', for Unknown port of embarkment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VesqtzGsfqKR",
        "outputId": "281d972d-0540-4f21-d498-58cf4affb31b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# get the median of age column using pandas\n",
        "median_age = raw_data[\"Age\"].median()\n",
        "median_age"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVksNV43fqKR"
      },
      "source": [
        "# use pandas to fill the na values in age column with the median age\n",
        "clean_data[\"Age\"] = clean_data[\"Age\"].fillna(median_age)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cf_Z2CzvfqKR"
      },
      "source": [
        "# use pandas to fill the na values in embarked column with 'U'\n",
        "\n",
        "clean_data[\"Embarked\"] = clean_data[\"Embarked\"].fillna('U')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAL-CsGyfqKR",
        "outputId": "239c7c07-3eaa-4d19-9f9a-26592bc16361",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "clean_data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ks6LMctufqKS",
        "outputId": "4ab3eae3-42c7-428e-b486-6a4407f7618d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "# view 10 rows of the clean dataset\n",
        "clean_data.head(10)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "5            6         0       3   \n",
              "6            7         0       1   \n",
              "7            8         0       3   \n",
              "8            9         1       3   \n",
              "9           10         1       2   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "5                                   Moran, Mr. James    male  28.0      0   \n",
              "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
              "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
              "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
              "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
              "\n",
              "   Parch            Ticket     Fare Embarked  \n",
              "0      0         A/5 21171   7.2500        S  \n",
              "1      0          PC 17599  71.2833        C  \n",
              "2      0  STON/O2. 3101282   7.9250        S  \n",
              "3      0            113803  53.1000        S  \n",
              "4      0            373450   8.0500        S  \n",
              "5      0            330877   8.4583        Q  \n",
              "6      0             17463  51.8625        S  \n",
              "7      1            349909  21.0750        S  \n",
              "8      2            347742  11.1333        S  \n",
              "9      0            237736  30.0708        C  "
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              "      <th>Survived</th>\n",
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              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1cb8a9a0-70ba-45ca-b3ac-d254aaebb312')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1cb8a9a0-70ba-45ca-b3ac-d254aaebb312 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1cb8a9a0-70ba-45ca-b3ac-d254aaebb312');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUfU0glCfqKS"
      },
      "source": [
        "### 12.2.3 Saving our data for the future"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtAY8WYTfqKS"
      },
      "source": [
        "# save the clean dataset to './clean_titanic_data.csv'\n",
        "clean_data.to_csv(\"./clean_titanic_data.csv\",index=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Y_4pk_RfqKS"
      },
      "source": [
        "## 12.3 Manipulating the features\n",
        "\n",
        "- One-hot encoding\n",
        "- Binning\n",
        "- Feature selection\n",
        "\n",
        "### 12.3.1 One-hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8pojgwefqKS",
        "outputId": "2392532a-ce0e-4592-8e60-a753d21fcdb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "preprocessed_data = pd.read_csv('clean_titanic_data.csv')\n",
        "preprocessed_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  28.0      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Embarked  \n",
              "0        0         A/5 21171   7.2500        S  \n",
              "1        0          PC 17599  71.2833        C  \n",
              "2        0  STON/O2. 3101282   7.9250        S  \n",
              "3        0            113803  53.1000        S  \n",
              "4        0            373450   8.0500        S  \n",
              "..     ...               ...      ...      ...  \n",
              "886      0            211536  13.0000        S  \n",
              "887      0            112053  30.0000        S  \n",
              "888      2        W./C. 6607  23.4500        S  \n",
              "889      0            111369  30.0000        C  \n",
              "890      0            370376   7.7500        Q  \n",
              "\n",
              "[891 rows x 11 columns]"
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              "      <th>888</th>\n",
              "      <td>889</td>\n",
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              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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              "      <td>7.7500</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09514b74-0d07-4163-b7ac-a80a7d0e9b0f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-09514b74-0d07-4163-b7ac-a80a7d0e9b0f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-09514b74-0d07-4163-b7ac-a80a7d0e9b0f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZivvKrlcfqKS",
        "outputId": "9e5de7d5-7a9f-48ea-83f8-fc4eeef5b48d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "# Use pandas method .get_dummies() to get the one hot encoding of “embarked”, “pclass” and “gender”\n",
        "\n",
        "data=pd.get_dummies(preprocessed_data,columns=['Embarked','Pclass','Sex'])\n",
        "\n",
        "\n",
        "data.head(10)\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived                                               Name  \\\n",
              "0            1         0                            Braund, Mr. Owen Harris   \n",
              "1            2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2            3         1                             Heikkinen, Miss. Laina   \n",
              "3            4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4            5         0                           Allen, Mr. William Henry   \n",
              "5            6         0                                   Moran, Mr. James   \n",
              "6            7         0                            McCarthy, Mr. Timothy J   \n",
              "7            8         0                     Palsson, Master. Gosta Leonard   \n",
              "8            9         1  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
              "9           10         1                Nasser, Mrs. Nicholas (Adele Achem)   \n",
              "\n",
              "    Age  SibSp  Parch            Ticket     Fare  Embarked_C  Embarked_Q  \\\n",
              "0  22.0      1      0         A/5 21171   7.2500           0           0   \n",
              "1  38.0      1      0          PC 17599  71.2833           1           0   \n",
              "2  26.0      0      0  STON/O2. 3101282   7.9250           0           0   \n",
              "3  35.0      1      0            113803  53.1000           0           0   \n",
              "4  35.0      0      0            373450   8.0500           0           0   \n",
              "5  28.0      0      0            330877   8.4583           0           1   \n",
              "6  54.0      0      0             17463  51.8625           0           0   \n",
              "7   2.0      3      1            349909  21.0750           0           0   \n",
              "8  27.0      0      2            347742  11.1333           0           0   \n",
              "9  14.0      1      0            237736  30.0708           1           0   \n",
              "\n",
              "   Embarked_S  Embarked_U  Pclass_1  Pclass_2  Pclass_3  Sex_female  Sex_male  \n",
              "0           1           0         0         0         1           0         1  \n",
              "1           0           0         1         0         0           1         0  \n",
              "2           1           0         0         0         1           1         0  \n",
              "3           1           0         1         0         0           1         0  \n",
              "4           1           0         0         0         1           0         1  \n",
              "5           0           0         0         0         1           0         1  \n",
              "6           1           0         1         0         0           0         1  \n",
              "7           1           0         0         0         1           0         1  \n",
              "8           1           0         0         0         1           1         0  \n",
              "9           0           0         0         1         0           1         0  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_U</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>26.0</td>\n",
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              "      <td>STON/O2. 3101282</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>35.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>54.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11eb2385-9520-4e7f-b2e0-630e5db93ec9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-11eb2385-9520-4e7f-b2e0-630e5db93ec9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-11eb2385-9520-4e7f-b2e0-630e5db93ec9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwEwCHPFfqKT",
        "outputId": "7a0bfa31-3a2c-4fbb-d272-d47aa0b76972",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived                                               Name  \\\n",
              "0            1         0                            Braund, Mr. Owen Harris   \n",
              "1            2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2            3         1                             Heikkinen, Miss. Laina   \n",
              "3            4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4            5         0                           Allen, Mr. William Henry   \n",
              "5            6         0                                   Moran, Mr. James   \n",
              "6            7         0                            McCarthy, Mr. Timothy J   \n",
              "7            8         0                     Palsson, Master. Gosta Leonard   \n",
              "8            9         1  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)   \n",
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          },
          "metadata": {},
          "execution_count": 45
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cV-AbnysfqKU"
      },
      "source": [
        "### 12.3.2 Binning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gThz2CLlfqKU",
        "outputId": "dffe0df5-4540-4cd9-8275-530947ab8849",
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          "base_uri": "https://localhost:8080/",
          "height": 991
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      "source": [
        "bins = [0, 10, 20, 30, 40, 50, 60, 70, 80]\n",
        "# Use .cut() method to make bins from the age column\n",
        "\n",
        "categorized_age = pd.cut(data['Age'],bins)\n",
        "data['Categorized_age'] = categorized_age\n",
        "data = data.drop([\"Age\"], axis=1)\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived                                               Name  \\\n",
              "0              1         0                            Braund, Mr. Owen Harris   \n",
              "1              2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2              3         1                             Heikkinen, Miss. Laina   \n",
              "3              4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4              5         0                           Allen, Mr. William Henry   \n",
              "..           ...       ...                                                ...   \n",
              "886          887         0                              Montvila, Rev. Juozas   \n",
              "887          888         1                       Graham, Miss. Margaret Edith   \n",
              "888          889         0           Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1                              Behr, Mr. Karl Howell   \n",
              "890          891         0                                Dooley, Mr. Patrick   \n",
              "\n",
              "     SibSp  Parch            Ticket     Fare  Embarked_C  Embarked_Q  \\\n",
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              "890      0      0            370376   7.7500           0           1   \n",
              "\n",
              "     Embarked_S  Embarked_U  Pclass_1  Pclass_2  Pclass_3  Sex_female  \\\n",
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              "\n",
              "     Sex_male Categorized_age  \n",
              "0           1        (20, 30]  \n",
              "1           0        (30, 40]  \n",
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              "4           1        (30, 40]  \n",
              "..        ...             ...  \n",
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              "889         1        (20, 30]  \n",
              "890         1        (30, 40]  \n",
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              "      <td>2</td>\n",
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
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              "      <td>(30, 40]</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>(20, 30]</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>8.0500</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>(10, 20]</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>(20, 30]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>(20, 30]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>(30, 40]</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 17 columns</p>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const dataTable =\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBtwwk_ofqKU",
        "outputId": "55d924bb-3ab1-4e9d-88f2-64fe1038d315",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "source": [
        "# Use pandas method .get_dummies() to get the one hot encoding of “Categorized_age”\n",
        "# Use pandas method .drop() to remove the old column and method .concat() to add the new columns\n",
        "data=pd.get_dummies(data,columns=['Categorized_age'])\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived                                               Name  \\\n",
              "0            1         0                            Braund, Mr. Owen Harris   \n",
              "1            2         1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2            3         1                             Heikkinen, Miss. Laina   \n",
              "3            4         1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4            5         0                           Allen, Mr. William Henry   \n",
              "\n",
              "   SibSp  Parch            Ticket     Fare  Embarked_C  Embarked_Q  \\\n",
              "0      1      0         A/5 21171   7.2500           0           0   \n",
              "1      1      0          PC 17599  71.2833           1           0   \n",
              "2      0      0  STON/O2. 3101282   7.9250           0           0   \n",
              "3      1      0            113803  53.1000           0           0   \n",
              "4      0      0            373450   8.0500           0           0   \n",
              "\n",
              "   Embarked_S  ...  Sex_female  Sex_male  Categorized_age_(0, 10]  \\\n",
              "0           1  ...           0         1                        0   \n",
              "1           0  ...           1         0                        0   \n",
              "2           1  ...           1         0                        0   \n",
              "3           1  ...           1         0                        0   \n",
              "4           1  ...           0         1                        0   \n",
              "\n",
              "   Categorized_age_(10, 20]  Categorized_age_(20, 30]  \\\n",
              "0                         0                         1   \n",
              "1                         0                         0   \n",
              "2                         0                         1   \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "\n",
              "   Categorized_age_(30, 40]  Categorized_age_(40, 50]  \\\n",
              "0                         0                         0   \n",
              "1                         1                         0   \n",
              "2                         0                         0   \n",
              "3                         1                         0   \n",
              "4                         1                         0   \n",
              "\n",
              "   Categorized_age_(50, 60]  Categorized_age_(60, 70]  \\\n",
              "0                         0                         0   \n",
              "1                         0                         0   \n",
              "2                         0                         0   \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "\n",
              "   Categorized_age_(70, 80]  \n",
              "0                         0  \n",
              "1                         0  \n",
              "2                         0  \n",
              "3                         0  \n",
              "4                         0  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0953a360-79c2-414f-9ea0-6cc58bb6507c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0953a360-79c2-414f-9ea0-6cc58bb6507c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0953a360-79c2-414f-9ea0-6cc58bb6507c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8251vH3fqKV"
      },
      "source": [
        "### 12.3.4 Feature selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_SErhJ9fqKV",
        "outputId": "630cd02a-5bb5-405f-b4c0-df6602b371c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "# drop these columns['Name', 'Ticket', 'PassengerId']\n",
        "\n",
        "data=data.drop([\"Name\",'Ticket','PassengerId'],axis=1)\n",
        "\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  SibSp  Parch     Fare  Embarked_C  Embarked_Q  Embarked_S  \\\n",
              "0         0      1      0   7.2500           0           0           1   \n",
              "1         1      1      0  71.2833           1           0           0   \n",
              "2         1      0      0   7.9250           0           0           1   \n",
              "3         1      1      0  53.1000           0           0           1   \n",
              "4         0      0      0   8.0500           0           0           1   \n",
              "\n",
              "   Embarked_U  Pclass_1  Pclass_2  ...  Sex_female  Sex_male  \\\n",
              "0           0         0         0  ...           0         1   \n",
              "1           0         1         0  ...           1         0   \n",
              "2           0         0         0  ...           1         0   \n",
              "3           0         1         0  ...           1         0   \n",
              "4           0         0         0  ...           0         1   \n",
              "\n",
              "   Categorized_age_(0, 10]  Categorized_age_(10, 20]  \\\n",
              "0                        0                         0   \n",
              "1                        0                         0   \n",
              "2                        0                         0   \n",
              "3                        0                         0   \n",
              "4                        0                         0   \n",
              "\n",
              "   Categorized_age_(20, 30]  Categorized_age_(30, 40]  \\\n",
              "0                         1                         0   \n",
              "1                         0                         1   \n",
              "2                         1                         0   \n",
              "3                         0                         1   \n",
              "4                         0                         1   \n",
              "\n",
              "   Categorized_age_(40, 50]  Categorized_age_(50, 60]  \\\n",
              "0                         0                         0   \n",
              "1                         0                         0   \n",
              "2                         0                         0   \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "\n",
              "   Categorized_age_(60, 70]  Categorized_age_(70, 80]  \n",
              "0                         0                         0  \n",
              "1                         0                         0  \n",
              "2                         0                         0  \n",
              "3                         0                         0  \n",
              "4                         0                         0  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_U</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>...</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Categorized_age_(0, 10]</th>\n",
              "      <th>Categorized_age_(10, 20]</th>\n",
              "      <th>Categorized_age_(20, 30]</th>\n",
              "      <th>Categorized_age_(30, 40]</th>\n",
              "      <th>Categorized_age_(40, 50]</th>\n",
              "      <th>Categorized_age_(50, 60]</th>\n",
              "      <th>Categorized_age_(60, 70]</th>\n",
              "      <th>Categorized_age_(70, 80]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d72b96a-d5ad-4545-98cd-82616e244011')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6d72b96a-d5ad-4545-98cd-82616e244011 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6d72b96a-d5ad-4545-98cd-82616e244011');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mw-TKDt1fqKV"
      },
      "source": [
        "### 12.3.5 Saving for future use"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzlAdRmffqKW"
      },
      "source": [
        "data.to_csv('./data1.csv', index=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bS3XGQrXfqKW"
      },
      "source": [
        "# 12.4 Training models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnmsC1G7fqKW",
        "outputId": "d7e25226-5443-4fda-c170-ed022d679877",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "dt = pd.read_csv('./data1.csv')\n",
        "dt.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  SibSp  Parch     Fare  Embarked_C  Embarked_Q  Embarked_S  \\\n",
              "0         0      1      0   7.2500           0           0           1   \n",
              "1         1      1      0  71.2833           1           0           0   \n",
              "2         1      0      0   7.9250           0           0           1   \n",
              "3         1      1      0  53.1000           0           0           1   \n",
              "4         0      0      0   8.0500           0           0           1   \n",
              "\n",
              "   Embarked_U  Pclass_1  Pclass_2  ...  Sex_female  Sex_male  \\\n",
              "0           0         0         0  ...           0         1   \n",
              "1           0         1         0  ...           1         0   \n",
              "2           0         0         0  ...           1         0   \n",
              "3           0         1         0  ...           1         0   \n",
              "4           0         0         0  ...           0         1   \n",
              "\n",
              "   Categorized_age_(0, 10]  Categorized_age_(10, 20]  \\\n",
              "0                        0                         0   \n",
              "1                        0                         0   \n",
              "2                        0                         0   \n",
              "3                        0                         0   \n",
              "4                        0                         0   \n",
              "\n",
              "   Categorized_age_(20, 30]  Categorized_age_(30, 40]  \\\n",
              "0                         1                         0   \n",
              "1                         0                         1   \n",
              "2                         1                         0   \n",
              "3                         0                         1   \n",
              "4                         0                         1   \n",
              "\n",
              "   Categorized_age_(40, 50]  Categorized_age_(50, 60]  \\\n",
              "0                         0                         0   \n",
              "1                         0                         0   \n",
              "2                         0                         0   \n",
              "3                         0                         0   \n",
              "4                         0                         0   \n",
              "\n",
              "   Categorized_age_(60, 70]  Categorized_age_(70, 80]  \n",
              "0                         0                         0  \n",
              "1                         0                         0  \n",
              "2                         0                         0  \n",
              "3                         0                         0  \n",
              "4                         0                         0  \n",
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              "[5 rows x 21 columns]"
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              "\n",
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              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Embarked_U</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>...</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Categorized_age_(0, 10]</th>\n",
              "      <th>Categorized_age_(10, 20]</th>\n",
              "      <th>Categorized_age_(20, 30]</th>\n",
              "      <th>Categorized_age_(30, 40]</th>\n",
              "      <th>Categorized_age_(40, 50]</th>\n",
              "      <th>Categorized_age_(50, 60]</th>\n",
              "      <th>Categorized_age_(60, 70]</th>\n",
              "      <th>Categorized_age_(70, 80]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c339695-06b0-4062-abbd-5bdd17f71486')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7c339695-06b0-4062-abbd-5bdd17f71486 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7c339695-06b0-4062-abbd-5bdd17f71486');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akXchKUDfqKW"
      },
      "source": [
        "### 12.4.1 Features-labels split and train-validation split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_lzo2QNufqKX"
      },
      "source": [
        "# drop [\"Survived\"] column and save that to features\n",
        "features = dt.drop('Survived',axis=1)\n",
        "\n",
        "# save the [\"Survived\"] column to labels\n",
        "labels = dt[\"Survived\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(features.shape)\n",
        "print(labels.shape)"
      ],
      "metadata": {
        "id": "UBhsaU0izckJ",
        "outputId": "7679a50b-2af6-4ae2-b90d-5686d94f68c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(891, 20)\n",
            "(891,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-gIWUBhfqKY"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3CZd8UGfqKY"
      },
      "source": [
        "# split data by 60% train ; use random_state=100\n",
        "features_train, features_validation_test, labels_train, labels_validation_test = train_test_split(features, labels, test_size=0.4, random_state=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZXTP5lgfqKY"
      },
      "source": [
        "# split test data by 50% validation and 50% test ; use random_state=100\n",
        "features_validation, features_test, labels_validation, labels_test = train_test_split(\n",
        "    features_validation_test, labels_validation_test, test_size=0.5, random_state=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCRpVXr0fqKY",
        "outputId": "cfc64efb-cb03-434f-9a1a-1d2fd2b74400",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(len(features_train))\n",
        "print(len(features_validation))\n",
        "print(len(features_test))\n",
        "print(len(labels_train))\n",
        "print(len(labels_validation))\n",
        "print(len(labels_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "534\n",
            "178\n",
            "179\n",
            "534\n",
            "178\n",
            "179\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dJMAfTUfqKZ"
      },
      "source": [
        "### 12.4.2 Training different models on our dataset\n",
        "\n",
        "We'll train six models:\n",
        "- Logistic regression (perceptron)\n",
        "- Decision tree\n",
        "- Support vector machine (SVM)\n",
        "- RandomForestClassifier\n",
        "- GradientBoostingClassifier\n",
        "- AdaBoostClassifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gP26kwwfqKZ",
        "outputId": "b08b94fd-b7a6-439a-fb59-5efe53e8de84",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train logistic regression model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_model = LogisticRegression()\n",
        "lr_model.fit(features_train, labels_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DThp9iwIfqKZ",
        "outputId": "79663b4c-a34d-4cfd-aa1b-eee70f3e7f2a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train decision tree model ; don't use any hyperparameter\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt_model = DecisionTreeClassifier()\n",
        "dt_model.fit(features_train, labels_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yMO82c9fqKa",
        "outputId": "8188c1c6-7b85-4dc4-e577-0190c4b12ed6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train SVM model ; don't use any hyperparameter\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svm_model = SVC()\n",
        "svm_model.fit(features_train, labels_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yhIfxavqfqKa",
        "outputId": "0d7a4c06-7df3-446e-f475-990c109a9001",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train random forest model ; don't use any hyperparameter\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_model = RandomForestClassifier()\n",
        "rf_model.fit(features_train, labels_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EW7wDPHIfqKa",
        "outputId": "4d77925b-2233-4e28-955b-dd926d7370ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train gradient boosting model ; don't use any hyperparameter\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gb_model =GradientBoostingClassifier()\n",
        "gb_model.fit(features_train,labels_train) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPmyHC-ZfqKa",
        "outputId": "69d0f6bc-ede9-4c5e-e5f3-f33b875c9a97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train Adaboost model ; don't use any hyperparameter\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "ab_model = AdaBoostClassifier()\n",
        "ab_model.fit(features_train,labels_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jF0KbS_fqKb"
      },
      "source": [
        "### 12.4.3 Evaluating the models\n",
        "\n",
        "#### Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "rkkPywqP2hl5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjeMJk4KfqKb",
        "outputId": "d81d1bd1-27ca-4755-8c33-8d1863488e1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print accuracy of each model on validation data\n",
        "print(\"Logistic Regression:\", lr_model.score(features_validation, labels_validation))\n",
        "print(\"Decision Tree:\", dt_model.score(features_validation, labels_validation))\n",
        "print(\"SVM:\", svm_model.score(features_validation, labels_validation))\n",
        "print(\"Random Forest:\", rf_model.score(features_validation, labels_validation))\n",
        "print(\"Gradient Boosting:\", gb_model.score(features_validation, labels_validation))\n",
        "print(\"AdaBoost:\", ab_model.score(features_validation, labels_validation))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression: 0.7696629213483146\n",
            "Decision Tree: 0.7696629213483146\n",
            "SVM: 0.6797752808988764\n",
            "Random Forest: 0.7808988764044944\n",
            "Gradient Boosting: 0.8146067415730337\n",
            "AdaBoost: 0.7640449438202247\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KG9iRP-f3hg1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCUK9NJXfqKb"
      },
      "source": [
        "#### F1-score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QU9uZMJkfqKb",
        "outputId": "a4266a95-fcaa-47c7-bf58-22d43c5b0eba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print F1-score of each model on validation data\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "print(\"F1-scores of the models:\")\n",
        "lr_pred=lr_model.predict(features_validation)\n",
        "print(\"Logistic regression:\", f1_score(labels_validation,lr_pred) )\n",
        "dt_pred=dt_model.predict(features_validation)\n",
        "print(\"Decision tree:\", f1_score(labels_validation,dt_pred))\n",
        "svm_pred=svm_model.predict(features_validation)\n",
        "print(\"SVM:\",f1_score(labels_validation,svm_pred) )\n",
        "\n",
        "rf_pred = rf_model.predict(features_validation)\n",
        "print(\"Random Forest:\", f1_score(labels_validation, rf_pred))\n",
        "\n",
        "gb_pred = gb_model.predict(features_validation)\n",
        "print(\"Gradient boosting:\", f1_score(labels_validation, gb_pred))\n",
        "\n",
        "ab_pred = ab_model.predict(features_validation)\n",
        "print(\"AdaBoost:\", f1_score(labels_validation, ab_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1-scores of the models:\n",
            "Logistic regression: 0.6870229007633588\n",
            "Decision tree: 0.6870229007633588\n",
            "SVM: 0.39999999999999997\n",
            "Random Forest: 0.7111111111111111\n",
            "Gradient boosting: 0.744186046511628\n",
            "AdaBoost: 0.6865671641791045\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cN_q429mfqKb"
      },
      "source": [
        "### 12.4.4 Testing the model\n",
        "\n",
        "Finding the accuracy and the F1-score of the model in the testing set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nfxWhppfqKb",
        "outputId": "4ec70683-3081-4838-c882-dab862b9fc3b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print accuracy of gradient boost model on testing data\n",
        "gb_model.score(features_test, labels_test)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8324022346368715"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oW0DEPGxfqKc",
        "outputId": "5f92da7e-bafe-4173-a39b-6f63b2654615",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print F1-score of gradient boost model on testing data\n",
        "gb_pred_test = gb_model.predict(features_test)\n",
        "f1_score(labels_test, gb_pred_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8026315789473685"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1XGztd2fqKc"
      },
      "source": [
        "# 12.5 Grid search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAK1vV5WfqKc"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrcPjLM1fqKc",
        "outputId": "936e7b98-6057-49cf-efe4-e5e7c4dda767",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "svm_parameters = {'kernel': ['rbf'],\n",
        "                  'C': [0.01, 0.1, 1 , 10, 100],\n",
        "                  'gamma': [0.01, 0.1, 1, 10, 100]\n",
        "                }\n",
        "# use gridsearch to find the best hyperparameters \n",
        "svm = SVC()\n",
        "svm_gs = GridSearchCV(estimator = svm,\n",
        "                      param_grid = svm_parameters)\n",
        "svm_gs.fit(features_train, labels_train)\n",
        "\n",
        "# git the best model\n",
        "svm_winner = svm_gs.best_estimator_\n",
        "svm_winner\n",
        "\n",
        "svm_winner.score(features_validation, labels_validation)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7191011235955056"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmtr0bLVfqKd",
        "outputId": "a60190d8-6122-411e-9ca7-de7b4f16aa81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "svm_winner"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=10, gamma=0.01)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDqUNXY1fqKd"
      },
      "source": [
        "# 12.6 Cross validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqi1xhi0fqKd",
        "outputId": "023abacd-daf5-4db3-9b19-ec2c65eb3431",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# print the k-fold cross validation output\n",
        "svm_gs.cv_results_ "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([0.00916309, 0.00926876, 0.01054025, 0.01238308, 0.01338844,\n",
              "        0.00847964, 0.00972753, 0.01128545, 0.01290512, 0.01493697,\n",
              "        0.00890899, 0.01008782, 0.01138844, 0.01360402, 0.01464605,\n",
              "        0.01118717, 0.01110263, 0.01364207, 0.0151432 , 0.01434565,\n",
              "        0.01248913, 0.01590419, 0.0126802 , 0.01494713, 0.01536751]),\n",
              " 'mean_score_time': array([0.00406451, 0.00402942, 0.00424867, 0.00450072, 0.00503249,\n",
              "        0.00361471, 0.00395255, 0.00447736, 0.00476484, 0.00509405,\n",
              "        0.00378895, 0.00401573, 0.00437088, 0.00486727, 0.00499082,\n",
              "        0.00362492, 0.00372634, 0.00426679, 0.0047317 , 0.00471582,\n",
              "        0.00302987, 0.00343809, 0.00418692, 0.00488453, 0.00504856]),\n",
              " 'mean_test_score': array([0.64607653, 0.64607653, 0.64607653, 0.64607653, 0.64607653,\n",
              "        0.65730912, 0.64607653, 0.64607653, 0.64607653, 0.64607653,\n",
              "        0.69851878, 0.76411568, 0.72294128, 0.69289367, 0.69664962,\n",
              "        0.8071945 , 0.75469935, 0.72294128, 0.6929113 , 0.69476283,\n",
              "        0.80340328, 0.74907424, 0.71359549, 0.69478046, 0.69476283]),\n",
              " 'param_C': masked_array(data=[0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1, 0.1,\n",
              "                    1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 100, 100, 100, 100,\n",
              "                    100],\n",
              "              mask=[False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'param_gamma': masked_array(data=[0.01, 0.1, 1, 10, 100, 0.01, 0.1, 1, 10, 100, 0.01,\n",
              "                    0.1, 1, 10, 100, 0.01, 0.1, 1, 10, 100, 0.01, 0.1, 1,\n",
              "                    10, 100],\n",
              "              mask=[False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'param_kernel': masked_array(data=['rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n",
              "                    'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n",
              "                    'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n",
              "                    'rbf'],\n",
              "              mask=[False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False, False, False, False, False, False, False, False,\n",
              "                    False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'C': 0.01, 'gamma': 0.01, 'kernel': 'rbf'},\n",
              "  {'C': 0.01, 'gamma': 0.1, 'kernel': 'rbf'},\n",
              "  {'C': 0.01, 'gamma': 1, 'kernel': 'rbf'},\n",
              "  {'C': 0.01, 'gamma': 10, 'kernel': 'rbf'},\n",
              "  {'C': 0.01, 'gamma': 100, 'kernel': 'rbf'},\n",
              "  {'C': 0.1, 'gamma': 0.01, 'kernel': 'rbf'},\n",
              "  {'C': 0.1, 'gamma': 0.1, 'kernel': 'rbf'},\n",
              "  {'C': 0.1, 'gamma': 1, 'kernel': 'rbf'},\n",
              "  {'C': 0.1, 'gamma': 10, 'kernel': 'rbf'},\n",
              "  {'C': 0.1, 'gamma': 100, 'kernel': 'rbf'},\n",
              "  {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'},\n",
              "  {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'},\n",
              "  {'C': 1, 'gamma': 1, 'kernel': 'rbf'},\n",
              "  {'C': 1, 'gamma': 10, 'kernel': 'rbf'},\n",
              "  {'C': 1, 'gamma': 100, 'kernel': 'rbf'},\n",
              "  {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'},\n",
              "  {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'},\n",
              "  {'C': 10, 'gamma': 1, 'kernel': 'rbf'},\n",
              "  {'C': 10, 'gamma': 10, 'kernel': 'rbf'},\n",
              "  {'C': 10, 'gamma': 100, 'kernel': 'rbf'},\n",
              "  {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'},\n",
              "  {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'},\n",
              "  {'C': 100, 'gamma': 1, 'kernel': 'rbf'},\n",
              "  {'C': 100, 'gamma': 10, 'kernel': 'rbf'},\n",
              "  {'C': 100, 'gamma': 100, 'kernel': 'rbf'}],\n",
              " 'rank_test_score': array([17, 17, 17, 17, 17, 16, 17, 17, 17, 17,  9,  3,  6, 15, 10,  1,  4,\n",
              "         6, 14, 12,  2,  5,  8, 11, 12], dtype=int32),\n",
              " 'split0_test_score': array([0.64485981, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.65420561, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.71962617, 0.73831776, 0.6728972 , 0.70093458, 0.70093458,\n",
              "        0.82242991, 0.71962617, 0.6728972 , 0.70093458, 0.70093458,\n",
              "        0.80373832, 0.74766355, 0.6635514 , 0.70093458, 0.70093458]),\n",
              " 'split1_test_score': array([0.64485981, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.65420561, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.64485981, 0.71028037, 0.68224299, 0.6635514 , 0.6635514 ,\n",
              "        0.73831776, 0.73831776, 0.6728972 , 0.65420561, 0.6635514 ,\n",
              "        0.76635514, 0.74766355, 0.64485981, 0.65420561, 0.6635514 ]),\n",
              " 'split2_test_score': array([0.64485981, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.6728972 , 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.71962617, 0.79439252, 0.73831776, 0.70093458, 0.71028037,\n",
              "        0.81308411, 0.79439252, 0.75700935, 0.70093458, 0.71028037,\n",
              "        0.82242991, 0.74766355, 0.73831776, 0.71028037, 0.71028037]),\n",
              " 'split3_test_score': array([0.64485981, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.64485981, 0.64485981, 0.64485981, 0.64485981, 0.64485981,\n",
              "        0.70093458, 0.77570093, 0.74766355, 0.70093458, 0.70093458,\n",
              "        0.81308411, 0.75700935, 0.73831776, 0.70093458, 0.70093458,\n",
              "        0.80373832, 0.74766355, 0.74766355, 0.70093458, 0.70093458]),\n",
              " 'split4_test_score': array([0.6509434 , 0.6509434 , 0.6509434 , 0.6509434 , 0.6509434 ,\n",
              "        0.66037736, 0.6509434 , 0.6509434 , 0.6509434 , 0.6509434 ,\n",
              "        0.70754717, 0.80188679, 0.77358491, 0.69811321, 0.70754717,\n",
              "        0.8490566 , 0.76415094, 0.77358491, 0.70754717, 0.69811321,\n",
              "        0.82075472, 0.75471698, 0.77358491, 0.70754717, 0.69811321]),\n",
              " 'std_fit_time': array([0.00091371, 0.00016537, 0.00018004, 0.00012907, 0.00024462,\n",
              "        0.00017096, 0.0008056 , 0.00104822, 0.00054602, 0.00094041,\n",
              "        0.00034025, 0.00036398, 0.00020711, 0.00041808, 0.00056031,\n",
              "        0.00225624, 0.00069871, 0.0010481 , 0.00167164, 0.00023746,\n",
              "        0.00082908, 0.00183634, 0.00040838, 0.0008214 , 0.00024991]),\n",
              " 'std_score_time': array([8.68155874e-05, 1.12698180e-04, 1.90326814e-04, 8.79230828e-05,\n",
              "        4.78310155e-04, 1.18054943e-04, 1.18613605e-04, 7.09313851e-04,\n",
              "        3.14360422e-04, 1.41806112e-04, 4.97112743e-05, 1.09642419e-04,\n",
              "        6.25059493e-05, 1.43061272e-04, 1.51172947e-04, 1.71036710e-04,\n",
              "        6.43158765e-05, 1.03031192e-04, 2.59821274e-04, 3.46708478e-04,\n",
              "        9.79997695e-05, 9.12438337e-05, 6.31729877e-05, 2.22897141e-04,\n",
              "        5.57209558e-04]),\n",
              " 'std_test_score': array([0.00243343, 0.00243343, 0.00243343, 0.00243343, 0.00243343,\n",
              "        0.00923744, 0.00243343, 0.00243343, 0.00243343, 0.00243343,\n",
              "        0.02777652, 0.03476014, 0.03891829, 0.01471177, 0.01695151,\n",
              "        0.03687258, 0.0251742 , 0.04235721, 0.01952157, 0.0161391 ,\n",
              "        0.02017857, 0.00282137, 0.05019872, 0.02061699, 0.0161391 ])}"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 12.7 Save the model"
      ],
      "metadata": {
        "id": "4DhPEZv_SCjn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use joblib to save the model\n",
        "import pickle"
      ],
      "metadata": {
        "id": "SVgPG_UrSLXC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('rf_model_pkl','wb') as file:\n",
        "    pickle.dump(svm_winner,file)"
      ],
      "metadata": {
        "id": "yIztxRKh9JRF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "QxRDaO979cV1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}