Titanic_Dataset - EDA_and_Modeling.ipynb

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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/IqbalLx/Data-Science-Project/blob/master/Titanic_Dataset%20-%20EDA_and_Modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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        "colab": {
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          "height": 71
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier as knn\n",
        "from sklearn.tree import DecisionTreeClassifier as dtc\n",
        "from sklearn.ensemble import RandomForestClassifier as rfc\n",
        "import tensorflow as tf\n",
        "\n",
        "sns.set()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOqReqCRfdVt",
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      "source": [
        "# Dataset Overview"
      ]
    },
    {
      "cell_type": "code",
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        "id": "8tfxn76kWIwV",
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        "outputId": "795976f2-4ac2-439a-9789-46b993b9d17f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv('titanic.csv')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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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",
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              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\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]"
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          "execution_count": 2
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      },
      "source": [
        "df_test = pd.read_csv('titanic_test.csv')\n",
        "df_test.head()"
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
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              "      <th>Sex</th>\n",
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            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0          892       3  ...   NaN        Q\n",
              "1          893       3  ...   NaN        S\n",
              "2          894       2  ...   NaN        Q\n",
              "3          895       3  ...   NaN        S\n",
              "4          896       3  ...   NaN        S\n",
              "\n",
              "[5 rows x 11 columns]"
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          "metadata": {
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          "execution_count": 3
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          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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      "source": [
        "df.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 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"
          ],
          "name": "stdout"
        }
      ]
    },
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        "colab": {
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      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
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        "colab": {
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          "height": 221
        }
      },
      "source": [
        "df_test.isnull().sum()"
      ],
      "execution_count": 0,
      "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"
            ]
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          "metadata": {
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          "execution_count": 7
        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3HhF-1obVTM",
        "colab_type": "text"
      },
      "source": [
        "#Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcqjYy8parc1",
        "colab_type": "code",
        "outputId": "32b900bc-af18-49d4-e1f9-af37adc99b87",
        "colab": {
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        }
      },
      "source": [
        "df.describe().transpose()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <td>891.0</td>\n",
              "      <td>446.000000</td>\n",
              "      <td>257.353842</td>\n",
              "      <td>1.00</td>\n",
              "      <td>223.5000</td>\n",
              "      <td>446.0000</td>\n",
              "      <td>668.5</td>\n",
              "      <td>891.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0000</td>\n",
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              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>891.0</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0000</td>\n",
              "      <td>3.0000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>714.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>0.42</td>\n",
              "      <td>20.1250</td>\n",
              "      <td>28.0000</td>\n",
              "      <td>38.0</td>\n",
              "      <td>80.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>891.0</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>891.0</td>\n",
              "      <td>32.204208</td>\n",
              "      <td>49.693429</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7.9104</td>\n",
              "      <td>14.4542</td>\n",
              "      <td>31.0</td>\n",
              "      <td>512.3292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count        mean         std  ...       50%    75%       max\n",
              "PassengerId  891.0  446.000000  257.353842  ...  446.0000  668.5  891.0000\n",
              "Survived     891.0    0.383838    0.486592  ...    0.0000    1.0    1.0000\n",
              "Pclass       891.0    2.308642    0.836071  ...    3.0000    3.0    3.0000\n",
              "Age          714.0   29.699118   14.526497  ...   28.0000   38.0   80.0000\n",
              "SibSp        891.0    0.523008    1.102743  ...    0.0000    1.0    8.0000\n",
              "Parch        891.0    0.381594    0.806057  ...    0.0000    0.0    6.0000\n",
              "Fare         891.0   32.204208   49.693429  ...   14.4542   31.0  512.3292\n",
              "\n",
              "[7 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKDrHlDacnSE",
        "colab_type": "text"
      },
      "source": [
        "Seems that from the descriptive statistics doesn't give much insight, except the std of Age is quite high, it's ranging from 0.42 to 80, so there are any baby passanger, lets check the distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL-CuraUcbof",
        "colab_type": "code",
        "outputId": "f0ea5fc4-8990-4a72-8c52-ec3977b97e98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "sns.distplot(df.Age)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiI1NsQ9d8X5",
        "colab_type": "code",
        "outputId": "841ee242-abe7-471d-a71b-95347203dbe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        }
      },
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.kdeplot(df['Age'][df.Survived == 0], label='Not Survived', color='red', shade=True)\n",
        "sns.kdeplot(df['Age'][df.Survived == 1], label='Survived', color='green', shade=True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PR-2NwYfqdg",
        "colab_type": "text"
      },
      "source": [
        "So the average Age is the most survived, also it's interesting that many Children also survived, maybe the passanger prioritized the kids to be saved first"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhEEiA4Kg-EU",
        "colab_type": "text"
      },
      "source": [
        "Pclass is stands for passanger class, lets figure it, is it going to correlated with the passanger survivalness "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JZ4ZObPfTeN",
        "colab_type": "code",
        "outputId": "c4819dc5-afb6-4541-ebc0-e1ab87b5a195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "df.groupby('Pclass').agg({'Survived':'mean'}).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4AuVteTh6VL",
        "colab_type": "text"
      },
      "source": [
        "Class 1 is tend to survive better"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9ABXYyzi1WZ",
        "colab_type": "code",
        "outputId": "45fa48f6-8d35-4005-849c-fb512a7edab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "df.groupby('Pclass').agg({'Fare':'mean'}).plot.bar()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f775106c518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfBHX4TajCbQ",
        "colab_type": "text"
      },
      "source": [
        "Absolutely like I thougt, class 1 is has higher Fare (because I dont know what Fare is, but Google said is Tarif, so class 1 pasti lebih mahal tarifnya, jadi sudah bisa diwakilkan sama column Pclass)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJ0DjCd8nRsU",
        "colab_type": "code",
        "outputId": "acb51efb-367e-4db6-a585-29b51171468f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "df.Embarked.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    644\n",
              "C    168\n",
              "Q     77\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEyc4fPMlXCj",
        "colab_type": "code",
        "outputId": "0f7f0462-20c5-44b7-d72f-8f90ffa80b92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "df.groupby('Embarked').agg({'Survived':'mean'}).plot.bar()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7750ff47f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Rf_hjtOlscE",
        "colab_type": "text"
      },
      "source": [
        "I'm not sure if Embarked columns really add value for our model later, because the point of embarkation is not related at all with passanger survivalness. But this chart trying to show us that people from embarkment point C tend to be survive better"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eAQcJLl0nbtP",
        "colab_type": "text"
      },
      "source": [
        "#Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2MxQNFbneGK",
        "colab_type": "code",
        "outputId": "13cac6b2-7d81-4bc9-9df6-2b2b2d64953f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
              "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLsy-7M8oUGH",
        "colab_type": "text"
      },
      "source": [
        "Dropped columns:\n",
        "- PassengerId, Ticket, Name = add no information\n",
        "- Fare = give same info as Pclass\n",
        "- Cabin = too many missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PpuT_z7nlEE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.drop(['PassengerId', 'Ticket', 'Fare', 'Cabin', 'Name'], axis=1)\n",
        "df_test = df_test.drop(['PassengerId', 'Ticket', 'Fare', 'Cabin', 'Name'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_k3dkQXOpOEZ",
        "colab_type": "text"
      },
      "source": [
        "I'm going to fill missing values in Age using its median, because the distribution is right skewed, so it's may cause bias if I fill it with mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLFHDelhoEtu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "median_age = df.Age.median()\n",
        "df['Age'].fillna(median_age, inplace=True)\n",
        "df_test['Age'].fillna(median_age, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DPAr-0epn3Q",
        "colab_type": "text"
      },
      "source": [
        "Next for missing values in Embarked, I'm going to use its Mode"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-x8l0h1pu_1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Embarked'].fillna('S', inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uUtAdPKp1GM",
        "colab_type": "code",
        "outputId": "4536eba6-f048-4479-a9d1-5063863dcc6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "df.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",
              "Embarked    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5Fire4Mp6dO",
        "colab_type": "code",
        "outputId": "cf418df7-9b4f-408e-fc53-a1947388754d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "df_test.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Embarked    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Az7a07kuqGGi",
        "colab_type": "text"
      },
      "source": [
        "Next I wanna deal with categorical data (Embarked and Pclass) using one hot encoding, and Sex by changing male into 1 and 0 for female. For embarked and sex it's needed because model only deal with numbers, and for Pclass since it's nominal type, so 1, 2 and 3 do not mean it's representing order, but just a label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhA6Q0sgqC1Y",
        "colab_type": "code",
        "outputId": "1943ba17-5267-424b-f842-89cc034dfe6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_embarked_oh = pd.get_dummies(df, columns=['Embarked'])\n",
        "df_clean = pd.get_dummies(df_embarked_oh, columns=['Pclass'])\n",
        "\n",
        "df_clean['Sex'] = np.where(df_clean.Sex == 'male', 1, 0)\n",
        "\n",
        "df_clean.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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>38.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Sex   Age  SibSp  ...  Embarked_S  Pclass_1  Pclass_2  Pclass_3\n",
              "0         0    1  22.0      1  ...           1         0         0         1\n",
              "1         1    0  38.0      1  ...           0         1         0         0\n",
              "2         1    0  26.0      0  ...           1         0         0         1\n",
              "3         1    0  35.0      1  ...           1         1         0         0\n",
              "4         0    1  35.0      0  ...           1         0         0         1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYF6f9QMsrbA",
        "colab_type": "code",
        "outputId": "f07f2784-4768-4177-df3a-cefa783f23be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_test_embarked_oh = pd.get_dummies(df_test, columns=['Embarked'])\n",
        "df_test_clean = pd.get_dummies(df_test_embarked_oh, columns=['Pclass'])\n",
        "\n",
        "df_test_clean['Sex'] = np.where(df_test.Sex == 'male', 1, 0)\n",
        "\n",
        "df_test_clean.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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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
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              "  </thead>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sex   Age  SibSp  Parch  ...  Embarked_S  Pclass_1  Pclass_2  Pclass_3\n",
              "0    1  34.5      0      0  ...           0         0         0         1\n",
              "1    0  47.0      1      0  ...           1         0         0         1\n",
              "2    1  62.0      0      0  ...           0         0         1         0\n",
              "3    1  27.0      0      0  ...           1         0         0         1\n",
              "4    0  22.0      1      1  ...           1         0         0         1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzay7Ms9kWfv",
        "colab_type": "text"
      },
      "source": [
        "# Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVj3PSRbtBoH",
        "colab_type": "text"
      },
      "source": [
        "I wanna add is_alone columns based on SibSp and Parch columns. From kaggle it's representing the family of passanger"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AMpb5zUUtTM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_clean['fams'] = df_clean.SibSp + df_clean.Parch\n",
        "df_clean['is_alone'] = np.where(df_clean['fams'] > 0, 0, 1)\n",
        "\n",
        "df_test_clean['fams'] = df_test_clean.SibSp + df_clean.Parch\n",
        "df_test_clean['is_alone'] = np.where(df_test_clean['fams'] > 0, 0, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJFpIkTet3Ba",
        "colab_type": "code",
        "outputId": "7f14fa73-1377-48f1-eea2-09ad7422364d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df_clean = df_clean.drop(['SibSp', 'Parch', 'fams'], axis=1)\n",
        "df_test_clean = df_test_clean.drop(['SibSp', 'Parch', 'fams'], axis=1)\n",
        "\n",
        "df_clean.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",
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              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>is_alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Sex   Age  Embarked_C  ...  Pclass_1  Pclass_2  Pclass_3  is_alone\n",
              "0         0    1  22.0           0  ...         0         0         1         0\n",
              "1         1    0  38.0           1  ...         1         0         0         0\n",
              "2         1    0  26.0           0  ...         0         0         1         1\n",
              "3         1    0  35.0           0  ...         1         0         0         0\n",
              "4         0    1  35.0           0  ...         0         0         1         1\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7KCLfA9u59N",
        "colab_type": "code",
        "outputId": "e0c0c2bb-d411-4242-98de-1797296e30d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "df_clean.groupby('is_alone').agg({'Survived':'mean'}).plot.bar()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7750f5ff28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtErTnsRxDps",
        "colab_type": "text"
      },
      "source": [
        "Those who travel alone is have less survivalness"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4NesTCMxP_N",
        "colab_type": "text"
      },
      "source": [
        "# Predictive Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_G2to9uggfJ",
        "colab_type": "text"
      },
      "source": [
        "## Build ML Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ubwyBJ6yRJK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df_clean.drop(['Survived'], axis=1)\n",
        "y = df_clean['Survived']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZ6joCFNnuZ2",
        "colab_type": "text"
      },
      "source": [
        "### KNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyXVxsE_yWvm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "error = []\n",
        "for i in range(1, 20):\n",
        "  model = knn(n_neighbors=i)\n",
        "  model.fit(X_train, y_train)\n",
        "  y_pred = model.predict(X_test)\n",
        "  error.append(np.mean((y_pred - y_test)**2))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LJzqDLW2JtX",
        "colab_type": "code",
        "outputId": "4f8c9400-cf0d-417b-bdd5-ac647ee2cd7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "plt.plot(range(1, 20), error, marker='o', markerfacecolor='red')\n",
        "plt.xlabel('K-Value')\n",
        "plt.ylabel('Error')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TchFrA1HVbcAUP+W5TV7/6Cz7u4A7whOdiRfR6j8RansPHKWu3oVk9fK7PrVrZ+6ZO5GHXviMP76ymR9//RxGDjo92ZzNmq0H2LjjEF+7bAT9encNRdjGnMJmiTdxJRr9J8KhsMg7Mna2nxpIox7dOrNg7kR6pXTh9y9vYkfJkYCPX3XsBC+s2M7QAalcOblVDysaEzBLICau9HI0tIv+AlpcyYC0rvRoYSiRHt27cO/cifTs1pk/vLyRXfurAjr+31YUUnuigdtyR+F02q0rEx6WQEzceGv1Xg67E3h01j1szhxLgzOBzZlj+eO1C8idnhPt8ALmdnvYvq/Sb/uHP71SvEmke3Iij760kT1fnD2JrNeDfLrtILMvHEpGeuz3fTLxK+b6gRjjz/K1Rbzy7k6mjBnA+KFjeLLXg5TUQFJDLb379WbKmNB2vAunooNHqalzkZ0VeJtG79QkFsydxO9e+IxHX9zIPTdPZHD/0zugVdfU8/w7hWT17c7MKVmhDNuY01gNxMS8FeuKeemfOzhvVF++O2s0F4wbwAN3X8H//uwKbpw1gZLDNXywOX6ewlJf+4dk+m9AP5O0HkksmDuRpM4JPPLiBooOnN4B7cVV2zlWU89t14ymU4L99zbhZVeYiWmr1u9jycrtnJudzvdm55DgPPWSvWTCQEZl9eSlf27ncFV8tKRrUSV9eyUHNR5Vn57J3Dt3Ip0TE3jkxY3sK/uyV/nmnYf4OP8LZk4dTFa/+B0ew8QPSyAmZr27oYS/rShk4sg+fH/OGL+/qJ0OB9+eOQqXy8Nzy5VYn6LZ7fG2f5zt6auW9O3VlQVzJ9IpwcFvnvuUn/9+Bd/57Sr+/MIaenVLZPa0IaEL2JizsARiYtL7m/bz3HLlnOFp3HHd2LPejunbqys3XDKczTvLWb3lQASjbL19B6s5VtsQcAP6mfTr3ZUrzxtEl8oK7vzbL3ntjzfyH0t/TafDh1i/LbY/A9N+WAIxMeejz0tZ9NY2xg7rzQ+uHxfQvfzp5w5ieEYqL6ws5Eh1XQSiDI4W+9o/WtGAfiYfr97JvXneSZg6uV2ML87n7tcfJm9lQZuPbUwgLIGYmPJJ/hf835tbyRnSi3+/fhyJnQK7RJ1OB7fljqau3s3iFYVhjjJ4hUWVpKUmhWRQw/bSqdLEL0sgJmasKTjAX94sYNTgXvz7V8fTOTGhVfsPSOvGnK8MYb2WsW7bwTBFGTyPx4MWV4ak9gE2CZOJPksgJiZ8uu0gz/yjgOxBPbnrq+Pp0srk0ejqKVkM7p/C4neUqmMnQhxl2+w/dIzqmvo2t380yp2ew8I5C07pVLlwTnx1qjTxzToSmqhbrwd5etkWhmekMv+m8XTpHFzyAEhwOrktdzQPPPspzyz7nHkzskMYaduEsv0D8A0eOYUnU7+chOm66TlxNaikiW+WQExUbSgs46llWxg6MIW7bzqHpM5tvyQz+3bnmgsG8/pHexg/tDcTRvQJQaRtp0WV9ErpQnrP0E3qZJMwmWiyBGIianV+KXkrCyiphfROLg41JDBkYA9+/LUJJHcJ3eU4a9oQNu0s57m3t5H93Sl0TUoM2bGD0dj+kTO4V5vmIzcmllgbiImY5pNBzV9yPz2OH+Hicf1DmjwAOiU4uevrEzly7AQv/2tHSI8djC8OH6fq2IlWjX9lTKyzBGIipulkUI39Fu558xFWvqdhOV92Vi+uPj+L9zd55wSPppPtHyFqQDcmFlgCMRETjX4Lc74ylH69u/LsW9uoPdEQvhO1oLCoktRunelvMwOadsQSiImYaPRb6JyYwG25ozhcVcvf390VvhOdxcn+H5k9rf3DtCuWQEzEzLh0FA/n/iTi/RZGDurJFecOYtVn+yj03UqKpLLKGiqO1oXs8V1jYoU9hWUi5sjxeiq79uB/vvErDtQ7I9pv4auXDGfjjkP8NW8rv7rt/Fb3cm+LL+f/sARi2peIJRARyQYWAWlAOTBPVbc32+YXwM2AC6gHfq6qy5vs/2egJ9AFeElV749U/KZtauoaWL62iPEj0rn7pnMifv4unRP49sxRPPLiRpZ+uJuvXTYiYufW4kq6JycysE+3iJ3TmEiI5C2sp4DHVTUbeBx42s82a4HJqjoeuA14SUQae109BLyqqhOAycCtInJ+BOI2IfDuhhKO1TYw+8IhUYshZ0hvLpkwkOVri9i1/+zziodSobV/mHYqIjUQEekLTAJm+IqWAH8SkXRVLWvcrrG24bMZcOCtsewDPEAP37quvuXYGzHPnKbuhIu31xYxdmhvhg/s0fIOYXTTpSPYvLOcJ/6+iWRXHftrHWQkeceVCsettPIjtRw6UsuMyZkhP7Yx0RapW1iZQImqugBU1SUi+33lZWfYZx6wU1X3+ZbvBv4hIj8AegH3quqe8IZtQuHdjSUcPV7PtRcOjXYodE3qxJTR6az+1+f8IO9Rckq2UpAxmoVVC4ApIU8iWlwBWPuHaZ9ishFdRC4BHuTLGgvA94HnVfVhERkAvCsi61R1TaDHTUvrHuJIQy89PT7msg40zrp6F8s/LeackX24YOKgMEd1On9xFmzZx715jzK+OB+A8cX5zF/2EE/3+i9mXxbawRf3HtxB9+REJuYMwOk88y2sePl3h/iJ1eIMv0glkGIgQ0QSfLWPBGCgr/wUInIBsBiYo6pNuyjfBQwDUNVSEfkncDEQcAIpL6/G7Y7dObPT01MoKzsa7TBa1Jo4V6wrpvJoHd+fnRPx93amOIuPefx2aCw+5gl5jJu2lzEiowfl5dWtjjMWxUusFmdoOJ2Os/7wjkgjuqoeBDYCc31Fc4ENTds/AERkMvAScKOqftbsMLuBq33bpQAXAfnhjNu0TX2Di7dW70UyeyJZvaIdzkmR6tBYcbSOgxU11v/DtFuRfArrduBOESkE7vQtIyJ5InKeb5sngGTgaRHZ6Pszzrfu28DtIrIJb63jZVV9K4Lxm1b6cHMpldUnovrklT+RmojpZPuHJRDTTkWsDURVtwFT/JTnNnk9+Sz7rwemhSc6E2oNLjdvrt7LiIwejB4cO7UPaDYRUw10qa9l1ozQP4VVWFRJcpcEsvrG7z1uY84mJhvRTfz76PNSDlfV8e2rR8Vk/4fGiZiqa+pZ8OTHFB08FvJzaHElIwf1PGvjuTHxzMbCMiHX4HLz5id7GToglTFDe0c7nLPqnpzIFecO4tOtB9l/KHRJ5MixE5SWH7fHd027ZgnEhNwnW77g0JFarr1wSEzWPpq7cnImnRMTeOOTPSE7ZuOgjdmWQEw7ZgnEhJTL7ebNj/cyuF8K44enRTucgKR07cxlkzJYU3CALw4fD8kxtaiCLokJDO5v7R+m/bIEYkJqTcEBDlbWxE3to9FV52eRmODkzY/3hOR4WlzJiIxUOiXYfzHTftnVbULG7fbwj4/3ktm3OxNG9ol2OK3So1tnLp2YwSdbDnCwom21kKPHT1BSdozsGOr7Ykw4BJRARMQpIpeLSOdwB2Ti19ptBzhw+Dizp8VX7aPR1VOycDodvPnJ3jYdp7D4CGDjX5n2L6AEoqpuYJmqnghzPCZOuT0e/vHRHjL6dGOSpEc7nKD07N6FSyYM5OP8LzhUWRP0cbS4gsROToYOSA1hdMbEntbcwnpfRKaGLRIT19ZrGaXlx5l94RCccVj7aDRzShYOB+StKQr6GIVFlQwfmEpiJ7tDbNq31nQk3Au8JSLL8A6CeHJUQlW9L9SBmfjRWPvo37sr50nfaIfTJr1Tk7ho/EDe37SfWRcMpndq6wbIOlZbT/HBaq79SvSHrjcm3FrzEykZWIo3cQzCO5dHpu+16cA2bj/EvrJqZk8b0i56XedOHQzAW6tbXwvZXnwED9b+YTqGgGsgqnprOAMx8cnj8fD6R7vp2yuZ83Piu/bRKK1HEheOG8B7m/aTe8FgeqV0CXhfLa6gU4KDYQOt/cO0f60aC0tERuIdij0DKAGWqOr2cARm4sOmneUUHajmttzRJDjbzz3/ay4YzIebS3l7TRFzp48MeD8tqmTYgFQ6JyaEMTpjYkPA/+NFZDawHhgFHAYEWCci14YpNhPjPB4P//hoN316JDF1TL9ohxNS6T2TmTa2P+9uLOFIdV1A+9TUNbD3wFHr/2E6jNb8ZPxvvLMEfkNVf6aq3wTm+MpNB5S/+zC7S48ya9qQdtnj+pppg2lwuVm+9rSJM/3avu8IHo/N/2E6jtb8rx8EfNCs7EOsEb1D8ng8vP7hbtJSuzBtbP9ohxMW/Xp1ZWpOf/65YR9Vx1vuAqXFFSQ4HYwY2CMC0RkTfa1JIBuBnzQr+7Gv3HQwBXsr2Lm/itwL2mfto9GsaYOpr3fzTgC1kMKiSoYMSKFLZ2v/MB1DaxrR7wD+ISLz8fYDyQSOA7PDEZiJXY21j14pXfjKuNDO4hdrBqR1Y/Lovqz6bB9XT8mie3Ki3+3qTrjY88VRrjo/K8IRGhM9AY+FBQwEJgJfAx71/T1aVbeGLzwTi/J3lrN93xFypw7uEL2tZ08bQt0JF+98euZayI79R3C5Pdb+YTqUgGogquoWkWWqmoK33cNE0Or8UvJWFlBSCxlJkDs99PN3tyqOGujaUEtSYvx3GgxERnp3zpN0Vq0v5urzM+madHotRIsqcTocjMiw9g/TcbTmFtb7IjJVVVeHLRpzmtX5pSx9bQ3zlz1ETslWCjJGs7BqATAloknEbxyeBSQ4nVFJZpE2a9oQ1mkZK9ft8ztMSWFRBYP7dye5S6u6VhkT12wsrBiXt7KA+cseYnxxPgDji/OZv+whnkx9MKJf3LESR7Rk9Uth4sg+vPNpMTMmZ56SKE7Uu9hVWsX0czOjGKExkdeaBNI4Fhac+uiux8+2pxGRbGARkAaUA/Oa92IXkV8ANwMuoB74uaoub7L+TuCHvnUuVZ3QivjjUkkt5JSc2syUU7KVktqOGUc0XXvhUH61/VNWrd/HrGlDTpbv2l9Fg8tDtrV/mA4moAQiIgl4ax2/VtXAuuWe7ingcVVdLCLfAp4GLm+2zVrgUVU9LiLnAO+JyABVrRGRG4CbgMmqelRE2lfX5zMYmOShIGP0yV/+AAUZoxmYFFDeDon6Bjcp7hN+48ho3WC1cW1w/xTOGZ7G8rVFXHHuoJO1EC2uxAFkD7L2D9OxBDqhlAvvY7z1wZxERPoCk4AlvqIlwCSRU2ceUtXlqto4n+hmwIG3xgLePij3q+pR37YHgokl3vQblM7DuT9hc+ZYGpwJbM4cy8O5P8HZvTv1Da6wn7/B5eapZflUORL5w+x7T4lj4ZwF5E7PCXsMsWT2hUM5VtvAuxtKTpZpUQWZ/br7bVw3pj1rzS2s54HbgSeCOE8mUOJLRKiqS0T2+8rLzrDPPGCnqu7zLecAU0Xkv4DOwNOq+kwQscSNogNH2bTrMMNGDubJWx48+RTW+OwBvL/5Cx7///L54fXjwvYobYPLzdPLtrBh+yG+eeUounVJ4MmeD7K/FgYmwXVRehosmoYNTGXssN68vbaIyycNwul0sHN/FZdOyIh2aMZEXGsSyPnAnSKygNMb0S8OZVAicgnwIDCjSXEC3oTzFaAP8JGIqKq+H+hx09K6hzLMsEhPTwG8X97/9fx6Urp15ld3XEhK11Onox/3yR4ef3UT//fWNn46b3LIk4jL5eaRv61nfWEZ350zljkXDwdg9mXZIT1PuDV+nqH0b9eM4d7/+YB1Ow4xMrMX9Q1uJo8d0KZzhSPOcImXWC3O8GtNAnnG96e5QG7GFwMZIpLgq30k4O2YeFrPLBG5AFiMd+BGbbKqCO/w8W7goIiswJvUAk4g5eXVuN2RaztorfT0FMrKjgLwxsd72FVyhB9eP5baY3XUHju16encEWl868psFr9TyK//dzXfnzMmZEOKuN0ennmjgDUFB/jaZSOYNrrvybiaxxnLwhVnWrdEcob04oW3t5JUX4vD04lnl6zm0KExQdXI4uXzhPiJ1eIMDafTcdYf3i1+44jIYwCqukhVFwGdGl/7lue0dAxVPYh3zKy5vqK5wAZVPeX2lYhMBl4CblTVz5od5gXgat923YCLgE0tnTselRw6xusf7ea8UX059yxTxF4+aQwfPgsAABT2SURBVBBzp49kfWEZf/5HAS63u83ndrs9/O+bW1lTcIAbLx3O1VNsaA5/RgxIpUtFOfe8/ACvLbyJHy6+j6WvrWF1fmm0QzMmYgL5yfrtZssPN1ueQWBux3sLrBC407eMiOSJyHm+bZ7A+7jw0yKy0fdnnG/dH4BMEdmC92mtxaq6IsBzxw2328Nf87aS1LkT35zR8u2iGedl8vXLR7Bu20GeaWMScXs8/PWtrXyy5Quuv3jYyaldzek+27CHe/MeZXxxPp3crpP9YvJWFkQ7NGMiJpBbWM3Hq2hp2S9V3QZM8VOe2+T15LPsXwPcEsi54tnKdcXs2l/F92bn0KNb55Z3AK46Pwu328Mr7+4kwengO9fktHpucrfHw3Nvb+Ojz7/g2guHMLtJPwdzOusXY0xgNZDmjQYtLZsg7T9UzWvv72LCiD5MyWldN5eZUwdzw8XD+GTLAf761lbcnsD/WTweD4vfKeT9TaXMmjaYOX6G6jCnykjy9oNpqqP1izEmkBpIJxG5jC9rGs2XbfKDEHB7PPzPK5tISHByy1WCw9H6gQpnTRuCy+1h2Ye7SXA6mHf1KJwtHMfj8fDCiu28u6GEmVOzuP6iYUGdu6PJnZ7DwqoFp44NNmcB13WwfjGmYwskgRwE/q/Jcnmz5YMhjaiDem9DCfk7y/n2zFH0SukS9HGuvdCbRN74eA9Oh+Osycjj8bBk1XZWfbaPq87P5MZLhlvyCJD3aaspPJn6Zf+cjtgvxnRsLSYQVR0SgTg6tENHanj53Z1MGJnORePb9gXkcDi4/qKhuN0e8lbvxel08M0Z2aclBo/Hw8v/2sHKdfuYft4gvnbZCEserTR17ABLGKZDs7Gno8zj8bDobQUP/PvXJuBwtX14EofDwVcvGYbb7eHttUU4nQ7mXjHyZILweDy8+t5Olq8t5vJJGaesM8aYQFkCibIPPy9ly+7DfHNGNv16dw1ZpyKHw8FNlw2nwe1m5bp9HCg/xuHSw+yvhZ6Oeircnbh04iC/tRNjjAmEJZAoqjhax4urdpA9qAeXTQr9WEoOh7fmsb+smuL8Xdyb9+jJBt9HZ93DyEE9LHkYY4LW/ie0jlEej4fnlysNLje35o5u8WmpYDkcDo4cqDit09tP3niEt6zTmzGmDSyBRMnarQfZuOMQ1180jH69u4b1XPut05sxJgwsgURB1fET/G1FIUMHpHLl5PBPg2qd3owx4WAJJApeWFFI7YkGbssd1eohR4KROz2HhXMWdPjJoIwxoWWN6BG2XstYu/Ug1188jIz0yMxPYp3ejDHhYAkkgqpr6ln8jpLVtzszIzxMunV6M8aEmt3CiqCXVm2nuqae264ZHbLJn4wxJlqsBhJmq/NLyVtZQEkNdKmvZXzOILL6xe8UlsYY08gSSBitzi9l6WtrTh2xlQWszu9rt5OMMXHP7qOEUd7KAuYve8hmrTPGtEuWQMLIZq0zxrRnlkDCyDrwGWPaM0sgYZQ7PYc/zL7XOvAZY9ola0QPo6ljB7B261D+y/kf1HZOsg58xph2xRJImFXXuRgwpB//Oe+8aIdijDEhFbEEIiLZwCIgDe+86vNUdXuzbX4B3Ay4gHrg56q6vNk2lwKrgPmq+qcIhB60unoXu/dXRWTARGOMibRItoE8BTyuqtnA48DTfrZZC0xW1fHAbcBLIpLcuFJEUoDfAW9FIN4221VyBJfbQ3Zmz2iHYowxIReRBCIifYFJwBJf0RJgkoikN91OVZer6nHf4mbAgbfG0uj3wMPAofBGHBpaXInDASMHWQIxxrQ/kaqBZAIlquoC8P2931d+JvOAnaq6D0BEZgI9VPXVcAcbKlpUSVbfFLomWVOTMab9iclvNhG5BHgQmOFb7gn8tnE5WGlpkRk+HeBEvYtdpVXkThtKenrgY1+1ZttosjhDK17ihPiJ1eIMv0glkGIgQ0QSVNUlIgnAQF/5KUTkAmAxMEdV1Vc8FhgArBURgD7AbBHpraoPBBpEeXk1brenjW8lMFpUQX2Dm6w+XSkrOxrQPunpKQFvG00WZ2jFS5wQP7FanKHhdDrO+sM7IglEVQ+KyEZgLt7kMBfYoKplTbcTkcnAS8CNqvpZk/0/BPo22e5ZYF0sP4WlxZU4gJHWgG6MaacieQvrdmCRiNwHVOBt40BE8oD7VHUd8ASQDDztq2kA3KKqn0cwzpAoLK4kI7073ZMTox2KMcaERcQSiKpuA6b4Kc9t8npygMf6dugiC70Gl5sdJUe4aPzAaIdijDFhY2NhhcGeL45yot6N2O0rY0w7ZgkkDLSoAoDsLEsgxpj2yxJIGGhxJQP7dCO1a+doh2KMMWFjCSTEXG432/cdsdtXxph2zxJIiBUdqKbuhAux21fGmHbOEkiIaVElgNVAjDHtniWQENOiCvr17kqP7l2iHYoxxoSVJZAQcrs9FFr7hzGmg7AEEkLFB6upqWuw9g9jTIdgCSSEtNjaP4wxHYclkBDSogrSeybROzUp2qEYY0zYWQIJEbfHQ2FxJZLZK9qhGGNMRFgCCZH9Zcc4VmvtH8aYjsMSSIhY+4cxpqOxBBIiWlRBWmoX+vRMjnYoxhgTEZZAQsDja//ItvYPY0wHYgkkBErLj1N1vN7aP4wxHYolkBAobGz/sARijOlALIGEgBZX0qN7Z/pa+4cxpgOxBNJGHo8HLapAMnvicDiiHY4xxkSMJZA2OlhZQ2X1CSTLGtCNMR2LJZA2svk/jDEdVadInUhEsoFFQBpQDsxT1e3NtvkFcDPgAuqBn6vqct+6x4ErgDqgGpivqusiFf+ZaFElqV0TGZDWNdqhGGNMREWyBvIU8LiqZgOPA0/72WYtMFlVxwO3AS+JSGPL9FvAOFU9B/gN8FIEYm5RYXEF2db+YYzpgCKSQESkLzAJWOIrWgJMEpH0ptup6nJVPe5b3Aw48NZYUNU3VLXet+4TYJCIRPUW3KHKGsqr6qz9wxjTIUXqFlYmUKKqLgBVdYnIfl952Rn2mQfsVNV9ftb9O/CmqrpbE0RaWvfWbN6izXsqAJh6Tgbp6SkhOWaojhNuFmdoxUucED+xWpzhF7E2kNYQkUuAB4EZftbdDHwDuLi1xy0vr8bt9rQ9QJ91W76gW1InkhOgrOxom4+Xnp4SkuOEm8UZWvESJ8RPrBZnaDidjrP+8I7ULaBiIENEEgB8fw/0lZ9CRC4AFgPXqao2W3c98GvgKlU9EPaoW6C+9g+ntX8YYzqgiCQQVT0IbATm+ormAhtU9ZTbVyIyGW/j+I2q+lmzdbOA3+NNHnvCHnQLDlfVUlZZa+0fxpgOK5K3sG4HFonIfUAF3jYORCQPuM/3SO4TQDLwtIg07neLqn4O/BU4AbzaZN0VqloeubfwJZv/wxjT0UUsgajqNmCKn/LcJq8nn2X/9DOtiwYtqiS5Sycy+4a2Yd4YY+KF9UQPkhZXkj2oB06ntX8YYzomSyBBqKyu48Dh49b+YYzp0CyBBMHm/zDGGEsgQdGiSpI6J5DVz9o/jDEdlyWQIBQWVzJiUA8SnPbxGWM6LvsGbKWjx09QcuiYPb5rjOnwLIG00pftH9aAbozp2CyBtJIWVdI50cmQ/vE7AJoxxoSCJZBW0uJKRmT0oFOCfXTGmI7NvgVb4VhtPfsOVlv7hzHGYAmkVQqLK/Fg7R/GGAOWQFpFiypJ7ORk6IDUaIdijDFRZwmkFbS4kuEDU0nsZB+bMcbYN2GAjtc2UHTgKNnW/mGMMYAlkIDtKKnE47H5P4wxppElkABpUSUJTgfDMnpEOxRjjIkJlkACpMWVDB2YSpfEhGiHYowxMcESSABqTzSwp/So3b4yxpgmLIEEYEfJEdwej83/YYwxTVgCCYAWVeJ0OBhh7R/GGHNSp2gHEMtW55eSt7KAkhro7q5jY2EZU8cOiHZYxhgTEyKWQEQkG1gEpAHlwDxV3d5sm18ANwMuoB74uaou963rCvwVOBdoAO5R1TfCFe/q/FKWvraG+cseIqdkKwUZo1noWgBMsSRijDFE9hbWU8DjqpoNPA487WebtcBkVR0P3Aa8JCLJvnX3AFWqOgKYDfxFRMI2p2zeygLmL3uI8cX5dHK7GF+cz/xlD5G3siBcpzTGmLgSkQQiIn2BScASX9ESYJKIpDfdTlWXq+px3+JmwIG3xgLwdXxJx1dzWQfMDFfMJbWQU7L1lLKckq2U1IbrjMYYE18iVQPJBEpU1QXg+3u/r/xM5gE7VXWfbzkL2NtkfVEL+7dJRhIUZIw+pawgYzQZSeE6ozHGxJeYbEQXkUuAB4EZoTxuWlrgd7xuvuFcHjv6U+5a+ruTbSCPXfdTbrnhXNLTwzcbYTiPHUoWZ2jFS5wQP7FanOEXqQRSDGSISIKqukQkARjoKz+FiFwALAbmqKo2WVUEDAbKfMtZwL9aE0R5eTVutyegbXOyejLn+vN5MuVBSmq9NZI503PIyepJWdnR1pw2YOnpKWE7dihZnKEVL3FC/MRqcYaG0+k46w/viCQQVT0oIhuBuXiTw1xgg6qWNd1ORCYDLwE3qupnzQ7zCvB9YJ2IjAQm+44TNlPHDrAnrowx5gwieQvrdmCRiNwHVOBt40BE8oD7VHUd8ASQDDwtIo373aKqnwMPA8+KyA68j/l+T1VjN3UbY0w7F7EEoqrbgCl+ynObvJ58lv2PATeFJzpjjDGtZUOZGGOMCYolEGOMMUGJycd4wyABvE8UxLp4iBEszlCLlzghfmK1ONuuSWx+J0JyeDyBPdYa574CfBDtIIwxJk5dBHzYvLCjJJAueB/7LcX7BJcxxpiWJQADgE+BuuYrO0oCMcYYE2LWiG6MMSYolkCMMcYExRKIMcaYoFgCMcYYExRLIMYYY4JiCcQYY0xQLIEYY4wJSkcZyiQmiEga8DwwHDgBbAe+72delGeB6cAhX9ErqvrrCIaKiOwBan1/AH6qqsubbdMV+CtwLtAA3KOqb0QwxiHA0iZFPYFUVe3dbLv7gR/gnUYZ4CNV/WGYY3sE+CowBBinqvm+8mxgEZAGlAPzVHW7n/0TgMeAqwEP8FtV/UukYg30WvXt/ywRuF7P8pnuoYVr1bddRK7XM3yeQwjgWvXtfz8Rvl6DZQkksjzAQ6r6LoCIPAz8FviOn21/q6p/imBs/tzY+J/0DO4BqlR1hG+Srw9EZISqVkciOFXdA0xoXBaRP3Lma/o5Vb0nEnH5LAUWcvoQOk8Bj6vqYhH5FvA0cLmf/b8JjABG4k02G0Rkpe89RyLW1lyrEJnr9UyfKbR8rULkrtfT4mzltQqRv16DYrewIkhVDzf+h/RZjXea3nj1dbxfgPh+Ra8DZkYjEBHpjPdL9/+icf7mVPVDVT1lymYR6QtMApb4ipYAk0Qk3c8hvg48o6pu36/+pYRpPhx/scbiteovzlaKyPXaUpyxdq22hdVAokREnMAdwOtn2OTHIvJ9YCfwM1XdGrHgvvQ3EXHgHUTt56pa2Wx9FrC3yXIRkBmp4Jq5FijxMxVyo5tF5ErgC+CXqvpJ5EI7KRNvjC4AVXWJyH5fefNbQzHz2QZwrUL0r9eWrlWInc+0pWsVYuN6bZHVQKLnf4BqwF+1/z+AEao6DngNeNt3TzySLlLVc/AOQunAf5yx5DbO/IvuKWCoqo7HOzXyMt89fhOYs12rEP3rtT1dqxBH16slkCjwNbKNBL6uqu7m61W1pLFcVZ8DugODIhljYxVcVevwzlV/oZ/Nijj1tkYW0JZbDEERkQzgEuBv/tar6heqWu97vQJvjGMjF+FJxUBG45er7++B+P/MYuWzPeu1CtG/XgO8ViEGPtOWrlWIqeu1RZZAIkxE/hvvUyDX+S54f9tkNHl9Fd4h6EsiEyGISDcR6eF77QBuBjb62fQV4Pu+7Ubi/QX4dqTibOLfgDdVtdzfymaf5wS8T8doZEL7kqoexPs5zvUVzQU2+HuyCe9n+/9ExOlrI7kOeDUykXoFcq36tova9dqKaxVi43o967UKsXO9BsKGc48gERkD5AOFQI2veLeqXi8iG4FcVd0vIiuBfoAbqALuVdXVEYxzGPB3vHMBJAAFwF2qWtoszm7As8BEvF8aC1R1WaTibBJvoS++t5uU5QH3qeo6EVmE94vQhfeR1F+qal6YY3oMuAHoj/fx1nJVHSMio/A+xtsLqMD7GK/6iTkB762YK32H/J2q/jlSsQJf4wzXqm+fiF+vZ4hzNme4Vv3EGZHr9Uz/9r51p12rvvKoXq/BsgRijDEmKHYLyxhjTFAsgRhjjAmKJRBjjDFBsQRijDEmKJZAjDHGBMUSiDExRkT2iMj0aMdhTEssgRjTSs2/4EXkZhGpEJFLfMtPichzfvY7R0TqROS0IbyNiUeWQIxpAxH5N+Bx4BpVfc9XvAi4wddxralbgDdU9XAkYzQmXGw0XmOC5Bt99tfAVaq6rrFcVT8RkRK8kwo959s2AfgGcIeIDAeeAc7BO+/GcuCH/kaQ9U3WtE9V/9O3fCmwWFUH+ZYH4h3s8GK8Ax7+QVUfC8sbNqYZq4EYE5w7gAeAK5omjyaeA+Y1WZ4OJAJ5eEeM/Q3egRRH4x1S/P7WBuAbZv0fwCYgA7gCuNs3HpUxYWc1EGOCMwP4F/D5GdY/D9wvIoNUdR/eZPKCb5TVHb4/AGUi8nvgl0HEMBlIV9UHfMu7ROQZvAMKnjalqzGhZgnEmODcAfwn8BcR+Q7egQcbhwqfqaofiMj7wLdE5E94R9O9GEBE+uGd8vQiIAXvnYCKIGIYDAwUkaa3vhLwP+WrMSFnCcSY4BzAe8voPeCJxtFWm1kE/BQoxTuS7Xpf+X/jbfsYp6qHReQ6zjwJ0jGga5Pl/k1eF/uOOzL4t2FM8KwNxJggqep+vEnkahH5g59N/o530qJf4U0mjVLwNngf8c39cO9ZTrMRyBWR3iLSH7i7ybq1wFER+amIJItIgoiMFZHJbXhbxgTMEogxbaCqRcDlwI0i8ptm647hTSKDOHUGul8Bk4AjwJt4p4E9k+fxNpLvAd4BXmpyfBcwC5gA7MY798RfgB5teU/GBMrmAzHGGBMUq4EYY4wJiiUQY4wxQbEEYowxJiiWQIwxxgTFEogxxpigWAIxxhgTFEsgxhhjgmIJxBhjTFAsgRhjjAnK/w9znb6ll/wAhQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFbBXuRI2Yxr",
        "colab_type": "code",
        "outputId": "fb4d5df4-dc2e-423e-b6f3-76569ead081c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "model_knn = knn(n_neighbors=1)\n",
        "model_knn.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCR4uzs64ST4",
        "colab_type": "code",
        "outputId": "7389348c-9595-47e3-c0fa-621d8a8105d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "y_pred_knn = model_knn.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_knn))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.85      0.83       104\n",
            "           1       0.77      0.73      0.75        75\n",
            "\n",
            "    accuracy                           0.80       179\n",
            "   macro avg       0.79      0.79      0.79       179\n",
            "weighted avg       0.80      0.80      0.80       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Ce2szhKny2g",
        "colab_type": "text"
      },
      "source": [
        "### Decision Tree Classsifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISBpKyY1n5Mf",
        "colab_type": "code",
        "outputId": "17c96834-1926-4c55-e7ce-247e1a5557b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "model_tree = dtc()\n",
        "model_tree.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=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, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zGkuDxVpDXn",
        "colab_type": "code",
        "outputId": "2316a879-8d1c-4e7c-bc0a-e9384d5665a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "y_pred_tree = model_tree.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_tree))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.88      0.83       104\n",
            "           1       0.80      0.69      0.74        75\n",
            "\n",
            "    accuracy                           0.80       179\n",
            "   macro avg       0.80      0.78      0.79       179\n",
            "weighted avg       0.80      0.80      0.80       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9a2XzzpplMr",
        "colab_type": "text"
      },
      "source": [
        "### Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKFfwGRwpkhM",
        "colab_type": "code",
        "outputId": "382f0442-eb69-4f05-82a6-de6917b0a303",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "model_forest = rfc()\n",
        "model_forest.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=None, max_features='auto',\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=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhtiJKuCp-5-",
        "colab_type": "code",
        "outputId": "147fc35c-d6e2-49e5-822e-274737a0f713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "y_pred_forest = model_forest.predict(X_test)\n",
        "print(classification_report(y_test, y_pred_forest))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.86      0.84       104\n",
            "           1       0.79      0.75      0.77        75\n",
            "\n",
            "    accuracy                           0.81       179\n",
            "   macro avg       0.81      0.80      0.80       179\n",
            "weighted avg       0.81      0.81      0.81       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KX553lnpgW4Q",
        "colab_type": "text"
      },
      "source": [
        "## Build Simple DL Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ued7bGn40iV",
        "colab_type": "code",
        "outputId": "fa5767a0-5f9f-4f9c-c967-f84170c3806e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "model_tf = tf.keras.models.Sequential([\n",
        "                                    tf.keras.layers.Dense(64, activation='relu', input_shape=(len(X_train.columns),)),\n",
        "                                    tf.keras.layers.Dense(64, activation='relu'),\n",
        "                                    tf.keras.layers.Dense(128, activation='relu'),\n",
        "                                    tf.keras.layers.Dropout(0.2),\n",
        "                                    tf.keras.layers.Dense(1, activation='sigmoid'),\n",
        "])\n",
        "\n",
        "model_tf.compile(\n",
        "    loss='binary_crossentropy',\n",
        "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
        "    metrics=['acc']\n",
        ")\n",
        "\n",
        "model_tf.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 64)                640       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 13,249\n",
            "Trainable params: 13,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KL3MW3O6B0w",
        "colab_type": "code",
        "outputId": "bdf71101-f606-4d86-85db-c2695eda48dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model_tf.fit(X_train, y_train,\n",
        "                    epochs=100,\n",
        "                    validation_split=0.2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "18/18 [==============================] - 0s 11ms/step - loss: 0.7315 - acc: 0.5606 - val_loss: 0.6425 - val_acc: 0.6643\n",
            "Epoch 2/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.6918 - acc: 0.5923 - val_loss: 0.6445 - val_acc: 0.6573\n",
            "Epoch 3/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.6650 - acc: 0.6380 - val_loss: 0.6177 - val_acc: 0.6573\n",
            "Epoch 4/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.6289 - acc: 0.6626 - val_loss: 0.5963 - val_acc: 0.6853\n",
            "Epoch 5/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.6175 - acc: 0.6731 - val_loss: 0.5879 - val_acc: 0.6923\n",
            "Epoch 6/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.5790 - acc: 0.7188 - val_loss: 0.6058 - val_acc: 0.7063\n",
            "Epoch 7/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.5394 - acc: 0.7417 - val_loss: 0.5860 - val_acc: 0.6853\n",
            "Epoch 8/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.5285 - acc: 0.7540 - val_loss: 0.5852 - val_acc: 0.6993\n",
            "Epoch 9/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.5250 - acc: 0.7627 - val_loss: 0.5625 - val_acc: 0.7133\n",
            "Epoch 10/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.5098 - acc: 0.7750 - val_loss: 0.5673 - val_acc: 0.7413\n",
            "Epoch 11/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4857 - acc: 0.7838 - val_loss: 0.5474 - val_acc: 0.7133\n",
            "Epoch 12/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4605 - acc: 0.7803 - val_loss: 0.5599 - val_acc: 0.7552\n",
            "Epoch 13/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4571 - acc: 0.8084 - val_loss: 0.5322 - val_acc: 0.7692\n",
            "Epoch 14/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4530 - acc: 0.8120 - val_loss: 0.5317 - val_acc: 0.7133\n",
            "Epoch 15/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4473 - acc: 0.8084 - val_loss: 0.5078 - val_acc: 0.7552\n",
            "Epoch 16/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4489 - acc: 0.7979 - val_loss: 0.5165 - val_acc: 0.7832\n",
            "Epoch 17/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4594 - acc: 0.8120 - val_loss: 0.5453 - val_acc: 0.6853\n",
            "Epoch 18/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4458 - acc: 0.8120 - val_loss: 0.5059 - val_acc: 0.7413\n",
            "Epoch 19/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4332 - acc: 0.8137 - val_loss: 0.5074 - val_acc: 0.7692\n",
            "Epoch 20/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4304 - acc: 0.8190 - val_loss: 0.4934 - val_acc: 0.7762\n",
            "Epoch 21/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4144 - acc: 0.8278 - val_loss: 0.5790 - val_acc: 0.6713\n",
            "Epoch 22/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4479 - acc: 0.8155 - val_loss: 0.5496 - val_acc: 0.7552\n",
            "Epoch 23/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4358 - acc: 0.8190 - val_loss: 0.4870 - val_acc: 0.7692\n",
            "Epoch 24/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4151 - acc: 0.8348 - val_loss: 0.5074 - val_acc: 0.7692\n",
            "Epoch 25/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4212 - acc: 0.8330 - val_loss: 0.4920 - val_acc: 0.7832\n",
            "Epoch 26/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4121 - acc: 0.8295 - val_loss: 0.4892 - val_acc: 0.7692\n",
            "Epoch 27/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4262 - acc: 0.8243 - val_loss: 0.4979 - val_acc: 0.7832\n",
            "Epoch 28/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4048 - acc: 0.8418 - val_loss: 0.5119 - val_acc: 0.7692\n",
            "Epoch 29/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4228 - acc: 0.8207 - val_loss: 0.4982 - val_acc: 0.7343\n",
            "Epoch 30/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4190 - acc: 0.8120 - val_loss: 0.4914 - val_acc: 0.7413\n",
            "Epoch 31/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4059 - acc: 0.8383 - val_loss: 0.5008 - val_acc: 0.7622\n",
            "Epoch 32/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3977 - acc: 0.8366 - val_loss: 0.4902 - val_acc: 0.7832\n",
            "Epoch 33/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4046 - acc: 0.8190 - val_loss: 0.5275 - val_acc: 0.7552\n",
            "Epoch 34/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4392 - acc: 0.8084 - val_loss: 0.4798 - val_acc: 0.7762\n",
            "Epoch 35/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.4217 - acc: 0.8190 - val_loss: 0.4884 - val_acc: 0.7762\n",
            "Epoch 36/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4076 - acc: 0.8348 - val_loss: 0.4853 - val_acc: 0.7483\n",
            "Epoch 37/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4037 - acc: 0.8260 - val_loss: 0.4833 - val_acc: 0.7692\n",
            "Epoch 38/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3976 - acc: 0.8418 - val_loss: 0.4814 - val_acc: 0.7692\n",
            "Epoch 39/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4090 - acc: 0.8313 - val_loss: 0.4849 - val_acc: 0.7832\n",
            "Epoch 40/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4232 - acc: 0.8260 - val_loss: 0.5160 - val_acc: 0.7552\n",
            "Epoch 41/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4025 - acc: 0.8172 - val_loss: 0.5258 - val_acc: 0.7552\n",
            "Epoch 42/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4127 - acc: 0.8313 - val_loss: 0.5330 - val_acc: 0.7552\n",
            "Epoch 43/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4157 - acc: 0.8155 - val_loss: 0.5018 - val_acc: 0.7622\n",
            "Epoch 44/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3935 - acc: 0.8401 - val_loss: 0.4832 - val_acc: 0.7692\n",
            "Epoch 45/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3975 - acc: 0.8418 - val_loss: 0.4820 - val_acc: 0.7762\n",
            "Epoch 46/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4083 - acc: 0.8260 - val_loss: 0.4939 - val_acc: 0.7483\n",
            "Epoch 47/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3907 - acc: 0.8436 - val_loss: 0.5001 - val_acc: 0.7622\n",
            "Epoch 48/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3962 - acc: 0.8436 - val_loss: 0.4872 - val_acc: 0.7762\n",
            "Epoch 49/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3945 - acc: 0.8453 - val_loss: 0.4812 - val_acc: 0.7413\n",
            "Epoch 50/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3992 - acc: 0.8383 - val_loss: 0.4840 - val_acc: 0.7413\n",
            "Epoch 51/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3898 - acc: 0.8418 - val_loss: 0.4853 - val_acc: 0.7692\n",
            "Epoch 52/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3858 - acc: 0.8383 - val_loss: 0.5312 - val_acc: 0.7203\n",
            "Epoch 53/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4018 - acc: 0.8348 - val_loss: 0.4887 - val_acc: 0.7622\n",
            "Epoch 54/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3949 - acc: 0.8453 - val_loss: 0.4852 - val_acc: 0.7762\n",
            "Epoch 55/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3854 - acc: 0.8418 - val_loss: 0.4973 - val_acc: 0.7762\n",
            "Epoch 56/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3972 - acc: 0.8453 - val_loss: 0.4836 - val_acc: 0.7483\n",
            "Epoch 57/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3888 - acc: 0.8453 - val_loss: 0.4924 - val_acc: 0.7413\n",
            "Epoch 58/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4032 - acc: 0.8330 - val_loss: 0.4909 - val_acc: 0.7762\n",
            "Epoch 59/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3970 - acc: 0.8383 - val_loss: 0.4946 - val_acc: 0.7762\n",
            "Epoch 60/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3899 - acc: 0.8418 - val_loss: 0.5152 - val_acc: 0.7622\n",
            "Epoch 61/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3905 - acc: 0.8383 - val_loss: 0.4842 - val_acc: 0.7622\n",
            "Epoch 62/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3852 - acc: 0.8418 - val_loss: 0.4871 - val_acc: 0.7622\n",
            "Epoch 63/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3980 - acc: 0.8436 - val_loss: 0.4846 - val_acc: 0.7622\n",
            "Epoch 64/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3896 - acc: 0.8453 - val_loss: 0.4988 - val_acc: 0.7762\n",
            "Epoch 65/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3882 - acc: 0.8366 - val_loss: 0.4864 - val_acc: 0.7622\n",
            "Epoch 66/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3935 - acc: 0.8348 - val_loss: 0.4892 - val_acc: 0.7692\n",
            "Epoch 67/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3816 - acc: 0.8436 - val_loss: 0.5031 - val_acc: 0.7692\n",
            "Epoch 68/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3851 - acc: 0.8418 - val_loss: 0.4870 - val_acc: 0.7622\n",
            "Epoch 69/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3857 - acc: 0.8366 - val_loss: 0.5076 - val_acc: 0.7552\n",
            "Epoch 70/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3878 - acc: 0.8436 - val_loss: 0.4891 - val_acc: 0.7762\n",
            "Epoch 71/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3777 - acc: 0.8453 - val_loss: 0.5023 - val_acc: 0.7692\n",
            "Epoch 72/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3912 - acc: 0.8471 - val_loss: 0.5018 - val_acc: 0.7692\n",
            "Epoch 73/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3873 - acc: 0.8453 - val_loss: 0.5103 - val_acc: 0.7622\n",
            "Epoch 74/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3824 - acc: 0.8453 - val_loss: 0.5098 - val_acc: 0.7622\n",
            "Epoch 75/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.4016 - acc: 0.8489 - val_loss: 0.5168 - val_acc: 0.7552\n",
            "Epoch 76/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3846 - acc: 0.8471 - val_loss: 0.5028 - val_acc: 0.7762\n",
            "Epoch 77/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3889 - acc: 0.8418 - val_loss: 0.5283 - val_acc: 0.7622\n",
            "Epoch 78/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3826 - acc: 0.8471 - val_loss: 0.5017 - val_acc: 0.7692\n",
            "Epoch 79/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3833 - acc: 0.8471 - val_loss: 0.4990 - val_acc: 0.7692\n",
            "Epoch 80/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3852 - acc: 0.8401 - val_loss: 0.4948 - val_acc: 0.7692\n",
            "Epoch 81/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3803 - acc: 0.8401 - val_loss: 0.4865 - val_acc: 0.7902\n",
            "Epoch 82/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3810 - acc: 0.8524 - val_loss: 0.5030 - val_acc: 0.7832\n",
            "Epoch 83/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3752 - acc: 0.8436 - val_loss: 0.5142 - val_acc: 0.7622\n",
            "Epoch 84/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3836 - acc: 0.8401 - val_loss: 0.5098 - val_acc: 0.7762\n",
            "Epoch 85/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3850 - acc: 0.8489 - val_loss: 0.4978 - val_acc: 0.7762\n",
            "Epoch 86/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3812 - acc: 0.8418 - val_loss: 0.5022 - val_acc: 0.7762\n",
            "Epoch 87/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3880 - acc: 0.8383 - val_loss: 0.5106 - val_acc: 0.7762\n",
            "Epoch 88/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3769 - acc: 0.8383 - val_loss: 0.5050 - val_acc: 0.7622\n",
            "Epoch 89/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3814 - acc: 0.8524 - val_loss: 0.5233 - val_acc: 0.7622\n",
            "Epoch 90/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3790 - acc: 0.8489 - val_loss: 0.4954 - val_acc: 0.7413\n",
            "Epoch 91/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3886 - acc: 0.8418 - val_loss: 0.5243 - val_acc: 0.7622\n",
            "Epoch 92/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3957 - acc: 0.8330 - val_loss: 0.5018 - val_acc: 0.7762\n",
            "Epoch 93/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3842 - acc: 0.8366 - val_loss: 0.5141 - val_acc: 0.7622\n",
            "Epoch 94/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3945 - acc: 0.8295 - val_loss: 0.5029 - val_acc: 0.7622\n",
            "Epoch 95/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3845 - acc: 0.8348 - val_loss: 0.5528 - val_acc: 0.7483\n",
            "Epoch 96/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3882 - acc: 0.8383 - val_loss: 0.5008 - val_acc: 0.7622\n",
            "Epoch 97/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3795 - acc: 0.8383 - val_loss: 0.5218 - val_acc: 0.7762\n",
            "Epoch 98/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3877 - acc: 0.8313 - val_loss: 0.4970 - val_acc: 0.7552\n",
            "Epoch 99/100\n",
            "18/18 [==============================] - 0s 4ms/step - loss: 0.3981 - acc: 0.8313 - val_loss: 0.5185 - val_acc: 0.7552\n",
            "Epoch 100/100\n",
            "18/18 [==============================] - 0s 3ms/step - loss: 0.3932 - acc: 0.8489 - val_loss: 0.4909 - val_acc: 0.7622\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDc0K9Jz8Xhh",
        "colab_type": "code",
        "outputId": "a1d94226-b5e0-4d4a-9967-ae082045fc02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "hist = history.history\n",
        "hist['epoch'] = history.epoch\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.plot(hist['epoch'], hist['loss'], label='Loss')\n",
        "plt.plot(hist['epoch'], hist['val_loss'], label='Val Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(hist['epoch'], hist['acc'], label='Acc')\n",
        "plt.plot(hist['epoch'], hist['val_acc'], label='Val Acc')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-2_dmE56AQR-",
        "colab_type": "text"
      },
      "source": [
        "Overfitting due to too little training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8x_H4BpM91tK",
        "colab_type": "code",
        "outputId": "2cbef379-17b7-4cc7-f44f-862625702ee3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "y_pred_tf = model_tf.predict(X_test)\n",
        "y_pred_tf = np.where(y_pred_tf > 0.5, 1, 0)\n",
        "print(classification_report(y_test, y_pred_tf))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.92      0.86       104\n",
            "           1       0.86      0.68      0.76        75\n",
            "\n",
            "    accuracy                           0.82       179\n",
            "   macro avg       0.83      0.80      0.81       179\n",
            "weighted avg       0.83      0.82      0.82       179\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGp_J7gO_iNx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "passenger_id = pd.read_csv('titanic_test.csv')['PassengerId']\n",
        "models = [model_knn, model_tree, model_forest]\n",
        "name = ['KNN', 'DT', 'RF']\n",
        "\n",
        "for i, model in enumerate(models):\n",
        "  y_pred = model.predict(df_test_clean)\n",
        "  pd_final = pd.DataFrame({\n",
        "      'PassengerId' : passenger_id,\n",
        "      'Survived' : y_pred,\n",
        "  })\n",
        "  pd_final.to_csv(f'submissions-{name[i]}.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGc7qJFyxnJy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred_tf_final = model_tf.predict(df_test_clean)\n",
        "y_pred_tf_final = np.where(y_pred_tf_final >= 0.5, 1, 0)\n",
        "pd_final = pd.DataFrame({\n",
        "      'PassengerId' : passenger_id,\n",
        "      'Survived' : y_pred.reshape(passenger_id.shape[0],),\n",
        "  })\n",
        "pd_final.to_csv('submissions-TF.csv', index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}