Titanic_Machine_Learning_from_Disaster.ipynb

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    "colab": {
      "name": "Titanic - Machine Learning from Disaster - Mine V3.ipynb",
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    "gpuClass": "standard"
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    {
      "cell_type": "markdown",
      "source": [
        "# Import Library"
      ],
      "metadata": {
        "id": "oq_Uxg5WOM4h"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "Qystfu9wJgaf"
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      "source": [
        "# Data analysis and visualization\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "\n",
        "# Machine learning models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "# Preprocessing and evaluation\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import classification_report, ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "cHDoQwptORiR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = pd.read_csv(\"titanic-train.csv\")\n",
        "test_df = pd.read_csv(\"titanic-test.csv\")\n",
        "train_df.shape, test_df.shape"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "A6XEJ4JOytsH",
        "outputId": "1ba1f7f2-f576-43c6-df22-945b5775c101"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 12), (418, 11))"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "nxiLfiy0Q5rK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initial Observation"
      ],
      "metadata": {
        "id": "1sXgqrce7D6-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the top 10 rows of dataframe\n",
        "train_df.head(10)"
      ],
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          "height": 442
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      "execution_count": 64,
      "outputs": [
        {
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          "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   NaN      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 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",
              "5      0            330877   8.4583   NaN        Q  \n",
              "6      0             17463  51.8625   E46        S  \n",
              "7      1            349909  21.0750   NaN        S  \n",
              "8      2            347742  11.1333   NaN        S  \n",
              "9      0            237736  30.0708   NaN        C  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Checking if the dataset is balanced or imbalanced\n",
        "train_df.Survived.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_nCDETWj1WGf",
        "outputId": "45acff47-987c-4826-f7ef-ca5a32466c10"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.616162\n",
              "1    0.383838\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View summary of datasets\n",
        "train_df.info()\n",
        "print('_'*40)\n",
        "test_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DB8uiFUnUt0E",
        "outputId": "102ab69b-bc7a-4512-97e9-5bb0ecf8f166"
      },
      "execution_count": 66,
      "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",
            "________________________________________\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution of numerical feature values across the samples\n",
        "train_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "VWzo3H4qVuiK",
        "outputId": "49407460-e3d1-48e6-f4e8-186a4350e854"
      },
      "execution_count": 67,
      "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  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
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              "      <th>SibSp</th>\n",
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              "      <th>Fare</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </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",
              "    </tr>\n",
              "    <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",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </tr>\n",
              "    <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",
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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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution of categorical features\n",
        "train_df.describe(include=['O'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "EVIgRtnXXR8a",
        "outputId": "81dc5933-132d-40fb-9068-47945f25a2f2"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Name   Sex  Ticket    Cabin Embarked\n",
              "count                       891   891     891      204      889\n",
              "unique                      891     2     681      147        3\n",
              "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
              "freq                          1   577       7        4      644"
            ],
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              "      <td>891</td>\n",
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              "      <td>3</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-e69788e8-d4ab-405c-bef3-c0abfe0af427');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the number of missing values in train data\n",
        "train_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDQ0NHCUHd0f",
        "outputId": "e3f1e5b7-8576-4b18-da7a-b0c2ac79e926"
      },
      "execution_count": 69,
      "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": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the number of missing values in test data\n",
        "test_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgTw4zk24goE",
        "outputId": "407fbac7-9f03-47f6-e473-875fe10b6e06"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking correlation between ticket feature and survival rate\n",
        "pd.crosstab(train_df.Survived, train_df.Ticket)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "ldK8m9xh3AZD",
        "outputId": "82bbb316-c478-410e-e7e6-7d265b3c4489"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ticket    110152  110413  110465  110564  110813  111240  111320  111361  \\\n",
              "Survived                                                                   \n",
              "0              0       1       2       0       0       1       1       0   \n",
              "1              3       2       0       1       1       0       0       2   \n",
              "\n",
              "Ticket    111369  111426  ...  STON/O2. 3101290  SW/PP 751  W./C. 14258  \\\n",
              "Survived                  ...                                             \n",
              "0              0       0  ...                 1          0            0   \n",
              "1              1       1  ...                 0          1            1   \n",
              "\n",
              "Ticket    W./C. 14263  W./C. 6607  W./C. 6608  W./C. 6609  W.E.P. 5734  \\\n",
              "Survived                                                                 \n",
              "0                   1           2           4           1            1   \n",
              "1                   0           0           0           0            0   \n",
              "\n",
              "Ticket    W/C 14208  WE/P 5735  \n",
              "Survived                        \n",
              "0                 1          1  \n",
              "1                 0          1  \n",
              "\n",
              "[2 rows x 681 columns]"
            ],
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              "      <th>Ticket</th>\n",
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              "      <th></th>\n",
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              "<p>2 rows × 681 columns</p>\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to initial observation:\n",
        "1. \"Name\" feature can be removed since it does not give us information except male and female which is already in another feature\n",
        "2. \"Ticket\" feature can be removed since it does not give us any information about survival rate except the family size which we can be derived from another feature.\n",
        "3. \"Passenger Id\" feature can be removed since it does not give us any information.\n",
        "4. \"Cabin\" feature can be removed since most of its values are missing."
      ],
      "metadata": {
        "id": "7m9Cb6c17bjG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Engineering\n"
      ],
      "metadata": {
        "id": "HyDUuwSE4w_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation between \"Survived\" and \"SibSp\"\n",
        "print(pd.crosstab(train_df.Survived, train_df.SibSp))\n",
        "print('_'*40)\n",
        "\n",
        "# Correlation between \"Survived\" and \"Parch\"\n",
        "print(pd.crosstab(train_df.Survived, train_df.Parch))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9Bn_IRRua85",
        "outputId": "876860ae-d1c7-4f2d-8558-d21093b7547b"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SibSp       0    1   2   3   4  5  8\n",
            "Survived                            \n",
            "0         398   97  15  12  15  5  7\n",
            "1         210  112  13   4   3  0  0\n",
            "________________________________________\n",
            "Parch       0   1   2  3  4  5  6\n",
            "Survived                         \n",
            "0         445  53  40  2  4  4  1\n",
            "1         233  65  40  3  0  1  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two new features, \"Parch_band\" and \"SibSp_band\" can be created to make the dimension of \"Parch\" and \"SibSp\" features smaller. More than 95 percent of the cases have \"Parch\" and \"SibSp\" values of 2 or smaller, so cases with values 3 or larger can be grouped into one."
      ],
      "metadata": {
        "id": "kexXGyu4NTbh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# All cases with \"Parch\" values larger than 2, will be assumed to have value 3\n",
        "for dataset in [train_df, test_df]:\n",
        "    dataset['Parch_band'] = 3\n",
        "    dataset.loc[dataset['Parch'] == 0 , 'Parch_band'] = 0\n",
        "    dataset.loc[dataset['Parch'] == 1 , 'Parch_band'] = 1\n",
        "    dataset.loc[dataset['Parch'] == 2 , 'Parch_band'] = 2\n",
        "\n",
        "train_df[['Parch_band', 'Survived']].groupby(['Parch_band'], as_index=False).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "eOS2sXzAZx86",
        "outputId": "2344e2b0-3d27-4037-c726-d720d07c9515"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Parch_band  Survived\n",
              "0           0  0.343658\n",
              "1           1  0.550847\n",
              "2           2  0.500000\n",
              "3           3  0.266667"
            ],
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              "\n",
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      "source": [
        "# All cases with \"SibSp\" values larger than 2, will be assumed to have value 3\n",
        "for dataset in [train_df, test_df]:\n",
        "    dataset['SibSp_band'] = 3\n",
        "    dataset.loc[dataset['SibSp'] == 0 , 'SibSp_band'] = 0\n",
        "    dataset.loc[dataset['SibSp'] == 1 , 'SibSp_band'] = 1\n",
        "    dataset.loc[dataset['SibSp'] == 2 , 'SibSp_band'] = 2\n",
        "\n",
        "train_df[['SibSp_band', 'Survived']].groupby(['SibSp_band'], as_index=False).mean()"
      ],
      "metadata": {
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      "execution_count": 74,
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        {
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          "data": {
            "text/plain": [
              "   SibSp_band  Survived\n",
              "0           0  0.345395\n",
              "1           1  0.535885\n",
              "2           2  0.464286\n",
              "3           3  0.152174"
            ],
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              "      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-e4430c6b-b549-466f-90f1-7121746257c2 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-e4430c6b-b549-466f-90f1-7121746257c2');\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": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating the Preprocessing Pipelines"
      ],
      "metadata": {
        "id": "bSxNOoON4rUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define different features and transformer pipelines\n",
        "categorical_features = ['Embarked', 'Sex', 'Pclass', 'Parch_band', 'SibSp_band']\n",
        "categorical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
        "\n",
        "numerical_features = ['Age', 'Fare']\n",
        "numerical_transformer = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('scaler', StandardScaler())])\n",
        "\n",
        "# Setup preprocessing steps\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('categorical', categorical_transformer, categorical_features),\n",
        "        ('numerical', numerical_transformer, numerical_features)])\n"
      ],
      "metadata": {
        "id": "vFwdjXHN99Vr"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model, Predict, Evaluate"
      ],
      "metadata": {
        "id": "3uGf1vBC4kBy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Since the ground truth for the test set is not provided, the training data is splitted to have a test set for evaluation\n",
        "X = train_df.drop(\"Survived\", axis=1)\n",
        "y = train_df[\"Survived\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "metadata": {
        "id": "6WZJHLox4r8v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "186a2ad1-54e7-4aa2-f8a0-bbd5ed34c2d7"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((801, 13), (801,), (90, 13), (90,))"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Comparison and Cross Validation"
      ],
      "metadata": {
        "id": "wBmE6K17dEjS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Put desired classifiers in a dictionary\n",
        "classifiers = {\n",
        "    \"Nearest Neighbors\": KNeighborsClassifier(),\n",
        "    \"Logistic Regression\": LogisticRegression(),\n",
        "    \"Random Forest\": RandomForestClassifier(),\n",
        "    \"RBF SVM\": SVC(),\n",
        "    \"Linear SVM\": SVC(kernel=\"linear\"),\n",
        "    \"Naive Bayes\": GaussianNB(),\n",
        "    \"AdaBoost\": AdaBoostClassifier()\n",
        "}\n",
        "\n",
        "# Random seed for reproducible results\n",
        "np.random.seed(42)\n",
        "\n",
        "# Define variables for model comparison\n",
        "model=[]\n",
        "score=[]\n",
        "std=[]\n",
        "\n",
        "# Loop through different classifiers\n",
        "for name, classifier in classifiers.items():\n",
        "  # Append classifier to preprocessing pipeline.\n",
        "  clf = Pipeline(steps=[('preprocessor', preprocessor),\n",
        "                        ('classifier', classifier)])\n",
        "  # Fit and score the model\n",
        "  clf.fit(X_train, y_train)\n",
        "  # Calculate Cross Validation Score\n",
        "  scores = cross_val_score(clf, X_train, y_train, cv=5)\n",
        "  # Append score and std to defined variables\n",
        "  model.append(f'{name}')\n",
        "  score.append(round(scores.mean()*100, 2))\n",
        "  std.append(round(scores.std()*100, 2))\n",
        "\n",
        "# Model Comparison\n",
        "models = pd.DataFrame({\n",
        "    'Model': model,\n",
        "    'Score': score,\n",
        "    'Standard Deviation': std})\n",
        "models.sort_values(by='Score', ascending=False, ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "md-PEWGJjYS1",
        "outputId": "2738e603-dec6-450f-b5b5-482b0ec4e428"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Score  Standard Deviation\n",
              "0              RBF SVM  82.65                3.78\n",
              "1  Logistic Regression  80.15                2.99\n",
              "2        Random Forest  80.15                1.71\n",
              "3             AdaBoost  79.65                3.29\n",
              "4           Linear SVM  79.40                3.79\n",
              "5    Nearest Neighbors  78.90                2.07\n",
              "6          Naive Bayes  76.77                3.78"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6082aeaa-350b-4a8c-8c7b-a35ec0015817\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Score</th>\n",
              "      <th>Standard Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RBF SVM</td>\n",
              "      <td>82.65</td>\n",
              "      <td>3.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>80.15</td>\n",
              "      <td>2.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>80.15</td>\n",
              "      <td>1.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>79.65</td>\n",
              "      <td>3.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Linear SVM</td>\n",
              "      <td>79.40</td>\n",
              "      <td>3.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nearest Neighbors</td>\n",
              "      <td>78.90</td>\n",
              "      <td>2.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>76.77</td>\n",
              "      <td>3.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6082aeaa-350b-4a8c-8c7b-a35ec0015817')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\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-6082aeaa-350b-4a8c-8c7b-a35ec0015817 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-6082aeaa-350b-4a8c-8c7b-a35ec0015817');\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": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter Tuning for Best Model"
      ],
      "metadata": {
        "id": "Ci7SuCSwP2GC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Support Vector Classifier - Hyperparameter tuning\n",
        "np.random.seed(42)\n",
        "\n",
        "clf = Pipeline(steps=[('preprocessor', preprocessor),\n",
        "                      ('classifier', SVC())])\n",
        "param_grid = {\n",
        "    'preprocessor__numerical__imputer__strategy': ['median', 'mean', 'most_frequent'],\n",
        "    'classifier__C': [0.1, 1, 10],\n",
        "    'classifier__gamma': [0.1, 0.5, 0.9]\n",
        "}\n",
        "grid_search = GridSearchCV(clf, param_grid, cv=10);\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "print('Best model parameters:')\n",
        "print(f\"Best Parameters for RBF Support Vector classifier: {grid_search.best_params_}\")\n",
        "print(\"\")\n",
        "print('Best model score after fine tuning:')\n",
        "print(f\"Score percentage for RBF Support Vector classifier: %.2f\" %(grid_search.score(X_train, y_train)*100))"
      ],
      "metadata": {
        "id": "dhb7r9-tIovB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1da90dec-6dff-4326-a72e-8a2c9f313160"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best model parameters:\n",
            "Best Parameters for RBF Support Vector classifier: {'classifier__C': 1, 'classifier__gamma': 0.1, 'preprocessor__numerical__imputer__strategy': 'median'}\n",
            "\n",
            "Best model score after fine tuning:\n",
            "Score percentage for RBF Support Vector classifier: 83.40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predict and Evaluate Test Dataset"
      ],
      "metadata": {
        "id": "oPTD9zZz2tYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction for test dataset\n",
        "y_pred = grid_search.predict(X_test)\n",
        "\n",
        "# Accuracy on test dataset\n",
        "accuracy = round(grid_search.score(X_test, y_test)*100, 2)\n",
        "print(f\"Accuracy on test dataset: {accuracy}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJG33r313ps5",
        "outputId": "146f8000-3bd8-4d0f-b305-cacb5f90fd24"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test dataset: 83.33%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot confusion matrix from predictions\n",
        "ConfusionMatrixDisplay.from_predictions(y_true=y_test, \n",
        "                                        y_pred=y_pred);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "sEKxf2ftCpvQ",
        "outputId": "ee1f65a5-0327-435c-9d92-97b9bbf4219d"
      },
      "execution_count": 80,
      "outputs": [
        {
          "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": [
        "# Classification report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EsxX1jkDTGY",
        "outputId": "606fb04d-a9bc-41e8-e182-3b5a48d85f33"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.85      0.86        54\n",
            "           1       0.78      0.81      0.79        36\n",
            "\n",
            "    accuracy                           0.83        90\n",
            "   macro avg       0.83      0.83      0.83        90\n",
            "weighted avg       0.83      0.83      0.83        90\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Summary"
      ],
      "metadata": {
        "id": "kRn-SYopFBZM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "After data exploration on raw data and comparing different classifiers, Support Vector classifier with RBF kernel was chosen as the best classifier for prediction of the survival rate in Titanic - Machine Learning problem. Accuracies of 83.33% and 83.40% were estimated for the test and training datasets so there is no sign of overfitting."
      ],
      "metadata": {
        "id": "LkvZV0HAFDwt"
      }
    }
  ]
}