PreProcessing_and_Feature_Engineering.ipynb

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    "colab": {
      "name": "PreProcessing and Feature Engineering",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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      "cell_type": "code",
      "metadata": {
        "id": "4BAC0eGr-dDd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wb1_WPUD-zxr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test = pd.read_csv(\"/content/test.csv\")\n",
        "train = pd.read_csv(\"/content/train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "id": "AoV6yp9L-9Cs",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
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      "metadata": {
        "id": "pzjL1XBQ_ejU",
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      },
      "source": [
        "columns = [\"Ticket\",\"Cabin\"]\n",
        "test = test.drop(columns=columns)\n",
        "train = train.drop(columns=columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "c21784fb-3e84-4d89-8f10-ced721ce0b93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Parch     Fare  Embarked\n",
              "0            1         0       3  ...     0   7.2500         S\n",
              "1            2         1       1  ...     0  71.2833         C\n",
              "2            3         1       3  ...     0   7.9250         S\n",
              "3            4         1       1  ...     0  53.1000         S\n",
              "4            5         0       3  ...     0   8.0500         S\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "lQB9suM4BeJz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.drop([\"PassengerId\"],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuewxN0YBsPg",
        "colab_type": "code",
        "outputId": "e5454cd0-eaab-4e9a-dc5b-40240258672e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
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            "text/plain": [
              "   Survived  Pclass  ...     Fare Embarked\n",
              "0         0       3  ...   7.2500        S\n",
              "1         1       1  ...  71.2833        C\n",
              "2         1       3  ...   7.9250        S\n",
              "3         1       1  ...  53.1000        S\n",
              "4         0       3  ...   8.0500        S\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsyuDZFeBttj",
        "colab_type": "code",
        "outputId": "f4c763bf-5e76-4f63-b857-286514fc6669",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "train[\"Name\"][0].split(',')[1].split('.')[0].strip()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Mr'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4ny1TcnCOuP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_titles(data):\n",
        "  data[\"Title\"] = data[\"Name\"].map(lambda x:x.split(',')[1].split('.')[0].strip())\n",
        "  return data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZA-zzI1JsCC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = get_titles(train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wRuFXxNJyPx",
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          "height": 195
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      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  ... Embarked Title\n",
              "0         0       3  ...        S    Mr\n",
              "1         1       1  ...        C   Mrs\n",
              "2         1       3  ...        S  Miss\n",
              "3         1       1  ...        S   Mrs\n",
              "4         0       3  ...        S    Mr\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUFpu7I9JzyZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test = get_titles(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5R44-yCMKcvB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.drop([\"Name\"],axis=1,inplace=True)\n",
        "test.drop([\"Name\"],axis=1,inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vc03N4CBKpQC",
        "colab_type": "code",
        "outputId": "6a5fc7b3-3bca-4198-adcb-ba87a5ef4b08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "train.Title.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Don', 'Rev', 'Dr', 'Mme', 'Ms',\n",
              "       'Major', 'Lady', 'Sir', 'Mlle', 'Col', 'Capt', 'the Countess',\n",
              "       'Jonkheer'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiQ-sS1-Kuuj",
        "colab_type": "code",
        "outputId": "967208be-de57-4a27-cd8c-419f42c941ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "test.Title.unique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mr', 'Mrs', 'Miss', 'Master', 'Ms', 'Col', 'Rev', 'Dr', 'Dona'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJ0IeCrXK1TF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_dict = {\n",
        "    \"Capt\": \"Rare\",\n",
        "    \"Col\": \"Rare\",\n",
        "    \"Major\": \"Rare\",\n",
        "    \"Jonkheer\": \"Rare\",\n",
        "    \"Don\": \"Rare\",\n",
        "    \"Dona\": \"Miss\",\n",
        "    \"Sir\" : \"Rare\",\n",
        "    \"Dr\": \"Rare\",\n",
        "    \"Rev\": \"Rare\",\n",
        "    \"the Countess\":\"Rare\",\n",
        "    \"Mme\": \"Mrs\",\n",
        "    \"Mlle\": \"Miss\",\n",
        "    \"Ms\": \"Mrs\",\n",
        "    \"Mr\" : \"Mr\",\n",
        "    \"Mrs\" : \"Mrs\",\n",
        "    \"Miss\" : \"Miss\",\n",
        "    \"Master\" : \"Master\",\n",
        "    \"Lady\" : \"Rare\"\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePxuyhzpLCvg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.Title = train.Title.map(title_dict)\n",
        "test.Title = test.Title.map(title_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a88HGAteLH0F",
        "colab_type": "code",
        "outputId": "5a85d5f3-542b-4a00-a3e7-cd43a474323c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked Title\n",
              "0         0       3    male  22.0      1      0   7.2500        S    Mr\n",
              "1         1       1  female  38.0      1      0  71.2833        C   Mrs\n",
              "2         1       3  female  26.0      0      0   7.9250        S  Miss\n",
              "3         1       1  female  35.0      1      0  53.1000        S   Mrs\n",
              "4         0       3    male  35.0      0      0   8.0500        S    Mr"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcZ5hk6QLOh1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train[\"Embarked\"].fillna(train[\"Embarked\"].mode()[0],inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD-rnMr7LtSv",
        "colab_type": "code",
        "outputId": "2aa26b56-77bc-46be-b9d4-15102ecba571",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      0\n",
              "Pclass        0\n",
              "Sex           0\n",
              "Age         177\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Embarked      0\n",
              "Title         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1xQsa7gLv4N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test[\"Embarked\"].fillna(test[\"Embarked\"].mode()[0],inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVA6mVy8Nd4F",
        "colab_type": "code",
        "outputId": "424683d9-1088-4ffe-9866-be1042e00a4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "test.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId     0\n",
              "Pclass          0\n",
              "Sex             0\n",
              "Age            86\n",
              "SibSp           0\n",
              "Parch           0\n",
              "Fare            1\n",
              "Embarked        0\n",
              "Title           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQf8yy2NNiIA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test[\"Fare\"].fillna(test[\"Fare\"].median(),inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQhc56nlN0RA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grp = test.groupby([\"Pclass\",\"Sex\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6FHsI1yOmE_",
        "colab_type": "code",
        "outputId": "dd500005-50e6-44af-ab35-4ed11fdad577",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "grp.median()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</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 rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>female</th>\n",
              "      <td>1088.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>79.02500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>1094.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.86250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>female</th>\n",
              "      <td>1122.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>1122.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>female</th>\n",
              "      <td>1070.5</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.08125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>1102.5</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.89580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               PassengerId   Age  SibSp  Parch      Fare\n",
              "Pclass Sex                                              \n",
              "1      female       1088.0  41.0    0.0    0.0  79.02500\n",
              "       male         1094.0  42.0    0.0    0.0  51.86250\n",
              "2      female       1122.0  24.0    0.0    0.0  26.00000\n",
              "       male         1122.0  28.0    0.0    0.0  13.00000\n",
              "3      female       1070.5  22.0    0.0    0.0   8.08125\n",
              "       male         1102.5  24.0    0.0    0.0   7.89580"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGWqtizVS_Hv",
        "colab_type": "code",
        "outputId": "75f34c72-a778-4705-bcbc-d7d8658f0b19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "grp.median().reset_index()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>1088.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>79.02500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.86250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>female</td>\n",
              "      <td>1122.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>1122.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>1070.5</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.08125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>1102.5</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.89580</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass     Sex  PassengerId   Age  SibSp  Parch      Fare\n",
              "0       1  female       1088.0  41.0    0.0    0.0  79.02500\n",
              "1       1    male       1094.0  42.0    0.0    0.0  51.86250\n",
              "2       2  female       1122.0  24.0    0.0    0.0  26.00000\n",
              "3       2    male       1122.0  28.0    0.0    0.0  13.00000\n",
              "4       3  female       1070.5  22.0    0.0    0.0   8.08125\n",
              "5       3    male       1102.5  24.0    0.0    0.0   7.89580"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECJc4N7MOoUb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "me = LabelEncoder()\n",
        "train[\"Sex\"] = le.fit_transform(train[\"Sex\"])\n",
        "test[\"Sex\"] = me.fit_transform(test[\"Sex\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJDJnePmP5sN",
        "colab_type": "code",
        "outputId": "43cc03b7-4a4a-49f8-e0b5-c65734d2013b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked Title\n",
              "0         0       3    1  22.0      1      0   7.2500        S    Mr\n",
              "1         1       1    0  38.0      1      0  71.2833        C   Mrs\n",
              "2         1       3    0  26.0      0      0   7.9250        S  Miss\n",
              "3         1       1    0  35.0      1      0  53.1000        S   Mrs\n",
              "4         0       3    1  35.0      0      0   8.0500        S    Mr"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04b0rVBkRdCZ",
        "colab_type": "code",
        "outputId": "4e77de58-cfcc-4603-8e3d-b6e1a923f290",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  Sex   Age  SibSp  Parch     Fare Embarked Title\n",
              "0          892       3    1  34.5      0      0   7.8292        Q    Mr\n",
              "1          893       3    0  47.0      1      0   7.0000        S   Mrs\n",
              "2          894       2    1  62.0      0      0   9.6875        Q    Mr\n",
              "3          895       3    1  27.0      0      0   8.6625        S    Mr\n",
              "4          896       3    0  22.0      1      1  12.2875        S   Mrs"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kdlGQH6ORkFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "grouped_median_train = train.groupby(['Sex','Pclass','Title']).median()\n",
        "grouped_median_train = grouped_median_train.reset_index()[['Sex', 'Pclass', 'Title', 'Age']]\n",
        "def fill_age(row):\n",
        "    condition = (\n",
        "        (grouped_median_train['Sex'] == row['Sex']) & \n",
        "        (grouped_median_train['Title'] == row['Title']) & \n",
        "        (grouped_median_train['Pclass'] == row['Pclass'])\n",
        "    ) \n",
        "    if np.isnan(grouped_median_train[condition]['Age'].values[0]):\n",
        "        print('true')\n",
        "        condition = (\n",
        "            (grouped_median_train['Sex'] == row['Sex']) & \n",
        "            (grouped_median_train['Pclass'] == row['Pclass'])\n",
        "        )\n",
        "\n",
        "    return grouped_median_train[condition]['Age'].values[0]\n",
        "\n",
        "\n",
        "def process_age(data):\n",
        "    \n",
        "    data.Age = data.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)\n",
        "    return data\n",
        "\n",
        "train = process_age(train)\n",
        "test = process_age(test) \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0qxpRYSWY97",
        "colab_type": "code",
        "outputId": "fa85cb68-e426-4172-95eb-eb000ef28c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0\n",
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "Embarked    0\n",
              "Title       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NXQSigUWc86",
        "colab_type": "code",
        "outputId": "13d50c86-b267-43b4-a995-a0727eddc0ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "test.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Pclass         0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Fare           0\n",
              "Embarked       0\n",
              "Title          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8iHL_DYWgHY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def process_family(data):\n",
        "    data['FamilySize'] = data['Parch'] + data['SibSp'] + 1 \n",
        "    return data\n",
        "train = process_family(train)\n",
        "test = process_family(test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkWouuz8XQbi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = train.drop(['SibSp'], axis = 1)\n",
        "test  = test.drop(['SibSp'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQW6LrwoXgYm",
        "colab_type": "code",
        "outputId": "ed37402c-b28e-43dd-e444-cd62a3c5d58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            "Survived      891 non-null int64\n",
            "Pclass        891 non-null int64\n",
            "Sex           891 non-null int64\n",
            "Age           891 non-null float64\n",
            "Parch         891 non-null int64\n",
            "Fare          891 non-null float64\n",
            "Embarked      891 non-null object\n",
            "Title         891 non-null object\n",
            "FamilySize    891 non-null int64\n",
            "dtypes: float64(2), int64(5), object(2)\n",
            "memory usage: 62.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-3tsc6qXiE5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embarked_map = {\n",
        "    'Q' : 0,\n",
        "    'S' : 1,\n",
        "    'C' : 2\n",
        "}\n",
        "\n",
        "title_map = {\n",
        "             \"Mr\": 1, \n",
        "             \"Master\": 2, \n",
        "             \"Mrs\": 3, \n",
        "             \"Miss\": 4, \n",
        "             \"Rare\": 5\n",
        "            }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gj1ZBoVbX1R2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.Embarked = train.Embarked.map(embarked_map)\n",
        "test.Embarked = test.Embarked.map(embarked_map)\n",
        "train.Title = train.Title.map(title_map)\n",
        "test.Title = test.Title.map(title_map)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ji2joktX9oz",
        "colab_type": "code",
        "outputId": "558a0819-626f-4bb7-8d04-0a37e301f930",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            "Survived      891 non-null int64\n",
            "Pclass        891 non-null int64\n",
            "Sex           891 non-null int64\n",
            "Age           891 non-null float64\n",
            "Parch         891 non-null int64\n",
            "Fare          891 non-null float64\n",
            "Embarked      891 non-null int64\n",
            "Title         891 non-null int64\n",
            "FamilySize    891 non-null int64\n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 62.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mudCVPKsX_kZ",
        "colab_type": "code",
        "outputId": "dcc86340-4126-4a57-efff-7bce961a2154",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "y = train[\"Survived\"]\n",
        "train = train.drop(columns = [\"Survived\"])\n",
        "X = train\n",
        "train.info()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            "Pclass        891 non-null int64\n",
            "Sex           891 non-null int64\n",
            "Age           891 non-null float64\n",
            "Parch         891 non-null int64\n",
            "Fare          891 non-null float64\n",
            "Embarked      891 non-null int64\n",
            "Title         891 non-null int64\n",
            "FamilySize    891 non-null int64\n",
            "dtypes: float64(2), int64(6)\n",
            "memory usage: 55.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKErYMjhBb-j",
        "colab_type": "code",
        "outputId": "8ecca515-774f-41e7-929d-37a07888ba2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "y.to_csv(\"/content/y.csv\",index=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: The signature of `Series.to_csv` was aligned to that of `DataFrame.to_csv`, and argument 'header' will change its default value from False to True: please pass an explicit value to suppress this warning.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CAExo-KYBb7M",
        "colab_type": "code",
        "outputId": "b844d601-60c5-4e66-e993-c4436f6e2114",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 8 columns):\n",
            "Pclass        891 non-null int64\n",
            "Sex           891 non-null int64\n",
            "Age           891 non-null float64\n",
            "Parch         891 non-null int64\n",
            "Fare          891 non-null float64\n",
            "Embarked      891 non-null int64\n",
            "Title         891 non-null int64\n",
            "FamilySize    891 non-null int64\n",
            "dtypes: float64(2), int64(6)\n",
            "memory usage: 55.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fikr5945Bb5L",
        "colab_type": "code",
        "outputId": "69874c80-4f1f-494d-f272-4f1655768a99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Parch</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>892</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Pclass  Sex   Age  Parch     Fare  Embarked  Title  FamilySize\n",
              "0          892       3    1  34.5      0   7.8292         0      1           1\n",
              "1          893       3    0  47.0      0   7.0000         1      3           2\n",
              "2          894       2    1  62.0      0   9.6875         0      1           1\n",
              "3          895       3    1  27.0      0   8.6625         1      1           1\n",
              "4          896       3    0  22.0      1  12.2875         1      3           3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqIvsXSfBb20",
        "colab_type": "code",
        "outputId": "3f44bb02-0ec0-4221-f3ae-adbc77e2911e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "test.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 9 columns):\n",
            "PassengerId    418 non-null int64\n",
            "Pclass         418 non-null int64\n",
            "Sex            418 non-null int64\n",
            "Age            418 non-null float64\n",
            "Parch          418 non-null int64\n",
            "Fare           418 non-null float64\n",
            "Embarked       418 non-null int64\n",
            "Title          418 non-null int64\n",
            "FamilySize     418 non-null int64\n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 29.5 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHnkDucaBbzq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.to_csv(\"/content/train_mod.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CZ1mRZiB39F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test.to_csv(\"/content/test_mod.csv\",index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bD_P76q5YwI6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import StratifiedKFold,cross_val_score,GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier()\n",
        "param_grid = { \n",
        "    'n_estimators': [200, 500],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4,5,6,7,8],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}\n",
        "kf = StratifiedKFold(n_splits=5)\n",
        "grfc = GridSearchCV(estimator = rfc,param_grid=param_grid,cv=10)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xf4M__m8bmbf",
        "colab_type": "code",
        "outputId": "e4c985ae-ab1f-4b35-93d6-20e8dd798af2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        }
      },
      "source": [
        "grfc.fit(X,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [4, 5, 6, 7, 8],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'n_estimators': [200, 500]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9sJ9m-rfbpPz",
        "colab_type": "code",
        "outputId": "3b7ef897-6a36-4cc3-a865-8365771f12b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grfc.best_score_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8395255930087391"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L6hPuaoZbwv-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rfc1 = RandomForestClassifier(n_estimators=500,max_depth=7,criterion='gini',max_features='log2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gXmyP4sb7Y0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_tes,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=101)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LxcRG_Yab9ei",
        "colab_type": "code",
        "outputId": "3214462d-424a-4aa4-a265-5f995bde9857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "rfc1.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=7, max_features='log2',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLFGNHU8cEl0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = rfc1.predict(X_tes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "696iyEIGcIr9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = cross_val_score(rfc1,X,y,scoring = 'accuracy',cv=kf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwFmXVq8t_4e",
        "colab_type": "code",
        "outputId": "b0f408ab-d714-4abc-fbc6-775361db9eb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "score\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.84357542, 0.82022472, 0.84831461, 0.81460674, 0.8258427 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDhF92YVt_1G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = rfc1.predict(X_tes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmg6kyUokMga",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "acc = accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJ5fLqQRvSyA",
        "colab_type": "code",
        "outputId": "da590a1e-2abc-4f66-87fc-1c151995f4ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "acc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8324022346368715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCW6cn9SkUBQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test = test\n",
        "X_test = X_test.drop(columns=[\"PassengerId\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmJvpBixvXEz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = rfc1.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwcXgRnfvcmM",
        "colab_type": "code",
        "outputId": "1c0ce81c-2037-4ac5-edab-6369b1974f66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "y_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
              "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
              "       1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
              "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "       0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
              "       1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
              "       0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
              "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOBIB9tYkVMY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rfc_grid = pd.DataFrame({\n",
        "        \"PassengerId\": test[\"PassengerId\"],\n",
        "        \"Survived\": y_pred\n",
        "    })"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5piJjkxrkfbK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rfc_grid.to_csv(\"/content/rfc_grid_data10.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_GFANYYklZE",
        "colab_type": "code",
        "outputId": "00f07d5b-b1c0-4493-c4b7-d47442009530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "n_estimators = [100,140,145,150,160, 170,175,180,185];\n",
        "learning_r = [0.1,1,0.01,0.5]\n",
        "\n",
        "parameters = {'n_estimators':n_estimators,\n",
        "              'learning_rate':learning_r\n",
        "              \n",
        "        }\n",
        "grid = GridSearchCV(AdaBoostClassifier(base_estimator= None, \n",
        "                                     ),\n",
        "                                 param_grid=parameters,\n",
        "                                 cv=10,\n",
        "                                 n_jobs = -1)\n",
        "grid.fit(X,y) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=AdaBoostClassifier(algorithm='SAMME.R',\n",
              "                                          base_estimator=None,\n",
              "                                          learning_rate=1.0, n_estimators=50,\n",
              "                                          random_state=None),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.1, 1, 0.01, 0.5],\n",
              "                         'n_estimators': [100, 140, 145, 150, 160, 170, 175,\n",
              "                                          180, 185]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3lz5S5_nNrP",
        "colab_type": "code",
        "outputId": "aa08e5c3-140f-4c73-ee6b-3a3ba8633f9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grid.best_score_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.823820224719101"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-EaDjxsnfH2",
        "colab_type": "code",
        "outputId": "9edb0d98-4604-46e6-a795-ceb7170d8565",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "ar = AdaBoostClassifier(learning_rate=1,n_estimators=150)\n",
        "ar.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1,\n",
              "                   n_estimators=150, random_state=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-DtgwazsbNe",
        "colab_type": "code",
        "outputId": "d737cd90-94ce-49a3-dfca-c49d7b48caf7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy_score(y_test,ar.predict(X_tes))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8324022346368715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2Lth5OPytmS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = cross_val_score(ar,X,y,scoring = 'accuracy',cv=kf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TteLS3dxytqi",
        "colab_type": "code",
        "outputId": "0624920f-2fe4-46a6-cf9b-28d0347cf9e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.81005587, 0.80898876, 0.83146067, 0.81460674, 0.8258427 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "02z7BftEsiqO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred1 = ar.predict(X_test)\n",
        "rfc_grid = pd.DataFrame({\n",
        "        \"PassengerId\": test[\"PassengerId\"],\n",
        "        \"Survived\": y_pred1\n",
        "    })\n",
        "rfc_grid.to_csv(\"/content/ada_grid_data2.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8w6POlds2lc",
        "colab_type": "code",
        "outputId": "2f9a8203-0802-4fca-992e-dee9c69615b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "gbk = GradientBoostingClassifier()\n",
        "gbk.fit(X_train, y_train)\n",
        "y_pred = gbk.predict(X_tes)\n",
        "acc_gbk = round(accuracy_score(y_pred, y_test) * 100, 2)\n",
        "print(acc_gbk)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "84.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txpqUi3XyQx5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xgb_params = {\n",
        "        'n_estimators': [200, 500],\n",
        "        'subsample': [0.6, 1.0],\n",
        "        'max_depth': [2,5,8],\n",
        "        'learning_rate': [0.1,0.01,0.02],\n",
        "        \"min_samples_split\": [2,5,10]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xh7xrf5LyXNw",
        "colab_type": "code",
        "outputId": "e37e1cff-c9c2-4d9f-cf84-dc05241965f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "source": [
        "xgb = GradientBoostingClassifier()\n",
        "\n",
        "xgb_cv_model = GridSearchCV(xgb, xgb_params, cv = 10, n_jobs = -1, verbose = 2)\n",
        "xgb_cv_model.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 108 candidates, totalling 1080 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:    9.2s\n",
            "[Parallel(n_jobs=-1)]: Done 158 tasks      | elapsed:   44.5s\n",
            "[Parallel(n_jobs=-1)]: Done 361 tasks      | elapsed:  2.7min\n",
            "[Parallel(n_jobs=-1)]: Done 644 tasks      | elapsed:  4.7min\n",
            "[Parallel(n_jobs=-1)]: Done 1009 tasks      | elapsed:  7.5min\n",
            "[Parallel(n_jobs=-1)]: Done 1080 out of 1080 | elapsed:  8.4min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=GradientBoostingClassifier(ccp_alpha=0.0,\n",
              "                                                  criterion='friedman_mse',\n",
              "                                                  init=None, learning_rate=0.1,\n",
              "                                                  loss='deviance', max_depth=3,\n",
              "                                                  max_features=None,\n",
              "                                                  max_leaf_nodes=None,\n",
              "                                                  min_impurity_decrease=0.0,\n",
              "                                                  min_impurity_split=None,\n",
              "                                                  min_samples_leaf=1,\n",
              "                                                  min_samples_split=2,\n",
              "                                                  min_weight_fraction_leaf=0.0,\n",
              "                                                  n_estimators=100,\n",
              "                                                  n_iter_no_...\n",
              "                                                  presort='deprecated',\n",
              "                                                  random_state=None,\n",
              "                                                  subsample=1.0, tol=0.0001,\n",
              "                                                  validation_fraction=0.1,\n",
              "                                                  verbose=0, warm_start=False),\n",
              "             iid='deprecated', n_jobs=-1,\n",
              "             param_grid={'learning_rate': [0.1, 0.01, 0.02],\n",
              "                         'max_depth': [2, 5, 8],\n",
              "                         'min_samples_split': [2, 5, 10],\n",
              "                         'n_estimators': [200, 500], 'subsample': [0.6, 1.0]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EIgEUWCymZ0",
        "colab_type": "code",
        "outputId": "c6a34e32-f53e-4398-a338-0ff3cea0bae9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "xgb_cv_model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.1,\n",
              " 'max_depth': 2,\n",
              " 'min_samples_split': 10,\n",
              " 'n_estimators': 200,\n",
              " 'subsample': 0.6}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "us8uvutY03VG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xgb = GradientBoostingClassifier(learning_rate = xgb_cv_model.best_params_[\"learning_rate\"], \n",
        "                    max_depth = xgb_cv_model.best_params_[\"max_depth\"],\n",
        "                    min_samples_split = xgb_cv_model.best_params_[\"min_samples_split\"],\n",
        "                    n_estimators = xgb_cv_model.best_params_[\"n_estimators\"],\n",
        "                    subsample = xgb_cv_model.best_params_[\"subsample\"])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBCx4WCr08kG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xgb_tuned =  xgb.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4Lp8Ov-1B1V",
        "colab_type": "code",
        "outputId": "1c08a3f3-1967-43d5-d292-443f0e3fc99c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred = xgb_tuned.predict(X_tes)\n",
        "acc_gbk = round(accuracy_score(y_pred, y_test) * 100, 2)\n",
        "print(acc_gbk)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "82.68\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOYwm6N61e1B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score = cross_val_score(xgb_tuned,X,y,scoring='accuracy',cv=kf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KrO9U001qZu",
        "colab_type": "code",
        "outputId": "c443a51e-4fcf-4e32-bc32-8a16bd9aa57a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.83798883, 0.82022472, 0.84269663, 0.81460674, 0.84831461])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOBxQDai1raA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred2 = xgb_tuned.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Upv7zUr_14nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rfc_grid = pd.DataFrame({\n",
        "        \"PassengerId\": test[\"PassengerId\"],\n",
        "        \"Survived\": y_pred2\n",
        "    })\n",
        "rfc_grid.to_csv(\"/content/gc_grid_data2.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jBXqpMor2z7f",
        "colab_type": "code",
        "outputId": "b9a637c7-bec5-4f01-e67c-dd263767a932",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "grfc.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini',\n",
              " 'max_depth': 8,\n",
              " 'max_features': 'log2',\n",
              " 'n_estimators': 800}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KhaClzR46mu_",
        "colab_type": "code",
        "outputId": "40591d30-2ee1-4a61-dd8f-45a88043964a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "rfc2 = RandomForestClassifier(n_estimators=800,criterion='gini',max_depth=8,max_features='log2',random_state=69)\n",
        "rfc2.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=8, max_features='log2',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=800,\n",
              "                       n_jobs=None, oob_score=False, random_state=69, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyyYXalw6_cL",
        "colab_type": "code",
        "outputId": "1a09d99c-e56e-45d0-9876-9649ded445c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy_score(y_test,rfc2.predict(X_tes))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8603351955307262"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM5uMmc_7FHp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score=cross_val_score(rfc2,X,y,scoring='accuracy',cv=kf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UiQkEQCZ7YML",
        "colab_type": "code",
        "outputId": "bbdc058d-3723-4c04-8b6f-ff48bb6521d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.84357542, 0.81460674, 0.85955056, 0.82022472, 0.83146067])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmpWEKW57aJ6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred3 = rfc2.predict(X_test)\n",
        "rfc_grid = pd.DataFrame({\n",
        "        \"PassengerId\": test[\"PassengerId\"],\n",
        "        \"Survived\": y_pred3\n",
        "    })\n",
        "rfc_grid.to_csv(\"/content/rfc_grid_data6.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJzCu9Pu9A3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}