Titanic_Survival.ipynb

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      "language": "python",
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      "codemirror_mode": {
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
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    "colab": {
      "name": "Titanic Survival.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49a0e7db-76b7-4413-b919-23c8b7f95de2"
      },
      "source": [
        "# Titanic Survial \n",
        "Using various classification methods to demonstrate how they work and explain them. The data is from https://www.kaggle.com/c/titanic"
      ],
      "id": "49a0e7db-76b7-4413-b919-23c8b7f95de2"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cRWgYjesqnWp",
        "outputId": "e4134bdb-d72c-468c-dc8c-eea1cdeea6cd"
      },
      "source": [
        "!pip install -U scikit-learn"
      ],
      "id": "cRWgYjesqnWp",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (0.24.2)\n",
            "Collecting scikit-learn\n",
            "  Using cached scikit_learn-1.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (23.1 MB)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.7.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.0.1)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.0.0)\n",
            "Installing collected packages: scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 0.24.2\n",
            "    Uninstalling scikit-learn-0.24.2:\n",
            "      Successfully uninstalled scikit-learn-0.24.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "auto-sklearn 0.14.0 requires scikit-learn<0.25.0,>=0.24.0, but you have scikit-learn 1.0 which is incompatible.\u001b[0m\n",
            "Successfully installed scikit-learn-1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aab0819-fc9b-4804-95d2-73980a67f9ca"
      },
      "source": [
        "import pandas as pd\n",
        "gender=pd.read_csv('gender_submission.csv')\n",
        "test=pd.read_csv('test.csv')\n",
        "train=pd.read_csv('train.csv')"
      ],
      "id": "1aab0819-fc9b-4804-95d2-73980a67f9ca",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "70d7ad1c-8030-45b5-8473-3b387c527221",
        "outputId": "e20c9c97-3e41-4c97-e74f-048de63e738f"
      },
      "source": [
        "train.head()"
      ],
      "id": "70d7ad1c-8030-45b5-8473-3b387c527221",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>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",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1ff38efd-3570-4967-a900-68a762d5a622",
        "outputId": "f325f542-a786-4da4-a041-9e39adffcb5c"
      },
      "source": [
        "total = train.isnull().sum().sort_values(ascending=False)\n",
        "percent = (train.isnull().sum()/train.isnull().count()).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing_data.head()"
      ],
      "id": "1ff38efd-3570-4967-a900-68a762d5a622",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <td>687</td>\n",
              "      <td>0.771044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>177</td>\n",
              "      <td>0.198653</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>2</td>\n",
              "      <td>0.002245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticket</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Total   Percent\n",
              "Cabin       687  0.771044\n",
              "Age         177  0.198653\n",
              "Embarked      2  0.002245\n",
              "Fare          0  0.000000\n",
              "Ticket        0  0.000000"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3796efd1-cdb1-4364-9863-4a3ce9473308"
      },
      "source": [
        "Cabin will now be dropped as it is to hard to fill and is not the most useful. As embarked is the how purpose of this project the 2 missing values will also be dropped. Age however will be looking into replacing as it will probably be very useful."
      ],
      "id": "3796efd1-cdb1-4364-9863-4a3ce9473308"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dc2e539-64bf-4701-994f-28ab0320367d"
      },
      "source": [
        "train.drop([\"Cabin\",\"Ticket\"], axis = 1, inplace = True)\n",
        "test.drop([\"Cabin\",\"Ticket\"], axis = 1, inplace = True)\n",
        "train.drop(train.loc[train['Embarked'].isnull()].index, inplace = True)\n",
        "train.drop(train.loc[train['Age'].isnull()].index, inplace = True)"
      ],
      "id": "3dc2e539-64bf-4701-994f-28ab0320367d",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbcc6109-c376-45b1-bde5-1e6fadad8da7",
        "outputId": "08aef72c-64c0-4f68-e61e-1ac864b7dbfc"
      },
      "source": [
        "train.info()"
      ],
      "id": "fbcc6109-c376-45b1-bde5-1e6fadad8da7",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 712 entries, 0 to 890\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  712 non-null    int64  \n",
            " 1   Survived     712 non-null    int64  \n",
            " 2   Pclass       712 non-null    int64  \n",
            " 3   Name         712 non-null    object \n",
            " 4   Sex          712 non-null    object \n",
            " 5   Age          712 non-null    float64\n",
            " 6   SibSp        712 non-null    int64  \n",
            " 7   Parch        712 non-null    int64  \n",
            " 8   Fare         712 non-null    float64\n",
            " 9   Embarked     712 non-null    object \n",
            "dtypes: float64(2), int64(5), object(3)\n",
            "memory usage: 61.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2e6fbb5-7666-487e-b976-8d8629a2b44a"
      },
      "source": [
        "## Data Exploration"
      ],
      "id": "f2e6fbb5-7666-487e-b976-8d8629a2b44a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9c18f08c-3fae-4e62-af77-c10f43901b65",
        "outputId": "8753a308-dacb-48ca-c4ab-980d5a677604"
      },
      "source": [
        "train.describe()"
      ],
      "id": "9c18f08c-3fae-4e62-af77-c10f43901b65",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>Survived</th>\n",
              "      <th>Pclass</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>count</th>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "      <td>712.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>448.589888</td>\n",
              "      <td>0.404494</td>\n",
              "      <td>2.240169</td>\n",
              "      <td>29.642093</td>\n",
              "      <td>0.514045</td>\n",
              "      <td>0.432584</td>\n",
              "      <td>34.567251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>258.683191</td>\n",
              "      <td>0.491139</td>\n",
              "      <td>0.836854</td>\n",
              "      <td>14.492933</td>\n",
              "      <td>0.930692</td>\n",
              "      <td>0.854181</td>\n",
              "      <td>52.938648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>222.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>445.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.645850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>677.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>33.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   712.000000  712.000000  712.000000  ...  712.000000  712.000000  712.000000\n",
              "mean    448.589888    0.404494    2.240169  ...    0.514045    0.432584   34.567251\n",
              "std     258.683191    0.491139    0.836854  ...    0.930692    0.854181   52.938648\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     222.750000    0.000000    1.000000  ...    0.000000    0.000000    8.050000\n",
              "50%     445.000000    0.000000    2.000000  ...    0.000000    0.000000   15.645850\n",
              "75%     677.250000    1.000000    3.000000  ...    1.000000    1.000000   33.000000\n",
              "max     891.000000    1.000000    3.000000  ...    5.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "1652d928-8411-47d4-b68d-7ce8ba06e028",
        "outputId": "b99a9ecc-cee8-41c7-cf3e-68a1447703b1"
      },
      "source": [
        "train.describe(include=['O'])"
      ],
      "id": "1652d928-8411-47d4-b68d-7ce8ba06e028",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>712</td>\n",
              "      <td>712</td>\n",
              "      <td>712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>712</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Maioni, Miss. Roberta</td>\n",
              "      <td>male</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>453</td>\n",
              "      <td>554</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Name   Sex Embarked\n",
              "count                     712   712      712\n",
              "unique                    712     2        3\n",
              "top     Maioni, Miss. Roberta  male        S\n",
              "freq                        1   453      554"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7d3e6956-0cb1-42ac-baa8-0a9ddf51bfa7"
      },
      "source": [
        "# Data Clearning and Feature Engineering"
      ],
      "id": "7d3e6956-0cb1-42ac-baa8-0a9ddf51bfa7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "7b64173b-e9ac-458f-ae46-b23872afc824",
        "outputId": "3330a1c4-79ff-47d0-9ff8-cadf443f31b7"
      },
      "source": [
        "train['Title'] = train.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "train.drop([\"Name\"], axis = 1, inplace = True)\n",
        "pd.crosstab(train['Title'], train['Sex'])"
      ],
      "id": "7b64173b-e9ac-458f-ae46-b23872afc824",
      "execution_count": 6,
      "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>Sex</th>\n",
              "      <th>female</th>\n",
              "      <th>male</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Capt</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Col</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Countess</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Don</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dr</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jonkheer</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lady</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Major</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Master</th>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miss</th>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mlle</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mme</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mr</th>\n",
              "      <td>0</td>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mrs</th>\n",
              "      <td>107</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ms</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rev</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sir</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Sex       female  male\n",
              "Title                 \n",
              "Capt           0     1\n",
              "Col            0     2\n",
              "Countess       1     0\n",
              "Don            0     1\n",
              "Dr             1     5\n",
              "Jonkheer       0     1\n",
              "Lady           1     0\n",
              "Major          0     2\n",
              "Master         0    36\n",
              "Miss         145     0\n",
              "Mlle           2     0\n",
              "Mme            1     0\n",
              "Mr             0   398\n",
              "Mrs          107     0\n",
              "Ms             1     0\n",
              "Rev            0     6\n",
              "Sir            0     1"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ef478b8e-aabc-46fa-9a17-04c926703d7b"
      },
      "source": [
        "train['Title'] = train['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "train['Title'] = train['Title'].replace('Mlle', 'Miss')\n",
        "train['Title'] = train['Title'].replace('Ms', 'Miss')\n",
        "train['Title'] = train['Title'].replace('Mme', 'Mrs')"
      ],
      "id": "ef478b8e-aabc-46fa-9a17-04c926703d7b",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bd45e3d5-822d-49c7-a9a4-707f5f83f88e"
      },
      "source": [
        "test['Title'] = test.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "test.drop([\"Name\"], axis = 1, inplace = True)\n",
        "test['Title'] = test['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
        "test['Title'] = test['Title'].replace('Mlle', 'Miss')\n",
        "test['Title'] = test['Title'].replace('Ms', 'Miss')\n",
        "test['Title'] = test['Title'].replace('Mme', 'Mrs')"
      ],
      "id": "bd45e3d5-822d-49c7-a9a4-707f5f83f88e",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cff49c48-89ac-47e6-b900-65c5084e32f2"
      },
      "source": [
        "This has now dealt with the name column and turns into something we are more likely to be able to use."
      ],
      "id": "cff49c48-89ac-47e6-b900-65c5084e32f2"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6f91da71-849f-4350-ae80-779355341ef3"
      },
      "source": [
        "## Note \n",
        "I will look at imputing the ages and make more features at a future point, for now I will move onto actually training the models."
      ],
      "id": "6f91da71-849f-4350-ae80-779355341ef3"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcf6fc01-1790-4e02-a637-eb587f002903"
      },
      "source": [
        "train.head()\n",
        "test.drop([\"PassengerId\"], axis = 1, inplace = True)\n",
        "train.drop([\"PassengerId\"], axis = 1, inplace = True)"
      ],
      "id": "dcf6fc01-1790-4e02-a637-eb587f002903",
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6aa66b63-c885-4667-8c4e-022d973a92e0"
      },
      "source": [
        "## Machine Learning\n",
        "Now we can move onto the actual machine learning, first however we will need to make a test train split and a evaluation function. Then the following models will be trained.\n",
        "* Decision Tree\n",
        "* Random Forrest\n",
        "* KNN or k-Nearest Neighbors\n",
        "* Support Vector Machines\n",
        "* Adaboost\n",
        "* Naive Bayes classifier\n",
        "* Logistic Regression"
      ],
      "id": "6aa66b63-c885-4667-8c4e-022d973a92e0"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fd880cc-55da-49ba-8975-cb43aa60d934"
      },
      "source": [
        "The first thing that needs to be done though is make a test train split for the data set. The data then needs to be transformed by scaling the features and encoding the categorical variables."
      ],
      "id": "0fd880cc-55da-49ba-8975-cb43aa60d934"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aa73c98-684b-4745-adba-d0c731368513"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "\n",
        "train_set, test_set = train_test_split(train,test_size=0.2)\n",
        "train_y=train_set[\"Survived\"]\n",
        "train_x=train_set.drop([\"Survived\"], axis=1)\n",
        "test_y=test_set[\"Survived\"]\n",
        "test_x=test_set.drop([\"Survived\"], axis=1)\n",
        "train_set_num=train_x.select_dtypes(include=[np.number])\n",
        "train_set_ob=train_x.select_dtypes(include=[object])\n",
        "num_column=list(train_set_num.columns)\n",
        "ob_column=list(train_set_ob.columns)"
      ],
      "id": "3aa73c98-684b-4745-adba-d0c731368513",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0075b975-ed5b-4bd5-accb-7692fc70e705"
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "num_pipeline = Pipeline([\n",
        "        ('std_scaler', StandardScaler()),\n",
        "    ])\n",
        "\n",
        "full_pipeline = ColumnTransformer([\n",
        "        (\"num\", num_pipeline, num_column),\n",
        "        (\"cat\", OrdinalEncoder(), ob_column),\n",
        "    ])\n",
        "\n",
        "train_x = full_pipeline.fit_transform(train_x)\n",
        "test_x = full_pipeline.fit_transform(test_x)"
      ],
      "id": "0075b975-ed5b-4bd5-accb-7692fc70e705",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "87781f97-fa9b-46f6-9565-b2098cc7d6b7"
      },
      "source": [
        "#### Metrics and Graphs\n",
        "The metrics that are used to measure the quality of a classifier are:\n",
        "* **Model score (score):** This method computes the accuracy score (accuracy is #correct_preds / #all_preds)\n",
        "* **Cross val score:** As some classifiers have the tendency to overfit it is sometimes useful to do a cross val score. This is when the training set in divided up into different folds and it goes through each fold as a test set while the rest the model is train on.\n",
        "* **f1 score:** The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. This is only for binary classifiers though.\n",
        "* **Confusion Matrix:** This is a very easy way to identify the number of mislabelled events. Simply it counts the number of class A's identified as class A and class B.\n",
        "* **Precision Recall:** The precision-recall curve shows the trade-off between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate. High scores for both show that the classifier is returning accurate results (high precision), as well as returning a majority of all positive results (high recall).\n",
        "* **ROC curve:** This is very similar to the precision recall graph however it plots true positive rate (recall) against false positive rate. This is the ratio of negative instances that are incorrectly classified as positive and is equal to 1-true negative rate."
      ],
      "id": "87781f97-fa9b-46f6-9565-b2098cc7d6b7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4689f74-7f95-4a8b-866d-81522ccf9f7c"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
        "from sklearn.metrics import f1_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import PrecisionRecallDisplay, ConfusionMatrixDisplay, RocCurveDisplay\n",
        "\n",
        "\n",
        "def eval(model,plots=True):\n",
        "    ymul=model.predict(test_x)\n",
        "    print(\"Model score: %.4f\" % model.score(test_x,test_y))  \n",
        "    print(\"The cross val score with 3 folds: \", cross_val_score(model, train_x, train_y, cv=3, scoring=\"accuracy\"))\n",
        "    print(\"F1-score: %.4f\" % f1_score(test_y , ymul) )\n",
        "    if(plots):\n",
        "        dis = ConfusionMatrixDisplay.from_estimator(model,test_x, test_y)\n",
        "        fig, ax = plt.subplots(figsize=(6, 6))\n",
        "        PrecisionRecallDisplay.from_estimator(model, test_x, test_y,ax=ax)\n",
        "        ax.set_title(\"2-class Precision-Recall curve\")\n",
        "        dis1 = RocCurveDisplay.from_estimator(model, test_x, test_y)"
      ],
      "id": "b4689f74-7f95-4a8b-866d-81522ccf9f7c",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17f70b4c-48e8-4570-9d1a-db7a2708e96a"
      },
      "source": [
        "### Desesion Trees and Random Forest\n",
        "A decision tree is normally used for classification but can also but done for regression. A decision tree works by asking a initial question at the root node for instance in this case is the sale condition normal and then depending on the answer you go down one of the nodes to another question if needed (for simple datasets it may not be needed to be able to divide the data up). You measure the impurity of the node called gini, if the node is completely pure then it would =0. In skearn it uses the CART algorithm, this works by splitting the data set into 2 subsets using a single feature and a threshold, it then continues to do this until the max depth is reached or a split will not reduce the impurity.                    \n",
        "A random forest algorithm is a ensemble of decision trees trained using the bagging method. This involves using the same algorithm and training them on a subsets of the training data, once this is done the ensemble makes predictions by aggregating the result (most frequent for classification and average for regression). This will normally perform better than a decision tree."
      ],
      "id": "17f70b4c-48e8-4570-9d1a-db7a2708e96a"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "d39cc8f1-168b-4c1a-9da4-e212002b3202",
        "outputId": "cea0e92c-0f8b-425b-b6bc-04fa9923a26e"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "tree_reg = DecisionTreeClassifier()\n",
        "tree_reg.fit(train_x,train_y)\n",
        "eval(tree_reg)"
      ],
      "id": "d39cc8f1-168b-4c1a-9da4-e212002b3202",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.7552\n",
            "The cross val score with 3 folds:  [0.71052632 0.75789474 0.70899471]\n",
            "F1-score: 0.7154\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sJ4uR9HWgN8m3fwGIiNGlKMrMzMon6+WjN5OcF7gAEPBtYLsS1mVmZmWS9aqh/SLiNGBJRIwC9gV2KF1ZZmZWLlmDYEX676eStgVWA9uUpiQzMyunrOcIHpK0BXAN8CLJFUO/KllVZmZWNlnHGqocYO4+SQ8BrWsYltrMzJqgYl8oO7aOZUTE/Q1fkpmZlVOxI4Kj61gWgIPAzKyJK/aFstPLVYiZmTWOrFcNmZlZM+UgMDPLOQeBmVnOZR1iYlNJl0n6ZTrdU9JRpS3NzMzKIesRwe0kN67fN51eAFxRkorMzKyssgZBj4j4KcnQEpU3s1fJqjIzs7LJGgSfSWpDejMaST1IjhDMzKyJyzrW0EjgEaCLpLuA/sCQEtVkZmZllHWsoamSZgL7kHQJfT8iPixpZWZmVhaZgkDSg8BvgEkR8UlpSzIzs3LKeo5gDLA/MEfSREnHSWpd7ElmZvbll7Vr6EngSUktgIOBs4HbgM1LWJuZmZXB+ty8vg3JaKQnAHsA40tVlJmZlU/WcwT3AnuRXDl0A/BkRHxRysLMzKw8sh4R3AqcFBGfl7IYMzMrv2J3KDs4IqYBbYHB0tpfJvYdyszMmr5iRwQHAtOo+U5lvkOZmVkzUOwOZZenD0dHxN8Kl0nqXrKqzMysbLJ+j+C+GuZNbMhCzMyscRQ7R7AT0BtoL+nYgkWbA/5CmZlZM1DsHMGOwFHAFqx9nuAjki+VmZlZE1fsHMEDwAOS9o2IZ8tUk5mZlVGxrqGL0xvSfEfSSdWXR8SFJavMzMzKoljX0KvpvzNKXYiZmTWOYl1DD6b/Vo0rJGkjoF1ELC9xbWZmVgaZLh+V9BtJm0tqC/yVZDjqYaUtzczMyiHr9wh6pUcA3wAeBroDpxZ7kqTDJb0uaa6k4TUs/7mkWenPG5KWrlf1ZmZWb1kHnWslqRVJENwQEaslRV1PSO9dcCMwCJgPTJc0KSLmVK4TET8oWP8CYPf1bYCZmdVP1iOCW4B5JIPPPSVpO6DYOYK9gLkR8XZEfAbcDQyuY/2TgAkZ6zEzswaSKQgi4hcR0SkijozE34GDijytE/BuwfT8dN460mDpTjLAnZmZlVHWG9O0By4HDkhnPQmMBpY1UB0nAhNru9+BpHOAcwC6du26wTuZ8/5yTrjF34szK7fBfTrxnb03/G/XSitr19BtJMNKHJ/+LAduL/KcBUCXgunO6byanEgd3UIRMTYi+kVEv4qKiowlr21wn0702sa3WDYrtznvL+eBWbX96duXQdaTxT0i4lsF06MkzSrynOlAz3S46gUkb/bfqb5SOrDdlkBJP6p/Z++u/kRi1gh8FP7ll/WIYIWkf6+ckNQfWFHXEyJiDXA+MIXkG8r3RsRsSaMlHVOw6onA3RFR51VIZmZWGlmPCIYCd6TnCgCWAN8t9qSImAxMrjZvRLXpkRlrMDOzEigaBJL6AF8l+eS+AMDDS5iZNR91dg1JGgHcC3wL+ANwgkPAzKx5KXZEcALQJyI+lbQ18Ajwy9KXZWZm5VLsZPGqiPgUICIWZVjfzMyamGJHBNtLmpQ+FtCjYJqIOKbmp5mZWVNRLAiqjw00plSFmJlZ4yh2Y5ony1WImZk1jmJXDT0o6eh0COrqy7ZPvxx2RunKMzOzUivWNXQ2cBFwnaTFwEKgNdANeIvk3gQPlLRCMzMrqWJdQ/8ALgYultQN2IZkaIk3Kq8mMjOzpi3rEBNExDySm9OYmVkz4u8FmJnlnIPAzCznHARmZjmX9VaV/YGRwHbpcwRERGxfutLMzKwcsp4svhX4ATATqPG+wmZm1jRlDYJlEfFwSSsxM7NGkTUIHpd0DXA/sKpyZkS8WJKqzMysbLIGwd7pv/0K5gVwcMOWY2Zm5ZYpCCLioFIXYmZmjSPT5aOS2ku6VtKM9OdnBTeyNzOzJizr9whuAz4Cjk9/lgO3l6ooMzMrn6znCHpExLcKpkdJmlWKgszMrLyyHhGskPTvlRPpF8xWlKYkMzMrp6xHBOcC49PzAgIWA0NKVZSZmZVP1quGZgG7Sdo8nV5e0qrMzKxs6gwCSadExK8lXVRtPgARcW0JazMzszIodkTQNv13s1IXYmZmjaPYrSpvSf8dVZ5yzMys3LJ+oeynkjaX1ErSHyUtlHRKqYszM7PSy3r56KHpCeKjSO5b/FVgWKmKMjOz8skaBJVdSF8HfhsRy0pUj5mZlVnW7xE8JOk1ki+RnSupAlhZurLMzKxcMh0RRMRwYD+gX0SsBj4BBpeyMDMzK49i3yM4OCKmSTq2YF7hKveXqjAzMyuPYl1DBwLTgKNrWBY4CMzMmrxi3yO4PP339PKUY2Zm5Zb1ewRXStqiYHpLSVeUriwzMyuXrJePHhERSysnImIJcGRpSjIzs3LKGgQtJG1SOSGpDbBJHeubmVkTkfV7BHcBf5RUeXvK04HxpSnJzMzKKev9CK6W9BJwSDrrxxExpXRlmZlZuWTtGgJ4FXgkIn4EPC2p6NDUkg6X9LqkuZKG17LO8ZLmSJot6TfrUY+ZmTWATEcEks4GzgG2AnoAnYCbgYF1PKcFcCMwCJgPTJc0KSLmFKzTE7gE6B8RSyR13NCGmJnZhsl6RHAe0B9YDhARbwLF3rT3AuZGxNsR8RlwN+sOS3E2cGN6FRIR8UHWws3MrGFkDYJV6Zs5AJJaknyzuC6dgHcLpuen8wrtAOwg6RlJz0k6PGM9ZmbWQLJeNfSkpP8HtJE0CPgP4MEG2n9PYADQGXhK0q6F31kAkHQOSdcUXbt2bYDdmplZpaxHBP8FLAReAb4HTAb+u8hzFgBdCqY7p/MKzQcmRcTqiPgb8AZJMKwlIsZGRL+I6FdRUZGxZDMzy6LoEUF60nd2ROwE/HI9tj0d6CmpO0kAnAh8p9o6vwdOAm6X1IGkq+jt9diHmZnVU9Ejgoj4HHhd0nr1yUTEGuB8YArJpaf3RsRsSaMlHZOuNgVYJGkO8DgwLCIWrVcLzMysXrKeI9gSmC3pBZKb0gAQEcfU/hSIiMkk3UiF80YUPA7govTHzMwaQdYguKykVZiZWaMpdoey1sBQ4KskJ4pvTbt8zMysmSh2jmA80I8kBI4AflbyiszMrKyKdQ31iohdASTdCrxQ+pLMzKycih0RrK584C4hM7PmqdgRwW6SlqePRfLN4uXp44iIzUtanZmZlVyxm9e3KFchZmbWONbnfgRmZtYMOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzJQ0CSYdLel3SXEnDa1g+RNJCSbPSn7NKWY+Zma2rZak2LKkFcCMwCJgPTJc0KSLmVFv1nog4v1R1mJlZ3Up5RLAXMDci3o6Iz4C7gcEl3J+ZmW2AUgZBJ+Ddgun56bzqviXpZUkTJXUpYT1mZlaDxj5Z/CDQLSK+BjwKjK9pJUnnSJohacbChQvLWqCZWXNXyiBYABR+wu+czqsSEYsiYlU6+Sugb00bioixEdEvIvpVVFSUpFgzs7wqZRBMB3pK6i5pY+BEYFLhCpK2KZg8Bni1hPWYmVkNSnbVUESskXQ+MAVoAdwWEbMljQZmRMQk4EJJxwBrgMXAkFLVY2ZmNStZEABExGRgcrV5IwoeXwJcUsoazMysbo19stjMzBqZg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLuZaNXUBDWL16NfPnz2flypWNXYpZk9C6dWs6d+5Mq1atGrsU+xJoFkEwf/58NttsM7p164akxi7H7EstIli0aBHz58+ne/fujV2OfQk0i66hlStXsvXWWzsEzDKQxNZbb+0jaKvSLIIAcAiYrQf/vVihZhMEZma2YRwEDaRFixb06dOH3r17s9tuu/Gzn/2ML774YoO2NWLECB577LFal998883ccccd673dKVOm0KdPH/r06UO7du3Ycccd6dOnD6eddtoG1VlozJgx7LTTTvTp04c999yzqr4BAwYwY8aMem8fYMaMGVx44YUArFq1ikMOOYQ+ffpwzz33cNZZZzFnzpx6bf+6665b63Vds2YNFRUVDB8+fK31BgwYwI477shuu+1G//79ef311+u1X4Dx48fTs2dPevbsyfjx42td7/rrr2ennXaid+/eXHzxxQA8+uij9O3bl1133ZW+ffsybdq0qvUPOeQQlixZUu/6rJmLiCb107dv36huzpw568wrt7Zt21Y9/uc//xkDBw6MESNGNGJFdTvwwANj+vTp68xfs2bNem/rpptuikMPPTSWLVsWERHLli2LcePG1bmf+nr22Wdj4MCBG/z86u1cvXp17LrrrrF69eqqeZMnT4799tsvtt9++/jiiy+q5he26ZZbbomjjz56g+uIiFi0aFF07949Fi1aFIsXL47u3bvH4sWL11lv2rRpMXDgwFi5cmVEJL9nEREvvvhiLFiwICIiXnnlldh2222rnjNu3Li44ooratxvuf5ujr/5z3H8zX8uy76sdsCMqOV9tVlcNVRo1IOzmfPe8gbdZq9tN+fyo3tnXr9jx46MHTuWPffck5EjR/LFF18wfPhwnnjiCVatWsV5553H9773PQCuvvpqfv3rX7PRRhtxxBFHcNVVVzFkyBCOOuoojjvuOIYPH86kSZNo2bIlhx56KGPGjGHkyJG0a9eOH/3oR8yaNYuhQ4fy6aef0qNHD2677Ta23HJLBgwYwN57783jjz/O0qVLufXWW9l///1rrLdbt26ccMIJPProo1x88cVstdVWXH755axatYoePXpw++23065dO2bOnMlFF13Exx9/TIcOHRg3bhzbbLMNV155JU888QSbb745AJtvvjnf/e5319nPueeey/Tp01mxYgXHHXcco0aNAqixjb/97W8ZNWoULVq0oH379jz11FM88cQTjBkzhttuu41TTjmFhQsX0qdPH+677z7OPPNMxowZQ79+/Zg6dWqN9Vdv54knnlhV27Rp09hjjz1o2fJffxITJkzg+9//PjfddBPPPvss++233zptOuCAA7juuusy/27UZMqUKQwaNIitttoKgEGDBvHII49w0kknrbXeTTfdxPDhw9lkk02A5PcMYPfdd69ap3fv3qxYsYJVq1axySabcMwxx7D//vtz6aWX1qvG+prz/nJOuOXZRq2hOVjf96Ksml0QfFlsv/32fP7553zwwQc88MADtG/fnunTp7Nq1Sr69+/PoYceymuvvcYDDzzA888/z6abbsrixYvX2saiRYv43e9+x2uvvYYkli5dus5+TjvtNK6//noOPPBARowYwahRo6remNasWcMLL7zA5MmTGTVqVJ3dTVtvvTUvvvgiH374IcceeyyPPfYYbdu25eqrr+baa6/lkksu4YILLuCBBx6goqKCe+65h0svvZTrrruOjz76iO23377oa/KTn/yErbbais8//5yBAwfy8ssv06lTpxrbOHr0aKZMmUKnTp3WaXfHjh351a9+xZgxY3jooYfWWvbhhx9yxRVXrFP/iBEj1mpndc888wx9+/atml65ciWPPfYYt9xyC0uXLmXChAk1BsGDDz7Irrvuus78a665hrvuumud+QcccAC/+MUv1pq3YMECunTpUjXduXNnFixYsM5z33jjDZ5++mkuvfRSWrduzZgxY9hzzz3XWue+++5jjz32qAqLLbfcklWrVrFo0SK23nrrdbZZDoP7dGqU/Vp2zS4ISpGW9TV16lRefvllJk6cCMCyZct48803eeyxxzj99NPZdNNNAao+EVZq3749rc+oqwAAAA5SSURBVFu35swzz+Soo47iqKOOWmv5smXLWLp0KQceeCAA3/3ud/n2t79dtfzYY48FoG/fvsybN6/OGk844QQAnnvuOebMmUP//v0B+Oyzz9h33315/fXX+etf/8qgQYMA+Pzzz9lmm23W63W49957GTt2LGvWrOH9999nzpw59OrVq8Y29u/fnyFDhnD88cdXtSOL2uqv3s7q3n//fXbeeeeq6YceeoiDDjqINm3a8K1vfYsf//jHXHfddbRo0QKAk08+mTZt2tCtWzeuv/76dbY3bNgwhg0blrnuLNasWcPixYt57rnnmD59Oscffzxvv/121RVAs2fP5r/+67+YOnXqWs/r2LEj7733XqMFwXf27sp39u7aKPu2bJpdEHxZvP3227Ro0YKOHTsSEVx//fUcdthha60zZcqUOrfRsmVLXnjhBf74xz8yceJEbrjhhrVOBBZT+amwRYsWrFmzps5127ZtCyTnjAYNGsSECRPWWv7KK6/Qu3dvnn123cP7du3a8fbbb9d5VPC3v/2NMWPGMH36dLbcckuGDBnCypUra23jzTffzPPPP88f/vAH+vbty8yZMzO1ubb6q7ezujZt2qx1Xf2ECRP405/+RLdu3YDk6GzatGlVQXjXXXfRr1+/WutYnyOCTp068cQTT1RNz58/nwEDBqzz3M6dO3Psscciib322ouNNtqIDz/8kIqKCubPn883v/lN7rjjDnr06LHW81auXEmbNm1qrdXMVw2VwMKFCxk6dCjnn38+kjjssMO46aabWL16NZAc4n/yyScMGjSI22+/nU8//RRgna6hjz/+mGXLlnHkkUfy85//nJdeemmt5e3bt2fLLbfk6aefBuDOO++sOjrYUPvssw/PPPMMc+fOBeCTTz7hjTfeYMcdd2ThwoVVQbB69Wpmz54NwCWXXMJ5553H8uXLq+quflXT8uXLadu2Le3bt+ef//wnDz/8cJ1tfOutt9h7770ZPXo0FRUVvPvuu/Wqv5idd9656jnLly/n6aef5p133mHevHnMmzePG2+8sdZwqcmwYcOYNWvWOj/VQwDgsMMOY+rUqSxZsoQlS5YwderUdT40AHzjG9/g8ccfB5Lfoc8++4wOHTqwdOlSvv71r3PVVVdVHQlVigj+8Y9/VAWaWU18RNBAVqxYQZ8+fVi9ejUtW7bk1FNP5aKLLgLgrLPOYt68eeyxxx5EBBUVFfz+97/n8MMPZ9asWfTr14+NN96YI488kiuvvLJqmx999BGDBw9m5cqVRATXXnvtOvsdP3581cni7bffnttvv71e7aioqGDcuHGcdNJJrFq1CoArrriCHXbYgYkTJ3LhhReybNky1qxZw3/+53/Su3dvzj33XD7++GP23HNPWrVqRatWrfjhD3+41nZ32203dt99d3baaSe6dOlS9YZVWxuHDRvGm2++SUQwcOBAdtttN5588sl61V+XI444glNPPRWA3/3udxx88MFVR1QAgwcP5uKLL67aZkPaaqutuOyyy6r6+0eMGFHVTXjWWWcxdOhQ+vXrxxlnnMEZZ5zBLrvswsYbb8z48eORxA033MDcuXMZPXo0o0ePBpLuyI4dOzJz5kz22WeftU6Cm1Wn5KqipqNfv35R/br0V199da3+XbMN8c1vfpOf/vSn9OzZs7FLaTDf//73OeaYYxg4cOA6y/x3ky+SZkZEjf2Z7hoyS1111VW8//77jV1Gg9pll11qDAGzQiUNAkmHS3pd0lxJw+tY71uSQlLtZ9/MSmzHHXfkgAMOaOwyGtTZZ5/d2CVYE1CyIJDUArgROALoBZwkqVcN620GfB94vj77a2pdXGaNyX8vVqiURwR7AXMj4u2I+Ay4Gxhcw3o/Bq4GNnhM3NatW7No0SL/cptlEOn9CFq3bt3YpdiXRCkvJegEFF7zNx/Yu3AFSXsAXSLiD5Jq/faNpHOAcwC6dl33iymdO3dm/vz5LFy4sCHqNmv2Ku9QZgaNePmopI2Aa4EhxdaNiLHAWEiuGqq+vFWrVr7TkpnZBipl19ACoEvBdOd0XqXNgF2AJyTNA/YBJvmEsZlZeZUyCKYDPSV1l7QxcCIwqXJhRCyLiA4R0S0iugHPAcdERMMMXm9mZpmULAgiYg1wPjAFeBW4NyJmSxot6ZhS7dfMzNZPk/tmsaSFwN838OkdgA8bsJymwG3OB7c5H+rT5u0ioqKmBU0uCOpD0ozavmLdXLnN+eA250Op2uwhJszMcs5BYGaWc3kLgrGNXUAjcJvzwW3Oh5K0OVfnCMzMbF15OyIwM7NqmmUQFBv+WtImku5Jlz8vqVv5q2xYGdp8kaQ5kl6W9EdJ2zVGnQ0pj8OcZ2mzpOPT/+vZkn5T7hobWobf7a6SHpf0l/T3+8jGqLOhSLpN0geS/lrLckn6Rfp6vJyO2VY/EdGsfoAWwFvA9sDGwEtAr2rr/Adwc/r4ROCexq67DG0+CNg0fXxuHtqcrrcZ8BTJN9f7NXbdZfh/7gn8Bdgyne7Y2HWXoc1jgXPTx72AeY1ddz3bfACwB/DXWpYfCTwMiGRonufru8/meESQZfjrwcD49PFEYKAklbHGhla0zRHxeER8mk4+RzL2U1NWtmHOv0SytPls4MaIWAIQER+UucaGlqXNAWyePm4PvFfG+hpcRDwFLK5jlcHAHZF4DthC0jb12WdzDIKahr/uVNs6kQyFsQzYuizVlUaWNhc6k+QTRVNWtM2Fw5yXs7ASyvL/vAOwg6RnJD0n6fCyVVcaWdo8EjhF0nxgMnBBeUprNOv7915Uow1DbY1D0ilAP+DAxq6llNZnmPNmpiVJ99AAkqO+pyTtGhFLG7Wq0joJGBcRP5O0L3CnpF0i4ovGLqypaI5HBMWGv15rHUktSQ4nF5WlutLI0mYkHQJcSjLK66oy1VYqeRzmPMv/83xgUkSsjoi/AW+QBENTlaXNZwL3AkTEs0BrkjF5mqtMf+/rozkGQZ3DX6cmAd9NHx8HTIv0LEwTVbTNknYHbiEJgabebwz5HOY8y+/270mOBpDUgaSr6O1yFtnAsrT5HWAggKSdSYKgOd+ucBJwWnr10D7Asoh4vz4bbHZdQxGxRlLl8NctgNsiHf4amBERk4BbSQ4f55KclDmx8Squv4xtvgZoB/w2PS/+TkQ02eHAM7a5WcnY5inAoZLmAJ8DwyKiyR7tZmzzD4FfSvoByYnjIU35g52kCSRh3iE973E50AogIm4mOQ9yJDAX+BQ4vd77bMKvl5mZNYDm2DVkZmbrwUFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yCwkpP0uaRZkv4q6UFJWzTw9uelX55C0se1rNNG0pOSWkjqJmlFWtMcSTenQ1Kszz77SfpF+niApP0Klg2VdFp92pRuZ6SkHxVZZ5yk49Zjm91qG9642no/kfRu9ddT0vmSzsi6P2saHARWDisiok9E7ELyBb7zGqGGM4D7I+LzdPqtiOgDfI1k6OJvrM/GImJGRFyYTg4A9itYdnNE3FH/khvVgyQjf1Z3G81/ULfccRBYuT1LOlKipB6SHpE0U9LTknZK539F0u8kvZT+7JfO/3267mxJ56znfk8GHqg+Mx199s/AV9NPy9P0r5v3dE33++30aOYlSU+l8wZIekjJTY2GAj9IjzD2r/wkL2knSS9U7ivd/ivp477pEcpMSVOKDSMs6WxJ09Ma7pO0acHiQyTNkPSGpKPS9VtIuiZ9zsuSvrc+L1ZEPFfTsAXpUObzJNUUEtZEOQisbCS1IBkTpnL4h7HABRHRF/gR8H/p/F8AT0bEbiQ36Jidzj8jXbcfcKGkTEOHp2PUbB8R82pYtmla0yvA9cD4iPgacFdaB8AI4LC0nrWG5Ui3eTPw8/So5+mCZa8BG0vqns46AbhHUqt0X8el7bkN+EmRZtwfEXumNbxKMtBapW4kn96/DtwsqXW6fFlE7AnsCZxdUEdl27eVNLnIfmsyA9h/A55nX1LNbqwh+1JqI2kWyZHAq8CjktqRdKdUjn0EsEn678HAaQBpV86ydP6Fkr6ZPu5CMqpmlnF0OgDVh2HukdYUwAMR8bCkO4Fj0+V3Aj9NHz8DjJN0L3B/hv0VupckAK5K/z0B2JFkZNRH07a3AIoNGraLpCuALUjGjJpSuI90yOU3Jb0N7AQcCnyt4PxBe5LX643KJ0XEeyRj1qyvD9J9WDPhILByWBERfdJP31NIzhGMA5am/fRFSRoAHALsGxGfSnqCZJTJTPuvYd23su47IoZK2pvkE/dMSX0z7hfgHpKwuz/ZVLwpaVdgdkTsux7bGQd8IyJekjSEdITRyhKrl0xyG8MLIqIwMFDD3J+7Nclras2Eu4asbNL+5QtJRov8FPibpG9D1Q25d0tX/SPJfZUr+7rbk3yiXZKGwE4k9xfIut8lQIu0y6Quf+ZfI9GeDDyd1tAjIp6PiBEkwxt3qfa8j0juf1DTvt8iGQX0MpJQAHgdqFByExUktZLUu0htmwHvp91KJ1db9m1JG0nqQXJv39dJAvfcdH0k7SCpbZF9ZLUDUPTKI2s6HARWVhHxF+BlkrtKnQycKeklkvMAlfei/T5wUHpidSbJVT2PAC0lvUrSzfLceu56KvDvRda5ADhd0svAqWkdANdIeiW97PLPJDdQL/Qg8M3Kk8U1bPce4BT+dfOUz0jug3F12vZZFFx1VIvLgOdJuqleq7bsHeAFktuPDo2IlcCvgDnAi2ndt1CtB6CucwSSfqpkCORNJc2XNLJgcX/g0SL1WhPiYagtF5Tcv/gHEXFqY9fSlCm5wdFFfh2bFx8RWC5ExIvA4+mVS7bhOpAcnVgz4iMCM7Oc8xGBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnl3P8Hlu5ov+HDrX0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "cc0fdd7a-5a0e-48eb-b5a3-c103bc6bb904",
        "outputId": "c69c91be-d8ca-4382-df02-ce299d562c5c"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "forest_clf.fit(train_x,train_y)\n",
        "eval(forest_clf)"
      ],
      "id": "cc0fdd7a-5a0e-48eb-b5a3-c103bc6bb904",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.7413\n",
            "The cross val score with 3 folds:  [0.79473684 0.84210526 0.7989418 ]\n",
            "F1-score: 0.6992\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19d68870-69b3-40e3-a375-67878211277f"
      },
      "source": [
        "### K Nearest Neighbors\n",
        "K-Nearest Neighbors is a supervised learning algorithm. Where the data is 'trained' with data points corresponding to their classification. To predict the class of a given data point, it takes into account the classes of the 'K' nearest data points and chooses the class in which the majority of the 'K' nearest data points belong to as the predicted class."
      ],
      "id": "19d68870-69b3-40e3-a375-67878211277f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "b5022fea-bae4-4c47-aaec-12442108fa94",
        "outputId": "9196592e-83a8-4bd3-9204-7ff6d0cc05e1"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "k=3\n",
        "\n",
        "neigh = KNeighborsClassifier(n_neighbors = k)\n",
        "neigh.fit(train_x,train_y)\n",
        "eval(neigh)"
      ],
      "id": "b5022fea-bae4-4c47-aaec-12442108fa94",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.7692\n",
            "The cross val score with 3 folds:  [0.75789474 0.79473684 0.75661376]\n",
            "F1-score: 0.7360\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56134e11-952a-43f2-b475-1c267f03ddff"
      },
      "source": [
        "This model is highly dependent on the value of k. This means that it is key to optimise this value. One way to do this is to loop over different values of k and plot the accuracy of the model against K, this will give you the best value for k."
      ],
      "id": "56134e11-952a-43f2-b475-1c267f03ddff"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ee327834-51e9-43ac-be83-0a3634ccf1d9",
        "outputId": "8bc5fd2f-5830-4c70-ccba-5208f1bfc279"
      },
      "source": [
        "Ks = 15\n",
        "mean_acc = np.zeros((Ks-1))\n",
        "\n",
        "for n in range(1,Ks):\n",
        "    #Train Model and Predict  \n",
        "    neigh = KNeighborsClassifier(n_neighbors = n).fit(train_x,train_y)\n",
        "    yhat=neigh.predict(test_x)\n",
        "    mean_acc[n-1] = neigh.score(test_x,test_y)\n",
        "\n",
        "plt.plot(range(1,Ks),mean_acc,'g')\n",
        "plt.legend(('Accuracy ', '+/- 1xstd','+/- 3xstd'))\n",
        "plt.ylabel('Accuracy ')\n",
        "plt.xlabel('Number of Neighbors (K)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "id": "ee327834-51e9-43ac-be83-0a3634ccf1d9",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7bbe9d19-8675-4bac-bbb2-f43daf7c82c7"
      },
      "source": [
        "This shows a high increase in the accuracy of the model when using k=7 instead of 3."
      ],
      "id": "7bbe9d19-8675-4bac-bbb2-f43daf7c82c7"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 981
        },
        "id": "7c8c925b-71df-4402-83a0-25cc7e4c0604",
        "outputId": "463c6195-0db7-41ef-ca9b-1ebf6ef27644"
      },
      "source": [
        "new_k=7\n",
        "neigh2 = KNeighborsClassifier(n_neighbors = new_k)\n",
        "neigh2.fit(train_x,train_y)\n",
        "eval(neigh2)"
      ],
      "id": "7c8c925b-71df-4402-83a0-25cc7e4c0604",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8252\n",
            "The cross val score with 3 folds:  [0.74736842 0.8        0.75132275]\n",
            "F1-score: 0.7899\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9750b7a5-59cb-4fde-ae7e-9162513f8d1f"
      },
      "source": [
        "### SVC\n",
        "This simply try's to divide the training data but keeps are far as possible away from any points in the training set. It can be thought of as fitting a street, with the edges of the street being the as a training instance."
      ],
      "id": "9750b7a5-59cb-4fde-ae7e-9162513f8d1f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "8da24c0b-5ee9-4b17-a6c5-2ffc715f0828",
        "outputId": "6a7bb180-e4a2-4b41-b11d-d6fd9ace9607"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svc_lin=SVC(kernel=\"linear\", C=0.025)\n",
        "svc_lin.fit(train_x,train_y)\n",
        "eval(svc_lin)"
      ],
      "id": "8da24c0b-5ee9-4b17-a6c5-2ffc715f0828",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.7972\n",
            "The cross val score with 3 folds:  [0.78421053 0.82631579 0.76190476]\n",
            "F1-score: 0.7434\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3ptsjJLmx18ysA8haIriYZMqI/wWIiIUkaxOYmVmZy5oRvJ/2+y/kEb5mZh1A1vUIlkj6KtBJ0mDg28Bf8wvLzMxKJWuJ4Fsk6xW/C/yeZDrqousRmJnZx1/WEsEnI+IC4II8gzEzs9LLWiK4QtIySf9Vuy6BmZl1DJkygog4hGRlshrgN5KekvS9XCMzM7OSyDygLCL+FhFXA/9GMqag0TmDzMysvGQdUPYpSRdLegr4OUmPob65RmZmZiWRtbH4WuAm4IsRsTrHeMzMrMQyZQQRsX/egZiZWftoNiOQdHNEnJBWCRWOJM6yQpmZmZWBYiWCs9PfR+YdiJmZtY9mG4sjYk368syIeLnwBzgz//DMzCxvWbuPfqGRfV9qy0DMzKx9FGsjOIPkm/9ukhYXHOoGPJxnYGZmVhrF2gh+D/wR+BFwfsH+tyLi9dyiMjOzkimWEURErJD0zfoHJO3kzMDMrPxlKREcCSwg6T6qgmMB7JZTXGZmViLNZgQRcWT628tSmpl1UFnnGjpQ0rbp6xMlXSmpf76hmZlZKWTtPvorYIOk4cB3gBeAG3KLyszMSiZrRvBBRARwDPCLiLiGpAupmZmVuayzj74l6bvAScBnJG0BdM4vLDMzK5WsJYIxJAvXfz0i/kayFsGk3KIyM7OSybpU5d+AacD2ko4E3omI3+UamZmZlUTWXkMnAI8BXwFOAOZJOj7DeaMlPStpuaTzGzk+UdJSSYsl3S9p15Y+gJmZtU7WNoILgH+OiFcBJPUC/gTc2tQJkjoB15BMWFcNPC5pZkQsLUj2JFAVERvSeY1+QlINZWZmJZK1jWCL2kwgtTbDufsCyyPixYh4D5hB0uuoTkTMiYgN6eajeB1kM7OSy1oiuFfSbGB6uj0GmFXknD7AyoLtamC/ZtKfSjLBXQOSJgATAPr39zg2M7O2lHXN4vMk/R/goHTX5Ii4va2CkHQiUAV8ton7TwYmA1RVVUVjaczMbPMUW49gMPBTYHfgKeDciFiV8dqrgH4F233TffXv8XmSNojPRsS7Ga9tZmZtpFg9/7XA3cBxJDOQ/rwF134cGCxpoKStgLHAzMIEkj4N/AY4ul4bhJmZlUixqqFuETElff2spCeyXjgiPpB0FjAb6ARcGxFLJF0KzI+ImSSD0rYDbpEE8EpEHN3ipzAzs81WLCPomn5rr12HYOvC7YhoNmOIiFnUa1SOiAsLXn++xRGbmVmbKpYRrAGuLNj+W8F2AJ/LIygzMyudYgvTHFKqQMzMrH1kHVBmZmYdlDMCM7MK54zAzKzCZZ19VOlaxRem2/0l7ZtvaGZmVgpZSwS/BPYHxqXbb5HMLGpmZmUu66Rz+0XESElPAkTEG+loYTMzK3NZSwTvp+sLBNStR/BhblGZmVnJZM0IrgZuB3aW9APgL8APc4vKzMxKJus01NMkLQAOJZle4tiIWJZrZGZmVhKZMgJJ/YENwF2F+yLilbwCMzOz0sjaWHwPSfuAgK7AQOBZYM+c4jIzsxLJWjW0V+G2pJHAmblEZGZmJbVZI4vT6aebW3/YzMzKRNY2gokFm1sAI4HVuURkZmYllbWNoFvB6w9I2gxua/twzMys1IpmBOlAsm4RcW4J4jEzsxJrto1A0pYRsRE4sETxmJlZiRUrETxG0h6wUNJM4BbgH7UHI+IPOcZmZmYlkLWNoCuwlmSN4trxBAE4IzAzK3PFMoKd0x5DT/NRBlArcovKzNrM+++/T3V1Ne+88057h2Il0LVrV/r27Uvnzp0zn1MsI+gEbMemGUAtZwRmZaC6uppu3boxYMAApMb+K1tHERGsXbuW6upqBg4cmPm8YhnBmoi4tHWhmVl7euedd5wJVAhJ9OjRg5qamhadV2xksf/lmHUAzgQqx+Z81sUygkM3LxQzMysXzWYEEfF6qQIxs47rBz/4AXvuuSd77703I0aMYN68eVxyySV897vf3STdwoUL+dSnPgXA+vXrOf3009l9993ZZ599GDVqFPPmzWtw7Yjgc5/7HG+++WbdvjvuuANJPPPMM3X75s6dy5FHHrnJuePHj+fWW28Fkkb1888/n8GDBzNy5Ej2339//vjHP7b62X/0ox8xaNAg9thjD2bPnt1omvvvv5+RI0cyYsQIDjroIJYvXw7Ayy+/zKGHHsree+/NqFGjqK6uBqCmpobRo0e3OrZamzXpnJlZVo888gh33303TzzxBIsXL+ZPf/oT/fr1Y9y4cdx0002bpJ0xYwbjxo0D4LTTTmOnnXbi+eefZ8GCBfzP//wPr732WoPrz5o1i+HDh9O9e/e6fdOnT+eggw5i+vTpmeP8/ve/z5o1a3j66ad54oknuOOOO3jrrbc286kTS5cuZcaMGSxZsoR7772XM888k40bNzZId8YZZzBt2jQWLlzIV7/6VS677DIAzj33XE4++WQWL17MhRdeWJdx9urVi969e/Pwww+3Kr5aWccRmFkHcMldS1i6+s3iCVtg6C7dueioppcmWbNmDT179qRLly4A9OzZs+7YjjvuyLx589hvv2Qy45tvvpnZs2fzwgsvMG/ePKZNm8YWWyTfVwcOHNhoT5hp06YxYcKEuu3169fzl7/8hTlz5nDUUUdxySWXFH2GDRs2MGXKFF566aW6OD/xiU9wwgknZHgHmnbnnXcyduxYunTpwsCBAxk0aBCPPfYY+++//ybpJNWVaNatW8cuu+wCJBnJlVdeCcAhhxzCscceW3fOsccey7Rp0zjwwNZP/OASgZnl6rDDDmPlypUMGTKEM888kwceeKDu2Lhx45gxYwYAjz76KDvttBODBw9myZIljBgxgk6dOhW9/sMPP8w+++xTt33nnXcyevRohgwZQo8ePViwYEHRayxfvpz+/ftvUqpoyjnnnMOIESMa/Fx++eUN0q5atYp+/frVbfft25dVq1Y1SDd16lQOP/xw+vbtyw033MD5558PwPDhw/nDH5Jxu7fffjtvvfUWa9euBaCqqoqHHnqoaLxZuERgVkGa++ael+22244FCxbw0EMPMWfOHMaMGcPll1/O+PHjGTNmDAcccABXXHHFJtVCLfH666/TrdtHEyRPnz6ds88+G4CxY8cyffp09tlnnyZ707S0l81VV13V4hizXHPWrFnst99+TJo0iYkTJzJ16lR++tOfctZZZ3Hddddx8MEH06dPn7rMceedd2b16rZZDSDXjEDSaOBnJAPTpkbE5fWOdwF+B+xDMoXFmIhYkWdMZlZ6nTp1YtSoUYwaNYq99tqL66+/nvHjx9OvXz8GDhzIAw88wG233cYjjzwCwJ577smiRYvYuHFj0VLBlltuyYcffsgWW2zB66+/zp///GeeeuopJLFx40YkMWnSJHr06MEbb7yxybmvv/46PXv2ZNCgQbzyyiu8+eabRUsF55xzDnPmzGmwf+zYsXXf5Gv16dOHlStX1m1XV1fTp0+fTdLU1NSwaNGiuuqxMWPG1DUE77LLLnUlgvXr13Pbbbexww47AMn4kK233rrZWLPKrWoonb76GuBLwFBgnKSh9ZKdCrwREYOAq4Af5xWPmbWPZ599lueff75ue+HChey666512+PGjeOcc85ht912o2/fvgDsvvvuVFVVcdFFFxGRTGKwYsUK7rnnngbX32OPPXjxxRcBuPXWWznppJN4+eWXWbFiBStXrmTgwIE89NBDDB48mNWrV7Ns2TIg6ZGzaNEiRowYwTbbbMOpp57K2WefzXvvvQckf6BvueWWBve76qqrWLhwYYOf+pkAwNFHH82MGTN49913eemll3j++efZd999N0mz4447sm7dOp577jkA7rvvvrqeU6+99hoffvghkPQ++vrXv1533nPPPcewYcOafe+zyrONYF9geUS8GBHvATOAY+qlOQa4Pn19K3CoPPLFrENZv349X/va1xg6dCh77703S5cu5eKLL647/pWvfIUlS5Y0qBaaOnUqf//73xk0aBDDhg1j/Pjx7Lzzzg2uf8QRRzB37lwgqRb68pe/vMnx4447junTp9OlSxduvPFGTjnlFEaMGMHxxx/P1KlT2X777QG47LLL6NWrF0OHDmXYsGEceeSRmdoMmrPnnntywgknMHToUEaPHs0111xTV8I5/PDDWb16NVtuuSVTpkzhuOOOY/jw4dxwww1MmjQJSLq87rHHHgwZMoS///3vXHDBBXXXnjNnDkcccUSr4qul2ty2rUk6HhgdEael2ycB+0XEWQVpnk7TVKfbL6RpXqt3rQnABID+/fvv8/LLL7c4nkvuWgK0Tx2pWXtatmxZ3TfMjmjNmjWcfPLJ3Hfffe0dSkkdfPDB3Hnnney4444NjjX2mUtaEBFVjV2rLBqLI2IyMBmgqqpqs3IuZwBmHVPv3r35xje+kal+v6Ooqalh4sSJjWYCmyPPjGAV0K9gu2+6r7E01ZK2BLYnaTQ2M8ustf39y02vXr02GVPQWnm2ETwODJY0UNJWwFhgZr00M4Gvpa+PB/4cedVVmVUw/7eqHJvzWeeWEUTEB8BZwGxgGXBzRCyRdKmko9NkvwV6SFoOTAQaNrubWat07dqVtWvXOjOoALXrEXTt2rVF5+XWWJyXqqqqmD9/fnuHYVY2vEJZZWlqhbKybyw2s83XuXPnFq1WZZXHcw2ZmVU4ZwRmZhXOGYGZWYUru8ZiSTVAy4cWJ3oCDVe26Nj8zJXBz1wZWvPMu0ZEr8YOlF1G0BqS5jfVat5R+Zkrg5+5MuT1zK4aMjOrcM4IzMwqXKVlBJPbO4B24GeuDH7mypDLM1dUG4GZmTVUaSUCMzOrxxmBmVmF65AZgaTRkp6VtFxSgxlNJXWRdFN6fJ6kAaWPsm1leOaJkpZKWizpfkm7NnadclLsmQvSHScpJJV9V8MszyzphPSzXiLp96WOsa1l+LfdX9IcSU+m/74Pb48424qkayW9mq7g2NhxSbo6fT8WSxrZ6ptGRIf6AToBLwC7AVsBi4Ch9dKcCfw6fT0WuKm94y7BMx8CbJO+PqMSnjlN1w14EHgUqGrvuEvwOQ8GngR2TLd3bu+4S/DMk4Ez0tdDgRXtHXcrn/lgYCTwdBPHDwf+CAj4F2Bea+/ZEUsE+wLLI+LFiHgPmAEcUy/NMcD16etbgUMlqYQxtrWizxwRcyJiQ7r5KMmKceUsy+cM8F/Aj4GOMAdzlmf+BnBNRLwBEBGvljjGtpblmQOoXaNye2B1CeNrcxHxIPB6M0mOAX4XiUeBHST1bs09O2JG0AdYWbBdne5rNE0kC+isA3qUJLp8ZHnmQqeSfKMoZ0WfOS0y94uIe0oZWI6yfM5DgCGSHpb0qKTRJYsuH1me+WLgREnVwCzgW6UJrd209P97UV6PoMJIOhGoAj7b3rHkSdIWwJXA+HYOpdS2JKkeGkVS6ntQ0l4R8b/tGlW+xgHXRcQVkvYHbpA0LCI+bO/AykVHLBGsAvoVbPdN9zWaRtKWJMXJtSWJLh9ZnhlJnwcuAI6OiHdLFFteij1zN2AYMFfSCpK61Jll3mCc5XOuBmZGxPsR8RLwHEnGUK6yPPOpwM0AEfEI0JVkcraOKtP/95boiBnB48BgSQMlbUXSGDyzXpqZwNfS18cDf460FaZMFX1mSZ8GfkOSCZR7vTEUeeaIWBcRPSNiQEQMIGkXOToiynmd0yz/tu8gKQ0gqSdJVdGLpQyyjWV55leAQwEkfYokI6gpaZSlNRM4Oe099C/AuohY05oLdriqoYj4QNJZwGySHgfXRsQSSZcC8yNiJvBbkuLjcpJGmbHtF3HrZXzmScB2wC1pu/grEXF0uwXdShmfuUPJ+MyzgcMkLQU2AudFRNmWdjM+83eAKZLOIWk4Hl/OX+wkTSfJzHum7R4XAZ0BIuLXJO0ghwPLgQ3AKa2+Zxm/X2Zm1gY6YtWQmZm1gDMCM7MK54zAzKzCOSMwM6twzgjMzCqcM4IKIGmjpIUFPwOaSbu+De53naSX0ns9kY72bOk1pkoamr7+z3rH/traGNPr1L4vT0u6S9IORdKP2JyZLSX1lnR3+nqUpHXpfZdJumgzrnd07Sycko6tfZ/S7UvTgYOtkn6GxxdJM7clA/TSZ787Q7pGZ9+U9FNJn5BHF0EAAAY0SURBVMt6P8vOGUFleDsiRhT8rCjBPc+LiBHA+SQD2VokIk6LiKXp5n/WO3ZAG8QHH70vw0jGk3yzSPoRJP23W2oiMKVg+6H0vakimSOnRdMIR8TMiLg83TyWZMbN2mMXRsSfNiPGj5PrgMbmSPo5yb8na2POCCqQpO2UrEnwhKSnJDWYtTP9FvtgwTfmz6T7D5P0SHruLZK2K3K7B4FB6bkT02s9Lenf033bSrpH0qJ0/5h0/1xJVZIuB7ZO45iWHluf/p4h6YiCmK+TdLykTpImSXpcyXztp2d4Wx4hnbhL0r7pMz4p6a+S9khHtV4KjEljGZPGfq2kx9K0jc1+CnAccG/9nRHxD2ABMCgtbTyaxnu7pB3TWL6tj9aRmJHuGy/pF5IOAI4GJqUx7V7wHoyWdEvBe1P3bbyln6GkC9P38mlJk6VNZuo9qeDfyL5p+qzvS6Oamn0zIl4Gekj6p5ZczzJoj/m2/VPaH5IRpgvTn9tJRpR3T4/1JBmhWDu4cH36+zvABenrTiRz9/Qk+cO+bbr//wIXNnK/64Dj09dfAeYB+wBPAduSjHBeAnya5I/klIJzt09/zyVdP6A2poI0tTF+Gbg+fb0VyYyMWwMTgO+l+7sA84GBjcS5vuD5bgFGp9vdgS3T158Hbktfjwd+UXD+D4ET09c7kMzrs229ewwEFhRsjwLuTl/3AFYAewKLgc+m+y8F/jt9vRroUnuP+nEUvteF2+ln/ErBZ/Ur4MTN/Ax3Kth/A3BUwWc0JX19MOn8+U29L/WevQqY2sy/2QE0Mh8/ScnquPb+P9XRfjrcFBPWqLcjqYoAQFJn4IeSDgY+JPkm/AngbwXnPA5cm6a9IyIWSvosSTXEw+mXwq1Ivkk3ZpKk75HM+XIqyVwwt0fyLRhJfwA+Q/JN+QpJPyb5I/FQC57rj8DPJHUhqUp4MCLelnQYsHdBHff2JBOvvVTv/K0lLUyffxlwX0H66yUNJpmyoHMT9z8MOFrSuel2V6B/eq1avWk4781nJD1J8t5fTjJR3A4R8UB6/HqSjAmSDGKapDtI5hHKJJKpGe4FjpJ0K3AE8B8ks85m/QxrHSLpP4BtgJ1IMvG70mPT0/s9KKm7knaWpt6XwvjmA6dlfZ4CrwK7bMZ51gxnBJXpX4FewD4R8b6S2Tm7FiZI/2MfTPIH5DpJVwJvAPdFxLgM9zgvIm6t3ZB0aGOJIuK5tI78cOAySfdHxKVZHiIi3pE0F/giMIZk0RJIVm76VkTMLnKJtyNihKRtSOay+SZwNcliNnMi4stKGtbnNnG+SL6dPtvcPaj33pK0ERxZdxFp+2bOP4Lk2/ZRwAWS9mombX0zgLNIqlnmR8RbabVO1s8QSV2BX5KUzlZKuphNn6f+HDVBE++LpE+0IPamdCV5T60NuY2gMm0PvJpmAocADdYvVrKm8d8jYgowlWTpvEeBAyXV1vlvK2lIxns+BBwraRtJ25JU6zwkaRdgQ0TcSDIxXmMNp++nJZPG3EQy6VZt6QKSP+pn1J4jaUh6z0ZFsnLbt4Hv6KNpyWun9R1fkPQtkiqyWrOBb9XWmSuZ4bW+50iqOZoUEeuAN5S2wwAnAQ8oWVOhX0TMIanC2Z6kWq1Q/ZgKPUDyfn6DjzLJln6GtX/0X0vbEur3JKpt0zmIZBbMdWR7XzbXEKDRtXxt8zkjqEzTgCpJTwEnA880kmYUsCitwhgD/Cwiakj+ME6XtJikSuGTWW4YEU+Q1Ds/RtJmMDUingT2Ah5Lq2guAi5r5PTJwGKljcX1/D+S6o4/RbKUISQZ11LgCSVdEH9DkdJvGstikkVOfgL8KH32wvPmAENrG4tJSg6d09iWpNv1r/sP4IXaP7zN+BpJddpikt5Jl5K0XdyYfk5PAldHwwVmZgDnpY2yu9e790bgbuBL6W9a+hmm95tC8sd3NkmVYaF30vfp1yRVgJDhfVHSEWBqY/dUMvvmI8AekqolnZru70zS8aCcpxL/WPLso2Y5k/Rlkmq477V3LOUsfR9HRsT32zuWjsZtBGY5i4jbJZXzmtgfF1sCV7R3EB2RSwRmZhXObQRmZhXOGYGZWYVzRmBmVuGcEZiZVThnBGZmFe7/A5CXmwkgX/ieAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "f3b4c90e-2d1e-455f-90e5-971067ee8b0a",
        "outputId": "8de8e580-1608-4def-f2bb-3180d3c0793f"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svc_gamma=SVC(gamma=2, C=1)\n",
        "svc_gamma.fit(train_x,train_y)\n",
        "eval(svc_gamma)"
      ],
      "id": "f3b4c90e-2d1e-455f-90e5-971067ee8b0a",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8112\n",
            "The cross val score with 3 folds:  [0.72105263 0.8        0.72486772]\n",
            "F1-score: 0.7907\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0n29ExIyMMZllcuiw/pv83jzap2t5C+imnFDreYL21X99ZE0E8yWdCvSQNBw4jwrzEUjqAVxBMrn9EuBhSdMjYkHJbv8G3BwR/1fSCGAGMLiTZTDbqBs+P6pL7692+3Qtmj+6kvzKyeME3RpjV/8+1nVZE8GXgX8F1gBTgJnApRXecyCwMCKeBZA0FRgLlCaCAFr/N2wLvJgxHstZIz641FV53F1Uq7b19qpxcs376t8JoPvIlAgi4k3gXyV9L1mM1zK8bQCwuGR5CdD+L38R8FtJXwa2Bo4sdyBJZwFnAey2m28XqwV32iW6WuZG/B6rXZuw7i/rXUMfBK4B+qTLK4HPVeHp4gnAdRFxuaSDgJ9L2ici1pXuFBFXAVcBtLS0VOybsOooaqddtcvcaN+jr9SLJ2vT0NXAFyPifgBJhwLXAvtt5D1LgUElywPTdaXOAI4GiIgHJfUG+gMvZYzLrFtopmcsrHiyDkP9bmsSAIiIB4C1Fd7zMDBc0hBJmwPjgent9nkB+EcASe8HegPLM8Zk1m2UGwoaun8zkBlUHmto//TlLEmTSTqKAxhH+kxBRyJiraRzSTqWewDXRMR8SZcAcyJiOvBPwE8knZ8ed2JEuOnH6qKrbeON1gRk1qpS09Dl7ZYvLHld8YSdPhMwo926C0peLwAOqXQcy1cR7xAqJ2vbeKOOXWTWkUpjDX2kVoFY/TTinS3djb8va2SdmaHsOGBvknZ8ACLikjyCstpzs0bn+fuyZpGps1jSlST9Al8GBHwS2D3HuMzMrEay1ggOjoj9JD0WERdLuhy4M8/AzLorP3BlzSZrIngr/f2mpF2BFcAu+YRk1r35gStrNlkTwR2StgMuAx4huWPop7lFZbnxHULWDMrduQWevnJTZR1rqHWAuV9JugPoHREr8wvL8uI7hKxZefrKTVfpgbKTNrKNiLi1+iFZ3nyHkDULT19ZHZVqBGM2si0AJwIzqzl32FdXpQfKTq9VIGZmWbnDvrqyDjpnZmZNyonAzKzgMg8xYY3Ht4qaWRZZh5jYStK3JP0kXR4u6fh8Q7OuKjdGvm8VNbP2stYIrgXmAq33ai0FfgnckUdQVj2+VdTMKsnaRzA0Ir4PvANtk9krt6jMzKxmstYI3pa0JelkNJKGAmtyi8rMrJM87MSmy5oILgJ+AwySdCPJrGITc4rJzKwqPOxENlnHGvqtpLnAh0iahL4SEX/PNTIzs03gYSc6L1MikHQ78AtgekS8kW9IZmad52EnNl3WpqEfkMxQNknSw8BU4I6IWJ1bZNYpfmbAiq7csBPuN8gm011DETErIr4IvA+YDJwCvJRnYNY5fmbALJsFy1ZtcNFUdJ2ZvH5LktFIxwH7A9fnFZRtGj8zYFae+w02Lmsfwc3AgSR3Dv0XMCsi1uUZmJlZV7nfIJusNYKrgQkR8W6ewZiZVZOHq86m0gxlR0TEPcDWwFhp/YeJPUOZmVnjq1QjOBy4h/IzlXmGMjOzJlBphrIL05eXRMRzpdskDcktKjMzq5msg879qsy6W6oZiJmZ1UelPoK9gL2BbSWdVLKpL9A7z8CsY354zMyqqVIfwZ7A8cB2rN9P8BpwZl5B2ca1PjxWeuL3w2Nmtqkq9RFMA6ZJOigi/BRGN+KHx8ysWio1DX09nZDmVEkT2m+PiPNyi8zMzGqiUtPQE+nvOXkHYmZm9VGpaej29HfbuEKSNgO2iYhVHb7RzMwaRqbbRyX9QlJfSVsDfwEWSPpavqGZmVktZH2OYERaA/g4cCcwBPh0blGZmVnNZE0EvST1IkkE0yPiHdKJ7M3MrLFlTQSTgUUkg8/9XtLugPsIzMyaQNbJ638M/Lhk1fOSPpJPSGZmVktZJ6bZFrgQ+HC6ahZwCbAyp7gs5eEkzCxvWZuGriEZVuKU9GcVcG1eQdl7PBexmeUt6wxlQyPi5JLliyXNyyMg25CHkzCzPGWtEbwl6dDWBUmHAG/lE5KZmdVS1hrBOcDP0r4CgFeAz+YTkpmZ1VLFRCBpJDAMGA8sBcg6vISko4EfAT2An0bEpHbb/wNovftoK2CniNguc/RmZtZlG20aknQBcDNwMvBrYFwnkkAP4ArgGGAEMEHSiNJ9IuL8iBgZESOB/4PnQDYzq7lKfQTjgJERMQH4IHBWJ459ILAwIp6NiLeBqcDYjew/AZjSieObmVkVVEoEayLiTYCIWJFh/1IDgMUly0vSdRtIn1QeAtzTwfazJM2RNGf58uWdCMHMzCqp1EfwPknT09cChpYsExEnVCmO8cAtEfFuuY0RcRVwFUBLS4vHODIzq6JKiaB9U84POnHspcCgkuWB6bpyxgNf6sSxzcysSipNTDOrC8d+GBguaQhJAhgPnNp+J0l7AdsDhZ8TefZzLwMwbvJ7X4WHkzCzvFW6a+h2SWPSIajbb3ufpEskfa7ceyNiLXAuMJNkysubI2J++p7SJqXxwNSIcJNPGR5OwszyVqlp6Ezgq8B/SnoZWA70BgYDzwD/FRHTOnpzRMwAZrRbd0G75Ys6HXWT83ASZlZLlZqG/gf4OvB1SYOBXUiGlniq9W4iMzNrbFmHmCAiFpFMTmNmZk2kM88FmJlZE3IiMDMruMxNQ2ZmzarcTIAAY0cO4NRRu9UhotrKVCOQdIikuyQ9JelZSc9Jejbv4MzMaqHcTIALlq0qmxyaUdYawdXA+cBcoOwwEGZmjaz9TIClD3Y2u6yJYGVE3JlrJGZmNeAn+DeUNRHcK+kykvkC1rSujIhHconKzKyGiv4Ef9ZEMCr93VKyLoAjqhuOmVlt+An+92RKBBHxkcp7mZl1f4cO61/vELqdTIkgnbT+QuDD6apZwCURsTKvwMzM8nDD50dV3onyfQnQnLeUZn2g7BrgNeCU9GcVcG1eQRXVocP6+2rFrBtr1ltKs/YRDI2Ik0uWL5Y0L4+AiizrlYqZ1U4RbinNWiN4S9KhrQuSDiEZhdTMzBpc1hrBF4Dr074CAS8DE/MKysys3orUTJv1rqF5wAck9U2XV1V4i5lZQytSU+1GE4GkT0XEDZK+2m49ABHxwxxjMzOzGqhUI9g6/d0n70DMzKw+Kk1VOTn9fXFtwjEzs1rLOgz19yX1ldRL0t2Slkv6VN7BmZlZ/rLePnpU2kF8PMm8xcOAr+UVlJmZ1U7WRNDahHQc8EsPLWFm1jyyPkdwh6S/kjxE9gVJOwKr8wvLzMxqJVONICK+ARwMtETEO8AbwNg8AzMzs9qo9BzBERFxj6STStaV7nJrXoGZmVltVGoaOhy4BxhTZlvgRGBmBdKsQ1NXeo7gwvT36bUJx8yssSxYloy407SJoJWkfwe+HxGvpsvbA/8UEf+WZ3BmZt1Rsw1NnfX20WNakwBARLwCHJtPSGZm3VOzTh6V9fbRHpK2iIg1AJK2BLbILywzs+6nWUckzZoIbgTultQ6PeXpwPX5hGRmZrWUdT6C70l6FDgyXXVpRMzMLywzM6uVrDUCgCeAtRHxO0lbSeoTEa/lFZiZmdVG1tFHzwRuASanqwYAt+UVlJmZ1U7Wu4a+BBwCrAKIiKeBnfIKyszMaidrIlgTEW+3LkjqSfJksZmZNbisiWCWpP8NbCnpo8AvgdvzC8vMzGolayL4F2A58DhwNjAD8FPFZmZNoOJdQ5J6APMjYi/gJ/mHZGZmtVSxRhAR7wJPSmrcEZXMzKxDWZ8j2B6YL+lPJJPSABARJ+QSlZmZ1UzWRPCtXKMwM7O6qTRDWW/gHGAYSUfx1RGxthaBmZlZbVTqI7geaCFJAscAl+cekZmZ1VSlRDAiIj4VEZOBTwCHdebgko6W9KSkhZK+0cE+p0haIGm+pF905vhmZtZ1lfoI3ml9ERFr201cv1HpbadXAB8FlgAPS5oeEQtK9hkOfBM4JCJekeRhK8zMaqxSIviApFXpa5E8WbwqfR0R0Xcj7z0QWBgRzwJImgqMBRaU7HMmcEU64xkR8dImlMHMzLqg0uT1Pbpw7AHA4pLlJUD76X32AJD0B6AHcFFE/Kb9gSSdBZwFsNtufpzBzKyasg4xkZeewHBgNDAB+Imk7drvFBFXRURLRLTsuOOONQ7RzKy55ZkIlgKDSpYHputKLQGmR8Q7EfEc8BRJYjAzsxrJMxE8DAyXNETS5sB4YHq7fW4jqQ0gqT9JU9GzOcZkZmbt5JYI0gfPzgVmkkxzeXNEzJd0iaTWoSlmAiskLQDuBb4WESvyisnMzDbUmTmLOy0iZpAMWV267oKS1wF8Nf0xM7M6qHdnsZmZ1ZkTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVXM96B2Bm1shmP/cyAOMmP7je+rEjB3DqqN3qEVKnuUZgZlZlC5atYtq8pfUOIzPXCMzMquCmsw9qe92+dtDdORGYmXXBocP61zuELnMiMDPrghs+P6reIXSZ+wjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAjMzArOicDMrOCcCMzMCs6JwMys4JwIzMwKzonAzKzgnAjMzAou10Qg6WhJT0paKOkbZbZPlLRc0rz05/N5xmNmZhvKbfJ6ST2AK4CPAkuAhyVNj4gF7Xa9KSLOzSsOMzPbuDxrBAcCCyPi2Yh4G5gKjM3x88zMbBPkmQgGAItLlpek69o7WdJjkm6RNKjcgSSdJWmOpDnLly/PI1Yzs8Kqd2fx7cDgiNgPuAu4vtxOEXFVRLRERMuOO+5Y0wDNzJpdnolgKVB6hT8wXdcmIlZExJp08afAATnGY2ZmZeSZCB4GhksaImlzYDwwvXQHSbuULJ4APJFjPGZmVkZudw1FxFpJ5wIzgR7ANRExX9IlwJyImA6cJ+kEYC3wMjAxr3jMzKy83BIBQETMAGa0W3dByetvAt/MMwYzM9u4encWm5lZnTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFVyut4+amVniF7NfYNq8pWW3jR05gFNH7VbjiN7jRGBmVmWzn3sZgHGTH9xg3aghO6y374JlqwCcCMzMiuKmsw9ab7k0WdSLE4GZWU5KT/qDv/HrOkaycU4EZmZVduiw/pnWdRdOBGZmVXbD50dlWtdd+PZRM7OCa4oawTvvvMOSJUtYvXp1vUOxKujduzcDBw6kV69e9Q7FrBCaIhEsWbKEPn36MHjwYCTVOxzrgohgxYoVLFmyhCFDhtQ7HLNCaIqmodWrV9OvXz8ngSYgiX79+rl2Z1ZDTVEjAJwEmoj/llYk5R4+g9o+bdwUNQIzs2ayYNmqDoejyIMTQZV85zvfYe+992a//fZj5MiRzJ49m4svvphvfnP9mTjnzZvH+9//fgBef/11zj77bIYOHcoBBxzA6NGjmT179gbHjgiOOOIIVq1a1bbutttuQxJ//etf29YtWrSILbfckpEjRzJixAjOOecc1q1b16VyrVmzhnHjxjFs2DBGjRrFokWLNtjnySefZOTIkW0/ffv25T//8z8BePTRRznooIPYd999GTNmTFsZHn/8cSZOnNil2MyayU1nH9T289rqtcx+7mXGTX5wvZ+Lb5+fy2c7EVTBgw8+yB133MEjjzzCY489xu9+9zsGDRrEhAkTuOmmm9bbd+rUqUyYMAGAz3/+8+ywww48/fTTzJ07l2uvvZa///3vGxx/xowZfOADH6Bv375t66ZMmcKhhx7KlClT1tt36NChzJs3j8cee4wFCxZw2223dalsV199Ndtvvz0LFy7k/PPP51/+5V822GfPPfdk3rx5zJs3j7lz57LVVltx4okntpVx0qRJPP7445x44olcdtllAOy7774sWbKEF154oUvxmTW6Q4f1r/vDZk3TR9Dq4tvns+DFVZV37IQRu/blwjF7d7h92bJl9O/fny222AKA/v3f+6Nuv/32zJ49m1GjkodJbr75ZmbOnMkzzzzD7NmzufHGG9lssyQfDxkypOydMjfeeMxDs1EAAA0CSURBVCNnnXVW2/Lrr7/OAw88wL333suYMWO4+OKLN3hPz549Ofjgg1m4cOGmFTo1bdo0LrroIgA+8YlPcO655xIRHbbj33333QwdOpTdd98dgKeeeooPf/jDAHz0ox/lYx/7GJdeeikAY8aMYerUqXz961/vUoxmjWxjD5q1H5coL64RVMFRRx3F4sWL2WOPPfjiF7/IrFmz2rZNmDCBqVOnAvDQQw+xww47MHz4cObPn8/IkSPp0aNHxeP/4Q9/4IADDmhbnjZtGkcffTR77LEH/fr1Y+7cuRu858033+Tuu+9m33333WDbYYcdtl5TTuvP7373uw32Xbp0KYMGDQKS5LLtttuyYsWKDmMtrfEA7L333kybNg2AX/7ylyxevLhtW0tLC/fff3/F8psVTa1rCU1XI9jYlXtettlmG+bOncv999/Pvffey7hx45g0aRITJ05k3LhxHHzwwVx++eUbnCSzevnll+nTp0/b8pQpU/jKV74CwPjx45kyZUpbonjmmWcYOXIkkhg7dizHHHPMBsfL6+T79ttvM336dL773e+2rbvmmms477zzuPTSSznhhBPYfPPN27bttNNOvPjii7nEYtbIaj0cRdMlgnrp0aMHo0ePZvTo0ey7775cf/31TJw4kUGDBjFkyBBmzZrFr371Kx58MLlFbO+99+bRRx/l3XffrVgr6NmzJ+vWrWOzzTbj5Zdf5p577uHxxx9HEu+++y6S2treW/sINuawww7jtdde22D9D37wA4488sj11g0YMIDFixczcOBA1q5dy8qVK+nXr1/Z4955553sv//+7Lzzzm3r9tprL377298CSTPRr3/93giMq1evZsstt9xorGaWPzcNVcGTTz7J008/3bY8b968tjZySJqHzj//fN73vvcxcOBAIDlht7S0cOGFFxIRQHLXT+mJstWee+7Js88+C8Att9zCpz/9aZ5//nkWLVrE4sWLGTJkSKeu8u+///62zt3Sn/ZJAOCEE07g+uuvb/vsI444osP+gSlTpmxQ43nppZcAWLduHd/+9rc555xz2rY99dRT7LPPPpnjNrN8OBFUweuvv85nP/tZRowYwX777ceCBQvaOlgBPvnJTzJ//vwNTpI//elP+dvf/sawYcPYZ599mDhxIjvttNMGxz/uuOO47777gORk23pHTquTTz55g7uHquWMM85gxYoVDBs2jB/+8IdMmjQJgBdffJFjjz22bb833niDu+66i5NOOmm990+ZMoU99tiDvfbai1133ZXTTz+9bdu9997Lcccdl0vcZpadWq9GG0VLS0vMmTNnvXVPPPFE2735zWjZsmV85jOf4a677qp3KFWzZs0aDj/8cB544AF69tywhbLZ/6ZmtSZpbkS0lNvmGkED2GWXXTjzzDPXe6Cs0b3wwgtMmjSpbBIws9ry/8IGccopp9Q7hKoaPnw4w4cPr3cYZkYT1QgarYnLOua/pVltNUUi6N27NytWrPAJpAm0zkfQu3fveodiVhhN0TQ0cOBAlixZwvLly+sdilVB6wxlZlYbTZEIevXq5dmszMw2UVM0DZmZ2aZzIjAzKzgnAjOzgmu4J4slLQee38S39wc2nPmlubnMxeAyF0NXyrx7ROxYbkPDJYKukDSno0esm5XLXAwuczHkVWY3DZmZFZwTgZlZwRUtEVxV7wDqwGUuBpe5GHIpc6H6CMzMbENFqxGYmVk7TZkIJB0t6UlJCyV9o8z2LSTdlG6fLWlw7aOsrgxl/qqkBZIek3S3pN3LHaeRVCpzyX4nSwpJDX+HSZYySzol/VvPl/SLWsdYbRn+be8m6V5Jf07/fR9b7jiNQtI1kl6S9JcOtkvSj9Pv4zFJ+3f5QyOiqX6AHsAzwPuAzYFHgRHt9vkicGX6ejxwU73jrkGZPwJslb7+QhHKnO7XB/g98BDQUu+4a/B3Hg78Gdg+Xd6p3nHXoMxXAV9IX48AFtU77i6W+cPA/sBfOth+LHAnIOBDwOyufmYz1ggOBBZGxLMR8TYwFRjbbp+xwPXp61uAf1RHM7I3hopljoh7I+LNdPEhoNGH98zydwa4FPgesLqWweUkS5nPBK6IiFcAIuKlGsdYbVnKHEDf9PW2wIs1jK/qIuL3wMsb2WUs8LNIPARsJ2mXrnxmMyaCAcDikuUl6bqy+0TEWmAl0K8m0eUjS5lLnUFyRdHIKpY5rTIPiohf1zKwHGX5O+8B7CHpD5IeknR0zaLLR5YyXwR8StISYAbw5dqEVjed/f9eUVMMQ23ZSfoU0AIcXu9Y8iRpM+CHwMQ6h1JrPUmah0aT1Pp+L2nfiHi1rlHlawJwXURcLukg4OeS9omIdfUOrFE0Y41gKTCoZHlguq7sPpJ6klQnV9QkunxkKTOSjgT+FTghItbUKLa8VCpzH2Af4D5Ji0jaUqc3eIdxlr/zEmB6RLwTEc8BT5EkhkaVpcxnADcDRMSDQG+SMXmaVab/753RjIngYWC4pCGSNifpDJ7ebp/pwGfT158A7om0F6ZBVSyzpP8FTCZJAo3ebgwVyhwRKyOif0QMjojBJP0iJ0TEnPqEWxVZ/m3fRlIbQFJ/kqaiZ2sZZJVlKfMLwD8CSHo/SSJo5ukKpwOfSe8e+hCwMiKWdeWATdc0FBFrJZ0LzCS54+CaiJgv6RJgTkRMB64mqT4uJOmUGV+/iLsuY5kvA7YBfpn2i78QESfULeguyljmppKxzDOBoyQtAN4FvhYRDVvbzVjmfwJ+Iul8ko7jiY18YSdpCkky75/2e1wI9AKIiCtJ+kGOBRYCbwKnd/kzG/j7MjOzKmjGpiEzM+sEJwIzs4JzIjAzKzgnAjOzgnMiMDMrOCcCM7OCcyKw3El6V9I8SX+RdLuk7ap8/EXpw1NIer2DfbaUNEtSD0mDJb2VxrRA0pXpkBSd+cwWST9OX4+WdHDJtnMkfaYrZUqPc5Gkf66wz3WSPtGJYw7uaHjjdvt9R9Li9t+npHMlfS7r51ljcCKwWngrIkZGxD4kD/B9qQ4xfA64NSLeTZefiYiRwH4kQxd/vDMHi4g5EXFeujgaOLhk25UR8bOuh1xXt5OM/NneNTT/oG6F40RgtfYg6UiJkoZK+o2kuZLul7RXun5nSf9P0qPpz8Hp+tvSfedLOquTn3saMK39ynT02T8Cw9Kr5Xv03uQ9u6Wf+8m0NvOopN+n60ZLukPJpEbnAOenNYzDWq/kJe0l6U+tn5Ue//H09QFpDWWupJmVhhGWdKakh9MYfiVpq5LNR0qaI+kpScen+/eQdFn6nscknd2ZLysiHio3bEE6lPkiSeWShDUoJwKrGUk9SMaEaR3+4SrgyxFxAPDPwH+n638MzIqID5BM0DE/Xf+5dN8W4DxJmYYOT8eoeV9ELCqzbas0pseB/wNcHxH7ATemcQBcAHwsjWe9YTnSY14J/Eda67m/ZNtfgc0lDUlXjQNuktQr/axPpOW5BvhOhWLcGhEfTGN4gmSgtVaDSa7ejwOulNQ73b4yIj4IfBA4sySO1rLvKmlGhc8tZw5w2Ca8z7qpphtryLqlLSXNI6kJPAHcJWkbkuaU1rGPALZIfx8BfAYgbcpZma4/T9KJ6etBJKNqZhlHpz/QfhjmoWlMAUyLiDsl/Rw4Kd3+c+D76es/ANdJuhm4NcPnlbqZJAFMSn+PA/YkGRn1rrTsPYBKg4btI+nbwHYkY0bNLP2MdMjlpyU9C+wFHAXsV9J/sC3J9/VU65si4kWSMWs666X0M6xJOBFYLbwVESPTq++ZJH0E1wGvpu30FUkaDRwJHBQRb0q6j2SUyUyfX2bfZ7J+dkScI2kUyRX3XEkHZPxcgJtIkt2tyaHiaUn7AvMj4qBOHOc64OMR8aikiaQjjLaG2D5kkmkMvxwRpQkDVWd+7t4k36k1CTcNWc2k7cvnkYwW+SbwnKRPQtuE3B9Id72bZF7l1rbubUmuaF9Jk8BeJPMLZP3cV4AeaZPJxvyR90aiPQ24P41haETMjogLSIY3HtTufa+RzH9Q7rOfIRkF9FskSQHgSWBHJZOoIKmXpL0rxNYHWJY2K53WbtsnJW0maSjJ3L5PkiTcL6T7I2kPSVtX+Iys9gAq3nlkjcOJwGoqIv4MPEYyq9RpwBmSHiXpB2idi/YrwEfSjtW5JHf1/AboKekJkmaWhzr50b8FDq2wz5eB0yU9Bnw6jQPgMkmPp7dd/pFkAvVStwMntnYWlznuTcCneG/ylLdJ5sH4Xlr2eZTcddSBbwGzSZqp/tpu2wvAn0imHz0nIlYDPwUWAI+kcU+mXQvAxvoIJH1fyRDIW0laIumiks2HAHdViNcaiIehtkJQMn/x+RHx6XrH0siUTHD0VX+PzcU1AiuEiHgEuDe9c8k2XX+S2ok1EdcIzMwKzjUCM7OCcyIwMys4JwIzs4JzIjAzKzgnAjOzgvv/QubcF3Y9pFgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddb20fb0-20de-4c0c-856c-509d7592f07c"
      },
      "source": [
        "### AdaBoost\n",
        "Using adaboost is when the next training instance pays more attention to previous training instances where it underfitted. This will keep going focussing on the harder to fit cases. For example when the first algorithm trains it will have some misclassified training instances, on the next instance it will increase the weight of the misclassified instances and then trains again. This will produces the final model."
      ],
      "id": "ddb20fb0-20de-4c0c-856c-509d7592f07c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "fae7e171-895d-4d6d-b68e-9edef8a0289c",
        "outputId": "9b1b25f6-e36b-460d-9824-7dded99915bd"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "clf = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
        "clf.fit(train_x,train_y)\n",
        "eval(clf)"
      ],
      "id": "fae7e171-895d-4d6d-b68e-9edef8a0289c",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.7902\n",
            "The cross val score with 3 folds:  [0.77368421 0.84736842 0.75661376]\n",
            "F1-score: 0.7727\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77e8215c-7cc5-491c-a8c5-2ba486d44863"
      },
      "source": [
        "### Naive Bayes\n",
        "This is done by applying Bayes theorem with the assumption that each pair of features are independent. There are a number of different bayes classifiers but they mainly differ by the distribution. These classifiers tend to be fast compared to more complex methods but still provide excellent results."
      ],
      "id": "77e8215c-7cc5-491c-a8c5-2ba486d44863"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "d158b754-c252-4475-84e5-36ea73d8a5fb",
        "outputId": "f6c5849f-2e44-49f0-9918-ada6c3327949"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(train_x,train_y)\n",
        "eval(gnb)"
      ],
      "id": "d158b754-c252-4475-84e5-36ea73d8a5fb",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.7622\n",
            "The cross val score with 3 folds:  [0.78421053 0.86842105 0.74074074]\n",
            "F1-score: 0.7344\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b730ad0b-51ae-4a69-91b8-d5d324991f03"
      },
      "source": [
        "### Logistic Regression\n",
        "This estimates the probability of the class belonging to one of the options, this will be between 0 and 1. If it is lower than 0.5 it will be assigned as 0."
      ],
      "id": "b730ad0b-51ae-4a69-91b8-d5d324991f03"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "b6b64d4b-e4c6-459f-9897-eaaede2cea1d",
        "outputId": "079d8b25-9e0b-422a-ab44-8f73950f58dc"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(train_x,train_y)\n",
        "eval(log_reg)"
      ],
      "id": "b6b64d4b-e4c6-459f-9897-eaaede2cea1d",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8322\n",
            "The cross val score with 3 folds:  [0.79473684 0.80526316 0.73544974]\n",
            "F1-score: 0.7857\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5b80d22e-7e7e-48b7-adc3-b497e095a7dc"
      },
      "source": [
        "### Trying to Improve model"
      ],
      "id": "5b80d22e-7e7e-48b7-adc3-b497e095a7dc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 984
        },
        "id": "b1a073aa-6d38-412b-a211-ace5d1b3f2bd",
        "outputId": "84feb71c-fd7c-47eb-f209-a4b2971bfccd"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "grad_boost = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0)\n",
        "grad_boost.fit(train_x, train_y)\n",
        "eval(grad_boost)"
      ],
      "id": "b1a073aa-6d38-412b-a211-ace5d1b3f2bd",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8252\n",
            "The cross val score with 3 folds:  [0.79473684 0.85789474 0.77777778]\n",
            "F1-score: 0.7934\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "758527cb-55dc-4e6e-8840-137f89a00db2",
        "outputId": "29070ac1-9d55-4c08-c7ed-e163920472dc"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier(use_label_encoder=False)\n",
        "xgb.fit(train_x, train_y)\n",
        "eval(xgb)"
      ],
      "id": "758527cb-55dc-4e6e-8840-137f89a00db2",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8531\n",
            "The cross val score with 3 folds:  [0.81052632 0.84210526 0.76719577]\n",
            "F1-score: 0.8235\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8dbfc8f-91ce-4e12-9818-35945a54fe1d",
        "outputId": "4599d545-b41d-42b3-b22d-71c7d11d133e"
      },
      "source": [
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "eclf = VotingClassifier( estimators=[('Grad', grad_boost), ('rf', log_reg), ('gnb', forest_clf),('Knn',neigh2),('svm',svc_lin)],voting='hard')\n",
        "eclf.fit(train_x, train_y)\n",
        "eval(eclf,False)"
      ],
      "id": "e8dbfc8f-91ce-4e12-9818-35945a54fe1d",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8322\n",
            "The cross val score with 3 folds:  [0.81578947 0.85263158 0.77777778]\n",
            "F1-score: 0.7931\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5065fc7-8a83-4b60-97a7-c57035048116"
      },
      "source": [
        "# Summary"
      ],
      "id": "e5065fc7-8a83-4b60-97a7-c57035048116"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0b4f9bea-8019-4e65-b3f0-59df87724c25",
        "outputId": "788cef4e-0af7-49b1-e57a-8ac624178792"
      },
      "source": [
        "for clf, label in zip([tree_reg, forest_clf, neigh2, svc_lin, svc_gamma,clf,gnb,log_reg,grad_boost], ['Decision Tree', 'Random Forest', 'k-Nearest Neighbors','SVM lin','SVM gamma','Adaboost', 'naive Bayes', 'Logistic Regression','Grad Boost']):\n",
        "    scores = cross_val_score(clf, train_x, train_y, scoring='accuracy', cv=5)\n",
        "    print(\"Accuracy: %0.5f (+/- %0.5f) [%s]\" % (scores.mean(), scores.std(), label))"
      ],
      "id": "0b4f9bea-8019-4e65-b3f0-59df87724c25",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.72400 (+/- 0.03684) [Decision Tree]\n",
            "Accuracy: 0.80840 (+/- 0.03277) [Random Forest]\n",
            "Accuracy: 0.78034 (+/- 0.02192) [k-Nearest Neighbors]\n",
            "Accuracy: 0.78201 (+/- 0.03163) [SVM lin]\n",
            "Accuracy: 0.74690 (+/- 0.04359) [SVM gamma]\n",
            "Accuracy: 0.79792 (+/- 0.04025) [Adaboost]\n",
            "Accuracy: 0.78381 (+/- 0.04295) [naive Bayes]\n",
            "Accuracy: 0.78733 (+/- 0.03216) [Logistic Regression]\n",
            "Accuracy: 0.80141 (+/- 0.03114) [Grad Boost]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fbeb8f9-8137-4369-a248-36c1888a3574"
      },
      "source": [
        "As expected the best classifier is the voting classifier combining different models."
      ],
      "id": "0fbeb8f9-8137-4369-a248-36c1888a3574"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed498fe7-f60a-4f73-8526-a0e4102a609f"
      },
      "source": [
        "## Extra\n",
        "I read about a automatic machine learning package called auto sklearn so I decided to try this out. This package allows for a full optimization of a full \n",
        "pipeline from data transformation to ensembled learning. This should provide a very good result. Should be noted that it working with sklearn 0.24 while the rest of this was written in v1. It also doesnt work in windows machines. https://towardsdatascience.com/auto-sklearn-an-automl-tool-based-on-bayesian-optimization-91a8e1b26c22 talks you through it."
      ],
      "id": "ed498fe7-f60a-4f73-8526-a0e4102a609f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "Dhp_-XXpr2-_",
        "outputId": "54cd213e-ff54-48e9-c0f7-e56d3fea9b44"
      },
      "source": [
        "!pip install auto-sklearn"
      ],
      "id": "Dhp_-XXpr2-_",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: auto-sklearn in /usr/local/lib/python3.7/dist-packages (0.14.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (3.7.4.3)\n",
            "Requirement already satisfied: threadpoolctl in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (3.0.0)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.7.1)\n",
            "Requirement already satisfied: pyrfr<0.9,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.8.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (3.13)\n",
            "Requirement already satisfied: smac>=0.14 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.0.1)\n",
            "Collecting scikit-learn<0.25.0,>=0.24.0\n",
            "  Using cached scikit_learn-0.24.2-cp37-cp37m-manylinux2010_x86_64.whl (22.3 MB)\n",
            "Requirement already satisfied: liac-arff in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (2.5.0)\n",
            "Requirement already satisfied: distributed<2021.07,>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (2021.6.2)\n",
            "Requirement already satisfied: pynisher>=0.6.3 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.6.4)\n",
            "Requirement already satisfied: dask<2021.07 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (2021.6.2)\n",
            "Requirement already satisfied: ConfigSpace<0.5,>=0.4.14 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (0.4.20)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (57.4.0)\n",
            "Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.19.5)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from auto-sklearn) (1.0.1)\n",
            "Requirement already satisfied: cython in /usr/local/lib/python3.7/dist-packages (from ConfigSpace<0.5,>=0.4.14->auto-sklearn) (0.29.24)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from ConfigSpace<0.5,>=0.4.14->auto-sklearn) (2.4.7)\n",
            "Requirement already satisfied: partd>=0.3.10 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (1.2.0)\n",
            "Requirement already satisfied: cloudpickle>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (2.0.0)\n",
            "Requirement already satisfied: fsspec>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (2021.10.0)\n",
            "Requirement already satisfied: toolz>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from dask<2021.07->auto-sklearn) (0.11.1)\n",
            "Requirement already satisfied: psutil>=5.0 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (5.4.8)\n",
            "Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (7.1.2)\n",
            "Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (2.4.0)\n",
            "Requirement already satisfied: tornado>=5 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (5.1.1)\n",
            "Requirement already satisfied: zict>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (2.0.0)\n",
            "Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (1.7.0)\n",
            "Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed<2021.07,>=2.2.0->auto-sklearn) (1.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->auto-sklearn) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0->auto-sklearn) (2018.9)\n",
            "Requirement already satisfied: locket in /usr/local/lib/python3.7/dist-packages (from partd>=0.3.10->dask<2021.07->auto-sklearn) (0.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.0->auto-sklearn) (1.15.0)\n",
            "Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed<2021.07,>=2.2.0->auto-sklearn) (1.0.1)\n",
            "Installing collected packages: scikit-learn\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0\n",
            "    Uninstalling scikit-learn-1.0:\n",
            "      Successfully uninstalled scikit-learn-1.0\n",
            "Successfully installed scikit-learn-0.24.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "sklearn"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QSyU2GUsGYJ",
        "outputId": "1ff4d7e5-962e-4233-8c1b-a7ff97142eab"
      },
      "source": [
        "import autosklearn.classification\n",
        "\n",
        "automl = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=3600) #The default time for the task is 3600s but this can be reduced \n",
        "automl.fit(train_x, train_y)\n",
        "print(\"Model score: %.4f\" % automl.score(test_x,test_y))  "
      ],
      "id": "2QSyU2GUsGYJ",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model score: 0.8112\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iG7G0gjtvEmd"
      },
      "source": [
        "This shows that my votting classifier my acutally perform very well with the data that I have. This indicates that to improve this score we will need to look at feature engineering"
      ],
      "id": "iG7G0gjtvEmd"
    }
  ]
}