kaggle_titanic_DS.ipynb

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      "name": "kaggle_titanic_DS.ipynb",
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      "name": "python"
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/bblackheart013/-/blob/main/kaggle_titanic_DS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "t-GYov9-_kr0"
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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAApaBPI_3if",
        "outputId": "9fbcec60-bf9e-4650-b3d8-ed417b7c4e29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training = pd.read_csv('/content/drive/MyDrive/titanic_train.csv')\n",
        "test = pd.read_csv('/content/drive/MyDrive/titanic_test.csv')\n",
        "\n",
        "training['train_test'] = 1\n",
        "test['train_test'] = 0\n",
        "test['Survived'] = np.NaN\n",
        "all_data = pd.concat([training,test])\n",
        "\n",
        "%matplotlib inline\n",
        "all_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z38lvocm_-WK",
        "outputId": "e702183f-f72c-4c29-b4b1-00c9d6bc7c27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'train_test'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#quick look at our data types & null counts \n",
        "training.info()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "oSjwHNycAkhX",
        "outputId": "72c8649a-3534-4458-9834-ceb8d72271df"
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      "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 13 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",
            " 12  train_test   891 non-null    int64  \n",
            "dtypes: float64(2), int64(6), object(5)\n",
            "memory usage: 90.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to better understand the numeric data, we want to use the .describe() method. This gives us an understanding of the central tendencies of the data \n",
        "training.describe()"
      ],
      "metadata": {
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        "id": "UWeRDPDzAnqq",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  train_test  \n",
              "count  891.000000  891.000000       891.0  \n",
              "mean     0.381594   32.204208         1.0  \n",
              "std      0.806057   49.693429         0.0  \n",
              "min      0.000000    0.000000         1.0  \n",
              "25%      0.000000    7.910400         1.0  \n",
              "50%      0.000000   14.454200         1.0  \n",
              "75%      0.000000   31.000000         1.0  \n",
              "max      6.000000  512.329200         1.0  "
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              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>14.454200</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "      <td>1.0</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#quick way to separate numeric columns\n",
        "training.describe().columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zOLeLufqAqXY",
        "outputId": "3d061394-936e-40bb-bc8a-3b91af630b17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare',\n",
              "       'train_test'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# look at numeric and categorical values separately \n",
        "df_num = training[['Age','SibSp','Parch','Fare']]\n",
        "df_cat = training[['Survived','Pclass','Sex','Ticket','Cabin','Embarked']]\n"
      ],
      "metadata": {
        "id": "lPaTJQhfAs-x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#distributions for all numeric variables \n",
        "for i in df_num.columns:\n",
        "    plt.hist(df_num[i])\n",
        "    plt.title(i)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
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        "outputId": "51488d2c-6e6b-4f76-a484-1d5378c49316"
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      "outputs": [
        {
          "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": "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": "code",
      "source": [
        "print(df_num.corr())\n",
        "sns.heatmap(df_num.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "RxUHkBUxAzeI",
        "outputId": "6b300805-2e9c-4482-aa73-604c5de76ea2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Age     SibSp     Parch      Fare\n",
            "Age    1.000000 -0.308247 -0.189119  0.096067\n",
            "SibSp -0.308247  1.000000  0.414838  0.159651\n",
            "Parch -0.189119  0.414838  1.000000  0.216225\n",
            "Fare   0.096067  0.159651  0.216225  1.000000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea5bbb6310>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compare survival rate across Age, SibSp, Parch, and Fare \n",
        "pd.pivot_table(training, index = 'Survived', values = ['Age','SibSp','Parch','Fare'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "e4q_mWevA3L4",
        "outputId": "b28e7f3a-98c4-4d0d-b789-f054c55731dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age       Fare     Parch     SibSp\n",
              "Survived                                          \n",
              "0         30.626179  22.117887  0.329690  0.553734\n",
              "1         28.343690  48.395408  0.464912  0.473684"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Parch</th>\n",
              "      <th>SibSp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>30.626179</td>\n",
              "      <td>22.117887</td>\n",
              "      <td>0.329690</td>\n",
              "      <td>0.553734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>28.343690</td>\n",
              "      <td>48.395408</td>\n",
              "      <td>0.464912</td>\n",
              "      <td>0.473684</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c8104d5-bfbc-4258-a8f1-454d554d69a0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \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-8c8104d5-bfbc-4258-a8f1-454d554d69a0 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-8c8104d5-bfbc-4258-a8f1-454d554d69a0');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df_cat.columns:\n",
        "    sns.barplot(df_cat[i].value_counts().index,df_cat[i].value_counts()).set_title(i)\n",
        "    plt.show()\\"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oR2wfn60A56o",
        "outputId": "4fbed5cb-96c0-4b1f-c08a-2c1939d91b29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\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": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\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": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\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": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\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": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\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": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
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            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing survival and each of these categorical variables \n",
        "print(pd.pivot_table(training, index = 'Survived', columns = 'Pclass', values = 'Ticket' ,aggfunc ='count'))\n",
        "print()\n",
        "print(pd.pivot_table(training, index = 'Survived', columns = 'Sex', values = 'Ticket' ,aggfunc ='count'))\n",
        "print()\n",
        "print(pd.pivot_table(training, index = 'Survived', columns = 'Embarked', values = 'Ticket' ,aggfunc ='count'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LB0tAEjA7dD",
        "outputId": "46ae8350-d552-4f39-8d7d-f212aefc2bef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pclass      1   2    3\n",
            "Survived              \n",
            "0          80  97  372\n",
            "1         136  87  119\n",
            "\n",
            "Sex       female  male\n",
            "Survived              \n",
            "0             81   468\n",
            "1            233   109\n",
            "\n",
            "Embarked   C   Q    S\n",
            "Survived             \n",
            "0         75  47  427\n",
            "1         93  30  217\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat.Cabin\n",
        "training['cabin_multiple'] = training.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))\n",
        "# after looking at this, we may want to look at cabin by letter or by number. Let's create some categories for this \n",
        "# letters \n",
        "# multiple letters \n",
        "training['cabin_multiple'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y2swqYP4A_Dz",
        "outputId": "eacde0aa-278a-495d-84d3-14426bb61031"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    687\n",
              "1    180\n",
              "2     16\n",
              "3      6\n",
              "4      2\n",
              "Name: cabin_multiple, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.pivot_table(training, index = 'Survived', columns = 'cabin_multiple', values = 'Ticket' ,aggfunc ='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "uDYkq0r2BESb",
        "outputId": "f166967d-7e78-4f9c-a724-0b15b6f7f159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cabin_multiple      0      1    2    3    4\n",
              "Survived                                   \n",
              "0               481.0   58.0  7.0  3.0  NaN\n",
              "1               206.0  122.0  9.0  3.0  2.0"
            ],
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              "          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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creates categories based on the cabin letter (n stands for null)\n",
        "#in this case we will treat null values like it's own category\n",
        "\n",
        "training['cabin_adv'] = training.Cabin.apply(lambda x: str(x)[0])"
      ],
      "metadata": {
        "id": "Do5Ul-91BHxG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#comparing surivial rate by cabin\n",
        "print(training.cabin_adv.value_counts())\n",
        "pd.pivot_table(training,index='Survived',columns='cabin_adv', values = 'Name', aggfunc='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "sCr1p_teBJJ5",
        "outputId": "f28a3695-494e-49c0-899a-8f8a0ba77a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n    687\n",
            "C     59\n",
            "B     47\n",
            "D     33\n",
            "E     32\n",
            "A     15\n",
            "F     13\n",
            "G      4\n",
            "T      1\n",
            "Name: cabin_adv, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cabin_adv    A     B     C     D     E    F    G    T      n\n",
              "Survived                                                    \n",
              "0          8.0  12.0  24.0   8.0   8.0  5.0  2.0  1.0  481.0\n",
              "1          7.0  35.0  35.0  25.0  24.0  8.0  2.0  NaN  206.0"
            ],
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              "\n",
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              "      <th>cabin_adv</th>\n",
              "      <th>A</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#understand ticket values better \n",
        "#numeric vs non numeric \n",
        "training['numeric_ticket'] = training.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)\n",
        "training['ticket_letters'] = training.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.','').replace('/','').lower() if len(x.split(' ')[:-1]) >0 else 0)"
      ],
      "metadata": {
        "id": "hSnGSwZLBRDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training['numeric_ticket'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AvlVTSemBSlA",
        "outputId": "1b8ffbf3-5f50-4015-f51e-d922ea189a39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    661\n",
              "0    230\n",
              "Name: numeric_ticket, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#lets us view all rows in dataframe through scrolling. This is for convenience \n",
        "pd.set_option(\"max_rows\", None)\n",
        "training['ticket_letters'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEdYy2YYBVxL",
        "outputId": "8db519a1-3830-4d69-caa7-9969654aecde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            665\n",
              "pc            60\n",
              "ca            41\n",
              "a5            21\n",
              "stono2        18\n",
              "sotonoq       15\n",
              "scparis       11\n",
              "wc            10\n",
              "a4             7\n",
              "soc            6\n",
              "fcc            5\n",
              "c              5\n",
              "sopp           3\n",
              "pp             3\n",
              "wep            3\n",
              "ppp            2\n",
              "scah           2\n",
              "sotono2        2\n",
              "swpp           2\n",
              "fc             1\n",
              "scahbasle      1\n",
              "as             1\n",
              "sp             1\n",
              "sc             1\n",
              "scow           1\n",
              "fa             1\n",
              "sop            1\n",
              "sca4           1\n",
              "casoton        1\n",
              "Name: ticket_letters, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#difference in numeric vs non-numeric tickets in survival rate \n",
        "pd.pivot_table(training,index='Survived',columns='numeric_ticket', values = 'Ticket', aggfunc='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "mQhzkD5-BZlD",
        "outputId": "78cb2e29-035a-48b9-f311-3c2a587ef56c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numeric_ticket    0    1\n",
              "Survived                \n",
              "0               142  407\n",
              "1                88  254"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e309da6-1a25-49ad-b165-f2f8c90f3033\">\n",
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              "\n",
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              "                                                     [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",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#survival rate across different tyicket types \n",
        "pd.pivot_table(training,index='Survived',columns='ticket_letters', values = 'Ticket', aggfunc='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "7et0nr30Beje",
        "outputId": "eac8dbbc-db34-475e-e2b8-53815ee3a427"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ticket_letters      0   a4    a5   as    c    ca  casoton   fa   fc  fcc  ...  \\\n",
              "Survived                                                                  ...   \n",
              "0               410.0  7.0  19.0  1.0  3.0  27.0      1.0  1.0  1.0  1.0  ...   \n",
              "1               255.0  NaN   2.0  NaN  2.0  14.0      NaN  NaN  NaN  4.0  ...   \n",
              "\n",
              "ticket_letters  soc  sop  sopp  sotono2  sotonoq   sp  stono2  swpp   wc  wep  \n",
              "Survived                                                                       \n",
              "0               5.0  1.0   3.0      2.0     13.0  1.0    10.0   NaN  9.0  2.0  \n",
              "1               1.0  NaN   NaN      NaN      2.0  NaN     8.0   2.0  1.0  1.0  \n",
              "\n",
              "[2 rows x 29 columns]"
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>ticket_letters</th>\n",
              "      <th>0</th>\n",
              "      <th>a4</th>\n",
              "      <th>a5</th>\n",
              "      <th>as</th>\n",
              "      <th>c</th>\n",
              "      <th>ca</th>\n",
              "      <th>casoton</th>\n",
              "      <th>fa</th>\n",
              "      <th>fc</th>\n",
              "      <th>fcc</th>\n",
              "      <th>...</th>\n",
              "      <th>soc</th>\n",
              "      <th>sop</th>\n",
              "      <th>sopp</th>\n",
              "      <th>sotono2</th>\n",
              "      <th>sotonoq</th>\n",
              "      <th>sp</th>\n",
              "      <th>stono2</th>\n",
              "      <th>swpp</th>\n",
              "      <th>wc</th>\n",
              "      <th>wep</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>410.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>255.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b25ee25-c571-40bb-a88b-adb191214fb6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \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",
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              "      fill: #1967D2;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-8b25ee25-c571-40bb-a88b-adb191214fb6');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#feature engineering on person's title \n",
        "training.Name.head(50)\n",
        "training['name_title'] = training.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
        "#mr., ms., master. etc"
      ],
      "metadata": {
        "id": "gAL1_MHHBf8u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training['name_title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjReAhkbBk0L",
        "outputId": "b0317570-41f0-4fd2-bc5a-e8a215a2c80d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr              517\n",
              "Miss            182\n",
              "Mrs             125\n",
              "Master           40\n",
              "Dr                7\n",
              "Rev               6\n",
              "Mlle              2\n",
              "Major             2\n",
              "Col               2\n",
              "the Countess      1\n",
              "Capt              1\n",
              "Ms                1\n",
              "Sir               1\n",
              "Lady              1\n",
              "Mme               1\n",
              "Don               1\n",
              "Jonkheer          1\n",
              "Name: name_title, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create all categorical variables that we did above for both training and test sets \n",
        "all_data['cabin_multiple'] = all_data.Cabin.apply(lambda x: 0 if pd.isna(x) else len(x.split(' ')))\n",
        "all_data['cabin_adv'] = all_data.Cabin.apply(lambda x: str(x)[0])\n",
        "all_data['numeric_ticket'] = all_data.Ticket.apply(lambda x: 1 if x.isnumeric() else 0)\n",
        "all_data['ticket_letters'] = all_data.Ticket.apply(lambda x: ''.join(x.split(' ')[:-1]).replace('.','').replace('/','').lower() if len(x.split(' ')[:-1]) >0 else 0)\n",
        "all_data['name_title'] = all_data.Name.apply(lambda x: x.split(',')[1].split('.')[0].strip())\n",
        "\n",
        "#impute nulls for continuous data \n",
        "#all_data.Age = all_data.Age.fillna(training.Age.mean())\n",
        "all_data.Age = all_data.Age.fillna(training.Age.median())\n",
        "#all_data.Fare = all_data.Fare.fillna(training.Fare.mean())\n",
        "all_data.Fare = all_data.Fare.fillna(training.Fare.median())\n",
        "\n",
        "#drop null 'embarked' rows. Only 2 instances of this in training and 0 in test \n",
        "all_data.dropna(subset=['Embarked'],inplace = True)\n",
        "\n",
        "#tried log norm of sibsp (not used)\n",
        "all_data['norm_sibsp'] = np.log(all_data.SibSp+1)\n",
        "all_data['norm_sibsp'].hist()\n",
        "\n",
        "# log norm of fare (used)\n",
        "all_data['norm_fare'] = np.log(all_data.Fare+1)\n",
        "all_data['norm_fare'].hist()\n",
        "\n",
        "# converted fare to category for pd.get_dummies()\n",
        "all_data.Pclass = all_data.Pclass.astype(str)\n",
        "\n",
        "#created dummy variables from categories (also can use OneHotEncoder)\n",
        "all_dummies = pd.get_dummies(all_data[['Pclass','Sex','Age','SibSp','Parch','norm_fare','Embarked','cabin_adv','cabin_multiple','numeric_ticket','name_title','train_test']])\n",
        "\n",
        "#Split to train test again\n",
        "X_train = all_dummies[all_dummies.train_test == 1].drop(['train_test'], axis =1)\n",
        "X_test = all_dummies[all_dummies.train_test == 0].drop(['train_test'], axis =1)\n",
        "\n",
        "\n",
        "y_train = all_data[all_data.train_test==1].Survived\n",
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "dJOod6BTBprH",
        "outputId": "202b843e-e7dd-43af-c9de-911e1f639f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(889,)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQBUlEQVR4nO3dX4zdZZ3H8fcXKiDM2iqYSW2bHRJJNwayCBPAsDFTurupQCwXamRZRcOmF4tsXXYj1Rvai03qBaKSjUlD3dRs11ELpg2oqwEmLhewtti1QGGtWKW1UhGoDqIs63cv5gFrnemcc+ac+c3v2fcrmczvz/M7v++TaT/zzHPOeU5kJpKkupzSdAGSpP4z3CWpQoa7JFXIcJekChnuklShRU0XAHDOOefkyMhIT9e++OKLnHXWWf0taJ7Zh4Wh7X1oe/1gH7q1Z8+eZzPzzdOdWxDhPjIywu7du3u6dmJigrGxsf4WNM/sw8LQ9j60vX6wD92KiB/NdM5pGUmqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqtCCeIfqXOw7fIwPbbi3o7YHN1814GokaWFw5C5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqlBH4R4Rfx8Rj0XEoxHxxYg4IyLOjYiHI+JARHwpIk4rbU8v+wfK+ZFBdkCS9IdmDfeIWAb8HTCamecDpwLvBz4J3J6ZbwWeB24ol9wAPF+O317aSZLmUafTMouA10fEIuBM4AhwBbCjnN8GXFO215Z9yvnVERH9KVeS1InIzNkbRawH/gl4CfgmsB54qIzOiYgVwNcz8/yIeBRYk5mHyrkfAJdm5rMnPOY6YB3A8PDwxePj4z114Ohzx3jmpc7aXrBscU/3GLTJyUmGhoaaLmNO7EPz2l4/2IdurVq1ak9mjk53btYPyI6INzI1Gj8XeAH4CrBmrkVl5hZgC8Do6GiOjY319Dh3bN/Jbfs6+5zvg9f1do9Bm5iYoNf+LxT2oXltrx/sQz91Mi3z58APM/Nnmfk/wN3A5cCSMk0DsBw4XLYPAysAyvnFwM/7WrUk6aQ6CfcfA5dFxJll7nw18DjwAPCe0uZ6YGfZ3lX2Kefvz07mfiRJfTNruGfmw0w9MfoIsK9cswW4Bbg5Ig4AZwNbyyVbgbPL8ZuBDQOoW5J0Eh1NVmfmrcCtJxx+Crhkmra/Bt4799IkSb3yHaqSVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVMtwlqUIdhXtELImIHRHxRETsj4h3RMSbIuJbEfH98v2NpW1ExGcj4kBEfC8iLhpsFyRJJ+p05P4Z4BuZ+SfAnwL7gQ3AfZl5HnBf2Qd4F3Be+VoHfK6vFUuSZjVruEfEYuCdwFaAzHw5M18A1gLbSrNtwDVley3whZzyELAkIpb2vXJJ0owiM0/eIOJCYAvwOFOj9j3AeuBwZi4pbQJ4PjOXRMQ9wObMfLCcuw+4JTN3n/C465ga2TM8PHzx+Ph4Tx04+twxnnmps7YXLFvc0z0GbXJykqGhoabLmBP70Ly21w/2oVurVq3ak5mj051b1MH1i4CLgJsy8+GI+Ay/m4IBIDMzIk7+W+IEmbmFqV8ajI6O5tjYWDeXv+aO7Tu5bV8n3YCD1/V2j0GbmJig1/4vFPaheW2vH+xDP3Uy534IOJSZD5f9HUyF/TOvTreU70fL+cPAiuOuX16OSZLmyazhnpk/BZ6OiJXl0Gqmpmh2AdeXY9cDO8v2LuCD5VUzlwHHMvNIf8uWJJ1MZ/MZcBOwPSJOA54CPszUL4YvR8QNwI+A95W2XwOuBA4AvyptJUnzqKNwz8y9wHST9qunaZvAjXOsS5I0B75DVZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVqNP13KUpG2f4HNqVm2Dj2gHe99jgHluqkCN3SaqQ4S5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUoY7DPSJOjYjvRsQ9Zf/ciHg4Ig5ExJci4rRy/PSyf6CcHxlM6ZKkmXQzcl8P7D9u/5PA7Zn5VuB54IZy/Abg+XL89tJOkjSPOgr3iFgOXAXcWfYDuALYUZpsA64p22vLPuX86tJekjRPOh25fxr4GPDbsn828EJmvlL2DwHLyvYy4GmAcv5YaS9JmieRmSdvEHE1cGVm/m1EjAH/CHwIeKhMvRARK4CvZ+b5EfEosCYzD5VzPwAuzcxnT3jcdcA6gOHh4YvHx8d76sDR547xzEudtb1g2eKe7jFok5OTDA0NNV1GZ47snfbw5OlvYeg3PxncfZdeOLjHLlr1c5hG2+sH+9CtVatW7cnM0enOLerg+suBd0fElcAZwBuAzwBLImJRGZ0vBw6X9oeBFcChiFgELAZ+fuKDZuYWYAvA6Ohojo2NddWpV92xfSe37eukG3Dwut7uMWgTExP02v95t3HttIcnVm5i7MlbB3ffa48N7rGLVv0cptH2+sE+9NOs0zKZ+fHMXJ6ZI8D7gfsz8zrgAeA9pdn1wM6yvavsU87fn7P9eSBJ6qu5vM79FuDmiDjA1Jz61nJ8K3B2OX4zsGFuJUqSutXZfEaRmRPARNl+Crhkmja/Bt7bh9okST3yHaqSVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkV6mpVSP2hkQ33dtz24OarBliJJP2OI3dJqpDhLkkVMtwlqUKGuyRVyHCXpAoZ7pJUIcNdkipkuEtShQx3SaqQ4S5JFTLcJalChrskVchwl6QKuSqkdDIbFzd472PN3Vut58hdkirkyF3tMB8j6JWbYOPawd9HmgeO3CWpQoa7JFXIcJekChnuklQhw12SKmS4S1KFDHdJqpDhLkkVmjXcI2JFRDwQEY9HxGMRsb4cf1NEfCsivl++v7Ecj4j4bEQciIjvRcRFg+6EJOn3dTJyfwX4h8x8G3AZcGNEvA3YANyXmecB95V9gHcB55WvdcDn+l61JOmkZg33zDySmY+U7V8C+4FlwFpgW2m2DbimbK8FvpBTHgKWRMTSvlcuSZpRZGbnjSNGgG8D5wM/zswl5XgAz2fmkoi4B9icmQ+Wc/cBt2Tm7hMeax1TI3uGh4cvHh8f76kDR587xjMvddb2gmX9X59k3+HOV+6b6f6Tk5MMDQ31q6TBOrJ32sOTp7+Fod/8ZJ6L6a8F14elF3bVvFX/jmZgH7qzatWqPZk5Ot25jhcOi4gh4C7go5n5i6k8n5KZGRGd/5aYumYLsAVgdHQ0x8bGurn8NXds38lt+zrrxsHrervHyXxow70dt53p/hMTE/Ta/3k3w8JaEys3MfbkrfNcTH8tuD5c292Sv636dzQD+9A/Hb1aJiJex1Swb8/Mu8vhZ16dbinfj5bjh4EVx12+vByTJM2TTl4tE8BWYH9mfuq4U7uA68v29cDO445/sLxq5jLgWGYe6WPNkqRZdDKfcTnwAWBfRLw64foJYDPw5Yi4AfgR8L5y7mvAlcAB4FfAh/tasSRpVrOGe3liNGY4vXqa9gncOMe6JElz4DtUJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXIcJekCnW8nrukebaxyw+XWblpxvX2u7tvd+vIa2Fy5C5JFTLcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoUMd0mqkOEuSRUy3CWpQoa7JFXItWUk/b5u17Tpp7Gdzd27Mo7cJalChrskVchwl6QKGe6SVCHDXZIq5KtlJC0cR/b259OkulXhp085cpekCjlyX6BGNtzbcduDm68aYCWS2siRuyRVyHCXpAoZ7pJUIcNdkirkE6oV6ObJ1075JK3Ubq0P9wtO+SEHz7i1s8Yb+3zzCl8bK6kOA5mWiYg1EfFkRByIiA2DuIckaWZ9H7lHxKnAPwN/ARwCvhMRuzLz8X7fS4Mz01TPwTPmuRBJPRnEtMwlwIHMfAogIsaBtYDh3icHz/irpkuQ6tLPDyhZuam7JRQGNL0bmdnfB4x4D7AmM/+m7H8AuDQzP3JCu3XAurK7Eniyx1ueAzzb47ULhX1YGNreh7bXD/ahW3+cmW+e7kRjT6hm5hZgy1wfJyJ2Z+ZoH0pqjH1YGNreh7bXD/ahnwbxhOphYMVx+8vLMUnSPBlEuH8HOC8izo2I04D3A7sGcB9J0gz6Pi2Tma9ExEeAfwdOBT6fmY/1+z7HmfPUzgJgHxaGtveh7fWDfeibvj+hKklqnmvLSFKFDHdJqlCrw73tyxxExOcj4mhEPNp0Lb2IiBUR8UBEPB4Rj0XE+qZr6lZEnBER/xkR/1X6sKnpmnoVEadGxHcj4p6ma+lFRByMiH0RsTcidjddT7ciYklE7IiIJyJif0S8o9F62jrnXpY5+G+OW+YAuLZNyxxExDuBSeALmXl+0/V0KyKWAksz85GI+CNgD3BNy34GAZyVmZMR8TrgQWB9Zj7UcGldi4ibgVHgDZl5ddP1dCsiDgKjmdnKNzFFxDbgPzLzzvJKwTMz84Wm6mnzyP21ZQ4y82Xg1WUOWiMzvw0813QdvcrMI5n5SNn+JbAfWNZsVd3JKZNl93Xlq3UjnohYDlwF3Nl0Lf8fRcRi4J3AVoDMfLnJYId2h/sy4Onj9g/RsmCpSUSMAG8HHm62ku6V6Yy9wFHgW5nZuj4AnwY+Bvy26ULmIIFvRsSesjxJm5wL/Az4lzI1dmdEnNVkQW0Ody0QETEE3AV8NDN/0XQ93crM/83MC5l6N/UlEdGqKbKIuBo4mpl7mq5ljv4sMy8C3gXcWKYt22IRcBHwucx8O/Ai0OjzgG0Od5c5WADKPPVdwPbMvLvpeuai/Bn9ALCm6Vq6dDnw7jJnPQ5cERH/2mxJ3cvMw+X7UeCrTE29tsUh4NBxf/XtYCrsG9PmcHeZg4aVJyO3Avsz81NN19OLiHhzRCwp269n6gn6J5qtqjuZ+fHMXJ6ZI0z9P7g/M/+64bK6EhFnlSflKdMZfwm05lVkmflT4OmIWFkOrabhZc5b+zF7DSxz0HcR8UVgDDgnIg4Bt2bm1mar6srlwAeAfWXOGuATmfm1Bmvq1lJgW3n11SnAlzOzlS8lbLlh4KtT4wUWAf+Wmd9otqSu3QRsL4PNp4APN1lMa18KKUmaWZunZSRJMzDcJalChrskVchwl6QKGe6SVCHDXZIqZLhLUoX+DxwqfwSFt2xYAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale data \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scale = StandardScaler()\n",
        "all_dummies_scaled = all_dummies.copy()\n",
        "all_dummies_scaled[['Age','SibSp','Parch','norm_fare']]= scale.fit_transform(all_dummies_scaled[['Age','SibSp','Parch','norm_fare']])\n",
        "all_dummies_scaled\n",
        "\n",
        "X_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(['train_test'], axis =1)\n",
        "X_test_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 0].drop(['train_test'], axis =1)\n",
        "\n",
        "y_train = all_data[all_data.train_test==1].Survived"
      ],
      "metadata": {
        "id": "ou0OBFKUBsIq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import tree\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "d_ZVdDG6Bv-w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#I usually use Naive Bayes as a baseline for my classification tasks \n",
        "gnb = GaussianNB()\n",
        "cv = cross_val_score(gnb,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_J6UrNFKBxEM",
        "outputId": "9d0da06a-93d0-43ed-da68-143db5d5db37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.66853933 0.70224719 0.75842697 0.74719101 0.73446328]\n",
            "0.7221735542436362\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(max_iter = 2000)\n",
        "cv = cross_val_score(lr,X_train,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I65AwlMHBzff",
        "outputId": "f350e630-b161-4bc2-b82c-06db4cef7498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.8258427  0.80898876 0.80337079 0.82022472 0.85310734]\n",
            "0.8223068621849807\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression(max_iter = 2000)\n",
        "cv = cross_val_score(lr,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOe57Yd2B2JD",
        "outputId": "2599def2-9ecc-4570-93d5-0bacde91acdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.8258427  0.80898876 0.80337079 0.82022472 0.85310734]\n",
            "0.8223068621849807\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = tree.DecisionTreeClassifier(random_state = 1)\n",
        "cv = cross_val_score(dt,X_train,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NiC1U-lrB4pL",
        "outputId": "8f685fe6-e4da-455c-ee1e-64606310f3c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.75842697 0.74719101 0.8258427  0.74719101 0.8079096 ]\n",
            "0.7773122579826065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = tree.DecisionTreeClassifier(random_state = 1)\n",
        "cv = cross_val_score(dt,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkM-w7R6B8FP",
        "outputId": "275c3fd8-cd76-47a5-e7d7-949b4ee2a8ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.75842697 0.74719101 0.8258427  0.74719101 0.8079096 ]\n",
            "0.7773122579826065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()\n",
        "cv = cross_val_score(knn,X_train,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXRuZ2gEB-uJ",
        "outputId": "a98b2c29-daf2-4cf8-c99b-fe7aa80e6d17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.76966292 0.80337079 0.80898876 0.82022472 0.85310734]\n",
            "0.8110709071288008\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()\n",
        "cv = cross_val_score(knn,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_q5DB1MBCDE4",
        "outputId": "e1ba4899-bb18-4266-9aff-c9610a411b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.79775281 0.79213483 0.83146067 0.79775281 0.85310734]\n",
            "0.8144416936456548\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(random_state = 1)\n",
        "cv = cross_val_score(rf,X_train,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NBkc0pUmCFik",
        "outputId": "22a91625-3af5-4294-b6bf-f149016aeab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.80898876 0.79213483 0.84831461 0.73595506 0.82485876]\n",
            "0.8020504030978227\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(random_state = 1)\n",
        "cv = cross_val_score(rf,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1OmNfraQCJbD",
        "outputId": "07791383-c18b-4ba9-d6d4-21c2eb01a007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.80337079 0.79213483 0.84831461 0.73595506 0.82485876]\n",
            "0.8009268075922046\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(probability = True)\n",
        "cv = cross_val_score(svc,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBdBOiE8CLHN",
        "outputId": "82e5c912-2e91-4d0e-de49-76264053cadb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.85393258 0.82022472 0.8258427  0.80337079 0.86440678]\n",
            "0.8335555132355742\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "xgb = XGBClassifier(random_state =1)\n",
        "cv = cross_val_score(xgb,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pipqkKyTCNpX",
        "outputId": "1f17249b-a701-4d0f-dc6c-13b28b90701e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.82022472 0.80898876 0.83707865 0.79213483 0.87570621]\n",
            "0.8268266361962802\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Voting classifier takes all of the inputs and averages the results. For a \"hard\" voting classifier each classifier gets 1 vote \"yes\" or \"no\" and the result is just a popular vote. For this, you generally want odd numbers\n",
        "#A \"soft\" classifier averages the confidence of each of the models. If a the average confidence is > 50% that it is a 1 it will be counted as such\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "voting_clf = VotingClassifier(estimators = [('lr',lr),('knn',knn),('rf',rf),('gnb',gnb),('svc',svc),('xgb',xgb)], voting = 'soft') "
      ],
      "metadata": {
        "id": "gk2-nTKNCQJ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv = cross_val_score(voting_clf,X_train_scaled,y_train,cv=5)\n",
        "print(cv)\n",
        "print(cv.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yA4JKWLxCT-Z",
        "outputId": "1a08138b-b87f-4184-9462-9c5eb97b01ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.82022472 0.80898876 0.83707865 0.79775281 0.85875706]\n",
            "0.8245604011934236\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "voting_clf.fit(X_train_scaled,y_train)\n",
        "y_hat_base_vc = voting_clf.predict(X_test_scaled).astype(int)\n",
        "basic_submission = {'PassengerId': test.PassengerId, 'Survived': y_hat_base_vc}\n",
        "base_submission = pd.DataFrame(data=basic_submission)\n",
        "base_submission.to_csv('base_submission.csv', index=False)"
      ],
      "metadata": {
        "id": "EFEslxlrCV7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV \n",
        "from sklearn.model_selection import RandomizedSearchCV "
      ],
      "metadata": {
        "id": "KdHPJplcCa7L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#simple performance reporting function\n",
        "def clf_performance(classifier, model_name):\n",
        "    print(model_name)\n",
        "    print('Best Score: ' + str(classifier.best_score_))\n",
        "    print('Best Parameters: ' + str(classifier.best_params_))"
      ],
      "metadata": {
        "id": "wX_VJ3K6CdTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "param_grid = {'max_iter' : [2000],\n",
        "              'penalty' : ['l1', 'l2'],\n",
        "              'C' : np.logspace(-4, 4, 20),\n",
        "              'solver' : ['liblinear']}\n",
        "\n",
        "clf_lr = GridSearchCV(lr, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_lr = clf_lr.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_lr,'Logistic Regression')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZ-iHssrCfYE",
        "outputId": "16bbe13c-419d-4049-ded2-fd81d7190911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 40 candidates, totalling 200 fits\n",
            "Logistic Regression\n",
            "Best Score: 0.8279375357074843\n",
            "Best Parameters: {'C': 1.623776739188721, 'max_iter': 2000, 'penalty': 'l1', 'solver': 'liblinear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier()\n",
        "param_grid = {'n_neighbors' : [3,5,7,9],\n",
        "              'weights' : ['uniform', 'distance'],\n",
        "              'algorithm' : ['auto', 'ball_tree','kd_tree'],\n",
        "              'p' : [1,2]}\n",
        "clf_knn = GridSearchCV(knn, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_knn = clf_knn.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_knn,'KNN')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KnkTgzd4CiHV",
        "outputId": "222bad7f-6644-4934-9ac7-6acbd01e76ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n",
            "KNN\n",
            "Best Score: 0.8290611312131023\n",
            "Best Parameters: {'algorithm': 'ball_tree', 'n_neighbors': 7, 'p': 2, 'weights': 'uniform'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(probability = True)\n",
        "param_grid = tuned_parameters = [{'kernel': ['rbf'], 'gamma': [.1,.5,1,2,5,10],\n",
        "                                  'C': [.1, 1, 10, 100, 1000]},\n",
        "                                 {'kernel': ['linear'], 'C': [.1, 1, 10, 100, 1000]},\n",
        "                                 {'kernel': ['poly'], 'degree' : [2,3,4,5], 'C': [.1, 1, 10, 100, 1000]}]\n",
        "clf_svc = GridSearchCV(svc, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_svc = clf_svc.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_svc,'SVC')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tJJ810GQEqwd",
        "outputId": "29ac9992-2d39-4c28-d4fb-770d63f013d6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 55 candidates, totalling 275 fits\n",
            "SVC\n",
            "Best Score: 0.8335555132355742\n",
            "Best Parameters: {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Because the total feature space is so large, I used a randomized search to narrow down the paramters for the model. I took the best model from this and did a more granular search \n",
        "\"\"\"\n",
        "rf = RandomForestClassifier(random_state = 1)\n",
        "param_grid =  {'n_estimators': [100,500,1000], \n",
        "                                  'bootstrap': [True,False],\n",
        "                                  'max_depth': [3,5,10,20,50,75,100,None],\n",
        "                                  'max_features': ['auto','sqrt'],\n",
        "                                  'min_samples_leaf': [1,2,4,10],\n",
        "                                  'min_samples_split': [2,5,10]}\n",
        "                                  \n",
        "clf_rf_rnd = RandomizedSearchCV(rf, param_distributions = param_grid, n_iter = 100, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_rf_rnd = clf_rf_rnd.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_rf_rnd,'Random Forest')\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "7pWrsrOuGR72",
        "outputId": "daf54c35-5c75-409f-bbd3-6b6d511d4aeb"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nrf = RandomForestClassifier(random_state = 1)\\nparam_grid =  {'n_estimators': [100,500,1000], \\n                                  'bootstrap': [True,False],\\n                                  'max_depth': [3,5,10,20,50,75,100,None],\\n                                  'max_features': ['auto','sqrt'],\\n                                  'min_samples_leaf': [1,2,4,10],\\n                                  'min_samples_split': [2,5,10]}\\n                                  \\nclf_rf_rnd = RandomizedSearchCV(rf, param_distributions = param_grid, n_iter = 100, cv = 5, verbose = True, n_jobs = -1)\\nbest_clf_rf_rnd = clf_rf_rnd.fit(X_train_scaled,y_train)\\nclf_performance(best_clf_rf_rnd,'Random Forest')\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(random_state = 1)\n",
        "param_grid =  {'n_estimators': [400,450,500,550],\n",
        "               'criterion':['gini','entropy'],\n",
        "                                  'bootstrap': [True],\n",
        "                                  'max_depth': [15, 20, 25],\n",
        "                                  'max_features': ['auto','sqrt', 10],\n",
        "                                  'min_samples_leaf': [2,3],\n",
        "                                  'min_samples_split': [2,3]}\n",
        "                                  \n",
        "clf_rf = GridSearchCV(rf, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_rf = clf_rf.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_rf,'Random Forest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EaUz4oTVGUUM",
        "outputId": "e7a8279c-ec99-4c85-93a9-abfc02432e7e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 288 candidates, totalling 1440 fits\n",
            "Random Forest\n",
            "Best Score: 0.8358027042468101\n",
            "Best Parameters: {'bootstrap': True, 'criterion': 'gini', 'max_depth': 15, 'max_features': 10, 'min_samples_leaf': 3, 'min_samples_split': 2, 'n_estimators': 550}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_rf = best_clf_rf.best_estimator_.fit(X_train_scaled,y_train)\n",
        "feat_importances = pd.Series(best_rf.feature_importances_, index=X_train_scaled.columns)\n",
        "feat_importances.nlargest(20).plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "5vd5xYqhK5IE",
        "outputId": "d5771878-832c-4910-af17-9aa42fdfea0b"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fea5c129110>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "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": "code",
      "source": [
        "\"\"\"xgb = XGBClassifier(random_state = 1)\n",
        "\n",
        "param_grid = {\n",
        "    'n_estimators': [20, 50, 100, 250, 500,1000],\n",
        "    'colsample_bytree': [0.2, 0.5, 0.7, 0.8, 1],\n",
        "    'max_depth': [2, 5, 10, 15, 20, 25, None],\n",
        "    'reg_alpha': [0, 0.5, 1],\n",
        "    'reg_lambda': [1, 1.5, 2],\n",
        "    'subsample': [0.5,0.6,0.7, 0.8, 0.9],\n",
        "    'learning_rate':[.01,0.1,0.2,0.3,0.5, 0.7, 0.9],\n",
        "    'gamma':[0,.01,.1,1,10,100],\n",
        "    'min_child_weight':[0,.01,0.1,1,10,100],\n",
        "    'sampling_method': ['uniform', 'gradient_based']\n",
        "}\n",
        "\n",
        "#clf_xgb = GridSearchCV(xgb, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\n",
        "#best_clf_xgb = clf_xgb.fit(X_train_scaled,y_train)\n",
        "#clf_performance(best_clf_xgb,'XGB')\n",
        "clf_xgb_rnd = RandomizedSearchCV(xgb, param_distributions = param_grid, n_iter = 1000, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_xgb_rnd = clf_xgb_rnd.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_xgb_rnd,'XGB')\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "2LhE_IZvK97u",
        "outputId": "599ed2f3-9956-411a-9fad-bda368c42c09"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"xgb = XGBClassifier(random_state = 1)\\n\\nparam_grid = {\\n    'n_estimators': [20, 50, 100, 250, 500,1000],\\n    'colsample_bytree': [0.2, 0.5, 0.7, 0.8, 1],\\n    'max_depth': [2, 5, 10, 15, 20, 25, None],\\n    'reg_alpha': [0, 0.5, 1],\\n    'reg_lambda': [1, 1.5, 2],\\n    'subsample': [0.5,0.6,0.7, 0.8, 0.9],\\n    'learning_rate':[.01,0.1,0.2,0.3,0.5, 0.7, 0.9],\\n    'gamma':[0,.01,.1,1,10,100],\\n    'min_child_weight':[0,.01,0.1,1,10,100],\\n    'sampling_method': ['uniform', 'gradient_based']\\n}\\n\\n#clf_xgb = GridSearchCV(xgb, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\\n#best_clf_xgb = clf_xgb.fit(X_train_scaled,y_train)\\n#clf_performance(best_clf_xgb,'XGB')\\nclf_xgb_rnd = RandomizedSearchCV(xgb, param_distributions = param_grid, n_iter = 1000, cv = 5, verbose = True, n_jobs = -1)\\nbest_clf_xgb_rnd = clf_xgb_rnd.fit(X_train_scaled,y_train)\\nclf_performance(best_clf_xgb_rnd,'XGB')\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb = XGBClassifier(random_state = 1)\n",
        "\n",
        "param_grid = {\n",
        "    'n_estimators': [450,500,550],\n",
        "    'colsample_bytree': [0.75,0.8,0.85],\n",
        "    'reg_alpha': [1],\n",
        "    'reg_lambda': [2, 5, 10],\n",
        "    'subsample': [0.55, 0.6, .65],\n",
        "    'learning_rate':[0.5],\n",
        "    'gamma':[.5,1,2],\n",
        "    'min_child_weight':[0.01],\n",
        "    'sampling_method': ['uniform']\n",
        "}\n",
        "\n",
        "clf_xgb = GridSearchCV(xgb, param_grid = param_grid, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_xgb = clf_xgb.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_xgb,'XGB')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHL8SHj-LPrI",
        "outputId": "0f3211f4-4e2a-441d-f735-1b89e9bdd7da"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n",
            "XGB\n",
            "Best Score: 0.8526566368310797\n",
            "Best Parameters: {'colsample_bytree': 0.8, 'gamma': 0.5, 'learning_rate': 0.5, 'min_child_weight': 0.01, 'n_estimators': 500, 'reg_alpha': 1, 'reg_lambda': 10, 'sampling_method': 'uniform', 'subsample': 0.65}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_hat_xgb = best_clf_xgb.best_estimator_.predict(X_test_scaled).astype(int)\n",
        "xgb_submission = {'PassengerId': test.PassengerId, 'Survived': y_hat_xgb}\n",
        "submission_xgb = pd.DataFrame(data=xgb_submission)\n",
        "submission_xgb.to_csv('xgb_submission3.csv', index=False)"
      ],
      "metadata": {
        "id": "WpdHJ3TfOP4H"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "best_lr = best_clf_lr.best_estimator_\n",
        "best_knn = best_clf_knn.best_estimator_\n",
        "best_svc = best_clf_svc.best_estimator_\n",
        "best_rf = best_clf_rf.best_estimator_\n",
        "best_xgb = best_clf_xgb.best_estimator_\n",
        "\n",
        "voting_clf_hard = VotingClassifier(estimators = [('knn',best_knn),('rf',best_rf),('svc',best_svc)], voting = 'hard') \n",
        "voting_clf_soft = VotingClassifier(estimators = [('knn',best_knn),('rf',best_rf),('svc',best_svc)], voting = 'soft') \n",
        "voting_clf_all = VotingClassifier(estimators = [('knn',best_knn),('rf',best_rf),('svc',best_svc), ('lr', best_lr)], voting = 'soft') \n",
        "voting_clf_xgb = VotingClassifier(estimators = [('knn',best_knn),('rf',best_rf),('svc',best_svc), ('xgb', best_xgb),('lr', best_lr)], voting = 'soft')\n",
        "\n",
        "print('voting_clf_hard :',cross_val_score(voting_clf_hard,X_train,y_train,cv=5))\n",
        "print('voting_clf_hard mean :',cross_val_score(voting_clf_hard,X_train,y_train,cv=5).mean())\n",
        "\n",
        "print('voting_clf_soft :',cross_val_score(voting_clf_soft,X_train,y_train,cv=5))\n",
        "print('voting_clf_soft mean :',cross_val_score(voting_clf_soft,X_train,y_train,cv=5).mean())\n",
        "\n",
        "print('voting_clf_all :',cross_val_score(voting_clf_all,X_train,y_train,cv=5))\n",
        "print('voting_clf_all mean :',cross_val_score(voting_clf_all,X_train,y_train,cv=5).mean())\n",
        "\n",
        "print('voting_clf_xgb :',cross_val_score(voting_clf_xgb,X_train,y_train,cv=5))\n",
        "print('voting_clf_xgb mean :',cross_val_score(voting_clf_xgb,X_train,y_train,cv=5).mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rgRl9NPOVqe",
        "outputId": "55d53215-7746-4925-b6fd-4a2d2d065a39"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "voting_clf_hard : [0.79213483 0.81460674 0.82022472 0.79775281 0.83615819]\n",
            "voting_clf_hard mean : 0.8121754586427983\n",
            "voting_clf_soft : [0.78651685 0.81460674 0.81460674 0.79775281 0.85310734]\n",
            "voting_clf_soft mean : 0.8144416936456548\n",
            "voting_clf_all : [0.80898876 0.83146067 0.8258427  0.80898876 0.85875706]\n",
            "voting_clf_all mean : 0.8268075922046595\n",
            "voting_clf_xgb : [0.83146067 0.8258427  0.8258427  0.80898876 0.8700565 ]\n",
            "voting_clf_xgb mean : 0.831314670221545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#in a soft voting classifier you can weight some models more than others. I used a grid search to explore different weightings\n",
        "#no new results here\n",
        "params = {'weights' : [[1,1,1],[1,2,1],[1,1,2],[2,1,1],[2,2,1],[1,2,2],[2,1,2]]}\n",
        "\n",
        "vote_weight = GridSearchCV(voting_clf_soft, param_grid = params, cv = 5, verbose = True, n_jobs = -1)\n",
        "best_clf_weight = vote_weight.fit(X_train_scaled,y_train)\n",
        "clf_performance(best_clf_weight,'VC Weights')\n",
        "voting_clf_sub = best_clf_weight.best_estimator_.predict(X_test_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJZor4AMOqK6",
        "outputId": "eac07283-31e2-4212-8983-e50ab7c125ec"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 7 candidates, totalling 35 fits\n",
            "VC Weights\n",
            "Best Score: 0.83244461372437\n",
            "Best Parameters: {'weights': [2, 2, 1]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Make Predictions \n",
        "voting_clf_hard.fit(X_train_scaled, y_train)\n",
        "voting_clf_soft.fit(X_train_scaled, y_train)\n",
        "voting_clf_all.fit(X_train_scaled, y_train)\n",
        "voting_clf_xgb.fit(X_train_scaled, y_train)\n",
        "\n",
        "best_rf.fit(X_train_scaled, y_train)\n",
        "y_hat_vc_hard = voting_clf_hard.predict(X_test_scaled).astype(int)\n",
        "y_hat_rf = best_rf.predict(X_test_scaled).astype(int)\n",
        "y_hat_vc_soft =  voting_clf_soft.predict(X_test_scaled).astype(int)\n",
        "y_hat_vc_all = voting_clf_all.predict(X_test_scaled).astype(int)\n",
        "y_hat_vc_xgb = voting_clf_xgb.predict(X_test_scaled).astype(int)"
      ],
      "metadata": {
        "id": "bQwuing3O4zt"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#convert output to dataframe \n",
        "final_data = {'PassengerId': test.PassengerId, 'Survived': y_hat_rf}\n",
        "submission = pd.DataFrame(data=final_data)\n",
        "\n",
        "final_data_2 = {'PassengerId': test.PassengerId, 'Survived': y_hat_vc_hard}\n",
        "submission_2 = pd.DataFrame(data=final_data_2)\n",
        "\n",
        "final_data_3 = {'PassengerId': test.PassengerId, 'Survived': y_hat_vc_soft}\n",
        "submission_3 = pd.DataFrame(data=final_data_3)\n",
        "\n",
        "final_data_4 = {'PassengerId': test.PassengerId, 'Survived': y_hat_vc_all}\n",
        "submission_4 = pd.DataFrame(data=final_data_4)\n",
        "\n",
        "final_data_5 = {'PassengerId': test.PassengerId, 'Survived': y_hat_vc_xgb}\n",
        "submission_5 = pd.DataFrame(data=final_data_5)\n",
        "\n",
        "final_data_comp = {'PassengerId': test.PassengerId, 'Survived_vc_hard': y_hat_vc_hard, 'Survived_rf': y_hat_rf, 'Survived_vc_soft' : y_hat_vc_soft, 'Survived_vc_all' : y_hat_vc_all,  'Survived_vc_xgb' : y_hat_vc_xgb}\n",
        "comparison = pd.DataFrame(data=final_data_comp)"
      ],
      "metadata": {
        "id": "vecGC57dO-Bv"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#track differences between outputs \n",
        "comparison['difference_rf_vc_hard'] = comparison.apply(lambda x: 1 if x.Survived_vc_hard != x.Survived_rf else 0, axis =1)\n",
        "comparison['difference_soft_hard'] = comparison.apply(lambda x: 1 if x.Survived_vc_hard != x.Survived_vc_soft else 0, axis =1)\n",
        "comparison['difference_hard_all'] = comparison.apply(lambda x: 1 if x.Survived_vc_all != x.Survived_vc_hard else 0, axis =1)"
      ],
      "metadata": {
        "id": "Cp00Kpl5PBXX"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comparison.difference_hard_all.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qcO4sj6NPD8N",
        "outputId": "95119cb7-ea52-43f1-8818-4f1e671e0aff"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    410\n",
              "1      8\n",
              "Name: difference_hard_all, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prepare submission files \n",
        "submission.to_csv('submission_rf.csv', index =False)\n",
        "submission_2.to_csv('submission_vc_hard.csv',index=False)\n",
        "submission_3.to_csv('submission_vc_soft.csv', index=False)\n",
        "submission_4.to_csv('submission_vc_all.csv', index=False)\n",
        "submission_5.to_csv('submission_vc_xgb2.csv', index=False)"
      ],
      "metadata": {
        "id": "b498EszuPGYu"
      },
      "execution_count": 70,
      "outputs": []
    }
  ]
}